A power equipment risk intelligent early warning method and system of multi-source data

Abstract

The application discloses a kind of multi-source data's electric power equipment risk intelligent early warning method and system, it is related to electric power equipment technical field, the present application includes step one, electric power data analysis, step two, electric power operation analysis and step three, electric power intelligent early warning.Judge whether the electric power equipment of power grid exists abnormal state, break through the limitation of single data, each abnormal electric power equipment is associated with other normal operation equipment of power grid Analysis, construct the influence network between equipment operation, obtain the power grid influence index of each abnormal electric power equipment of power grid, match multi-level risk early warning, and carry out early warning, realize the systematization of the correlation between abnormal electric power equipment and normal equipment, break the limitation of single device analysis in prior art, can comprehensively capture the influence transmission path of abnormal equipment, reduce the risk of power grid fault expansion, guarantee the overall operation stability of power grid.

Description

Technical Field

[0001] This invention relates to the field of power equipment technology, and specifically to a method and system for intelligent early warning of power equipment risks based on multi-source data. Background Technology

[0002] With the continuous expansion of the power grid and the increasing number of power devices, the operational relationships between these devices are becoming increasingly complex. When abnormal power devices appear in the power grid, their impact on the overall stability of the grid often exhibits both conductive and diffuse characteristics. Current technologies for handling abnormal power devices primarily focus on the fault diagnosis and repair of individual devices, lacking a systematic analysis of the operational relationships between abnormal and normal devices. This makes it difficult to quantify the impact of abnormal devices on the overall stability of the power grid, resulting in insufficient targeting and accuracy of risk warnings. Consequently, these warnings cannot provide precise decision support for power grid operation and maintenance, potentially leading to problems such as the expansion of power grid faults and decreased stability. Therefore, it is necessary to analyze a multi-source data-driven intelligent early warning method and system for power device risks.

[0003] Existing technology, such as the invention application patent with publication number CN120873747A, discloses a target detection and early warning method and system for proactive intervention in power equipment safety. The method includes: continuously monitoring the operating environment of the power equipment to obtain multi-source data; based on the multi-source data, identifying potential safety hazards using a target detection algorithm; when a potential safety hazard is identified, extracting corresponding multi-source risk features from the multi-source data according to the type of the potential safety hazard to construct a risk feature spectrum; quantifying the risk of the potential safety hazard based on the risk feature spectrum to obtain a quantified risk vector; determining the risk level corresponding to the quantified risk vector and executing an early warning strategy corresponding to the risk level.

[0004] Existing technologies for intelligent early warning methods and systems for power equipment risks based on multi-source data can meet basic requirements, but they also have some potential defects and challenges, specifically in the following aspects: First, in existing technologies, the analysis of the operational compliance index of various power equipment in the power grid is not accurate enough, which affects the judgment of whether there are abnormal states of power equipment in the power grid, affects the screening of abnormal power equipment, reduces the quality of the fused dataset, affects the judgment of abnormal states, increases the occurrence of problems in subsequent control measures due to incorrect identification of abnormal equipment, and affects the link control of power grid risks.

[0005] Second, in existing technologies, there is insufficient attention paid to the correlation analysis between various abnormal power devices and other normally operating equipment in the power grid. This affects the construction of the operational impact network between devices, the analysis of the power grid impact index of various abnormal power devices, the matching of multi-level risk warnings, the limitations of single-device analysis in existing technologies, the capture of the impact transmission path of abnormal devices, the risk of power grid fault expansion, and the reduction of the overall operational stability of the power grid. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for intelligent early warning of power equipment risks based on multi-source data, which solves the problems existing in the background technology.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for intelligent early warning of power equipment risks based on multi-source data, including step one, power data analysis, step two, power operation analysis, and step three, intelligent early warning of power risks.

[0008] Step 1: Power Data Analysis: Based on the extraction of sensor data, meteorological data, and equipment status data of power grid equipment, and the standardization of multi-source data through a dynamic weight allocation algorithm, a fused dataset is generated, and then the operational compliance index of each power grid equipment is analyzed.

[0009] Step 2: Power Operation Analysis: Based on the obtained operating compliance index of each power equipment in the power grid, determine whether there are any abnormal states of the power equipment in the power grid, and screen out each abnormal power equipment.

[0010] Step 3: Intelligent Power Early Warning: Based on the identified abnormal power equipment, the abnormal power equipment is correlated with other normally operating equipment in the power grid to construct an operational impact network between equipment. A graph neural network model is used to evaluate the impact index of abnormal equipment on the overall power grid stability, obtain the power grid impact index of each abnormal power equipment, match multi-level risk warnings, and issue warnings.

[0011] Furthermore, the fused dataset specifically includes: operating parameter data, external meteorological data, and operating status data.

[0012] Furthermore, the operational compliance index of each power device in the power grid is analyzed using the following method: based on the obtained operating parameter data, external meteorological data, and equipment status data, whereby the operating parameter data includes: voltage difference, current difference, and vibration frequency of each power device in the power grid at different time periods; external meteorological data includes: humidity difference and surface temperature difference of each power device in the power grid at different time periods; and equipment status data includes: number of fault outages, number of maintenance operations, and operating time of each power device in the power grid at different time periods, the operational impact index of each power device in the power grid is then analyzed. External meteorological impact index and equipment condition impact index Furthermore, the operational compliance index of various power equipment in the power grid is analyzed, and its specific calculation formula is as follows: ,in, This is represented by the serial number of each electrical device. , This represents the quantity of each electrical device.

[0013] Furthermore, the specific analysis method for the operational impact index of each power device in the power grid is as follows: Based on the voltage difference, current difference, and vibration frequency of each power device in the power grid for each time period, the average historical voltage difference, average historical current difference, and average historical vibration frequency are extracted from the database, and the difference is processed. Then, the difference is divided by the historical standard deviation of the operating parameter data to obtain the standard deviation of voltage, standard deviation of current, and standard vibration frequency of each power device in the power grid for each time period. The standard deviation of voltage, standard deviation of current, and standard vibration frequency of each power device in the power grid for each time period are compared with the safe range of the standard allowable voltage difference, safe range of the standard allowable current difference, and safe range of the standard allowable vibration frequency of the power device in the power grid stored in the database. If the standard deviation of voltage, the standard deviation of current, and the standard vibration frequency of a certain power device in the power grid for a certain time period are within the safe range of the standard allowable voltage difference, safe range of the standard current difference, and safe range of the standard vibration frequency, then the operational impact index of that power device in the power grid is recorded as 1; otherwise, it is recorded as -1. Thus, the operational impact index of each power device in the power grid is obtained. ,in, The values ​​include 1 and -1.

[0014] Furthermore, the specific analysis method for the external meteorological impact index of each power equipment in the power grid is as follows: Based on the humidity difference and surface temperature difference values ​​of each power equipment in the power grid for each time period, the humidity difference and surface temperature difference values ​​of each power equipment in the power grid for each time period are averaged to obtain the average humidity difference and average surface temperature difference of each power equipment in the power grid. The average humidity difference and average surface temperature difference of each power equipment in the power grid are compared with the safe range of the average allowable humidity difference and the safe range of the average allowable surface temperature difference of the power equipment in the power grid stored in the database. If the average humidity difference of a certain power equipment in the power grid is within the safe range of the average humidity difference and the average surface temperature difference, then the external meteorological impact index of that power equipment in the power grid is recorded as 1, otherwise it is recorded as -1, thus obtaining the external meteorological impact index of each power equipment in the power grid. ,in, Includes 1 and -1.

[0015] Furthermore, the specific analysis method for the equipment status impact index of each power device in the power grid is as follows: Based on the obtained fault outage number, maintenance number, and operating time of each power device in the power grid for each time period, and compared with the safe intervals for the number of fault outages, maintenance numbers, and operating time of the power devices in the power grid stored in the database, if the number of fault outages, maintenance numbers, and operating time of a certain power device in the power grid for a certain time period are within the safe intervals for the number of fault outages, maintenance numbers, and operating time, then the equipment status impact index of that power device in the power grid is recorded as 1; otherwise, it is recorded as -1, thus obtaining the equipment status impact index of each power device in the power grid. ,in, The values ​​include 1 and -1.

[0016] Furthermore, the specific analysis method for determining whether the power equipment of the power grid is in an abnormal state and screening each abnormal power equipment is as follows: based on the obtained operating compliance index of each power equipment of the power grid, the operating compliance index of each power equipment of the power grid is compared with the safe range of the operating compliance index of the power equipment of the power grid stored in the database. When the operating compliance index of a certain power equipment of the power grid is within the safe range of the operating compliance index, it indicates that the power equipment of the power grid is in an abnormal state, and the power equipment is recorded as an abnormal power equipment, thereby obtaining each abnormal power equipment of the power grid.

[0017] Furthermore, the construction of the inter-device operation influence network and the use of a graph neural network model to evaluate the impact index of abnormal devices on the overall power grid stability yields the power grid influence index of each abnormal power device. The specific analysis method is as follows: based on the obtained abnormal power devices of the power grid, each abnormal power device is removed to obtain each normally operating power device of the power grid. The operation correlation dataset between each abnormal power device and each normally operating power device is obtained, and an inter-device operation influence network is established. The inter-device operation influence network is input into the graph neural network model to obtain the power grid influence index of each abnormal power device of the power grid.

[0018] Furthermore, the specific analysis method for matching and issuing multi-level risk warnings is as follows: extract the preset multi-level risk warning standards from the database, match the power grid impact index of each abnormal power equipment in the power grid with the multi-level risk warning standards to obtain the warning level corresponding to each abnormal power equipment, and trigger the corresponding warning mechanism according to the matched warning level, and send it to the power grid operation and maintenance management platform to complete the warning execution.

[0019] A second aspect of the present invention provides a system for executing the aforementioned method for intelligent early warning of power equipment risks based on multi-source data, characterized in that it includes: a power data analysis module: based on the extraction of sensor data, meteorological data and equipment status data of power equipment in the power grid, and the standardization processing of multi-source data through a dynamic weight allocation algorithm to generate a fused dataset, and then analyzing the operational compliance index of each power equipment in the power grid.

[0020] Power Operation Analysis Module: Based on the obtained operating compliance index of each power equipment in the power grid, it determines whether there are abnormal states of the power equipment in the power grid and filters out each abnormal power equipment.

[0021] Power Intelligent Early Warning Module: Based on the identified abnormal power equipment, the module performs correlation analysis between the abnormal power equipment and other normally operating equipment in the power grid, constructs an operational impact network between equipment, uses a graph neural network model to evaluate the impact index of abnormal equipment on the overall power grid stability, obtains the power grid impact index of each abnormal power equipment, matches multi-level risk warnings, and issues warnings.

[0022] The beneficial effects of this invention are as follows: In step one, power data analysis, and step two, power operation analysis, sensor data, meteorological data, and equipment status data of the power grid's power equipment are extracted, and multi-source data are standardized through a dynamic weight allocation algorithm to generate a fused dataset. This dataset is then used to analyze the operational compliance index of each power grid device, determine whether any abnormal conditions exist, and screen for abnormal devices. This overcomes the limitations of single data sources, improves the quality and relevance of the fused dataset, enhances the judgment of abnormal conditions, avoids subsequent control measures due to incorrect identification of abnormal devices, and ensures link control of power grid risks.

[0023] Step 3: Intelligent Power Early Warning: Based on the identified abnormal power equipment, the abnormal power equipment is correlated with other normally operating equipment in the power grid to construct an operational impact network between equipment. A graph neural network model is used to evaluate the impact index of abnormal equipment on the overall power grid stability, obtaining the power grid impact index of each abnormal power equipment. Multi-level risk warnings are matched and issued, realizing a systematic sorting of the correlation between abnormal power equipment and normal equipment. This breaks through the limitations of single-equipment analysis in existing technologies, comprehensively captures the impact transmission path of abnormal equipment, reduces the risk of power grid fault expansion, and ensures the overall operational stability of the power grid. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the implementation steps of the method of the present invention.

[0026] Figure 2 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1As shown, the present invention provides a method for intelligent early warning of power equipment risks based on multi-source data, including: step one, power data analysis, step two, power operation analysis, and step three, intelligent early warning of power risks.

[0029] It should be noted that Step 1, Power Data Analysis, is connected to Step 2, Power Operation Analysis, and Step 2, Power Operation Analysis is connected to Step 3, Power Intelligent Early Warning.

[0030] Step 1: Power Data Analysis: Based on the extraction of sensor data, meteorological data, and equipment status data of power grid equipment, and the standardization of multi-source data through a dynamic weight allocation algorithm, a fused dataset is generated, and then the operational compliance index of each power grid equipment is analyzed.

[0031] In the above embodiments, the fused dataset specifically includes: operating parameter data, external meteorological data, and operating status data.

[0032] In the above embodiments, the operational compliance index of each power device in the power grid is analyzed using the following method: based on obtained operating parameter data, external meteorological data, and equipment status data, wherein the operating parameter data includes: voltage difference, current difference, and vibration frequency of each power device in the power grid at different time periods; the external meteorological data includes: humidity difference and surface temperature difference of each power device in the power grid at different time periods; and the equipment status data includes: number of fault outages, number of repairs, and operating time of each power device in the power grid at different time periods, the operational impact index of each power device in the power grid is then analyzed. External meteorological impact index and equipment condition impact index Furthermore, the operational compliance index of various power equipment in the power grid is analyzed, and its specific calculation formula is as follows: ,in, This is represented by the serial number of each electrical device. , This represents the quantity of each electrical device.

[0033] In the above embodiments, the specific analysis method for the operation impact index of each power device in the power grid is as follows: Based on the obtained voltage difference, current difference, and vibration frequency of each power device in the power grid for each time period, the average historical voltage difference, average historical current difference, and average historical vibration frequency are extracted from the database, and the difference is processed. Then, the difference is divided by the historical standard deviation of the operating parameter data to obtain the standard deviation of voltage, standard deviation of current, and standard vibration frequency of each power device in the power grid for each time period. The standard deviation of voltage, standard deviation of current, and standard vibration frequency of each power device in the power grid for each time period are compared with the safe range of the standard allowable voltage difference, safe range of the standard allowable current difference, and safe range of the standard allowable vibration frequency of the power device in the power grid stored in the database. If the standard deviation of voltage, the standard deviation of current, and the standard vibration frequency of a certain power device in the power grid for a certain time period are within the safe range of the standard allowable voltage difference, safe range of the standard current difference, and safe range of the standard vibration frequency, then the operation impact index of that power device in the power grid is recorded as 1; otherwise, it is recorded as -1. Thus, the operation impact index of each power device in the power grid is obtained. ,in, The values ​​include 1 and -1.

[0034] In the above embodiments, the specific analysis method for the external meteorological impact index of each power equipment in the power grid is as follows: Based on the obtained humidity difference and surface temperature difference values ​​of each power equipment in the power grid for each time period, the humidity difference and surface temperature difference values ​​of each power equipment in the power grid for each time period are averaged to obtain the average humidity difference and average surface temperature difference of each power equipment in the power grid. The average humidity difference and average surface temperature difference of each power equipment in the power grid are compared with the safe range of the allowable average humidity difference and the safe range of the allowable average surface temperature difference of the power equipment in the power grid stored in the database. If the average humidity difference of a certain power equipment in the power grid is within the safe range of the allowable average humidity difference and the safe range of the allowable average surface temperature difference, then the external meteorological impact index of that power equipment in the power grid is recorded as 1, otherwise it is recorded as -1, thus obtaining the external meteorological impact index of each power equipment in the power grid. ,in, Includes 1 and -1.

[0035] In the above embodiments, the specific analysis method for the equipment status impact index of each power device in the power grid is as follows: Based on the obtained fault outage number, maintenance number, and operating time of each power device in the power grid for each time period, and compared with the safe intervals for the number of fault outages, maintenance numbers, and operating time of the power devices in the power grid stored in the database, if the number of fault outages, maintenance numbers, and operating time of a certain power device in the power grid for a certain time period are within the safe intervals for the number of fault outages, maintenance numbers, and operating time, then the equipment status impact index of that power device in the power grid is recorded as 1; otherwise, it is recorded as -1, thereby obtaining the equipment status impact index of each power device in the power grid. ,in, The values ​​include 1 and -1.

[0036] Step 2: Power Operation Analysis: Based on the obtained operating compliance index of each power equipment in the power grid, determine whether there are any abnormal states of the power equipment in the power grid, and screen out each abnormal power equipment.

[0037] In the above embodiments, the specific analysis method for determining whether the power equipment of the power grid is in an abnormal state and screening each abnormal power equipment is as follows: based on the obtained operating compliance index of each power equipment of the power grid, the operating compliance index of each power equipment of the power grid is compared with the safe range of the operating compliance index of the power equipment of the power grid stored in the database. When the operating compliance index of a certain power equipment of the power grid is within the safe range of the operating compliance index, it indicates that the power equipment of the power grid is in an abnormal state, and the power equipment is recorded as an abnormal power equipment, thereby obtaining each abnormal power equipment of the power grid.

[0038] Step 3: Intelligent Power Early Warning: Based on the identified abnormal power equipment, the abnormal power equipment is correlated with other normally operating equipment in the power grid to construct an operational impact network between equipment. A graph neural network model is used to evaluate the impact index of abnormal equipment on the overall power grid stability, obtain the power grid impact index of each abnormal power equipment, match multi-level risk warnings, and issue warnings.

[0039] In the above embodiments, the construction of the inter-device operation influence network and the use of a graph neural network model to evaluate the impact index of abnormal devices on the overall power grid stability, thereby obtaining the power grid influence index of each abnormal power device in the power grid, are specifically analyzed as follows: based on the obtained abnormal power devices in the power grid, each abnormal power device is removed to obtain each normally operating power device in the power grid, an operation association dataset between each abnormal power device and each normally operating power device is obtained, and an inter-device operation influence network is established. The inter-device operation influence network is then input into the graph neural network model to obtain the power grid influence index of each abnormal power device in the power grid.

[0040] It should be noted that the associated dataset includes, but is not limited to, electrical connection relationships, load transmission path relationships, topology dependency relationships, and operating parameter coupling relationships. Specifically, it includes the load transmission amount, parameter coupling value, and connection tightness value between abnormal power equipment and normal power equipment, and between normal power equipment.

[0041] It should be noted that the network of operational impacts between devices is established with individual power devices as network nodes and the operational impact relationships between devices as network edges. Based on the associated operational dataset, a corresponding weight value is assigned to each network edge.

[0042] It should be noted that the weight value ranges from 0 to 1. The larger the weight value, the stronger the operational impact between the corresponding two devices.

[0043] It should be noted that the grid impact index of each abnormal power device in the power grid refers to the impact on the entire power equipment.

[0044] It should be noted that the core innovation of the graph neural network model involved in this invention lies in the feature processing logic for the network design affected by the operation of power grid equipment. All other parameter settings and operational details are common knowledge in the prior art, and those skilled in the art can flexibly set them according to actual scenarios. Specifically, apart from the core feature processing and output calculation logic, the standardized settings of the model do not require special limitations, including model selection, basic network structure parameters, training process parameters, and routine graph data preprocessing operations. Basic network structure parameters include the number of hidden layers, feature dimensions of each layer, and activation function selection. Training process parameters include batch size, number of training iterations, regularization coefficient, optimizer type, and corresponding learning rate adjustment strategy. Routine graph data preprocessing operations include node feature normalization and standardization methods, edge feature encoding format, and graph data batch sampling strategy. The above standardized settings are all general configurations for graph neural network models, and those skilled in the art can adjust them according to the scale of power grid equipment data, computational resource conditions, and evaluation accuracy requirements without affecting the implementation and reproduction of the technical solution of this invention.

[0045] In the above embodiments, the specific analysis method for matching and issuing multi-level risk warnings is as follows: extracting preset multi-level risk warning standards from the database, matching the grid impact index of each abnormal power equipment in the power grid with the multi-level risk warning standards to obtain the warning level corresponding to each abnormal power equipment, and triggering the corresponding warning mechanism according to the matched warning level, and sending it to the power grid operation and maintenance management platform to complete the warning execution.

[0046] It should be noted that the warning levels include: Level 1 warning and Level 2 warning.

[0047] It should be noted that the corresponding early warning mechanisms include: for a Level 1 early warning, the corresponding early warning mechanism is to immediately arrange maintenance personnel to conduct on-site inspections, reduce the load on related lines, and issue an early warning signal; for a Level 2 early warning, the corresponding early warning mechanism is to continuously monitor abnormal power grid equipment.

[0048] Reference Figure 2 As shown, the present invention provides a system for intelligent early warning of power equipment risks based on multi-source data. The system is characterized by including: a power data analysis module: extracting sensor data, meteorological data and equipment status data of power equipment in the power grid, and standardizing the multi-source data through a dynamic weight allocation algorithm to generate a fused dataset, and then analyzing the working operation compliance index of each power equipment in the power grid.

[0049] Power Operation Analysis Module: Based on the obtained operating compliance index of each power equipment in the power grid, it determines whether there are abnormal states of the power equipment in the power grid and filters out each abnormal power equipment.

[0050] Power Intelligent Early Warning Module: Based on the identified abnormal power equipment, the module performs correlation analysis between the abnormal power equipment and other normally operating equipment in the power grid, constructs an operational impact network between equipment, uses a graph neural network model to evaluate the impact index of abnormal equipment on the overall power grid stability, obtains the power grid impact index of each abnormal power equipment, matches multi-level risk warnings, and issues warnings.

[0051] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A power equipment risk intelligent early warning method of multi-source data, characterized in that, Comprising: Step one, power data analysis: based on the power equipment of power grid sensor data, meteorological data and equipment state data extraction, and through the dynamic weight distribution algorithm to standardize the processing of multi-source data, generate fusion data set, and then analyze the work running compliance index of each power equipment of power grid; The fusion data set specifically includes: operating parameter data, external meteorological data and operating state data; The working operation of each power equipment of the power grid conforms to the index, and the specific analysis method is: based on the obtained operation parameter data, external meteorological data and equipment state data, wherein the operation parameter data includes: voltage difference, current difference and vibration frequency of each time period of each power equipment of the power grid, the external meteorological data includes: humidity difference and external surface temperature difference of each time period of each power equipment of the power grid, and the equipment state data includes: fault stop number, maintenance number and operation time length of each time period of each power equipment of the power grid, and then the operation influence index of each power equipment of the power grid is analyzed , external meteorological influence index and equipment state influence index , and then the working operation conformity index of each power equipment of the power grid is analyzed, and the specific calculation formula is: , wherein represents the number of each power equipment, , represents the number of each power equipment; Step two, power operation analysis: based on the work running compliance index of each power equipment of power grid, judge whether the power equipment of power grid exists abnormal state, and screen each abnormal power equipment; Step three, power intelligent early warning: based on each abnormal power equipment, the correlation analysis of each abnormal power equipment and other normal operation equipment of power grid is carried out, the running influence network between equipment is constructed, the influence index of abnormal equipment on the stability of whole power grid is evaluated by using graph neural network model, the power grid influence index of each abnormal power equipment of power grid is obtained, the multi-level risk early warning is matched, and early warning is carried out; The specific analysis method of the power grid influence index of each abnormal power equipment of power grid is: based on the obtained each abnormal power equipment of power grid, and the each abnormal power equipment is excluded, the each normal operation power equipment of power grid is obtained, the running correlation data set between each abnormal power equipment and each normal operation power equipment is obtained, and the running influence network between equipment is established, the running influence network between equipment is input into the graph neural network model, and the power grid influence index of each abnormal power equipment of power grid is obtained; The correlation data set is the load transmission quantity, parameter coupling value and connection close value between abnormal power equipment and normal operation power equipment. 2.The power equipment risk intelligent early warning method of multi-source data according to claim 1, characterized in that, The specific analysis method of the running influence index of each power equipment of power grid is: The voltage standard difference value, the current standard difference value and the vibration standard frequency of each power equipment of the power grid in each time period are obtained by subtracting the historical voltage difference mean value, the historical current difference mean value and the historical vibration frequency mean value from the database, and dividing by the historical standard deviation of the operation parameter data. The voltage standard difference value, the current standard difference value and the vibration standard frequency of each power equipment of the power grid are compared with the voltage standard allowable difference safety interval, the current standard allowable difference safety interval and the vibration standard allowable frequency safety interval of the power equipment of the power grid stored in the database. If the voltage standard difference value of a certain power equipment of the power grid in a certain time period is in the voltage standard allowable difference safety interval, the current standard difference value is in the current standard allowable difference safety interval, and the vibration standard frequency is in the vibration standard allowable frequency safety interval, the operation influence index of the power equipment of the power grid is recorded as 1, otherwise as -1, and the operation influence index of each power equipment of the power grid is obtained wherein the values of the operation influence index include 1 and -1.

3. The power equipment risk intelligent early warning method of multi-source data according to claim 2, characterized in that, The specific analysis method of the external meteorological influence index of each power equipment of power grid is: Based on the humidity difference value and the surface temperature difference value of each power equipment of the power grid in each time period, the humidity difference value and the surface temperature difference value of each power equipment of the power grid in each time period are respectively processed by averaging to obtain the humidity difference average value and the surface temperature difference average value of each power equipment of the power grid. The humidity difference average value and the surface temperature difference average value of each power equipment of the power grid are compared with the humidity difference allowable average value safety interval and the surface temperature difference allowable average value safety interval of the power equipment of the power grid stored in the database. If the humidity difference average value of a certain power equipment of the power grid is in the humidity difference allowable average value safety interval and the surface temperature difference average value is in the surface temperature difference allowable average value safety interval, the external meteorological influence index of the power equipment of the power grid is recorded as 1, otherwise as -1, and then the external meteorological influence index of each power equipment of the power grid is obtained wherein comprises 1 and -1.

4. The power equipment risk intelligent early warning method of multi-source data according to claim 3, characterized in that, The specific analysis method of the equipment state influence index of each power equipment of power grid is: Based on the obtained fault stop number, repair number and operation time length of each power equipment of the power grid in each time period, and compared with the fault stop allowed number safety interval, repair allowed number safety interval and operation time length safety interval of the power equipment of the power grid stored in the database, if the fault stop number of a certain power equipment of the power grid in a certain time period is in the fault stop allowed number safety interval, the repair number is in the repair allowed number safety interval and the operation time length is in the operation time length safety interval, the equipment state influence index of the power equipment of the power grid is recorded as 1, otherwise as -1, and then the equipment state influence index of each power equipment of the power grid is obtained wherein, The value of the equipment state influence index includes 1 and -1.

5. The power equipment risk intelligent early warning method of multi-source data according to claim 4, characterized in that, The specific analysis method of judging whether the power equipment of power grid exists abnormal state and screening each abnormal power equipment is: Based on the work running compliance index of each power equipment of power grid, the work running compliance index of each power equipment of power grid is compared with the work running compliance index safety interval of power equipment of power grid stored in the database, when the work running compliance index of power equipment of power grid is in the work running compliance index safety interval, it is proved that the power equipment of power grid exists abnormal state, and the power equipment is recorded as abnormal power equipment, and then the each abnormal power equipment of power grid is obtained.

6. The power equipment risk intelligent early warning method of multiple source data according to claim 1, characterized in that, The specific analysis method of matching multi-level risk early warning and early warning is: The preset multi-level risk early warning standard is extracted from the database, the power grid influence index of each abnormal power equipment is matched with the multi-level risk early warning standard, the corresponding early warning grade of each abnormal power equipment is obtained, and according to the early warning grade obtained by matching, the corresponding early warning mechanism is triggered, and sent to the power grid operation and management platform, and the early warning execution is completed.

7. A system for performing the power equipment risk intelligent early warning method of any one of claims 1-6, characterized in that, Comprising: The power data analysis module: based on the power equipment of power grid sensor data, meteorological data and equipment state data extraction, and through the dynamic weight distribution algorithm, the multi-source data is standardized, the fusion data set is generated, and the working operation compliance index of each power equipment of the power grid is analyzed; The power operation analysis module: based on the working operation compliance index of each power equipment of the power grid, whether the power equipment of the power grid exists abnormal state is judged, and each abnormal power equipment is screened; The power intelligent early warning module: based on each abnormal power equipment, the correlation analysis of each abnormal power equipment and other normal operation equipment of the power grid is carried out, the running influence network between the equipment is constructed, the influence index of the abnormal equipment on the stability of the whole power grid is evaluated by using the graph neural network model, the power grid influence index of each abnormal power equipment of the power grid is obtained, the multi-level risk early warning is matched, and early warning is carried out.

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