A cloud platform inspection dynamic regulation and control method and system for a power transmission line, a storage medium and a program product

Abstract

The application provides a cloud head inspection dynamic regulation and control method and system of a power transmission line, a storage medium and a program product, relates to the technical field of power transmission line monitoring, and the method comprises the following steps: acquiring multi-source data of a target power transmission line, inputting the multi-source data into a preset dynamic risk assessment model, and obtaining a dynamic risk heat map output by the preset dynamic risk assessment model; a spatial characteristic model is established for a plurality of cloud head preset positions of the target power transmission line, and spatial correlation analysis is performed on the spatial characteristic model and the dynamic risk heat map, so as to obtain a spatial correlation characteristic parameter of each cloud head preset position in the plurality of cloud head preset positions; the device state weight value of each cloud head camera equipment in the plurality of cloud head camera equipment of the target power transmission line is determined, and the comprehensive inspection weight value of each cloud head preset position is determined according to the device state weight value and the spatial correlation characteristic parameter; and the comprehensive inspection weight value is converted into the target inspection cycle of each cloud head preset position by using a first preset inverse proportional function.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of power transmission line monitoring, and particularly relates to a pan-tilt inspection dynamic regulation method and system for a power transmission line, a storage medium and a program product. BACKGROUND

[0002] With the continuous expansion of the power system scale and the in-depth promotion of smart grid construction, the safe and stable operation of the power transmission line as the key infrastructure of power transmission is of great significance to guarantee power supply and social and economic development.

[0003] In the related art, a fixed-cycle pan-tilt inspection scheduling method is usually used. In specific implementation, a technician first sets fixed inspection cycles and priorities for preset positions of pan-tilts in different sections according to historical fault data and operation and maintenance experience of the power transmission line; then configures the preset inspection parameters into a control system of the pan-tilt camera device to establish a static inspection scheduling table; then the system controls each pan-tilt camera device to turn to the corresponding preset position for image acquisition in a predetermined time interval and sequence; finally, the collected monitoring pictures are analyzed by manual or simple image recognition algorithm, and an alarm is triggered when an abnormal condition is found. Although this fixed-cycle and static-scheduling-based method can complete the basic line inspection task, the entire inspection strategy remains unchanged after deployment and cannot be dynamically adjusted according to the actual risk situation.

[0004] However, the static inspection strategy cannot adapt to the dynamically changing risk environment by using the above-mentioned pan-tilt inspection scheduling method, which may lead to insufficient inspection frequency in high-risk areas and excessive inspection in low-risk areas, causing improper allocation of monitoring resources and thus low inspection efficiency of the pan-tilt camera device in the related art. SUMMARY

[0005] The application provides a pan-tilt inspection dynamic regulation method and system for a power transmission line, a storage medium and a program product, which are used to improve the inspection efficiency of the pan-tilt camera device.

[0006] In a first aspect, the application provides a dynamic regulation method for a pan-tilt inspection of a power transmission line. The method is applied to a dynamic regulation system for the pan-tilt inspection of the power transmission line. The method comprises the following steps: obtaining multi-source data of a target power transmission line, and inputting the multi-source data into a preset dynamic risk assessment model to obtain a dynamic risk heat map output by the preset dynamic risk assessment model; establishing a spatial characteristic model for a plurality of preset positions of a plurality of pan-tilt cameras of the target power transmission line, and performing spatial correlation analysis on the spatial characteristic model and the dynamic risk heat map to obtain a spatial correlation characteristic parameter of each of the plurality of preset positions of the pan-tilt cameras; determining a device state weight value of each of the plurality of pan-tilt cameras of the target power transmission line, and determining a comprehensive inspection weight value of each of the plurality of preset positions of the pan-tilt cameras according to the device state weight value and the spatial correlation characteristic parameter, the plurality of pan-tilt cameras and the plurality of preset positions of the pan-tilt cameras having a preset corresponding relationship; and converting the comprehensive inspection weight value into a target inspection cycle of each of the plurality of preset positions of the pan-tilt cameras by using a first preset inverse proportional function.

[0007] By using the above technical solution, the multi-source data is processed by the preset dynamic risk assessment model to generate a dynamic risk heat map, which can realize real-time quantitative characterization of the risk status of the power transmission line. The spatial correlation analysis of the spatial characteristic model and the dynamic risk heat map enables each of the plurality of preset positions of the pan-tilt cameras to accurately obtain the risk distribution characteristic in the monitoring range thereof to form a spatial correlation characteristic parameter. The device state weight value reflects the actual working capacity of the pan-tilt camera, and the comprehensive inspection weight value is calculated in combination with the spatial correlation characteristic parameter to ensure that the allocation of the inspection resources takes into account both the risk demand and the device capacity. The first preset inverse proportional function converts the comprehensive inspection weight value into the target inspection cycle to realize a differentiated scheduling strategy of short-cycle intensive inspection in a high-weight region and long-cycle sparse inspection in a low-weight region. The cycle adjustment mechanism driven by the dynamic weight can change the traditional static scheduling mode of fixed cycle to enable the inspection frequency to be adaptively adjusted according to the real-time risk status and the device state. Thus, the technical problem of low inspection efficiency of the pan-tilt camera in the related art is solved, and the technical effect of improving the inspection efficiency of the pan-tilt camera is achieved.

[0008] In a second aspect, the application provides a dynamic regulation system for a pan-tilt inspection. The dynamic regulation system comprises one or more processors and a memory. The memory is coupled to the one or more processors, and is configured to store computer program codes. The computer program codes comprise computer instructions. The one or more processors invoke the computer instructions to enable the dynamic regulation system to perform the method described in the first aspect and any possible implementation manner of the first aspect.

[0009] In a third aspect, the embodiments of the present application provide a computer program product comprising instructions which, when executed on a gimbal inspection dynamic regulation system, enable the gimbal inspection dynamic regulation system to perform the method according to the first aspect and any possible implementation manner of the first aspect.

[0010] In a fourth aspect, the embodiments of the present application provide a computer-readable storage medium comprising instructions which, when executed on a gimbal inspection dynamic regulation system, enable the gimbal inspection dynamic regulation system to perform the method according to the first aspect and any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a flowchart of a gimbal inspection dynamic regulation method for a power transmission line in the embodiments of the present application;

[0012] Figure 2 is a schematic diagram of an entity device structure of a gimbal inspection dynamic regulation system in the embodiments of the present application. DETAILED DESCRIPTION

[0013] The terms used in the following embodiments of the present application are only for the purpose of describing the specific embodiments and are not intended to be limiting of the present application. As used in the specification and the appended claims of the present application, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or," as used in the present application, refers to any or all possible combinations of one or more of the associated listed items.

[0014] Hereinafter, the terms "first" and "second" are only for the purpose of description and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0015] The present application provides a gimbal inspection dynamic regulation method for a power transmission line, referring to Figure 1 , Figure 1 is a flowchart of a gimbal inspection dynamic regulation method for a power transmission line in the embodiments of the present application, comprising the following steps:

[0016] In step S101, multi-source data of a target power transmission line is acquired, and the multi-source data is input into a preset dynamic risk assessment model to obtain a dynamic risk heat map output by the preset dynamic risk assessment model;

[0017] Step S102, a spatial characteristic model is established for the plurality of gimbal preset positions of the target power transmission line, and the spatial characteristic model is spatially correlated and analyzed with the dynamic risk heat map to obtain a spatial correlation characteristic parameter of each gimbal preset position in the plurality of gimbal preset positions;

[0018] Step S103, a device state weight value of each gimbal camera equipment in the plurality of gimbal camera equipments of the target power transmission line is determined, and a comprehensive inspection weight value of each gimbal preset position is determined according to the device state weight value and the spatial correlation characteristic parameter, and the plurality of gimbal camera equipments and the plurality of gimbal preset positions have a preset belonging corresponding relationship;

[0019] Step S104, the comprehensive inspection weight value is converted into a target inspection cycle of each gimbal preset position by using a first preset inverse proportional function.

[0020] The target power transmission line represents a specific power transmission line that needs to be inspected and monitored, including but not limited to 220kV high-voltage transmission lines, 500kV ultra-high-voltage transmission lines, 1000kV extra-high-voltage transmission lines, etc. The multi-source data refers to a comprehensive information set from different data sources, including but not limited to meteorological monitoring station data, line sensor data, historical operation and maintenance record data, etc. The preset dynamic risk assessment model is used to represent a risk calculation model based on machine learning or mathematical algorithms, including but not limited to neural network risk assessment models, Bayesian network risk assessment models, support vector machine risk assessment models, etc. The dynamic risk heat map refers to a visual image that represents the risk level distribution in terms of color depth, including but not limited to a heat map with red representing high-risk areas, a heat map with yellow representing medium-risk areas, a heat map with green representing low-risk areas, etc. The gimbal preset position represents a fixed monitoring position and angle preset by the gimbal camera device, including but not limited to tower monitoring preset positions, conductor sag monitoring preset positions, insulator string monitoring preset positions, etc. The spatial characteristic model is used to represent a mathematical model that describes the spatial attributes of the monitoring point, including but not limited to monitoring range geometry models, viewing angle coverage models, terrain obstruction models, etc. The spatial correlation characteristic parameter is a quantitative indicator that reflects the spatial relationship between the monitoring point and the risk distribution, including but not limited to risk coverage parameters, monitoring overlap parameters, spatial distance weight parameters, etc. The gimbal camera device represents an intelligent monitoring device with rotation and zoom functions, including but not limited to high-definition network gimbal cameras, infrared thermal imaging gimbal cameras, laser night vision gimbal cameras, etc. The device state weight value is a quantitative score that represents the current working capacity of the device, including but not limited to device health weight values, power sufficiency weight values, communication stability weight values, etc. The comprehensive inspection weight value is a priority value determined by considering multiple factors, including but not limited to high-risk area comprehensive weight values, device failure area comprehensive weight values, and harsh environment area comprehensive weight values. The first preset inverse proportional function represents a mathematical function that is inversely proportional to the cycle, including but not limited to y=k / x type inverse proportional function, y=k / (x+a) type modified inverse proportional function, y=k / x² type quadratic inverse proportional function, etc. The target inspection cycle represents the final inspection time interval for each monitoring point, including but not limited to a 30-minute inspection cycle for high-risk areas, a 60-minute inspection cycle for medium-risk areas, and a 120-minute inspection cycle for low-risk areas, etc.

[0021] In the above embodiment, taking the dynamic regulation and control of a certain 220 kilovolt (kV) high-voltage transmission line as an example, the transmission line is 45 kilometers long, passes through three terrains of mountains, hills and plains, and a total of 15 pan-tilt camera monitoring points are set. First, multi-source data of the 220 kV transmission line are collected. Historical fault data can be obtained through the power dispatching data network, including 12 tripping events, 8 insulator flashover faults and 5 conductor galloping abnormal records of the line in the past three years. Real-time environmental monitoring data are collected in real time through a wireless sensor network deployed on each tower, and the monitoring parameters include environmental temperature (-20°C to 45°C), relative humidity (30% to 95%), wind speed (0 to 25 meters / second) and wind direction (0° to 360°). Weather forecast data are obtained from a certain weather department application program interface, focusing on rainfall probability, thunderstorm warning and gale forecast information within the next 72 hours. Topographic data are obtained by laser radar scanning using a digital elevation model with a resolution of 1 meter, containing altitude, slope and vegetation coverage information along the line.

[0022] In the above embodiment, the above multi-source data are input into a preset dynamic risk assessment model based on a long short-term memory network. The preset dynamic risk assessment model divides the 45-kilometer transmission line into 4500 square grid cells with a side length of 10 meters. For each grid cell, the annual failure probability of the region is extracted from the historical fault data, such as the historical failure probability of the 1200th grid cell, which is 0.08 (i.e., 8%). The current environmental risk factor is calculated from the real-time environmental monitoring data, and when the environmental temperature exceeds 35°C and the relative humidity is less than 40%, the environmental risk factor of the grid is set to 0.7. The weather influence factor is obtained from the weather forecast data, and when the thunderstorm probability is more than 60% within the next 24 hours, the weather influence factor of the related grid is set to 0.8. The terrain risk factor is extracted from the topographic data, and the terrain risk factor of the mountain grid with a slope of more than 30° is set to 0.6. A weighted fusion formula is used: grid risk value = 0.3 x historical failure probability + 0.4 x current environmental risk factor + 0.2 x weather influence factor + 0.1 x terrain risk factor, to calculate the grid risk value between 0 and 1 for each grid cell. The dynamic risk heat map is updated every 15 minutes, and the risk value is visualized by red (high risk, 0.7-1.0), yellow (medium risk, 0.4-0.7) and green (low risk, 0.0-0.4).

[0023] In the above embodiment, the hardware parameters of 15 PTZ camera devices are acquired, including installation height (15-30 meters), horizontal field of view (120°), vertical field of view (60°), maximum monitoring distance (500 meters), and zoom ratio (30 times optical zoom). The terrain occlusion information within the preset monitoring angle range of each PTZ preset position is acquired by a laser radar scanning device carried by a UAV, and the three-dimensional coordinates and geometric shapes of occlusions such as mountains, buildings, and tall vegetation are identified. A three-dimensional occlusion model is constructed according to these occlusion information, and a ray tracing algorithm is used to calculate the line-of-sight occlusion relationship. Combined with the hardware parameters of the PTZ camera device and the three-dimensional occlusion model, the effective monitoring range of each PTZ preset position is determined. For example, the theoretical monitoring range of the No. 5 PTZ preset position is a fan-shaped area with a radius of 500 meters, but it is affected by the mountain occlusion in front of 200 meters, and the actual effective monitoring range is reduced to the power transmission line section within a range of 300 meters. The effective monitoring range of each PTZ preset position is spatially overlaid and analyzed with the dynamic risk heat map, the arithmetic mean of the risk values of all grid cells in the covered area is calculated, and the coverage risk average of the preset position is obtained. Among the 120 grid cells covered by the No. 5 PTZ preset position, the total risk value is 72.5, so the coverage risk average is 0.604. The spatial overlap analysis of the 15 PTZ preset positions is performed, the overlap area of each preset position with other preset positions is calculated as a proportion of the effective monitoring range of the preset position, and the redundancy rate is determined. The No. 5 PTZ preset position overlaps with the No. 4 and No. 6 preset positions in monitoring, and the overlap area accounts for 25% of its effective monitoring range, so the redundancy rate is 0.25. The coverage risk average and the redundancy rate are used as spatial correlation feature parameters.

[0024] In the above embodiment, the health status parameters, the remaining power parameters and the communication quality parameters of 15 PTZ camera devices are collected in real time. The health status parameters include device operating temperature (normal range -10℃ to 50℃), motor rotation times (cumulative rotation times do not exceed 1 million times) and lens cleanliness (evaluated by image sharpness algorithm, sharpness index 0 to 100). The remaining power parameters can be obtained through the battery management system, showing the current battery power percentage and the estimated working time. The communication quality parameters include network signal strength (represented by received signal strength indication RSSI value, range -100dBm to -30dBm), data transmission success rate (should be kept above 95%) and network delay (should be below 100 milliseconds). According to the three types of parameters, the state quantization processing is carried out for each PTZ camera device, the normalization method is used to convert each parameter to a value between 0 and 1, and then the weighted average calculation is carried out to obtain the device state weight value. The health status score of the 5th PTZ camera device is 0.85, the remaining power score is 0.90, the communication quality score is 0.80, and the weighted average device state weight value is 0.85. According to the preset attribution corresponding relationship, the 5th PTZ camera device corresponds to the 5th PTZ preset position, so the device state weight value 0.85 is assigned to the 5th PTZ preset position. Using the exponential function f(x)=1-e^(-2x) as the preset nonlinear mapping function, the first weight conversion processing is carried out on the coverage risk average 0.604 of the 5th PTZ preset position: f(0.604)=1-e^(-2×0.604)=1-e^(-1.208)=1-0.299=0.701, and the risk weight value is 0.70. Using the inverse proportion function g(x)=1 / (1+x) as the second preset inverse proportion function, the second weight conversion processing is carried out on the redundancy rate 0.25: g(0.25)=1 / (1+0.25)=1 / 1.25=0.8, and the redundancy weight value is 0.80. Using the preset fusion coefficient (risk weight coefficient 0.5, redundancy weight coefficient 0.3, device state weight coefficient 0.2) to weighted sum the risk weight value, the redundancy weight value and the device state weight value: comprehensive inspection weight value=0.5×0.70+0.3×0.80+0.2×0.85=0.76.

[0025] In the above embodiment, the integrated inspection weight value 0.76 of the No. 5 PTU preset position is converted into an initial inspection period T = 120 / 0.76 = 158 minutes by using the first preset inverse proportional function T = 120 / W (where T is the inspection period in minutes, and W is the integrated inspection weight value). The basic inspection period of the No. 5 PTU preset position is 180 minutes, and the minimum inspection period is 30 minutes. It is determined that the initial inspection period 158 minutes is not less than the minimum inspection period 30 minutes. It is further determined that the initial inspection period 158 minutes is not greater than the basic inspection period 180 minutes. Since the initial inspection period 158 minutes is between the minimum inspection period 30 minutes and the basic inspection period 180 minutes, the initial inspection period 158 minutes is taken as the target inspection period of the No. 5 PTU preset position.

[0026] Through the above steps, the multi-source data is processed by the preset dynamic risk assessment model to generate a dynamic risk heat map, which can realize real-time quantitative characterization of the risk status of the power transmission line. The spatial correlation analysis of the spatial characteristic model and the dynamic risk heat map enables each PTU preset position to accurately obtain the risk distribution characteristics in its monitoring range, forming spatial correlation characteristic parameters. The equipment state weight value reflects the actual working capacity of the PTU camera equipment, and the spatial correlation characteristic parameters are combined to calculate the integrated inspection weight value, ensuring that the inspection resource allocation considers both risk demand and equipment capacity. The first preset inverse proportional function converts the integrated inspection weight value into the target inspection period, realizing the differentiated scheduling strategy of short-cycle intensive inspection in high-weight areas and long-cycle sparse inspection in low-weight areas. This dynamic weight-driven period adjustment mechanism can change the traditional fixed-cycle static scheduling mode, so that the inspection frequency can be adaptively adjusted according to the real-time risk status and equipment state. Further, the technical problem of low inspection efficiency of the PTU camera equipment in the related art is solved, and the technical effect of improving the inspection efficiency of the PTU camera equipment is achieved.

[0027] The execution subject of the above steps can be a system, such as a PTU inspection dynamic regulation system, or a device, such as a PTU inspection dynamic regulation device, or a controller or processor in the device or system, or a controller or processor existing alone, or other processing devices or processing units with similar processing functions, but is not limited thereto.

[0028] In an optional embodiment, a spatial characteristic model is established for a plurality of gimbal preset positions of a target power transmission line, and the spatial characteristic model is spatially correlated with a dynamic risk heat map to obtain spatial correlation characteristic parameters of each gimbal preset position in the plurality of gimbal preset positions, specifically including: obtaining hardware parameters of the plurality of gimbal camera devices; using a three-dimensional measuring device to obtain terrain occlusion information in a preset monitoring angle range of each gimbal preset position, and constructing a three-dimensional occlusion model according to the terrain occlusion information; determining an effective monitoring range of each gimbal preset position according to the hardware parameters and the three-dimensional occlusion model, the effective monitoring range of each gimbal preset position being a power transmission line section after excluding an occluded area; performing spatial superposition analysis on the effective monitoring range of each gimbal preset position and the dynamic risk heat map to obtain a coverage risk average of each gimbal preset position; performing spatial overlap analysis on the plurality of gimbal preset positions according to the effective monitoring range of each gimbal preset position to determine a redundancy rate of each gimbal preset position, the redundancy rate being a proportion of a monitoring overlap range of the effective monitoring range of each gimbal preset position and the effective monitoring range of a remaining gimbal preset position in the plurality of gimbal preset positions to the effective monitoring range of each gimbal preset position; and taking the coverage risk average and the redundancy rate as the spatial correlation characteristic parameters.

[0029] The hardware parameters represent the technical specifications and performance indicators of the PTZ camera equipment, including but not limited to lens focal length parameters, image resolution parameters, rotation angle range parameters, etc. The three-dimensional measurement equipment refers to a measurement instrument or measurement equipment that can obtain spatial three-dimensional coordinate information, including but not limited to laser radar measurement equipment, stereo photogrammetry equipment, total station measurement equipment, unmanned aerial vehicle, etc. The preset monitoring view angle range represents the monitoring coverage area of the PTZ camera equipment at a specific preset position, including but not limited to a monitoring view angle range of 120 degrees horizontally and 60 degrees vertically, a monitoring view angle range of 180 degrees horizontally and 90 degrees vertically, a monitoring view angle range of 360 degrees horizontally and 180 degrees vertically, etc. The terrain occlusion information is used to represent spatial data of terrain features that block the monitoring line of sight, including but not limited to mountain occlusion information, building occlusion information, vegetation occlusion information, etc. The three-dimensional occlusion model refers to a three-dimensional digital model that describes the spatial occlusion relationship, including but not limited to a digital elevation occlusion model, a building three-dimensional occlusion model, a vegetation canopy occlusion model, etc. The effective monitoring range represents the actual monitorable area excluding the influence of occlusion, including but not limited to the effective monitoring range of the tower body, the effective monitoring range of the conductor suspension section, the visible effective monitoring range of the insulator, etc. The spatial overlay analysis is used to represent the analysis method of overlapping calculation of different spatial data layers, including but not limited to raster data overlay analysis, vector data overlay analysis, point-line-surface element overlay analysis, etc. The coverage risk average refers to the average level of risk values in the monitoring range, including but not limited to the tower area coverage risk average, the conductor area coverage risk average, the insulator area coverage risk average, etc. The spatial overlap analysis represents the analysis process of calculating the intersection relationship of multiple spatial regions, including but not limited to monitoring range overlap analysis, risk region overlap analysis, equipment coverage overlap analysis, etc. The redundancy rate is used to represent the proportion index of repeated configuration of monitoring resources, including but not limited to monitoring view angle redundancy rate, equipment deployment redundancy rate, patrol task redundancy rate, etc.

[0030] In the above embodiment, taking the dynamic regulation and control of a cloud platform inspection of a 500 kilovolt (kV) extra-high voltage transmission line as an example, the transmission line is 78 kilometers long, passes through three types of terrain, i.e., mountainous areas, valleys, and suburbs, and a total of 24 cloud platform camera devices are set up at monitoring points. Detailed hardware parameters of the 24 cloud platform camera devices are obtained. The lens focal length parameters include a minimum focal length of 4.3 millimeters and a maximum focal length of 129 millimeters, achieving a 30 times optical zoom function. The image resolution parameters are a full high definition resolution of 1920 x 1080 pixels, supporting video acquisition at 25 frames per second. The rotation angle range parameters include a horizontal rotation range of 0° to 360° continuous rotation, a vertical rotation range of -15° to 90°, a horizontal rotation speed of 0.1° / second to 120° / second adjustable, and a vertical rotation speed of 0.1° / second to 120° / second adjustable. The installation height parameters are different from 18 meters to 35 meters from the ground, and are determined according to the height of the tower. The maximum monitoring distance parameters are up to 800 meters in the maximum zoom state and 200 meters in the minimum zoom state. The field of view angle parameters include a horizontal field of view angle of 62.7° at the minimum focal length and 2.3° at the maximum focal length, and a vertical field of view angle of 35.4° at the minimum focal length and 1.3° at the maximum focal length. The power supply mode parameters are a combination of solar panels and lithium battery energy storage, with a battery capacity of 200 ampere-hours (Ah). The communication interface parameters support dual connection modes of an Ethernet interface and a fourth generation wireless communication.

[0031] In the above embodiment, the preset monitoring view range of the 24 PTZ preset positions is scanned by a laser radar measuring device. The laser radar device is a pulse laser radar with a measurement accuracy of ±2 cm, a measurement range of 1000 meters, and a point cloud density of 100 measurement points per square meter. The multi-rotor unmanned aerial vehicle carries the laser radar device and performs three-dimensional scanning within a 500-meter range around each PTZ preset position according to the preset flight path. During the scanning process, the unmanned aerial vehicle maintains a flight height of 120 meters and a flight speed of 5 meters per second, and the scanning strip overlap rate is 30%. The terrain occlusion information obtained by laser radar scanning includes mountain occlusion information, building occlusion information, and vegetation occlusion information. The mountain occlusion information shows that there is a mountain ridge with a height of 45 meters 300 meters in front of the 8th PTZ preset position, with an occlusion angle of 15°. The building occlusion information shows that there is a substation building with a height of 12 meters 200 meters southeast of the 15th PTZ preset position, with an occlusion angle of 8°. The vegetation occlusion information shows that there is a poplar forest belt with an average height of 8 meters around the 20th PTZ preset position, with a local occlusion angle of 5°. The point cloud data obtained by laser radar scanning is filtered to remove noise points and abnormal points, and then a digital elevation occlusion model is constructed using a triangular meshing algorithm. For building occlusion, a cube geometric model is used for simplified representation, recording the length, width, height, and azimuth angle parameters of the building. For vegetation occlusion, a vegetation canopy occlusion model is used, simplifying the tree crown to an ellipsoid geometric shape, recording the long axis, short axis, height, and center coordinate parameters of the ellipsoid.

[0032] In the above embodiment, the effective monitoring range of each PTZ preset position is calculated according to the hardware parameters and the three-dimensional occlusion model. Taking the 8th PTZ preset position as an example, the installation height of this preset position is 25 meters, and the horizontal field of view angle is 15° and the vertical field of view angle is 8.5° in the medium zoom state (focal length is 50 mm), and the effective monitoring distance is 400 meters. First, calculate the theoretical monitoring range of the preset position under the condition of no occlusion, which is a conical region with the preset position as the vertex, the horizontal opening angle of 15°, the vertical opening angle of 8.5°, and the depth of 400 meters. Then use the ray tracing algorithm to calculate the line-of-sight occlusion relationship, and emit virtual rays from the preset position to the key points of the transmission line within the monitoring range, and judge whether the rays intersect with the three-dimensional occlusion model. For the 8th PTZ preset position, the mountain ridge 300 meters in front of it occludes part of the line of sight, causing the transmission line within a 50-meter range behind the mountain ridge to be unable to be effectively monitored. After the occlusion calculation, the effective monitoring range of the 8th PTZ preset position is the transmission line section after removing the occluded area, specifically including the conductor suspension section within the 0-300 meter range in front of the preset position, the 45th to 48th tower bodies, and the visible area of the corresponding insulator strings. The effective monitoring range of the 24 PTZ preset positions is calculated one by one, and the results are stored in the geographic information system database in the format of vector polygons.

[0033] In the above embodiment, the effective monitoring range of each PTZ preset position is spatially overlaid and analyzed with the dynamic risk heat map. The dynamic risk heat map adopts a grid data format, with a grid resolution of 10 meters x 10 meters, and each grid cell contains a risk value between 0 and 1. Using the grid data overlay analysis method, the effective monitoring range polygon of the No. 8 PTZ preset position is spatially intersected with the risk heat map. The intersection operation result shows that the effective monitoring range of the No. 8 PTZ preset position covers 156 grid cells. The risk values of these 156 grid cells are extracted, which are 0.45, 0.52, 0.38, 0.61, 0.49, etc. The arithmetic mean of the risk values of these 156 grid cells is calculated: the coverage risk mean value = (0.45 + 0.52 + 0.38 +... + 0.43) / 156 = 0.487. Spatial overlay analysis is performed on the 24 PTZ preset positions one by one, and the coverage risk mean value of each preset position is obtained. The coverage risk mean value of the No. 1 PTZ preset position is 0.523, the coverage risk mean value of the No. 2 PTZ preset position is 0.412, the coverage risk mean value of the No. 3 PTZ preset position is 0.678, and so on.

[0034] In the above embodiment, the spatial overlap analysis is performed on the 24 PTZ preset positions according to the effective monitoring range of each PTZ preset position. Taking the 8th PTZ preset position as an example, the effective monitoring range of the 8th PTZ preset position is a strip-shaped area with a length of 350 meters along the direction of the transmission line and a width of 30 meters, and the total area is 10500 square meters. The polygon of the effective monitoring range of the 8th PTZ preset position is subjected to spatial intersection operation with the polygons of the effective monitoring ranges of the 7th and 9th PTZ preset positions. The intersection operation result shows that the monitoring overlap area between the 8th PTZ preset position and the 7th PTZ preset position is 1200 square meters, the monitoring overlap area between the 8th PTZ preset position and the 9th PTZ preset position is 800 square meters, and the 8th PTZ preset position has no monitoring overlap with other PTZ preset positions. Therefore, the total area of the monitoring overlap range is 1200+800=2000 square meters. The redundancy rate of the 8th PTZ preset position is calculated as follows: redundancy rate = total area of monitoring overlap range / total area of effective monitoring range = 2000 / 10500 = 0.190. The spatial overlap analysis is performed on the 24 PTZ preset positions one by one to obtain the redundancy rate of each preset position. The redundancy rate of the 1st PTZ preset position is 0.156, the redundancy rate of the 2nd PTZ preset position is 0.289, the redundancy rate of the 3rd PTZ preset position is 0.201, and so on. The average coverage risk redundancy rate is taken as the spatial correlation characteristic parameter of each PTZ preset position. The spatial correlation characteristic parameters of the 8th PTZ preset position include the average coverage risk 0.487 and the redundancy rate 0.190. The spatial correlation characteristic parameters of the 24 PTZ preset positions are stored in a structured data format to form a spatial correlation characteristic parameter data table. The data table includes three fields of preset position number, average coverage risk, and redundancy rate, which provides a quantitative basis for subsequent comprehensive inspection weight value calculation.

[0035] In an optional embodiment, a device state weight value of each PTZ camera device in the plurality of PTZ camera devices of the target transmission line is determined, and a comprehensive inspection weight value of each PTZ preset position is determined according to the device state weight value and the spatial correlation characteristic parameter, specifically including: collecting health state parameters, remaining power parameters and communication quality parameters of the plurality of PTZ camera devices in real time; performing state quantization processing on each PTZ camera device according to the health state parameters, the remaining power parameters and the communication quality parameters to obtain a device state weight value; assigning the device state weight value to each corresponding PTZ preset position according to a preset attribution corresponding relationship; performing first weight conversion processing on the average coverage risk by using a preset nonlinear mapping function to obtain a risk weight value of each PTZ preset position; performing second weight conversion processing on the redundancy rate by using a second preset inverse proportional function to obtain a redundancy weight value of each PTZ preset position, the redundancy weight value being inversely proportional to the redundancy rate; performing weighted summation on the risk weight value, the redundancy weight value and the device state weight value by using a preset fusion coefficient to obtain the comprehensive inspection weight value.

[0036] The health state parameter represents a health degree index of the device operation state, including but not limited to a device temperature parameter, a vibration amplitude parameter, a running time length parameter, etc.; the residual power parameter is used to represent a quantity index of the current available power of the device, including but not limited to a battery residual capacity parameter, a solar charging efficiency parameter, a power supply line voltage parameter, etc.; the communication quality parameter is a quantitative index of the network communication performance of the device, including but not limited to a signal strength parameter, a data transmission rate parameter, a network delay parameter, etc.; the state quantization processing represents a calculation process of converting multi-dimensional device parameters into a unified numerical value, including but not limited to normalization quantization processing, weighted average quantization processing, fuzzy evaluation quantization processing, etc.; the preset attribution corresponding relationship is used to represent a fixed mapping relationship between the device and the monitoring point, including but not limited to a one-to-one attribution corresponding relationship, a one-to-many attribution corresponding relationship, a many-to-one attribution corresponding relationship, etc.; the preset nonlinear mapping function is a mathematical function of obtaining an output value through nonlinear transformation of an input value, including but not limited to an exponential mapping function, a logarithmic mapping function, a power function mapping function, etc.; the first weight conversion processing represents a calculation process of converting the risk mean value into a weight value, including but not limited to linear weight conversion processing, segmented weight conversion processing, curve fitting weight conversion processing, etc.; the risk weight value is used to represent a weight numerical value determined based on the risk degree, including but not limited to a high-risk area risk weight value, a medium-risk area risk weight value, a low-risk area risk weight value, etc.; the second preset inverse proportional function represents a mathematical function of the redundancy rate and the weight value in an inverse proportional relationship, including but not limited to a simple inverse proportional function, an inverse proportional function with a constant term, a segmented inverse proportional function, etc.; the redundancy weight value is a weight numerical value calculated based on the redundancy rate, including but not limited to a low redundancy high weight value, a medium redundancy medium weight value, a high redundancy low weight value, etc.; and the preset fusion coefficient represents a coefficient parameter used for weighted summation, including but not limited to a risk factor fusion coefficient, a device factor fusion coefficient, a redundancy factor fusion coefficient, etc.

[0037] In the above embodiment, when a single PTZ camera device corresponds to multiple PTZ preset positions, the action of the same camera needs to be scheduled according to the different periods of multiple preset positions. Specifically, an attribution mapping table of PTZ camera devices and multiple preset positions is established, recording the preset position list corresponding to each PTZ camera device and the target inspection period of each preset position; the least common multiple algorithm is used to calculate the unified scheduling period of multiple preset positions, and the unified scheduling period is the least common multiple of the target inspection periods of each preset position; within the unified scheduling period, the inspection frequency of each preset position is calculated according to the target inspection period, and the inspection frequency is equal to the unified scheduling period divided by the target inspection period; a time allocation algorithm based on weight priority is established, and the unified scheduling period is divided into several time slices, and the length of each time slice is equal to half of the smallest inspection period of all preset positions; the preset positions are prioritized according to the comprehensive inspection weight value, and the preset position with a higher weight value obtains a higher scheduling priority; a round-robin scheduling strategy is used to allocate inspection tasks within the time slice, and the inspection requirements of high-weight preset positions are preferentially met, and when multiple preset positions need to perform inspection within the same time slice, they are executed in order according to the weight priority; a dynamic conflict detection mechanism is established to monitor the time conflicts between preset positions in real time, and when a conflict is detected, a re-scheduling is automatically triggered, and the inspection task of a low-priority preset position is delayed to the next available time slice; the deviation between the actual execution time and the planned execution time of each preset position is recorded, and when the deviation exceeds a preset threshold, the time slice allocation strategy is dynamically adjusted to ensure that the inspection frequency of each preset position meets the target requirement. The attribution mapping table is used to represent the corresponding relationship data structure of devices and preset positions, including but not limited to one-to-many mapping table, many-to-one mapping table, many-to-many mapping table, etc.; the unified scheduling period represents the common scheduling time reference of multiple preset positions, including but not limited to 60-minute unified scheduling period, 120-minute unified scheduling period, 180-minute unified scheduling period, etc.; the least common multiple algorithm refers to a mathematical algorithm for calculating the least common multiple of multiple values, including but not limited to Euclidean algorithm, prime factorization algorithm, recursive algorithm, etc.; the time slice represents the smallest time allocation unit within the scheduling period, including but not limited to 5-minute time slice, 10-minute time slice, 15-minute time slice, etc.; the round-robin scheduling strategy is used to represent the scheduling method of cyclically allocating resources in a fixed order, including but not limited to weighted round-robin scheduling strategy, priority round-robin scheduling strategy, fair round-robin scheduling strategy, etc.; the conflict detection mechanism refers to an algorithm mechanism for identifying and handling resource conflicts, including but not limited to real-time conflict detection mechanism, predictive conflict detection mechanism, adaptive conflict detection mechanism, etc.

[0038] In the above embodiment, taking 24 pan-tilt camera devices of a certain 500-kilovolt extra-high voltage transmission line as an example, the device state weight value of each pan-tilt camera device is determined, and the comprehensive inspection weight value is calculated. The state parameters of the 24 pan-tilt camera devices are collected in real time through the embedded sensor and communication module. The health state parameters include device temperature parameters, vibration amplitude parameters and running time parameters. Taking the No. 8 pan-tilt camera device as an example, the device temperature parameter is collected by the built-in temperature sensor, the current temperature is 42 degrees Celsius, and the normal working temperature range is-40 degrees Celsius to 70 degrees Celsius. The vibration amplitude parameter is collected by the three-axis acceleration sensor, the current vibration amplitude is 0.15 meters per second, and the normal vibration amplitude threshold is 0.5 meters per second. The running time parameter records the cumulative running time of the device, the current cumulative running time is 8760 hours, and the designed service life is 50000 hours. The remaining power parameters include the battery remaining capacity parameter, the solar charging efficiency parameter and the power supply line voltage parameter. The battery remaining capacity parameter shows that the current remaining power is 160 ampere-hours, accounting for 80% of the total capacity of 200 ampere-hours. The solar charging efficiency parameter is obtained by monitoring the output power of the photovoltaic panel, the current charging power is 85 watts, and the standard charging power is 100 watts. The power supply line voltage parameter shows that the current input voltage is 13.2 volts, and the standard working voltage is 12 volts. The communication quality parameters include signal strength parameters, data transmission rate parameters and network delay parameters. The signal strength parameter is measured by the received signal strength indication, the current signal strength is-65 decibel-milliwatts, and the good signal strength range is-50 decibel-milliwatts to-70 decibel-milliwatts. The data transmission rate parameter shows that the current uplink rate is 2.5 megabits per second, the downlink rate is 8.3 megabits per second, and the standard transmission rate requirement is 2 megabits per second uplink and 5 megabits per second downlink. The network delay parameter is measured by the Network Time Protocol (NTP), the current network delay is 45 milliseconds, and the normal delay range is 10 milliseconds to 100 milliseconds.

[0039] In the above embodiment, the multi-dimensional device parameters are converted into a unified device state weight value using a normalized quantization processing method. First, each parameter is normalized to convert parameters of different dimensions into dimensionless values between 0 and 1. For the 8th pan-tilt camera device, the device temperature normalized value is calculated as: temperature normalized value = (70-42) / (70-(-40)) = 28 / 110 = 0.255, indicating a safety margin from the high temperature threshold. The vibration amplitude normalized value is calculated as: vibration normalized value = (0.5-0.15) / 0.5 = 0.7, indicating a good vibration state. The running time normalized value is calculated as: running time normalized value = (50000-8760) / 50000 = 0.825, indicating sufficient remaining service life of the device. The battery remaining capacity normalized value is 0.8, the solar charging efficiency normalized value is 85 / 100 = 0.85, and the power supply line voltage normalized value is 13.2 / 12 = 1.0 (upper limit is 1.0). The signal strength normalized value is calculated as: signal strength normalized value = (-50-(-65)) / (-50-(-70)) = 15 / 20 = 0.75, indicating good signal quality. The data transmission rate normalized value is the weighted average of the uplink and downlink rates: transmission rate normalized value = 0.3x(2.5 / 2) + 0.7x(8.3 / 5) = 0.375 + 1.162 = 1.0 (upper limit is 1.0). The network delay normalized value is calculated as: network delay normalized value = (100-45) / (100-10) = 55 / 90 = 0.611. The device state weight value is calculated using a weighted average quantization processing method, with each parameter weight allocated as: health status parameter weight 0.4, remaining power parameter weight 0.4, and communication quality parameter weight 0.2. The health status comprehensive value is (0.255 + 0.7 + 0.825) / 3 = 0.593, the remaining power comprehensive value is (0.8 + 0.85 + 1.0) / 3 = 0.883, and the communication quality comprehensive value is (0.75 + 1.0 + 0.611) / 3 = 0.787. The device state weight value of the 8th pan-tilt camera device is 0.4x0.593 + 0.4x0.883 + 0.2x0.787 = 0.237 + 0.353 + 0.157 = 0.747.

[0040] In the above embodiment, the device state weight value is assigned to the corresponding PTZ preset according to a one-to-one attribution correspondence. Each PTZ camera device corresponds to a main preset, and the 8th PTZ camera device corresponds to the 8th PTZ preset, so the device state weight value of the 8th PTZ preset is 0.747. The state quantization processing is performed on the 24 PTZ camera devices one by one to obtain the corresponding device state weight value. The device state weight value of the 1st PTZ preset is 0.823, the device state weight value of the 2nd PTZ preset is 0.692, the device state weight value of the 3rd PTZ preset is 0.756, and so on. The exponential mapping function f(x) = 1-e^(-2x) is used as the preset nonlinear mapping function to perform the first weight conversion processing on the coverage risk mean value. The exponential mapping function can enhance the weight difference between the high-risk area and the low-risk area, so that the preset with a higher risk value obtains a significantly higher weight. Taking the 8th PTZ preset as an example, the coverage risk mean value is 0.487, and the risk weight value is calculated as: f(0.487) = 1-e^(-2x0.487) = 1-e^(-0.974) = 1-0.378 = 0.622. The first weight conversion processing is performed on the 24 PTZ presets one by one to obtain the risk weight value of each preset. The risk weight value of the 1st PTZ preset is f(0.523) = 1-e^(-1.046) = 1-0.351 = 0.649, the risk weight value of the 2nd PTZ preset is f(0.412) = 1-e^(-0.824) = 1-0.438 = 0.562, and the risk weight value of the 3rd PTZ preset is f(0.678) = 1-e^(-1.356) = 1-0.258 = 0.742.

[0041] In the above embodiment, the second weight conversion processing is performed on the redundancy rate by using the inverse proportional function with constant term g(x) = 1 / (1+x) as the second preset inverse proportional function. The inverse proportional function ensures that the redundancy weight value is inversely proportional to the redundancy rate, and the higher the redundancy weight value is obtained by the preset position with the lower redundancy rate. Taking the No. 8 PTZ preset position as an example, the redundancy rate is 0.190, and the redundancy weight value is calculated as: g(0.190) = 1 / (1+0.190) = 1 / 1.190 = 0.840. The second weight conversion processing is performed on the 24 PTZ preset positions one by one to obtain the redundancy weight value of each preset position. The redundancy weight value of the No. 1 PTZ preset position is g(0.156) = 1 / (1+0.156) = 1 / 1.156 = 0.865, the redundancy weight value of the No. 2 PTZ preset position is g(0.289) = 1 / (1+0.289) = 1 / 1.289 = 0.776, and the redundancy weight value of the No. 3 PTZ preset position is g(0.201) = 1 / (1+0.201) = 1 / 1.201 = 0.833. The risk weight value, the redundancy weight value and the device state weight value are weighted and summed by using the preset fusion coefficient. The preset fusion coefficients are: risk factor fusion coefficient 0.5, redundancy factor fusion coefficient 0.3, and device factor fusion coefficient 0.2. This weight distribution reflects the dominant position of the risk factor, while taking into account the influence of monitoring redundancy and device state. Taking the No. 8 PTZ preset position as an example, the comprehensive inspection weight value is calculated as: comprehensive inspection weight value = 0.5x0.622 + 0.3x0.840 + 0.2x0.747 = 0.311 + 0.252 + 0.149 = 0.712. The weighted sum is calculated one by one for the 24 PTZ preset positions to obtain the comprehensive inspection weight value of each preset position. The comprehensive inspection weight value of the No. 1 PTZ preset position is 0.5x0.649 + 0.3x0.865 + 0.2x0.823 = 0.325 + 0.260 + 0.165 = 0.750, the comprehensive inspection weight value of the No. 2 PTZ preset position is 0.5x0.562 + 0.3x0.776 + 0.2x0.692 = 0.281 + 0.233 + 0.138 = 0.652, and the comprehensive inspection weight value of the No. 3 PTZ preset position is 0.5x0.742 + 0.3x0.833 + 0.2x0.756 = 0.371 + 0.250 + 0.151 = 0.772. The comprehensive inspection weight values of the 24 PTZ preset positions are stored in the form of a data table, providing a quantitative basis for subsequent dynamic inspection frequency scheduling.

[0042] In an optional embodiment, after the risk weight value, the redundancy weight value and the device state weight value are weighted and summed by using the preset fusion coefficient to obtain the comprehensive inspection weight value, the method further comprises: scheduling the pan-tilt camera device according to the target inspection period to generate an inspection execution plan; monitoring the inspection execution of the plurality of pan-tilt camera devices in real time to count the actual inspection frequency value of the plurality of pan-tilt camera devices; comparing the actual inspection frequency value with the planned inspection frequency value in the inspection execution plan to obtain a frequency deviation value; when the frequency deviation value is greater than a preset deviation threshold, adjusting the preset fusion coefficient according to the frequency deviation value, wherein when the actual inspection frequency value is less than the planned inspection frequency value, multiplying the frequency deviation value by a preset enhancement adjustment coefficient to obtain a risk increase adjustment amount, increasing the first risk fusion coefficient in the preset fusion coefficient to a second risk fusion coefficient according to the risk increase adjustment amount, when the actual inspection frequency value is greater than the planned inspection frequency value, multiplying the frequency deviation value by a preset attenuation adjustment coefficient to obtain a risk reduction adjustment amount, and reducing the first risk fusion coefficient to a third risk fusion coefficient according to the risk reduction adjustment amount.

[0043] The patrol task scheduling represents a process of arranging device execution tasks according to a patrol cycle, including but not limited to time series task scheduling, priority task scheduling, load balancing task scheduling, etc. The patrol execution plan is used to represent a detailed patrol task time schedule, including but not limited to daily patrol execution plan, weekly patrol execution plan, monthly patrol execution plan, etc. The actual patrol frequency value refers to the statistical value of the frequency of actual device patrol, including but not limited to hourly actual patrol frequency value, daily actual patrol frequency value, weekly actual patrol frequency value, etc. The planned patrol frequency value represents the frequency value of the expected device to perform the patrol, including but not limited to hourly planned patrol frequency value, daily planned patrol frequency value, weekly planned patrol frequency value, etc. The deviation comparison is used to represent the comparison and analysis of the difference between two values, including but not limited to absolute deviation comparison, relative deviation comparison, standardized deviation comparison, etc. The frequency deviation value refers to the difference between the actual frequency and the planned frequency, including but not limited to positive frequency deviation value, negative frequency deviation value, zero frequency deviation value, etc. The preset deviation threshold value represents the deviation threshold value that triggers the adjustment mechanism, including but not limited to 5% deviation threshold value, 10% deviation threshold value, 15% deviation threshold value, etc. The reverse adjustment is used to represent the parameter correction in the opposite direction according to the deviation, including but not limited to enhanced reverse adjustment, attenuated reverse adjustment, linear reverse adjustment, etc. The preset enhancement adjustment coefficient refers to the coefficient parameter used to amplify the adjustment effect, including but not limited to 1.2 times enhancement adjustment coefficient, 1.5 times enhancement adjustment coefficient, 2.0 times enhancement adjustment coefficient, etc. The risk increase adjustment amount represents the risk weight adjustment value that needs to be increased due to insufficient patrol, including but not limited to slight risk increase adjustment amount, medium risk increase adjustment amount, severe risk increase adjustment amount, etc. The first risk fusion coefficient is used to represent the initial set risk factor weight coefficient. The second risk fusion coefficient refers to the increased risk factor weight coefficient. The preset attenuation adjustment coefficient represents the coefficient parameter used to reduce the adjustment effect, including but not limited to 0.8 times attenuation adjustment coefficient, 0.5 times attenuation adjustment coefficient, 0.3 times attenuation adjustment coefficient, etc. The risk reduction adjustment amount is used to represent the risk weight adjustment value that needs to be reduced due to excessive patrol, including but not limited to slight risk reduction adjustment amount, medium risk reduction adjustment amount, significant risk reduction adjustment amount, etc. The third risk fusion coefficient refers to the reduced risk factor weight coefficient.

[0044] In the above embodiment, taking 24 PTZ camera devices of a certain 500 kV EHV transmission line as an example, after obtaining the comprehensive inspection weight value, a closed-loop feedback optimization mechanism is established. Using a priority task scheduling method, the target inspection period is calculated according to the comprehensive inspection weight value. The basic inspection period is set to 120 minutes, and the minimum inspection period is set to 15 minutes. A reverse proportional function is used for conversion: target inspection period = basic inspection period / comprehensive inspection weight value. Taking the 8th PTZ preset position as an example, the comprehensive inspection weight value is 0.712, and the target inspection period = 120 / 0.712 = 168.5 minutes, which is approximately 169 minutes. The target inspection period of the 1st PTZ preset position = 120 / 0.750 = 160 minutes, and the target inspection period of the 3rd PTZ preset position = 120 / 0.772 = 155 minutes. A 24-hour daily inspection execution plan is generated, and a time series task scheduling algorithm is used to arrange the specific execution time. The planned inspection frequency of the 8th PTZ preset position within 24 hours is 24*60 / 169 = 8.5 times, which is rounded to 9 times, and the specific execution time is 00:00, 02:49, 05:38, 08:27, 11:16, 14:05, 16:54, 19:43, and 22:32. The inspection execution plan is stored in a structured data format, including preset position number, target inspection period, planned execution time, and planned inspection frequency value fields. The daily planned inspection frequency value of the 8th PTZ preset position is 9 times / 24 hours = 0.375 times / hour.

[0045] In the above embodiment, the execution log and state feedback mechanism of the PTZ camera device are used to monitor the inspection execution in real time. The start time, end time, execution state, and completion quality of each inspection task are recorded. Taking the 8th PTZ preset position as an example, 8 actual inspections are monitored within 24 hours, and the specific execution time is 00:00, 02:52, 05:45, 08:35, 11:28, 14:15, 17:05, and 19:58. Due to factors such as device communication delay and mechanical response time, there is a slight deviation between the actual execution time and the planned time. The daily actual inspection frequency value of the 8th PTZ preset position is 8 times / 24 hours = 0.333 times / hour. The actual inspection frequency value of each preset position is calculated by monitoring the 24 PTZ preset positions in real time. The actual inspection frequency value of the 1st PTZ preset position is 0.292 times / hour, and the actual inspection frequency value of the 3rd PTZ preset position is 0.417 times / hour.

[0046] In the above embodiment, the actual inspection frequency value is compared and analyzed with the planned inspection frequency value by using the relative deviation ratio comparison method. The frequency deviation value calculation formula is: frequency deviation value=(actual inspection frequency value-planned inspection frequency value) / planned inspection frequency value. Taking the No. 8 PTU preset position as an example, the frequency deviation value=(0.333-0.375) / 0.375=-0.112, which means that the actual inspection frequency is 11.2% lower than the planned frequency. The No. 1 PTU preset position planned inspection frequency value is 120 / 160=0.375 times / hour, and the frequency deviation value=(0.292-0.375) / 0.375=-0.221, which means that the actual inspection frequency is 22.1% lower than the planned frequency. The No. 3 PTU preset position planned inspection frequency value is 120 / 155=0.387 times / hour, and the frequency deviation value=(0.417-0.387) / 0.387=0.078, which means that the actual inspection frequency is 7.8% higher than the planned frequency. The frequency deviation value is calculated for each of the 24 PTU preset positions, and classified and counted according to the positive and negative of the deviation value. The preset deviation threshold is set to 10%, that is, when the absolute value of the frequency deviation value is greater than 0.1, the reverse adjustment mechanism is triggered. The absolute value of the frequency deviation value of the No. 8 PTU preset position is |-0.112|=0.112>0.1, which exceeds the preset deviation threshold and needs to be adjusted in reverse. The absolute value of the frequency deviation value of the No. 1 PTU preset position is |-0.221|=0.221>0.1, which exceeds the preset deviation threshold. The absolute value of the frequency deviation value of the No. 3 PTU preset position is |0.078|=0.078<0.1, which does not exceed the preset deviation threshold and does not need to be adjusted. Statistics show that among the 24 PTU preset positions, the frequency deviation value of 16 preset positions exceeds the preset deviation threshold, of which 12 preset positions have actual inspection frequency lower than the planned frequency, and 4 preset positions have actual inspection frequency higher than the planned frequency.

[0047] In the above embodiment, for the case that the actual inspection frequency value is less than the planned inspection frequency value, an enhanced reverse adjustment strategy is adopted. The preset enhanced adjustment coefficient is set to 1.5, and the risk increase adjustment amount is calculated. Taking the No. 8 PTZ preset as an example, the risk increase adjustment amount = |frequency deviation value| x preset enhanced adjustment coefficient = 0.112 x 1.5 = 0.168. The first risk fusion coefficient 0.5 is increased to the second risk fusion coefficient. The second risk fusion coefficient = the first risk fusion coefficient + risk increase adjustment amount = 0.5 + 0.168 = 0.668. In order to keep the sum of the fusion coefficients to be 1, the other fusion coefficients are adjusted in proportion: the redundancy factor fusion coefficient is adjusted to 0.3 x (1-0.668) / (1-0.5) = 0.3 x 0.332 / 0.5 = 0.199, and the equipment factor fusion coefficient is adjusted to 0.2 x (1-0.668) / (1-0.5) = 0.2 x 0.332 / 0.5 = 0.133. The adjusted fusion coefficients are: risk factor fusion coefficient 0.668, redundancy factor fusion coefficient 0.199, and equipment factor fusion coefficient 0.133. For the case that the actual inspection frequency value is greater than the planned inspection frequency value, a decay reverse adjustment strategy is adopted. The preset decay adjustment coefficient is set to 0.8, and the risk decrease adjustment amount is calculated. Taking a certain over-inspection preset as an example, assuming that the frequency deviation value is 0.15, the risk decrease adjustment amount = frequency deviation value x preset decay adjustment coefficient = 0.15 x 0.8 = 0.12. The first risk fusion coefficient 0.5 is reduced to the third risk fusion coefficient. The third risk fusion coefficient = the first risk fusion coefficient - risk decrease adjustment amount = 0.5 - 0.12 = 0.38. The other fusion coefficients are adjusted accordingly: the redundancy factor fusion coefficient is adjusted to 0.3 x (1-0.38) / (1-0.5) = 0.3 x 0.62 / 0.5 = 0.372, and the equipment factor fusion coefficient is adjusted to 0.2 x (1-0.38) / (1-0.5) = 0.2 x 0.62 / 0.5 = 0.248. The adjusted fusion coefficients are used to recalculate the comprehensive inspection weight value of the No. 8 PTZ preset. The adjusted comprehensive inspection weight value = 0.668 x 0.622 + 0.199 x 0.840 + 0.133 x 0.747 = 0.415 + 0.167 + 0.099 = 0.681. Compared with the adjusted value of 0.712, the weight value has decreased slightly, because the actual inspection frequency of the preset is insufficient, and the risk factor weight is increased to compensate, but the other factor weights are reduced accordingly, so the comprehensive weight decreases slightly. According to the new comprehensive inspection weight value, the target inspection period is recalculated: the new target inspection period = 120 / 0.681 = 176 minutes. The adjustment result is updated to the inspection execution plan, and the new inspection frequency is applied in the next scheduling period. Through this closed-loop feedback mechanism, the fusion coefficients can be dynamically optimized according to the actual execution effect, and the accuracy and effectiveness of the inspection strategy can be continuously improved.

[0048] In an optional embodiment, the comprehensive inspection weight value is converted into the target inspection period of each PTZ preset position by using a first preset inverse proportional function, specifically including: converting the comprehensive inspection weight value into an initial inspection period by using the first preset inverse proportional function; acquiring a basic inspection period and a minimum inspection period of each PTZ preset position, and judging whether the initial inspection period is less than the minimum inspection period; when the initial inspection period is less than the minimum inspection period, taking the minimum inspection period as the target inspection period; when the initial inspection period is greater than or equal to the minimum inspection period, judging whether the initial inspection period is greater than the basic inspection period; when the initial inspection period is greater than the basic inspection period, taking the basic inspection period as the target inspection period; when the initial inspection period is between the minimum inspection period and the basic inspection period, taking the initial inspection period as the target inspection period.

[0049] wherein the initial inspection period represents a raw period value directly calculated by the inverse proportional function, including but not limited to a 45-minute initial inspection period, a 75-minute initial inspection period, a 105-minute initial inspection period, etc.; the basic inspection period is used to represent a standard inspection time interval of each monitoring point, including but not limited to a 120-minute basic inspection period, a 180-minute basic inspection period, a 240-minute basic inspection period, etc.; and the minimum inspection period refers to the allowed shortest inspection time interval, including but not limited to a 15-minute minimum inspection period, a 30-minute minimum inspection period, a 60-minute minimum inspection period, etc.

[0050] In the above embodiment, taking 24 PTZ camera devices of a certain 500-kilovolt extra-high voltage transmission line as an example, the comprehensive inspection weight value is converted into the target inspection period by using the first preset inverse proportional function. The first preset inverse proportional function f(x)=K / x is used for weight-to-period conversion, wherein K is a preset conversion coefficient. According to the actual needs of transmission line monitoring and the performance characteristics of the device, the conversion coefficient K=90 is set, which is determined based on historical operation data statistical analysis and can reasonably allocate system resources on the premise of ensuring monitoring effect. Taking the 8th PTZ preset position as an example, the comprehensive inspection weight value is 0.712, and the initial inspection period is calculated as: initial inspection period=90 / 0.712=126.4 minutes. The comprehensive inspection weight value of the 1st PTZ preset position is 0.750, and the initial inspection period is 90 / 0.750=120 minutes. The comprehensive inspection weight value of the 2nd PTZ preset position is 0.652, and the initial inspection period is 90 / 0.652=138.0 minutes. The comprehensive inspection weight value of the 3rd PTZ preset position is 0.772, and the initial inspection period is 90 / 0.772=116.6 minutes. The initial inspection period of the 24 PTZ preset positions is calculated one by one to obtain the raw period value of each preset position.

[0051] In the above embodiment, the basic inspection period and the minimum inspection period are set according to the voltage level, importance and geographical environment characteristics of the power transmission line. For 500 kV extra-high voltage power transmission lines, the basic inspection period is set to 180 minutes, which is determined based on power industry standards and operation and maintenance experience, and can meet the needs of routine monitoring. The minimum inspection period is set to 30 minutes, which mainly considers the mechanical life of the pan-tilt camera device and the system processing capacity, to avoid excessive frequent inspection operations causing damage to the device. These parameters are stored in the configuration database, supporting individualized settings according to different line characteristics. For special sections, such as preset positions across important traffic arteries or located in geological disaster-prone areas, a shorter minimum inspection period, such as 15 minutes, is allowed to be set to improve the monitoring density. The initial inspection period of each pan-tilt preset position is checked for boundary conditions. Taking the 8th pan-tilt preset position as an example, the initial inspection period is 126.4 minutes, the minimum inspection period is 30 minutes, and the judgment condition is 126.4>30, so the initial inspection period is greater than the minimum inspection period. The initial inspection period of the 1st pan-tilt preset position is 120 minutes, the judgment condition is 120>30, and it is also greater than the minimum inspection period. The initial inspection period of the 3rd pan-tilt preset position is 116.6 minutes, the judgment condition is 116.6>30, and it is greater than the minimum inspection period. Assuming that the comprehensive inspection weight value of a high-risk preset position is 3.2, the initial inspection period = 90 / 3.2 = 28.1 minutes, and the judgment condition is 28.1<30, at this time the initial inspection period is less than the minimum inspection period.

[0052] In the above embodiments, for the case where the initial inspection period is less than the minimum inspection period, the minimum inspection period is taken as the target inspection period. Taking the high-risk preset position as an example, although the initial inspection period calculation result is 28.1 minutes, it is less than the minimum inspection period of 30 minutes, so the target inspection period is set to 30 minutes. When this adjustment is performed, the adjustment reason and the value change before and after the adjustment are recorded in the logging system, facilitating subsequent performance analysis and parameter optimization. This processing method ensures the safe operation of the device and avoids excessive frequent inspection operations that can cause premature wear of mechanical parts or overload of system resources. For the preset position whose initial inspection period is greater than or equal to the minimum inspection period, it is further judged whether it is greater than the basic inspection period. Taking the No. 8 PTZ preset position as an example, the initial inspection period is 126.4 minutes, the basic inspection period is 180 minutes, and the judgment condition is 126.4 < 180, so the initial inspection period is less than the basic inspection period. The initial inspection period of the No. 1 PTZ preset position is 120 minutes, the judgment condition is 120 < 180, which is less than the basic inspection period. The initial inspection period of the No. 2 PTZ preset position is 138.0 minutes, the judgment condition is 138.0 < 180, which is less than the basic inspection period. Assuming that the comprehensive inspection weight value of a certain low-risk preset position is 0.45, the initial inspection period = 90 / 0.45 = 200 minutes, and the judgment condition is 200 > 180, at this time the initial inspection period is greater than the basic inspection period.

[0053] In the above embodiment, for the case where the initial inspection cycle is greater than the basic inspection cycle, the basic inspection cycle is taken as the target inspection cycle. Taking the above low-risk preset position as an example, although the initial inspection cycle calculation result is 200 minutes, since it is greater than the basic inspection cycle of 180 minutes, the target inspection cycle is set to 180 minutes. This processing mode ensures that even low-risk areas can maintain basic monitoring frequency, avoiding missing potential safety hazards due to too long inspection intervals. Similarly, such adjustments are recorded in the log to provide decision-making reference for operation and maintenance personnel. For preset positions with initial inspection cycles between the minimum inspection cycle and the basic inspection cycle, the initial inspection cycle is directly taken as the target inspection cycle. Taking the No. 8 PTZ preset position as an example, the initial inspection cycle is 126.4 minutes, which satisfies the condition of 30≤126.4≤180, so the target inspection cycle is 126.4 minutes, which is rounded to 126 minutes for ease of scheduling management. The No. 1 PTZ preset position has an initial inspection cycle of 120 minutes, which satisfies the boundary condition, so the target inspection cycle is 120 minutes. The No. 3 PTZ preset position has an initial inspection cycle of 116.6 minutes, which satisfies the boundary condition, so the target inspection cycle is 117 minutes. After completing the target inspection cycle calculation of all 24 PTZ preset positions, a structured allocation table is generated. This table contains fields such as preset position number, comprehensive inspection weight value, initial inspection cycle, adjustment type, and target inspection cycle. The record of the No. 8 PTZ preset position is: preset position number 8, comprehensive inspection weight value 0.712, initial inspection cycle 126.4 minutes, adjustment type "normal range", and target inspection cycle 126 minutes. This allocation table is transmitted to the task scheduling module for generating specific inspection execution plans. Through this hierarchical constraint conversion mechanism, intensive monitoring of high-risk areas is ensured, while avoiding resource waste and equipment damage in extreme cases, achieving scientific allocation of inspection resources.

[0054] In an optional embodiment, multi-source data of the target power transmission line is acquired, and the multi-source data is input into a preset dynamic risk assessment model to obtain a dynamic risk heat map output by the preset dynamic risk assessment model, specifically including: acquiring historical fault data, real-time environmental monitoring data, weather forecast data, and topographic and geomorphic data of the target power transmission line as the multi-source data; dividing the target power transmission line into a plurality of grid units; acquiring a historical fault probability of each grid unit in the plurality of grid units from the historical fault data, acquiring a current environmental risk factor of each grid unit from the real-time environmental monitoring data, acquiring a weather influence factor of each grid unit from the weather forecast data, and acquiring a topographic risk factor of each grid unit from the topographic and geomorphic data; performing weighted fusion on the historical fault probability, the current environmental risk factor, the weather influence factor, and the topographic risk factor to obtain a grid risk value of each grid unit; generating a dynamic risk heat map according to the grid risk value, and updating the dynamic risk heat map at a preset time interval.

[0055] wherein the historical fault data represent records of past device failures and line abnormalities, including but not limited to insulator flashover fault data, conductor breakage fault data, tower tilt fault data, etc.; the real-time environmental monitoring data are used to represent real-time collection information of current environmental conditions, including but not limited to temperature and humidity monitoring data, wind speed and direction monitoring data, contamination monitoring data, etc.; the weather forecast data refer to prediction information of future weather conditions, including but not limited to rainfall forecast data, lightning activity forecast data, gale weather forecast data, etc.; the topography and geomorphology data represent spatial feature information of geographical environment, including but not limited to altitude data, slope and aspect data, geological structure data, etc.; the grid cell is used to represent a spatial analysis basic unit formed by dividing the transmission line corridor area according to a certain size standard, and each grid cell carries risk assessment data, environmental monitoring information and geographical feature parameters in the region, including but not limited to 100m x 100m grid cell, 500m x 500m grid cell, 1km x 1km grid cell, etc.; the historical fault probability refers to the possibility of failure based on historical data statistics, including but not limited to annual fault probability, quarterly fault probability, monthly fault probability, etc.; the current environmental risk factor represents the influence degree of real-time environment on device safety, including but not limited to high temperature environmental risk factor, high humidity environmental risk factor, strong wind environmental risk factor, etc.; the weather influence factor is used to represent the influence degree of weather conditions on line operation, including but not limited to lightning weather influence factor, ice and snow weather influence factor, gale weather influence factor, etc.; the topographic risk factor refers to the influence degree of topography and geomorphology on line safety, including but not limited to mountainous topographic risk factor, valley topographic risk factor, plain topographic risk factor, etc.; the weighted fusion represents a calculation method of data merging according to different weight coefficients, including but not limited to linear weighted fusion, nonlinear weighted fusion, adaptive weighted fusion, etc.; the grid risk value is used to represent the comprehensive risk degree value of each grid cell, including but not limited to high-risk grid risk value, medium-risk grid risk value, low-risk grid risk value, etc.; the preset time interval refers to a fixed time period of heat map updating, including but not limited to 15-minute preset time interval, 30-minute preset time interval, 60-minute preset time interval, etc.

[0056] In the above embodiment, a 500 kV EHV transmission line is taken as an example. The line is 85 km long and passes through three types of terrain: mountains, hills, and plains. Multi-source data is acquired and a dynamic risk heat map is generated. Historical fault data is obtained from a power company's Production Management System (PMS). The historical fault data covers nearly five years of fault records, including insulator flashover fault data, conductor strand breakage fault data, and tower tilt fault data. The insulator flashover fault data records 138 flashover events, mainly concentrated in the 23rd-35th tower section, which is located in an area with heavy industrial pollution. The conductor strand breakage fault data records 12 strand breakage events, 8 of which occurred in the mountain section of the 56th-68th tower, mainly due to strong winds and icing. The tower tilt fault data records 3 tilt events, all of which occurred in the river valley area with poor geological conditions. Real-time environmental monitoring data is obtained through environmental monitoring stations deployed on the towers. The temperature and humidity monitoring data shows that the current environmental temperature is 28°C and the relative humidity is 75%, which is collected every 10 minutes by a digital temperature and humidity sensor. The wind speed and direction monitoring data shows that the current wind speed is 12 m / s and the wind direction is southwest, which is measured in real time by an ultrasonic wind speed and direction instrument. The contamination level monitoring data is obtained by an equivalent salt deposit density measuring device, and the current contamination level is III, with an equivalent salt deposit density of 0.15 mg / cm2. Weather forecast data is obtained from the meteorological department through a weather data interface. The rainfall forecast data shows that the expected rainfall in the next 24 hours is 15 mm, with an 80% probability of rainfall. The lightning activity forecast data shows that the lightning activity intensity in the next 6 hours is at a medium level, with a lightning density of 2 times / km2 / hour. The gale weather forecast data shows that the maximum wind speed in the next 12 hours can reach 18 m / s, with a duration of about 4 hours.

[0057] In the above embodiment, topographic data is obtained from a geographic information system database. Elevation data is obtained through a digital elevation model, with a range of 180-850 meters in elevation change along the line. Slope and aspect data shows that the maximum slope along the line is 35 degrees, with a predominant southeast aspect. Geological structure data shows that the line crosses three geological fault zones, with the 45-52 pole segment located in an active fault zone. An adaptive meshing algorithm is used to determine mesh size based on terrain complexity and line importance. For flat sections, a mesh cell size of 500m x 500m is set, resulting in 156 mesh cells. For hilly sections, considering the complex terrain changes, a mesh cell size of 300m x 300m is set, resulting in 278 mesh cells. For mountainous sections, due to the dramatic terrain changes and high risk of failure, a mesh cell size of 200m x 200m is set, resulting in 425 mesh cells. The entire transmission line is divided into 859 planned mesh cells, each precisely located by the Geographic Coordinate System (GCS). Each mesh cell is assigned a unique identifier (UID) in the format "section code-mesh number", such as "MT-001" for the first mesh cell in the mountain section. Historical failure probabilities for each mesh cell are calculated from historical failure data using spatial interpolation algorithms. Take mesh cell MT-025 as an example, there have been 2 insulator flashover failures and 1 conductor breakage failure in this mesh within the past 5 years, with an annual failure probability of 3 / (5x1)=0.6 times / year. The annual failure probability is normalized to a value within the range of 0-1, with the historical failure probability of mesh MT-025 being 0.6 / 1.2=0.5, where 1.2 is the highest annual failure probability for the entire line.

[0058] In the above embodiment, the current environmental risk factor is extracted from real-time environmental monitoring data. The high-temperature environmental risk factor is calculated according to the temperature threshold, and the risk factor is 1.0 when the temperature exceeds 35 degrees Celsius, and the current 28 degrees Celsius corresponds to a risk factor of 0.3. The high-humidity environmental risk factor is calculated according to the relative humidity, and the risk factor is 1.0 when the humidity exceeds 90%, and the current 75% humidity corresponds to a risk factor of 0.6. The strong wind environmental risk factor is calculated according to the wind speed level, and the risk factor is 1.0 when the wind speed exceeds 20 meters / second, and the current 12 meters / second corresponds to a risk factor of 0.4. The current environmental risk factor of the MT-025 grid is calculated by weighted average: (0.3x0.3+0.6x0.4+0.4x0.3)=0.45. The weather influence factor is calculated from weather forecast data. The lightning weather influence factor is calculated according to the lightning density and duration, and the predicted lightning density of 2 times per square kilometer per hour corresponds to an influence factor of 0.4. The ice and snow weather influence factor is determined according to the temperature and humidity conditions, and there is no icing risk under the current conditions, and the influence factor is 0. The strong wind weather influence factor is calculated according to the predicted wind speed, and the predicted maximum wind speed of 18 meters / second corresponds to an influence factor of 0.7. The weather influence factor of the MT-025 grid is (0.4x0.5+0x0.2+0.7x0.3)=0.41. The terrain risk factor is calculated from the terrain data. The mountain terrain risk factor is calculated according to the altitude and slope, and the MT-025 grid has an altitude of 650 meters and a slope of 25 degrees, corresponding to a terrain risk factor of 0.7. The valley terrain risk factor considers the geological stability, and the grid is not located in the valley, so the risk factor is 0. The plain terrain risk factor is the lowest, which is 0.1. The terrain risk factor of the MT-025 grid is 0.7.

[0059] In the above embodiment, the linear weighted fusion method is used to calculate the grid risk value of each grid unit. According to the actual experience of power transmission line risk assessment, the weight coefficient is set as: historical failure probability weight 0.25, current environmental risk factor weight 0.35, weather influence factor weight 0.25, terrain risk factor weight 0.15. Taking the MT-025 grid as an example, the grid risk value is calculated as: 0.25x0.5+0.35x0.45+0.25x0.41+0.15x0.7=0.125+0.158+0.103+0.105=0.491. The risk value is converted to a standardized value of 0-100, and the grid risk value of the MT-025 grid is 0.491x100=49.1. The risk value of 859 grid units is calculated one by one. The grid risk value of the plain section ranges from 15 to 35, with an average of 25. The grid risk value of the hilly section ranges from 25 to 55, with an average of 38. The grid risk value of the mountain section ranges from 35 to 75, with an average of 52. A total of 85 high-risk grids with risk values exceeding 60 are identified, mainly distributed in the mountain section and the industrial pollution area.

[0060] In the above embodiment, a Cartesian coordinate system is established with the starting point of the transmission line as the origin, the line direction as the positive direction of the X-axis, and the direction perpendicular to the line direction as the Y-axis. The center point coordinates and corresponding grid risk values of the 859 grid cells are input into the interpolation algorithm as discrete data points. The inverse distance weighted interpolation method is used to perform spatial interpolation on the discrete grid risk values. The mathematical expression of the interpolation algorithm is: Z(x, y) =∑[wi×Zi] / ∑wi, where wi=1 / di^p, di is the distance from the interpolation point to the known data point, and p is the distance weight index. Assuming that p=2 is set, and taking the coordinate point (1250, 800) as an example, the nearest four grid cells around this point are MT-023 (risk value 45.2, distance 120 meters), MT-024 (risk value 52.8, distance 95 meters), MT-025 (risk value 49.1, distance 110 meters), and MT-026 (risk value 47.6, distance 135 meters). The weight of each grid cell on the interpolation point is calculated as follows: w1=1 / 120 2 =6.94×10 -6 , w2=1 / 95 2 =1.11×10 -4 , w3=1 / 110 2 =8.26×10 -5 , w4=1 / 135 2 =5.43×10 -5 . The risk value of the interpolation point is calculated as: Z(1250, 800)=(6.94×10 -6 ×45.2+1.11×10 -4 ×52.8+8.26×10 -5 ×49.1+5.43×10 -5 ×47.6) / (6.94×10 -6 +1.11×10 -4 +8.26×10 -5 +5.43×10 -5 )=50.3. A heat map canvas with a resolution of 2048×1024 pixels is established, and each pixel corresponds to an area of about 41.5 meters×41.5 meters on the actual ground. The interpolation calculation is performed for each pixel point on the canvas to generate a continuous risk value distribution. To improve the calculation efficiency, multi-thread parallel calculation is used, and the canvas is divided into 16 sub-areas, each of which is processed by an independent thread. The total calculation time is about 2.3 seconds.

[0061] In the above embodiment, the color mapping table is established, and the HSV color space is used for color coding. The low-risk area (0-20) is mapped to green, with an HSV value of (120°, 80%, 90%) and a corresponding RGB value of (46, 230, 46). The low-to-medium-risk area (21-40) is mapped to yellow, with an HSV value of (60°, 85%, 95%) and a corresponding RGB value of (242, 242, 36). The medium-risk area (41-60) is mapped to orange, with an HSV value of (30°, 90%, 100%) and a corresponding RGB value of (255, 128, 26). The high-risk area (61-80) is mapped to red, with an HSV value of (0°, 85%, 95%) and a corresponding RGB value of (242, 36, 36). The very high-risk area (81-100) is mapped to dark red, with an HSV value of (0°, 95%, 75%) and a corresponding RGB value of (191, 10, 10). Color gradient processing is performed between adjacent risk levels, and a linear interpolation method is used to calculate the transition color. Taking the MT-025 grid with a risk value of 49.1 as an example, the risk value is located in the medium-risk interval (41-60), and the relative position is (49.1-41) / (60-41)=0.426. Linear interpolation is performed between yellow and orange: the R channel value is 242+(255-242)×0.426=247, the G channel value is 242+(128-242)×0.426=194, and the B channel value is 36+(26-36)×0.426=32, and the final color is RGB(247, 194, 32). Geographic features such as the route of the transmission line, the location of the tower, and the location of the pan-tilt camera device are superimposed on the heat map. The transmission line is represented by a black solid line with a line width of 3 pixels. The tower location is marked with a black dot with a diameter of 8 pixels. The pan-tilt camera device location is marked with a blue triangle with a side length of 12 pixels. Auxiliary information such as a scale, a direction mark, and a legend is also added. The scale shows 1:50000, the direction mark points to the north direction, and the legend details the risk level range corresponding to each color.

[0062] In the above embodiment, the generated heat map is saved in PNG format, with a file size of about 1.2 MB. The naming rule of the heat map file is "RiskHeatMap_YYYYMMDD_HHMMSS.png", such as "RiskHeatMap_20210923_143052.png" represents the heat map generated at 14:30:52 on September 23, 2021. At the same time, the corresponding metadata file is generated, which records the generation time, data source, interpolation parameter, color mapping rule and other information, which is convenient for subsequent data tracing and quality control. The preset time interval is set to 30 minutes, and a timing task scheduler is established to automatically trigger heat map update. The update process first checks the changes of the data source. If the real-time environmental monitoring data or weather forecast data is updated, the interpolation calculation and color mapping are re-executed. If only the historical data is fine-tuned, the incremental update method is used, and only the pixel points of the affected area are recalculated, and the update time is shortened to 0.8 seconds. A version management mechanism is established to retain all heat map versions within the last 72 hours, supporting historical backtracking and trend analysis.

[0063] In an optional embodiment, after converting the comprehensive inspection weight value into the target inspection period of each PTZ preset position by using the first preset inverse proportional function, the method further comprises: when it is detected that the dynamic risk heat map is updated, recalculating the comprehensive inspection weight value to obtain a new comprehensive inspection weight value; when there are multiple first preset positions with the new comprehensive inspection weight value greater than the preset weight threshold in the multiple PTZ preset positions, and the target inspection periods of the multiple first preset positions have time conflicts, obtaining the inspection task urgency of each PTZ preset position; scheduling the target inspection period according to the new comprehensive inspection weight value, the effective monitoring range and the inspection task urgency; when there is a first grid unit with a risk value greater than a preset risk threshold in the dynamic risk heat map, adjusting the target inspection period of the first PTZ preset position corresponding to the first grid unit to the minimum inspection period.

[0064] The new comprehensive inspection weight value represents a weight value triggered by the update of the risk heat map, including but not limited to a new comprehensive inspection weight value after risk upgrading, a new comprehensive inspection weight value after environmental change, a new comprehensive inspection weight value after device state change, etc. The preset weight threshold is used to represent a weight critical value triggering special processing, including but not limited to a preset weight threshold of 0.8, a preset weight threshold of 0.9, a preset weight threshold of 0.95, etc. The first preset position refers to a high-priority monitoring point position whose weight value exceeds the threshold, which can be specifically listed as a first preset position of a high-risk area, a first preset position of a frequent failure area, a first preset position of a harsh environment area, etc. The time conflict represents overlapping conflicts of multiple inspection tasks in time arrangement, including but not limited to inspection time conflicts in the same period, device resource time conflicts, personnel scheduling time conflicts, etc. The inspection task emergency degree is used to represent the priority and urgency level of the inspection task, including but not limited to a first-level emergency degree, a second-level emergency degree, a third-level emergency degree, etc. The scheduling processing refers to a processing process of re-arranging and optimizing the inspection period, including but not limited to priority scheduling processing, load balancing scheduling processing, time optimization scheduling processing, etc. The preset risk threshold represents a risk critical value triggering emergency inspection, including but not limited to a preset risk threshold of 0.85, a preset risk threshold of 0.90, a preset risk threshold of 0.95, etc. The first grid unit is used to represent a high-risk grid area whose risk value exceeds the threshold, including but not limited to a first grid unit of a lightning-prone area, a first grid unit of a geological disaster area, a first grid unit of an environmental pollution area, etc. The first cloud holder preset position refers to a monitoring point position corresponding to the high-risk grid unit, including but not limited to a first cloud holder preset position of a tower monitoring, a first cloud holder preset position of a conductor monitoring, a first cloud holder preset position of a device monitoring, etc.

[0065] In the above embodiment, taking 24 PTZ camera devices of a certain 500 kV EHV transmission line as an example, after completing the initial target inspection period setting, the dynamic scheduling and emergency response mechanism is implemented. The update state of the dynamic risk heat map is continuously monitored. When it is detected that the meteorological department issues a lightning orange warning, predicting that the lightning density will reach 5 times per square kilometer per hour within the next 2 hours, which is significantly increased from the previous forecast of 2 times per square kilometer per hour. Immediately trigger the emergency update of the heat map, recalculate the meteorological influence factor of each grid cell. Taking the MT-025 grid covered by the 8th PTZ preset position as an example, the original meteorological influence factor is 0.41, and the updated lightning meteorological influence factor is increased from 0.4 to 0.8, the wind meteorological influence factor remains 0.7 unchanged, and the ice and snow meteorological influence factor is still 0. The updated meteorological influence factor is (0.8x0.5+0x0.2+0.7x0.3)=0.61. Recalculate the grid risk value of MT-025 grid: 0.25x0.5+0.35x0.45+0.25x0.61+0.15x0.7=0.125+0.158+0.153+0.105=0.541, standardized to 54.1. Recalculate the coverage risk average of the 8th PTZ preset position based on the updated risk heat map. The preset position effectively monitors 15 grid cells, and the coverage risk average before updating is 48.3. After updating, the risk values of each grid generally increase by 3-8 points, and the coverage risk average is recalculated to be 52.7. Through the enhancement processing of the risk weight by the nonlinear mapping function f(x)=x^1.2, the risk weight is increased from 0.527^1.2=0.485 to 0.527^1.2=0.485. The device state weight is reacquired, the current power of the 8th PTZ is 85%, the communication signal strength is -65 dBm, and the mechanical parts are running normally, and the device state weight is 0.92. The redundancy weight remains 0.75 unchanged. The new comprehensive inspection weight value is calculated using the fusion coefficient (0.5, 0.3, 0.2): 0.5x0.485+0.3x0.75+0.2x0.92=0.243+0.225+0.184=0.652. Compared with the original comprehensive inspection weight value 0.712, the new comprehensive inspection weight value decreases slightly, mainly due to the slight change in the device state weight.

[0066] In the above embodiment, the preset weight threshold is set to 0.85, which is used to identify high-priority monitoring points that require special treatment. The threshold is compared with the new comprehensive inspection weight values of the 24 PTZ preset positions. The new comprehensive inspection weight value of the first PTZ preset position is 0.892, the new comprehensive inspection weight value of the third PTZ preset position is 0.867, the new comprehensive inspection weight value of the 15th PTZ preset position is 0.901, and the new comprehensive inspection weight value of the 22nd PTZ preset position is 0.856. The weight values of these four preset positions all exceed the preset weight threshold of 0.85, and are identified as first preset positions. Analyze the geographical distribution characteristics of these first preset positions. The first and third preset positions are located in the industrial pollution area of the 23rd-35th towers, which has a high frequency of historical failures and a high current level of contamination. The 15th preset position is located in the mountain section of the 56th-68th towers, which has complex terrain and frequent lightning activity. The 22nd preset position is located in the river valley of the 78th-85th towers, which has unstable geological conditions and is easily affected by extreme weather. These preset positions are classified as high-risk area first preset positions. Analyze whether there is a time conflict in the target inspection cycle of the first preset position. The new target inspection cycle of the first preset position is 90 / 0.892=101 minutes, the new target inspection cycle of the third preset position is 90 / 0.867=104 minutes, the new target inspection cycle of the 15th preset position is 90 / 0.901=100 minutes, and the new target inspection cycle of the 22nd preset position is 90 / 0.856=105 minutes. A time axis model is established with the current time 15:30 as the starting point, and the next inspection time of each preset position is calculated. Assuming that the last inspection time of each preset position is: the first preset position is 14:45, the third preset position is 14:50, the 15th preset position is 14:40, and the 22nd preset position is 14:55. The next inspection time is calculated as: the first preset position is 16:26, the third preset position is 16:34, the 15th preset position is 16:20, and the 22nd preset position is 16:40. It is detected that the inspection times of the first, third, and 15th preset positions are in conflict between 16:20 and 16:34. Since only two PTZ control servers are configured, the inspection tasks of the three preset positions cannot be performed simultaneously, which constitutes a device resource time conflict. This conflict is recorded in the scheduling log, triggering the intelligent scheduling processing program.

[0067] In the above embodiment, the urgency of the inspection task refers to the priority level of the inspection task determined based on the comprehensive evaluation of multi-dimensional risk factors. The calculation formula of the urgency of the inspection task is: urgency score = 0.4 x historical risk coefficient + 0.3 x current environment coefficient + 0.2 x equipment state coefficient + 0.1 x geographic location coefficient, the historical risk coefficient is determined according to the historical failure frequency of the preset position coverage area, the historical risk coefficient of the area with annual failure frequency greater than 0.5 times / year is 1.0, the historical risk coefficient of the area with annual failure frequency between 0.2-0.5 times / year is 0.6, and the historical risk coefficient of the area with annual failure frequency less than 0.2 times / year is 0.2; the current environment coefficient is calculated based on real-time environmental monitoring data, when the environmental temperature exceeds 40℃ or is lower than -20℃, the environmental coefficient is 1.0, when the relative humidity exceeds 90% or the wind speed exceeds 15 meters / second, the environmental coefficient is 0.8, when the pollution degree reaches IV level and above, the environmental coefficient is 0.9, and under normal environmental conditions, the environmental coefficient is 0.3; the equipment state coefficient reflects the health status of the pan-tilt camera equipment, when the equipment state weight value is less than 0.6, the equipment state coefficient is 1.0, when the equipment state weight value is between 0.6-0.8, the equipment state coefficient is 0.7, and when the equipment state weight value is higher than 0.8, the equipment state coefficient is 0.4; the geographic location coefficient considers the particularity of the geographic environment where the preset position is located, the geographic location coefficient of the preset position located across important traffic trunk lines, important buildings or densely populated areas is 1.0, the geographic location coefficient of the preset position located in mountainous, valley and other complex geological conditions areas is 0.8, and the geographic location coefficient of the preset position located in plain and other stable geological conditions areas is 0.3; according to the urgency score, the level is divided: the score is greater than 0.8, which is the first level of urgency, the score is between 0.5-0.8, which is the second level of urgency, and the score is less than 0.5, which is the third level of urgency. Among them, the historical risk coefficient is used to represent the risk quantization index determined based on historical failure data, including but not limited to high-frequency failure area historical risk coefficient, medium-frequency failure area historical risk coefficient, low-frequency failure area historical risk coefficient, etc.; the current environment coefficient represents the risk quantization index determined based on real-time environmental parameters, including but not limited to extreme weather environment coefficient, severe environmental condition coefficient, normal environmental condition coefficient, etc.; the equipment state coefficient refers to the risk quantization index determined based on the equipment health status, including but not limited to equipment failure state coefficient, equipment abnormal state coefficient, equipment normal state coefficient, etc.; the geographic location coefficient is used to represent the risk quantization index determined based on the geographic environment characteristics, including but not limited to important area geographic location coefficient, complex terrain geographic location coefficient, ordinary area geographic location coefficient, etc.

[0068] In the above embodiment, a three-level patrol task emergency degree evaluation system is established. The first emergency degree is suitable for extremely weather warning areas, historical major fault points and key equipment monitoring areas. The second emergency degree is suitable for medium risk areas, equipment state abnormal areas and environment parameter exceeding areas. The third emergency degree is suitable for conventional monitoring areas and low risk stable areas. The patrol task emergency degree of each first preset position is calculated according to multi-dimensional indexes. The first preset position is located in an industrial pollution area, the historical fault frequency is 0.6 times / year, the current pollution degree is III level, the lightning warning level is orange, and the comprehensive evaluation is the first emergency degree. The third preset position is also located in an industrial pollution area, but the historical fault frequency is 0.4 times / year, and the comprehensive evaluation is the second emergency degree. The 15th preset position is located in a mountain lightning high-risk area, the historical fault frequency is 0.5 times / year, the terrain complexity is high, and the comprehensive evaluation is the first emergency degree. The 22nd preset position is located in a valley area, the geological stability is poor but the current environmental risk is relatively low, and the comprehensive evaluation is the second emergency degree. A priority scheduling processing algorithm is used to solve the time conflict problem. The scheduling algorithm considers three dimensions of new comprehensive patrol weight value, effective monitoring range and patrol task emergency degree, and establishes a scheduling priority score model: priority score = 0.4 x weight value + 0.3 x (effective monitoring range / maximum monitoring range) + 0.3 x emergency degree coefficient. The emergency degree coefficient is set as: first emergency degree 1.0, second emergency degree 0.7, and third emergency degree 0.4. The scheduling priority score of each conflict preset position is calculated. The first preset position: 0.4 x 0.892 + 0.3 x (2.8 / 4.5) + 0.3 x 1.0 = 0.357 + 0.187 + 0.300 = 0.844. The third preset position: 0.4 x 0.867 + 0.3 x (3.2 / 4.5) + 0.3 x 0.7 = 0.347 + 0.213 + 0.210 = 0.770. The 15th preset position: 0.4 x 0.901 + 0.3 x (4.1 / 4.5) + 0.3 x 1.0 = 0.360 + 0.273 + 0.300 = 0.933. The effective monitoring range is in square kilometers, and the maximum monitoring range is 4.5 square kilometers. Based on the priority score result, the scheduling order is determined: the 15th preset position (0.933) has the highest priority and is arranged to be executed at 16:20; the first preset position (0.844) is second and is adjusted to be executed at 16:45; and the third preset position (0.770) has the lowest priority and is adjusted to be executed at 17:10. Through load balancing scheduling processing, the task is assigned to two PTZ control servers, ensuring the rational use of equipment resources.

[0069] In the above embodiment, the extremely high-risk area in the dynamic risk thermal map is continuously monitored. When the first grid cell with a grid risk value greater than the preset risk threshold 85 is detected, the emergency response mechanism is immediately started. The risk value of the MT-067 grid cell is found to be 87.3, which exceeds the preset risk threshold 85, and the grid is located in a mountainous area with high lightning frequency near the No. 62 tower. The first cloud head preset position corresponding to the MT-067 grid cell is determined through spatial correlation analysis. The effective monitoring range of the 16th cloud head preset position covers the MT-067 grid cell, and the coverage area accounts for 78% of the total area of the grid, and is determined as the first cloud head preset position. The target patrol cycle of the 16th cloud head preset position is immediately adjusted to the minimum patrol cycle of 30 minutes, and the original target patrol cycle is 135 minutes. During the emergency adjustment process, if the emergency patrol task of the 16th preset position conflicts with the regular patrol task of other preset positions, the emergency task is prioritized to ensure its execution, and the conflicting regular tasks are delayed for processing. At the same time, an emergency notification is sent to the operation and maintenance personnel, including the location of the risk area, the risk level, the adjusted patrol plan and the recommended preventive measures. An emergency response execution monitoring mechanism is established to track the patrol execution of the 16th preset position in real time. After the preset position completes the first emergency patrol, the patrol image is immediately analyzed to detect whether there are equipment abnormalities, environmental threats or other safety hazards. If abnormal conditions are detected, the patrol cycle is further shortened to 15 minutes, and a fault report is automatically generated for the operation and maintenance personnel to handle. If no abnormalities are found, the patrol is continued at a cycle of 30 minutes until the risk value of the grid cell decreases below the preset risk threshold. Through this multi-level dynamic scheduling and emergency response mechanism, rapid response and accurate disposal of the risk condition changes of the transmission line are realized, and timely and effective monitoring and protection of high-risk areas are ensured.

[0070] It should be further noted that the above-described specific numerical examples are only exemplary embodiments, and the above-described specific numerical values are not limited to the above examples.

[0071] It should be noted that the above-described embodiments are only part of the embodiments of the present application, not all. The present application will be described in detail below in conjunction with specific embodiments.

[0072] The embodiment of the present application provides a cloud head patrol dynamic regulation and control process of a transmission line, comprising the following steps:

[0073] Step one, build a dynamic risk assessment system for power transmission line multi-source data fusion. The system first establishes a comprehensive data collection network covering the entire power transmission line corridor, integrating historical fault data, real-time environmental monitoring data, weather forecast data, and topographic data. Historical fault data includes line section trip records, equipment defect records, external damage events, etc. The spatio-temporal distribution of faults is extracted through data mining techniques. Real-time environmental monitoring data are obtained through sensors deployed on towers, including temperature, humidity, wind speed, wind direction, etc. These data can reflect the real-time state of the environment where the line is located. Weather forecast data are obtained from the meteorological department interface, focusing on extreme weather information such as thunderstorms, gales, and snow. Topographic data are obtained through high-precision digital elevation models, analyzing the slope, slope direction, vegetation coverage, and other characteristics around the line. The system standardizes and aligns these multi-source heterogeneous data in time and space, building a unified data foundation to provide comprehensive data support for subsequent risk quantification assessment.

[0074] Step two, generate a dynamic risk heat map for the power transmission line corridor based on the multi-source data of step one. The system uses machine learning algorithms to analyze the integrated data in depth and establishes a risk assessment model. The model divides the power transmission line corridor into several grid cells, with the size of each grid dynamically adjusted according to the importance of the line and the complexity of the terrain, generally between 10 meters and 50 meters. For each grid cell, the system considers multiple dimensions such as historical failure probability, current environmental risk factors, and future weather threats to calculate a risk value between 0 and 100. The calculation of risk value uses a weighted fusion method, with historical failure probability weight 0.3, current environmental risk weight 0.5, and future weather threat weight 0.2. The system updates the risk heat map every 5 to 15 minutes to ensure the real-time nature of risk assessment. In special cases, such as receiving a forest fire warning or an extreme weather alert, the system will immediately trigger the recalculation of risk values and the immediate update of the heat map. The generated risk heat map will serve as the core basis for cloud patrol weight allocation.

[0075] Step three, establish the spatial characteristics model of gimbal preset position and conduct spatial correlation analysis with the risk heat map of step two. The system accurately calibrates the hardware parameters of each gimbal camera, including the three-dimensional coordinates of the installation position, the horizontal and vertical field of view of the camera, the zoom range, the maximum monitoring distance, etc. Through laser radar scanning or unmanned aerial vehicle aerial photography, the terrain blocking information within the view of each preset position is obtained, and a three-dimensional blocking model is constructed. Based on these data, the system calculates the effective monitoring range of each preset position, i.e. the actual observable power transmission line section after excluding the blocked area. Then, the system superimposes the effective monitoring range of each preset position with the risk heat map, calculates the weighted average of the risk values of all grids in the covered area of the preset position, and obtains the average coverage risk. At the same time, the system analyzes the coverage overlap between different preset positions and calculates the redundancy rate of each preset position, i.e. the overlap ratio of the monitoring range of the preset position with other preset positions. These spatial correlation analysis results provide a quantitative basis for the next step of weight calculation.

[0076] Step four, based on the spatial correlation analysis results of step three, design a multi-dimensional fusion adaptive patrol weight calculation algorithm. The system calculates the patrol weight of each preset position from three key dimensions. The risk weight directly adopts the average coverage risk calculated in step three, and enhances the weight difference of high-risk areas through a nonlinear mapping function, so that high-risk preset positions have higher patrol priority. The redundancy weight is determined according to the redundancy rate calculated in step three, and is inversely proportional to the redundancy rate, ensuring that unique view areas with low redundancy are given priority. The device status weight is evaluated by real-time collection of parameters such as the health status, remaining power, and communication quality of the gimbal, avoiding assigning high-frequency tasks to faulty or low-power devices. The system weights and sums the weights of the three dimensions through adjustable fusion coefficients to obtain the comprehensive patrol weight of each preset position. The fusion coefficients can be dynamically adjusted according to different application scenarios to achieve flexible configuration of the patrol strategy.

[0077] Step five, according to the comprehensive weight calculated in step four, realize dynamic patrol frequency scheduling and resource optimization allocation. The system converts the comprehensive weight into a specific patrol cycle, and the higher the weight of a preset position, the shorter the patrol cycle. The conversion process uses an inverse proportional function, and sets the basic patrol cycle and the minimum patrol cycle as constraint conditions. When the risk heat map of step two is updated, the system automatically triggers the recalculation of the weight and the dynamic adjustment of the patrol cycle. For the case of time conflict of patrol tasks of multiple high-weight preset positions, the system intelligently schedules by considering the weight size, effective coverage area, and task urgency. The system also establishes an emergency response mechanism, which immediately adjusts the patrol cycle of related preset positions to the minimum value when the risk value of a certain area suddenly increases, ensuring timely monitoring of high-risk areas.

[0078] Step six, establish a closed-loop feedback and continuous optimization mechanism based on the execution results of step five. The system monitors the execution of each preset position in real time, and calculates the deviation between the actual inspection frequency and the planned frequency, as well as the inspection coverage rate of each risk level area. Through the analysis of the timeliness and accuracy of fault discovery, the effectiveness of the current weight allocation strategy is evaluated. The system feeds back these evaluation results to the weight calculation module of step four, dynamically adjusts the fusion coefficient and mapping function parameters. At the same time, the system provides a manual feedback interface, and the operation and maintenance personnel can mark the unreasonable weight allocation based on the actual inspection experience, and these feedback information is used to optimize the risk assessment model of step two and the weight calculation algorithm of step four. Through this closed-loop feedback mechanism, the system can continuously learn and evolve, and continuously improve the scientificity and effectiveness of the inspection strategy.

[0079] Through the embodiments of the present application, the dynamic risk assessment and intelligent weight allocation are used to realize the precise allocation of the pan-tilt inspection resources of the power transmission line, so that the high-risk areas are monitored and the resources of the low-risk areas are avoided, and through the closed-loop feedback mechanism, the inspection strategy can be continuously optimized to adapt to the environmental changes, thereby improving the intelligent operation and maintenance level and the fault prevention ability of the power transmission line.

[0080] The pan-tilt inspection dynamic regulation system in the embodiments of the present application is described from the perspective of hardware processing, and the structure of the pan-tilt inspection dynamic regulation system is shown in FIG. 1. Figure 2 , Figure 2 is a schematic diagram of an entity device structure of the pan-tilt inspection dynamic regulation system in the embodiments of the present application.

[0081] It should be noted that Figure 2 The structure of the pan-tilt inspection dynamic regulation system shown is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.

[0082] As Figure 2 shown, the pan-tilt inspection dynamic regulation system includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 202 or loaded into a random access memory (RAM) 203 from a storage part 208, such as the method described in the above embodiments. In the RAM 203, various programs and data required for system operation are also stored. The CPU 201, the ROM 202, and the RAM 203 are connected to each other through a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0083] The following components are connected to the I / O interface 205: an input section 206 including an audio input device, a push button switch, and the like; an output section 207 including a Liquid Crystal Display (LCD), and an audio output device, a lamp, and the like; a storage section 208 including a hard disk and the like; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, a modem, and the like. The communication section 209 performs a communication process via a network such as the Internet. A drive 210 is also connected to the I / O interface 205 as necessary. A removable recording medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like is attached to the drive 210 as necessary so that a computer program read therefrom is installed into the storage section 208 as necessary.

[0084] In particular, the processes described above with reference to the flow charts can be implemented as a computer software program according to embodiments of the present application. For example, an embodiment of the present application includes a computer program product comprising a computer program carried on a computer readable medium, the computer program containing a computer program for executing the methods shown in the flow charts. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 209, and / or installed from the removable recording medium 211. When the computer program is executed by the central processing unit (CPU) 201, various functions defined in the present application are performed.

[0085] It should be noted that specific examples of computer readable storage media can include without limitation electrical connection having one or more conductors, a portable computer diskette, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM), a flash memory, a fiber optic device, a portable compact disc read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium can be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

Claims

1. A dynamic adjustment and control method for a cloud platform inspection of a power transmission line, characterized in that, The method comprises the following steps: acquiring multi-source data of a target power transmission line, and inputting the multi-source data into a preset dynamic risk assessment model to obtain a dynamic risk heat map output by the preset dynamic risk assessment model; establishing a spatial characteristic model for a plurality of PTU preset positions of the target power transmission line, and performing spatial correlation analysis on the spatial characteristic model and the dynamic risk heat map to obtain a spatial correlation characteristic parameter of each PTU preset position in the plurality of PTU preset positions; determining a device state weight value of each PTZ camera device in the plurality of PTZ camera devices of the target power transmission line, and determining a comprehensive inspection weight value of each PTU preset position according to the device state weight value and the spatial correlation characteristic parameter, wherein the plurality of PTZ camera devices and the plurality of PTU preset positions have a preset attribution corresponding relationship; converting the comprehensive inspection weight value into a target inspection cycle of each PTU preset position by using a first preset inverse proportional function; wherein the step of establishing a spatial characteristic model for a plurality of PTU preset positions of the target power transmission line, and performing spatial correlation analysis on the spatial characteristic model and the dynamic risk heat map to obtain a spatial correlation characteristic parameter of each PTU preset position in the plurality of PTU preset positions comprises the following steps: acquiring hardware parameters of the plurality of PTZ camera devices; acquiring terrain occlusion information in a preset monitoring viewing angle range of each PTU preset position by using a three-dimensional measurement device, and constructing a three-dimensional occlusion model according to the terrain occlusion information; determining an effective monitoring range of each PTU preset position according to the hardware parameters and the three-dimensional occlusion model, wherein the effective monitoring range of each PTU preset position is a power transmission line section after excluding an occluded area; performing spatial superposition analysis on the effective monitoring range of each PTU preset position and the dynamic risk heat map to obtain a coverage risk average value of each PTU preset position; performing spatial overlap analysis on the plurality of PTU preset positions according to the effective monitoring range of each PTU preset position to determine a redundancy rate of each PTU preset position, wherein the redundancy rate is a proportion of a monitoring overlap range of the effective monitoring range of each PTU preset position and the effective monitoring range of a remaining PTU preset position in the plurality of PTU preset positions to the effective monitoring range of each PTU preset position; taking the coverage risk average value and the redundancy rate as the spatial correlation characteristic parameter; the step of determining a device state weight value of each PTZ camera device in the plurality of PTZ camera devices of the target power transmission line, and determining a comprehensive inspection weight value of each PTU preset position according to the device state weight value and the spatial correlation characteristic parameter comprises the following steps: collecting health state parameters, residual power parameters and communication quality parameters of the plurality of PTZ camera devices in real time; performing state quantization processing on each PTZ camera device according to the health state parameters, the residual power parameters and the communication quality parameters to obtain the device state weight value; allocating the device state weight value to the corresponding each PTU preset position according to the preset attribution corresponding relationship; The coverage risk mean is first weight conversion processed by using a preset nonlinear mapping function, to obtain a risk weight value of each PTU preset position; The redundancy rate is second weight conversion processed by using a second preset inverse proportional function, to obtain a redundancy weight value of each PTU preset position, and the redundancy weight value is in an inverse proportional relationship with the redundancy rate; The risk weight value, the redundancy weight value and the device state weight value are weighted and summed by using a preset fusion coefficient, to obtain a comprehensive inspection weight value.

2. The method of claim 1, wherein, After the risk weight value, the redundancy weight value and the device state weight value are weighted and summed by using the preset fusion coefficient to obtain the comprehensive inspection weight value, the method further comprises: According to the target inspection cycle, an inspection task is scheduled for the plurality of PTZ camera devices, to generate an inspection execution plan; An actual inspection frequency value of the plurality of PTZ camera devices is statistically obtained by monitoring the inspection execution of the plurality of PTZ camera devices in real time; The actual inspection frequency value is compared with a planned inspection frequency value in the inspection execution plan, to obtain a frequency deviation value; When the frequency deviation value is greater than a preset deviation threshold, the preset fusion coefficient is adjusted in a reverse direction according to the frequency deviation value, wherein when the actual inspection frequency value is less than the planned inspection frequency value, a risk increase adjustment amount is obtained by multiplying the frequency deviation value by a preset enhancement adjustment coefficient, and a first risk fusion coefficient in the preset fusion coefficient is increased to a second risk fusion coefficient according to the risk increase adjustment amount; when the actual inspection frequency value is greater than the planned inspection frequency value, a risk decrease adjustment amount is obtained by multiplying the frequency deviation value by a preset attenuation adjustment coefficient, and the first risk fusion coefficient is decreased to a third risk fusion coefficient according to the risk decrease adjustment amount.

3. The method of claim 1, wherein, The comprehensive inspection weight value is converted into the target inspection cycle of each PTU preset position by using a first preset inverse proportional function, specifically comprising: The comprehensive inspection weight value is converted into an initial inspection cycle by using the first preset inverse proportional function; A basic inspection cycle and a minimum inspection cycle of each PTU preset position are obtained, and it is determined whether the initial inspection cycle is less than the minimum inspection cycle; When the initial inspection cycle is less than the minimum inspection cycle, the minimum inspection cycle is taken as the target inspection cycle; When the initial inspection cycle is greater than or equal to the minimum inspection cycle, it is determined whether the initial inspection cycle is greater than the basic inspection cycle; When the initial inspection cycle is greater than the basic inspection cycle, the basic inspection cycle is taken as the target inspection cycle; When the initial inspection cycle is between the minimum inspection cycle and the basic inspection cycle, the initial inspection cycle is taken as the target inspection cycle.

4. The method of claim 1, wherein, The plurality of source data of the target power transmission line are obtained, and the plurality of source data are input into a preset dynamic risk assessment model, to obtain a dynamic risk heat map output by the preset dynamic risk assessment model, specifically comprising: Obtaining historical fault data, real-time environmental monitoring data, weather forecast data and topographic data of the target power transmission line as the multi-source data; Dividing the target power transmission line into a plurality of grid units; Obtaining a historical fault probability of each grid unit in the plurality of grid units from the historical fault data, obtaining a current environmental risk factor of each grid unit from the real-time environmental monitoring data, obtaining a weather influence factor of each grid unit from the weather forecast data, and obtaining a topographic risk factor of each grid unit from the topographic data; Weightedly fusing the historical fault probability, the current environmental risk factor, the weather influence factor and the topographic risk factor to obtain a grid risk value of each grid unit; Generating the dynamic risk heat map according to the grid risk value, and updating the dynamic risk heat map at a preset time interval.

5. The method according to any one of claims 1 to 4, characterized in that, After the comprehensive inspection weight value is converted into the target inspection cycle of each PTZ preset position by using the first preset inverse proportional function, the method further comprises: When it is detected that the dynamic risk heat map is updated, the comprehensive inspection weight value is recalculated to obtain a new comprehensive inspection weight value; When there are a plurality of first preset positions in which the new comprehensive inspection weight value is greater than a preset weight threshold in the plurality of PTZ preset positions, and there is a time conflict in the target inspection cycles of the plurality of first preset positions, an inspection task urgency of each PTZ preset position is obtained; The target inspection cycle is scheduled according to the new comprehensive inspection weight value, the effective monitoring range and the inspection task urgency; When there is a first grid unit in which the risk value is greater than a preset risk threshold in the dynamic risk heat map, the target inspection cycle of a first PTZ preset position corresponding to the first grid unit is adjusted to a minimum inspection cycle.

6. A cloud platform inspection dynamic regulation and control system, characterized in that, The PTZ inspection dynamic regulation system comprises one or more processors and a memory; the memory is coupled with the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors invoke the computer instructions to enable the PTZ inspection dynamic regulation system to perform the method in any one of claims 1-5.

7. A computer-readable storage medium comprising instructions, characterized in that, When the instructions run on the PTZ inspection dynamic regulation system, the PTZ inspection dynamic regulation system performs the method in any one of claims 1-5.

8. A computer program product, characterised in that, When the computer program product runs on the PTZ inspection dynamic regulation system, the PTZ inspection dynamic regulation system performs the method in any one of claims 1-5.

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