AI-powered intelligent control method for drone-based full-domain inspection

Abstract

This application relates to a method for intelligent control and management of UAV full-domain AI inspection, belonging to the field of UAV technology. The method includes: determining a target area and first association information of the target area; dividing the target area into multiple partitions; obtaining second association information of the target partitions; determining the inspection type and priority of the target partitions based on the second association information; and generating a UAV inspection task request; in response to receiving the UAV inspection task request, obtaining the status information and environmental information of UAVs in the standby queue; determining the target UAV associated with the target partition and the entry / exit information of the target UAV in the target partition; determining the communication link of the target UAV based on the entry / exit information of the UAVs corresponding to the multiple partitions; and controlling the target UAV to execute the inspection task corresponding to the UAV inspection task request. This application improves inspection efficiency, accuracy, and security.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method for intelligent control and management of UAVs through AI-based all-domain inspection. Background Technology

[0002] With the rapid development of drone technology, its advantages of high flexibility, low cost, and high efficiency have led to its widespread application in inspection work in many fields such as power lines, oil pipelines, wind farms, photovoltaic power stations, rail transit, smart parks, and emergency disaster relief. Traditional regular manual inspection methods suffer from problems such as high labor intensity, high safety risks, low efficiency, geographical limitations, and difficulty in detecting hidden defects. Drone inspection has effectively overcome these problems and has become an important means of modern infrastructure operation and maintenance. Although drone inspection technology has replaced traditional manual inspection, problems still exist, such as the difficulty of achieving large-scale full-area coverage in single-machine operation mode, the separation of data collection and analysis leading to information delays and data silos, insufficient automation and AI recognition intelligence, and reduced safety and reliability in complex environments. Therefore, there is an urgent need to propose a drone-based full-area AI inspection intelligent control method that can solve problems such as multi-machine collaboration, real-time intelligent analysis, and centralized control, in order to achieve safe, efficient, and accurate full-area inspection. Summary of the Invention

[0003] Therefore, it is necessary to provide a method for intelligent control of all-domain AI-based drone inspection that can achieve safe, efficient, and accurate all-domain inspection, addressing the aforementioned technical issues.

[0004] Firstly, a method for intelligent control and management of drone-based AI-powered inspection across all domains is provided, the method comprising:

[0005] Determine the target area and the first association information of the target area, and divide the target area based on the first association information to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0006] Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0007] In response to receiving the UAV inspection task request, the status information and environmental information of the UAVs in the standby queue are obtained. Based on the UAV inspection task request, status information and environmental information, the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition are determined. The cut-in and cut-out information includes at least time and location.

[0008] Based on the entry and exit information of drones corresponding to multiple partitions, the communication link of the target drone is determined, and based on the entry and exit information of the target drone in the target partition and the communication link, the target drone is controlled to execute the inspection task corresponding to the drone inspection task request.

[0009] Optionally, determining the target region and the first associated information of the target region includes:

[0010] Receive inspection requests from user terminals, wherein the inspection requests from user terminals include at least a geographical area.

[0011] The target area is determined based on the geographical region.

[0012] Based on the target area, first association information of the target area is extracted from the database. The first association information includes at least the inspection object, inspection accuracy, and key inspection targets.

[0013] Optionally, based on the first association information, the target region is divided to generate multiple partitions, including:

[0014] Based on the object attributes of the inspected object, the first division rule for the target area is determined, including:

[0015] In response to the fact that the first object attribute of the inspected object is a geographical feature, the target area is divided based on the boundary of the geographical feature to generate a first partition;

[0016] In response to the fact that the first object attribute of the inspected object is a non-geographical feature, the target area is divided based on the area balancing mechanism to generate a second partition;

[0017] In response to the completion of the second partitioning, based on the inspection accuracy and the key inspection targets, a second partitioning rule for the target area is determined, including:

[0018] Obtain the level corresponding to the inspection accuracy and the attributes of the key inspection targets, and determine the workload of the second partition based on the level and the attributes;

[0019] Based on the task load balancing mechanism and the task load of the second partition, the partitioning result of the second partition is adjusted to generate the third partition;

[0020] The first partition and / or the third partition are defined as multiple partitions generated by dividing the target region.

[0021] Optionally, obtaining second association information of the target partition, and determining the inspection type and priority of the target partition based on the second association information, includes:

[0022] Obtain the second association information of the target partition, the second association information including at least the inspection object;

[0023] Based on the second object attribute of the inspection object, the inspection type of the target partition is determined, wherein the second object attribute includes at least the inspection method, inspection frequency and inspection accuracy;

[0024] Based on the inspection type of the target partition, determine the factors that affect the execution order of the inspection tasks of the target partition, and determine the comprehensive evaluation value based on the factors.

[0025] Based on the comprehensive evaluation value, the priority of the target partition is determined.

[0026] Optionally, based on the second association information, inspection type, and priority, generating a drone inspection task request includes:

[0027] Obtain the second association information of the target partition, the inspection type, and the priority;

[0028] The second associated information, the inspection type, and the priority are mapped to generate a mapping relationship including a unique identifier. The mapping relationship is then filled into the corresponding field of the task request template to generate the UAV inspection task request.

[0029] Optionally, in response to receiving the UAV inspection task request, obtaining the status information and environmental information of the UAVs in the standby queue includes:

[0030] In response to receiving the drone inspection task request, determine the drones that are in an idle state and / or whose remaining task execution time is less than a first preset threshold, and set the drone identifiers corresponding to the drones in the standby queue in sequence according to the task execution status;

[0031] Obtain the status information of the UAVs in the standby queue, the status information including at least health status, equipment status, task execution status and real-time operation status;

[0032] Obtain environmental information of the target partition, wherein the environmental information includes at least meteorological information, geographic information, airspace information and electromagnetic information;

[0033] The status information and environmental information of the drones in the standby queue are mapped to generate a mapping relationship and saved.

[0034] Optionally, based on the UAV inspection task request, status information, and environmental information, determining the target UAV associated with the target partition and the target UAV's entry / exit information in the target partition includes:

[0035] Based on the environmental information of the target partition, a first set of unmanned aerial vehicles (UAVs) associated with the target partition is determined;

[0036] Based on the inspection type, the priority, and the status information, a second set of drones associated with the target partition is selected from the first set of drones.

[0037] Define the drones in the second drone set as target drones associated with the target partition;

[0038] Based on the spatiotemporal coordination mechanism and the inspection type, the entry and exit information of the target UAV in the target partition is determined.

[0039] Optionally, determining the communication link of the target drone based on the inbound and outbound information of drones corresponding to multiple partitions includes:

[0040] Based on the entry and exit information of the target UAV, the path point sequence of the target UAV in the target partition is determined;

[0041] Based on the path point sequence, determine the reachability of the communication link between the target UAV and the first target communication base station at the target path points;

[0042] In response to the communication link reachability not meeting a preset standard, the first target communication base station of the target UAV on the target path node is switched to the second target communication base station so that the communication link reachability meets the preset standard;

[0043] Based on the entry and exit information of drones corresponding to multiple partitions, the communication load and interference level of the communication link within the target time period are obtained, and the target time period is determined based on the entry time and exit time.

[0044] In response to the communication load and the interference level corresponding to the collaborative optimization value being greater than a first preset threshold, the second target communication base station of the target UAV on the target path node is switched to the third target communication base station, so that the reachability of the communication link meets the preset standard and the collaborative optimization value is less than or equal to the first preset threshold.

[0045] The communication link generated between the target UAV and the third target communication base station at the target path node is defined as the target communication link;

[0046] The set of communication links generated by multiple target communication links is defined as the communication link of the target UAV.

[0047] Optionally, based on the target UAV's entry / exit information in the target partition and the communication link, controlling the target UAV to execute the inspection task corresponding to the UAV inspection task request includes:

[0048] Based on the entry and exit information of the target drone in the target partition, control the target drone to execute the inspection task corresponding to the drone inspection task request;

[0049] Based on the communication link, the image data generated by the target UAV in performing the inspection task is transmitted back to the data receiving end.

[0050] Optionally, the method further includes:

[0051] The image data transmitted back by the target drone is acquired and analyzed to determine whether there are any anomalies in the target partition.

[0052] In response to abnormal situations, generate and push alarm information.

[0053] In a second aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0054] Determine the target area and the first association information of the target area, and divide the target area based on the first association information to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0055] Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0056] In response to receiving the UAV inspection task request, the status information and environmental information of the UAVs in the standby queue are obtained. Based on the UAV inspection task request, status information and environmental information, the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition are determined. The cut-in and cut-out information includes at least time and location.

[0057] Based on the entry and exit information of drones corresponding to multiple partitions, the communication link of the target drone is determined. Then, based on the entry and exit information of the target drone in the target partition and the communication link, the target drone is controlled to execute the inspection task corresponding to the drone inspection task request.

[0058] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0059] Determine the target area and the first association information of the target area, and divide the target area based on the first association information to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0060] Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0061] In response to receiving the UAV inspection task request, the status information and environmental information of the UAVs in the standby queue are obtained. Based on the UAV inspection task request, status information and environmental information, the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition are determined. The cut-in and cut-out information includes at least time and location.

[0062] Based on the entry and exit information of drones corresponding to multiple partitions, the communication link of the target drone is determined. Then, based on the entry and exit information of the target drone in the target partition and the communication link, the target drone is controlled to execute the inspection task corresponding to the drone inspection task request.

[0063] Fourthly, a computer program product is provided, the computer program product comprising a computer program, which, when executed by a processor, performs the following steps:

[0064] Determine the target area and the first association information of the target area, and divide the target area based on the first association information to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0065] Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0066] In response to receiving the UAV inspection task request, the status information and environmental information of the UAVs in the standby queue are obtained. Based on the UAV inspection task request, status information and environmental information, the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition are determined. The cut-in and cut-out information includes at least time and location.

[0067] Based on the entry and exit information of drones corresponding to multiple partitions, the communication link of the target drone is determined. Then, based on the entry and exit information of the target drone in the target partition and the communication link, the target drone is controlled to execute the inspection task corresponding to the drone inspection task request.

[0068] The aforementioned intelligent control method for full-domain AI inspection of unmanned aerial vehicles (UAVs) includes: determining a target area and first association information of the target area; dividing the target area into multiple partitions based on the first association information, wherein the association information is at least task attribute data describing the AI ​​inspection task of the area; obtaining second association information of the target partitions; determining the inspection type and priority of the target partitions based on the second association information; generating a UAV inspection task request based on the second association information, the inspection type, and the priority; and, in response to receiving the UAV inspection task request, obtaining the status information and environmental information of the UAVs in the standby queue. Based on the UAV inspection task request, status information, and environmental information, the target UAV associated with the target partition and the entry / exit information of the target UAV in the target partition are determined. The entry / exit information includes at least time and location. Based on the entry / exit information of UAVs corresponding to multiple partitions, the communication link of the target UAV is determined. Based on the entry / exit information of the target UAV in the target partition and the communication link, the target UAV is controlled to execute the inspection task corresponding to the UAV inspection task request. This application improves inspection efficiency, accuracy, and safety by decomposing the task across the entire domain, coordinating multi-UAV planning, and performing real-time AI analysis. Attached Figure Description

[0069] Figure 1 This is an application environment diagram of the UAV full-domain AI inspection and intelligent control method in one embodiment;

[0070] Figure 2 This is a flowchart illustrating a method for intelligent control and management of drone-based AI-powered inspection across the entire domain, as shown in one embodiment.

[0071] Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0073] It should be understood that, in the description of this application, unless the context explicitly requires it, words such as "including" or "comprising" throughout the specification should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to".

[0074] It should also be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0075] It should be noted that the terms "S1," "S2," etc., are used only for descriptive purposes and do not specifically refer to the order or sequence, nor are they intended to limit this application. They are merely for the convenience of describing the method of this application and should not be construed as indicating the sequential order of the steps. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0076] The UAV-based AI-powered intelligent inspection and control method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with a data processing platform set on server 104 via a network. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. Server 104 can be implemented as a standalone server or a server cluster consisting of multiple servers.

[0077] In one embodiment, such as Figure 2 As shown, a method for intelligent control and management of drones through AI-powered full-domain inspection is provided, which is then applied to... Figure 1 Taking the terminal in the example, the explanation includes the following steps:

[0078] S1: Determine the target area and the first association information of the target area, and divide the target area based on the first association information to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task.

[0079] It should be noted that the target area refers to the area that needs to be inspected based on the user's request. It is determined by latitude and longitude coordinates, geographical boundaries, or by importing GIS map data. The first associated information may include the inspection object, inspection accuracy, and key inspection targets. The inspection object refers to the specific assets that need to be inspected, such as power lines, photovoltaic panels, pipelines, etc. The inspection accuracy is the specified flight altitude and image resolution used to ensure that the collected image data can meet the analysis requirements. Key inspection targets refer to areas or equipment points that require special attention, such as equipment with known potential hazards. A target area may include multiple key inspection targets.

[0080] S2: Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority.

[0081] It should be noted that the type of inspection can be determined by the inspection method, inspection frequency, and inspection accuracy. For example, the inspection method can include special loads such as thermal imaging and multispectral imaging. The inspection frequency can be regular inspection or special inspection. Regular inspection is performed according to a pre-established maintenance calendar, such as monthly, quarterly, or annual inspections of power lines. Special inspection is triggered after a specific event, such as a special inspection after a typhoon or rainstorm, which focuses on checking the impact of the disaster. The inspection accuracy can be determined according to the attributes of the inspection object. For example, key core equipment (such as substation main transformers, pipeline valves, etc.) requires high-frequency refined inspection or detection-type inspection (such as using special loads such as thermal imaging and multispectral imaging).

[0082] S3: In response to receiving the UAV inspection task request, obtain the status information and environmental information of the UAV in the standby queue, and determine the target UAV associated with the target partition and the entry and exit information of the target UAV in the target partition based on the UAV inspection task request, status information and environmental information. The entry and exit information includes at least time and location.

[0083] It should be noted that the status information can include health status, equipment status, mission execution status, and real-time operational status. Health status can be determined by the self-test status of core components (such as motors), historical maintenance records, and remaining lifespan prediction. Equipment status includes the type of payload (such as visible light, infrared, or lidar) and its operational status. Mission execution status includes whether the UAV is idle or the remaining duration of its mission. Real-time operational status can include current battery level, altitude, speed, and heading. Environmental information can include meteorological information, geographic information, airspace information, and electromagnetic information. Meteorological information includes real-time forecasts of wind speed, wind direction, precipitation, visibility, and temperature; geographic information includes a high-precision digital elevation model (DEM) of the target area and the distribution of obstacles (such as buildings and mountains); airspace information can include real-time no-fly zones, restricted zones, and other air traffic activities; electromagnetic information mainly includes interference intensity distribution maps of communication frequency bands; cut-in and cut-out information includes cut-in time, cut-out time, cut-in location, and cut-out location. Among these, a target area can have multiple associated target UAVs, which can perform inspection tasks at the same time or at different times.

[0084] S4: Based on the entry and exit information of drones corresponding to multiple partitions, determine the communication link of the target drone, and based on the entry and exit information of the target drone in the target partition and the communication link, control the target drone to execute the inspection task corresponding to the drone inspection task request.

[0085] It should be noted that the communication link is established by the communication between the target UAV and the communication base station. The target UAV enters the target partition according to the entry time and entry position, and exits the target partition according to the exit position and exit time. During the time period between the entry time and exit time, the target partition is inspected. Multiple target partitions can be inspected by multiple UAVs at the same time.

[0086] In some specific implementations, determining the target region and the first associated information of the target region includes:

[0087] The system receives inspection requests from users. These requests include at least a geographical area. The user can input the inspection request in real time or the system can issue it periodically. The geographical area refers to the target area to be inspected, determined by latitude and longitude coordinates, geographical boundaries, or imported GIS map data.

[0088] Based on the geographical area, the target area is determined. For example, the target area may be the area corresponding to a linear pipeline or the area corresponding to a regularly arranged photovoltaic panel array.

[0089] Based on the target area, the first association information of the target area is extracted from the database. The first association information includes at least the inspection object, inspection accuracy, and key inspection targets. The database is used to store task attribute data of multiple target areas, such as the first association information and the second association information.

[0090] In some specific implementations, based on the first association information, the target region is divided to generate multiple partitions, including:

[0091] Based on the object attributes of the inspected object, the first division rule for the target area is determined, including:

[0092] In response to the first object attribute of the inspected object being a geographic feature, the target area is divided based on the boundary of the geographic feature to generate a first partition. The geographic feature refers to an area containing natural barriers (such as rivers or ridges) or obvious geographic separations. These areas are preferentially divided based on these features to ensure the geographic continuity of each partition.

[0093] In response to the fact that the first object attribute of the inspection object is a non-geographical feature, the target area is divided based on the area balancing mechanism to generate a second partition. Here, non-geographical features refer to areas within the target area without obvious geographical separation, that is, areas where the terrain and inspection objects are evenly distributed. According to the set partition size, the target area is divided into multiple sub-partitions with equal areas, and the sub-partition is defined as the second partition.

[0094] In response to the completion of the second partitioning, based on the inspection accuracy and the key inspection targets, a second partitioning rule for the target area is determined, including:

[0095] The inspection accuracy level and the attributes of the key inspection targets are obtained. Based on the level and the attributes, the workload of the second partition is determined. The inspection accuracy level is determined by a pre-set mapping relationship, which describes the correspondence between inspection accuracy and inspection level. Generally, the higher the inspection accuracy, the higher the inspection level. The attributes of the key inspection targets include the number of key targets and the inspection duration of key targets. The inspection level, the number of key targets, and the inspection duration of key targets are normalized. Based on the normalized data, the workload of the second partition is determined by the workload evaluation function. The workload evaluation function is: R = w1X1 + w2X2 + w3X3, where R represents the workload evaluation value, w1, w2, and w3 are weight coefficients, X1 represents the inspection level, X2 represents the number of key targets, and X3 represents the inspection duration of key targets.

[0096] Based on the task load balancing mechanism and the task load of the second partition, the division result of the second partition is adjusted to generate a third partition. The task load balancing mechanism includes: determining multiple task load evaluation values ​​for the second partition, and subtracting the task load evaluation values ​​of any two partitions to obtain the absolute value of the difference. Based on multiple subtraction processes, multiple absolute values ​​of difference are obtained. In response to the fact that all multiple absolute values ​​of difference are less than a second preset threshold or the number of absolute values ​​of difference greater than or equal to the second preset threshold is less than or equal to a third preset threshold, the second partition is not adjusted. At this time, the first partition and / or the second partition are defined as multiple partitions generated by dividing the target area. In response to the fact that the number of absolute values ​​of difference greater than or equal to the second preset threshold is greater than the third preset threshold, the second partition is adjusted according to the task load evaluation value until the number of absolute values ​​of difference greater than or equal to the second preset threshold is less than or equal to the third preset threshold. The second preset threshold and the third preset threshold can be set according to actual needs.

[0097] The first partition and / or the third partition are defined as multiple partitions generated by dividing the target region.

[0098] In some specific implementations, obtaining second association information of the target partition, and determining the inspection type and priority of the target partition based on the second association information, includes:

[0099] Obtain the second association information of the target partition, the second association information including at least the inspection object;

[0100] Based on the second object attribute of the inspection object, the inspection type of the target partition is determined, wherein the second object attribute includes at least the inspection method, inspection frequency and inspection accuracy;

[0101] Based on the inspection type of the target zone, factors affecting the execution order of inspection tasks in the target zone are determined, and a comprehensive evaluation value is determined based on these factors. These influencing factors may include risks determined by the inspection type, the criticality of the inspection object in the industrial chain, timeliness requirements, and task dependencies. For example, risk refers to emergency inspections, targeted inspections, etc. Post-disaster assessments have a higher risk level and therefore higher priority; targeted inspections for hazard information have a medium risk level and therefore medium priority; the higher the criticality of the inspection object in the industrial chain, the higher its priority; the higher the timeliness requirements, the higher the priority; and if subsequent tasks depend on the inspection results of the current task, the higher the priority of the current task. Based on this, the above influencing factors are assigned values ​​using an expert assignment method and standardized. The comprehensive evaluation value is calculated based on the standardized results. The calculation method is as follows:

[0102]

[0103] in, This represents the overall evaluation value, where m represents the number of influencing factors. This represents the weight coefficient of the nth influencing factor. This represents the nth influencing factor;

[0104] Based on the comprehensive evaluation value, the priority of the target partition is determined. The higher the comprehensive evaluation value, the higher the priority of the target partition. Multiple partitions in the target area are sorted by comprehensive evaluation value, and their priority is determined according to the sorting result. For example, if the comprehensive evaluation value is the largest, it is ranked first and has the highest priority, and so on. This will not be elaborated further.

[0105] In some specific implementations, generating a drone inspection task request based on the second association information, inspection type, and priority includes:

[0106] Obtain the second association information of the target partition, the inspection type, and the priority;

[0107] The second associated information, the inspection type, and the priority are mapped to generate a mapping relationship including a unique identifier. The mapping relationship is then filled into the corresponding field of the task request template to generate the UAV inspection task request. The unique identifier refers to a unique ID, which is used to represent the relevant information of the target partition. The task request template is a pre-set inspection task request template.

[0108] In some specific implementations, in response to receiving the UAV inspection task request, obtaining the status information and environmental information of the UAVs in the standby queue includes:

[0109] In response to receiving the drone inspection task request, the system identifies drones that are idle and / or whose remaining task execution time is less than a first preset threshold. Based on the task execution status, the system sequentially sets the drone identifiers corresponding to the drones in the standby queue. The first preset threshold can be set according to actual needs. Generally, it can be determined based on the available working time of drones in an idle state. For example, if the available working time of an idle drone is 2.5 hours, then the first preset threshold must be less than 2.5 hours, and so on. Further details are omitted.

[0110] Obtain the status information of the UAVs in the standby queue, the status information including at least health status, equipment status, task execution status and real-time operation status;

[0111] Obtain environmental information of the target partition, wherein the environmental information includes at least meteorological information, geographic information, airspace information and electromagnetic information;

[0112] The status information and environmental information of the drones in the standby queue are mapped to generate a mapping relationship and saved.

[0113] In some specific implementations, determining the target drone associated with the target partition and the target drone's entry / exit information in the target partition based on the drone inspection task request, status information, and environmental information includes:

[0114] Based on the environmental information of the target partition, a first set of UAVs associated with the target partition is determined, wherein the degree of matching between the UAVs and the environmental information of the target partition is determined based on a matching degree evaluation function, which is:

[0115]

[0116] in, Indicates the degree of matching. Indicates the number of environmental parameters. This represents the weight coefficient of the i-th environmental parameter. This represents the standardized value of the i-th environmental parameter. This represents the conversion coefficient of the UAV function corresponding to the i-th environmental parameter. This represents the standardized value of the drone function corresponding to the i-th environmental parameter. For example, the standardized value of the drone function can be the standardized value of wind resistance level, the standardized value of payload penetration, etc., and the standardized value of the corresponding environmental parameter can be wind speed, visibility, etc. The standardized value is assigned by experts and standardized. The standardization method is a common method, and the specific process will not be described here.

[0117] If the matching degree is greater than the fourth preset threshold, the drone is associated with the target partition. The fourth preset threshold can be set according to actual needs.

[0118] Based on the inspection type, the priority, and the status information, a second set of drones associated with the target partition is selected from the first set of drones. Specifically, the first set of drones is initially screened based on the inspection type to identify drones matching the inspection type. Then, the drones in the initial screening result are further screened based on the priority and status information to obtain the second set of drones. The drone status evaluation value is calculated based on the status information, and the calculation method includes:

[0119]

[0120] in, This indicates the drone's status assessment value. Indicates the number of state parameters. This represents the weight coefficient of the b-th state parameter. This represents the b-th state parameter;

[0121] Based on the status evaluation value, the corresponding partition priority is matched. For example, the higher the status evaluation value, the higher the priority of the matched partition, and so on. This will not be elaborated further.

[0122] Define the drones in the second drone set as target drones associated with the target partition;

[0123] Based on the spatiotemporal coordination mechanism and the inspection type, the entry and exit information of the target UAV in the target partition is determined. Specifically, based on parameters such as the partition boundary of the target partition, the 3D model of the inspection object, and the required resolution, the UAV flight altitude is calculated using the formula H = (D × f) / d, where H is the flight altitude, D represents the sampling distance (determined by the required resolution), f represents the focal length of the camera sensor, and d represents the size of a single pixel of the camera sensor. All parameters are in meters. Furthermore, based on the flight altitude and overlap rate, adjacent image acquisition nodes are calculated using the formula... ,in, The image acquisition spacing is represented by L, and the image scaling ratio is represented by L. This indicates the camera's field of view angle corresponding to the heading. The overlap rate is used to determine the acquisition path points. These path points are then connected and smoothed. Local adjustments are made to the smoothed path based on environmental data such as obstacles to ensure a safe distance between the path and obstacles, thus generating the corresponding UAV inspection path. The path smoothing method uses a polynomial curve to connect the path points, a common method; the specific connection process is not detailed here. The path start point is the node closest to the UAV nest in the partition, i.e., the UAV's entry point in the target partition. The UAV's exit point in the target partition is the intersection of the UAV inspection path and the partition boundary. The entry time is the time from the path start point to the node closest to the UAV nest in the partition. Furthermore, the smoothed path is divided into multiple segments, and an optimal speed profile is generated for each segment, including acceleration, deceleration, and cruising segments. Based on the acceleration, deceleration, and cruising segments, as well as the dwell time at each acquisition path point, the entire inspection task time is determined. Based on this time and the entry time, the final exit time is determined. The calculation method for the entire inspection task time is as follows:

[0124]

[0125] in, This indicates the total time of the inspection task. Indicates the number of line segments. Indicates the first The inspection time corresponding to each line segment. Indicates the number of data collection path points. Indicates the first The dwell time at each data collection path point;

[0126] in, The calculation method is as follows:

[0127]

[0128] in, Indicates cruising speed. Indicates the maximum acceleration. Indicates the length of the line segment;

[0129] In some specific implementations, determining the communication link of the target drone based on the inbound and outbound information of drones corresponding to multiple partitions includes:

[0130] Based on the entry and exit information of the target UAV, the path point sequence of the target UAV in the target partition is determined, that is, the path point sequence generated by the above-mentioned collected path points.

[0131] Based on the path point sequence, the reachability of the communication link between the target UAV and the first target communication base station at the target path point is determined. The reachability of the communication link is determined by whether there are obstacles between the target UAV and the first target communication base station and the degree of obstruction by the obstacles. The two are inversely proportional, that is, the larger the obstruction range, the smaller the reachability of the communication link.

[0132] In response to the communication link reachability not meeting a preset standard, the first target communication base station of the target UAV on the target path node is switched to the second target communication base station so that the communication link reachability meets the preset standard. The preset standard is that the communication link reachability is within a preset range, which can be set according to actual needs, such as the occurrence of communication blind spots.

[0133] Based on the entry and exit information of UAVs corresponding to multiple partitions, the communication load and interference level of the communication link within the target time period are obtained. The target time period is determined based on the entry time and exit time. The communication load refers to the amount of communication bandwidth resources used by a communication link within a time period. The interference level refers to the degree of communication interference between multiple UAVs, such as same-channel interference and adjacent-channel interference.

[0134] In response to the communication load and the interference level corresponding to the collaborative optimization value being greater than a first preset threshold, the second target communication base station of the target UAV on the target path node is switched to a third target communication base station, so that the reachability of the communication link meets the preset standard and the collaborative optimization value is less than or equal to the first preset threshold. The collaborative optimization value is calculated as B = w4X4 + w5X5, where B represents the collaborative optimization value, w4 and w5 represent weight coefficients, X4 represents the communication load, and X5 represents the interference level. The first preset threshold can be set according to actual needs.

[0135] The communication link generated between the target UAV and the third target communication base station at the target path node is defined as the target communication link;

[0136] The set of communication links generated by multiple target communication links is defined as the communication link of the target UAV.

[0137] In some specific implementations, based on the target UAV's entry and exit information in the target partition and the communication link, controlling the target UAV to execute the inspection task corresponding to the UAV inspection task request includes:

[0138] Based on the entry and exit information of the target drone in the target partition, control the target drone to execute the inspection task corresponding to the drone inspection task request;

[0139] Based on the communication link, the image data generated by the target UAV in performing the inspection task is transmitted back to the data receiving end.

[0140] In some specific embodiments, the method further includes:

[0141] The image data transmitted back by the target UAV is acquired and analyzed to determine whether there are any anomalies in the target partition. The image analysis process is executed by the central processing unit according to the pre-set code. The specific analysis method is a common method, such as similarity matching. The specific process will not be described in detail here.

[0142] In response to any abnormal situation, an alarm message is generated and pushed to the user's device to promptly remind the user.

[0143] The aforementioned intelligent control method for full-domain AI inspection of unmanned aerial vehicles (UAVs) includes: determining a target area and first association information of the target area; dividing the target area into multiple partitions based on the first association information, wherein the association information is at least task attribute data describing the AI ​​inspection task of the area; obtaining second association information of the target partitions; determining the inspection type and priority of the target partitions based on the second association information; generating a UAV inspection task request based on the second association information, the inspection type, and the priority; and, in response to receiving the UAV inspection task request, obtaining the status information and environmental information of the UAVs in the standby queue, and... Based on the UAV inspection task request, status information, and environmental information, the target UAV associated with the target partition and the entry / exit information of the target UAV in the target partition are determined. The entry / exit information includes at least time and location. Based on the entry / exit information of UAVs corresponding to multiple partitions, the communication link of the target UAV is determined. Based on the entry / exit information of the target UAV in the target partition and the communication link, the target UAV is controlled to execute the inspection task corresponding to the UAV inspection task request. This application improves inspection efficiency, accuracy, and safety by decomposing the task across the entire domain, coordinating multi-UAV planning, and performing real-time AI analysis.

[0144] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0145] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for intelligent control and management of UAV full-domain AI inspection. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0146] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0148] S1: Determine the target area and the first association information of the target area; based on the first association information, divide the target area to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0149] S2: Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0150] S3: In response to receiving the UAV inspection task request, obtain the status information and environmental information of the UAV in the standby queue, and determine the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition based on the UAV inspection task request, status information and environmental information, wherein the cut-in and cut-out information includes at least time and location.

[0151] S4: Based on the entry and exit information of drones corresponding to multiple partitions, determine the communication link of the target drone, and based on the entry and exit information of the target drone in the target partition and the communication link, control the target drone to execute the inspection task corresponding to the drone inspection task request.

[0152] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0153] S1: Determine the target area and the first association information of the target area; based on the first association information, divide the target area to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0154] S2: Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0155] S3: In response to receiving the UAV inspection task request, obtain the status information and environmental information of the UAV in the standby queue, and determine the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition based on the UAV inspection task request, status information and environmental information, wherein the cut-in and cut-out information includes at least time and location.

[0156] S4: Based on the entry and exit information of drones corresponding to multiple partitions, determine the communication link of the target drone, and based on the entry and exit information of the target drone in the target partition and the communication link, control the target drone to execute the inspection task corresponding to the drone inspection task request.

[0157] In one embodiment, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, performs the following steps:

[0158] S1: Determine the target area and the first association information of the target area; based on the first association information, divide the target area to generate multiple partitions, wherein the association information is at least task attribute data used to describe the regional AI inspection task;

[0159] S2: Obtain the second association information of the target partition, determine the inspection type and priority of the target partition based on the second association information, and generate a UAV inspection task request based on the second association information, inspection type and priority;

[0160] S3: In response to receiving the UAV inspection task request, obtain the status information and environmental information of the UAV in the standby queue, and determine the target UAV associated with the target partition and the cut-in and cut-out information of the target UAV in the target partition based on the UAV inspection task request, status information and environmental information, wherein the cut-in and cut-out information includes at least time and location.

[0161] S4: Based on the entry and exit information of drones corresponding to multiple partitions, determine the communication link of the target drone, and based on the entry and exit information of the target drone in the target partition and the communication link, control the target drone to execute the inspection task corresponding to the drone inspection task request.

[0162] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] The embodiments described above are merely examples of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A global AI inspection intelligent management and control method for a UAV, characterized in that, The method comprises: determining a target area and first associated information of the target area, dividing the target area based on the first associated information, and generating a plurality of partitions, wherein the associated information is at least task attribute data for describing an AI inspection task of the area; obtaining second associated information of a target partition, determining an inspection type and a priority of the target partition based on the second associated information, and generating a UAV inspection task request based on the second associated information, the inspection type, and the priority; in response to receiving the UAV inspection task request, obtaining state information and environmental information of a UAV in a standby queue, determining a target UAV associated with the target partition and entry and exit information of the target UAV in the target partition based on the UAV inspection task request, the state information, and the environmental information, the entry and exit information at least including time and position; determining a communication link of the target UAV based on the entry and exit information of the UAV corresponding to the plurality of partitions, and controlling the target UAV to perform an inspection task corresponding to the UAV inspection task request based on the entry and exit information of the target UAV in the target partition and the communication link; dividing the target area based on the first associated information to generate a plurality of partitions comprises: determining a first division rule of the target area according to an object attribute of an inspection object, comprising: in response to the first object attribute of the inspection object being a geographical feature, dividing the target area based on a boundary of the geographical feature to generate a first partition; in response to the first object attribute of the inspection object being a non-geographical feature, dividing the target area based on an area balancing mechanism to generate a second partition; in response to the second partition having been divided, determining a second division rule of the target area based on inspection accuracy and an inspection key target, comprising: obtaining a level corresponding to the inspection accuracy and an attribute of the inspection key target, and determining a task amount of the second partition based on the level and the attribute; adjusting a division result of the second partition based on a task amount balancing mechanism and the task amount of the second partition to generate a third partition; defining the first partition and / or the third partition as the plurality of partitions generated by dividing the target area. 2.The method of claim 1, wherein, determining a target area and first associated information of the target area comprises: receiving an inspection request of a user end, the inspection request of the user end at least including a geographical area range; determining the target area based on the geographical area range; extracting first associated information of the target area from a database according to the target area, the first associated information at least including an inspection object, an inspection accuracy, and an inspection key target. 3.The method of claim 2, wherein, obtaining second associated information of a target partition, and determining an inspection type and a priority of the target partition based on the second associated information comprises: obtaining second associated information of a target partition, the second associated information at least including an inspection object; determine, based on the second object attribute of the inspection object, an inspection type of the target partition, the second object attribute including at least an inspection manner, an inspection frequency, and an inspection accuracy; determine, according to the inspection type of the target partition, a factor affecting an execution order of the target partition inspection task, and determine a comprehensive evaluation value according to the factor; determine, based on the comprehensive evaluation value, a priority of the target partition. 4.The method of claim 3, wherein, generating the UAV inspection task request based on the second correlation information, the inspection type, and the priority includes: obtaining the second correlation information, the inspection type, and the priority of the target partition; mapping the second correlation information, the inspection type, and the priority, generating a mapping relationship including a unique identifier, and filling the mapping relationship into a corresponding field of a task request template to generate the UAV inspection task request. 5.The method of claim 4, wherein, in response to receiving the UAV inspection task request, obtaining state information and environment information of a UAV in a standby queue includes: in response to receiving the UAV inspection task request, determining a UAV in an idle state and / or having a remaining task execution time less than a first preset threshold, and arranging, according to a task execution state, UAV identifiers corresponding to the UAVs in the standby queue in order; obtaining state information of the UAVs in the standby queue, the state information including at least a health state, an equipment state, a task execution state, and a real-time running state; obtaining environment information of the target partition, the environment information including at least meteorological information, geographical information, airspace information, and electromagnetic information; mapping the state information and the environment information of the UAVs in the standby queue to generate a mapping relationship and save the mapping relationship. 6.The method of claim 5, wherein, determining, according to the UAV inspection task request, the state information, and the environment information, a target UAV associated with the target partition and cut-in and cut-out information of the target UAV in the target partition includes: determining, according to the environment information of the target partition, a first set of UAVs associated with the target partition; screening, according to the inspection type, the priority, and the state information, a second set of UAVs associated with the target partition from the first set of UAVs; defining the UAVs in the second set of UAVs as target UAVs associated with the target partition; determining, based on a space-time coordination mechanism and the inspection type, cut-in and cut-out information of the target UAV in the target partition. 7.The method of claim 6, wherein, determining, according to cut-in and cut-out information of a plurality of partition corresponding UAVs, a communication link of the target UAV includes: determining, based on the cut-in and cut-out information of the target UAV, a path point sequence of the target UAV in the target partition; determining, according to the path point sequence, a communication link accessibility degree of the target UAV to a first target communication base station at a target path point; in response to the communication link accessibility degree not meeting a preset standard, switching the first target communication base station at the target path point to a second target communication base station to make the communication link accessibility degree meet the preset standard; According to the cut-in and cut-out information of the unmanned aerial vehicle corresponding to the multiple partitions, communication load and interference degree of the communication link in a target time period are obtained, the target time period being determined based on the cut-in time and the cut-out time; In response to a cooperative optimization value corresponding to the communication load and the interference degree being greater than a first preset threshold, a second target communication base station of the target unmanned aerial vehicle at a target path node is switched to a third target communication base station, so that the communication link reachability degree meets a preset standard and the cooperative optimization value is less than or equal to the first preset threshold; A communication link generated by the target unmanned aerial vehicle at the target path node and the third target communication base station is defined as a target communication link; A set of communication links generated by the multiple target communication links is defined as a communication link of the target unmanned aerial vehicle. 8.The method of claim 7, wherein, Based on the cut-in and cut-out information of the target unmanned aerial vehicle in the target partition and the communication link, the target unmanned aerial vehicle is controlled to perform an inspection task corresponding to the unmanned aerial vehicle inspection task request, including: According to the cut-in and cut-out information of the target unmanned aerial vehicle in the target partition, the target unmanned aerial vehicle is controlled to perform an inspection task corresponding to the unmanned aerial vehicle inspection task request; Based on the communication link, image data generated by the target unmanned aerial vehicle performing the inspection task is transmitted back to a data receiving end. 9.The method of claim 8, wherein, The method further includes: Obtaining the image data transmitted back by the target unmanned aerial vehicle, and analyzing the image data to determine whether there is an abnormal situation in the target partition; In response to the existence of an abnormal situation, alarm information is generated and pushed.

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