A charging pile monitoring system based on big data

Abstract

The present invention discloses a charging pile monitoring system based on big data, which specifically relates to the field of charging piles; the system collects feedback parameters and load parameters of a built-in monitoring module of the charging pile, obtains a concealment coefficient after normalization, compares the concealment coefficient with a preset concealment threshold, and generates a latent signal or an observable signal. Furthermore, for a monitoring module that generates an observable signal, the concealment coefficient is collected multiple times, the average value and the oscillation value are calculated, and compared with the corresponding average threshold and vibration threshold, thereby generating a consistent signal or an observed signal, which helps to improve the stability and reliability of the charging pile monitoring system and ensure the long-term stable operation of the charging pile.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and more specifically, to a charging pile monitoring system based on big data. Background Art

[0002] With the large-scale listing of new energy vehicles, as supporting facilities, the importance of charging piles to new energy vehicles lies in providing charging services for electric vehicles, enabling electric vehicles to easily obtain energy and extend their driving mileage, promoting the popularization and development of electric vehicles. Without the support of charging piles, the scope of use of electric vehicles will be limited, and users may face problems such as charging inconvenience and mileage anxiety. Therefore, the construction and improvement of charging piles is crucial to promoting the development of the new energy vehicle industry. The charging state of the charging pile directly affects the service life of the car battery. During the charging process, the battery will go through multiple stages such as charging, discharging and resting, and different charging states will have different degrees of impact on the battery.

[0003] Therefore, it is necessary to monitor the charging pile to ensure the safety and stability of the charging process and avoid damage to the battery. Existing charging piles usually monitor the charging process through a built-in monitoring module to ensure the stability and safety of charging. However, the monitoring module itself is also likely to fail, and traditional measures usually do not monitor this, which may result in inaccurate data obtained through the monitoring module, and may even mislead the system and provide wrong decision-making information.

[0004] 1. Failure of the monitoring module may result in failure to promptly detect abnormal conditions during charging, such as battery overheating, abnormal current, etc. This may cause the charging system to continue to operate without detecting abnormalities, increasing the risk of fire or other safety accidents;

[0005] 2. Failure of the monitoring module will result in an inability to accurately understand the actual status of the charging pile and the vehicle, making it impossible to adjust the charging power or voltage according to actual needs. This may lead to reduced charging efficiency, increased charging time and energy waste;

[0006] 3. Incorrect decision information will cause the charging pile to output inappropriate charging power or charging current, which will cause overcharging or undercharging of the battery. In the long run, this may reduce the battery life and shorten the battery life;

[0007] 4. Failure of the monitoring module itself can also cause failure of the charging pile, making it unable to work properly and affecting the availability of charging services;

[0008] 5. Inaccurate data can cause problems during the charging process, such as charging interruptions, charging rate fluctuations, etc., affecting the user experience and reducing the user's trust in the charging pile.

[0009] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0010] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides, which collects the feedback parameters and load parameters of the built-in monitoring module of the charging pile, obtains the concealment coefficient after normalization, compares the concealment coefficient with the preset concealment threshold, and generates a latent signal or an observable signal. Furthermore, for the monitoring module that generates an observable signal, its concealment coefficient is collected multiple times, the average value and oscillation value are calculated, and compared with the corresponding average threshold and vibration threshold, so as to generate a consistent signal or an observed signal, which helps to improve the stability and reliability of the charging pile monitoring system, ensure the long-term stable operation of the charging pile, and solve the problems raised in the above-mentioned background technology.

[0011] To achieve the above-mentioned purpose, the present invention provides the following technical solution: comprising a data acquisition unit, a model building unit, an analysis and judgment unit, and a summary and analysis unit;

[0012] The data acquisition unit is used to collect feedback parameters and load parameters of the built-in monitoring module of the charging pile, and send the feedback parameters and load parameters to the model building unit;

[0013] The model building unit is used to obtain a hidden coefficient by normalizing the feedback parameter and the load parameter, and send the hidden coefficient to the analysis and judgment unit;

[0014] The analysis and judgment unit is used to compare the concealment coefficient with the concealment threshold, and if the concealment coefficient is greater than or equal to the concealment threshold, a latent signal is generated; if the concealment coefficient is less than the concealment threshold, a visible signal is generated;

[0015] The summary analysis unit is used to collect the concealment coefficient of the monitoring module that generates the observable signal for multiple times according to the judgment of the analysis and judgment unit, calculate the average value and oscillation value of the concealment coefficient, compare the average value, vibration value and the corresponding average threshold and vibration threshold, and generate a consistent signal and an observation signal according to the comparison result.

[0016] In a preferred embodiment, the feedback parameter includes a heartbeat abnormality ratio, and the load parameter includes an energy load index.

[0017] In a preferred embodiment, the logic for obtaining the abnormal heartbeat ratio is:

[0018] Step S11: In a unit time, the heartbeat collector collects the heartbeat signal data of the monitoring module, and counts the number of heartbeat missing and the total number of heartbeats, and calculates the heartbeat missing rate, and the calculation formula is: heartbeat missing rate = heartbeat missing number / total heartbeat number;

[0019] Step S12: Compare the heartbeat missing rate with a preset threshold. If the heartbeat missing rate is greater than the threshold, mark it as abnormal.

[0020] Step S13: when an abnormality is found, the timestamp A of the abnormality is recorded, and then the heartbeat signal is continuously monitored during the entire operation cycle. When the heartbeat signal returns to normal, the timestamp B of the recovery is recorded;

[0021] Step S14: using the recorded abnormal interval time and normal interval time, the ratio of the normal interval time to the abnormal interval time, ie, the heartbeat abnormality ratio, is calculated using the formula: heartbeat abnormality ratio = normal interval time / abnormal interval time.

[0022] In a preferred embodiment, the logic for obtaining the energy load index is:

[0023] Step S21: During each charging process, the memory usage of the module is monitored at fixed time intervals and recorded;

[0024] Step S22: based on the recorded memory usage data, draw a curve graph showing the change of memory usage over time;

[0025] Step S23: In the drawn graph, find the part where the memory usage exceeds the set threshold, and calculate the area enclosed by the part of the graph, and the calculation formula is: area exceeding the threshold = Σ(memory usage - threshold) * time interval;

[0026] Step S24: Calculate the area enclosed by the entire memory usage graph, using the following calculation formula: total area = Σ(memory usage)*time interval;

[0027] Step S25: Divide the area exceeding the threshold calculated in step 3 by the total area calculated in step 4 to obtain the ratio of the area exceeding the threshold to the total area, i.e., the energy loading index. The calculation formula W is: energy loading index = area exceeding the threshold / total area.

[0028] In a preferred embodiment, the summary analysis unit operation includes the following:

[0029] Step S31: for the analysis and judgment unit to determine that the generated signal is observable, within the time t, the concealment coefficient of the monitoring module is collected multiple times to obtain a series of concealment coefficient values;

[0030] Step S32: Add all the collected concealment coefficient values ​​and divide by the number of collections to obtain an average value;

[0031] Subtract the average value from all the hidden coefficient values ​​and take the absolute value, then average it to get the oscillation value;

[0032] Step S33: If the average value is less than or equal to the average threshold, and the oscillation value is less than or equal to the vibration threshold, a consistent signal is generated; otherwise, an observation signal is generated.

[0033] The technical effects and advantages of the charging pile monitoring system based on big data of the present invention are as follows:

[0034] 1. The invention monitors the heartbeat abnormality ratio and the energy load index to understand the operation of the monitoring module, not only focusing on its stability but also considering the load level. The normalization of the hidden coefficient allows data at different scales to be compared and evaluated, ensuring the accuracy of the evaluation. By comparing with the hidden threshold, possible problems with the monitoring module are discovered, and corresponding latent signals or observable signals are generated, so that abnormalities can be discovered in a timely manner and corresponding measures can be taken for optimization and adjustment to ensure the stable operation of the monitoring system;

[0035] 2. By collecting the concealment coefficient value multiple times and calculating the average value, the stability and load of the monitoring module are effectively comprehensively considered. It can more comprehensively reflect the operating status of the monitoring module and avoid the possible accidental influence of single data collection; calculating the oscillation value can accurately determine the volatility of the concealment coefficient of the monitoring module, that is, the stability of the data. If the oscillation value is less than or equal to the vibration threshold, it means that the volatility of the data is small and the system operation is relatively stable. On the contrary, if the oscillation value exceeds the vibration threshold, it means that the data fluctuates greatly and there may be potential problems; calculating the oscillation value can accurately determine the volatility of the concealment coefficient of the monitoring module, that is, the stability of the data. If the oscillation value is less than or equal to the vibration threshold, it means that the volatility of the data is small and the system operation is relatively stable. On the contrary, if the oscillation value exceeds the vibration threshold, it means that the data fluctuates greatly and there may be potential problems; accurately evaluating the concealment coefficient and volatility, and generating consistent signals or observation signals based on the evaluation results, can help system administrators quickly discover and solve potential faults and problems, improve the reliability and stability of the charging pile monitoring system, and ensure user charging experience and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a structural schematic diagram of a charging pile monitoring system based on big data of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1

[0039] Figure 1 The present invention provides a charging pile monitoring system based on big data, including a data acquisition unit, a model building unit, an analysis and judgment unit, and a summary and analysis unit;

[0040] The data acquisition unit is used to collect feedback parameters and load parameters of the built-in monitoring module of the charging pile, and send the feedback parameters and load parameters to the model building unit;

[0041] The model building unit is used to obtain a hidden coefficient by normalizing the feedback parameter and the load parameter, and send the hidden coefficient to the analysis and judgment unit;

[0042] The analysis and judgment unit is used to compare the concealment coefficient with the concealment threshold, and if the concealment coefficient is greater than or equal to the concealment threshold, a latent signal is generated; if the concealment coefficient is less than the concealment threshold, a visible signal is generated;

[0043] The summary analysis unit is used to collect the concealment coefficient of the monitoring module that generates the observable signal for multiple times according to the judgment of the analysis and judgment unit, calculate the average value and oscillation value of the concealment coefficient, compare the average value, vibration value and the corresponding average threshold and vibration threshold, and generate a consistent signal and an observation signal according to the comparison result.

[0044] The importance of the built-in monitoring module of the charging pile cannot be underestimated. The monitoring module can monitor the key parameters of the charging pile, such as the status, current, voltage, power, etc. in real time to ensure the safe control and stable operation of the charging process. Through the fault diagnosis and early warning functions, abnormal conditions of the charging pile can be discovered in time, ensuring that the operation and maintenance personnel can respond and solve problems quickly, and ensuring the continuity and reliability of the charging service. At the same time, the data recording and analysis functions provide decision-making support for operators, optimize the layout and planning of charging piles, and improve the efficiency of charging piles and user satisfaction. Therefore, it is necessary to conduct a detailed analysis of the monitoring module to ensure the safe and efficient operation of the charging pile and provide a better charging service experience.

[0045] The data acquisition unit operation includes the following:

[0046] The feedback parameters include the heartbeat abnormality ratio, and the load parameters include the energy load index.

[0047] The logic for obtaining the heartbeat abnormality ratio is:

[0048] Step S11: In a unit time, the heartbeat collector collects the heartbeat signal data of the monitoring module, and counts the number of heartbeat missing and the total number of heartbeats, and calculates the heartbeat missing rate, and the calculation formula is: heartbeat missing rate = heartbeat missing number / total heartbeat number;

[0049] Step S12: Compare the heartbeat missing rate with a preset threshold. If the heartbeat missing rate is greater than the threshold, mark it as abnormal.

[0050] Step S13: When an abnormality is found, record the abnormal timestamp A. Then continue to monitor the heartbeat signal during the entire operation cycle. When the heartbeat signal returns to normal, record the recovery timestamp B;

[0051] Step S14: using the recorded abnormal interval time and normal interval time, the ratio of the normal interval time to the abnormal interval time, ie, the heartbeat abnormality ratio, is calculated using the formula: heartbeat abnormality ratio = normal interval time / abnormal interval time.

[0052] The heartbeat abnormality ratio is used to reflect the reliability of the monitoring module. The smaller the heartbeat abnormality ratio, the longer the interval between abnormal heartbeats of the charging pile during the operation cycle, that is, the fewer the number of missing heartbeats under abnormal conditions. This shows that the operation status of the charging pile is relatively stable, and the missing rate of the heartbeat signal under abnormal conditions is low. A smaller abnormality ratio usually indicates that the charging pile is in normal working condition. A larger heartbeat abnormality ratio indicates that the interval between abnormal heartbeats of the charging pile during the operation cycle is relatively short, that is, the missing rate of the heartbeat under abnormal conditions is high. This suggests that there are serious problems or faults in the charging pile, resulting in a higher missing rate of the heartbeat signal. A larger abnormality ratio usually indicates that the operation of the charging pile is unstable and requires timely maintenance and troubleshooting.

[0053] The logic for obtaining the energy load index is:

[0054] Step S21: During each charging process, the memory usage of the module is monitored at fixed time intervals and recorded;

[0055] Step S22: based on the recorded memory usage data, draw a curve graph showing the change of memory usage over time;

[0056] Step S23: In the drawn graph, find the part where the memory usage exceeds the set threshold, and calculate the area enclosed by the part of the graph, and the calculation formula is: area exceeding the threshold = Σ(memory usage - threshold) * time interval;

[0057] Step S24: Calculate the area enclosed by the entire memory usage graph, using the following calculation formula: total area = Σ(memory usage)*time interval;

[0058] Step S25: Divide the area exceeding the threshold calculated in step 3 by the total area calculated in step 4 to obtain the ratio of the area exceeding the threshold to the total area, i.e., the energy loading index. The calculation formula W is: energy loading index = area exceeding the threshold / total area.

[0059] The energy load index is used to reflect the load level of the monitoring module's memory usage. A larger energy load index means that the memory usage exceeds the set threshold most of the time. The monitoring module runs out of memory when processing large amounts of data or complex tasks, which can cause the system to run slowly, or experience delays or crashes. A smaller energy load index means that the memory usage is within the set threshold most of the time. The system memory load is relatively light and is in normal working condition. There are sufficient memory resources to process tasks, and the monitoring module has stable operating performance.

[0060] Building a model unit run consists of the following:

[0061] The heartbeat abnormality ratio and the energy loading index are normalized to obtain the concealment coefficient. For example, the concealment coefficient can be calculated by the following formula: CI = f1 × hrir + f2 × wei, where CI is the concealment coefficient, hrir and wei are the heartbeat abnormality ratio and the energy loading index, respectively, and f1 and f2 are the preset proportional coefficients of the heartbeat abnormality ratio and the energy loading index, respectively, and both f1 and f2 are greater than 0;

[0062] The concealment coefficient is used to reflect the stability and performance of the monitoring module during operation. The smaller the concealment coefficient, the relatively stable operation of the monitoring module, the memory usage is within the set threshold most of the time, and the heartbeat signal is less abnormal. This means that the performance and stability of the monitoring module are good, and the system is running normally. The larger the concealment coefficient, the more unstable the operation of the heart monitoring module, with more memory overload and heartbeat signal abnormalities. This causes problems in system operation, and corresponding measures need to be taken to optimize and adjust to ensure the stability and performance of the monitoring module.

[0063] The analysis and judgment unit operation includes the following:

[0064] Compare the concealment coefficient with the concealment threshold;

[0065] If the concealment coefficient is greater than or equal to the concealment threshold, it means that the stability and load level of the monitoring module exceed the set acceptable range, which means that there is a potential problem with the monitoring module, indicating that the monitoring system is abnormal, issuing an early warning signal and generating a latent signal;

[0066] If the concealment coefficient is less than the concealment threshold, it means that the stability and load level of the monitoring module are within an acceptable range. This means that the monitoring module is operating normally, indicating that the monitoring system is working normally, there is no obvious abnormality, and an observable signal is generated.

[0067] The present invention understands the operation of the monitoring module by monitoring the heartbeat abnormality ratio and the energy load index, not only focusing on its stability but also considering the load level. The normalization of the concealment coefficient allows data at different scales to be compared and evaluated, ensuring the accuracy of the evaluation. By comparing with the concealment threshold, possible problems of the monitoring module are discovered, and corresponding latent signals or observable signals are generated, so that abnormalities are discovered in time and corresponding measures are taken for optimization and adjustment, ensuring the stable operation of the monitoring system.

[0068] The summary analysis unit runs include the following:

[0069] Step S31: for the analysis and judgment unit to determine that the generated signal is observable, within the time t, the concealment coefficient of the monitoring module is collected multiple times to obtain a series of concealment coefficient values;

[0070] Step S32: Add all the collected concealment coefficient values ​​and divide by the number of collections to obtain an average value;

[0071] Subtract the average value from all the hidden coefficient values ​​and take the absolute value, then average it to get the oscillation value;

[0072] Step S33: If the average value is less than or equal to the average threshold, and the oscillation value is less than or equal to the vibration threshold, it means that the concealment coefficient of the monitoring module is stable and within the normal range, the data volatility is small, and the overall stability is good, and a consistent signal is generated; otherwise, it means that the concealment coefficient of the monitoring module may fluctuate or deviate from the normal range, the data volatility is large, and the overall stability is poor, and an observation signal is generated.

[0073] The present invention effectively comprehensively considers the stability and load conditions of the monitoring module by collecting the concealment coefficient value multiple times and calculating the average value. It can more comprehensively reflect the operating status of the monitoring module and avoid the possible accidental influence of single data collection; the calculation of the oscillation value can accurately determine the volatility of the concealment coefficient of the monitoring module, that is, the stability of the data. If the oscillation value is less than or equal to the vibration threshold, it means that the volatility of the data is small and the system operation is relatively stable. On the contrary, if the oscillation value exceeds the vibration threshold, it means that the data fluctuates greatly and there may be potential problems; the calculation of the oscillation value can accurately determine the volatility of the concealment coefficient of the monitoring module, that is, the stability of the data. If the oscillation value is less than or equal to the vibration threshold, it means that the volatility of the data is small and the system operation is relatively stable. On the contrary, if the oscillation value exceeds the vibration threshold, it means that the data fluctuates greatly and there may be potential problems; accurately evaluating the concealment coefficient and volatility, and generating a consistent signal or observation signal according to the evaluation results, can help system administrators quickly discover and solve potential faults and problems, improve the reliability and stability of the charging pile monitoring system, and ensure user charging experience and safety.

[0074] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0075] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0076] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0077] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0079] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0081] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging pile monitoring system based on big data, characterized in that: The method comprises the following steps: a data acquisition unit, a model building unit, an analysis and judgment unit, and a summary and analysis unit; The data acquisition unit is used to collect feedback parameters and load parameters of the built-in monitoring module of the charging pile, and send the feedback parameters and load parameters to the model building unit; The model building unit is used to obtain a hidden coefficient by normalizing the feedback parameter and the load parameter, and send the hidden coefficient to the analysis and judgment unit; The analysis and judgment unit is used to compare the concealment coefficient with the concealment threshold, and if the concealment coefficient is greater than or equal to the concealment threshold, a latent signal is generated; if the concealment coefficient is less than the concealment threshold, a visible signal is generated; The summary analysis unit is used for collecting the concealment coefficients of the monitoring modules that generate the observable signals for multiple times according to the observable signals generated by the analysis and judgment unit, calculating the average value and the oscillation value of the concealment coefficients, comparing the average value and the oscillation value with the corresponding average threshold and the oscillation threshold, and generating a consistent signal and an observation signal according to the comparison result; The feedback parameters include the heartbeat abnormality ratio, and the load parameters include the energy load index; The logic for obtaining the heartbeat abnormality ratio is: Step S11: In a unit time, the heartbeat collector collects the heartbeat signal data of the monitoring module, and counts the number of heartbeat missing and the total number of heartbeats, and calculates the heartbeat missing rate, and the calculation formula is: heartbeat missing rate = heartbeat missing number / total heartbeat number; Step S12: Compare the heartbeat missing rate with a preset threshold. If the heartbeat missing rate is greater than the threshold, mark it as abnormal. Step S13: when an abnormality is found, the timestamp A of the abnormality is recorded, and then the heartbeat signal is continuously monitored during the entire operation cycle. When the heartbeat signal returns to normal, the timestamp B of the recovery is recorded; Step S14: using the recorded abnormal interval time and normal interval time, the ratio of the normal interval time to the abnormal interval time, i.e., the heartbeat abnormality ratio, is calculated using the formula: heartbeat abnormality ratio = normal interval time / abnormal interval time; The logic for obtaining the energy load index is: Step S21: During each charging process, the memory usage of the module is monitored at fixed time intervals and recorded; Step S22: based on the recorded memory usage data, draw a curve graph showing the change of memory usage over time; Step S23: In the drawn graph, find the part where the memory usage exceeds the set threshold, and calculate the area enclosed by the part of the graph, and the calculation formula is: area exceeding the threshold = Σ(memory usage - threshold) * time interval; Step S24: Calculate the area enclosed by the entire memory usage graph, using the following calculation formula: total area = Σ(memory usage)*time interval; Step S25: Divide the area exceeding the threshold calculated in step 3 by the total area calculated in step 4 to obtain the ratio of the area exceeding the threshold to the total area, i.e., the energy loading index. The calculation formula W is: energy loading index = area exceeding the threshold / total area.

2. The charging pile monitoring system based on big data according to claim 1 is characterized in that: The summary analysis unit runs include the following: Step S31: for the analysis and judgment unit to determine that the generated signal is observable, within the time t, the concealment coefficient of the monitoring module is collected multiple times to obtain a series of concealment coefficient values; Step S32: Add all the collected concealment coefficient values ​​and divide by the number of collections to obtain an average value; Subtract the average value from all the hidden coefficient values ​​and take the absolute value, then average it to get the oscillation value; Step S33: If the average value is less than or equal to the average threshold, and the oscillation value is less than or equal to the vibration threshold, a consistent signal is generated; otherwise, an observation signal is generated.

Images