A user-side power failure fault analysis method and system

Abstract

The application discloses a user-side power failure analysis method and system, which comprises the following steps: acquiring and sorting power grid state data and user terminal power consumption data sequences of a target area in chronological order; intelligently judging whether there is a power grid abnormal event in the power grid state data sequence based on a threshold strategy dynamically corrected based on environmental temperature; if there is, then according to the event time node, the associated sub-sequence is intercepted from each user power consumption data sequence, and the power consumption rate of change and the power failure duration are extracted as key features; then, the features are input into a pre-trained power failure classification model to identify the power failure type; finally, the density clustering algorithm is used to automatically generate a visual power failure influence area in combination with the spatial distribution information of the user terminal, and the visual power failure influence area is pushed to the user terminal. The power grid and user-side multi-source data are effectively fused, and the rapid and accurate identification of the power failure and the visual positioning of the influence range are realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power failure analysis, and particularly relates to a user-side power failure analysis method and system. BACKGROUND

[0002] With the rapid development of smart grid and advanced measurement system (AMI), the data dimension and scale available to the power system are growing explosively, especially the massive real-time power consumption data provided by the user-side smart meters, which provides a new opportunity for the fine management and fault diagnosis of the power grid. The accurate and rapid analysis of user-side power failure is a key link to improve power supply reliability and user experience.

[0003] The traditional power failure analysis method mainly relies on the substation alarm signal, the report of the distribution automation terminal (FTU / DTU) or the user's call for repair. These methods have obvious hysteresis and limitations: the dependence on hardware alarms cannot cover the end of the distribution network, and the signal transmission itself may be interrupted due to failure; and the user repair method is limited by the user's subjective will and communication channels, the information is ambiguous and difficult to locate, which makes the operation and maintenance personnel unable to accurately determine the nature of the fault and the scope of the impact in the first time, thereby prolonging the time for troubleshooting and restoring power supply.

[0004] In recent years, although some researches have attempted to use user power consumption data (such as voltage, current, power consumption) for power failure analysis, the existing methods usually have the following shortcomings: first, the grid-side state data and user-side power consumption data are not time-series correlated and analyzed cooperatively, there is a lack of cross-validation of fault events, and the false positive rate is high; second, the extraction of power failure features is relatively single, and the multi-dimensional features such as power consumption rate and power loss duration are not comprehensively considered, making it difficult to accurately distinguish different types of events such as planned power failure, fault power failure and transient voltage sag; finally, in determining the power failure impact range, it is usually limited to the tabular user account, lacking clustering visualization means based on spatial geographic information, and unable to intuitively and automatically generate power failure impact areas, providing limited support for dispatching and repair decision-making.

[0005] Therefore, there is an urgent need for a new analysis method that can integrate grid and user-side multi-source data, automatically and accurately identify fault types, and intelligently generate visual power failure areas, to overcome the shortcomings of the existing technology and improve the intelligent level and response efficiency of power failure management. SUMMARY

[0006] The present application provides a user-side power failure analysis method and system, which solves the technical problem that the traditional power failure analysis method mainly relies on the substation alarm signal and the report of the distribution automation terminal, and has obvious hysteresis and limitations.

[0007] In a first aspect, the present application provides a user-side power failure analysis method, comprising:

[0008] obtaining power grid state data of a target area and power consumption data of each user terminal in the target area within a preset time period, and sorting each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal;

[0009] determining whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and using a preset power grid abnormality analysis strategy;

[0010] if there is at least one power grid abnormal event, then according to the time node corresponding to the at least one power grid abnormal event, using a preset data selection strategy to intercept at least one power consumption data sub-sequence associated with the at least one power grid abnormal event in the each power consumption data sequence;

[0011] determining a certain power consumption feature corresponding to a certain power grid abnormal event according to a certain power consumption data sub-sequence, and inputting the certain power consumption feature into a pre-trained power failure classification model corresponding to the power failure type of the certain power grid abnormal event, wherein the certain power consumption feature includes power consumption rate and power failure duration;

[0012] based on the power failure type and the spatial distribution information of each user terminal, using a preset clustering strategy to generate a power failure influence area, and sending the power failure influence area to the user terminal.

[0013] In a second aspect, the present application provides a user-side power failure analysis system, comprising:

[0014] an acquisition module configured to obtain power grid state data of a target area and power consumption data of each user terminal in the target area within a preset time period, and sort each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal;

[0015] a determination module configured to determine whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and using a preset power grid abnormality analysis strategy;

[0016] an intercepting module configured to, if there is at least one power grid abnormal event, according to the time node corresponding to the at least one power grid abnormal event, using a preset data selection strategy to intercept at least one power consumption data sub-sequence associated with the at least one power grid abnormal event in the each power consumption data sequence;

[0017] an output module configured to determine a certain power consumption feature corresponding to a certain power grid abnormal event according to a certain power consumption data subsequence, and input the certain power consumption feature into a pre-trained power failure classification model corresponding to a power failure type of the certain power grid abnormal event, wherein the certain power consumption feature includes a power consumption rate of change and a power failure duration;

[0018] a generation module configured to generate a power failure influence area based on the power failure type and spatial distribution information of each user terminal, and send the power failure influence area to the user terminal.

[0019] In a third aspect, an electronic device is provided, which includes at least one processor and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the user-side power failure analysis method of any embodiment of the present application.

[0020] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the user-side power failure analysis method of any embodiment of the present application.

[0021] The user-side power failure analysis method and system of the present application achieve accurate detection and classification of power failure by time sequence correlation analysis of power grid state data and user terminal power consumption data. The system first identifies power grid abnormal events based on a threshold value corrected by dynamic ambient temperature, significantly improving the adaptability and accuracy of abnormal state discrimination, and effectively avoiding misjudgment caused by environmental factors. By extracting a user power consumption data subsequence associated with the abnormal event and focusing on key features such as power consumption rate of change and power failure duration, a pre-trained classification model is used to accurately identify the power failure type (such as planned power failure, fault power failure, etc.). Further combined with user spatial distribution information, a density clustering algorithm is used to automatically generate a geographical range description of the power failure influence area, achieving rapid spatial visualization positioning of power failure influence. This method comprehensively improves the automation and response speed of power failure analysis, provides accurate decision support for power grid operation and maintenance personnel, and facilitates the accurate push of power failure influence range information to the user terminal, enhancing the transparency and satisfaction of user service. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0023] Figure 1 A flow chart of a user side power failure analysis method provided by an embodiment of the present application is shown in

[0024] Figure 2 A structural block diagram of a user side power failure analysis system provided by an embodiment of the present application is shown in

[0025] Figure 3 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in DETAILED DESCRIPTION

[0026] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present application.

[0027] Please refer to Figure 1 A flow chart of a user side power failure analysis method provided by an embodiment of the present application is shown in

[0028] As shown in Figure 1 , the user side power failure analysis method specifically includes the following steps:

[0029] In step S101, the power grid state data of a target area in a preset time period and the power consumption data of each user terminal in the target area are acquired, and each power grid state data and each power consumption data are sorted based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal.

[0030] In step S102, whether there is a power grid abnormal event in the preset time period is judged by using a preset power grid abnormal analysis strategy according to the power grid state data sequence.

[0031] In this step, the normal power grid state data of the target area in a historical time period is acquired, and each normal power grid state data is averaged to obtain normal average power grid state data.

[0032] correct the normal average power grid state data based on the historical environment temperature of the historical time period and each real-time environment temperature of the preset time period to obtain each target normal average power grid state data, and define the each target normal average power grid state data as a dynamic state threshold value;

[0033] obtain a state deviation degree between each power grid state data in the power grid state data sequence and the dynamic state threshold value, and sort each state deviation degree in time sequence to obtain a state deviation degree sequence;

[0034] sequentially determine whether each state deviation degree in the state deviation degree sequence is greater than a preset threshold value;

[0035] If at least two state deviation degrees are greater than the preset threshold value, determine whether a time interval between the first state deviation degree and the second state deviation degree is greater than a preset time threshold value, wherein the first state deviation degree and the second state deviation degree are two state deviation degrees with the shortest time interval in the at least two state deviation degrees, and the collection time of the power grid state data corresponding to the first state deviation degree is earlier than the collection time of the power grid state data corresponding to the second state deviation degree; specifically, if only one state deviation degree is greater than the preset threshold value, or all state deviation degrees are not greater than the preset threshold value, it is directly determined that there is no power grid abnormal event in the target region within the preset time period;

[0036] If the time interval is not greater than the preset time threshold value, it is determined that there is a power grid abnormal event in the target region within the preset time period, and the occurrence time corresponding to the power grid abnormal event is the collection time of the power grid state data corresponding to the first state deviation degree;

[0037] If the time interval is greater than the preset time threshold value, it is determined that there is no power grid abnormal event in the target region within the preset time period.

[0038] It should be noted that the correction of the normal average power grid state data based on the historical environment temperature of the historical time period and each real-time environment temperature of the preset time period to obtain each target normal average power grid state data includes:

[0039] calculate a historical ratio of the normal average power grid state data and the historical environment temperature, and multiply the real-time environment temperature of each time node in the preset time period by the historical ratio to obtain each target normal average power grid state data.

[0040] After determining whether there is a power grid abnormal event in the preset time period by using a preset power grid abnormal analysis strategy based on the power grid state data sequence, if there is no at least one power grid abnormal event, the user-side power failure analysis is stopped.

[0041] In one specific embodiment, first, the system obtains a sequence of power grid state data (such as voltage, frequency, load rate, etc.) of the target area in a historical time period (e.g. the past 30 days) under normal operation from the historical database. The arithmetic mean of these historical normal data is calculated to obtain the "normal average power grid state data" of the area as the static baseline value for abnormality judgment.

[0042] To adapt to the objective influence of environmental factors on the operation state of the power grid (for example, temperature changes will cause changes in line impedance and load characteristics), the system introduces the ambient temperature as a dynamic correction factor. Specifically, the ratio of the historical normal average power grid state data to the average ambient temperature of the corresponding historical time period is calculated as the "unit temperature state coefficient". In the real-time monitoring stage, for each sampling time node in the preset time period, the real-time collected ambient temperature is multiplied by the coefficient to dynamically generate the "target normal average power grid state data" at that moment, which is defined as the "dynamic state threshold value" at that moment. This process enables the judgment threshold to be self-adaptively adjusted with temperature fluctuations, better fitting the actual operation conditions of the power grid.

[0043] Subsequently, the system compares each data point in the real-time collected sequence of power grid state data with the dynamic state threshold value at the corresponding moment, calculates the absolute value or percentage of the relative deviation, and obtains the "state deviation degree" at each moment. The state deviation degrees of all moments are arranged in chronological order to form a "state deviation degree sequence".

[0044] Next, the system uses a dual-criterion mechanism for abnormality judgment. The first criterion is the amplitude criterion: the state deviation degree sequence is traversed in turn to determine whether each value exceeds a preset fixed deviation threshold (e.g. 15%). If the state deviation degrees of at least two consecutive moments in the sequence exceed the threshold, the second criterion - the time persistence criterion - is triggered. The system selects the pair of points with the shortest time interval from the two points and calculates the time difference between them. If the time difference is less than or equal to a preset time window threshold (e.g. 5 minutes), it is determined that a power grid abnormal event has occurred in the target area within the preset time period, and the data collection time corresponding to the earlier abnormal point is taken as the "occurrence time" of the abnormal event. Otherwise, if the points are too far apart, it is considered to be an incidental disturbance, and no power grid abnormal event is determined.

[0045] It should be noted that if the entire judgment process ultimately determines that there is no power grid abnormal event, the system will terminate the user-side power failure analysis process to save computing resources.

[0046] In summary, the dynamic threshold compensates for normal fluctuations in the power grid state caused by factors such as temperature, effectively avoiding false alarms caused by changes in environmental conditions. Secondly, the dual criterion mechanism (exceeding the amplitude and continuously occurring in a short period of time) effectively distinguishes between real persistent power grid anomalies and transient random disturbances or measurement noise, significantly reducing the false alarm rate and enhancing the reliability of the detection results. Finally, the method can provide accurate time anchors (occurrence time) for identified abnormal events, which lays the foundation for accurately extracting data segments associated with abnormal events from massive user power consumption data in subsequent steps, ensuring the accuracy of time alignment in the entire analysis process and the effectiveness of subsequent feature extraction and fault classification, thereby improving the intelligence level and judgment accuracy of the user-side power failure analysis system as a whole.

[0047] Step S103, if there is at least one power grid anomaly event, according to the time node corresponding to the at least one power grid anomaly event, a preset data selection strategy is used to extract at least one power consumption data subsequence associated with the at least one power grid anomaly event from the respective power consumption data sequences.

[0048] In this step, if there is at least one power grid anomaly event, the occurrence time of a certain power grid anomaly event is taken as the reference, a first power consumption data subsequence of a first preset time length is extracted forwardly and a second power consumption data subsequence of a second preset time length is extracted backwardly in each power consumption data sequence; the first power consumption data subsequence and the second power consumption data subsequence are spliced based on time sequence to obtain a certain power consumption data subsequence associated with a certain power grid anomaly event.

[0049] Step S104, determining a certain power consumption feature corresponding to a certain power grid anomaly event according to a certain power consumption data subsequence, and inputting the certain power consumption feature into a pre-trained power failure classification model corresponding to the power failure type of the certain power grid anomaly event, wherein the certain power consumption feature includes power consumption rate and power failure duration.

[0050] Step S105, based on the power failure type and the spatial distribution information of each user terminal, a preset clustering strategy is used to generate a power failure impact area, and the power failure impact area is sent to the user terminal.

[0051] In this step, a spatial distribution matrix is constructed based on the latitude and longitude coordinates of the user terminal, and a density clustering algorithm is used to identify power failure user aggregation areas according to the spatial distribution matrix; the center coordinates and influence radius of each power failure user aggregation area are calculated to generate a power failure impact area.

[0052] In summary, the method of the present application realizes accurate detection and classification of power failure by time sequence correlation analysis of power grid state data and user terminal power consumption data. The system first identifies power grid abnormal events based on a threshold corrected by dynamic ambient temperature, significantly improving the adaptability and accuracy of abnormal state discrimination, effectively avoiding misjudgment caused by environmental factors. By extracting the user power consumption data subsequence associated with the abnormal event and focusing on key features such as power consumption rate and power loss duration, a pre-trained classification model is used to accurately identify the type of power failure (such as planned power failure, fault power failure, etc.). Further combined with user spatial distribution information, a density clustering algorithm is used to automatically generate a geographical range description of the power failure impact area, realizing rapid spatial visualization positioning of power failure impact. This method comprehensively improves the automation level and response speed of power failure analysis, provides accurate decision support for power grid operation and maintenance personnel, and facilitates the accurate push of power failure impact range information to the user end, enhancing the transparency and satisfaction of user services.

[0053] Referring to Figure 2 , a structural block diagram of a user-side power failure analysis system of the present application is shown.

[0054] As Figure 2 shown, the user-side power failure analysis system 200 includes an acquisition module 210, a judgment module 220, a cutting module 230, an output module 240, and a generation module 250.

[0055] The acquisition module 210 is configured to acquire power grid state data of a target area in a preset time period and power consumption data of each user terminal in the target area, and sort each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal.

[0056] It should be understood that Figure 2 The modules described in the above detailed description and illustrated in the accompanying drawings Figure 1 The modules described in the above detailed description and illustrated in the accompanying drawings Figure 2 The modules described in the above detailed description and illustrated in the accompanying drawings

[0057] In some embodiments, the present application further provides a computer readable storage medium having stored thereon computer program, the program instructions being executed by a processor to cause the processor to perform the user side power failure analysis method in any of the above method embodiments.

[0058] As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, and the computer executable instructions are configured to:

[0059] acquire power grid state data of a target area in a preset time period and power consumption data of each user terminal in the target area, and sort each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal;

[0060] Based on the power grid status data sequence, a preset power grid anomaly analysis strategy is used to determine whether there are any power grid anomaly events within the preset time period;

[0061] If there is at least one power grid abnormal event, then according to the time node corresponding to the at least one power grid abnormal event, a preset data selection strategy is used to extract at least one power consumption data subsequence associated with the at least one power grid abnormal event from each power consumption data sequence.

[0062] Based on a certain electricity consumption data subsequence, a certain electricity consumption feature corresponding to a certain power grid abnormal event is determined, and the certain electricity consumption feature is input into a pre-trained power outage fault classification model. The power outage fault classification model corresponds to the power outage type of the certain power grid abnormal event. The certain electricity consumption feature includes the electricity consumption change rate and the power outage duration.

[0063] Based on the power outage type and the spatial distribution information of each user terminal, a preset clustering strategy is used to generate the power outage impact area, and the power outage impact area is sent to the user terminal.

[0064] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the user-side power outage fault analysis system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the user-side power outage fault analysis system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0065] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3The bus is taken as an example. The memory 320 is the computer readable storage medium described above. The processor 310 executes various functions of the server and data processing by running the non-volatile software programs, instructions and modules stored in the memory 320, that is, implements the user side power failure analysis method of the method embodiment described above. The input device 330 can receive input digital or character information, and generate key signal input related to user settings and function control of the user side power failure analysis system. The output device 340 can include a display device such as a display screen.

[0066] The electronic device described above can execute the method provided by the embodiment of the application, and has the corresponding function modules and beneficial effects of executing the method. Technical details not described in detail in the embodiment can be referred to the method provided by the embodiment of the application.

[0067] As an implementation manner, the electronic device described above is applied to a user side power failure analysis system, and is used for a client, and includes at least one processor and a memory in communication connection with the at least one processor. The memory stores instructions executable by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to:

[0068] obtain power grid state data of a target region and power consumption data of each user terminal in the target region within a preset time period, and sort each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal;

[0069] determine whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and by using a preset power grid abnormal analysis strategy;

[0070] if there is at least one power grid abnormal event, then according to a time node corresponding to the at least one power grid abnormal event, at least one power consumption data sub-sequence associated with the at least one power grid abnormal event is intercepted from the each power consumption data sequence by using a preset data selection strategy;

[0071] determine a certain power consumption feature corresponding to a certain power grid abnormal event according to a certain power consumption data sub-sequence, and input the certain power consumption feature into a pre-trained power failure classification model corresponding to a power failure type of the certain power grid abnormal event, wherein the certain power consumption feature includes a power consumption rate of change and a power failure duration;

[0072] based on the power failure type and spatial distribution information of each user terminal, a power failure influence region is generated by using a preset clustering strategy, and the power failure influence region is sent to the user terminal.

[0073] Those skilled in the art can clearly understand the implementation of the embodiments by means of software and necessary general hardware platforms through the description of the above embodiments, and of course, the embodiments can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, and the computer software product can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the embodiments or some parts of the embodiments.

[0074] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for some technical features thereof; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A user side power failure analysis method, characterized by, The method comprises: acquiring power grid state data of a target region within a preset time period and power consumption data of each user terminal in the target region, and sorting each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal; judging whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and using a preset power grid abnormality analysis strategy; if there is at least one power grid abnormal event, then according to the time node corresponding to the at least one power grid abnormal event, using a preset data selection strategy to intercept at least one power consumption data sub-sequence associated with the at least one power grid abnormal event in the each power consumption data sequence; determining a certain power consumption feature corresponding to a certain power grid abnormal event according to a certain power consumption data sub-sequence, and inputting the certain power consumption feature into a pre-trained power failure classification model, wherein the certain power consumption feature includes power consumption rate and power failure duration, and the power failure classification model outputs a power failure type corresponding to the certain power grid abnormal event; based on the power failure type and the spatial distribution information of each user terminal, using a preset clustering strategy to generate a power failure influence area, and sending the power failure influence area to the user terminal.

2. The user-side power failure analysis method according to claim 1, characterized in that, The method according to the power grid state data sequence, using a preset power grid abnormality analysis strategy to judge whether there is a power grid abnormal event within the preset time period comprises: acquiring normal power grid state data of the target region within a historical time period, and averaging each normal power grid state data to obtain a normal average power grid state data; based on the historical environment temperature of the historical time period and each real-time environment temperature of the preset time period, correcting the normal average power grid state data to obtain each target normal average power grid state data, and defining the each target normal average power grid state data as a dynamic state threshold; acquiring the state deviation degree between each power grid state data in the power grid state data sequence and the dynamic state threshold, and sorting each state deviation degree based on time sequence to obtain a state deviation degree sequence; sequentially judging whether each state deviation degree in the state deviation degree sequence is greater than a preset threshold; if at least two state deviation degrees are greater than the preset threshold, then judging whether the time interval between a first state deviation degree and a second state deviation degree is greater than a preset time threshold, wherein the first state deviation degree and the second state deviation degree are the two state deviation degrees with the shortest time interval in the at least two state deviation degrees, and the collection time of the power grid state data corresponding to the first state deviation degree is earlier than the collection time of the power grid state data corresponding to the second state deviation degree; if not greater than the preset time threshold, then determining that the target region has a power grid abnormal event within the preset time period, and the occurrence time of the power grid abnormal event is the collection time of the power grid state data corresponding to the first state deviation degree. If greater than the preset time threshold, it is determined that the target area does not exist power grid abnormal event within the preset time period.

3. The user-side power failure analysis method according to claim 2, wherein The historical environment temperature of the historical time period and each real-time environment temperature of the preset time period are used to correct the normal average power grid state data to obtain each target normal average power grid state data. The normal average power grid state data and the historical ratio of the historical environment temperature are calculated, and each real-time environment temperature of each time node within the preset time period is multiplied by the historical ratio to obtain each target normal average power grid state data.

4. The user-side power failure analysis method according to claim 1, characterized in that, After determining whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and using a preset power grid abnormal analysis strategy, the method further comprises: If there is no at least one power grid abnormal event, the user side power failure analysis is stopped.

5. The user-side power failure analysis method according to claim 1, characterized in that, The at least one power consumption data sub-sequence associated with the at least one power grid abnormal event is intercepted from each power consumption data sequence according to the time node corresponding to the at least one power grid abnormal event and using a preset data selection strategy. A first power consumption data sub-sequence of a first preset time length is intercepted forwardly and a second power consumption data sub-sequence of a second preset time length is intercepted backwardly in each power consumption data sequence based on the occurrence time of a certain power grid abnormal event; The first power consumption data sub-sequence and the second power consumption data sub-sequence are spliced based on time sequence to obtain a certain power consumption data sub-sequence associated with the certain power grid abnormal event.

6. The user-side power failure analysis method according to claim 1, characterized in that, The power failure influence area is generated based on the power failure type and the spatial distribution information of each user terminal and using a preset clustering strategy, comprising: A spatial distribution matrix is constructed based on the latitude and longitude coordinates of the user terminal, and a power failure user aggregation area is identified according to the spatial distribution matrix and using a density clustering algorithm; The center coordinates and the influence radius of each power failure user aggregation area are calculated to generate a power failure influence area.

7. A user side power outage failure analysis system characterized by, The method comprises: The acquisition module is configured to acquire power grid state data of a target area within a preset time period and power consumption data of each user terminal in the target area, and sort each power grid state data and each power consumption data based on time sequence to obtain a power grid state data sequence and each power consumption data sequence, wherein one power consumption data sequence corresponds to one power consumption terminal; The judgment module is configured to determine whether there is a power grid abnormal event within the preset time period according to the power grid state data sequence and using a preset power grid abnormal analysis strategy; The interception module is configured to, if there is at least one power grid abnormal event, intercept at least one power consumption data sub-sequence associated with the at least one power grid abnormal event from each power consumption data sequence according to the time node corresponding to the at least one power grid abnormal event and using a preset data selection strategy; The output module is configured to determine a certain power consumption feature corresponding to a certain power grid abnormal event according to a certain power consumption data subsequence, and input the certain power consumption feature into a pre-trained power failure classification model, and the power failure classification model outputs a power failure type corresponding to the certain power grid abnormal event, wherein the certain power consumption feature includes a power consumption rate of change and a power failure duration; The generation module is configured to generate a power failure influence area based on the power failure type and spatial distribution information of each user terminal by using a preset clustering strategy, and send the power failure influence area to the user terminal.

8. An electronic device, comprising: Comprise: At least one processor, and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1 to 6.

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