Household storage equipment abnormity monitoring method and device, computer equipment and storage medium

Abstract

The invention relates to a household storage equipment abnormity monitoring method and device, computer equipment and a storage medium. The method comprises the steps of obtaining operation parameters of household storage equipment in real time, and judging whether an abnormal state exists or not; in response to the existence of the abnormal state, obtaining abnormal log data of the user storage equipment, and judging whether the user storage equipment has a first type of equipment fault or not; in response to the fact that the user storage device does not have the first type of device fault, obtaining geographical location of the user storage device, and obtaining a feature reference domain in combination with regional feature analysis; based on the feature reference domain, obtaining a plurality of reference devices and a plurality of corresponding reference log data; and according to the abnormal log data, in combination with the plurality of reference log data, judging whether the user storage equipment has a second type of equipment fault or regional operation fluctuation. By adopting the method, the source of the abnormal state of the household storage equipment can be judged and adaptive adjustment is performed in combination with regional feature analysis, so that the accuracy of fault detection and anomaly identification is improved, and the stability and reliability of the equipment are enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of abnormality monitoring of household storage equipment, and in particular to a method, apparatus, computer equipment and storage medium for abnormality monitoring of household storage equipment. Background Art

[0002] With the development of energy storage technology, household storage devices are playing an increasingly important role in energy management. However, during actual operation, household storage devices may experience performance anomalies or failures due to device failures or external environmental factors. Moreover, since household storage devices are often located in areas far from urban areas, they are more susceptible to external environmental factors than in more stable urban environments. Furthermore, on-site repairs for household storage devices in these areas are often more costly. While equipment failures typically require prompt attention and human intervention, performance anomalies caused by external environmental factors can often be resolved through remote control of the device. Therefore, accurately identifying equipment failures and distinguishing their impact from regional fluctuations has become a pressing issue.

[0003] Existing anomaly monitoring technologies mostly focus on fault diagnosis based on the device's own parameters and historical data. These methods rely primarily on single device log data, typically comparing it with standard parameters to determine if a fault exists. However, this approach carries a high risk of misjudgment, making it difficult to determine whether an abnormal device condition stems from an internal fault or an external environmental change, leading to unnecessary repairs and equipment downtime. Summary of the Invention

[0004] Based on this, it is necessary to provide a household storage equipment abnormality monitoring method, device, computer equipment and storage medium that can combine regional feature analysis, determine the source of equipment abnormality and make adaptive equipment adjustments, thereby improving the accuracy of fault detection and abnormality identification, and enhancing equipment stability and reliability.

[0005] In one aspect, a method for monitoring abnormalities of household storage devices is provided, the method comprising:

[0006] Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality;

[0007] In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data;

[0008] In response to the fact that the household storage device does not have the first type of device failure, the geographical location of the household storage device is obtained, and combined with regional feature analysis, a feature reference domain is obtained;

[0009] Based on the characteristic reference domain, a plurality of reference devices and corresponding plurality of reference log data are obtained;

[0010] Based on the abnormal log data and combined with multiple reference log data, it is determined whether the household storage device has a second-category equipment failure or whether there is regional operation fluctuation.

[0011] In one embodiment, before obtaining the operating parameters of the household storage device in real time and comparing them with the preset standard parameters to determine whether there is an abnormal state, the following steps are also included:

[0012] Based on the type of household storage equipment, establish the corresponding basic parameter framework;

[0013] According to the type of household storage equipment, obtain one or more historical operation data of the same type of equipment;

[0014] Extract key features of the basic parameter framework based on one or more historical operating data to obtain feature data items;

[0015] Performing data analysis and processing on one or more historical operation data to extract normal operation thresholds corresponding to characteristic data items;

[0016] Based on the characteristic data items and the corresponding normal operation thresholds, standard parameters are generated.

[0017] In one embodiment, a first-category device fault is correspondingly provided with a first-category fault code. In response to the presence of an abnormal state, abnormal log data of the household storage device is obtained, and based on the abnormal log data, it is determined whether the household storage device has a first-category device fault, including:

[0018] Analyze abnormal log data and obtain alarm information;

[0019] In response to the presence of a first type of fault code in the alarm information, determining that a first type of device fault corresponding to the first type of fault code exists in the household storage device;

[0020] In response to the absence of the first type fault code in the alarm information, further parsing the abnormality log data to obtain abnormality parameters;

[0021] Send a device self-test request to the user storage device and obtain the device self-test execution result;

[0022] In response to the device self-test execution result being completely normal, it is determined that the household storage device does not have a first type of device failure;

[0023] In response to the device self-test result being a detection failure, determining whether the abnormal parameters match the manifestation parameters of the first type of device failure, and if so, determining that the household storage device has a first type of device failure, and if not, determining that the household storage device does not have a first type of device failure;

[0024] In response to the device self-test result being partially abnormal, and the abnormality parameters matching the manifestation parameters of the first type of device failure, obtaining the component of the household storage device with the self-test abnormality, and determining whether it matches the component of the first type of device failure; if so, determining that the household storage device has the first type of device failure; otherwise, determining that the household storage device does not have the first type of device failure;

[0025] In response to the device self-test result being partially abnormal, and the abnormal parameters not matching the manifestation parameters of the first type of device failure, it is determined that the household storage device does not have the first type of device failure.

[0026] In one embodiment, in response to the absence of a first type of device failure in the household storage device, the geographic location of the household storage device is obtained, and combined with regional feature analysis to obtain a feature reference domain, including:

[0027] According to the geographical location of the household storage device, an initial domain and a central domain are generated with a first distance and a second distance as radii, respectively, wherein the second distance is smaller than the first distance;

[0028] Obtain the number of devices in the central domain that are of the same or similar type as the household storage device, perform device feature analysis on the central domain, and generate a central device feature factor;

[0029] Based on the central device characteristic factor, a device reference domain with the closest device quantity and specifications to the central domain is selected from the initial domain, and a device weight coefficient is set for the device reference domain to generate a first reference domain;

[0030] Acquire weather data of the central domain, analyze weather characteristics of the central domain, and obtain central weather characteristic factors of the central domain;

[0031] Based on the central weather characteristic factor, a weather reference domain with the weather characteristics closest to the central domain is selected from the initial domain, and a weather weight coefficient is set for the weather reference domain to generate a second reference domain.

[0032] Obtaining the grid load data of the central domain, performing grid characteristic analysis on the central domain, obtaining the grid load rating of the central domain, and generating a central grid characteristic factor;

[0033] Based on the central grid characteristic factors, a grid reference domain with the closest grid load rating to the central domain is selected from the initial domain, and a grid weight coefficient is set for the grid reference domain to generate a third reference domain;

[0034] The equipment weight coefficient, the weather weight coefficient, and the power grid weight coefficient are adjusted so that at least one intersection domain exists among the first reference domain, the second reference domain, and the third reference domain, and the intersection domain is used as the feature reference domain.

[0035] In one embodiment, obtaining a plurality of reference devices and corresponding reference log data based on a characteristic reference domain includes:

[0036] In the feature reference domain, the operation log data of multiple reference devices are obtained, and the multiple reference devices are screened for abnormal devices based on the standard parameters to obtain standard reference devices and abnormal reference devices;

[0037] Determine whether the abnormal reference device has a first type of device failure. If no first type of device failure exists, determine that the abnormal reference device is available and record it as an abnormal available reference device. Otherwise, determine that the abnormal reference device is unavailable.

[0038] Parse the abnormal log data to obtain the abnormal start time and abnormal running time;

[0039] Generate an observation time window based on the abnormal start time and the preset time range before and after;

[0040] According to the abnormal operation time, the observation time window is modified to obtain the reference time window;

[0041] Based on a reference time window, the operation log data of a plurality of standard reference devices and abnormal available reference devices are screened in sequence to obtain a plurality of reference log data.

[0042] In one embodiment, based on the abnormal log data and in combination with multiple reference log data, determining whether the household storage device has a second type of device failure or regional operation fluctuations includes:

[0043] Obtaining historical regional features of corresponding feature reference domains based on multiple reference log data;

[0044] According to the historical regional characteristics, combined with regional characteristic analysis, the historical regional characteristic factors are obtained;

[0045] Based on the types of multiple reference devices, multiple reference log data are divided to obtain standard reference logs and abnormal available reference logs;

[0046] Parse the standard reference log to obtain standard historical equipment operating parameters;

[0047] Based on the standard historical equipment operating parameters and combined with historical regional characteristic factors, a multidimensional regression model was established to extract the initial regional characteristic influence coefficients;

[0048] To analyze anomalies, you can refer to the log to obtain the abnormal historical equipment operating parameters;

[0049] According to the abnormal historical equipment operating parameters and the historical regional characteristic factors, the regional characteristic influence coefficient is corrected to obtain the corrected regional characteristic influence coefficient;

[0050] Get the current regional characteristic factor of the area where the household storage device is located;

[0051] According to the current regional characteristic factors and the correction regional characteristic influence coefficient, the abnormal parameters are simulated and corrected to obtain the simulated correction parameters;

[0052] Extract the standard reference log to obtain the current standard operating parameters;

[0053] Obtain the current reference area characteristic factors of the reference domain, combine them with the current standard operating parameters, and generate regional fluctuation parameter indicators;

[0054] In response to the simulated correction parameter meeting the regional fluctuation parameter index, it is determined that the household storage device has regional operation fluctuation;

[0055] In response to the simulation correction parameter not meeting the regional fluctuation parameter index, it is determined that the household storage device has a second type of device failure.

[0056] In one embodiment, the method further comprises:

[0057] In response to a second type of device failure in a household storage device, the household storage device is marked, an alarm signal is generated, and the geographic location of the household storage device and abnormal log data of the household storage device are encapsulated into a maintenance information package and fed back to the monitoring database;

[0058] In response to regional operation fluctuations of household storage equipment, current abnormal parameters are obtained based on abnormal log data;

[0059] Obtain the current regional characteristic factor, combine it with the modified regional characteristic influence coefficient and standard parameters, and generate the regional characteristic adaptation parameter;

[0060] Adapt parameters based on regional characteristics and adjust household storage devices accordingly.

[0061] In another aspect, a device for monitoring abnormalities of household storage equipment is provided, the device comprising:

[0062] The abnormality detection module is used to obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is an abnormal state;

[0063] a first fault judgment module, configured to obtain abnormal log data of the household storage device in response to an abnormal state, and judge whether the household storage device has a first type of device fault based on the abnormal log data;

[0064] A reference domain generation module is configured to obtain the geographic location of the household storage device in response to the absence of the first type of device failure in the household storage device, and obtain a feature reference domain in combination with regional feature analysis;

[0065] A reference data acquisition module, configured to acquire a plurality of reference devices and corresponding reference log data based on a characteristic reference domain;

[0066] The second fault judgment module is used to judge whether the household storage device has a second type of equipment failure or regional operation fluctuation based on the abnormal log data in combination with multiple reference log data.

[0067] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed:

[0068] Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality;

[0069] In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data;

[0070] In response to the fact that the household storage device does not have the first type of device failure, the geographical location of the household storage device is obtained, and combined with regional feature analysis, a feature reference domain is obtained;

[0071] Based on the characteristic reference domain, a plurality of reference devices and corresponding plurality of reference log data are obtained;

[0072] Based on the abnormal log data and combined with multiple reference log data, it is determined whether the household storage device has a second-category equipment failure or whether there is regional operation fluctuation.

[0073] In another aspect, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the following steps:

[0074] Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality;

[0075] In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data;

[0076] In response to the fact that the household storage device does not have the first type of device failure, the geographical location of the household storage device is obtained, and combined with regional feature analysis, a feature reference domain is obtained;

[0077] Based on the characteristic reference domain, a plurality of reference devices and corresponding plurality of reference log data are obtained;

[0078] Based on the abnormal log data and combined with multiple reference log data, it is determined whether the household storage device has a second-category equipment failure or whether there is regional operation fluctuation.

[0079] The above-mentioned household storage equipment abnormality monitoring method, device, computer equipment and storage medium introduce regional feature analysis and feature reference domain to obtain reference equipment with similar characteristics to the area where the household storage equipment in the abnormal state is located, which is used to analyze the abnormal logs of the household storage equipment, thereby achieving accurate distinction between equipment failures and regional fluctuations, effectively reducing misjudgments, and improving the accuracy of abnormality identification and fault detection; at the same time, based on the log data of the reference equipment under regional feature analysis, the household storage equipment can be adjusted more accurately, reducing on-site maintenance costs and improving the equipment's operating stability and regional adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 2. It is an application environment diagram of a method for monitoring abnormalities of household storage devices in one embodiment;

[0081] Figure 2 1 is a flow chart of a method for monitoring abnormalities of household storage devices in one embodiment;

[0082] Figure 3 1 is a structural block diagram of a device for monitoring abnormalities of household storage equipment in one embodiment;

[0083] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0085] The present application provides a method for monitoring abnormalities of household storage devices, which can be applied to Figure 1 In the application environment shown, household storage device 102 communicates with server 104 via a network. Household storage device 102 records operating data in real time, generates logs, and transmits them to server 104. Server 104 performs real-time status monitoring and anomaly detection on household storage device 102 based on the received operating data and logs. Household storage device 102 can be, but is not limited to, a household energy storage cabinet, household photovoltaic energy storage device, distributed energy storage device, or energy storage battery. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0086] In one embodiment, Figure 2 As shown, a method for monitoring abnormalities of household storage equipment is provided, which is applied to Figure 1 Taking the server 104 in the example as an example, the following steps are included:

[0087] Step 201: Obtain the operating parameters of the household storage device in real time, compare them with the preset standard parameters, and determine whether there is an abnormal state.

[0088] Among them, household storage equipment refers to home energy storage systems (HESS), which are generally combined with renewable energy (such as solar power generation systems), or store electrical energy when electricity demand is low for use during peak electricity demand, or provide backup power when the power grid is out of power. It usually includes but is not limited to the following components: batteries, inverters, battery management systems (BMS), power interfaces, and temperature control systems.

[0089] Specifically, based on the type of household storage equipment, a corresponding basic parameter framework is established; according to the type of household storage equipment, historical operating data of equipment of the same type is obtained, key features of the basic parameter framework are extracted, characteristic data items and corresponding normal operating thresholds are obtained, and standard parameters are generated; according to the generated standard parameters, the operating parameters of the household storage equipment are compared to determine whether the operating parameters of the household storage equipment meet the standard parameters. If so, the household storage equipment does not have an abnormal state; otherwise, the household storage equipment has an abnormal state.

[0090] Step 202: In response to the abnormal state, abnormal log data of the household storage device is obtained, and based on the abnormal log data, it is determined whether the household storage device has a first type of device failure.

[0091] Among them, the first type of equipment failure refers to a failure that causes the equipment to be unable to continue operating or to shut down. Its characteristics are that it will affect the core functions of the equipment, causing the equipment to lose its working ability, and even be unable to complete self-diagnosis or perform self-test procedures. Examples include: battery damage, circuit short circuit, inverter failure, and power interface damage.

[0092] Specifically, the abnormal log data is parsed to obtain alarm information; in response to the presence of a first-category fault code corresponding to a first-category device fault in the alarm information, it is determined that a first-category device fault exists in the household storage device; in response to the absence of a first-category fault code in the alarm information, the abnormal log data is further parsed to obtain abnormal parameters; a device self-test request is sent to the household storage device, and the device self-test execution result is obtained; based on the abnormal parameters and the device self-test execution result, it is determined whether a first-category device fault exists in the household storage device.

[0093] Step 203: In response to the fact that the household storage device does not have the first type of device failure, the geographical location of the household storage device is obtained, and combined with regional feature analysis, a feature reference domain is obtained.

[0094] Among them, regional characteristic analysis refers to analyzing the regional characteristic factors of the area where the household storage equipment is located, such as weather, power grid, etc., to further analyze the impact of these regional characteristic factors on the household storage equipment.

[0095] Specifically, based on the geographic positioning of household storage equipment, an initial domain and a central domain are generated; the equipment characteristics of the central domain are analyzed, and an equipment reference domain with the equipment quantity and specifications closest to those of the central domain is selected in the initial domain, and combined with the set equipment weight coefficient, a first reference domain is generated; the weather characteristics of the central domain are analyzed, and a weather reference domain with the weather characteristics closest to those of the central domain is selected in the initial domain, and combined with the set weather weight coefficient, a second reference domain is generated; the power grid characteristics of the central domain are analyzed, and a power grid reference domain with the power grid load rating closest to that of the central domain is selected in the initial domain, and combined with the set power grid weight coefficient, a third reference domain is generated; the equipment weight coefficient, the weather weight coefficient, and the power grid weight coefficient are adjusted so that there is at least one intersection domain among the first reference domain, the second reference domain, and the third reference domain, and the intersection domain is used as the feature reference domain.

[0096] Step 204: Based on the characteristic reference domain, obtain multiple reference devices and corresponding multiple reference log data.

[0097] Specifically, in the feature reference domain, operation log data of multiple reference devices are obtained, and combined with standard parameters, the multiple reference devices are screened for abnormal devices to obtain standard reference devices and abnormal reference devices; it is determined whether the abnormal reference device has a first-category device failure, and if it does not exist, the abnormal reference device is determined to be available and recorded as an abnormally available reference device, otherwise, the abnormal reference device is determined to be unavailable; based on the abnormal log data, a reference time window is generated; based on the reference time window, the operation log data of multiple standard reference devices and abnormally available reference devices are screened in turn to obtain multiple reference log data.

[0098] Step 205: Based on the abnormal log data and in combination with a plurality of reference log data, determine whether the household storage device has a second type of equipment failure or whether there is a regional operation fluctuation.

[0099] Among them, the second type of equipment failure refers to a failure that will not immediately cause the equipment to shut down or completely lose function. Household storage equipment can usually still operate when there is a second type of equipment failure, but the equipment performance will decline, for example: battery charging and discharging efficiency decreases, temperature control system failure, and inverter efficiency attenuation; regional operation fluctuations refer to the impact of external factors in a specific area on the performance of household storage equipment, causing the operating conditions of the equipment in the area to fluctuate. The equipment will not necessarily fail under such influence, but it will affect the efficiency, stability and long-term performance of the equipment, for example: ice and snow or high temperature extreme weather affects the heat dissipation of the temperature control system and battery performance, grid load and frequency fluctuations affect battery charging and discharging efficiency, and regional grid maintenance or failures affect battery charging.

[0100] Specifically, based on multiple reference log data, historical regional characteristics of the corresponding feature reference domain are obtained; based on the types of multiple reference devices, multiple reference log data are divided to obtain standard reference logs and abnormal available reference logs; based on the standard reference logs, combined with historical regional characteristics, a multidimensional regression model is established to extract the initial regional characteristic influence coefficient; based on the abnormal available reference logs, combined with historical regional characteristics, the regional characteristic influence coefficient is corrected to obtain the corrected regional characteristic influence coefficient; the current regional characteristics of the area where the household storage device is located are obtained, combined with the corrected regional characteristic influence coefficient, the abnormal parameters are simulated and corrected to obtain simulated correction parameters; based on the standard reference logs, combined with the current reference area characteristics of the reference domain, a regional fluctuation parameter index is generated; in response to the simulated correction parameter being consistent with the regional fluctuation parameter index, it is determined that the household storage device has regional operation fluctuations; in response to the simulated correction parameter not being consistent with the regional fluctuation parameter index, it is determined that the household storage device has a second type of equipment failure.

[0101] In the above-mentioned household storage equipment abnormality monitoring method, by introducing regional feature analysis and feature reference domain, a reference device with similar characteristics to the area where the household storage device in the abnormal state is located is obtained, which is used to analyze the abnormal logs of the household storage equipment, thereby achieving accurate distinction between equipment failures and regional fluctuations, effectively reducing misjudgments, and improving the accuracy of abnormality identification and fault detection; at the same time, based on the log data of the reference device under regional feature analysis, the household storage equipment can be adjusted more accurately, reducing on-site maintenance costs and improving the equipment's operating stability and regional adaptability.

[0102] In one embodiment, before obtaining the operating parameters of the household storage device in real time and comparing them with the preset standard parameters to determine whether there is an abnormal state, the following steps are also included:

[0103] Based on the type of household storage equipment, establish the corresponding basic parameter framework;

[0104] According to the type of household storage equipment, obtain one or more historical operation data of the same type of equipment;

[0105] Extract key features of the basic parameter framework based on one or more historical operating data to obtain feature data items;

[0106] Performing data analysis and processing on one or more historical operation data to extract normal operation thresholds corresponding to characteristic data items;

[0107] Based on the characteristic data items and the corresponding normal operation thresholds, standard parameters are generated.

[0108] Specifically, in this embodiment, by generating standard parameters based on the type of equipment and historical data, errors caused by unified standard parameters are avoided, and the performance threshold of each household storage device can be set more accurately, thereby realizing customization of standard parameters; and, the standard parameters generated by extracting key feature data items can be highly matched with the equipment type characteristics when making fault judgments, thereby improving the accuracy of fault detection.

[0109] In one embodiment, a first-category device fault is correspondingly provided with a first-category fault code. In response to the presence of an abnormal state, abnormal log data of the household storage device is obtained, and based on the abnormal log data, it is determined whether the household storage device has a first-category device fault, including:

[0110] Analyze abnormal log data and obtain alarm information;

[0111] In response to the presence of a first type of fault code in the alarm information, determining that a first type of device fault corresponding to the first type of fault code exists in the household storage device;

[0112] In response to the absence of the first type fault code in the alarm information, further parsing the abnormality log data to obtain abnormality parameters;

[0113] Send a device self-test request to the user storage device and obtain the device self-test execution result;

[0114] In response to the device self-test execution result being completely normal, it is determined that the household storage device does not have a first type of device failure;

[0115] In response to the device self-test result being a detection failure, determining whether the abnormal parameters match the manifestation parameters of the first type of device failure, and if so, determining that the household storage device has a first type of device failure, and if not, determining that the household storage device does not have a first type of device failure;

[0116] In response to the device self-test result being partially abnormal, and the abnormality parameters matching the manifestation parameters of the first type of device failure, obtaining the component of the household storage device with the self-test abnormality, and determining whether it matches the component of the first type of device failure; if so, determining that the household storage device has the first type of device failure; otherwise, determining that the household storage device does not have the first type of device failure;

[0117] In response to the device self-test result being partially abnormal, and the abnormal parameters not matching the manifestation parameters of the first type of device failure, it is determined that the household storage device does not have the first type of device failure.

[0118] Specifically, in this embodiment, by parsing the abnormal log and combining it with the self-test results, a preliminary fault judgment can be made on the household storage equipment in the abnormal state, and the type and source of the fault can be quickly located, avoiding unnecessary manual investigation and misdiagnosis; at the same time, by quickly identifying the first type of equipment failure, it is conducive to the subsequent rapid fault repair or shutdown processing, thereby effectively reducing the downtime of the equipment caused by the failure and ensuring normal operation.

[0119] In one embodiment, in response to the absence of a first type of device failure in the household storage device, the geographic location of the household storage device is obtained, and combined with regional feature analysis, a feature reference domain is obtained, including:

[0120] According to the geographical location of the household storage device, an initial domain and a central domain are generated with a first distance and a second distance as radii, respectively, wherein the second distance is smaller than the first distance;

[0121] Obtain the number of devices in the central domain that are of the same or similar type as the household storage device, perform device feature analysis on the central domain, and generate a central device feature factor;

[0122] Based on the central device characteristic factor, a device reference domain with the closest device quantity and specifications to the central domain is selected from the initial domain, and a device weight coefficient is set for the device reference domain to generate a first reference domain;

[0123] Acquire weather data of the central domain, analyze weather characteristics of the central domain, and obtain central weather characteristic factors of the central domain;

[0124] Based on the central weather characteristic factor, a weather reference domain with the weather characteristics closest to the central domain is selected from the initial domain, and a weather weight coefficient is set for the weather reference domain to generate a second reference domain.

[0125] Obtaining the grid load data of the central domain, performing grid characteristic analysis on the central domain, obtaining the grid load rating of the central domain, and generating a central grid characteristic factor;

[0126] Based on the central grid characteristic factors, a grid reference domain with the closest grid load rating to the central domain is selected from the initial domain, and a grid weight coefficient is set for the grid reference domain to generate a third reference domain;

[0127] The equipment weight coefficient, the weather weight coefficient, and the power grid weight coefficient are adjusted so that at least one intersection domain exists among the first reference domain, the second reference domain, and the third reference domain, and the intersection domain is used as the feature reference domain.

[0128] Specifically, in this embodiment, through geographic positioning and regional feature analysis, the feature reference domain can be more consistent with the actual situation of the area where the equipment is located, so as to accurately select a feature reference domain for household storage equipment in an abnormal state, avoiding misjudgment due to environmental differences; at the same time, the feature reference domain obtained in combination with regional feature analysis is conducive to the subsequent selection of reference devices in the domain to obtain reference data, capture the impact of regional fluctuations on equipment operation, thereby providing an important basis for the subsequent judgment of faults and regional fluctuations, and improving the accuracy of abnormality identification and fault detection.

[0129] In one embodiment, performing weather characteristic analysis on the central domain includes the following steps:

[0130] Get the type of household storage device, which includes: solar energy storage device and peak-valley energy storage device;

[0131] Based on the type of household storage device, several meteorological parameters with the highest correlation with the operating parameters of the corresponding household storage device are selected based on Pearson coefficient calculation and analysis. For example, the operation of solar energy storage devices is generally strongly correlated with meteorological parameters such as sunshine duration and light intensity, while the operation of peak-valley energy storage devices is generally only strongly correlated with temperature changes. These types of household storage devices will be severely affected by extreme ice, snow and thunderstorms.

[0132] Based on the selected meteorological parameters, obtain the corresponding statistical values, such as maximum value, minimum value, mean value, variance, coefficient of variation, kurtosis, skewness, and range;

[0133] The weather characteristic factor is calculated based on several statistical values ​​of several meteorological parameters in combination with the corresponding meteorological parameter weights, wherein the meteorological parameter weights can be set according to the importance of the meteorological parameter in the calculation and analysis of the Pearson coefficient.

[0134] In one embodiment, performing power grid characteristic analysis on the central domain includes the following steps:

[0135] Obtain grid operation data records and select several grid parameters, such as grid load fluctuation, voltage fluctuation, electricity price fluctuation, frequency, power factor, grid fault records, and grid maintenance records;

[0136] Based on the type of household storage device, we select several grid parameters that are most relevant to the performance parameters of the corresponding household storage device based on mutual information entropy calculation and analysis. For example, solar energy storage devices focus more on grid parameters related to grid stability, while peak-valley energy storage devices focus more on grid parameters related to electricity prices and grid output efficiency.

[0137] Based on several selected grid parameters, obtain corresponding grid statistical values, such as maximum value, minimum value, mean value, variance, coefficient of variation, kurtosis, skewness, and range;

[0138] The grid characteristic factor is calculated based on several statistical values ​​of several grid parameters in combination with the corresponding grid parameter weights, wherein the grid parameter weights can be set according to the importance of the grid parameter in the mutual information entropy calculation analysis.

[0139] In one embodiment, based on a characteristic reference domain, obtaining multiple reference devices and corresponding multiple reference log data includes:

[0140] In the feature reference domain, the operation log data of multiple reference devices are obtained, and the multiple reference devices are screened for abnormal devices based on the standard parameters to obtain standard reference devices and abnormal reference devices;

[0141] Determine whether the abnormal reference device has a first type of device failure. If no first type of device failure exists, determine that the abnormal reference device is available and record it as an abnormal available reference device. Otherwise, determine that the abnormal reference device is unavailable.

[0142] Parse the abnormal log data to obtain the abnormal start time and abnormal running time;

[0143] Generate an observation time window based on the abnormal start time and the preset time range before and after;

[0144] According to the abnormal operation time, the observation time window is modified to obtain the reference time window;

[0145] Based on a reference time window, the operation log data of a plurality of standard reference devices and abnormal available reference devices are screened in sequence to obtain a plurality of reference log data.

[0146] Specifically, in this embodiment, by selecting multiple reference devices and their log data in the reference domain, it is possible to effectively obtain device data similar to the abnormal log data of the target household storage device, thereby reducing misjudgments caused by the abnormality of a single device; at the same time, by setting a time window based on the abnormal log data to filter the operation log data of the reference device, it is possible to not only reduce data redundancy and improve the efficiency of subsequent calculation and analysis, but also further improve the similarity and referenceability of the data, and improve the accuracy of subsequent analysis.

[0147] In one embodiment, based on the abnormal log data and in combination with multiple reference log data, determining whether the household storage device has a second type of device failure or regional operation fluctuations includes:

[0148] Obtaining historical regional features of corresponding feature reference domains based on multiple reference log data;

[0149] According to the historical regional characteristics, combined with regional characteristic analysis, the historical regional characteristic factors are obtained;

[0150] Based on the types of multiple reference devices, multiple reference log data are divided to obtain standard reference logs and abnormal available reference logs;

[0151] Parse the standard reference log to obtain standard historical equipment operating parameters;

[0152] Based on the standard historical equipment operating parameters and combined with historical regional characteristic factors, a multidimensional regression model was established to extract the initial regional characteristic influence coefficients;

[0153] To analyze anomalies, you can refer to the log to obtain the abnormal historical equipment operating parameters;

[0154] According to the abnormal historical equipment operating parameters and the historical regional characteristic factors, the regional characteristic influence coefficient is corrected to obtain the corrected regional characteristic influence coefficient;

[0155] Get the current regional characteristic factor of the area where the household storage device is located;

[0156] According to the current regional characteristic factors and the correction regional characteristic influence coefficient, the abnormal parameters are simulated and corrected to obtain the simulated correction parameters;

[0157] Extract the standard reference log to obtain the current standard operating parameters;

[0158] Obtain the current reference area characteristic factors of the reference domain, combine them with the current standard operating parameters, and generate regional fluctuation parameter indicators;

[0159] In response to the simulated correction parameter meeting the regional fluctuation parameter index, it is determined that the household storage device has regional operation fluctuation;

[0160] In response to the simulation correction parameter not meeting the regional fluctuation parameter index, it is determined that the household storage device has a second type of device failure.

[0161] Specifically, in this embodiment, a multidimensional regression model is constructed by combining the historical data of the reference equipment and the historical characteristics of the area where it is located, and the relationship between the equipment operating parameters and the regional characteristics is obtained, so that the household storage equipment in an abnormal state can be simulated and corrected, and then it can be determined whether its abnormal state is caused by changes in regional characteristics or whether there is a fault in the equipment itself, which is beneficial to improving the accuracy of abnormal monitoring and fault detection and reducing the risk of misjudgment; at the same time, on the basis of constructing a multidimensional regression model with standard reference equipment, the data of abnormal available reference equipment is introduced for further correction. Since the parameters of the abnormal available reference equipment change more obviously when the regional characteristics change, the accuracy of the relationship between the obtained equipment operating parameters and regional characteristics can be improved, thereby improving the accuracy of subsequent fault judgment and equipment adjustment.

[0162] In one embodiment, the method includes:

[0163] In response to a second type of device failure in a household storage device, the household storage device is marked, an alarm signal is generated, and the geographic location of the household storage device and abnormal log data of the household storage device are encapsulated into a maintenance information package and fed back to the monitoring database;

[0164] In response to regional operation fluctuations of household storage equipment, current abnormal parameters are obtained based on abnormal log data;

[0165] Obtain the current regional characteristic factor, combine it with the modified regional characteristic influence coefficient and standard parameters, and generate the regional characteristic adaptation parameter;

[0166] Adapt parameters based on regional characteristics and adjust household storage devices accordingly.

[0167] Specifically, in this embodiment, by obtaining the identification results of abnormal states, it is possible to accurately distinguish abnormal states caused by regional fluctuations or equipment failures, and generate corresponding adjustment strategies; in the case of regional fluctuations, the adjustment strategy formulated in combination with regional feature analysis can ensure the adaptability of the equipment to regional changes, thereby improving the stability and efficiency of the equipment and reducing unnecessary manpower operation and maintenance costs; in the case of equipment failure, the system will issue an alarm in a timely manner and generate a maintenance information package to ensure that the fault can be handled quickly, reduce equipment downtime, and improve the overall adaptability and reliability of the equipment.

[0168] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0169] In one embodiment, Figure 3 As shown, a household storage device abnormality monitoring device is provided, comprising: an abnormality detection module, a first fault judgment module, a reference domain generation module, a reference data acquisition module, a second fault judgment module and an adjustment strategy generation module, wherein:

[0170] The abnormality detection module is used to obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is an abnormal state;

[0171] a first fault judgment module, configured to obtain abnormal log data of the household storage device in response to an abnormal state, and judge whether the household storage device has a first type of device fault based on the abnormal log data;

[0172] a reference domain generating module, configured to obtain the geographic location of the household storage device in response to the absence of the first type of device failure in the household storage device, and obtain a feature reference domain in combination with regional feature analysis;

[0173] A reference data acquisition module, configured to acquire a plurality of reference devices and corresponding reference log data based on the characteristic reference domain;

[0174] The second fault judgment module is used to judge whether the household storage device has a second type of equipment failure or regional operation fluctuation based on the abnormal log data and the multiple reference log data.

[0175] The anomaly detection module is also used to establish a corresponding basic parameter framework based on the type of household storage equipment; obtain one or more historical operating data of equipment of the same type according to the type of household storage equipment; extract key features of the basic parameter framework based on the one or more historical operating data to obtain characteristic data items; perform data analysis and processing on the one or more historical operating data to extract the normal operating threshold corresponding to the characteristic data item; and generate standard parameters based on the characteristic data item and the corresponding normal operating threshold.

[0176] The first fault judgment module is further used to parse abnormal log data and obtain alarm information; in response to the presence of a first-class fault code in the alarm information, it is determined that the household storage device has a first-class device fault corresponding to the first-class fault code; in response to the absence of the first-class fault code in the alarm information, it is further used to parse the abnormal log data and obtain abnormal parameters; send a device self-test request to the household storage device and obtain the device self-test execution result; in response to the device self-test execution result being all normal, it is determined that the household storage device does not have a first-class device fault; in response to the device self-test execution result being a detection failure, it is determined that the abnormal parameters are consistent with the performance parameters of the first-class device fault. Whether they match, if so, it is determined that the household storage device has a first-category device fault, if not, it is determined that the household storage device does not have a first-category device fault; in response to the device self-test execution result being partially abnormal, and the abnormal parameters matching the manifestation parameters of the first-category device fault, the self-test abnormal component of the household storage device is obtained to determine whether it matches the component of the first-category device fault, if so, it is determined that the household storage device has a first-category device fault, if not, it is determined that the household storage device does not have a first-category device fault; in response to the device self-test execution result being partially abnormal, and the abnormal parameters not matching the manifestation parameters of the first-category device fault, it is determined that the household storage device does not have a first-category device fault.

[0177] The reference domain generation module is further used to generate an initial domain and a central domain based on the geographic location of the household storage device, using a first distance and a second distance as radii, respectively, wherein the second distance is smaller than the first distance; obtain the number of devices in the central domain that are the same or similar in type to the household storage device, perform device feature analysis on the central domain, and generate a central device feature factor; based on the central device feature factor, select a device reference domain that is closest in number and specification to the central domain in the initial domain, and set a device weight coefficient for the device reference domain to generate a first reference domain; obtain weather data for the central domain, perform weather feature analysis on the central domain, and obtain a central weather feature factor for the central domain; based on the central weather feature factor , select the weather reference domain with the weather characteristics closest to the central domain in the initial domain, set the weather weight coefficient for the weather reference domain, generate the second reference domain to obtain the grid load data of the central domain, perform grid characteristic analysis on the central domain, obtain the grid load rating of the central domain, and generate the central grid characteristic factor; based on the central grid characteristic factor, select the grid reference domain with the grid load rating closest to the central domain in the initial domain, set the grid weight coefficient for the grid reference domain, and generate the third reference domain; adjust the equipment weight coefficient, weather weight coefficient and grid weight coefficient so that there is at least one intersection domain among the first reference domain, the second reference domain and the third reference domain, and use the intersection domain as the characteristic reference domain.

[0178] The reference data acquisition module is also used to obtain operation log data of multiple reference devices in the feature reference domain, and in combination with standard parameters, screen the multiple reference devices for abnormal devices respectively to obtain standard reference devices and abnormal reference devices; determine whether the abnormal reference device has a first-category device failure, and in response to the response that it does not exist, determine that the abnormal reference device is available and recorded as an abnormally available reference device, otherwise, determine that the abnormal reference device is unavailable; parse the abnormal log data to obtain the abnormal start time and the abnormal operation time; generate an observation time window based on the abnormal start time and a preset before and after time range; correct the observation time window based on the abnormal operation time to obtain a reference time window; based on the reference time window, screen the operation log data of multiple standard reference devices and abnormally available reference devices in turn to obtain multiple reference log data.

[0179] The second fault judgment module is also used to obtain the historical regional characteristics of the corresponding feature reference domain based on multiple reference log data, and obtain the historical regional characteristic factors based on the historical regional characteristics in combination with regional characteristic analysis; divide the multiple reference log data based on the types of multiple reference devices to obtain standard reference logs and abnormal available reference logs; parse the standard reference logs to obtain standard historical equipment operating parameters; establish a multidimensional regression model based on the standard historical equipment operating parameters in combination with historical regional characteristic factors to extract the initial regional characteristic influence coefficient; parse the abnormal available reference logs to obtain abnormal historical equipment operating parameters; based on the abnormal historical equipment operating parameters in combination with historical regional characteristics Factor, correct the regional characteristic influence coefficient to obtain the corrected regional characteristic influence coefficient; obtain the current regional characteristic factor of the area where the household storage device is located; according to the current regional characteristic factor, combined with the corrected regional characteristic influence coefficient, simulate and correct the abnormal parameters to obtain simulated correction parameters; extract the standard reference log to obtain the current standard operation parameters; obtain the current reference regional characteristic factor of the reference domain, combine the current standard operation parameters to generate a regional fluctuation parameter index; in response to the simulation correction parameter being consistent with the regional fluctuation parameter index, it is determined that the household storage device has regional operation fluctuations; in response to the simulation correction parameter not being consistent with the regional fluctuation parameter index, it is determined that the household storage device has a second type of equipment failure.

[0180] The second fault judgment module is also used to, in response to a second type of equipment failure in the household storage device, mark the household storage device, generate an alarm signal, and encapsulate the geographic location of the household storage device and the abnormal log data of the household storage device into a maintenance information package, and feed it back to the monitoring database; in response to regional operation fluctuations in the household storage device, obtain the current abnormal parameters based on the abnormal log data; obtain the current regional characteristic factor, combine the corrected regional characteristic influence coefficient and the standard parameter, and generate the regional characteristic adaptation parameter; based on the regional characteristic adaptation parameter, adjust the household storage device accordingly.

[0181] The specific definitions of the household storage device anomaly monitoring device can be found in the definitions of the household storage device anomaly monitoring method described above and will not be further elaborated here. Each module in the aforementioned household storage device anomaly monitoring device can be implemented in whole or in part via software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0182] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store user storage device abnormality monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a user storage device abnormality monitoring method is implemented.

[0183] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0184] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0185] Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality;

[0186] In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data;

[0187] In response to the household storage device not having the first type of device failure, obtaining the geographic location of the household storage device, combining it with regional feature analysis to obtain a feature reference domain;

[0188] Based on the characteristic reference domain, obtaining a plurality of reference devices and a corresponding plurality of reference log data;

[0189] According to the abnormal log data and in combination with the plurality of reference log data, it is determined whether the household storage device has a second type of equipment failure or whether there is a regional operation fluctuation.

[0190] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0191] Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality;

[0192] In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data;

[0193] In response to the household storage device not having the first type of device failure, obtaining the geographic location of the household storage device, combining it with regional feature analysis to obtain a feature reference domain;

[0194] Based on the characteristic reference domain, obtaining a plurality of reference devices and a corresponding plurality of reference log data;

[0195] According to the abnormal log data and in combination with the plurality of reference log data, it is determined whether the household storage device has a second type of equipment failure or whether there is a regional operation fluctuation.

[0196] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0197] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0198] The above embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A method for monitoring abnormalities of household storage equipment, characterized in that: include: Obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is any abnormality; In response to the abnormal state, obtaining abnormal log data of the household storage device, and determining whether the household storage device has a first type of device failure based on the abnormal log data; In response to the household storage device not having the first type of device failure, obtaining the geographic location of the household storage device, combining it with regional feature analysis to obtain a feature reference domain; Based on the characteristic reference domain, obtaining a plurality of reference devices and a corresponding plurality of reference log data; According to the abnormal log data and in combination with the plurality of reference log data, it is determined whether the household storage device has a second type of equipment failure or whether there is a regional operation fluctuation.

2. A household storage device abnormality monitoring method according to claim 1, characterized in that: Before obtaining the operating parameters of the household storage device in real time and comparing them with the preset standard parameters to determine whether there is an abnormal state, the following steps are also included: Based on the type of the household storage device, a corresponding basic parameter framework is established; According to the type of the household storage device, obtain one or more historical operation data of the same type of device; Extract key features of the basic parameter framework based on the one or more historical operation data to obtain feature data items; Performing data analysis on the one or more historical operation data to extract a normal operation threshold corresponding to the characteristic data item; The standard parameters are generated according to the characteristic data items in combination with the corresponding normal operation thresholds.

3. The method for monitoring abnormalities of household storage equipment according to claim 1, characterized in that: The first type of device failure is correspondingly provided with a first type of fault code. In response to the abnormal state, abnormal log data of the household storage device is obtained, and according to the abnormal log data, whether the household storage device has the first type of device failure is determined, including: Parsing the abnormal log data to obtain alarm information; In response to the presence of the first type of fault code in the alarm information, determining that the household storage device has the first type of device fault corresponding to the first type of fault code; In response to the absence of the first type of fault code in the alarm information, further parsing the abnormality log data to obtain abnormality parameters; Sending a device self-test request to the user storage device and obtaining the device self-test execution result; In response to the device self-test being completely normal, determining that the household storage device does not have the first type of device failure; In response to the device self-test result being a detection failure, determining whether the abnormal parameters match the manifestation parameters of the first type of device failure, and if so, determining that the household storage device has the first type of device failure; otherwise, determining that the household storage device does not have the first type of device failure; In response to the device self-test result being partially abnormal, and the abnormal parameters matching the manifestation parameters of the first type of device failure, obtaining the component of the household storage device with the self-test abnormality, and determining whether it matches the component of the first type of device failure; if so, determining that the household storage device has the first type of device failure; otherwise, determining that the household storage device does not have the first type of device failure; In response to the device self-test result being partially abnormal, and the abnormal parameters not matching the manifestation parameters of the first type of device failure, it is determined that the household storage device does not have the first type of device failure.

4. The method for monitoring abnormality of household storage equipment according to claim 1, characterized in that: In response to the fact that the household storage device does not have the first type of device failure, obtaining the geographic location of the household storage device, and combining regional feature analysis to obtain a feature reference domain, including: generating an initial domain and a central domain based on the geographic location of the household storage device, using a first distance and a second distance as radii, respectively, wherein the second distance is smaller than the first distance; Obtaining the number of devices in the central domain that are of the same or similar type as the household storage device, performing device feature analysis on the central domain, and generating a central device feature factor; Based on the central device characteristic factor, selecting a device reference domain in the initial domain that is closest to the central domain in terms of device quantity and specifications, and setting a device weight coefficient for the device reference domain to generate a first reference domain; Acquiring weather data of the central domain, performing weather characteristic analysis on the central domain, and obtaining a central weather characteristic factor of the central domain; Based on the central weather characteristic factor, selecting a weather reference domain in the initial domain that has weather characteristics closest to those of the central domain, and setting a weather weight coefficient for the weather reference domain to generate a second reference domain; Acquiring grid load data of the central region, performing grid characteristic analysis on the central region, obtaining a grid load rating of the central region, and generating a central grid characteristic factor; Based on the central power grid characteristic factor, selecting a power grid reference domain in the initial domain that has a power grid load rating closest to that of the central domain, and setting a power grid weight coefficient for the power grid reference domain to generate a third reference domain; The device weight coefficient, the weather weight coefficient, and the power grid weight coefficient are adjusted so that at least one intersection domain exists among the first reference domain, the second reference domain, and the third reference domain, and the intersection domain is used as the feature reference domain.

5. The method for monitoring abnormalities of household storage equipment according to claim 1, characterized in that: Based on the characteristic reference domain, a plurality of reference devices and corresponding plurality of reference log data are obtained, including: In the characteristic reference domain, operation log data of the plurality of reference devices are obtained, and abnormal device screening is performed on the plurality of reference devices respectively in combination with the standard parameters to obtain standard reference devices and abnormal reference devices; Determining whether the abnormal reference device has the first type of device failure, and if not, determining that the abnormal reference device is available and recording it as an abnormal available reference device; otherwise, determining that the abnormal reference device is unavailable; Parse the abnormal log data to obtain the abnormal start time and abnormal running time; Generate an observation time window based on the abnormal start time and the preset time range before and after; According to the abnormal operation time, the observation time window is corrected to obtain a reference time window; Based on the reference time window, the operation log data of the plurality of standard reference devices and the abnormal available reference devices are screened in sequence to obtain the plurality of reference log data.

6. A household storage device abnormality monitoring method according to claim 5, characterized in that: The determining, based on the abnormal log data and in combination with the plurality of reference log data, whether the household storage device has a second type of device failure or regional operation fluctuations includes: Acquiring historical regional features of the corresponding feature reference domain according to the plurality of reference log data; According to the historical regional characteristics, combined with the regional characteristics analysis, a historical regional characteristic factor is obtained; Based on the types of the multiple reference devices, the multiple reference log data are divided to obtain standard reference logs and abnormal available reference logs; Parsing the standard reference log to obtain standard historical equipment operating parameters; Based on the standard historical equipment operating parameters and the historical regional characteristic factors, a multidimensional regression model is established to extract the initial regional characteristic influence coefficient; The abnormality can be analyzed by referring to the log to obtain the abnormal historical equipment operating parameters; According to the abnormal historical equipment operating parameters and in combination with the historical regional characteristic factors, the regional characteristic influence coefficient is corrected to obtain a corrected regional characteristic influence coefficient; Obtaining a current regional characteristic factor of the area where the household storage device is located; According to the current regional characteristic factor and in combination with the modified regional characteristic influence coefficient, the abnormal parameter is simulated and corrected to obtain a simulated correction parameter; Extracting the standard reference log to obtain current standard operating parameters; Obtaining a current reference area characteristic factor of the reference domain and generating a regional fluctuation parameter index in combination with the current standard operating parameters; In response to the simulation correction parameter meeting the regional fluctuation parameter index, determining that the household storage device has regional operation fluctuation; In response to the simulation correction parameter not meeting the regional fluctuation parameter index, it is determined that the household storage device has a second type of device failure.

7. A household storage device abnormality monitoring method according to claim 6, characterized in that: The method comprises: In response to a second type of device failure occurring in the household storage device, marking the household storage device, generating an alarm signal, and encapsulating the geographic location of the household storage device and abnormal log data of the household storage device into a maintenance information package, and feeding the package back to a monitoring database; In response to regional operation fluctuations of the household storage device, obtaining current abnormal parameters according to the abnormal log data; Obtaining the current regional characteristic factor, combining the modified regional characteristic influence coefficient and the standard parameter to generate a regional characteristic adaptation parameter; Based on the regional characteristic adaptation parameters, the household storage device is adjusted accordingly.

8. A household storage equipment abnormality monitoring device, characterized in that: The device comprises: The abnormality detection module is used to obtain the operating parameters of household storage equipment in real time, compare them with the preset standard parameters, and determine whether there is an abnormal state; a first fault judgment module, configured to obtain abnormal log data of the household storage device in response to an abnormal state, and judge whether the household storage device has a first type of device fault based on the abnormal log data; a reference domain generating module, configured to obtain the geographic location of the household storage device in response to the absence of the first type of device failure in the household storage device, and obtain a feature reference domain in combination with regional feature analysis; A reference data acquisition module, configured to acquire a plurality of reference devices and corresponding reference log data based on the characteristic reference domain; The second fault judgment module is used to judge whether the household storage device has a second type of equipment failure or regional operation fluctuation based on the abnormal log data and in combination with the multiple reference log data, and generate an abnormal state identification result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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