Battery management system of FTU terminal equipment

Abstract

The invention relates to the field of battery management data processing, in particular to a battery management system of FTU (Feeder Terminal Unit) terminal equipment, which comprises a data acquisition module for acquiring battery management data, and a data preprocessing module for preprocessing the battery management data, the system constructs a fault feature recognition model through a model prediction module, a decision generation module generates a basic decision, and an instruction generation module performs on-off processing on an execution layer according to a configuration instruction. The aging condition of the battery is predicted, data comprehensiveness and battery data precision are still kept in a severe environment, interruption is not likely to happen in the face of strong interference, and therefore the management and monitoring precision of the battery state is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of battery management data processing, and in particular to a battery management system of an FTU terminal device. BACKGROUND

[0002] In the application background of the FTU terminal device, its battery management system is directly related to the long-term reliability of the device in remote and harsh environments. The traditional scheme faces core challenges: on the one hand, the terminal is often deployed in weak signal or extreme environment (such as high and low temperature, strong electromagnetic interference), resulting in frequent interruption or distortion of transmission of key battery data such as voltage and current, causing "data black box", so that the management system cannot accurately estimate the state of charge based on complete information. On the other hand, under such uncontrollable charging and discharging cycles and adverse environmental stress, the chemical aging process of the battery will be accelerated sharply, therefore, an intelligent battery management system that can cope with incomplete data and actively optimize strategies to prolong the actual service life of the battery is urgently needed.

[0003] Chinese Patent No. CN119482868B discloses a battery management system, which comprises a battery module, a current sampling circuit, a first resistor, an analog front-end circuit, a relay driving circuit, a control module, and a charging and discharging circuit. The current sampling circuit is used to detect the working current of the first resistor. The analog front-end circuit is used to collect a plurality of working parameters of the battery module at the current time when the working current does not meet the preset condition. The control module is used to determine the target working state of the battery module according to the plurality of working parameters. When the target working state is an abnormal state, a control instruction corresponding to the target working state is generated, and the control instruction is used to control the relay to be attracted or opened. It can be seen that this scheme still has the problems of being unable to adapt to battery aging, lacking battery aging prediction, and incomplete monitoring data and low data precision in adverse environments. SUMMARY

[0004] Therefore, the present application provides a battery management system of an FTU terminal device to overcome the problems of being unable to adapt to battery aging, lacking battery aging prediction, and incomplete monitoring data and low data precision in adverse environments in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides a battery management system of an FTU terminal device, which comprises: a data acquisition module for acquiring battery management data; a data preprocessing module for preprocessing the battery management data to obtain processed battery management data; The model prediction module is configured to construct a fault feature recognition model, acquire early fault signals based on the processed battery management data and the fault feature recognition model, and acquire the battery health based on the fault feature recognition model. The decision generation module is configured to generate a basic decision based on the battery health, perform dual-mode communication transmission based on the basic decision, query a configuration instruction based on the dual-mode communication transmission result, and perform proportion optimization on the battery health based on the negative proportion. The instruction generation module is configured to perform on-off processing on the execution level based on the configuration instruction, evaluate the effect of the on-off processing to obtain an effect evaluation value, and adjust the negative proportion based on the effect evaluation value.

[0006] Further, the data preprocessing module preprocesses the battery management data through a data preprocessing method, which includes: Step A01, performing format unification processing on the battery management data to obtain unified battery management data; Step A02, performing identification and marking processing on the unified battery management data to obtain marked battery management data; Step A03, performing association processing on the marked battery management data to obtain associated battery management data; Step A04, performing synonymous classification on the associated battery management data to obtain processed battery management data.

[0007] Further, the model prediction module constructs a fault feature recognition model, divides a preset fault database into a 70% fault training set, a 20% fault validation set, and a 10% fault test set, inputs the fault training set into a weighted Kalman filter algorithm model for training to obtain a trained weighted Kalman filter algorithm model, inputs the fault validation set into the trained weighted Kalman filter algorithm model to iteratively optimize the parameters of the trained weighted Kalman filter algorithm model, inputs the fault test set into the iteratively optimized weighted Kalman filter algorithm model to test the iteratively optimized weighted Kalman filter algorithm model, obtains a registration accuracy U, compares the registration accuracy U with a preset registration accuracy U0, judges whether the training of the weighted Kalman filter algorithm model meets the standard based on the comparison result, and outputs the weighted Kalman filter algorithm model based on the judgment result, wherein: When U≥U0, it is determined that the training of the weighted Kalman filter algorithm model meets the standard, and the weighted Kalman filter algorithm model is output as the fault feature recognition model; When U < U0, it is determined that the weighted Kalman filtering algorithm model training is not up to standard, the weighted Kalman filtering algorithm model parameters are iteratively optimized, and the training is repeated until the weighted Kalman filtering algorithm model training is up to standard, and the model is output as a fault feature recognition model.

[0008] Further, the model prediction module acquires an early fault signal according to the processed battery management data and the fault feature recognition model, inputs the processed battery management data into the fault feature recognition model, and obtains the early fault signal output by the fault feature recognition model.

[0009] Further, the model prediction module acquires a battery health degree according to the processed battery management data and the fault feature recognition model, inputs the processed battery management data into the fault feature recognition model, and obtains the battery health degree Js output by the fault feature recognition model.

[0010] Further, the decision generation module compares the battery health degree Js with a preset battery health degree Js0, judges the standard reaching condition of the battery health degree according to the comparison result, and generates a basic decision according to the judgment result, wherein: When Js is greater than or equal to Js0, the decision generation module determines that the standard reaching condition of the battery health degree is up to standard, and does not generate the basic decision; When Js is less than Js0, the decision generation module determines that the standard reaching condition of the battery health degree is not up to standard, and generates the basic decision: to establish a strict quality assurance system.

[0011] Further, the decision generation module performs dual-mode communication transmission according to the basic decision, and the dual-mode communication transmission simultaneously adopts two kinds of heterogeneous communication techniques to perform data transceiving on the processed battery management data. When the main channel encounters interference or attenuation, causing the signal quality to decrease, the terminal module will detect in real time and automatically switch to the standby channel, and the receiving end will selectively combine.

[0012] Further, the decision generation module queries the configuration instruction according to the dual-mode communication transmission result. The user inputs the format specification to the configuration instruction through a command line interface. The target configuration object is identified through an instruction parser, and the configuration instruction is fed back to the user interface according to the data structure in the configuration database or the memory accessed by the query engine.

[0013] Further, the instruction generation module obtains the negative proportion, and performs proportion optimization on the battery health degree according to the negative proportion, inputs the processed battery management data into a negative proportion calculation model to obtain a negative proportion Fy output by the negative proportion calculation model, compares the negative proportion Fy with a preset negative proportion Fy0, judges the state of the negative proportion according to the comparison result, and performs proportion optimization on the battery health degree according to the judgment result, wherein: When Fy≤Fy0, the instruction generation module determines that the state of the negative proportion is a high proportion state, performs proportion optimization on the battery health degree, performs proportion optimization on the preset battery health degree Js0 according to a proportion optimization coefficient zn, and zn=0.75, sets the optimized preset battery health degree as Js1, and Js1=Js0×zn, outputs the optimized preset battery health degree Js1 as the preset battery health degree Js0, and recompares the battery health degree Js with the preset battery health degree Js0; When Fy>Fy0, the instruction generation module determines that the state of the negative proportion is a low proportion state, and does not perform proportion optimization on the battery health degree.

[0014] Further, the instruction generation module performs on-off processing on the execution level according to the configuration instruction, and performs effect evaluation on the on-off processing process to obtain an effect evaluation value Pg, compares the effect evaluation value Pg with a preset effect evaluation value Pg0, judges the standard reaching degree of the effect evaluation value according to the comparison result, and performs effect evaluation on the on-off processing process according to the judgment result, wherein: When Pg≥Pg0, the instruction generation module determines that the standard reaching degree of the effect evaluation value is up to standard, and does not perform effect evaluation on the on-off processing process; When Pg

[0015] Compared with the prior art, the FTU terminal device has the beneficial effects that the battery management system of the FTU terminal device identifies the fault characteristics, and generates basic decisions and configuration instructions, so as to predict the aging condition of the battery and still maintain the comprehensiveness of data and the accuracy of battery data in a harsh environment, and is not easy to be interrupted in the face of strong interference, thereby improving the management and monitoring accuracy of the battery state. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 FIG. 1 is a structural schematic diagram of a battery management system of an FTU terminal device according to the present embodiment. DETAILED DESCRIPTION

[0017] In order to make the objects, technical schemes and advantages of the present application clearer, the following further describes the present application with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0018] The preferred embodiments of the present application are described below with reference to the drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0019] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0020] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium; it can be the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0021] Please refer to Figure 1 As shown in the figure, it is a structural schematic diagram of the battery management system of the FTU terminal device of the present embodiment, which comprises: A data acquisition module is used to acquire battery management data; A data preprocessing module is used to preprocess the battery management data to obtain processed battery management data; A model prediction module is used to construct a fault feature recognition model, and to acquire early fault signals according to the processed battery management data and the fault feature recognition model, and to acquire the battery health degree according to the fault feature recognition model; A decision generation module is used to generate a basic decision according to the battery health degree, and to perform dual-mode communication transmission according to the basic decision, and is also used to query a configuration instruction according to the dual-mode communication transmission result, and is also used to optimize the proportion of the battery health degree according to the negative proportion; An instruction generation module is used to perform on-off processing on the execution level according to the configuration instruction, and to evaluate the effect of the on-off processing process to obtain an effect evaluation value, and to adjust the negative proportion according to the effect evaluation value.

[0022] Specifically, the battery management system of the FTU terminal device identifies fault features and generates basic decisions and configuration instructions to predict the aging of the battery and maintain the comprehensiveness of data and the accuracy of battery data in harsh environments, face strong interference and not be easily interrupted, thereby improving the management and monitoring accuracy of the battery state.

[0023] Specifically, the data acquisition module acquires battery management data at a frequency of 10 times per second through the MCU.

[0024] Specifically, the battery management data includes remaining power, battery voltage, battery current, battery temperature, and environmental humidity. The remaining power refers to the current battery remaining power, the battery voltage refers to the current battery voltage value, the battery current refers to the current battery current value, and the battery temperature refers to the current battery temperature value. In this embodiment, the battery voltage is acquired by a voltage sensor, the battery current is acquired by a current sensor, and the battery temperature is acquired by a temperature sensor.

[0025] Specifically, the data preprocessing module preprocesses the battery management data through a data preprocessing method, and the data preprocessing method includes: Step A01, performing format uniformity processing on the battery management data to obtain uniform battery management data; Step A02, identifying and marking the uniform battery management data to obtain marked battery management data; Step A03, associating the marked battery management data to obtain associated battery management data; Step A04, synonymically classifying the associated battery management data to obtain processed battery management data.

[0026] Specifically, the format uniformity processing refers to the process of removing random codes in the battery management data, the identification and marking processing refers to the process of marking the uniform battery management data, the association processing refers to the process of associating the marked battery management data with relationship attributes, and the synonymically classifying refers to the process of classifying battery management data with the same attributes to construct a knowledge graph. The specific way of synonymically classifying is not limited in this embodiment, and a person skilled in the art can freely choose according to actual needs.

[0027] Specifically, the data preprocessing module preprocesses the battery management data to enhance data accuracy and improve the comprehensiveness and environmental adaptability of data.

[0028] Specifically, the model prediction module constructs a fault feature recognition model, divides a preset fault database into a 70% fault training set, a 20% fault verification set, and a 10% fault test set, inputs the fault training set into a weighted Kalman filtering algorithm model for training to obtain a trained weighted Kalman filtering algorithm model, inputs the fault verification set into the trained weighted Kalman filtering algorithm model to iteratively optimize parameters of the trained weighted Kalman filtering algorithm model, inputs the fault test set into the iteratively optimized weighted Kalman filtering algorithm model to test the iteratively optimized weighted Kalman filtering algorithm model, obtains a registration accuracy U, compares the registration accuracy U with a preset registration accuracy U0, judges whether the training of the weighted Kalman filtering algorithm model meets a standard according to a comparison result, and outputs the weighted Kalman filtering algorithm model according to a judgment result, wherein: When U ≥ U0, it is determined that the training of the weighted Kalman filtering algorithm model meets the standard, and the weighted Kalman filtering algorithm model is output as the fault feature recognition model; When U < U0, it is determined that the training of the weighted Kalman filtering algorithm model does not meet the standard, the parameters of the weighted Kalman filtering algorithm model are iteratively optimized, and the training is repeated until the training of the weighted Kalman filtering algorithm model meets the standard, and the weighted Kalman filtering algorithm model is output as the fault feature recognition model.

[0029] The adaptive weighted Kalman filtering algorithm is a signal processing and state estimation technology, which introduces an adaptive adjustment mechanism on the basis of a classical Kalman filtering algorithm, estimates and corrects the uncertainty of a system model and the statistical characteristics of sensor noise in real time to improve filtering accuracy and stability, and outputs a probability that the SOH decreases to 80% in the next 30 days to realize early warning.

[0030] Specifically, the model prediction module obtains early fault signals according to the processed battery management data and the fault feature recognition model, inputs the processed battery management data into the fault feature recognition model to obtain the early fault signals output by the fault feature recognition model. The model prediction module obtains the battery health degree according to the processed battery management data and the fault feature recognition model, inputs the processed battery management data into the fault feature recognition model to obtain the battery health degree Js output by the fault feature recognition model.

[0031] Specifically, the early fault signal refers to a fault tendency of the battery in an early stage obtained according to the fault feature recognition model, and the battery health degree refers to a numerical value for measuring the health degree of the battery obtained according to the fault feature recognition model.

[0032] Specifically, the decision generation module compares the battery health degree Js with the preset battery health degree Js0, judges the compliance of the battery health degree according to the comparison result, and generates the basic decision according to the judgment result, wherein: When Js≥Js0, the decision generation module determines that the compliance of the battery health degree is up to standard, and does not generate the basic decision; When Js<Js0, the decision generation module determines that the compliance of the battery health degree is not up to standard, and generates the basic decision: establish a strict quality assurance system.

[0033] Specifically, the preset battery health degree refers to the preset value for judging the compliance of the battery health degree, and the specific value of the preset battery health degree is not limited in the embodiment, and a person skilled in the art can freely select according to actual needs, such as setting Js0=0.85 in the embodiment, the compliance of the battery health degree refers to the up-to-standard degree of the battery health degree judged according to the battery health degree Js and the preset battery health degree Js0, and the compliance of the battery health degree includes up to standard and not up to standard.

[0034] Specifically, the decision generation module judges the compliance of the battery health degree, and when the compliance of the battery health degree is not up to standard, the basic decision is generated in time, so as to improve the management and monitoring accuracy of the battery state.

[0035] Specifically, the decision generation module performs dual-mode communication transmission according to the basic decision, and the dual-mode communication transmission simultaneously adopts two kinds of heterogeneous communication techniques to process the battery management data for data transmission and reception. When the main channel encounters interference or attenuation, causing the signal quality to decrease, the terminal module will detect in real time and automatically switch to the standby channel, and the receiving end will select and combine.

[0036] Specifically, the decision generation module automatically switches to the standby channel through dual-mode communication transmission without manual intervention, dynamically selects the optimal transmission path through the adaptive routing algorithm, effectively overcomes the limitations of single communication mode, and constructs a high-reliability, full-coverage local communication network in a complex environment, providing stable data transmission guarantee for smart grid and other applications.

[0037] Specifically, the decision generation module queries the configuration instruction according to the dual-mode communication transmission result, the user inputs the configuration instruction through the command line interface in a standard format, identifies the target configuration object through the instruction parser, and then feeds back the configuration instruction to the user interface according to the query engine accessing the configuration database or the data structure in the memory.

[0038] Specifically, the decision generation module querying the configuration instruction ensures that the administrator can master the system configuration in real time and accurately, which is the basis for network operation and fault troubleshooting.

[0039] Specifically, the instruction generation module obtains the negative proportion and optimizes the battery health degree according to the negative proportion. The processed battery management data is input into the negative proportion calculation model to obtain the negative proportion Fy output by the negative proportion calculation model. The negative proportion Fy is compared with the preset negative proportion Fy0. The state of the negative proportion is judged according to the comparison result, and the battery health degree is optimized according to the judgment result. Wherein: When Fy≤Fy0, the instruction generation module determines that the state of the negative proportion is a high proportion state, and optimizes the battery health degree. The preset battery health degree Js0 is optimized according to the proportion optimization coefficient zn, and zn=0.75. The optimized preset battery health degree is Js1, and Js1=Js0×zn. The optimized preset battery health degree Js1 is output as the preset battery health degree Js0, and the battery health degree Js is compared with the preset battery health degree Js0 again.

[0040] When Fy>Fy0, the instruction generation module determines that the state of the negative proportion is a low proportion state, and does not optimize the battery health degree.

[0041] Specifically, the preset negative proportion is a preset value for judging the state of the negative proportion. The specific value of the preset negative proportion is not limited in the embodiment, and can be freely selected by those skilled in the art according to actual needs. For example, Fy0=0.23 is set in the embodiment. The state of the negative proportion refers to the high or low degree of the negative proportion according to the comparison between the negative proportion and the preset negative proportion. The state of the negative proportion includes high proportion state and low proportion state.

[0042] Specifically, the instruction generation module performs on-off processing on the execution level according to the configuration instruction, and evaluates the effect of the on-off processing to obtain an effect evaluation value Pg. The effect evaluation value Pg is compared with a preset effect evaluation value Pg0. The standard degree of the effect evaluation value is judged according to the comparison result, and the effect of the on-off processing is evaluated according to the judgment result. Wherein: When Pg≥Pg0, the instruction generation module determines that the standard degree of the effect evaluation value is up to standard, and does not evaluate the effect of the on-off processing; When Pg<Pg0, the instruction generation module determines that the standard degree of the effect evaluation value is not up to standard, and evaluates the effect of the on-off processing.

[0043] The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A battery management system for an FTU terminal device, characterized in that, The system includes: The data acquisition module is used to collect battery management data; The data preprocessing module is used to preprocess the battery management data to obtain the processed battery management data. The model prediction module is used to construct a fault feature identification model, obtain early fault signals based on the processed battery management data and the fault feature identification model, and obtain battery health based on the fault feature identification model. The decision generation module is used to generate basic decisions based on battery health, and to perform dual-mode communication transmission based on the basic decisions. It is also used to query configuration commands based on the dual-mode communication transmission results, and to optimize battery health based on the negative percentage. The instruction generation module is used to perform on / off processing on the execution layer according to the configuration instructions, evaluate the effect of the on / off processing, obtain the effect evaluation value, and adjust the negative proportion according to the effect evaluation value.

2. The battery management system of the FTU terminal device according to claim 1, characterized in that, The data preprocessing module preprocesses the battery management data using a data preprocessing method, which includes: Step A01: Perform format unification processing on the battery management data to obtain unified battery management data; Step A02: Identify and label the unified battery management data to obtain labeled battery management data; Step A03: Perform association processing on the tagged battery management data to obtain the associated battery management data; Step A04: Perform synonym categorization on the associated battery management data to obtain the processed battery management data.

3. The battery management system of the FTU terminal device according to claim 2, characterized in that, The model prediction module constructs a fault feature recognition model, dividing a preset fault database into a 70% fault training set, a 20% fault validation set, and a 10% fault test set. The fault training set is input into a weighted Kalman filter (WKF) algorithm model for training, resulting in a trained WKF model. The fault validation set is input into the trained WKF model to iteratively optimize its parameters. The fault test set is input into the iteratively optimized WKF model for testing, obtaining a registration accuracy U. The registration accuracy U is compared with a preset registration accuracy U0. Based on the comparison result, the training success of the WKF model is judged, and the WKF model is output based on the judgment result. When U≥U0, the weighted Kalman filter algorithm model is deemed to have met the training target, and the weighted Kalman filter algorithm model is output as the fault feature identification model. When U < U0, the weighted Kalman filter algorithm model is deemed unqualified during training. The parameters of the weighted Kalman filter algorithm model are iteratively optimized and the training is repeated until the weighted Kalman filter algorithm model is qualified for training and then output as the fault feature recognition model.

4. The battery management system of the FTU terminal device according to claim 3, characterized in that, The model prediction module acquires early fault signals based on the processed battery management data and the fault feature recognition model, and inputs the processed battery management data into the fault feature recognition model to obtain the early fault signals output by the fault feature recognition model.

5. The battery management system of the FTU terminal device according to claim 4, characterized in that, The model prediction module obtains the battery health status based on the processed battery management data and the fault feature recognition model, and inputs the processed battery management data into the fault feature recognition model to obtain the battery health status Js output by the fault feature recognition model.

6. The battery management system of the FTU terminal device according to claim 5, characterized in that, The decision generation module compares the battery health level Js with the preset battery health level Js0, judges the compliance status of the battery health level based on the comparison result, and generates a basic decision based on the judgment result, wherein: When Js≥Js0, the decision generation module determines that the battery health meets the standard and does not generate a basic decision. When Js < Js0, the decision generation module determines that the battery health status does not meet the standard and generates a basic decision: establish a strict quality assurance system.

7. The battery management system of the FTU terminal device according to claim 6, characterized in that, The decision generation module performs dual-mode communication transmission based on the basic decision. The dual-mode communication transmission uses two heterogeneous communication technologies to send and receive the processed battery management data. When the primary channel encounters interference or attenuation that causes the signal quality to deteriorate, the terminal module will detect it in real time and automatically switch to the backup channel. The receiving end selects the best channel to merge the signals.

8. The battery management system of the FTU terminal device according to claim 7, characterized in that, The decision generation module queries the configuration commands based on the dual-mode communication transmission results, and the user queries the configuration commands by inputting the format specifications through the command line interface; The system identifies the target configuration object through the instruction parser, and then, based on the query engine, accesses the runtime configuration database or the data structure in memory to feed back the configuration instructions to the user interface.

9. The battery management system of the FTU terminal device according to claim 8, characterized in that, The instruction generation module acquires the negative percentage and optimizes the battery health percentage based on the negative percentage. It inputs the processed battery management data into the negative percentage calculation model to obtain the negative percentage Fy output by the model. The negative percentage Fy is compared with a preset negative percentage Fy0. Based on the comparison result, the state of the negative percentage is determined, and the battery health percentage is optimized based on the determination result. Wherein: When Fy≤Fy0, the instruction generation module determines that the negative percentage state is a high percentage state, optimizes the battery health percentage, optimizes the preset battery health Js0 according to the percentage optimization coefficient zn, and zn=0.75, sets the optimized preset battery health Js1, and Js1=Js0×zn, outputs the optimized preset battery health Js1 as the preset battery health Js0, and compares the battery health Js with the preset battery health Js0 again; When Fy > Fy0, the instruction generation module determines that the negative percentage state is a low percentage state and does not optimize the battery health percentage.

10. The battery management system of the FTU terminal device according to claim 9, characterized in that, The instruction generation module performs on / off processing on the execution layer according to the configuration instructions, and evaluates the effect of the on / off processing to obtain an effect evaluation value Pg. The effect evaluation value Pg is compared with a preset effect evaluation value Pg0. Based on the comparison result, the degree of compliance of the effect evaluation value is judged, and the effect of the on / off processing is evaluated based on the judgment result. Wherein: When Pg≥Pg0, the instruction generation module determines that the degree of compliance of the effect evaluation value is compliant and does not evaluate the effect of the on / off process. When Pg < Pg0, the instruction generation module determines that the compliance level of the effect evaluation value is not met, and performs an effect evaluation on the on / off process.

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