CN119248510A - A banking information intelligent management method and system based on big data - Google Patents
A banking information intelligent management method and system based on big data Download PDFInfo
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Abstract
The invention relates to a bank informatization intelligent management method and system based on big data, belonging to the technical field of bank informatization management, and finally, acquiring the stored data to be released according to the analysis result of the data storage amount, and formulating a data release strategy according to the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp and the stored data to be released. The invention fully considers whether the data storage state of the bank informatization management platform reaches the preset standard, and when the data storage state reaches the preset standard, the data storage amount of the bank informatization management platform is released by setting the optimal maximum information release amount, so that the clamping phenomenon of the bank informatization management platform is reduced.
Description
Technical Field
The invention relates to the technical field of bank informatization management, in particular to a bank informatization intelligent management method and system based on big data.
Background
The traditional intelligent operation management information system of the bank adopts a traditional manual management mode, and a great deal of time and cost are required to be spent facing massive data. The big data processing technology is already mature and applied to an operation management information system, the mature technology framework at present is a dynamic enterprise data warehouse ACTIVE EDW of Teradata-day Rui company, which mainly focuses on the underlying data storage and data processing level, and the banking system management solution of IBM company relies on a SPSS CLEMENTINE data mining platform and a cognis analysis data visualization analysis tool to develop a set of data management models. However, as the data of the client increases, the bank informatization management platform can continuously increase the data storage amount after a certain time, and a lot of data becomes useless data after a certain time limit, and the data is released and then the update of the platform data is completed through the synchronization of the data, however, the influence of network fluctuation on the data deletion or the data release is not considered in the prior art, so that the bank informatization management platform is stuck in the process of deleting or releasing the data.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a bank informatization intelligent management method and system based on big data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The first aspect of the invention provides a bank informatization intelligent management method based on big data, which comprises the following steps:
acquiring data storage capacity data of a bank informatization management platform, and acquiring a data storage capacity analysis result by identifying and analyzing the data storage capacity data of the bank informatization management platform;
Acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time through the big data, and constructing an information release amount prediction model according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time;
Predicting the maximum information release amount of the bank informatization management platform in the current time stamp within unit time by the information release amount prediction model;
And acquiring the stored data to be released according to the data storage amount analysis result, and formulating a data release strategy according to the maximum information release amount of the bank informatization management platform in unit time in the current time stamp and the stored data to be released.
Further, in the method, the data storage capacity data of the bank informatization management platform is obtained, and the data storage capacity analysis result is obtained by identifying and analyzing the data storage capacity data of the bank informatization management platform, which specifically comprises the following steps:
Acquiring data storage capacity data of a bank informatization management platform, setting a data storage capacity data threshold value, and judging whether the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value or not;
When the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as an over-storage capacity state;
when the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as a normal storage capacity state;
and generating an over-storage state according to the over-storage state or the normal storage state, and outputting the over-storage state.
Further, in the method, the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time is obtained through big data, and an information release amount prediction model is constructed according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time, specifically:
Acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time through big data, and inputting the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time into the graph neural network;
taking the network fluctuation data as a first graph node of the graph neural network, taking the maximum information release amount of a bank informatization management platform in unit time as a second graph node, and constructing a directed description relationship;
Connecting the first graph node and the second graph node based on the directed description relationship to form a topological structure diagram, acquiring a related adjacency matrix based on the topological structure diagram, and constructing an information release quantity prediction model based on a deep neural network;
And inputting the related adjacency matrix into the information release quantity prediction model, and storing model parameters of the information release quantity prediction model after the loss function of the information release quantity prediction model is converged to a preset value.
Further, in the method, the maximum information release amount of the bank informatization management platform in the current timestamp in unit time is predicted by the information release amount prediction model, specifically:
Acquiring network fluctuation data of a current time stamp, and inputting the network fluctuation data of the current time stamp into the information release amount prediction model for prediction;
And obtaining the maximum information release amount of the bank informatization management platform in the current time stamp within unit time through prediction, and outputting the maximum information release amount of the bank informatization management platform in the current time stamp within unit time.
Further, in the method, the stored data to be released is obtained according to the analysis result of the data storage amount, specifically:
when the data storage amount analysis result is in an over storage amount state, acquiring storage time limit data of each storage data in the bank informatization management platform, and setting a storage time limit data threshold;
when the storage time limit data is larger than the storage time limit data threshold, taking the storage data corresponding to the storage time limit data larger than the storage time limit data threshold as the storage data to be released, and outputting the storage data to be released.
Further, in the method, a data release strategy is formulated according to the maximum information release amount of the bank informatization management platform in the unit time and the stored data to be released in the current timestamp, specifically:
Acquiring stored data to be released in a current time stamp, and judging whether the stored data to be released in the current time stamp is larger than the maximum information release amount of a bank informatization management platform in the current time stamp in unit time;
When the stored data to be released in the current timestamp is larger than the maximum information release amount of the bank informatization management platform in the current timestamp in unit time, generating a first release strategy from the maximum information release amount of the bank informatization management platform in the current timestamp in unit time;
When the stored data to be released in the current time stamp is not more than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time, generating a second release strategy from the storage amount corresponding to the stored data to be released in the current time stamp;
And generating a data release strategy based on the first release strategy or the second release strategy, and releasing stored data to the bank informatization management platform according to the data release strategy.
The second aspect of the invention provides a bank informatization intelligent management system based on big data, which comprises a memory and a processor, wherein the memory comprises a bank informatization intelligent management method program based on the big data, and when the bank informatization intelligent management method program based on the big data is executed by the processor, the steps of any bank informatization intelligent management method based on the big data are realized.
The third aspect of the present invention provides a computer readable storage medium, which is characterized by comprising a bank informatization intelligent management method program based on big data, wherein when the bank informatization intelligent management method program based on big data is executed by a processor, the steps of any bank informatization intelligent management method based on big data are realized.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
The invention obtains the data storage quantity data of the bank informatization management platform, and obtains the data storage quantity analysis result by identifying and analyzing the data storage quantity data of the bank informatization management platform, further obtains the maximum information release quantity of the bank informatization management platform in unit time under each network fluctuation data through big data, and constructs an information release quantity prediction model according to the maximum information release quantity of the bank informatization management platform in unit time under each network fluctuation data, thereby predicting the maximum information release quantity of the bank informatization management platform in unit time in the current timestamp through the information release quantity prediction model, finally obtaining the storage data to be released according to the data storage quantity analysis result, and formulating a data release strategy according to the maximum information release quantity of the bank informatization management platform in unit time in the current timestamp and the storage data to be released. The invention fully considers whether the data storage state of the bank informatization management platform reaches the preset standard, and when the data storage state reaches the preset standard, the data storage amount of the bank informatization management platform is released by setting the optimal maximum information release amount, so that the clamping phenomenon of the bank informatization management platform is reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall flow chart of a big data based bank informatization intelligent management method;
FIG. 2 shows a partial flow chart of a big data based bank informatization intelligent management method;
Fig. 3 shows a system block diagram of a big data based bank informatization intelligent management system.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides a bank informationized intelligent management method based on big data, comprising the following steps:
S102, acquiring data storage capacity data of a bank informatization management platform, and acquiring a data storage capacity analysis result by identifying and analyzing the data storage capacity data of the bank informatization management platform;
S104, acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time through big data, and constructing an information release amount prediction model according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time;
s106, predicting the maximum information release amount of the bank informatization management platform in the current time stamp in unit time through an information release amount prediction model;
S108, acquiring the stored data to be released according to the analysis result of the data storage amount, and formulating a data release strategy according to the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp and the stored data to be released.
It should be noted that, the invention fully considers whether the data storage state of the bank informatization management platform reaches the preset standard, and when the data storage state reaches the preset standard, the data storage amount of the bank informatization management platform is released by setting the optimal maximum information release amount, so as to reduce the clamping phenomenon of the bank informatization management platform.
Further, in the method, the data storage amount data of the bank informatization management platform is obtained, and the data storage amount analysis result is obtained by identifying and analyzing the data storage amount data of the bank informatization management platform, which specifically comprises the following steps:
Acquiring data storage capacity data of the bank informatization management platform, setting a data storage capacity data threshold value, and judging whether the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value;
when the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as an over-storage capacity state;
When the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as a normal storage capacity state;
and generating an over-storage state according to the over-storage state or the normal storage state, and outputting the over-storage state.
The data storage state of the bank informatization management platform is acquired through the method. The over-storage state is stored data exceeding a predetermined amount and size. The storage amount data includes consumption information, basic material information, loan information, and the like of the user.
As shown in fig. 2, further, in the method, the maximum information release amount of the bank informatization management platform under each network fluctuation data in a unit time is obtained through big data, and an information release amount prediction model is constructed according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in the unit time, specifically:
S202, acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time through big data, and inputting the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time into a graph neural network;
S204, taking the network fluctuation data as a first graph node of the graph neural network, taking the maximum information release amount of the bank informatization management platform in unit time as a second graph node, and constructing a directed description relationship;
s206, connecting the first graph node and the second graph node based on the directed description relationship to form a topological structure diagram, acquiring a related adjacency matrix based on the topological structure diagram, and constructing an information release quantity prediction model based on a deep neural network;
S208, inputting the related adjacency matrix into the information release quantity prediction model, and storing model parameters of the information release quantity prediction model after the loss function of the information release quantity prediction model is converged to a preset value.
It should be noted that, since the bank informatization management platform is usually a network platform, and secondly, due to the influence of network fluctuation data (such as network delay, error rate, etc.), a maximum information release amount exists in the bank informatization management platform within a unit time, and an information release amount prediction model can be constructed by the method, so that the maximum information release amount (such as an amount of 500MB of data capable of being released within 1 second) under different network fluctuation data is predicted.
Further, in the method, the maximum information release amount of the bank informatization management platform in the current timestamp in unit time is predicted by an information release amount prediction model, and specifically comprises the following steps:
acquiring network fluctuation data of a current time stamp, and inputting the network fluctuation data of the current time stamp into an information release amount prediction model for prediction;
And obtaining the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp through prediction, and outputting the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp.
Further, in the method, the stored data to be released is obtained according to the analysis result of the data storage amount, specifically:
When the data storage amount analysis result is in an over storage amount state, acquiring storage time limit data of each storage data in the bank informatization management platform, and setting a storage time limit data threshold;
When the storage time limit data is larger than the storage time limit data threshold value, the storage data corresponding to the storage time limit data larger than the storage time limit data threshold value is used as the storage data to be released, and the storage data to be released is output.
Further, in the method, a data release strategy is formulated according to the maximum information release amount of the bank informatization management platform in the unit time and the stored data to be released in the current timestamp, specifically:
acquiring the stored data to be released in the current time stamp, and judging whether the stored data to be released in the current time stamp is larger than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time;
When the stored data to be released in the current time stamp is larger than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time, generating a first release strategy from the maximum information release amount of the bank informatization management platform in the current time stamp in unit time;
when the stored data to be released in the current time stamp is not more than the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp, generating a second release strategy from the storage amount corresponding to the stored data to be released in the current time stamp;
and generating a data release strategy based on the first release strategy or the second release strategy, and releasing stored data to the bank informatization management platform according to the data release strategy.
It should be noted that, by the method, the stored data of the bank informatization management platform can be released, so that the storage capacity of the bank informatization management platform is kept within a preset standard range, and the blocking of the bank informatization management platform is avoided.
In addition, the method can further comprise the following steps:
acquiring the maximum information storage amount of the bank informatization management platform and the storage amount corresponding to the storage data of which the storage time limit data is not more than the storage time limit data threshold value;
judging whether the storage capacity corresponding to the storage data of which the storage time limit data is not more than the storage time limit data threshold value is more than the maximum information storage capacity of the bank informatization management platform or not;
Outputting a normal storage state if the storage capacity corresponding to the storage data of which the storage time limit data is not more than the storage time limit data threshold value is not more than the maximum information storage capacity of the bank informatization management platform;
and if the storage quantity corresponding to the storage data of which the storage time limit data is not greater than the storage time limit data threshold value is greater than the maximum information storage quantity of the bank informatization management platform, performing expansion processing on the maximum information storage quantity of the bank informatization management platform.
By the method, the management rationality of the bank informatization management platform can be further improved.
Furthermore, the method further comprises:
acquiring data storage capacity increased by a bank informatization management platform within preset time, constructing a data storage capacity prediction model based on LSTM, and inputting the data storage capacity increased by the bank informatization management platform within the preset time into the data storage capacity prediction model for training;
acquiring a data storage quantity prediction model after training through training, and acquiring data storage quantity preference characteristics added by a bank informatization management platform within preset time through the data storage quantity prediction model after training;
Initializing the memory capacity to be expanded according to the preference characteristics of the data memory capacity added by the bank informatization management platform within the preset time, and calculating the total memory capacity after expansion according to the memory capacity to be expanded;
acquiring the data storage amount of the bank informatization management platform in the current time stamp, and acquiring the predicted total storage amount according to the preference characteristic of the data storage amount added by the bank informatization management platform within the preset time and the data storage amount of the bank informatization management platform in the current time stamp;
And when the predicted total storage amount is larger than the expanded total storage amount, readjusting the storage amount required to be expanded until the predicted total storage amount is not larger than the expanded total storage amount.
It should be noted that, by predicting the preference characteristics of the data storage amount added by the bank informatization management platform within the preset time, the bank informatization management platform is expanded and optimized, and the management rationality is improved.
As shown in fig. 3, the second aspect of the present invention provides a bank informatization intelligent management system 4 based on big data, which includes a memory 41 and a processor 42, wherein the memory includes a bank informatization intelligent management method program based on big data, and when the bank informatization intelligent management method program based on big data is executed by the processor 42, the following steps are implemented:
acquiring data storage capacity data of the bank informatization management platform, and acquiring a data storage capacity analysis result by identifying and analyzing the data storage capacity data of the bank informatization management platform;
acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time through the big data, and constructing an information release amount prediction model according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time;
predicting the maximum information release amount of the bank informatization management platform in the current time stamp in unit time by using an information release amount prediction model;
And acquiring the stored data to be released according to the analysis result of the data storage quantity, and formulating a data release strategy according to the maximum information release quantity of the bank informatization management platform in the unit time in the current time stamp and the stored data to be released.
Further, in the system, the data storage amount data of the bank informatization management platform is obtained, and the data storage amount analysis result is obtained by identifying and analyzing the data storage amount data of the bank informatization management platform, which specifically comprises the following steps:
Acquiring data storage capacity data of the bank informatization management platform, setting a data storage capacity data threshold value, and judging whether the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value;
when the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as an over-storage capacity state;
When the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as a normal storage capacity state;
and generating an over-storage state according to the over-storage state or the normal storage state, and outputting the over-storage state.
Further, in the system, the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time is obtained through big data, and an information release amount prediction model is constructed according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time, specifically:
acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time through the big data, and inputting the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time into the graph neural network;
Taking the network fluctuation data as a first graph node of the graph neural network, taking the maximum information release amount of the bank informatization management platform in unit time as a second graph node, and constructing a directed description relationship;
connecting the first graph node and the second graph node based on the directed description relationship to form a topological structure diagram, acquiring a related adjacency matrix based on the topological structure diagram, and constructing an information release quantity prediction model based on a deep neural network;
And inputting the related adjacency matrix into the information release quantity prediction model, and storing model parameters of the information release quantity prediction model after the loss function of the information release quantity prediction model is converged to a preset value.
Further, in the system, the maximum information release amount of the bank informatization management platform in the current time stamp in unit time is predicted by the information release amount prediction model, specifically:
acquiring network fluctuation data of a current time stamp, and inputting the network fluctuation data of the current time stamp into an information release amount prediction model for prediction;
And obtaining the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp through prediction, and outputting the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp.
Further, in the system, the stored data to be released is obtained according to the analysis result of the data storage amount, specifically:
When the data storage amount analysis result is in an over storage amount state, acquiring storage time limit data of each storage data in the bank informatization management platform, and setting a storage time limit data threshold;
When the storage time limit data is larger than the storage time limit data threshold value, the storage data corresponding to the storage time limit data larger than the storage time limit data threshold value is used as the storage data to be released, and the storage data to be released is output.
Further, in the system, a data release strategy is formulated according to the maximum information release amount of the bank informatization management platform in the unit time and the stored data to be released in the current time stamp, specifically:
acquiring the stored data to be released in the current time stamp, and judging whether the stored data to be released in the current time stamp is larger than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time;
When the stored data to be released in the current time stamp is larger than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time, generating a first release strategy from the maximum information release amount of the bank informatization management platform in the current time stamp in unit time;
when the stored data to be released in the current time stamp is not more than the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp, generating a second release strategy from the storage amount corresponding to the stored data to be released in the current time stamp;
and generating a data release strategy based on the first release strategy or the second release strategy, and releasing stored data to the bank informatization management platform according to the data release strategy.
The third aspect of the present invention provides a computer-readable storage medium, which is characterized by comprising a bank informatization intelligent management method program based on big data, and implementing the steps of any bank informatization intelligent management method based on big data when the bank informatization intelligent management method program based on big data is executed by a processor.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be additional divisions of actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place or distributed on a plurality of network units, and may select some or all of the units according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of hardware plus a form of software functional unit.
It will be appreciated by those of ordinary skill in the art that implementing all or part of the steps of the above method embodiments may be implemented by hardware associated with program instructions, where the above program may be stored in a computer readable storage medium, where the program when executed performs the steps comprising the above method embodiments, where the above storage medium includes a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic or optical disk, or other various media that may store program code.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. The storage medium includes various media capable of storing program codes such as a removable storage device, a ROM, a RAM, a magnetic disk or an optical disk.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The bank informatization intelligent management method based on big data is characterized by comprising the following steps:
acquiring data storage capacity data of a bank informatization management platform, and acquiring a data storage capacity analysis result by identifying and analyzing the data storage capacity data of the bank informatization management platform;
Acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time through the big data, and constructing an information release amount prediction model according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time;
Predicting the maximum information release amount of the bank informatization management platform in the current time stamp within unit time by the information release amount prediction model;
And acquiring the stored data to be released according to the data storage amount analysis result, and formulating a data release strategy according to the maximum information release amount of the bank informatization management platform in unit time in the current time stamp and the stored data to be released.
2. The intelligent bank informatization management method based on big data according to claim 1, wherein the method is characterized in that the data storage capacity data of the bank informatization management platform is obtained, and the data storage capacity analysis result is obtained by identifying and analyzing the data storage capacity data of the bank informatization management platform, and specifically comprises the following steps:
Acquiring data storage capacity data of a bank informatization management platform, setting a data storage capacity data threshold value, and judging whether the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value or not;
When the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as an over-storage capacity state;
when the data storage capacity data of the bank informatization management platform is larger than the data storage capacity data threshold value, the current data storage capacity state of the bank informatization management platform is used as a normal storage capacity state;
and generating an over-storage state according to the over-storage state or the normal storage state, and outputting the over-storage state.
3. The intelligent bank informatization management method based on big data according to claim 1, wherein the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time is obtained through the big data, and an information release amount prediction model is constructed according to the maximum information release amount of the bank informatization management platform under each network fluctuation data in unit time, specifically:
Acquiring the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time through big data, and inputting the maximum information release amount of the bank informatization management platform under each network fluctuation data within unit time into the graph neural network;
taking the network fluctuation data as a first graph node of the graph neural network, taking the maximum information release amount of a bank informatization management platform in unit time as a second graph node, and constructing a directed description relationship;
Connecting the first graph node and the second graph node based on the directed description relationship to form a topological structure diagram, acquiring a related adjacency matrix based on the topological structure diagram, and constructing an information release quantity prediction model based on a deep neural network;
And inputting the related adjacency matrix into the information release quantity prediction model, and storing model parameters of the information release quantity prediction model after the loss function of the information release quantity prediction model is converged to a preset value.
4. The intelligent management method of bank informatization based on big data according to claim 1, wherein the maximum information release amount of the bank informatization management platform in the unit time in the current time stamp is predicted by the information release amount prediction model, specifically:
Acquiring network fluctuation data of a current time stamp, and inputting the network fluctuation data of the current time stamp into the information release amount prediction model for prediction;
And obtaining the maximum information release amount of the bank informatization management platform in the current time stamp within unit time through prediction, and outputting the maximum information release amount of the bank informatization management platform in the current time stamp within unit time.
5. The bank informationized intelligent management method based on big data according to claim 1, wherein the acquiring of the stored data to be released according to the analysis result of the data storage amount is specifically as follows:
when the data storage amount analysis result is in an over storage amount state, acquiring storage time limit data of each storage data in the bank informatization management platform, and setting a storage time limit data threshold;
when the storage time limit data is larger than the storage time limit data threshold, taking the storage data corresponding to the storage time limit data larger than the storage time limit data threshold as the storage data to be released, and outputting the storage data to be released.
6. The intelligent management method of bank informatization based on big data according to claim 1, wherein the data release strategy is formulated according to the maximum information release amount of the bank informatization management platform in unit time in the current time stamp and the stored data to be released, specifically:
Acquiring stored data to be released in a current time stamp, and judging whether the stored data to be released in the current time stamp is larger than the maximum information release amount of a bank informatization management platform in the current time stamp in unit time;
When the stored data to be released in the current timestamp is larger than the maximum information release amount of the bank informatization management platform in the current timestamp in unit time, generating a first release strategy from the maximum information release amount of the bank informatization management platform in the current timestamp in unit time;
When the stored data to be released in the current time stamp is not more than the maximum information release amount of the bank informatization management platform in the current time stamp in unit time, generating a second release strategy from the storage amount corresponding to the stored data to be released in the current time stamp;
And generating a data release strategy based on the first release strategy or the second release strategy, and releasing stored data to the bank informatization management platform according to the data release strategy.
7. The bank informatization intelligent management system based on big data is characterized by comprising a memory and a processor, wherein the memory comprises a bank informatization intelligent management method program based on big data, and the bank informatization intelligent management method program based on big data realizes the steps of the bank informatization intelligent management method based on big data according to any one of claims 1-6 when the bank informatization intelligent management method program based on big data is executed by the processor.
8. A computer-readable storage medium, comprising a big data based bank informatization intelligent management method program, which when executed by a processor, implements the steps of the big data based bank informatization intelligent management method according to any one of claims 1-6.
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