Residential charging facility state monitoring method and system, electronic equipment and storage medium

Abstract

The invention discloses a resident charging facility state monitoring method and system, electronic equipment and a storage medium, and the resident charging facility state monitoring method processes total load data through employing an adaptive weighted moving average algorithm, can effectively eliminate the interference of short-time high-frequency fluctuation of household appliances, and improves the reliability of the household appliances. Weight distribution can be dynamically adjusted according to the change of load data, the stable trend component reflecting the charging load of the electric vehicle can be more accurately extracted, and the stable trend component is subjected to time-frequency domain analysis processing, so that the charging power of the electric vehicle and the electric power of household appliances are not consistent in frequency distribution. According to the method, the charging load data of the electric vehicle can be accurately extracted from the total load data of the residential electricity meter, so that the charging event of the electric vehicle can be accurately detected, and the charging process of the electric vehicle can be accurately and safely monitored.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power monitoring, in particular, relates to a resident charging facility state monitoring method and system, electronic equipment and computer readable storage medium. BACKGROUND

[0002] With the continuous development of economy, the number of resident charging facilities (such as electric vehicles, electric vehicles, household energy storage, etc.) is rapidly increasing, but due to equipment aging, environmental factors and other influences, the safety and reliability of resident charging equipment in the charging process are facing serious challenges, therefore, real-time monitoring of the working state of resident charging facilities and timely detection of potential safety hazards are of great significance to ensure the safe operation of resident charging facilities and the personal and property safety of residents. Among them, electric vehicles are the most widely used resident charging facilities, and it is particularly important to monitor their working state. However, in actual application, the resident user's smart meter collects total load data of all household electrical facilities (including resident charging facilities), and the charging data of electric vehicles is also included in the total load data, and in the electric vehicle charging process, the rest of the household appliances are also enabled at the same time, and the rated power of some household appliances is close to the charging power of electric vehicles, for example, the rated power of air conditioners is close to the charging power of general electric vehicles, which makes it difficult to accurately identify the charging data of electric vehicles from the total load data of the resident user's smart meter, thereby making it difficult to accurately monitor the working state of electric vehicles. SUMMARY

[0003] The present application provides a resident charging facility state monitoring method and system, electronic equipment and computer readable storage medium, which can accurately extract the load data of electric vehicle charging from the total load data of resident meter, which is beneficial to accurately detect electric vehicle charging event, so as to accurately monitor the charging process of electric vehicle.

[0004] According to one aspect of the present application, a resident charging facility state monitoring method is provided, comprising the following contents:

[0005] Collecting total load data of resident user's smart meter;

[0006] Adopting adaptive weighted moving average algorithm and time-frequency domain analysis method to extract load data of electric vehicle charging from total load data;

[0007] Based on the load data of electric vehicle charging, the charging event detection is carried out, and the charging process of electric vehicle is monitored according to the charging event detection result.

[0008] Further, the process of extracting the load data of the electric vehicle charging from the total load data by using the adaptive weighted moving average algorithm and the time-frequency domain analysis method comprises the following contents:

[0009] The adaptive weighted moving average algorithm is used to smooth the total load data to extract the low-frequency trend component.

[0010] The short-time Fourier transform is used to analyze the low-frequency trend component in the time-frequency domain, and the low-frequency trend component is filtered in a specific frequency range to extract the characteristic component of the electric vehicle charging.

[0011] Further, the expression of the adaptive weighted moving average algorithm is as follows:

[0012]

[0013] Wherein, T low (t) represents the low-frequency trend component at the current t moment, P(t-n) represents the power data at the n th moment before the current t moment, P(t) represents the power data at the current t moment, a n (t) represents the weight coefficient of the power data at the n th moment before the current t moment, b(t) represents the weight coefficient of the power data at the current t moment, N represents the length of the review time window, λ represents the sensitivity parameter, T low (t-n) represents the low-frequency trend component at the (t-n) moment.

[0014] Further, the process of detecting the charging event based on the load data of the electric vehicle charging comprises the following contents:

[0015] The power change characteristic quantity in the electric vehicle charging load data is calculated.

[0016] The charging start and end time of the electric vehicle is determined based on the power change characteristic quantity.

[0017] Further, the power change characteristic quantity is calculated based on the following formula:

[0018] ΔP(t) = P" (t) + k·P'(t) Wherein, ΔP(t) represents the power change characteristic quantity, P'(t) represents the speed of power change, P"(t) represents the acceleration of power change, and k represents the adjustment coefficient.

[0019] Further, the charging start and end time of the electric vehicle is determined based on the following formula:

[0020] t s = {t | ΔP(t) > λ'}, t e = {t | ΔP(t) < -λ'}

[0021] Wherein, ts represents a charging start time, λ' represents a preset power variation feature quantity threshold value, t e represents a charging end time.

[0022] Further, the following is also included:

[0023] When an abnormality in charging of the electric vehicle is monitored, a warning is issued to remind.

[0024] In addition, the present application also provides a resident charging facility state monitoring system, comprising:

[0025] A data acquisition module is configured to acquire total load data of a resident user smart meter;

[0026] A data processing module is configured to extract load data of electric vehicle charging from the total load data by using an adaptive weighted moving average algorithm and a time-frequency domain analysis method;

[0027] A state monitoring module is configured to detect charging events based on the load data of electric vehicle charging, and to perform safety monitoring on the charging process of the electric vehicle according to the charging event detection result.

[0028] In addition, the present application also provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method described above by calling the computer program stored in the memory.

[0029] In addition, the present application also provides a computer readable storage medium for storing a computer program for resident charging facility state monitoring, wherein the computer program performs the steps of the method described above when running on a computer.

[0030] The present application has the following advantages:

[0031] The resident charging facility state monitoring method of the present application can effectively eliminate the interference of short-time high-frequency fluctuations of household appliances by processing the total load data by using an adaptive weighted moving average algorithm, and can dynamically adjust the weight distribution according to the change of the load data, so as to more accurately extract the smooth trend component reflecting the electric vehicle charging load. Furthermore, by performing time-frequency domain analysis processing on the smooth trend component, the electric vehicle charging load data can be accurately extracted from the total load data of the resident meter by using the characteristic that the frequency distribution of the electric vehicle charging power is inconsistent with that of the household appliance power, which is beneficial to accurately detecting the electric vehicle charging event, so as to accurately perform safety monitoring on the charging process of the electric vehicle.

[0032] In addition, the resident charging facility state monitoring system of the present application also has the above advantages.

[0033] In addition to the above-described objects, features and advantages, the present application has other objects, features and advantages. The present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0034] The accompanying drawings, which form a part of this application, are included to provide a further understanding of the application, illustrate the preferred embodiment of the application and assist in

[0035] Figure 1 is a flowchart of a resident charging facility status monitoring method according to a preferred embodiment of the present application;

[0036] Figure 2 is a flowchart of step S2 in Figure 1

[0037] Figure 3 is a flowchart of step S3 in Figure 1

[0038] Figure 4 is another flowchart of a resident charging facility status monitoring method according to a preferred embodiment of the present application;

[0039] Figure 5 is a block diagram of a resident charging facility status monitoring system according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0040] It should be noted that the embodiments and features of the present application can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0041] With reference to Figure 1 , a preferred embodiment of the present application provides a resident charging facility status monitoring method, which includes the following contents:

[0042] Step S1: Collect total load data of a resident user's smart meter;

[0043] Step S2: Extract load data of electric vehicle charging from the total load data by using an adaptive weighted moving average algorithm and a time-frequency domain analysis method;

[0044] Step S3: Perform charging event detection based on the load data of electric vehicle charging, and perform safety monitoring on the charging process of the electric vehicle according to the charging event detection result.

[0045] ​​It can be understood that the resident charging facility state monitoring method of the embodiment can effectively eliminate the interference of short-time high-frequency fluctuations of household appliances by adopting the adaptive weighted moving average algorithm to process the total load data, can dynamically adjust the weight distribution according to the change of the load data, can more accurately extract the smooth trend component reflecting the electric vehicle charging load, and can accurately extract the load data of the electric vehicle charging from the total load data of the resident electric meter by using the frequency distribution inconsistency between the electric vehicle charging power and the household appliance power through time-frequency domain analysis processing of the smooth trend component, which is beneficial to accurately detecting the electric vehicle charging event, so that the charging process of the electric vehicle can be accurately and safely monitored.

[0046] In the step S1, the cloud platform can collect the total load data of the resident user smart electric meter, and the load data includes current, voltage or power, etc. In addition, after collecting the total load data, the cloud platform can compare the collected load data with the preset load threshold to monitor the power safety of the resident user. For example, the cloud platform can compare the total load power of the resident user smart electric meter with the preset power threshold to monitor whether the resident user has a power overload problem. In addition, the cloud platform can receive the load data of multiple smart electric meters in real time to realize remote monitoring of the power safety of multiple resident users.

[0047] In addition, in the step S2, since the total load data collected in the step S1 not only contains the load data of the electric vehicle charging, but also contains the load data of the remaining household appliances of the resident user, in order to accurately extract the load data of the electric vehicle charging from the total load data, the adaptive weighted moving average algorithm and the time-frequency domain analysis method are adopted to process the total load data to extract the load data of the electric vehicle charging. As shown in the step S2, the process of extracting the load data of the electric vehicle charging from the total load data by using the adaptive weighted moving average algorithm and the time-frequency domain analysis method includes the following contents: Figure 2

[0048] Step S21: adopting the adaptive weighted moving average algorithm to process the total load data to extract the low-frequency trend component;

[0049] Step S22: adopting the short-time Fourier transform to analyze the low-frequency trend component in time-frequency domain, and filtering the low-frequency trend component in a specific frequency range to extract the characteristic component of the electric vehicle charging.

[0050] ​Specifically, in order to eliminate the interference of short-time high-frequency fluctuations (such as random start, transient interference, etc.) of household appliances from the total load data of the residential user smart meter, and accurately extract the smooth trend component reflecting the electric vehicle charging, the adaptive weighted moving average algorithm is introduced to realize the smoothing operation in the time domain. The expression of the adaptive weighted moving average algorithm is:

[0051]

[0052] wherein T low (t) represents the low-frequency trend component at the current t moment, P(t-n) represents the power data at the n th moment before the current t moment, P(t) represents the power data at the current t moment, a n (t) represents the weight coefficient of the power data at the n th moment before the current t moment, b(t) represents the weight coefficient of the power data at the current t moment, N represents the length of the review time window, for example, N = 10 represents that the data of the last 10 time points are used to calculate the current low-frequency trend component, λ represents the sensitivity parameter, by adjusting λ, the reflection speed of the weight coefficient to the load fluctuation can be changed, a larger λ value makes the weight coefficient more sensitive to the load fluctuation, so as to adjust the weight faster, which is suitable for fast-changing signals, and a smaller λ value makes the weight coefficient change relatively slowly, which is suitable for processing relatively stable load data, but may not be timely enough to respond to transient fluctuations, T low (t-n) represents the low-frequency trend component at the (t-n) moment.

[0053] It can be seen that in the above formula, the weight coefficients a n (t) and b(t) are automatically calculated according to the load fluctuation, when the difference between the historical data P(t-n) and the low-frequency trend component T low (t-n) corresponding thereto is large, it means that the signal fluctuation at this moment is large, which may be noise or short-term fluctuation, therefore the weight of the historical data at this moment is reduced, that is, a n (t) becomes small, when the difference between the historical data P(t-n) and the low-frequency trend component T low (t-n) is small, it means that the signal at this moment is relatively stable, the past data has a greater contribution to the current trend, therefore the weight of the historical data is increased, that is, a n (t) becomes large; similarly, if the difference between the power value P(t) at the current moment and the low-frequency trend component T low (t) is large, it means that the signal fluctuation is large, at this time, the weight of the data at the current moment needs to be small, in order to reduce the influence of the fluctuation on the low-frequency trend component, if the difference between the power value P(t) at the current moment and the low-frequency trend component T low(t) is closer, it indicates that the signal is stationary, and a higher weight is maintained to ensure the contribution of the latest data to the low-frequency trend. Therefore, the present application can effectively eliminate the interference of short-time high-frequency fluctuations of household appliances, and can also dynamically adjust the weight distribution according to the change of the load data, so as to more accurately extract the stationary trend component reflecting the electric vehicle charging load.

[0054] Then, the electric vehicle charging related feature component needs to be further extracted from the low-frequency trend component T low (t). The low-frequency trend component is subjected to time-frequency domain analysis by using short-time Fourier transform, and is subjected to filtering processing in a specific frequency range to extract the feature component of electric vehicle charging, wherein the expression of the filtering processing is: T low (f) represents the frequency domain signal of the low-frequency trend component, G(f) represents the band-pass filter, f1 and f2 represent the lower limit and upper limit of the frequency of the filtering, and T ev (t) represents the feature component of electric vehicle charging, represents the inverse Fourier transform.

[0055] It can be understood that the electric vehicle charging process is a continuous and stable process, and its power signal is concentrated in a specific low-frequency band, that is, the feature component of electric vehicle charging has the characteristics of low-frequency stationary fluctuation, and the power signal of the household appliance is not in the low-frequency band. Therefore, the present application can effectively remove the interference of household appliances by filtering the low-frequency trend component in a specific low-frequency band, so as to accurately extract the feature component of electric vehicle charging, so as to accurately extract the load data of electric vehicle charging from the total load data.

[0056] In addition, the load data of electric vehicle charging is accurately extracted from the total load data in step S2, and in step S3, the charging event detection is performed according to the load data of electric vehicle charging, and the safety monitoring of the charging process of the electric vehicle is performed according to the charging event detection result. As shown in the figure, Figure 3 the process of performing charging event detection based on the load data of electric vehicle charging includes the following contents:

[0057] Step S31: calculating the power change feature quantity in the electric vehicle charging load data;

[0058] Step S32: determining the charging start and stop time of the electric vehicle based on the power change feature quantity.

[0059] Specifically, the power change characteristics of the electric vehicle at the beginning and end of charging are different from other household electrical appliances, for example, the power change of the electric vehicle charging is obvious and rapid at the beginning and end of charging, while the power change of the air conditioner, washing machine and other household electrical appliances is gradual at the start and stop, so the electric vehicle charging event can be accurately detected according to the power change characteristics. The application constructs the power change characteristic quantity as an evaluation index by jointly analyzing the change rate and trend of the power, which can accurately represent the power change characteristics of the electric vehicle at the beginning and end of charging. The power change characteristic quantity is calculated based on the following formula:

[0060] ΔP(t)=P″(t)+k·P′(t)

[0061] Wherein, ΔP(t) represents the power change characteristic quantity, P'(t) represents the speed of power change, which reflects the instantaneous increase or decrease rate of power with time, P''(t) represents the acceleration of power change, which reflects the trend of power change rate, k represents an adjustment coefficient for balancing the contribution of the first and second derivatives of power, which is set according to experience. When the electric vehicle starts charging, the power signal will jump from 0 or a low value to a higher value, P'(t) will appear a significant positive value, and the power change trend will present a sharp turning point, P''(t) will significantly increase; while when the electric vehicle ends charging, the power signal will suddenly drop from a high value to a low value or 0, P'(t) will appear a significant negative value, and the power change trend will present a sharp turning point, P''(t) will significantly decrease. Therefore, the power change characteristic quantity of the electric vehicle at the beginning and end of charging will be much larger than that of ordinary household electrical appliances.

[0062] Then, the charging start and end time of the electric vehicle is determined based on the following formula:

[0063] t s ={t|ΔP(t)>λ′},t e ={t|ΔP(t)<-λ′}

[0064] Wherein, t s represents the charging start time, λ' represents the preset power change characteristic quantity threshold, t e represents the charging end time. That is, when the power change characteristic quantity ΔP(t) is greater than the preset threshold λ', it indicates that the power has a sharp positive change, that is, it is identified as the start time of the electric vehicle charging behavior, and when the power change characteristic quantity ΔP(t) is less than -λ', it indicates that the power has a sharp negative change, which is identified as the end time of the electric vehicle charging behavior.

[0065] It can be understood that the application can accurately identify the electric vehicle charging event by constructing the power change feature quantity as an evaluation index and comprehensively analyzing the change rate and change trend of the power, thereby improving the accuracy of the charging event detection.

[0066] In addition, after detecting the electric vehicle charging start time and the charging end time, the cloud platform can monitor the working state of the electric vehicle during the charging process to determine whether there is an abnormal charging state, such as overcurrent, overvoltage, overload, etc.

[0067] In addition, as shown in Figure 4 The resident charging facility state monitoring method further includes the following contents:

[0068] Step S4: When the abnormal charging of the electric vehicle is monitored, a pre-warning is issued.

[0069] It can be understood that when the abnormal charging of the electric vehicle is monitored, the cloud platform can issue a pre-warning in the form of a short message, an email, an App notification, etc., to notify the resident user to check the charging condition of the electric vehicle in time.

[0070] In addition, as shown in Figure 5 Another embodiment of the application further provides a resident charging facility state monitoring system, which preferably adopts the resident charging facility state monitoring method as described above, and includes:

[0071] The data acquisition module is configured to acquire total load data of the smart meter of the resident user.

[0072] The data processing module is configured to extract the load data of the electric vehicle charging from the total load data by using the adaptive weighted moving average algorithm and the time-frequency domain analysis method.

[0073] The state monitoring module is configured to detect the charging event based on the load data of the electric vehicle charging, and to monitor the charging process of the electric vehicle based on the charging event detection result.

[0074] It can be understood that the resident charging facility state monitoring system of the embodiment can effectively eliminate the interference of the short-time high-frequency fluctuation of the household appliances by processing the total load data by using the adaptive weighted moving average algorithm, and can dynamically adjust the weight distribution according to the change of the load data, so as to more accurately extract the smooth trend component reflecting the charging load of the electric vehicle. In addition, by performing the time-frequency domain analysis on the smooth trend component, the load data of the electric vehicle charging can be accurately extracted from the total load data of the resident meter by using the characteristic that the frequency distribution of the charging power of the electric vehicle is inconsistent with that of the household appliance power, which is beneficial to accurately detecting the electric vehicle charging event, so as to accurately monitor the charging process of the electric vehicle.

[0075] In addition, the resident charging facility state monitoring system further comprises:

[0076] The early warning module is configured to issue a warning when an abnormal charging of the electric vehicle is detected.

[0077] In addition, another embodiment of the present application provides an electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method described above by invoking the computer program stored in the memory.

[0078] In addition, another embodiment of the present application provides a computer readable storage medium for storing a computer program for monitoring the state of a resident charging facility, wherein the computer program performs the steps of the method described above when executed on a computer.

[0079] The general computer readable storage medium includes floppy disks, flexible disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tapes, any other physical media with patterns of holes, random access memories (RAMs), programmable read-only memories (PROMs), erasable programmable read-only memories (EPROMs), flash erasable programmable read-only memories (FLASH-EPROMs), any other memories of same type or different types, or any other medium of same type or different types that can be used to store instructions and codes of the task-executing machine, and any medium that can carry or represent computer programs in a transitory or non-transitory manner. The instructions can be further transmitted or received in a transmission medium. The term "transmission medium" shall include any intangible or tangible medium that is used to store, encode or carry the instructions for execution by or to program a machine and includes digital or analog communications signals or other intangible media to facilitate communication of such software. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise a bus that carries digital signals of a computer.

[0080] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, such as object-oriented programming languages Java and interpreted scripting language JavaScript.

[0081] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0082] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0083] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0084] Although preferred embodiments of the application have been described, those skilled in the art will recognize that many modifications and variations of this application are possible. Accordingly, it is intended to embrace all such modifications and variations as fall within the scope of the application.

[0085] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

[0086] The above description is only preferred embodiments of the application, not intended to limit the application. The application can be variously changed and modified by those skilled in the art without departing from the spirit and scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall within the scope of the application.

Claims

1. A residential charging infrastructure status monitoring method, characterized by, The method comprises the following steps: Collecting total load data of a smart electricity meter of a resident user; Extracting load data of electric vehicle charging from the total load data by using an adaptive weighted moving average algorithm and a time-frequency domain analysis method; Detecting a charging event based on the load data of electric vehicle charging, and performing safety monitoring on the charging process of the electric vehicle according to the detection result of the charging event.

2. The resident charging facility status monitoring method according to claim 1, wherein The process of extracting the load data of electric vehicle charging from the total load data by using the adaptive weighted moving average algorithm and the time-frequency domain analysis method comprises the following steps: Performing smoothing processing on the total load data by using the adaptive weighted moving average algorithm to extract a low-frequency trend component; Performing time-frequency domain analysis on the low-frequency trend component by using a short-time Fourier transform, and performing filtering processing on the low-frequency trend component in a specific frequency range to extract a characteristic component of electric vehicle charging.

3. The resident charging facility status monitoring method according to claim 2, wherein The expression of the adaptive weighted moving average algorithm is as follows: wherein T low (t) represents the low-frequency trend component at the current t moment, P(t-n) represents the power data at the n-th moment before the current t moment, P(t) represents the power data at the current t moment, a n (t) represents the weight coefficient of the power data at the n-th moment before the current t moment, b(t) represents the weight coefficient of the power data at the current t moment, N represents the length of the review time window, λ represents the sensitivity parameter, T low (t-n) represents the low-frequency trend component at the (t-n) moment.

4. The resident charging facility status monitoring method according to claim 1, wherein The process of detecting the charging event based on the load data of electric vehicle charging comprises the following steps: Calculating a power change characteristic quantity in the load data of electric vehicle charging; Determining charging start and stop times of the electric vehicle based on the power change characteristic quantity.

5. The resident charging facility status monitoring method according to claim 4, wherein The power change characteristic quantity is calculated based on the following formula: ΔP(t) = P''(t) + k·P'(t) Wherein, ΔP(t) represents the power change characteristic quantity, P'(t) represents the speed of power change, P''(t) represents the acceleration of power change, and k represents an adjustment coefficient.

6. The resident charging facility status monitoring method according to claim 5, wherein The charging start and stop times of the electric vehicle are determined based on the following formula: t s = {t | ΔP(t) > λ'}, t e = {t | ΔP(t) < -λ'} where t s represents the charging start time, λ' represents the preset power variation characteristic quantity threshold, t e represents the charging end time.

7. The resident charging facility status monitoring method of claim 1, wherein, The method further comprises the following steps: When an abnormality in electric vehicle charging is monitored, issuing a pre-warning reminder.

8. A residential charging infrastructure status monitoring system, characterized by, The method comprises the following steps: A data collection module is configured to collect total load data of a smart electricity meter of a resident user; A data processing module is configured to extract load data of electric vehicle charging from the total load data by using an adaptive weighted moving average algorithm and a time-frequency domain analysis method; A state monitoring module is configured to detect a charging event based on the load data of electric vehicle charging, and perform safety monitoring on the charging process of the electric vehicle according to the detection result of the charging event.

9. An electronic device, comprising: The computer program is stored in the memory and is configured to perform the steps of the method according to any one of claims 1-7 when the computer program is executed on the computer.

10. A computer-readable storage medium for storing a computer program for monitoring the state of a residential charging facility, characterized by The computer program is stored in the memory and is configured to perform the steps of the method according to any one of claims 1-7 when the computer program is executed on the computer.

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