Intelligent power grid data management system and method based on computer analysis

Abstract

The invention discloses an intelligent power grid data management system and method based on computer analysis, and relates to the technical field of power grid data management.The intelligent power grid data management method comprises the steps that each monitoring node is monitored through a sensor, and monitoring feature information of each monitoring node of each power grid line in a to-be-monitored area is generated; carrying out image recognition on the monitoring feature information through an image recognition technology, evaluating and analyzing the monitoring feature information of each monitoring node, and building a power grid feature change model of the power grid line of the to-be-detected region; analyzing the potential risk condition of each monitoring node in the to-be-monitored area monitored most recently; permutation and combination are carried out on the obtained potential risk nodes, and a corresponding routing inspection line scheme is generated; analyzing an inspection reference value of the inspection line scheme; and pushing an inspection line scheme to an administrator according to the inspection reference value. The resource quantity consumed by monitoring is reduced, and the operation safety of the power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data management, and in particular to an intelligent power grid data management system and method based on computer analysis. Background Art

[0002] The power grid cable is used as a carrier to transmit electric energy. When the power grid cable is connected to the supporting pole or other grounding objects, the current will automatically flow to the ground, forming a current loop, causing waste of electric energy and potential safety hazards. The function of the insulator is to isolate the power grid cable from the supporting pole or other grounding objects to prevent current leakage and electric shock.

[0003] However, when insulators are dirty, frozen, or damaged, arc discharges can easily occur, affecting the performance of the insulators and even causing power outages, endangering the safe operation of the power grid. In the existing technology for maintaining insulators, most of them use manual regular inspections, but manual inspections are inefficient, and long power outages for maintenance can easily affect production activities and daily life. Machine monitoring often uses real-time monitoring, which can easily lead to a waste of resources.

[0004] Therefore, the present invention discloses an intelligent management system and method for power grid data based on computer analysis to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a computer-analyzed intelligent management system and method for power grid data to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for intelligent management of power grid data based on computer analysis, the method comprising the following steps: S1: Obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; S2: Use image recognition technology to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; S3: Obtain the most recent monitoring feature information, combine it with the grid feature change model of the grid line in the area to be tested, analyze the potential risk of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes; S4: Arrange and combine the obtained potential risk nodes, and generate a corresponding inspection route plan according to each arrangement and combination; analyze the inspection reference value of the inspection route plan in combination with the sequence of each potential risk node and the corresponding abnormal risk situation; and push the inspection route plan to the administrator according to the inspection reference value.

[0007] According to the above scheme, in S1, the monitoring characteristic information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specifications corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node, and the monitoring image of the insulator; the current value of the monitoring node, the voltage value of the monitoring node, and the cable temperature of the monitoring node in the monitoring characteristic information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring characteristic information is obtained through regular monitoring; The dirtiness or damage of insulators requires a long time to accumulate. If real-time dynamic monitoring is used for insulator monitoring images, it is easy to cause waste of power and material resources. Regular monitoring can effectively reduce the amount of resources consumed by monitoring. For The number of monitoring nodes of the power grid lines is counted. [1,I], where I represents the total number of power grid lines in the area to be tested; The monitoring nodes of the power grid lines are uniformly labeled and form a line monitoring node set, denoted as ; The monitoring nodes are recorded as ; [1,J], J represents the The total number of monitoring nodes on the power grid line. According to the above scheme, in S2, the following contents are included: S201: Perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the test area by using image recognition technology, perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; The image evaluation value of the insulator monitoring image is recorded as ; The specific calculation formula is as follows:

[0008] in, , and are evaluation coefficients respectively, and the evaluation coefficients are system preset constants; Represents the average grayscale value of the image, which is the monitoring node The average value of the grayscale value corresponding to each pixel after the grayscale processing of the insulator monitoring image; It represents the average value of the image average grayscale value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node Grayscale feature deviation value; It represents the average value of the grayscale feature deviation value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node The flatness difference of It represents the average value of the flatness difference of the insulator monitoring image before the insulator is replaced in the historical data; The monitoring feature information is quantified from three aspects: the average grayscale value of the image, the grayscale feature deviation value and the flatness difference value, which facilitates the establishment of a connection with the operating status of the corresponding power grid lines in the subsequent steps and provides data support for building a power grid characteristic change model for the power grid lines in the area to be tested.

[0009] Monitoring Node Grayscale feature deviation value The specific calculation formula is as follows:

[0010] in, Indicates monitoring node After grayscale processing of the insulator monitoring image, The gray value corresponding to each pixel; ,in Indicates monitoring node The total number of pixels in the insulator monitoring image; Monitoring Node Flatness difference The specific calculation formula is as follows:

[0011] in, Indicates monitoring node Insulator No. Block deformation area, ,in Indicates monitoring node The total number of insulator deformation areas; Indicates monitoring node The average area of ​​the insulator deformation region; S202: extract the monitoring node current value, monitoring node voltage value and monitoring node cable temperature at the same time as the insulator monitoring image is acquired, set different weights for the monitoring node current value, monitoring node voltage value and monitoring node cable temperature, and calculate the monitoring node The operational evaluation value ; S203: Construct an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, denoted as ; With o as the origin, the operation evaluation value as the x-axis and the image evaluation value as the y-axis, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the evaluation pairs are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the power grid characteristic change model of the power grid line in the test area.

[0012] The purpose of building a grid characteristic change model for the power grid lines in the tested area is to analyze the relationship between the image evaluation value and the operation evaluation value of the power grid lines in combination with historical data, and to provide a basis for predicting the abnormal risk situation corresponding to each power grid line in the most recent monitoring characteristic information in subsequent steps.

[0013] According to the above scheme, S3 includes the following: S301: Obtain the most recent monitoring feature information, and calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; S302: inputting the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the test area respectively, and generating a virtual image evaluation value corresponding to each monitoring node of the power grid line in the test area after the most recent monitoring; S303: Preset a first threshold value for the virtual image evaluation value system. If the obtained virtual image evaluation value is less than the first threshold value, it is determined that there is no abnormal risk in the corresponding monitoring node of the power grid line in the area to be tested after the most recent monitoring; otherwise, it is determined that there is an abnormal risk in the corresponding monitoring node of the power grid line in the area to be tested after the most recent monitoring; S304: Acquire the real-time insulator monitoring image of the monitoring node with abnormal risk, calculate the image evaluation value of the real-time insulator monitoring image, and preset a second threshold value for the image evaluation value system. If the obtained image evaluation value is less than the second threshold value, it is determined that the corresponding monitoring node of the real-time insulator monitoring image does not have potential risk; otherwise, it is determined that the corresponding monitoring node of the real-time insulator monitoring image has potential risk; the monitoring node with potential risk is recorded as a potential risk node.

[0014] The operation evaluation value is calculated based on the current value, voltage value and cable temperature of the monitoring node obtained through real-time dynamic monitoring. The abnormal risk situation of the monitoring node is analyzed in combination with the grid characteristic change model. For the monitoring nodes with abnormal risk situation, sensors are turned on to obtain real-time insulator monitoring images. This can effectively avoid the problem of untimely acquisition of regular monitoring of insulator monitoring images, and can more accurately discover the potential risks of insulators and ensure the safety of the grid.

[0015] According to the above scheme, S4 includes the following contents: S401: extract all potential risk nodes, classify the potential risk nodes according to the power grid lines they belong to, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; arrange and combine all potential risk nodes according to the power grid lines they belong to, and generate inspection route plans corresponding to each arrangement and combination plan; adjacent potential risk nodes are spliced ​​using the shortest route in the area to be tested; S402: Calculate the inspection reference value ACV for the inspection line plan. The specific calculation formula is as follows: ACV= ; in, represents a reference coefficient, which is a system preset constant; Indicates the total length of the power grid lines in the area to be tested; Indicates the length of the power grid line through which the inspection line plan passes; Indicates the length of the power grid line that the inspection line plan does not pass through; Indicates the total number of potential risk nodes in the inspection route plan; Indicates the inspection route plan Image evaluation value corresponding to potential risk nodes; ; The inspection line plan with the smallest inspection reference value is taken as the best inspection plan, and the corresponding administrator is recommended to adopt the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid lines in the tested area.

[0016] By using the image evaluation values ​​corresponding to the risk nodes to calculate the inspection reference values ​​of the inspection line plans, and selecting the inspection line plan with the smallest inspection reference value as the optimal inspection plan, the potential risk nodes with larger image evaluation values ​​can be effectively put in front, providing maintenance personnel with the opportunity to perform insulator maintenance as early as possible, further ensuring the safe operation of the power grid.

[0017] Another aspect of the present application provides a power grid data intelligent management system based on computer analysis, the system is applied to the above-mentioned power grid data intelligent management method based on computer analysis, the system includes a power grid data acquisition module, a data feature analysis module, a potential risk analysis module and a line inspection route analysis module; The power grid data acquisition module is used to obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; The data feature analysis module is used to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested through image recognition technology, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; The potential risk analysis module is used to obtain the most recent monitoring feature information, analyze the potential risk of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes in combination with the power grid feature change model of the power grid line in the area to be tested; The obtained potential risk nodes are arranged and combined, and a corresponding inspection route plan is generated according to each arrangement and combination; the inspection reference value of the inspection route plan is analyzed in combination with the sequence of each potential risk node and the corresponding abnormal risk situation; and the inspection route plan is pushed to the administrator according to the inspection reference value.

[0018] According to the above solution, the power grid data acquisition module includes a power grid line acquisition unit and a monitoring data acquisition unit; The power grid line acquisition unit is used to acquire the direction distribution of the power grid lines in the area to be tested, the distribution of monitoring nodes of each power grid line, and the insulator specifications of each power grid line; The monitoring data acquisition unit is used to monitor each monitoring node of each power grid line in the measured area through a sensor, and generate monitoring feature information of each monitoring node of each power grid line in the measured area; the monitoring feature information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specification corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node and the monitoring image of the insulator; the current value of the monitoring node, the voltage value of the monitoring node and the cable temperature of the monitoring node in the monitoring feature information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring feature information is obtained through regular monitoring.

[0019] According to the above scheme, the data feature analysis module includes a monitoring data analysis unit and a model building unit; The monitoring data analysis unit is used to perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the measured area through image recognition technology, and perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; set different weights for the monitoring node current value, the monitoring node voltage value and the monitoring node cable temperature, and calculate the operation evaluation value of the monitoring node; The model building unit is used to build an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, and to build a power grid characteristic change model of the power grid line in the area to be tested according to the evaluation pair of monitoring nodes.

[0020] According to the above scheme, the potential risk analysis module includes a model prediction unit and a risk confirmation unit; The model prediction unit is used to obtain the most recent monitoring feature information, calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; input the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the area to be tested, and generate a virtual image evaluation value corresponding to each monitoring node of the power grid line in the area to be tested after the most recent monitoring; The risk confirmation unit presets a first threshold value for the virtual image evaluation value system to determine the abnormal risk situation of the corresponding monitoring nodes of the power grid lines in the tested area after the most recent monitoring; based on the abnormal risk situation, obtains the real-time insulator monitoring image of the monitoring node with abnormal risk, calculates the image evaluation value of the real-time insulator monitoring image, and determines the potential risk situation of the corresponding monitoring node of the real-time insulator monitoring image.

[0021] According to the above scheme, the patrol route analysis module includes a patrol route scheme generation unit and a patrol reference analysis unit; The inspection route plan generation unit is used to extract all potential risk nodes, classify the potential risk nodes according to the power grid lines to which they belong, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; arrange and combine all potential risk nodes according to the power grid lines to which they belong, and generate an inspection route plan corresponding to each arrangement and combination plan; adjacent potential risk nodes are spliced ​​using the shortest route in the area to be tested; The inspection reference analysis unit is used to calculate the inspection reference value for the inspection line plan, take the inspection line plan with the smallest inspection reference value as the best inspection plan, and recommend the corresponding administrator to use the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid line in the tested area.

[0022] Compared with the prior art, the beneficial effects of the present invention are as follows: the dirtiness or damage of insulators requires a long period of accumulation. If real-time dynamic monitoring is adopted for the monitoring images of insulators, it is easy to cause waste of electricity and material resources. Regular monitoring can effectively reduce the amount of resources required for monitoring; the monitoring feature information is quantified from three aspects: the average gray value of the image, the gray feature deviation value and the flatness difference value, which is convenient for establishing a connection with the operating status of the corresponding power grid line in the subsequent steps, and provides data support for building a power grid feature change model for the power grid line in the area to be tested; the power grid feature change model for the power grid line in the area to be tested is built in order to analyze the relationship between the image evaluation value and the operation evaluation value of the power grid line in combination with historical data , which provides a basis for predicting the abnormal risk situation corresponding to each power grid line in the most recent monitoring feature information in the subsequent steps; turning on the sensor for the monitoring node with abnormal risk situation to obtain the real-time insulator monitoring image can effectively avoid the problem of untimely regular monitoring and acquisition of insulator monitoring images, and can more accurately discover the potential risks of insulators to ensure the safety of the power grid; by calculating the inspection reference value by using the image evaluation value corresponding to the risk node to calculate the inspection line plan, and selecting the inspection line plan with the smallest inspection reference value as the best inspection plan, it can effectively put the potential risk nodes with larger image evaluation values ​​in the front, so as to provide maintenance personnel with the opportunity to perform insulator maintenance as soon as possible, and further ensure the safe operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of a flow chart of a method for intelligent management of power grid data based on computer analysis according to the present invention; Figure 2 The present invention is a schematic structural diagram of an intelligent management system for power grid data based on computer analysis. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0025] See also Figure 1 The present invention provides a technical solution: a method for intelligent management of power grid data based on computer analysis, the method comprising the following steps: S1: Obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; S2: Use image recognition technology to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; S3: Obtain the most recent monitoring feature information, combine it with the grid feature change model of the grid line in the area to be tested, analyze the potential risk of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes; S4: Arrange and combine the obtained potential risk nodes, and generate a corresponding inspection route plan according to each arrangement and combination; analyze the inspection reference value of the inspection route plan in combination with the sequence of each potential risk node and the corresponding abnormal risk situation; and push the inspection route plan to the administrator according to the inspection reference value.

[0026] In S1, the monitoring feature information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specifications corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node, and the monitoring image of the insulator; the current value of the monitoring node, the voltage value of the monitoring node, and the cable temperature of the monitoring node in the monitoring feature information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring feature information is obtained through regular monitoring; For The number of monitoring nodes of the power grid lines is counted. [1,I], where I represents the total number of power grid lines in the area to be tested; The monitoring nodes of the power grid lines are uniformly labeled and form a line monitoring node set, denoted as ; The monitoring nodes are recorded as ; [1,J], J represents the The total number of monitoring nodes on the power grid lines In S2, the following are included: S201: Perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the test area by using image recognition technology, perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; The image evaluation value of the insulator monitoring image is recorded as ; The specific calculation formula is as follows:

[0027] in, , and are evaluation coefficients respectively, and the evaluation coefficients are system preset constants; Embodiment 1: In this embodiment, , and All equal to 1 / 3; Therefore, monitoring node Image evaluation value of insulator monitoring images The calculation formula is adjusted to: ; Represents the average grayscale value of the image, which is the monitoring node The average value of the grayscale value corresponding to each pixel after the grayscale processing of the insulator monitoring image; It represents the average value of the image average grayscale value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node Grayscale feature deviation value; It represents the average value of the grayscale feature deviation value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node The flatness difference of It represents the average value of the flatness difference of the insulator monitoring image before the insulator is replaced in the historical data; Monitoring Node Grayscale feature deviation value The specific calculation formula is as follows:

[0028] in, Indicates monitoring node After grayscale processing of the insulator monitoring image, The gray value corresponding to each pixel; ,in Indicates monitoring node The total number of pixels in the insulator monitoring image; Embodiment 2: In this embodiment, the pixel matrix generated after the grayscale processing of the insulator monitoring image is: ; In this embodiment, the pixels are sorted from left to right and from top to bottom; therefore, ; Furthermore, .

[0029] Monitoring Node Flatness difference The specific calculation formula is as follows:

[0030] in, Indicates monitoring node Insulator No. Block deformation area, ,in Indicates monitoring node The total number of insulator deformation areas; Indicates monitoring node The average area of ​​the insulator deformation region; Example 3: In this example, there are three deformation areas. =5cm 2 , =6cm 2 , =7cm 2 ; Therefore .

[0031] S202: extract the monitoring node current value, monitoring node voltage value and monitoring node cable temperature at the same time as the insulator monitoring image is acquired, set different weights for the monitoring node current value, monitoring node voltage value and monitoring node cable temperature, and calculate the monitoring node The operational evaluation value ; S203: Construct an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, denoted as ; With o as the origin, the operation evaluation value as the x-axis and the image evaluation value as the y-axis, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the evaluation pairs are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the power grid characteristic change model of the power grid line in the test area.

[0032] In S3, include the following: S301: Obtain the most recent monitoring feature information, and calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; S302: inputting the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the test area respectively, and generating a virtual image evaluation value corresponding to each monitoring node of the power grid line in the test area after the most recent monitoring; S303: Preset a first threshold value for the virtual image evaluation value system. If the obtained virtual image evaluation value is less than the first threshold value, it is determined that there is no abnormal risk in the corresponding monitoring node of the power grid line in the area to be tested after the most recent monitoring; otherwise, it is determined that there is an abnormal risk in the corresponding monitoring node of the power grid line in the area to be tested after the most recent monitoring; S304: Acquire the real-time insulator monitoring image of the monitoring node with abnormal risk, calculate the image evaluation value of the real-time insulator monitoring image, and preset a second threshold value for the image evaluation value system. If the obtained image evaluation value is less than the second threshold value, it is determined that the corresponding monitoring node of the real-time insulator monitoring image does not have potential risk; otherwise, it is determined that the corresponding monitoring node of the real-time insulator monitoring image has potential risk; the monitoring node with potential risk is recorded as a potential risk node.

[0033] In S4, the following are included: S401: extract all potential risk nodes, classify the potential risk nodes according to the power grid lines they belong to, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; arrange and combine all potential risk nodes according to the power grid lines they belong to, and generate inspection route plans corresponding to each arrangement and combination plan; adjacent potential risk nodes are spliced ​​using the shortest route in the area to be tested; S402: Calculate the inspection reference value ACV for the inspection line plan. The specific calculation formula is as follows: ACV= ; in, represents a reference coefficient, which is a system preset constant; Indicates the total length of the power grid lines in the area to be tested; Indicates the length of the power grid line through which the inspection line plan passes; Indicates the length of the power grid line that the inspection line plan does not pass through; Indicates the total number of potential risk nodes in the inspection route plan; Indicates the inspection route plan Image evaluation value corresponding to potential risk nodes; ; The inspection line plan with the smallest inspection reference value is taken as the best inspection plan, and the corresponding administrator is recommended to adopt the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid lines in the tested area.

[0034] See also Figure 2 , the present invention provides a technical solution, a computer-analyzed intelligent management system for power grid data, the system comprising a power grid data acquisition module, a data feature analysis module, a potential risk analysis module, and a line inspection route analysis module; The power grid data acquisition module is used to obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; The data feature analysis module is used to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested through image recognition technology, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; The potential risk analysis module is used to obtain the most recent monitoring feature information, combine the power grid feature change model of the power grid line in the area to be tested, analyze the potential risk situation of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes; Arrange and combine the obtained potential risk nodes, and generate corresponding inspection route plans according to each arrangement and combination; analyze the inspection reference value of the inspection route plan based on the sequence of each potential risk node and the corresponding abnormal risk situation; push the inspection route plan to the administrator based on the inspection reference value.

[0035] The power grid data acquisition module includes a power grid line acquisition unit and a monitoring data acquisition unit; The power grid line acquisition unit is used to obtain the direction distribution of the power grid lines in the area to be tested, the distribution of monitoring nodes of each power grid line, and the insulator specifications of each power grid line; The monitoring data acquisition unit is used to monitor each monitoring node of each power grid line in the area to be measured through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be measured; the monitoring feature information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specification corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node and the monitoring image of the insulator; the current value of the monitoring node, the voltage value of the monitoring node and the cable temperature of the monitoring node in the monitoring feature information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring feature information is obtained through regular monitoring.

[0036] The data feature analysis module includes a monitoring data analysis unit and a model building unit; The monitoring data analysis unit is used to perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the measured area through image recognition technology, and perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; set different weights for the monitoring node current value, the monitoring node voltage value and the monitoring node cable temperature, and calculate the operation evaluation value of the monitoring node; The model building unit is used to build an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, and to build a power grid characteristic change model of the power grid line in the area to be tested according to the evaluation pair of monitoring nodes.

[0037] The potential risk analysis module includes a model prediction unit and a risk confirmation unit; The model prediction unit is used to obtain the most recent monitoring feature information, calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; input the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the area to be tested, and generate a virtual image evaluation value corresponding to each monitoring node of the power grid line in the area to be tested after the most recent monitoring; The risk confirmation unit presets a first threshold value for the virtual image evaluation value system to determine the abnormal risk situation of the corresponding monitoring nodes of the power grid lines in the tested area after the most recent monitoring; based on the abnormal risk situation, the real-time insulator monitoring image of the monitoring node with abnormal risk is obtained, the image evaluation value of the real-time insulator monitoring image is calculated, and the potential risk situation of the corresponding monitoring node of the real-time insulator monitoring image is determined.

[0038] The patrol route analysis module includes a patrol route plan generation unit and a patrol reference analysis unit; The inspection route plan generation unit is used to extract all potential risk nodes, classify the potential risk nodes according to the power grid lines they belong to, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; all potential risk nodes are arranged and combined according to the power grid lines they belong to, and the inspection route plan corresponding to each arrangement and combination plan is generated; adjacent potential risk nodes are spliced ​​using the shortest route in the area to be tested; The inspection reference analysis unit is used to calculate the inspection reference value for the inspection line plan, take the inspection line plan with the smallest inspection reference value as the best inspection plan, and recommend the corresponding administrator to adopt the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid lines in the tested area.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0040] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for intelligent management of power grid data based on computer analysis, characterized in that: The method comprises the following steps: S1: Obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; S2: Use image recognition technology to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; S3: Obtain the most recent monitoring feature information, combine it with the grid feature change model of the grid line in the area to be tested, analyze the potential risk of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes; S4: Arrange and combine the obtained potential risk nodes, and generate a corresponding inspection route plan according to each arrangement and combination; analyze the inspection reference value of the inspection route plan in combination with the sequence of each potential risk node and the corresponding abnormal risk situation; Push inspection route plans to administrators based on inspection reference values.

2. The method for intelligent management of power grid data based on computer analysis according to claim 1, characterized in that: In S1, the monitoring characteristic information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specifications corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node, and the monitoring image of the insulator; the current value of the monitoring node, the voltage value of the monitoring node, and the cable temperature of the monitoring node in the monitoring characteristic information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring characteristic information is obtained through regular monitoring; For The number of monitoring nodes of the power grid lines is counted. [1,I], where I represents the total number of power grid lines in the area to be tested; For The monitoring nodes of the power grid lines are uniformly labeled and form a line monitoring node set, denoted as ; The monitoring nodes are recorded as ; [1,J], J represents the The total number of monitoring nodes on the power grid line.

3. The method for intelligent management of power grid data based on computer analysis according to claim 2, characterized in that: In S2, the following are included: S201: Perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the test area by using image recognition technology, perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; The image evaluation value of the insulator monitoring image is recorded as ; The specific calculation formula is as follows: in, , and are evaluation coefficients respectively, and the evaluation coefficients are system preset constants; Represents the average grayscale value of the image, which is the monitoring node The average value of the grayscale value corresponding to each pixel after the grayscale processing of the insulator monitoring image; It represents the average value of the image average grayscale value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node Grayscale feature deviation value; It represents the average value of the grayscale feature deviation value of the insulator monitoring image before the insulator replacement in the historical data; Indicates monitoring node Flatness difference; It represents the average value of the flatness difference of the insulator monitoring image before the insulator is replaced in the historical data; Monitoring Node Grayscale feature deviation value The specific calculation formula is as follows: in, Indicates monitoring node After grayscale processing of the insulator monitoring image, The gray value corresponding to each pixel; ,in Indicates monitoring node The total number of pixels in the insulator monitoring image; Monitoring Node Flatness difference The specific calculation formula is as follows: in, Indicates monitoring node Insulator No. Block deformation area, ,in Indicates monitoring node The total number of insulator deformation areas; Indicates monitoring node The average area of ​​the insulator deformation region; S202: extract the monitoring node current value, monitoring node voltage value and monitoring node cable temperature at the same time as the insulator monitoring image is acquired, set different weights for the monitoring node current value, monitoring node voltage value and monitoring node cable temperature, and calculate the monitoring node The operational evaluation value ; S203: Construct an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, denoted as ; With o as the origin, the operation evaluation value as the x-axis and the image evaluation value as the y-axis, a plane rectangular coordinate system is constructed, the coordinate points corresponding to the evaluation pairs are marked in the plane rectangular coordinate system, and the adjacent marked coordinate points in the plane rectangular coordinate system are connected in order from small to large in the x-axis coordinate value, and the function corresponding to the obtained broken line is used as the power grid characteristic change model of the power grid line in the test area.

4. The method for intelligent management of power grid data based on computer analysis according to claim 3, characterized in that: In S3, include the following: S301: Obtain the most recent monitoring feature information, and calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; S302: inputting the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the test area respectively, and generating a virtual image evaluation value corresponding to each monitoring node of the power grid line in the test area after the most recent monitoring; S303: Preset a first threshold value for the virtual image evaluation value system. If the obtained virtual image evaluation value is less than the first threshold value, it is determined that there is no abnormal risk for the corresponding monitoring node of the power grid line in the tested area after the most recent monitoring; Otherwise, it is determined that the corresponding monitoring nodes of the power grid lines in the tested area have abnormal risks after the most recent monitoring; S304: obtaining a real-time insulator monitoring image of a monitoring node with abnormal risk, calculating an image evaluation value for the real-time insulator monitoring image, and presetting a second threshold for the image evaluation value system. If the obtained image evaluation value is less than the second threshold, it is determined that the corresponding monitoring node of the real-time insulator monitoring image does not have a potential risk; Otherwise, it is determined that the corresponding monitoring node of the real-time insulator monitoring image has potential risk; the monitoring node with potential risk is recorded as a potential risk node.

5. The method for intelligent management of power grid data based on computer analysis according to claim 4, characterized in that: In S4, the following are included: S401: extract all potential risk nodes, classify the potential risk nodes according to the power grid lines they belong to, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; arrange and combine all potential risk nodes according to the power grid lines they belong to, and generate inspection route plans corresponding to each arrangement and combination plan; adjacent potential risk nodes are spliced ​​using the shortest route in the area to be tested; S402: Calculate the inspection reference value ACV for the inspection line plan. The specific calculation formula is as follows: ACV= ; in, represents a reference coefficient, which is a system preset constant; Indicates the total length of the power grid lines in the area to be tested; Indicates the length of the power grid line through which the inspection line plan passes; Indicates the length of the power grid line that the inspection line plan does not pass through; Indicates the total number of potential risk nodes in the inspection route plan; Indicates the inspection route plan Image evaluation value corresponding to potential risk nodes; ; The inspection line plan with the smallest inspection reference value is taken as the best inspection plan, and the corresponding administrator is recommended to adopt the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid lines in the tested area.

6. An intelligent management system for power grid data based on computer analysis, the system is applied to the intelligent management method for power grid data based on computer analysis as claimed in any one of claims 1 to 5, characterized in that: The system includes a power grid data acquisition module, a data feature analysis module, a potential risk analysis module and a line inspection route analysis module; The power grid data acquisition module is used to obtain the distribution of the power grid lines in the area to be tested and the specifications of the insulators of each power grid line; monitor each monitoring node of each power grid line in the area to be tested through sensors, and generate monitoring feature information of each monitoring node of each power grid line in the area to be tested; The data feature analysis module is used to perform image recognition on the monitoring feature information of each monitoring node of the power grid line in the area to be tested through image recognition technology, evaluate and analyze the monitoring feature information of each monitoring node, and build a power grid feature change model of the power grid line in the area to be tested; The potential risk analysis module is used to obtain the most recent monitoring feature information, analyze the potential risk of each monitoring node in the area to be tested in the most recent monitoring, and generate potential risk nodes in combination with the power grid feature change model of the power grid line in the area to be tested; The obtained potential risk nodes are arranged and combined, and a corresponding inspection route plan is generated according to each arrangement and combination; the inspection reference value of the inspection route plan is analyzed in combination with the sequence of each potential risk node and the corresponding abnormal risk situation; Push inspection route plans to administrators based on inspection reference values.

7. The intelligent management system for power grid data based on computer analysis according to claim 6, characterized in that: The power grid data acquisition module includes a power grid line acquisition unit and a monitoring data acquisition unit; The power grid line acquisition unit is used to acquire the direction distribution of the power grid lines in the area to be tested, the distribution of monitoring nodes of each power grid line, and the insulator specifications of each power grid line; The monitoring data acquisition unit is used to monitor each monitoring node of each power grid line in the area to be measured through a sensor, and generate monitoring characteristic information of each monitoring node of each power grid line in the area to be measured; The monitoring characteristic information includes the corresponding position of the monitoring node, the power grid line to which the monitoring node belongs, the cable specifications corresponding to the monitoring node, the current value of the monitoring node, the voltage value of the monitoring node, the cable temperature of the monitoring node and the insulator monitoring image; the current value of the monitoring node, the voltage value of the monitoring node and the cable temperature of the monitoring node in the monitoring characteristic information are obtained through real-time dynamic monitoring, and the insulator monitoring image in the monitoring characteristic information is obtained through regular monitoring.

8. The intelligent management system for power grid data based on computer analysis according to claim 6, characterized in that: The data feature analysis module includes a monitoring data analysis unit and a model building unit; The monitoring data analysis unit is used to perform image recognition on the insulator monitoring images of each monitoring node of the power grid line in the measured area through image recognition technology, and perform grayscale processing, evaluate and analyze the insulator monitoring images of each monitoring node, and calculate the image evaluation value of the insulator monitoring image of each monitoring node; Different weights are set for the monitoring node current value, the monitoring node voltage value and the monitoring node cable temperature to calculate the operation evaluation value of the monitoring node; The model building unit is used to build an evaluation pair of monitoring nodes according to the operation evaluation value and the image evaluation value, and to build a power grid characteristic change model of the power grid line in the area to be tested according to the evaluation pair of monitoring nodes.

9. The intelligent management system for power grid data based on computer analysis according to claim 6, characterized in that: The potential risk analysis module includes a model prediction unit and a risk confirmation unit; The model prediction unit is used to obtain the most recent monitoring feature information, calculate the operation evaluation value corresponding to each monitoring node in the power grid line in the area to be tested at the most recent monitoring time; input the obtained operation evaluation values ​​into the power grid characteristic change model of the power grid line in the area to be tested, and generate a virtual image evaluation value corresponding to each monitoring node of the power grid line in the area to be tested after the most recent monitoring; The risk confirmation unit presets a first threshold value for the virtual image evaluation value system to determine the abnormal risk situation of the corresponding monitoring nodes of the power grid line in the area to be tested after the most recent monitoring; According to the abnormal risk situation, the real-time insulator monitoring image of the monitoring node with abnormal risk is obtained, the image evaluation value is calculated for the real-time insulator monitoring image, and the potential risk situation of the corresponding monitoring node of the real-time insulator monitoring image is determined.

10. The intelligent management system for power grid data based on computer analysis according to claim 6, characterized in that: The inspection route analysis module includes an inspection route plan generation unit and an inspection reference analysis unit; The inspection line plan generation unit is used to extract all potential risk nodes, classify the potential risk nodes according to the power grid lines to which they belong, and sort the potential risk nodes belonging to the same power grid line according to the transmission direction of the power grid line; Arrange and combine all potential risk nodes according to the power grid lines they belong to, and generate inspection line plans corresponding to each arrangement and combination plan; Adjacent potential risk nodes are connected using the shortest route in the area to be tested; The inspection reference analysis unit is used to calculate the inspection reference value for the inspection line plan, take the inspection line plan with the smallest inspection reference value as the best inspection plan, and recommend the corresponding administrator to use the best inspection plan to perform insulator maintenance on the monitoring nodes in the power grid line in the tested area.

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