A power distribution intelligent operation and maintenance system based on power big data

Abstract

The present invention discloses an intelligent power distribution operation and maintenance system based on electric power big data, which relates to the field of intelligent operation and maintenance technology. The present invention is based on electric power big data and combines intelligent operation and maintenance technology to realize accurate monitoring and dynamic evaluation of the power distribution system through real-time collection and analysis of various data in the power distribution network; the system can perform real-time detection and early warning of key indicators such as the operating status of the distribution cabinet, meteorological factors, line wear, etc., effectively reducing the occurrence of equipment failures and power outages; at the same time, the system also has a powerful stability analysis function, which can identify potential risks in advance and optimize resource allocation, thereby improving the reliability and stability of power supply; in addition, the system uses an intelligent evaluation module, combined with factors such as meteorological risks and line wear, to provide targeted maintenance suggestions and power outage protection solutions to ensure the safe operation of the power distribution network under extreme conditions.

Description

Technical Field

[0001] The present invention relates to the field of intelligent operation and maintenance technology, and specifically to a power distribution intelligent operation and maintenance system based on power big data. Background Art

[0002] With the ever-increasing demand for electricity in modern society, the reliability and stability of distribution systems, as a crucial link in power transmission, directly impact the safety and economic efficiency of power supply. Traditional distribution networks rely heavily on manual inspections and routine maintenance. While these methods ensure the normal operation of distribution systems to a certain extent, they still present challenges such as untimely equipment fault detection, low O&M efficiency, and irrational resource allocation. Especially in complex weather environments and aging equipment from long-term operation, traditional methods often fail to detect potential risks in a timely manner, leading to power outages.

[0003] In recent years, with the rapid development of the Internet of Things, cloud computing, and big data technologies, the concept of smart grids has gradually emerged, and intelligent operation and maintenance of distribution systems has become a development trend in the power industry. Big data-based intelligent distribution operation and maintenance systems, through real-time collection and in-depth analysis of various data from the distribution system, can implement multiple intelligent functions such as status monitoring, fault diagnosis, risk prediction, and optimized scheduling of distribution equipment, greatly improving operation and maintenance efficiency and system reliability. However, existing technologies mostly focus on equipment fault monitoring and anomaly detection. There is still a lack of comprehensive solutions for dynamically assessing system operating status and risks by comprehensively considering multiple influencing factors such as external meteorological factors, line wear, and power load changes.

[0004] Therefore, there is an urgent need for an intelligent distribution operation and maintenance system based on power big data. Through multi-dimensional data collection and analysis, the various operating parameters of the distribution network can be monitored in real time, and comprehensive assessments can be conducted in combination with multiple factors such as weather and line wear to identify potential system risks in advance, thereby improving the reliability, safety and operation and maintenance efficiency of the distribution system, reducing the probability of failures, and achieving rational allocation and optimized management of power resources. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention provides a power distribution intelligent operation and maintenance system based on power big data, which solves the problems in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power distribution intelligent operation and maintenance system based on power big data, comprising:

[0007] The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status based on the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk based on the operation status coefficient. If it is judged that there is a failure risk, it will generate a distribution cabinet early warning information;

[0008] The stability index determination module is used to obtain the operating status coefficient for stability analysis when the distribution cabinet operating status module does not generate early warning information, determine the stability index of the operating status coefficient, and then determine the abnormal distribution cabinet based on the stability index of the operating status coefficient and generate abnormal information of the distribution cabinet;

[0009] Meteorological data collection module, which obtains meteorological data of the target area at a preset time;

[0010] Meteorological risk coefficient determination module, which determines the meteorological risk coefficient of the target area based on meteorological data;

[0011] A line wear coefficient determination module is used to obtain the historical line failure counts of all distribution cabinets in the target area and determine the line wear coefficient of each distribution cabinet based on the historical line failure counts of each distribution cabinet;

[0012] The power outage protection assessment module obtains the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient and line wear coefficient of each distribution cabinet in the target area, performs power outage protection assessment to determine the power outage protection coefficient of each distribution cabinet in the target area, and determines whether to generate a power outage protection instruction based on the power outage protection coefficient. When a power outage protection instruction is generated, the power outage protection information of the distribution cabinet is generated at the same time.

[0013] As a further solution of the present invention: in the distribution cabinet operation status module, the monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the distribution cabinet.

[0014] As a further solution of the present invention: In the distribution cabinet operation status module, the specific method of evaluating the distribution cabinet operation status based on the monitoring data of the distribution cabinet, determining the operation status coefficient, and judging whether the distribution cabinet has risks based on the operation status coefficient is as follows:

[0015] BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data;

[0016] The health index of each data in the monitoring data is determined according to the specific value of each data in the monitoring data, including the voltage health index RSI V , Current Health Index RSI I , Temperature Health Index RSI T , Vibration Health Index RSI A , Power Factor Health Index RSI pf, Insulation Resistance Health Index

[0017] BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the distribution cabinet using the following formula:

[0018]

[0019] Among them, RSI represents the operating status coefficient of the distribution cabinet, and β, γ and α represent weight factors;

[0020] BS3: Obtain the distribution cabinet operating status coefficient RSI and compare the operating status coefficient RSI with the preset threshold GR1:

[0021] If RSI ≤ GR1, it indicates that there is a risk of failure in the operation of the power distribution cabinet, and a power distribution cabinet warning message is generated;

[0022] If RSI>GR1, it means the power distribution cabinet is in good operating condition and no action is required.

[0023] As a further solution of the present invention: in the stability index determination module, the operation state coefficient is obtained to perform stability analysis, the stability index of the operation state coefficient is determined, and then the abnormal distribution cabinet is determined according to the stability index of the operation state coefficient, and the specific method of generating abnormal information of the distribution cabinet is as follows:

[0024] CS1: Get all the operating status coefficients for the past two hours from the current moment;

[0025] CS2: Calculate the average of all the operating status coefficients and mark it as RSI enp ;

[0026] CS3: Then use the formula Determine the discrete values ​​EU of all operating state coefficients, where 1≤j≤m, and m represents the total number of all current operating state coefficients;

[0027] CS4: Obtain the discrete value EU and the preset threshold GR2, and determine the stability index of the operating status coefficient using the following formula:

[0028]

[0029] Among them, WD represents the stability index of the operating state coefficient;

[0030] CS5: Obtain the stability index WD of the operating status coefficient and compare the stability index WD with the abnormal stability index GR3:

[0031] If WD>GR3, the distribution cabinet is marked as an abnormal distribution cabinet and the distribution cabinet abnormal information is generated. No processing is performed in other cases. The abnormal stability index GR3 is a preset value.

[0032] As a further solution of the present invention: in the meteorological data acquisition module, the meteorological data specifically includes temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters and frost parameters.

[0033] As a further solution of the present invention: in the meteorological risk coefficient determination module, the specific method of determining the meteorological risk coefficient of the target area based on meteorological data is:

[0034] DS1: Preprocess the collected meteorological data, including the following steps:

[0035] DS11: Handle missing values ​​and outliers to ensure data integrity and accuracy;

[0036] DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter X i The normalized value can be expressed as X′ i :

[0037]

[0038] Where, 1≤i≤6, X1 represents the temperature parameter, X2 represents the humidity parameter, X3 represents the wind speed parameter, X4 represents the precipitation parameter, X5 represents the lightning activity parameter, and X6 represents the frost parameter; X max-i Indicates the preset maximum value of the corresponding meteorological parameter, X min-i Indicates the preset minimum value of the corresponding meteorological parameter;

[0039] DS2: For each meteorological parameter X′ i The deviation value of each meteorological parameter is determined by the following formula

[0040]

[0041] Among them, X mid-i are expressed as ideal values ​​of corresponding meteorological parameters, and X range-i It is expressed as the allowable deviation range of the corresponding meteorological parameters, and X range-i =X max-i -X min-i ;

[0042] DS3: Determine the meteorological risk factor CBR using the following formula:

[0043]

[0044] Among them, w i represents the weight coefficient corresponding to the deviation value of the meteorological parameter, and w1 + w2 + w3 + w4 + w5 + w6 = 1.

[0045] As a further solution of the present invention: in the line wear coefficient determination module, the specific method for obtaining the historical line fault times of all distribution cabinets in the target area and determining the line wear coefficient of each distribution cabinet according to the historical line fault times of each distribution cabinet is as follows:

[0046] Obtain all distribution cabinets in the abnormal area, and determine the line wear coefficient HX of each distribution cabinet through the following formula HX = δ·YL according to the historical line fault times of each distribution cabinet; where, YL represents the historical line fault times, and δ represents the influence factor.

[0047] As a further solution of the present invention: in the power-off protection evaluation module, the specific method for obtaining the operation state coefficient, the stability index of the operation state coefficient, the meteorological risk coefficient and the line wear coefficient of each distribution cabinet in the target area, performing the power-off protection evaluation to determine the power-off protection coefficient of each distribution cabinet in the target area, and judging whether to generate a power-off protection instruction according to the power-off protection coefficient is as follows:

[0048] Perform the power-off protection evaluation on it through the following formula to determine the power-off protection coefficient:

[0049]

[0050] Among them, PRG represents the power-off protection coefficient, and k1 and k2 represent the weight factors;

[0051] Then judge whether to generate a power-off protection instruction according to the power-off protection coefficient:

[0052] If the power-off protection coefficient PRG < GR5, then generate a power-off protection instruction.

[0053] As a further solution of the present invention: it further includes:

[0054] The maintenance end display module, which obtains and displays the distribution cabinet warning information, the distribution cabinet abnormal information and the distribution cabinet power-off protection information, reminds the maintenance personnel to understand the corresponding distribution cabinet situation at the first moment and take corresponding treatment measures.

[0055] The present invention provides a distribution intelligent operation and maintenance system based on power big data. Compared with the prior art, it has the following beneficial effects:

[0056] Based on big data from power generation, combined with intelligent operation and maintenance technologies, this system enables precise monitoring and dynamic evaluation of power distribution systems through real-time collection and analysis of various data from the distribution network. The system provides real-time monitoring and early warning of key indicators such as the operating status of distribution cabinets, meteorological factors, and line wear, effectively reducing equipment failures and power outages. Furthermore, the system also features powerful stability analysis capabilities, enabling proactive identification of potential risks and optimizing resource allocation, thereby improving the reliability and stability of power supply.

[0057] Furthermore, the system uses intelligent assessment modules, taking into account factors such as meteorological risks and line wear, to provide targeted maintenance recommendations and power outage protection solutions, ensuring the safe operation of the distribution network under extreme conditions. Operations and maintenance personnel can view system status and risk forecasts in real time through the maintenance terminal display module, enabling timely responses and optimizing operations and maintenance decisions. Overall, this intelligent operation and maintenance system has significantly improved the distribution network's operational efficiency, reduced maintenance costs, and enhanced the ability to respond to emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below with reference to the accompanying drawings.

[0059] Figure 1 This is a structural framework diagram of a power distribution intelligent operation and maintenance system based on power big data in the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] Example 1

[0062] See also Figure 1 ,The present invention provides a power distribution intelligent operation and maintenance system based on power big data, including;

[0063] The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status based on the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk based on the operation status coefficient. If it is judged that there is a failure risk, it will generate a distribution cabinet early warning information;

[0064] The monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the distribution cabinet;

[0065] Specifically, the data for monitoring the operating status of the power distribution cabinet are obtained and determined through relevant sensors;

[0066] The specific method of evaluating the operating status of the distribution cabinet based on the monitoring data of the distribution cabinet, determining the operating status coefficient, and judging whether the distribution cabinet has risks based on the operating status coefficient is as follows:

[0067] BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data;

[0068] Determining the health index of each piece of monitoring data according to the specific value of each piece of monitoring data includes:

[0069] By formula Determine the voltage health index RSI V ; Among them, V measured Indicates the actual measured voltage value, V rated Expressed as standard voltage value;

[0070] By formula Determine the Current Health Index RSI I ; Among them, I measured Indicates the actual measured current value, I rated Expressed as standard current value;

[0071] By formula Determine the temperature health index RSI T ; Among them, T measured Indicates the actual measured temperature, T max Indicates the maximum operating temperature allowed in the distribution cabinet, T max Indicates the minimum operating temperature allowed in the power distribution cabinet;

[0072] By formula Determine the Vibration Health Index (RSI) A Among them, A measured Indicates the actual measured vibration level, A max Expressed as the maximum vibration level allowed in the distribution cabinet;

[0073] By formula Determine the power factor health index RSI pf ; Among them, pf measured Indicates the actual measured power factor, pf rated Expressed as standard power factor;

[0074] By formula Determining the Insulation Resistance Health Index in, Indicates the actual measured insulation resistance value. It is expressed as the minimum insulation resistance value required for normal operation in the distribution cabinet;

[0075] BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the distribution cabinet using the following formula:

[0076]

[0077] Among them, RSI represents the operating status coefficient of the distribution cabinet, and β, γ and α represent weight factors;

[0078] BS3: Obtain the distribution cabinet operating status coefficient RSI and compare the operating status coefficient RSI with the preset threshold GR1:

[0079] If RSI ≤ GR1, it indicates that there is a risk of failure in the operation of the power distribution cabinet, and a power distribution cabinet warning message is generated;

[0080] If RSI>GR1, it means the power distribution cabinet is in good operating condition and no action is required;

[0081] By real-time monitoring of the operating data of the power distribution cabinet, such as current, voltage, temperature and other parameters, it is possible to accurately determine whether it is in normal working condition. If an abnormality occurs, the module can quickly identify and alarm, preventing the equipment from being in a long-term fault state and reducing the risk of fault escalation. Through this module, operation and maintenance personnel can obtain real-time data and health status of the power distribution cabinet, thereby improving the reliability of the power distribution system, reducing power outage time and extending the service life of the equipment.

[0082] The stability index determination module is used to obtain the operating status coefficient for stability analysis when the distribution cabinet operating status module does not generate early warning information, determine the stability index of the operating status coefficient, and then determine the abnormal distribution cabinet based on the stability index of the operating status coefficient and generate abnormal information of the distribution cabinet;

[0083] The specific method of obtaining the operating state coefficient for stability analysis, determining the stability index of the operating state coefficient, then determining the abnormal distribution cabinet according to the stability index of the operating state coefficient, and generating abnormal information of the distribution cabinet is as follows:

[0084] CS1: Get all the operating status coefficients for the past two hours from the current moment;

[0085] It should be noted that obtaining all the operating status coefficients for the two hours before the current moment means that within the two hours, professional staff set a time period of t2 to collect monitoring data and obtain the operating status coefficient. In general, monitoring data is collected and the operating status coefficient is calculated every time period of t2.

[0086] CS2: Calculate the average of all the operating status coefficients and mark it as RSI enp ;

[0087] CS3: Then use the formula Determine the discrete values ​​EU of all operating state coefficients, where 1≤j≤m, and m represents the total number of all current operating state coefficients;

[0088] CS4: Obtain the discrete value EU and the preset threshold GR2, and determine the stability index of the operating status coefficient using the following formula:

[0089]

[0090] Among them, WD represents the stability index of the operating status coefficient, and the specific parameters of the preset threshold GR2 are set by professional staff;

[0091] CS5: Obtain the stability index WD of the operating status coefficient and compare the stability index WD with the abnormal stability index GR3:

[0092] If WD>GR3, the PDC is marked as abnormal and abnormal information is generated. In other cases, no action is taken. The abnormal stability index GR3 is a preset value and is determined by professional staff.

[0093] This module can help operation and maintenance personnel identify potential risk factors in advance, reduce the occurrence of sudden failures, and improve system efficiency by optimizing operating modes. This module also provides data support for subsequent preventive maintenance measures to ensure the reliability of the power distribution system in long-term operation.

[0094] Meteorological data collection module, which obtains meteorological data of the target area at a preset time;

[0095] The meteorological data at the preset time is represented as the meteorological data at a time period T1 after the current time, calculated from the current time; wherein the T1 time period is set by professional staff, and in this embodiment, the T1 time period is 12 hours;

[0096] The meteorological data specifically include temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters and frost parameters;

[0097] It should be noted that obtaining meteorological data for a target area at a preset time can be achieved through a variety of means, including weather forecast models, weather forecast service APIs, satellite remote sensing data, and ground weather stations. Obtaining meteorological data for a target area at a preset time is an existing mature technology and will not be described in detail here. The target area can be represented by a city or a preset region, which is determined by professional staff.

[0098] Meteorological risk coefficient determination module, which determines the meteorological risk coefficient of the target area based on meteorological data;

[0099] The specific method of determining the meteorological risk coefficient of the target area based on meteorological data is:

[0100] DS1: Preprocess the collected meteorological data, including the following steps:

[0101] DS11: Handle missing values ​​and outliers to ensure data integrity and accuracy;

[0102] DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter X i The normalized value can be expressed as X′ i :

[0103]

[0104] Where, 1≤i≤6, X1 represents the temperature parameter, X2 represents the humidity parameter, X3 represents the wind speed parameter, X4 represents the precipitation parameter, X5 represents the lightning activity parameter, and X6 represents the frost parameter; X max-i Indicates the preset maximum value of the corresponding meteorological parameter, X min-i Indicates the preset minimum value of the corresponding meteorological parameter, X max-i and X min-i The specific parameters are set by professional staff;

[0105] DS2: For each meteorological parameter X′ i The deviation value of each meteorological parameter is determined by the following formula

[0106]

[0107] Among them, X mid-i are expressed as ideal values ​​of corresponding meteorological parameters, and X range-i It is expressed as the allowable deviation range of the corresponding meteorological parameters, and X range-i =X max-i -X min-i ;

[0108] DS3: Determine the meteorological risk factor CBR using the following formula:

[0109]

[0110] Among them, w i It is expressed as the weight coefficient of the corresponding meteorological parameter deviation value, and w1+w2+w3+w4+w5+w6=1;

[0111] Based on collected meteorological data, the meteorological risk coefficient determination module further quantifies the impact of different meteorological conditions on the distribution system through big data analysis and machine learning algorithms, and calculates the meteorological risk coefficient. This coefficient reflects the risk level that power equipment may face under specific weather conditions. For example, in conditions of high wind speeds or heavy rain, the system can initiate equipment protection or adjust operating modes in advance based on the meteorological risk coefficient to avoid equipment failures caused by excessive load or extreme weather. The module's beneficial effect is that by quantifying the risk coefficient, it helps operation and maintenance personnel understand the potential threats posed by weather in real time, thereby improving the efficiency and accuracy of emergency response and ensuring the safe operation of the power grid.

[0112] A line wear coefficient determination module is used to obtain the historical line failure counts of all distribution cabinets in the target area and determine the line wear coefficient of each distribution cabinet based on the historical line failure counts of each distribution cabinet;

[0113] The specific method of obtaining the historical line failure counts of all distribution cabinets in the target area and determining the line wear coefficient of each distribution cabinet according to the historical line failure counts of each distribution cabinet is as follows:

[0114] Obtain all distribution cabinets in the abnormal area and determine the line wear coefficient HX of each distribution cabinet based on the historical number of line failures of each distribution cabinet using the following formula HX = δ·YL; where YL represents the historical number of line failures and δ represents the impact factor;

[0115] It should be noted that a distribution cabinet usually supplies power to a specific area or a group of equipment. The function of a distribution cabinet is to distribute power from the main power source to various loads or areas. It distributes and protects power according to the required power level and equipment configuration. In large buildings, factories, or industrial parks, a distribution cabinet may be responsible for providing power to a specific area (such as a floor, workshop, or work section). Different areas have different power demands, so different distribution cabinets are configured. The distribution cabinets transmit power through wires. Historical line faults refer to past short circuit faults, open circuit faults, overload faults, etc. on all lines connected to a distribution cabinet. Each fault is recorded as a historical line fault.

[0116] The power outage protection assessment module obtains the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient, and line wear coefficient of each distribution cabinet in the target area, performs a power outage protection assessment to determine the power outage protection coefficient of each distribution cabinet in the target area, and determines whether to generate a power outage protection instruction based on the power outage protection coefficient. When a power outage protection instruction is generated, the power outage protection information of the distribution cabinet is generated at the same time;

[0117] The specific method for obtaining the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient, and line wear coefficient of each power distribution cabinet in the target area, and performing power-off protection evaluation to determine the power-off protection coefficient of each power distribution cabinet in the target area, and then judging whether to generate a power-off protection instruction based on the power-off protection coefficient is as follows:

[0118] Specifically, power-off protection evaluation is performed through the following formula to determine the power-off protection coefficient:

[0119]

[0120] Among them, PRG represents the power-off protection coefficient, and k1 and k2 represent weighting factors;

[0121] Then, it is judged whether to generate a power-off protection instruction according to the power-off protection coefficient:

[0122] If the power-off protection coefficient PRG < GR5, a power-off protection instruction is generated, and no other processing is performed in other cases; where GR5 is a preset value, which is specifically set by professional staff;

[0123] Generate a power-off protection instruction to perform a power-off operation on the power distribution cabinet, and prevent the power distribution cabinet and the involved power distribution lines from being damaged and malfunctioning during subsequent bad weather. [[ID=二十]]

[0124] Embodiment 2

[0125] In the specific implementation process of this embodiment, based on Embodiment 1 and different from Embodiment 1, this embodiment further includes a maintenance end display module;

[0126] The maintenance end display module obtains and displays the warning information, abnormal information, and power-off protection information of the power distribution cabinet, reminding the maintenance personnel to understand the corresponding power distribution cabinet situation at the first moment and take corresponding treatment measures;

[0127] ]>The maintenance end display module is the decision-making support center of the entire system, responsible for presenting the data collected by all modules, evaluation results, and risk predictions to the operation and maintenance personnel through a visual interface; through forms such as charts, alarms, and trend analysis, the operation and maintenance personnel can quickly understand the operating status, potential problems, and risk points that need to be focused on of the power distribution system; the beneficial effect of this module is that through clear visual display, the work efficiency of the operation and maintenance personnel is improved, enabling them to quickly identify the fault points and take corresponding measures.<000]

[0128] Embodiment 3

[0129] In the specific implementation process of this embodiment, it includes all the implementation processes of the above two groups of embodiments.

[0130] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0131] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power distribution intelligent operation and maintenance system based on power big data, characterized by: include: The distribution cabinet operation status module is used to evaluate the distribution cabinet operation status based on the monitoring data of the distribution cabinet, determine the operation status coefficient, and judge whether the distribution cabinet has a failure risk based on the operation status coefficient. If it is judged that there is a failure risk, it will generate a distribution cabinet early warning information; The stability index determination module is used to obtain the operating status coefficient for stability analysis when the distribution cabinet operating status module does not generate early warning information, determine the stability index of the operating status coefficient, and then determine the abnormal distribution cabinet based on the stability index of the operating status coefficient and generate abnormal information of the distribution cabinet; The specific method of generating abnormal information of the power distribution cabinet is: CS1: Get all the operating status coefficients for the past two hours from the current moment; CS2: Calculate the average value of all operating state coefficients and mark it as ; CS3: Then use the formula Determine the discrete values ​​of all operating state coefficients ,in, Represents any operating state coefficient, , It is expressed as the total number of all current operating status coefficients; CS4: Get discrete values and preset thresholds , the stability index of the operating state coefficient is determined by the following formula: ； in, The stability index expressed as the operating status coefficient; CS5: Obtaining stability indicators of operating status coefficients , the stability index Abnormal stability index For comparison: like When the abnormality occurs, the distribution cabinet is marked as abnormal and abnormal information of the distribution cabinet is generated. No other processing is done; the abnormal stability index is the default value; Meteorological data collection module, which obtains meteorological data of the target area at a preset time; Meteorological risk coefficient determination module, which determines the meteorological risk coefficient of the target area based on meteorological data; A line wear coefficient determination module is used to obtain the historical line failure counts of all distribution cabinets in the target area and determine the line wear coefficient of each distribution cabinet based on the historical line failure counts of each distribution cabinet; The power outage protection assessment module obtains the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient and line wear coefficient of each distribution cabinet in the target area, performs power outage protection assessment to determine the power outage protection coefficient of each distribution cabinet in the target area, and determines whether to generate a power outage protection instruction based on the power outage protection coefficient. When a power outage protection instruction is generated, the power outage protection information of the distribution cabinet is generated at the same time.

2. The power distribution intelligent operation and maintenance system based on power big data according to claim 1 is characterized in that: In the distribution cabinet operation status module, the monitoring data includes monitoring the voltage, current, temperature, vibration, power factor and insulation resistance in the distribution cabinet.

3. The power distribution intelligent operation and maintenance system based on power big data according to claim 2 is characterized in that: In the distribution cabinet operation status module, the specific method of evaluating the distribution cabinet operation status based on the monitoring data of the distribution cabinet, determining the operation status coefficient, and judging whether the distribution cabinet has risks based on the operation status coefficient is as follows: BS1: Determine the health index of each data in the monitoring data according to the specific value of each data in the monitoring data; The health index of each data in the monitoring data is determined according to the specific value of each data in the monitoring data, including the voltage health index , Current Health Index , Temperature Health Index , vibration health index , Power Factor Health Index , Insulation Resistance Health Index ; BS2: Obtain the health index of each monitoring data and determine the operating status coefficient of the distribution cabinet using the following formula: ； in, Expressed as the operating status coefficient of the distribution cabinet, 、 and Expressed as a weight factor; BS3: Get the operating status coefficient of the power distribution cabinet , the operating state coefficient With the preset threshold For comparison: like , indicating that there is a risk of failure in the operation status of the distribution cabinet, and a distribution cabinet warning message is generated; like , indicating that the power distribution cabinet is in good operating condition and no action is required.

4. The power distribution intelligent operation and maintenance system based on power big data according to claim 1 is characterized in that: In the meteorological data acquisition module, the meteorological data specifically include temperature parameters, humidity parameters, wind speed parameters, precipitation parameters, lightning activity parameters and frost parameters.

5. The power distribution intelligent operation and maintenance system based on power big data according to claim 4 is characterized in that: In the meteorological risk coefficient determination module, the specific method of determining the meteorological risk coefficient of the target area based on meteorological data is: DS1: Preprocess the collected meteorological data, including the following steps: DS11: Handle missing values ​​and outliers to ensure data integrity and accuracy; DS12: Standardize meteorological parameters of different dimensions in meteorological data. For each meteorological parameter The normalized value can be expressed as : ； in, , Expressed as temperature parameter, Expressed as humidity parameter, Expressed as wind speed parameter, Expressed as precipitation parameter, Expressed as lightning activity parameters, Expressed as frost parameter; Indicates the preset maximum value of the corresponding meteorological parameter. Indicates the preset minimum value of the corresponding meteorological parameter; DS2: For each meteorological parameter The deviation value of each meteorological parameter is determined by the following formula : ； in, are expressed as ideal values ​​of corresponding meteorological parameters, and ; is expressed as the allowable deviation range of the corresponding meteorological parameters, and ; DS3: Determine the meteorological risk factor using the following formula : ； in, is expressed as the weight coefficient corresponding to the deviation value of the meteorological parameter, and .

6. The power distribution intelligent operation and maintenance system based on power big data according to claim 5 is characterized in that: In the line wear coefficient determination module, the specific method of obtaining the historical line failure counts of all distribution cabinets in the target area and determining the line wear coefficient of each distribution cabinet based on the historical line failure counts of each distribution cabinet is as follows: Get all the distribution cabinets in the abnormal area, and use the following formula based on the historical line failure times of each distribution cabinet Determine the line wear factor for each distribution cabinet ;in, Expressed as the number of historical line failures, Expressed as impact factor.

7. The power distribution intelligent operation and maintenance system based on power big data according to claim 6 is characterized in that: In the power outage protection assessment module, the operating status coefficient, stability index of the operating status coefficient, meteorological risk coefficient, and line wear coefficient of each distribution cabinet in the target area are obtained, and a power outage protection assessment is performed to determine the power outage protection coefficient of each distribution cabinet in the target area. The specific method for determining whether to generate a power outage protection instruction based on the power outage protection coefficient is as follows: The power failure protection is evaluated using the following formula to determine the power failure protection coefficient: ； in, Expressed as the power-off protection coefficient, and Expressed as a weight factor; Then determine whether to generate a power-off protection instruction based on the power-off protection coefficient: If the power failure protection factor , a power-off protection instruction is generated.

8. The power distribution intelligent operation and maintenance system based on power big data according to claim 1 is characterized in that: Also includes: The maintenance end display module obtains and displays the distribution cabinet warning information, distribution cabinet abnormal information and distribution cabinet power-off protection information, reminding maintenance personnel to understand the corresponding distribution cabinet situation at the first moment and take corresponding treatment measures.

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