CN121309995B - Industrial Internet of things temperature transmitter data low-power-consumption transmission system - Google Patents
Industrial Internet of things temperature transmitter data low-power-consumption transmission systemInfo
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Abstract
本发明涉及工业物联网及工业自动化监测技术领域，具体为工业物联网温度变送器数据低功耗传输系统，包括：局部趋势特征提取单元，用于维护滑动时间窗口，解算出当前物理过程的变化速率与变化加速度；生成当前模型参数；虚拟影子预测单元，用于得到虚拟预测值；动态置信区间判决单元，用于生成动态置信阈值；将偏差与动态置信阈值进行比较；当偏差大于动态置信阈值时，生成模型失效触发信号；模型参数化重同步单元，用于将当前模型参数构建为数据帧；通过无线模块发送数据帧；将当前模型参数回写至虚拟影子预测单元，以更新已发送参数；本发明有效解决了传统死区方案因离散化导致关键过程细节丢失的问题，提升了工业监控数据的分析价值。
This invention relates to the field of industrial Internet of Things (IIoT) and industrial automation monitoring technology, specifically to a low-power data transmission system for industrial IoT temperature transmitters. The system includes: a local trend feature extraction unit for maintaining a sliding time window and calculating the rate and acceleration of change of the current physical process; generating current model parameters; a virtual shadow prediction unit for obtaining virtual predicted values; a dynamic confidence interval decision unit for generating a dynamic confidence threshold; comparing the deviation with the dynamic confidence threshold; generating a model failure trigger signal when the deviation exceeds the dynamic confidence threshold; a model parameterization resynchronization unit for constructing data frames from the current model parameters; transmitting the data frames via a wireless module; and writing the current model parameters back to the virtual shadow prediction unit to update the transmitted parameters. This invention effectively solves the problem of loss of key process details due to discretization in traditional dead-zone schemes, thereby enhancing the analytical value of industrial monitoring data.
Description
Technical Field
The invention relates to the technical fields of industrial Internet of things and industrial automatic monitoring, in particular to a low-power-consumption data transmission system of an industrial Internet of things temperature transmitter.
Background
Along with the wide application of the industrial Internet of things technology, the demand for the remote real-time monitoring of the running state of equipment, particularly the temperature parameter, is increasingly remarkable, and the industrial wireless temperature transmitter is taken as a key sensing node and is usually limited by the power supply capacity of a battery, so that the energy efficiency ratio of a data transmission strategy directly determines the service life and the maintenance cost of the equipment;
At present, the traditional data transmission and monitoring method mainly depends on a dead zone transmission mechanism for periodically reporting at regular time or based on a static threshold value, in a timing reporting mode, a transmitter mechanically transmits discrete sampling points containing a large amount of redundant information, so that a wireless radio frequency module is frequently activated to cause serious battery energy loss, and in a technology adopting fixed dead zone transmission, although part of invalid data can be filtered, the judgment logic is only based on a single numerical value difference value, and the dynamic characteristic of continuous change of a physical process is ignored. The processing mode causes that a receiving end can only acquire discrete values and loses key process details representing temperature change rate and trend turning, in addition, a fixed judgment threshold cannot adapt to changeable industrial field environments, namely, under the environment of strong electromagnetic interference or thermal noise, random fluctuation is easy to cause false awakening and transmission, invalid power consumption is increased, under the stable working condition, a cured threshold can cause missed detection on tiny trend change due to insufficient sensitivity, therefore, on the premise of strictly limiting power consumption, the process details are prevented from being lost, the contradiction between noise resistance and sensitivity of the fixed threshold is solved, and therefore, high-fidelity monitoring on a physical process is realized, and the problem to be solved urgently in the field is solved.
Disclosure of Invention
In order to solve the technical problems, the invention provides a low-power-consumption transmission system for industrial Internet of things temperature transmitter data, which comprises the following specific technical scheme:
industrial internet of things temperature transmitter data low-power transmission system includes:
the system comprises a local trend feature extraction unit, a current model parameter generation unit, a local trend feature extraction unit and a local trend feature extraction unit, wherein the local trend feature extraction unit is used for maintaining a sliding time window, the sliding time window stores a plurality of latest sampling points, and performing differential operation or least square operation on an original data stream in the sliding time window to calculate the change rate and the change acceleration of a current physical process;
the virtual shadow predicting unit is used for storing the transmitted parameters, deducing a theoretical temperature value at the current moment by utilizing the transmitted parameters and obtaining a virtual predicting value;
The dynamic confidence interval judging unit is used for receiving the real sampling value and the virtual predicted value, calculating the absolute value of the difference value between the real sampling value and the virtual predicted value to obtain deviation, generating a dynamic confidence threshold value based on the historical fluctuation variance of the equipment in a steady state and the preset precision requirement, comparing the deviation with the dynamic confidence threshold value, and generating a model failure triggering signal when the deviation is larger than the dynamic confidence threshold value;
The model parameterization resynchronization unit is used for responding to the model failure triggering signal, acquiring the current model parameters, constructing the current model parameters into data frames, wherein the data frames comprise new datum points, new change rates, new acceleration coefficients and effective time stamps, transmitting the data frames through a wireless module, and writing the current model parameters back to the virtual shadow prediction unit so as to update the transmitted parameters.
Preferably, the current model parameter is a polynomial coefficient defining the shape of the temperature change curve for a current short time.
Preferably, the virtual shadow prediction unit defaults to storing a null model or invalid state during the system power-up initialization phase such that the bias must be greater than the dynamic confidence threshold during the first sampling period, thereby triggering a first synchronization.
Preferably, the process of generating the dynamic confidence threshold by the dynamic confidence interval judging unit comprises the steps of calling a dynamic noise reference evaluation submodule, analyzing historical fluctuation variance of equipment in a steady state, determining a noise floor level, and generating a tolerance interval which changes along with environmental noise in a self-adaptive mode by combining with accuracy requirements set by a user to serve as the dynamic confidence threshold.
Preferably, the write-back operation of the model parameterized resynchronization unit is used for enabling the model in the virtual shadow prediction unit to agree with the current physical process, so that the deviation calculated by the dynamic confidence interval judgment unit at the next moment is reset to zero, and thus the subsequent communication request is restrained.
Preferably, the change rate corresponds to a first derivative and represents the speed of heating or cooling, and the change acceleration corresponds to a second derivative and represents the turning condition of the trend.
Preferably, the dynamic confidence threshold is not a fixed value, but is the tolerance interval that varies adaptively with the noise floor level, the tolerance interval representing the maximum reasonable error range that the system allows for model prediction to exist.
Compared with the prior art, the invention has the following beneficial effects:
1. The system utilizes the transmitted parameters to deduce a theoretical value by constructing a forward coupling mechanism of the virtual shadow prediction unit and a physical process, and triggers communication only when the real deviation exceeds a threshold value; the on-demand transmission mode changes the energy waste caused by the traditional periodic reporting and mechanical dead zone transmission, and greatly reduces the activation frequency of the radio frequency module while ensuring the monitoring precision, thereby remarkably prolonging the service life of battery power supply and realizing the balance of low power consumption and high fidelity;
2. The system adopts a local trend feature extraction technology to convert discrete sampling points into model parameters comprising change rate and change acceleration, so that the up-dimensional transmission of data dimension is realized; the receiving end can restore a continuous and smooth temperature curve according to polynomial coefficients, so that dynamic characteristics such as heating speed, trend turning and the like are completely reserved, the problem that details of a key process are lost due to discretization in a traditional dead zone scheme is effectively solved, and the analysis value of industrial monitoring data is improved;
3. the system introduces a dynamic confidence interval judging mechanism, can evaluate the background noise level in real time according to the historical fluctuation variance of the equipment in steady state and generate a self-adaptive variation tolerance interval according to the background noise level;
4. The system forcedly updates the local shadow model to be in agreement with the current physical process immediately after completing data transmission through a write-back mechanism of the model parameterized resynchronization unit, and the closed loop calibration eliminates continuous deviation generated by model hysteresis, so that the communication module quickly returns to a silence state after one-time calibration, thereby ensuring real-time synchronization of remote and local states, avoiding redundant handshake protocol and further improving channel utilization rate and system energy efficiency.
Drawings
The invention is further explained below with reference to the drawings and examples:
fig. 1 is a block diagram of the system of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
Example 1:
Referring to fig. 1, an industrial internet of things temperature transmitter data low-power transmission system includes:
The local trend feature extraction unit is used for maintaining a sliding time window, wherein the sliding time window stores a plurality of latest sampling points, differential operation or least square operation is carried out on an original data stream in the sliding time window, the change rate and the change acceleration of a current physical process are calculated, and a current model parameter is generated based on the change rate and the change acceleration, and the current model parameter comprises a current reference value, a first-order slope coefficient and a second-order acceleration coefficient;
The virtual shadow predicting unit is used for storing the transmitted parameters, and deducing a theoretical temperature value at the current moment by using the transmitted parameters to obtain a virtual predicting value;
The dynamic confidence interval judgment unit is used for receiving the real sampling value and the virtual prediction value, calculating the absolute value of the difference value between the real sampling value and the virtual prediction value to obtain deviation, generating a dynamic confidence threshold value based on the historical fluctuation variance of the equipment in a steady state and the preset precision requirement, comparing the deviation with the dynamic confidence threshold value, and generating a model failure triggering signal when the deviation is larger than the dynamic confidence threshold value;
the model parameterization resynchronization unit is used for responding to the model failure triggering signal, acquiring the current model parameters and constructing the current model parameters into data frames, wherein the data frames comprise new datum points, new change rates, new acceleration coefficients and effective time stamps, and the data frames are sent through the wireless module, and the current model parameters are written back to the virtual shadow prediction unit so as to update the sent parameters.
The local trend feature extraction unit is configured as a core perception calculation module of the system and is responsible for stripping continuous physical evolution rules from a discrete original sampling sequence; the unit continuously ingests the raw data stream generated by the temperature sensor through a hardware interface; in order to achieve accurate trend capturing, the unit opens up and maintains a sliding time window which follows the first-in first-out principle in the memory, wherein the size of the window is set to cover the number of sampling points which are enough to reflect the minimum characteristic period of the current physical process, for example, the number of sampling points which are larger than 3 to 5 times of the thermal response time constant of the measured object is set, so that the window data has statistical significance; in the operation process, the system calculates a group of key indexes representing the dynamic characteristics of the current physical process by constructing a functional relation between time and temperature, namely the change rate representing the speed of temperature change and the change acceleration representing the turning of a change trend, and after the calculation is completed, the unit packages the calculated data into a group of structured current model parameters to be transmitted to a subordinate unit, wherein the parameter set clearly comprises the intercept, the primary term coefficient and the quadratic term coefficient of a fitting curve at the current moment;
The virtual shadow prediction unit constructs a calculation model completely synchronous with the state of a remote server side locally in a transmitter, and takes the calculation model as a logic reference standard of communication decision; on the operation mechanism, the unit performs polynomial extrapolation calculation according to the currently stored parameter set and a real-time system clock, and the calculation product is defined as a virtual predicted value and is transmitted to a judgment unit on the premise that the physical rule is not changed, wherein the calculation product is a virtual predicted value, and is essentially a mathematical state machine running in a local microcontroller, and the state variable is strictly controlled by each wireless transmission action, so that the real-time consistency of the local and remote cognition on the state of a measured object is ensured;
The dynamic confidence interval judging unit is used as a logic gating hub of the system and bears the decision task of balancing between data fidelity and energy consumption, the unit receives a real sampling value from a sensor and a virtual predicted value from a shadow unit in parallel, when the operation logic is started, the unit calculates the absolute value of the difference value of the real sampling value and the virtual predicted value to obtain instantaneous deviation, more importantly, the unit abandons static threshold judgment and generates a dynamic confidence threshold in real time through a weighting algorithm according to the historical fluctuation variance of the equipment in steady-state operation and the accuracy index pre-configured by a user;
The execution flow comprises directly retrieving the latest current model parameters from the feature extraction unit, packaging the latest current model parameters with the current timestamp information, constructing a special data frame containing a new datum point, a new change rate, a new acceleration coefficient and an effective timestamp, driving the wireless radio frequency module to transmit the data frame to a remote server, immediately executing key write-back operation by the unit after completing the transmission action, and forcedly overwriting the latest parameter set in a register of the virtual shadow prediction unit;
Compared with the prior art, the scheme does not mechanically transmit redundant data points or lose process details due to dead zones, but realizes on-demand communication, namely energy is consumed only at the moment when the physical rule is changed substantially, and the scheme realizes high-fidelity reconstruction and synchronization of the process curve form while ensuring microampere-level power consumption constraint, thereby remarkably prolonging the service life of a battery and improving the data value.
Example 2:
The current model parameters are polynomial coefficients defining the shape of the temperature change curve in a current short time.
The system adopts a second order polynomial model to fit the temperature sequence in a sliding window for the purpose of describing a continuous physical process by using the minimum data load, under the definition, the current model parameters are embodied into a coefficient set of the polynomial, an operation unit adopts least square regression analysis with end point constraint, namely, lagrange multiplier is introduced in fitting operation or the current moment is set as a fixed constraint point, the intercept c of the forced fitting curve is strictly equal to the real sampling value of the current moment so as to ensure the numerical continuity when the new model and the old model are switched, and the best fitting curve is determined under the constraintWhereinAs the constant term corresponding to the reference value,As the slope of the coefficient correspondence of the first order term,As the curvature corresponding to the quadratic coefficient;
The method has the advantages that polynomial coefficients are adopted as a transmission carrier, the dimension of the transmission content is essentially increased, a receiving end can restore a smooth and continuous temperature change curve through integral operation only according to the received three coefficients and time stamps, the dynamic trend characteristics of temperature change are completely reserved while the data transmission quantity is greatly compressed by the technical means, and the technical problem that key process information is lost due to data discretization in a traditional dead zone scheme is effectively solved.
Example 3:
the virtual shadow prediction unit defaults to storing an empty model or invalid state during the system power-up initialization phase such that the bias must be greater than the dynamic confidence threshold during the first sampling period, thereby triggering a first synchronization.
The initialization logic of the virtual shadow prediction unit in the cold start stage is specified in detail; in order to ensure that the reference synchronization can be quickly established after the system is powered on, a register of the unit is preset as an empty model or a specific invalid flag bit in an initialization program, under the configuration, when the system acquires first real temperature data and sends the first real temperature data into a judgment unit, the calculated deviation value is expressed as a maximum value or a logic infinity mathematically due to the invalid or zero output of a shadow model, and the value is beyond any dynamic confidence threshold set based on physical significance, so that a model failure triggering signal is forcedly triggered;
The initialization strategy skillfully utilizes the error correction mechanism of the system to finish the first data synchronization by presetting the inevitably invalid judgment condition, and the design eliminates the data blind area possibly caused by lack of a reference model in the initial stage of system start, ensures the seamless connection of the monitoring process, and simultaneously avoids the need of additionally writing complex handshake protocol codes.
Example 4:
The process of generating the dynamic confidence threshold by the dynamic confidence interval judging unit comprises the steps of calling a dynamic noise reference evaluation submodule, analyzing historical fluctuation variance of equipment in a steady state, determining a background noise level, and generating a tolerance interval which changes along with environmental noise in a self-adaptive mode by combining with the precision requirement set by a user to serve as the dynamic confidence threshold.
The dynamic noise reference evaluation submodule embedded in the dynamic confidence interval judging unit executes a strict threshold value calculation flow, the submodule adopts a rolling statistical algorithm to analyze sampling data of equipment in a latest steady-state period in real time, performs differential operation matched with the current model order on a data sequence, such as adopting second-order differential to eliminate the influence of acceleration items or high-pass filtering processing aiming at a second-order model, so as to strip low-frequency trend components with time-varying temperature, only calculates variance or standard deviation of the stripped high-frequency residual sequence and quantizes the current noise floor level, the specific generation logic of the threshold value follows the principle that the calculated noise floor level is multiplied by a preset confidence coefficient, and then an absolute precision requirement value set by a user is overlapped to finally synthesize a floating dynamic confidence threshold value, wherein the noise floor level reflects the environmental electromagnetic interference and the thermal noise intensity of the sensor, and the dynamic confidence threshold value defines a tolerance interval which stretches adaptively along with the environmental working condition;
The self-adaptive threshold mechanism solves the adaptability problem of a fixed threshold scheme under a complex working condition, automatically widens the threshold under a strong noise environment to filter false triggering caused by random interference, prevents unnecessary consumption of battery energy, automatically narrows the threshold under a low-noise stable environment to improve the capturing sensitivity to tiny trend change, and ensures that the system is always at an optimal detection performance and energy efficiency balance point under different working conditions.
Example 5:
and the write-back operation of the model parameterized resynchronization unit is used for enabling the model in the virtual shadow prediction unit to agree with the current physical process, so that the deviation calculated by the dynamic confidence interval judgment unit at the next moment is reset to zero, and further, the subsequent communication request is restrained.
The logic closed-loop effect of the model parameterized resynchronization unit for executing the write-back operation is deeply described; when the dynamic confidence interval judging unit compares in the following sampling period, the inputted virtual predicted value will be generated by the new model, the value is highly approximate to the real sampling value mathematically, the difference between the two will quickly converge to zero or only contain tiny random noise component, the deviation value must fall into the range allowed by the dynamic confidence threshold value;
The write-back mechanism builds the digital remodelling anticipation capability of the system, automatically eliminates a series of transmission requirements possibly generated by model hysteresis through one-time parameter calibration, enables the communication module to quickly return to the silence state, greatly improves the channel utilization rate and the overall energy efficiency of the system through one-time calibration in a long-term silence working mode, and embodies the silence, namely controlled advanced monitoring concept.
Example 6:
the change rate corresponds to the first derivative, which represents the speed of heating or cooling, and the change acceleration corresponds to the second derivative, which represents the turning condition of the trend.
The change speed strictly corresponds to the first derivative of temperature versus time in mathematic, the physical meaning of the change speed is to quantify the temperature rise or fall speed of the measured medium at the current moment, the change acceleration corresponds to the second derivative of temperature versus time, the physical meaning of the change acceleration is to describe the curvature or turning characteristic of the temperature change trend, namely, the change speed reflects whether the temperature change process is in acceleration or tends to be gentle;
By establishing clear physical-mathematical mapping, the invention ensures that the transmitted data has clear physical process indication, so that a receiving end system can directly utilize the parameters to carry out process diagnosis based on the change rate, the defects of the prior art that only discrete values are transmitted and process dynamic information is lost are overcome, and the availability and the analysis value of industrial monitoring data are obviously improved.
Example 7:
The dynamic confidence threshold is not a fixed value, but is the tolerance interval that varies adaptively with the noise floor level, which represents the maximum reasonable error range that the system allows for model prediction to exist.
The threshold is not a hard-coded constant but a variable which has a positive correlation function with the background noise level, and logically, the tolerance interval defined by the threshold represents the acceptance boundary of the system for the prediction error, and all the deviations falling into the interval are judged by the system as reasonable fluctuation caused by environmental noise or measurement uncertainty without triggering communication;
The embodiment emphasizes the robustness of the technical scheme in a non-ideal environment, the system can effectively avoid reporting of pseudo changes caused by environmental interference by introducing a tolerance interval floating along with the noise level, and simultaneously ensures the sharp perception of real physical changes when the noise background is low, and the design endows the sensor with biological self-adaptive adjustment capability, so that the sensor can adapt to wide application scenes from laboratories to severe industrial sites.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention.
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