Intelligent substation unmanned aerial vehicle inspection system

Abstract

The invention relates to the technical field of unmanned aerial vehicle inspection adjustment, and particularly discloses an intelligent substation unmanned aerial vehicle inspection system, which is characterized in that firstly, a scale distribution diagram of a substation and environment monitoring information of an area to which the substation belongs are acquired, the influence of distribution of the substation on unmanned aerial vehicle inspection can be obtained, and each unmanned aerial vehicle needing inspection is preliminarily screened out; the flight state of each unmanned aerial vehicle to be patrolled is detected, the abnormal state of each unmanned aerial vehicle to be patrolled can be obtained, and each patrolled unmanned aerial vehicle in a good state is screened out; based on the patrol process of each patrol unmanned aerial vehicle, the operation data of the transformer substation is monitored, and the patrol result of each patrol unmanned aerial vehicle is obtained through comparative analysis, so that each abnormal patrol unmanned aerial vehicle with the abnormal patrol process can be counted, and finally, the patrol state of each abnormal patrol unmanned aerial vehicle is early warned and prompted. Therefore, the patrol efficiency of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) patrol and regulation technology, and in particular to an intelligent substation UAV patrol system. Background Art

[0002] Drones have the advantages of high efficiency, flexibility, and safety. They can cover areas that are difficult to reach or dangerous for traditional manual inspections, reducing the labor intensity and risks of manual inspections. However, when inspecting substations, due to the various types and performances of drones, as well as the different sizes of substations, how to choose the most suitable drone to perform specific inspection tasks becomes a key issue. If mismatched drones are used to perform inspection tasks, it will lead to unreasonable utilization of drone resources and increase additional inspection costs. At the same time, if the data obtained by drone inspections is inaccurate or unreliable, it will not only affect the accuracy of the inspection results, but also reduce the overall inspection efficiency of the substation, which is not conducive to the stable operation of the substation.

[0003] For example, the invention patent with announcement number CN113946163B announced a method for optimizing the route of autonomous patrol of substation drones based on electromagnetic field analysis. The method adopts technologies such as substation three-dimensional modeling, route modeling, route re-measurement and autonomous patrol, fully considers the electromagnetic field strength of the substation and the needs of operation and maintenance patrol, and adjusts the route according to the electromagnetic field strength and the optimal shooting distance during route planning and autonomous patrol, so as to avoid the drone being disturbed by the electromagnetic field of the substation during patrol and losing control, and at the same time improve the quality and efficiency of drone patrol. It solves the problems that the existing autonomous patrol routes of drones are formulated based on experience, lack of adjustment during flight patrol, and inability to avoid strong electromagnetic interference areas, and improves the safety and quality and efficiency of drone patrol.

[0004] For example, the invention patent with the announcement number CN113687661B announced the method, device and system for automatic analysis and management of data in unmanned substations. The method includes associating the patrol equipment and route number in each route in the database table; configuring the relationship between the route photo and the patrol equipment to form an equipment configuration table; creating patrol tasks based on the selected equipment to be patrolled; sending the patrol tasks and the routes corresponding to each equipment to be patrolled to the airport in the substation, so that the drones in the airport perform the patrol tasks based on the received routes; receiving the route photos sent back by the drone, and identifying the patrol equipment in the route photos; comparing and verifying the identification results with the configuration results in the equipment configuration table; and generating patrol reports based on the comparison and verification results. It can solve the problems of intelligent patrol and data maintenance of substations.

[0005] Combining the above technical solutions, it is found that in the current technical solutions for inspecting substations by drones, the substations are usually inspected directly, but there is a possibility that the drone fails to effectively complete the inspection task, that is, the drone’s own status cannot guarantee the inspection mission, so that the drone’s inspection results are not efficient, and ultimately the substation’s operating efficiency cannot be improved through the inspection results. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an intelligent substation drone inspection system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent substation drone inspection system, including a drone preliminary screening module, which is used to collect the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs, and comprehensively evaluate the distribution impact index of the substation, so as to preliminarily screen out the drones that need to be inspected; a drone status abnormal screening module, which is used to detect the flight status of each drone that needs to be inspected, obtain the status abnormality index of each drone that needs to be inspected, and screen out each patrol drone; a drone inspection result analysis module, which is used to obtain the inspection data of each patrol drone based on the inspection process of each patrol drone, and at the same time monitor the operation data of the substation, and comprehensively analyze the inspection results of each patrol drone; a drone inspection abnormality prompt module, which is used to compare the inspection results of each patrol drone with the preset inspection preset results, count each abnormal patrol drone, and finally issue an early warning prompt for the patrol status of each abnormal patrol drone.

[0008] As a further solution, the preliminary screening of the drones that need to be inspected is carried out, and the specific screening process is as follows: The distribution impact index of the substation is matched with the permitted patrol data set of the drone corresponding to each distribution impact index interval preset in the patrol data management library to obtain the permitted patrol data set of the drone; the patrol data set of each drone to be patrolled is counted, and if the patrol data set of a drone to be patrolled is larger than the permitted patrol data set, the drone to be patrolled is recorded as a drone to be patrolled, thereby screening out the drones to be patrolled.

[0009] As a further solution, the patrol drones are screened out, and the specific screening process is as follows: The state abnormality index of each UAV that needs to be inspected is compared with the state abnormality threshold preset in the inspection data management library. If the state abnormality index of a UAV that needs to be inspected is less than the state abnormality threshold, the UAV that needs to be inspected is recorded as a patrol UAV. Thus, the UAVs that need to be inspected whose state abnormality index is less than the state abnormality threshold are counted and marked as patrol UAVs.

[0010] As a further solution, the patrol status of each abnormal patrol drone is warned, and the specific warning process is as follows: The inspection results of each patrol drone are compared with the preset inspection results in the patrol data management library. If the inspection result of a patrol drone is lower than the preset inspection result, an early warning will be issued for the patrol status of the patrol drone. Thus, the patrol drones whose inspection results are lower than the preset patrol results are counted and marked as abnormal patrol drones, and an early warning will be issued for the patrol status of each abnormal patrol drone.

[0011] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides an intelligent substation drone inspection system. First, the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs are collected. The influence of the distribution of the substation on the drone inspection can be obtained, and the drones that need to be inspected can be preliminarily screened out; the flight status of each drone that needs to be inspected is detected, and the abnormal status of each drone that needs to be inspected can be obtained, and the inspection drones in good status can be screened out; based on the inspection process of each inspection drone, the inspection data of each inspection drone is obtained, and the operation data of the substation is monitored at the same time, and the inspection results of each inspection drone are obtained by comparative analysis. The abnormal inspection drones with abnormal inspection processes can be counted, and finally the inspection status of each abnormal inspection drone is warned. By taking corresponding inspection efficiency improvement measures, the inspection efficiency of the drone is improved.

[0012] (2) The present invention collects the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs, and comprehensively evaluates the distribution impact index of the substation. It can preliminarily screen out the patrol drones that meet the distribution requirements of the substation, so as to reduce the possibility of collision between the drone and other accessories and ensure the non-destructive patrol of the drone.

[0013] (3) The present invention detects the flight status of each UAV that needs to be inspected, obtains the abnormal status index of each UAV that needs to be inspected, and screens out each patrol UAV, thereby reducing the situation where the UAV's patrol mission is affected by factors such as insufficient power, which is conducive to the UAV's complete and efficient completion of the patrol mission.

[0014] (4) The present invention obtains the patrol data of each patrol drone and monitors the operation data of the substation at the same time, performs comparative analysis to obtain the patrol results of each patrol drone, and compares the patrol results of each patrol drone with the preset patrol results, and counts the abnormal patrol drones. It is possible to understand the data collection uncertainty of the abnormal patrol drones, and finally issue early warning prompts for the patrol status of each abnormal patrol drone, which can improve the accuracy of the patrol results, improve the overall patrol efficiency of the substation, and facilitate the stable operation of the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0016] Figure 1 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, an embodiment of the present invention provides a technical solution: a smart substation drone inspection system, including a drone preliminary screening module, a drone status abnormal screening module, a drone inspection result analysis module and a drone inspection abnormality prompt module.

[0019] The intelligent substation drone inspection system provided by the present invention also includes an inspection data management library, which is used to store the drone's permitted inspection data set corresponding to each preset distribution influence index interval, the initial center position point corresponding to each mechanical component of each drone that needs to be inspected, the preset state abnormality threshold, the preset inspection operation temperature deviation value corresponding to the unit value influence factor, the preset ultraviolet radiation intensity deviation value corresponding to the unit value influence factor, the preset inspection noise intensity deviation value corresponding to the unit value influence factor, the preset inspection preset results, and at the same time store the following data in this example.

[0020] The drone preliminary screening module is connected to the drone status abnormal screening module, the drone status abnormal screening module is connected to the drone patrol result analysis module, the drone patrol result analysis module is connected to the drone patrol abnormal prompt module, and the drone preliminary screening module, the drone status abnormal screening module, the drone patrol result analysis module and the drone patrol abnormal prompt module are all connected to the patrol data management library.

[0021] The drone preliminary screening module is used to collect the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs, and comprehensively evaluate the distribution impact indicators of the substation, so as to preliminarily screen out the drones that need to be patrolled.

[0022] Specifically, the preliminary screening of the drones that need to be inspected is carried out in the following specific process: The distribution impact index of the substation is matched with the drone-owned permitted inspection data set corresponding to each distribution impact index interval preset in the inspection data management library to obtain the drone-owned permitted inspection data set. The specific matching process is as follows: Each distribution impact index interval corresponds to a permitted patrol data set of the UAV. When the distribution impact index interval of the substation is queried, the permitted patrol data set of the UAV corresponding to the distribution impact index interval is the permitted patrol data set of the UAV.

[0023] The inspection data set of each unmanned aerial vehicle to be inspected is counted, wherein the statistical method is to use the unmanned aerial vehicle state monitoring system to perform statistics. The unmanned aerial vehicle state monitoring system can monitor the operating status of each unmanned aerial vehicle in real time, including the flight path, battery power, sensor data (such as temperature, vibration, etc.) and communication signal strength. When the unmanned aerial vehicle is in standby mode, fault mode, and working mode, the system will automatically record and classify it. By querying the classification in the unmanned aerial vehicle state monitoring system, each unmanned aerial vehicle to be inspected can be obtained; the unmanned aerial vehicle to be inspected refers to a unmanned aerial vehicle that is not currently in a fault state and is not in an inspection state; the inspection data set includes but is not limited to the maximum flight altitude and the maximum electromagnetic interference intensity that can be tolerated, and the above-mentioned permitted inspection data set corresponds to the maximum permitted flight altitude and the permitted electromagnetic interference intensity; if the inspection data set of a certain unmanned aerial vehicle to be inspected is greater than the permitted inspection data set, that is, the above-mentioned maximum flight altitude is greater than the maximum permitted flight altitude, and the maximum electromagnetic interference intensity that can be tolerated is greater than the permitted electromagnetic interference intensity, then the unmanned aerial vehicle to be inspected is recorded as a unmanned aerial vehicle to be inspected, thereby screening out the unmanned aerial vehicles to be inspected.

[0024] Furthermore, the comprehensive evaluation of the distribution impact index of the substation is as follows: The minimum straight-line distance of the transmission line, the highest altitude of the substation, and the minimum straight-line distance between the locations of the electrical equipment are extracted from the scale distribution map of the substation. The corresponding acquisition method of the minimum straight-line distance of the transmission line can be obtained through GIS software, such as ArcGIS (Geographic Information System). The scale distribution map of the substation is imported into ArcGIS. Through the spatial analysis tool, the shortest straight-line distance between any two transmission lines can be calculated. All the minimum distance data are collected, and the minimum distance with the minimum value is screened out, which is the minimum straight-line distance of the transmission line. The transmission line represents the combination of the main bus, grounding line, backup line, secondary circuit and other lines in the substation; the substation The highest altitude is obtained by measuring using a laser radar scanning method; the minimum straight-line distance between the locations of electrical equipment can be obtained by using GIS software, such as ArcGIS (Geographic Information System), importing the scale distribution map of the substation into ArcGIS, and converting the equipment location information in the scale distribution map of the substation into calculable spatial data. The minimum straight-line distance between adjacent equipment is then calculated using spatial analysis tools, thereby screening out the minimum straight-line distance with the smallest value, which is the minimum straight-line distance between the locations of electrical equipment. The electrical equipment in the substation includes but is not limited to main transformers, circuit breakers, mutual inductors, relays, and grounding devices.

[0025] The maximum concentration values ​​of various types of particulate matter are extracted through environmental monitoring information of the substation area. The acquisition method is to query the portable particulate matter monitor installed in the substation. The types of particulate matter include but are not limited to PM2.5, PM10, and total suspended particulate matter; the maximum intensity of electromagnetic interference is obtained by using the electromagnetic interference monitoring equipment installed in the substation, such as the electromagnetic field intensity meter, to monitor and record the intensity of the electromagnetic field around the substation in real time, so as to obtain multiple electromagnetic interference intensity values ​​within a period of time, and to obtain the maximum electromagnetic interference intensity by screening the maximum value; the above-mentioned maximum concentration of particulate matter and the maximum intensity of electromagnetic interference are the maximum values ​​obtained during the monitoring period of the substation, and finally the distribution impact index of the substation is comprehensively evaluated. The evaluation expression is as follows:

[0026] In the formula, is the distribution influencing index of the substation. In this example, before the drone inspects the substation, it is usually necessary to consider whether the drone's flight inspection is lower than the scale inspection standard of the substation, that is, whether the maximum distance of the drone's fuselage is greater than the straight-line distance of the substation, and whether the maximum flight altitude of the drone is less than the highest altitude of the substation. If the above situation exists, it means that the drone cannot effectively inspect the substation and will not be able to inspect the substation completely. At the same time, the environmental factors around the substation will also affect the inspection of the drone, that is, if the drone's transparent clarity is less than the concentration of particulate matter in the environment, and the drone's anti-electromagnetic interference strength is less than the electromagnetic interference strength in the environment, the inspection efficiency of the drone will be reduced, and it may seriously cause the drone to be forced to land, so as to damage the drone. In summary, it is necessary to combine the scale of the substation and the influencing factors of the surrounding environment to determine the drones that can be inspected, so as to screen out drones that fit the scale of the substation and the surrounding environment, and ultimately improve their inspection efficiency.

[0027] The impact factor of the unit value corresponding to the straight-line distance preset for the patrol data management library is a mapping set of straight-line distance and its corresponding weight factor constructed according to the relationship between the historical straight-line distance and the distribution influence index, and the real-time straight-line distance is input into the mapping set to obtain the impact factor of the unit value corresponding to the straight-line distance. At the same time, its value range in this example is [0, 1].

[0028] It is the minimum straight-line distance of the transmission line, which refers to the minimum straight-line distance of the line in the substation.

[0029] It is the minimum straight-line distance between the locations of electrical equipment, which refers to the minimum straight-line distance between the locations of electrical equipment in the substation.

[0030] The highest altitude of the substation refers to the height of the highest point of the geographical location of the substation relative to the sea level.

[0031] The impact factor of the unit value corresponding to the altitude preset in the patrol data management library is a mapping set of altitude and its corresponding weight factor constructed according to the relationship between the historical altitude and the distribution influence index, and the real-time altitude is input into the mapping set to obtain the impact factor of the unit value corresponding to the altitude. At the same time, its value range in this example is [0, 1].

[0032] The influence factor of the unit value of the particle matter concentration preset for the patrol data management library is a mapping set of the particle matter concentration and its corresponding weight factor constructed according to the relationship between the historical particle matter concentration and the distribution influencing index, and the real-time particle matter concentration is input into the mapping set to obtain the influence factor of the unit value of the particle matter concentration. At the same time, its value range in this example is [0, 1].

[0033] It is the maximum concentration value of Class B particulate matter, which refers to the highest level of particulate matter content in the environment around the substation during a specific monitoring period.

[0034] The maximum intensity of electromagnetic interference refers to the peak intensity of electromagnetic energy in the surrounding environment of the substation during a specific monitoring period.

[0035] The influence factor of the unit value corresponding to the maximum electromagnetic interference intensity preset for the patrol data management library is a mapping set of the maximum electromagnetic interference intensity and its corresponding weight factor constructed according to the relationship between the historical maximum electromagnetic interference intensity and the distribution influence index, and the real-time maximum electromagnetic interference intensity is input into the mapping set to obtain the influence factor of the unit value corresponding to the maximum electromagnetic interference intensity. At the same time, its value range in this example is [0, 1].

[0036] b is the number of each type of particle, , g is the total number of particle types.

[0037] In a specific embodiment, the present invention collects the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs, comprehensively evaluates the distribution impact index of the substation, and can preliminarily screen out the patrol drones that meet the distribution requirements of the substation to reduce the possibility of collision between the drone and other accessories, thereby ensuring the non-destructive patrol of the drone.

[0038] The drone status abnormality screening module is used to detect the flight status of each drone that needs to be patrolled, obtain the status abnormality index of each drone that needs to be patrolled, and screen out each patrol drone.

[0039] Specifically, the specific screening process of screening out the patrol drones is as follows: The state abnormality index of each UAV that needs to be inspected is compared with the state abnormality threshold preset in the inspection data management library, where the state abnormality threshold represents the minimum value of the state abnormality index within a reasonable range. If the state abnormality index of a UAV that needs to be inspected is less than the state abnormality threshold, it means that the current operating state of the UAV that needs to be inspected is considered to be within the safe and normal range of the substation that can be inspected. The UAV that needs to be inspected is recorded as a patrol UAV. Thus, the UAVs that need to be inspected whose state abnormality index is less than the state abnormality threshold are counted and marked as patrol UAVs.

[0040] Furthermore, the flight status of each drone that needs to be inspected is detected, and the specific detection data is the flight status data of each drone that needs to be inspected.

[0041] The flight status data of each drone that needs to be inspected specifically includes the existing power level, which can be obtained through a built-in power monitoring system. The power monitoring system will monitor the remaining capacity of the battery in real time and send the data to the display interface of the drone through the drone's communication module, so as to query the current power status of the drone and the current center position of each mechanical component. The acquisition method is a GPS positioning system, which can provide real-time information on the position, posture and motion status of the drone to obtain the center position of each mechanical component in the drone. Mechanical components refer to various components on the surface of the drone, including but not limited to wings, tail wings, landing gear, and cameras.

[0042] Specifically, the specific analysis process of the abnormal status indicators of each drone that needs to be inspected is as follows: The initial center position points corresponding to the mechanical parts of each UAV that needs to be inspected are extracted from the inspection data management library. At the same time, based on the flight status data of each UAV that needs to be inspected, the abnormal status indicators of each UAV that needs to be inspected can be analyzed. The analysis expression is as follows:

[0043] In the formula, is the abnormal status indicator of each drone that needs to be inspected. In this example, after the idle and fault-free drones are initially screened out as mentioned above, it is necessary to further determine the drones that can be inspected based on factors such as the inspection route and flight speed. Therefore, based on factors such as the inspection route, it is necessary to determine whether the existing power of the drone can support the complete inspection mission this time. At the same time, if the surface parts of the drone are loose, etc., this drone needs to be eliminated, because if a drone with loose parts is inspected, it will not only cause further damage to the drone, but also cause the sensors and other configurations carried to fall, thereby increasing the maintenance cost and inspection cost of the configuration; in summary, by evaluating the current power of the drone and the factors of loose parts, the drones that can perform inspection missions can be further determined, which is conducive to the drones completing complete and safe inspection missions and improving the inspection efficiency of drones.

[0044] The influencing factor of the unit value of the power deviation preset for the patrol data management library is a mapping set of the power deviation and its corresponding weight factor constructed according to the relationship between the historical power deviation and the abnormal status index, and the real-time power deviation is input into the mapping set to obtain the influencing factor of the unit value of the power deviation. At the same time, its value range in this example is [0, 1].

[0045] It is the current power of each drone that needs to be inspected, indicating the remaining power of the drone at the current time point.

[0046] is the power limit value of the patrol drone, indicating the minimum power required by the drone to support the patrol mission. It is obtained by matching the patrol route of the drone (according to the patrol requirements, this patrol route needs to patrol all electrical equipment in the substation) through the scale distribution map of the substation and the patrol requirements (for example, focusing on patrolling electrical equipment). The patrol route is divided into multiple patrol sections, and the average flight energy consumption of the drone is obtained at the same time. The flight energy consumption and stay energy consumption of the drone in each patrol section are calculated, and then the total energy consumption of the entire patrol route is accumulated, which is the power limit value of the drone.

[0047] The correction factor corresponding to the mechanical component position movement distance preset in the patrol data management library is a mapping set of the mechanical component position movement distance and its corresponding weight factor constructed according to the relationship between the historical mechanical component position movement distance and the state abnormality index, and the real-time mechanical component position movement distance is input into the mapping set to obtain the correction factor corresponding to the mechanical component position movement distance. At the same time, in this example, its value range is [0, 1].

[0048] It is the current center position point of each mechanical component of each drone that needs to be inspected, indicating the coordinate position of the physical center of each key component of the drone in the current state in three-dimensional space.

[0049] It is the initial center position point corresponding to each mechanical component of each drone that needs to be inspected, indicating the initial coordinate position of the physical center of each key component of the drone in the factory state in three-dimensional space.

[0050] is the moving distance of the corresponding position of the mechanical parts of the UAV to be inspected, which is the maximum spatial distance between the coordinates corresponding to the current center position point and the coordinates corresponding to the initial center position point.

[0051] f is the number of each drone that needs to be inspected, , m is the total number of drones to be inspected, k is the number of each mechanical component, , u is the total number of mechanical parts.

[0052] In a specific embodiment, the present invention detects the flight status of each drone that needs to be patrolled, obtains the abnormal status index of each drone that needs to be patrolled, and screens out each patrol drone, which can reduce the situation where the drone’s patrol mission is affected by factors such as insufficient power, and is conducive to the drone’s complete and efficient completion of the patrol mission.

[0053] The UAV inspection result analysis module is used to obtain the inspection data of each patrol UAV based on the inspection process of each patrol UAV, monitor the operation data of the substation at the same time, and comprehensively analyze the inspection results of each patrol UAV.

[0054] Specifically, the patrol data of each patrol drone includes the patrol operation temperature value of each patrol drone corresponding to each patrol electrical equipment at each patrol monitoring time point, the ultraviolet radiation intensity value of each patrol drone corresponding to each patrol electrical equipment at each patrol monitoring time point, and the patrol noise intensity value of each patrol drone corresponding to each measurement position point at each patrol monitoring time point; wherein the patrol operation temperature value, ultraviolet radiation intensity value, and patrol noise intensity value are all obtained by using the infrared thermal imager, ultraviolet radiation sensor, and acoustic sensor carried by the drone; at the same time, the patrol electrical equipment refers to the substation monitored by the infrared thermal imager and ultraviolet radiation sensor carried by the drone. The inspection monitoring time points are obtained by dividing the inspection monitoring cycle and other time intervals. For example, the inspection monitoring cycle is 50 seconds. If the time interval is 25 seconds, the inspection monitoring time points are 0 seconds, 25 seconds and 50 seconds. The inspection monitoring cycle refers to the time period during which the drone inspects the substation. The duration varies according to factors such as the flight path of the drone and the size of the substation. This example does not make any special restrictions. The measurement location point refers to the location point in the substation that needs to be inspected. It can be the measurement point of multiple electrical equipment in the substation or the measurement point of multiple cables in the substation. It will vary according to the specific scale of the substation, the purpose of the inspection and the equipment capabilities of the drone.

[0055] Furthermore, the operating data of the substation specifically includes the operating temperature monitoring value of each patrol electrical equipment at each patrol monitoring time point, the ultraviolet radiation intensity monitoring value of each patrol electrical equipment at each patrol monitoring time point, and the corresponding noise intensity monitoring value of each measurement position point at each patrol monitoring time point, wherein the operating temperature monitoring value, the ultraviolet radiation intensity monitoring value and the noise intensity monitoring value are all obtained through the infrared thermal imager, ultraviolet sensor and sound level meter installed in the substation.

[0056] Specifically, the patrol results of each patrol drone are specifically expressed as follows:

[0057] in,

[0058] In the formula, is the inspection result of the ith inspection drone. In this example, due to the differences in the performance of the drones themselves and the performance of the sensors they carry, the inspection results of each drone will also be biased. By comparing the inspection results of the drones with the results of the substation's own monitoring, such as the operating temperature of the equipment, the ultraviolet intensity on the equipment's surface, and the noise intensity around the substation, we can find out which drones' inspection results are more abnormal, and optimize their quality to improve the inspection efficiency of the drones.

[0059] The influencing factor of the unit value corresponding to the patrol operation temperature deviation value preset in the patrol data management library is a mapping set of the patrol operation temperature deviation value and its corresponding weight factor constructed according to the relationship between the historical patrol operation temperature deviation value and the patrol result, and the real-time patrol operation temperature deviation value is input into the mapping set to obtain the influencing factor of the unit value corresponding to the patrol operation temperature deviation value. At the same time, its value range in this example is [0, 1].

[0060] is the patrol operating temperature deviation value of the p-th patrol electrical equipment corresponding to the ith patrol UAV at the a-th patrol monitoring time point, which refers to the difference between the equipment operating temperature patrolled by the UAV and the equipment operating temperature monitored by the substation.

[0061] The influencing factor of the unit value corresponding to the ultraviolet radiation intensity deviation value preset for the patrol data management library is a mapping set of the ultraviolet radiation intensity deviation value and its corresponding weight factor constructed according to the relationship between the historical ultraviolet radiation intensity deviation value and the patrol result, and the real-time ultraviolet radiation intensity deviation value is input into the mapping set to obtain the influencing factor of the unit value corresponding to the ultraviolet radiation intensity deviation value. At the same time, its value range in this example is [0, 1].

[0062] is the deviation value of the ultraviolet radiation intensity of the p-th patrol electrical equipment corresponding to the ith patrol UAV at the a-th patrol monitoring time point, which refers to the difference between the ultraviolet radiation intensity of the equipment patrolled by the UAV and the ultraviolet radiation intensity of the equipment monitored in the substation.

[0063] The influencing factor of the patrol noise intensity deviation value corresponding to the unit value preset in the patrol data management library is a mapping set of patrol noise intensity deviation values ​​and their corresponding weight factors constructed based on the relationship between historical patrol noise intensity deviation values ​​and patrol results, and the real-time patrol noise intensity deviation value is input into the mapping set to obtain the influencing factor of the patrol noise intensity deviation value corresponding to the unit value. At the same time, its value range in this example is [0, 1].

[0064] It is the patrol noise intensity deviation value corresponding to the dth measurement position point of the ith patrol UAV at the ath patrol monitoring time point, which refers to the difference between the noise intensity around the substation patrolled by the UAV and the surrounding noise intensity monitored by the substation.

[0065] is the patrol operating temperature value of the p-th patrol electrical equipment corresponding to the ith patrol UAV at the a-th patrol monitoring time point, that is, the operating temperature of the electrical equipment patrolled by the infrared thermal imager carried by the UAV.

[0066] is the operating temperature monitoring value of the pth patrol electrical equipment at the ath patrol monitoring time point, that is, the equipment operating temperature monitored by the built-in infrared thermal imager of the electrical equipment of the substation.

[0067] is the ultraviolet radiation intensity value of the p-th patrol electrical equipment corresponding to the ith patrol UAV at the a-th patrol monitoring time point, that is, the ultraviolet radiation intensity of the electrical equipment patrolled by the ultraviolet radiation sensor carried by the UAV.

[0068] is the ultraviolet radiation intensity monitoring value of the p-th patrolled electrical equipment at the a-th patrol monitoring time point, that is, the ultraviolet radiation intensity of the equipment monitored by the built-in ultraviolet sensor of the electrical equipment of the substation.

[0069] is the patrol noise intensity value corresponding to the dth measurement position of the ith patrol UAV at the ath patrol monitoring time point, that is, the noise intensity of the acoustic sensor carried by the UAV patrolling around the substation.

[0070] is the noise intensity monitoring value corresponding to the dth measurement location at the ath patrol monitoring time point, that is, the noise intensity monitored by the built-in sound level meter of the substation around the substation.

[0071] i is the number of each patrol drone, , j is the total number of patrol drones, p is the number of each patrol electrical equipment, , q is the total number of patrolled electrical equipment, a is the number of each patrol monitoring time point, , h is the total number of patrol monitoring time points, d is the number of each measurement location point, , n is the total number of measurement locations.

[0072] The drone patrol abnormality prompt module is used to compare the patrol results of each patrol drone with the preset patrol preset results, count the abnormal patrol drones, and finally issue an early warning prompt for the patrol status of each abnormal patrol drone.

[0073] In a specific embodiment, the present invention obtains the patrol data of each patrol drone and monitors the operating data of the substation at the same time, performs comparative analysis to obtain the patrol results of each patrol drone, and compares the patrol results of each patrol drone with the preset patrol preset results, and counts the abnormal patrol drones. It is possible to understand the data collection uncertainty of the abnormal patrol drones, and finally issue early warning prompts for the patrol status of each abnormal patrol drone, which can improve the accuracy of the patrol results, improve the overall patrol efficiency of the substation, and facilitate the stable operation of the substation.

[0074] Specifically, the patrol status of each abnormal patrol drone is warned, and the specific warning process is as follows: The inspection results of each patrol drone are compared with the preset inspection results in the patrol data management library, where the preset inspection result indicates the minimum value of the drone's inspection result within a reasonable patrol range. If the patrol result of a patrol drone is lower than the preset patrol result, it means that the patrol result of the patrol drone is not within a reasonable patrol range, and an early warning prompt is issued for the patrol status of the patrol drone. The specific early warning prompt process is as follows: The drone status monitoring system senses the inspection results corresponding to the drone's completed inspection mission. If the inspection result of a drone is lower than the reasonable inspection range, the drone status monitoring system will reflect the inspection abnormality of the drone and automatically turn on the warning signal light to feedback to the drone management personnel, so that the drone can be repaired in time, thereby improving the effective utilization rate of the drone.

[0075] Thus, each patrol drone whose patrol results are statistically lower than the preset patrol results is marked as an abnormal patrol drone, and an early warning prompt is given for the patrol status of each abnormal patrol drone.

[0076] In this example, the number of the above-mentioned drones is arranged in the following order: the number of drones to be inspected ≥ the number of drones required to be inspected ≥ the number of patrol drones ≥ the number of abnormal patrol drones, that is, the drones to be inspected are the preliminary selected patrol drones. If the number of drones to be inspected is 10, the number of drones required to be inspected, patrol drones, and abnormal patrol drones will not exceed 10.

[0077] In a specific embodiment, the present invention provides an intelligent substation drone inspection system. First, the scale distribution map of the substation and the environmental monitoring information of the area to which the substation belongs are collected, so as to obtain the impact of the distribution of the substation on the drone inspection, and preliminarily screen out the drones that need to be inspected; the flight status of each drone that needs to be inspected is detected, and the abnormal status of each drone that needs to be inspected can be obtained, and the inspection drones in good status can be screened out; based on the inspection process of each inspection drone, the inspection data of each inspection drone is obtained, and the operation data of the substation is monitored at the same time, and the inspection results of each inspection drone are obtained by comparative analysis. The abnormal inspection drones with abnormal inspection processes can be counted, and finally the inspection status of each abnormal inspection drone is warned, and the inspection efficiency of the drone is improved by taking corresponding inspection efficiency improvement measures.

[0078] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. The intelligent substation drone inspection system is characterized by: include: The drone preliminary screening module is used to collect the scale distribution map of the substation and the environmental monitoring information of the area where the substation belongs, and comprehensively evaluate the distribution impact indicators of the substation, so as to preliminarily screen the drones that need to be inspected; The drone status abnormality screening module is used to detect the flight status of each drone that needs to be patrolled, obtain the status abnormality index of each drone that needs to be patrolled, and screen out each patrol drone; The UAV inspection result analysis module is used to obtain the inspection data of each UAV based on the inspection process of each UAV, monitor the operation data of the substation at the same time, and comprehensively analyze the inspection results of each UAV; The drone patrol abnormality prompt module is used to compare the patrol results of each patrol drone with the preset patrol results, count the abnormal patrol drones, and finally issue an early warning prompt for the patrol status of each abnormal patrol drone.

2. According to claim 1, the intelligent substation drone inspection system is characterized by: The comprehensive evaluation of the distribution impact index of the substation is as follows: Extract the minimum straight-line distance of the transmission line, the highest altitude of the substation, and the minimum straight-line distance between the locations of electrical equipment from the scale distribution map of the substation; Through the environmental monitoring information of the substation area, the maximum concentration values ​​of various types of particulate matter and the maximum intensity of electromagnetic interference are extracted, and finally the distribution impact indicators of the substation are comprehensively evaluated.

3. According to claim 1, the intelligent substation drone inspection system is characterized by: The preliminary screening of the drones that need to be inspected is as follows: Match the distribution impact index of the substation with the drone-owned permitted inspection data set corresponding to each distribution impact index interval preset in the inspection data management library to obtain the drone-owned permitted inspection data set; The inspection data set of each unmanned aerial vehicle to be inspected is counted. If the inspection data set of a certain unmanned aerial vehicle to be inspected is greater than the permitted inspection data set, the unmanned aerial vehicle to be inspected is recorded as a drone that needs to be inspected, thereby screening out the drones that need to be inspected.

4. The intelligent substation drone inspection system according to claim 1 is characterized by: The flight status of each drone that needs to be inspected is detected, and the specific detection data is the flight status data of each drone that needs to be inspected; The flight status data of each drone that needs to be inspected specifically includes the existing power and the current center position of each mechanical component.

5. According to claim 4, the intelligent substation drone inspection system is characterized by: The specific analysis process of the abnormal status indicators of the drones to be inspected is as follows: The initial center position points corresponding to the mechanical parts of each UAV that needs to be inspected are extracted from the inspection data management library. At the same time, based on the flight status data of each UAV that needs to be inspected, the abnormal status indicators of each UAV that needs to be inspected can be analyzed.

6. The intelligent substation drone inspection system according to claim 5 is characterized by: The specific screening process of screening out the patrol drones is as follows: The state abnormality index of each UAV that needs to be inspected is compared with the state abnormality threshold preset in the inspection data management library. If the state abnormality index of a UAV that needs to be inspected is less than the state abnormality threshold, the UAV that needs to be inspected is recorded as a patrol UAV. Thus, the UAVs that need to be inspected whose state abnormality index is less than the state abnormality threshold are counted and marked as patrol UAVs.

7. The intelligent substation drone inspection system according to claim 1 is characterized by: The inspection data of each patrol drone specifically includes the patrol operating temperature value of each patrol electrical equipment corresponding to each patrol drone at each patrol monitoring time point, the ultraviolet radiation intensity value of each patrol electrical equipment corresponding to each patrol drone at each patrol monitoring time point, and the patrol noise intensity value of each measurement position point corresponding to each patrol drone at each patrol monitoring time point.

8. The intelligent substation drone inspection system according to claim 1 is characterized by: The operating data of the substation specifically includes the operating temperature monitoring value of each patrol electrical equipment at each patrol monitoring time point, the ultraviolet radiation intensity monitoring value of each patrol electrical equipment at each patrol monitoring time point, and the noise intensity monitoring value corresponding to each measurement position point at each patrol monitoring time point.

9. The intelligent substation drone inspection system according to claim 8 is characterized by: The patrol results of each patrol drone are specifically expressed as follows: in, In the formula, is the patrol result of the i-th patrol drone, The impact factor of the patrol operation temperature deviation value corresponding to the unit value preset in the patrol data management library, is the patrol operation temperature deviation value of the pth patrol electrical equipment corresponding to the i-th patrol UAV at the a-th patrol monitoring time point, The influence factor of the unit value corresponding to the ultraviolet radiation intensity deviation value preset in the patrol data management database, is the UV radiation intensity deviation value of the p-th patrol electrical equipment corresponding to the i-th patrol UAV at the a-th patrol monitoring time point, The impact factor of the patrol noise intensity deviation value corresponding to the unit value preset in the patrol data management library, is the patrol noise intensity deviation value corresponding to the dth measurement position of the ith patrol UAV at the ath patrol monitoring time point, is the patrol operating temperature value of the p-th patrol electrical equipment corresponding to the ith patrol UAV at the a-th patrol monitoring time point, is the operating temperature monitoring value of the pth patrol electrical equipment at the ath patrol monitoring time point, is the ultraviolet radiation intensity value of the p-th patrol electrical equipment corresponding to the i-th patrol UAV at the a-th patrol monitoring time point, is the ultraviolet radiation intensity monitoring value of the pth patrol electrical equipment at the ath patrol monitoring time point, is the patrol noise intensity value corresponding to the dth measurement position of the ith patrol UAV at the ath patrol monitoring time point, is the noise intensity monitoring value corresponding to the dth measurement location at the ath patrol monitoring time point, i is the number of each patrol UAV, , j is the total number of patrol drones, p is the number of each patrol electrical equipment, , q is the total number of patrolled electrical equipment, a is the number of each patrol monitoring time point, , h is the total number of patrol monitoring time points, d is the number of each measurement location point, , n is the total number of measurement locations.

10. The smart substation drone inspection system according to claim 1 is characterized by: The specific warning process of the patrol status of each abnormal patrol drone is as follows: The inspection results of each patrol drone are compared with the preset inspection results in the patrol data management library. If the inspection result of a patrol drone is lower than the preset inspection result, an early warning will be issued for the patrol status of the patrol drone. Thus, the patrol drones whose inspection results are lower than the preset patrol results are counted and marked as abnormal patrol drones, and an early warning will be issued for the patrol status of each abnormal patrol drone.

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