Inspection robot control system and method based on inspection information

Abstract

The invention belongs to the technical field of automation control, and discloses an inspection robot control system and method based on inspection information, and the inspection robot control system based on the inspection information comprises a path planning module, an obstacle detection module, a region evaluation module, a path reconstruction module and a state recovery module. By acquiring the coordinate point set of the inspection map and the start-stop position of the robot and analyzing the path steering change and distance accumulation, the navigation precision of the inspection robot in a complex environment is optimized, roundabout and repeated movement caused by inaccurate path planning is reduced, the real-time distance between an obstacle and the path is measured and judged, and the navigation precision of the inspection robot is improved. And the response speed and accuracy of dealing with sudden obstacles are improved, when the path is reconstructed, through analysis of connection curve data, the smoothness of the path and the movement continuity of the inspection robot are ensured, the operation efficiency of the inspection robot is improved, the manpower monitoring demand is reduced, and the resource configuration is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control, and in particular, relates to a patrol robot control system based on patrol information and a method thereof. Background Art

[0002] The field of automation and control technology includes a wide range of application technologies and tools to enhance the independent operation capabilities of equipment, machines and systems. This field covers everything from simple mechanical automation to complex electronic and computer control systems. Its core content includes improving production efficiency, reducing human operating errors and optimizing resource utilization. Automation and control systems can be used in various industries, businesses and daily life, such as manufacturing, traffic control, home automation, etc. Automation and control systems rely on sensor data to make decisions and perform tasks. Their design and implementation require comprehensive consideration of multiple technical fields such as mechanical engineering, electronic engineering and software development.

[0003] The inspection robot control system refers to the use of collected on-site data to manage and control the behavior and path of the inspection robot, covering technical matters such as the inspection robot's path planning, data collection, and real-time monitoring.

[0004] The Chinese invention patent with patent application number: CN202311606621.X discloses a multi-scenario robot intelligent inspection method, which includes: generating robot inspection tasks and calling digital information of the inspection area; interactively obtaining basic data; outputting path planning results and using them as standard reference paths; performing path image acquisition; generating obstacle features and road features; configuring preview distance and obstacle avoidance reference; generating regression paths; and controlling the robot to complete the inspection tasks.

[0005] The above-mentioned existing robot intelligent inspection method adopts the method of analyzing the data collected by the robot to dynamically adjust the robot's operation path and inspection tasks; the system mainly uses data analysis technology and path planning technology to achieve effective control of the inspection robot; in this way, the inspection robot can perform the predetermined tasks more accurately while ensuring accurate collection of information and real-time feedback.

[0006] Although the existing technology in automated control systems can handle various mechanical and electronic control tasks, it still has limitations in path planning and obstacle handling in complex environments. For example, traditional path planning cannot be adjusted in real time, and the response to sudden obstacles is not flexible enough, which can easily cause the robot to suspend work or detour, affecting efficiency. The old system lacks instant updates and analysis of detailed information on obstacles, making it difficult to accurately calculate safe distances, increasing the risk of erroneous operations. These shortcomings limit the application scope and effectiveness of automated systems in complex environments. For example, in industrial sites with multiple obstacles or in ever-changing outdoor environments, improper path planning and untimely obstacle handling can lead to unstable task execution and even affect the safety of the overall operation. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an inspection robot control system based on inspection information and a method thereof, which are used to solve the limitations of path planning and obstacle handling in complex environments in the prior art. Traditional path planning cannot be adjusted in real time, the response to sudden obstacles is not flexible enough, and the detailed information of obstacles cannot be updated and analyzed in real time, making it difficult to accurately calculate the safe distance, increasing the risk of erroneous operations; and it cannot better adapt to industrial sites with multiple obstacles or changing outdoor environments, reducing the effect and scope of use.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: A patrol robot control system based on patrol information, the system comprising: The path planning module obtains the two-dimensional coordinate point set of the inspection map, the robot's starting and ending positions, and the starting inspection path data, segments the path according to the degree of change in the direction of the path, assigns a unique logical segment identifier and a spatial block number to each path segment, completes the number assignment through the spatial projection mapping of the two-dimensional map coordinate range, and generates a path number spatial data table; The obstacle detection module collects obstacle point information detected by the inspection robot based on the path number space data table, measures the straight-line distance between the obstacle center and the real-time point of the path, and determines whether the straight-line distance is shorter than the stopping distance. If so, the real-time path execution is suspended to obtain a temporary obstacle avoidance path data set; The regional assessment module continuously updates the real-time occupancy status corresponding to the temporary obstacle avoidance path number area according to the temporary obstacle avoidance path data set, calculates the connection distance and the safety distance value from the obstacle boundary point to the path segment point set, and generates a regressed reachable path identifier; The path reconstruction module uses the target path number recorded in the regressive reachable path identifier and the position of the real-time inspection robot to verify the continuity of the direction, speed and angle change of the inspection path and obtain the path connection curve data.

[0009] The following is a further optimization of the above technical solution by the present invention: The path number spatial data table includes a logical segment identifier, a spatial block number, and a coordinate mapping relationship; the temporary obstacle avoidance path data set includes a path segment interruption identifier, a position coordinate, and an interruption status; the regressive reachable path identifier includes an occupancy status, a connectivity distance, and a safety distance value; the path connection curve data includes a direction difference, a distance value, and a deflection angle.

[0010] Further optimization: The path planning module includes: The path point recognition submodule obtains the two-dimensional coordinate point set, the robot's starting and ending positions, and the starting inspection path data in the inspection map, extracts the coordinate change value and coordinate point sequence between consecutive points in the path, and selects the coordinate point sequence that meets the path connectivity conditions according to the arrangement order of the consecutive coordinate points and the path connectivity between the robot's starting and ending positions, and generates the path point sequence quantity value; The path segment identification submodule extracts a multi-segment path point sequence based on the path point sequence quantity value, assigns continuous logical numbers to the path point segments in sequence order, evaluates the association between the logical segment identification value and the path segment, and obtains a path logical segment number record; The spatial block number mapping submodule calculates the coordinate projection value of the logical segment path point according to the path logical segment number record and the two-dimensional coordinate value range of the path point in the path segment, and assigns the corresponding spatial number to the logical segment according to the map area grid division and matching spatial block number where the projection value is located, and generates a path number spatial data table.

[0011] Further optimization: The obstacle detection module includes: The obstacle information extraction submodule collects the two-dimensional coordinate information of the obstacle points detected by the inspection robot and the coordinates of the center position of the obstacle points, extracts the path point coordinate range and the path segment identification value in the path number space data table, performs regional comparison on the obstacle point coordinates and selects the center coordinates of the obstacle points within the path point coordinate range, and generates the obstacle point path attribution quantity value; The path distance measurement submodule calls the obstacle point path attribution quantity value, obtains the coordinate value of the center position of each obstacle point and the real-time execution point of the corresponding path segment, calculates the Euclidean distance value between the center of the obstacle point and the real-time path point, counts the distance value between the obstacle point and the path point in the path segment, and generates the path segment obstacle distance value; The path interruption judgment submodule makes a difference judgment between the obstacle distance value and the stop distance threshold in each path segment according to the obstacle distance value of the path segment and the stop distance threshold set by the inspection robot. When the distance value is shorter than the stop distance threshold, the logical number of the real-time path segment, the coordinate position of the path point and the path execution interruption status mark are recorded, and the interruption point information of the path segment is integrated to obtain a temporary obstacle avoidance path data set.

[0012] Further optimization: The regional assessment module includes: The status update submodule extracts the path segment number and the interruption status mark in the path number area based on the temporary obstacle avoidance path data set, updates the occupancy status of the interruption status path segment number in real time, and obtains the occupancy status record of the path number area by referring to the mapping relationship between the occupancy status and the path number; The boundary distance calculation submodule calls the path number area occupancy status record, extracts the coordinate values ​​of the obstacle boundary point set and the path segment point set in the corresponding area, calculates the distance value between the boundary point and the path segment point, compares the safety critical distance value of each set of boundary points and the path segment point set, obtains the safety distance judgment result under each path segment, and generates the path segment boundary safety distance value; The formula for calculating the distance value between the boundary point and the path segment point is as follows: ; in, Represents the boundary point set and path segment point set The average distance between Represents the boundary point set Middle The x-coordinate of the point, Represents a set of path segment points Middle The x-coordinate of the point, Represents the boundary point set Middle The y coordinate of the point, Represents a set of path segment points Middle The y coordinate of the point, Represents the boundary point set Middle The z coordinate of the point, Represents a set of path segment points Middle The z coordinate of the point, For the Directional weight coefficient for coordinate difference, For the Adjustment parameters for the effect of z-coordinate differences on distance, is the total number of point pairs involved in the calculation; The path feasibility judgment submodule performs interval judgment on the numerical relationship between the path connectivity distance value and the safety distance value according to the path segment boundary safety distance value and the connectivity distance value of each path segment in the path numbering area. When the connectivity distance exceeds the safety distance threshold from the path interruption point to the boundary point, the path is marked as a regressible path, and the logical numbers of the regressible path segments are integrated to generate a regressive reachable path identifier.

[0013] Further optimization: the path reconstruction module includes: The coordinate extraction submodule extracts the starting point coordinate value of the target path segment and the position coordinate of the robot during the actual inspection based on the target path number recorded in the regressive reachable path identifier and the real-time position of the inspection robot, calculates the coordinate difference between the two points and extracts the coordinate attribute combination of the two points to generate the logarithmic value of the path start and end coordinates; The connection curve construction submodule calls the path start and end coordinate pairs, obtains the direction difference, straight line distance and deflection angle between each set of coordinate pairs, arranges the direction difference, distance and deflection angle as curve parameters in order, and generates a path segment connection curve parameter group; The continuity verification submodule extracts the direction value, speed value and angle change value of the real-time segment of the inspection path according to the path segment connection curve parameter group, and performs sequence comparison and continuous change trend judgment on the three parameters of the connection curve and the real-time path segment direction, speed and angle change values. When the change range of the three is within the continuous change threshold interval, it is marked as a successful connection, and the path connection curve data is obtained.

[0014] Further optimization: Calculate the coordinate difference between two points using the formula: ; in, Representative The path difference feature value between the group path points, The first The horizontal coordinate value, Represents the number of times the inspection robot actually inspects The horizontal coordinate value, The first The vertical coordinate value, Represents the number of inspections performed by the inspection robot. The vertical coordinate value, Represents the total number of coordinate pairs within the target path segment.

[0015] Further optimization: the system also includes a state recovery module: The state recovery module detects the number of obstacle points in the connection area according to the straight-line distance between the path starting point and the real-time position of the robot recorded in the path connection curve data, and measures the angle offset value between the path direction and the original inspection path direction, and determines whether the value meets the conditions for continuing to execute. If so, the original inspection task is continued to be executed to generate a path state recovery result; The path status recovery result includes the number of obstacle point distribution, path direction angle offset value, and path executable status.

[0016] Further optimization: the state recovery module includes: The distance detection submodule calculates the straight-line distance between the two points based on the coordinate value between the path starting point and the real-time position of the robot recorded in the path connection curve data, extracts the coordinates of the obstacle points in the connection area, counts the number of obstacle points within the coverage range of the path segment, and generates a distribution value of obstacle points in the connection area; The direction deviation measurement submodule calls the obstacle point distribution quantity value of the connection area, extracts the data direction of the path connection curve and the direction data of the original inspection path, calculates the direction vector angle between the two, identifies the path connection angle deviation interval, and generates the path direction angle deviation value; The execution judgment submodule compares the obstacle point distribution value with the path pass critical value based on the path direction angle offset value and the obstacle point distribution value in the connection area. When the two values ​​are within the range of continuing execution conditions, the path connection state is restored and the original inspection task identifier is activated to generate a path state recovery result.

[0017] The present invention also provides a patrol robot control method based on patrol information, which is executed based on the above-mentioned patrol robot control system based on patrol information, and includes the following steps: S1: Obtain the two-dimensional coordinate point set, the robot's starting and ending positions, and the initial inspection path data in the inspection map, calculate the cumulative distance value of adjacent coordinate points in the path and the direction angle change value between consecutive points, perform regional mapping and segment identification allocation according to the spatial coordinate point positions within the two-dimensional map range, and generate a path number spatial data table; S2: calling the path number space data table, collecting the obstacle center coordinate value detected by the inspection robot, measuring the straight-line distance value between the obstacle center coordinate value and the real-time point of the path, comparing the distance value with the inspection robot stop distance threshold, and judging whether it is less than the stop distance threshold. If so, marking the real-time path segment as an interrupted state, and obtaining a temporary obstacle avoidance path data set; S3: According to the temporary obstacle avoidance path data set, continuously update the obstacle coverage area status under the corresponding path segment number, obtain the distance value between the obstacle boundary coordinate value and the connection point coordinate of the path segment point set, calculate the total value of the connected distance of the feasible connection segment number, determine whether the reachable number meets the pass condition, and generate a regressed reachable path identifier; S4: using the regressive reachable path identification, extracting the coordinate values ​​of two points between the target numbered path point and the real-time robot position, calculating the direction angle difference, distance value and angle deflection between the two points, constructing a connection curve segment with the direction angle difference as the reference angle and the distance as the radius, and obtaining the path connection curve data; S5: Using the path connection curve data, count the number of obstacle points in the connection path area, calculate the angle offset value between the connection path direction angle and the original path direction angle, and determine whether the number of obstacle points is less than the obstacle density. If so, continue the task by connecting to the original path segment and generate a path state recovery result.

[0018] The present invention adopts the above technical solution, which has at least the following beneficial effects: The present invention achieves refined management of the path by acquiring the coordinate point set of the inspection map and the start and end positions of the robot, analyzing the path turning changes and distance accumulation. This precise path segmentation and number allocation can optimize the navigation accuracy of the inspection robot in complex environments and reduce detours and repeated movements caused by inaccurate path planning; measure and judge the real-time distance between the obstacle and the path, and generate an obstacle avoidance path to improve the reaction speed and accuracy to sudden obstacles; continuously evaluate the occupancy status of the obstacle avoidance area and calculate the safe distance, provide the inspection robot with effective regression path selection, and ensure the continuity and efficiency of the task.

[0019] When reconstructing the path in the present invention, the smoothness of the path and the continuity of the inspection robot's movement are ensured by analyzing the connection curve data; during the state recovery process, the deviation of the path from the original plan is monitored and adjusted in time to ensure the accuracy and reliability of the inspection, improve the operating efficiency of the inspection robot, reduce the demand for human monitoring, and optimize resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a schematic diagram of a control system in an embodiment of the present invention; Figure 2Schematic diagram of the framework of the control system in the embodiment of the present invention; Figure 3 Flow chart of the control method in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In the embodiments of the present invention, words such as "example" and "for example" are used to indicate examples, illustrations or explanations; any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or advantageous than other embodiments or designs; to be precise, the use of the word "example" is intended to present concepts in a concrete way; in addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0023] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent.

[0024] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0025] In order to make the technical problems to be solved, technical solutions and beneficial effects of the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0026] like Figure 1-2 As shown together, a patrol robot control system based on patrol information, the system includes: The path planning module obtains the two-dimensional coordinate point set of the inspection map, the robot's starting and ending positions, and the starting inspection path data, and segments the path according to the degree of turning change between consecutive points in the path and the cumulative distance between adjacent points. A unique logical segment identifier and spatial block number is assigned to each path segment, and the number assignment is completed through spatial projection mapping of the two-dimensional map coordinate range to generate a path number spatial data table.

[0027] The obstacle detection module collects the obstacle point information detected by the inspection robot based on the path number spatial data table, measures the straight-line distance between the obstacle center and the real-time point of the path, and determines whether the straight-line distance is shorter than the stopping distance. If so, the real-time path execution is suspended, and the real-time path segment interruption mark, position coordinates and interruption status are recorded to obtain a temporary obstacle avoidance path data set.

[0028] The regional assessment module continuously updates the real-time occupancy status of the temporary obstacle avoidance path number area based on the temporary obstacle avoidance path data set, calculates the connectivity distance and the safety distance value from the obstacle boundary point to the path segment point set, determines the feasibility of the regression path, and generates a regression reachable path identifier.

[0029] The path reconstruction module uses the target path number recorded in the regressive reachable path identification and the position of the real-time inspection robot to extract the coordinate value between the two points, calculate the direction difference, distance value and deflection angle of the two points, and construct a connection curve according to the three values ​​to verify the continuity of the direction, speed and angle changes of the inspection path, and obtain the path connection curve data.

[0030] The state recovery module detects the number of obstacle points in the connection area based on the straight-line distance between the path starting point and the robot's real-time position recorded in the path connection curve data, and measures the angular offset value between the path direction and the original inspection path direction to determine whether the value meets the conditions for continuing execution. If so, the original inspection task will continue to be executed to generate a path state recovery result.

[0031] The path number spatial data table includes the logical segment identifier, spatial block number, and coordinate mapping relationship; the temporary obstacle avoidance path data set includes the path segment interruption identifier, position coordinates, and interruption status; the regression reachable path identifier includes the occupancy status, connectivity distance, and safety distance value; the path connection curve data includes the direction difference, distance value, and deflection angle; the path state recovery results include the number of obstacle point distribution, path direction angle offset value, and path executable status.

[0032] Specifically, if Figure 2 As shown, the path planning module includes: The path point recognition submodule obtains the two-dimensional coordinate point set, the robot's starting and ending positions, and the starting inspection path data in the inspection map, extracts the coordinate change values ​​and coordinate point sequences between consecutive points in the path, and selects the coordinate point sequences that meet the path connectivity conditions based on the arrangement order of consecutive coordinate points and the path connectivity between the robot's starting and ending positions, and generates the path point sequence quantity value.

[0033] The inspection map is loaded into the recognition device and converted into a standard two-dimensional pixel coordinate grid structure. Each pixel block represents a space of a certain physical size. One grid is set to 0.5 meters. The map layer information is parsed to extract the passable area and obstacle area. After extracting the specific pixel coordinates of the robot's starting point and end point, the system extracts the path points of the starting path. The path points are continuously sampled in the walkable area with a certain step length to form a path point sequence. Every two consecutive path points are analyzed pair by pair, and their horizontal and vertical offsets are calculated. The connectivity is judged based on whether there is continuity between the path points and whether they are in the passable area. If there is an obstacle area between a pair of points or the horizontal and vertical jumps are greater than the set step length limit, the path is deemed to be disconnected and excluded. The recognition process is repeated at each step. After the first judgment, the legal point pairs are appended to the connected path point sequence. Based on the starting position of the robot, a point-to-point connectivity structure is constructed in a step-by-step recursive manner to ensure that each path has a complete and passable continuous point connection from the starting point to the end point. If there is a branch path, each branch path is also subjected to connectivity analysis to screen out several groups of point sequences that meet the complete path connectivity conditions, and the number of groups is counted as the path point sequence quantity value. The above process can be explained in conjunction with a typical industrial scenario in a certain area. During the internal inspection of the factory, the path point data is extracted from the factory map, and multiple passable path segments are formed after avoiding the factory building and equipment area. Five groups of independent connected paths can be identified, and each group of paths can be used as the basis for subsequent scheduling and path optimization to generate a path point sequence quantity value.

[0034] The path segmentation identification submodule extracts multi-paragraph path point sequences based on the number value of the path point sequence, assigns continuous logical numbers to the path point segments in sequence order, evaluates the association between the logical segment identification value and the path segment, and obtains the path logical segment number record.

[0035] The path point sequence is segmented. Taking the starting path as a reference, the entire path is divided into multiple sections according to the logical structure between points. The number of path points in each section is automatically set according to the actual connectivity and direction consistency. Each direction change or path branch is defined as a new section. After the division is completed, a logical number is assigned to each section. The numbering sequence is automatically generated according to the arrangement sequence of the path points in the entire path. The relationship between the logical section and the path section is established. By comparing the coordinates of the start and end points of each section, it is determined whether there is continuity between the start and end points with the previous section. If continuous, the two sections are defined as logically adjacent. By establishing inter-segment adjacency records, a complete path logic segment number record table is constructed, which not only It contains the number of each section, the information of the starting and ending points, and the connection relationship between sections. Segment 1 is set to start at path point 1 and end at path point 8, and segment 2 is set to start at path point 9 and end at path point 15. Their logical numbers are L1 and L2 respectively. The system records L1 and L2 as a sequential relationship, which is convenient for building an overall path structure diagram. This logical relationship can be reflected in the indoor floor inspection path in practical applications. It is set from the channel inspection in area A to the equipment room B. The system divides the path into 3 sections according to the nodes, and forms a logical structure table after numbering them respectively, which provides a structural basis for the subsequent optimization and scheduling of the path. It also facilitates the reallocation of section numbers and the update of logical topology information when the path changes, and obtains the path logical segment number record.

[0036] The spatial block number mapping submodule calculates the coordinate projection value of the path point in the logical segment according to the path logical segment number record and the two-dimensional coordinate value range of the path point in the path segment. It assigns the corresponding spatial number to the logical segment according to the map area grid division and matching spatial block number where the projection value is located, and generates the path number spatial data table.

[0037] Identify and classify the spatial range of the path point coordinate data in each logical segment, collect the two-dimensional coordinate values ​​of all path points in each logical segment, analyze the spatial distribution of the coordinate points in the map grid, determine the map space range covered by the path segment by identifying the maximum and minimum coordinate boundaries of the path points, correspond the range to the preset map grid, set the map to be divided into grid areas with a unit of 1 meter, each grid has a unique number, extract all the grid numbers covered by the path segment as the spatial mapping block of the logical segment, assign a corresponding set of spatial numbers to each logical segment, and establish a mapping relationship record table between the logical segment and the spatial block number. For example, in a patrol task in a certain factory area, a path segment L1 covers The coordinate range of the covered area is from (2, 3) to (5, 6). After grid number matching, its spatial block number set is [203, 204, 304, 305, 306], etc. A correspondence is established between the number set and the L1 logical number, recorded as L1→B1=[203, 204, 304, 305, 306]. In this way, the system can accurately mark the actual spatial position of each path segment and uniformly use the map grid number for management, providing a unified data basis for subsequent path matching, map scheduling and other operations. The whole process is particularly applicable in indoor warehouses and equipment workshops, and can help realize the accurate binding and calling of robot paths and map spaces, and generate path number space data tables.

[0038] Specifically, if Figure 2 As shown, the obstacle detection module includes: The obstacle information extraction submodule collects the two-dimensional coordinate information of the obstacle points detected by the inspection robot and the coordinates of the center position of the obstacle points, extracts the path point coordinate range and path segment identification value in the path number space data table, performs regional comparison on the obstacle point coordinates and filters the center coordinates of the obstacle points within the path point coordinate range, and generates the obstacle point path attribution quantity value.

[0039] The two-dimensional coordinate information of the obstacle points detected by the inspection robot and the coordinates of the center position of the obstacle points are collected. This process relies on the robot's navigation system and the environment perception module to work in conjunction. During the inspection process, the robot uses multimodal devices such as the laser radar, stereo camera or infrared sensor to scan the physical objects in the surrounding environment in real time and identify the target objects that constitute obstacles. After the data collection is completed, the system will perform preliminary processing on the collected spatial point information, screen out point clusters with obvious edges and prominent shapes, and then divide the point clusters into different obstacle areas through clustering algorithms. On this basis, the system further obtains the center coordinates of each obstacle area, that is, by collectively processing the obstacle edge coordinate data, the center of mass position in the area is extracted as The representative point is called, and the path segment information stored in the path number space data table is called. Each path in the data table is composed of multiple path points. The starting point and the end point of each path segment clearly mark the spatial coordinate range. The system reads the coordinate boundary of the path segment and establishes a mapping relationship between the obstacle point and the path coordinate. The obstacle point is determined by comparing whether the center coordinate of the obstacle point falls within the boundary range of a certain path segment. If an obstacle point identified by the robot is located in the area between the starting point and the end point of the path number 001, the obstacle point belongs to path 001. The system performs coordinate comparison operations on all identified obstacle points one by one, and completes the path segment attribution statistics, laying a data foundation for subsequent distance calculation and path evaluation, and generating the obstacle point path attribution quantity value.

[0040] The path distance measurement submodule calls the obstacle point path attribution quantity value, obtains the coordinate value of the center position of each obstacle point and the corresponding path segment real-time execution point, calculates the Euclidean distance value between the center of the obstacle point and the real-time path point, counts the distance value between the obstacle point and the path point in the path segment, and generates the path segment obstacle distance value.

[0041] The center coordinates of the obstacle points of the assigned path segment are extracted one by one, and the spatial distance is compared with the coordinates of the path point where the robot is currently located. The real-time point information actually executed by the robot in the current path segment must be extracted from the system first. This point is the nearest execution coordinate point of the robot on this path segment. The system determines the execution index value of the path by querying the execution status of the path segment, and extracts the coordinates of the real-time point from the path point sequence. Then, the center position coordinates of all obstacle points in the path segment are called one by one to form a one-to-one coordinate pair with the coordinates of the current path point. For each pair of coordinates, the system uses the distance difference between the coordinates to compare the spatial relationship between the two. If the distance between an obstacle point and a path point is short, This indicates that the obstacle is close to the robot's path and needs to be marked. The system will traverse the coordinates of the obstacle points in all path segments, calculate and record the distances to the path points, and summarize, classify and number all distance values ​​in each path segment to generate an obstacle distance value sequence for path evaluation. Based on this sequence, a spatial distance mapping relationship between obstacles and path segments is established. For example, path segment 002 contains 3 obstacle points, and their distances to the current path points are 0.8 meters, 0.6 meters and 1.2 meters respectively. The obstacle distance sequence of path segment 002 is [0.8, 0.6, 1.2]. This sequence becomes the key data for subsequent judgment of the path interruption status and generates the path segment obstacle distance value.

[0042] The path interruption judgment submodule makes a difference judgment between the obstacle distance value and the stop distance threshold in each path segment according to the obstacle distance value of the path segment and the stop distance threshold set by the inspection robot. When the distance value is shorter than the stop distance threshold, it records the logical number of the real-time path segment, the coordinate position of the path point and the path execution interruption status mark, integrates the interruption point information of the path segment, and obtains a temporary obstacle avoidance path dataset.

[0043] It is necessary to obtain the stopping distance threshold set by the inspection robot from the system configuration. This value is set by the technicians according to the actual application environment during the initialization stage of the robot system. The robot movement speed is set to be low in the indoor environment, and the obstacle response range is set to 0.5 meters. In the complex factory environment, considering the irregular path and frequent obstacles, it is set to 0.7 meters or even higher. After the system reads the threshold parameter, it enters the path interruption judgment logic, compares the obstacle distance value in each path segment one by one, and judges whether it is shorter than the preset threshold, which is set in the path segment number 003. If the distance between the obstacle point and the current path point is only 0.4 meters, which is shorter than the set threshold, the obstacle point will be stopped. If the specified stopping threshold of 0.5 meters is exceeded, it is determined that there is an obstacle risk on this path segment. The system will immediately record the path segment number, obstacle point coordinates, and path point position, and attach an interruption mark. During the entire path execution process, the system continues to patrol the path segment and updates the interruption status record in real time when it detects that the obstacle distance is lower than the threshold. For example, if an obstacle point with a distance of 0.3 meters is detected in path segment 004, the system will set this segment as an interrupted path segment. Finally, the information of all interrupted path segments, including their logical numbers, corresponding obstacle point coordinates, real-time execution point positions, and path interruption status, is integrated to obtain a temporary obstacle avoidance path dataset.

[0044] Specifically, if Figure 2 As shown, the regional assessment module includes: Based on the temporary obstacle avoidance path dataset, the status update submodule extracts the path segment number and the interruption status mark in the path number area, updates the occupancy status of the interruption status path segment number in real time, and obtains the occupancy status record of the path number area by referring to the mapping relationship between the occupancy status and the path number.

[0045] It is necessary to extract the path segment numbers and their corresponding interruption status marks in the path number area from the temporary obstacle avoidance path data set. The path number area can be defined as a set of continuous path segment logical numbers in the task area. The robot operation interval is set to the path segment numbers from A001 to A050. Each path segment will be assigned a status mark in real time, such as "0" for normal, "1" for interruption, and "2" for unknown. This mark is updated based on real-time perception data. Sensor devices such as lidar, cameras or ultrasonic modules perceive obstacles on the path segment and feed them back to the system in real time. After extracting the path segment number and status mark, the system needs to execute the status update process for the path segment numbers that are currently in the interruption state one by one. The update action is based on the data changes between the robot's current position and the perception range. If the robot moves to A025 and detects that there is no obstacle blocking the path segment A027, the original mark "1" can be updated to "0". If the robot detects a large static obstacle on the path segment A034, the system marks it as interrupted and updates the status of the path segment to "1" in real time. Then the system maps the path number with its current occupancy status, compares the path number with its status value, and determines the path passability. The record structure needs to reflect the path number, status value, update time and other fields. The status record at a certain time node is set as: {A023:0, A024:1, A025:0, A026:1}, indicating that the current A024 and A026 path segments are in an interrupted state, the path is blocked, and the path number area occupancy status record is obtained.

[0046] The boundary distance measurement submodule calls the path number area occupancy status record, extracts the coordinate set of obstacle boundary points and the coordinate value of the path segment point set in the corresponding area, calculates the distance value between the boundary point and the path segment point, compares the safety critical distance value of each set of boundary points and the path segment point set, obtains the safety distance judgment result under each path segment, and generates the path segment boundary safety distance value.

[0047] The formula for calculating the distance value between the boundary point and the path segment point is as follows: ; in, Represents the boundary point set and path segment point set The average distance between Represents the boundary point set Middle The x-coordinate of the point, Represents a set of path segment points Middle The x-coordinate of the point, Represents the boundary point set Middle The y coordinate of the point, Represents a set of path segment points Middle The y coordinate of the point, Represents the boundary point set Middle The z coordinate of the point, Represents a set of path segment points Middle The z coordinate of the point, For the Directional weight coefficient for coordinate difference, For the Adjustment parameters for the effect of z-coordinate differences on distance, is the total number of point pairs involved in the calculation.

[0048] Parameter meaning and formula calculation derivation process: Let the boundary point set be numbered , the path segment point set number is , the number of point pairs involved in the calculation is selected as , the coordinates of each point are collected by the laser radar sensor, the collection frequency is 10Hz, the accuracy is controlled within the range of ±2cm, the point coordinate unit is meter, and the data is filtered and processed as follows: Point 1: , , , , , ; Point 2: , , , , , ; Point 3: , , , , , ; Weight coefficient Characterize the degree of influence caused by the angle between the path direction and the offset direction of the boundary point. According to the measurement result of the direction difference angle between the boundary point and the path segment, it is converted into a weight coefficient. The weight coefficient value range is defined as 0.5 to 1.5. When the measured angle is within 15°, the weight coefficient is set to 0.8; when the measured angle is within 30°, the weight coefficient is set to 1.0; when the measured angle is within 45°, the weight coefficient is set to 1.2. The angles of the current point pair measured by the angle sensor are 18°, 12°, and 25°, respectively. Therefore, the weight coefficients of the three groups of points are , , ; Adjustment parameters The adjustment value that characterizes the overall distance judgment of different height differences is derived from the standard site height tolerance database setting, with a setting range of 0 to 0.5. It is set based on the quantification of the slope change rate of the path segment and the ground undulation coefficient. The adjustment parameters of the height difference of the three groups of points on the distance are: , , ; The item-by-item calculation process is as follows: Molecular calculation: Point 1: ; ; Multiply by the weight factor : ; Add the absolute difference: ; Point 2: ; ; Multiply by the weight factor : ; Add the absolute difference: ; Point 3: ; ; Multiply by the weight factor : ; Add the absolute difference: ; The sum of the numerators is: ; Denominator calculation: Point 1: , multiplied by the adjustment parameter ; Point 2: , multiplied by the adjustment parameter ; Point 3: , multiplied by the adjustment parameter ; And is: , the absolute value is ; The denominator is: ; Substitute into the calculation formula: ; The result shows that the average boundary distance value between the current path segment 1 and the boundary point set 1 is 0.716 meters, which is the basic measurement for calculating the path segment boundary safety distance and serves as the core reference value for subsequent comparison with the safety critical distance.

[0049] The path feasibility judgment submodule makes an interval judgment on the numerical relationship between the path connectivity distance value and the safety distance value based on the safety distance value of the path segment boundary and the connectivity distance value of each path segment in the path numbering area. When the connectivity distance exceeds the safety distance threshold from the path interruption point to the boundary point, the path is marked as a regressible path, and the logical numbers of the regressible path segments are integrated to generate a regressive reachable path identifier.

[0050] Compare the boundary safety distance value of the path segment with the connectivity distance value of the path segment in the path number area to determine whether the current path segment set can form a connected and regressive path. During the execution process, the system needs to organize and summarize the continuous path segments in a certain area and calculate the total connectivity length. The path segments numbered A031 to A036 are each 1.0 to 1.5 meters long. After calculation, the total connectivity distance is 7.2 meters. The system then searches for the boundary safety distance value of the corresponding path segment, such as A032 is 0.8 meters, A033 is 1.3 meters, A034 is 1.0 meters, etc. The system needs to identify the minimum boundary safety distance value in the segment and combine it with the set passage The judgment standard threshold is used for judgment. The threshold can be set as the minimum boundary safety distance plus the fixed redundant distance. The redundant distance is set to 0.2 meters and the minimum boundary distance value is 0.9 meters. The path segment needs to meet the connection distance greater than 1.1 meters to be considered as a regressible path segment. In the above example, the connection distance reaches 7.2 meters, which is much larger than the threshold. The system marks the path segment interval A031 to A036 as a path with regression conditions and generates a corresponding logical number and regression path identifier. The identifier will be accompanied by the path segment set number, connection distance value, judgment status, timestamp and other information for the scheduling system to use in subsequent path replanning or path optimization to generate a regression reachable path identifier.

[0051] Specifically, if Figure 2 As shown, the path reconstruction module includes: The coordinate extraction submodule extracts the starting coordinate value of the target path segment and the position coordinates of the robot during actual inspection based on the target path number recorded in the regressed reachable path identifier and the real-time position of the inspection robot, calculates the coordinate difference between the two points and extracts the coordinate attribute combination of the two points to generate the logarithmic value of the path start and end coordinates.

[0052] To calculate the coordinate difference between two points, the formula used is: ; in, Representative The path difference feature value between the group path points, The first The horizontal coordinate values, Represents the number of times the inspection robot actually inspects The horizontal coordinate values, The first The vertical coordinate value, Represents the number of inspections performed by the inspection robot. The vertical coordinate value, Represents the total number of coordinate pairs within the target path segment.

[0053] Parameter meaning and formula calculation derivation process: parameter , , , The coordinate data is collected in real time through laser radar positioning combined with SLAM map. The coordinate unit is meter and the resolution is 0.01 meter. Based on the actual data collected by the inspection robot, in the collection of the 8th group of coordinate points in the path segment, the following is detected: , , , ; The mean term of the horizontal total difference is: , select the path segment with a total of The coordinate points of each group are collected and analyzed, and the absolute values ​​of the lateral differences are detected in sequence: , , , , , , , , , ; After summing up: ; The mean of the horizontal difference is: ; Substituting into the main formula, it expands as follows: Item 1: ; Item 2: ; Item 3: ; Substitute the formula into the overall calculation: ; The results show that in the 8th coordinate pair of the path segment, the path difference eigenvalue between the robot inspection position and the starting point of the target path is 1.7026, which is a distance-type composite value reflecting the degree to which the robot inspection trajectory deviates from the target path. The path difference eigenvalue is used as an input parameter of the subsequent path correction module to participate in the deviation trend judgment and regression path control execution.

[0054] The connection curve construction submodule calls the path start and end coordinate pair values, obtains the direction difference, straight-line distance and deflection angle values ​​between each set of coordinate pairs, arranges the direction difference, distance value and deflection angle as curve parameters in order, and generates a path segment connection curve parameter group.

[0055] It is necessary to calculate the direction difference, straight-line distance and deflection angle value for each set of coordinate pairs. The direction difference can determine the angle direction through the relative coordinate positions of the initial and final points. Set the direction vector between the starting point (32.4, 18.6) and the end point (30.7, 17.9). Calculate the initial direction difference through the azimuth angles of adjacent path segments. If the current path direction is northwest by north, and the inspection robot is facing due west, the direction difference is about 45 degrees. The system obtains the straight-line distance between the start and end points according to the geometric coordinate distance calculation method. Set the coordinate difference of the two points to (1.7, 0.7), then the distance is about 1.83 meters, which is the length of the connecting line. Then, according to the original robot The system extracts the deflection angle value based on the angle difference between the travel direction and the direction of the intended path segment. This value reflects the degree of change in the direction of the path curvature. For example, if the original direction is 10 degrees north by east and the intended path direction is 35 degrees west by north, the deflection angle is 45 degrees. The system combines the above three parameters in sequence into a set of path segment connection curve parameter groups, which is the basic control vector of a certain connection curve. The parameter group format can be set to {B025, direction difference: 45, distance value: 1.83, deflection angle: 45}. If there are multiple path connection requirements, the system will continuously generate several curve parameter groups and arrange them according to the target path number to form a path segment connection curve parameter group.

[0056] The continuity verification submodule extracts the direction value, speed value and angle change value of the real-time segment of the inspection path according to the path segment connection curve parameter group, and performs sequence comparison and continuous change trend judgment on the three parameters of the connection curve and the real-time path segment direction, speed and angle change values. When the change range of the three is within the continuous change threshold interval, it is marked as a successful connection and the path connection curve data is obtained.

[0057] It is necessary to compare the direction value, speed value and angle change value of the path segment connection curve parameter group with the real-time segment of the inspection path. The system extracts the direction value of the robot's current path segment and sets the robot's direction of travel in the B024 path segment to 15 degrees northeast by north. The speed value is measured by the wheel speed meter and is currently 1.2 meters per second. The angle change value is provided by the IMU module, and the current angular velocity changes by 6 degrees per second. The system calls the direction difference, distance value and deflection angle information in the connection curve parameter group, and compares and analyzes whether this group of parameters has continuous transition characteristics with the current inspection segment direction, speed and angle changes, and sets the curve direction difference to 30 degrees. , the deflection angle is 40 degrees. The system makes a judgment based on the set continuous change threshold range. If the direction difference change is controlled between 0 and 45 degrees, the speed change is less than 0.8 m / s, and the angle difference is controlled within 60 degrees, it is considered continuous. The current actual direction difference is 30 degrees, the speed has not changed suddenly, and the angle change is also within the specified range, meeting the continuity requirements. The system determines that the connection is successful and records the connection relationship between the current path segment and the target segment. The data includes the source path number, the target path number, the curve parameters, the continuity status and the generation time, which are used for the navigation system to dynamically update the feasible path and form the path connection curve data.

[0058] Specifically, if Figure 2 As shown, the state recovery module includes: The distance detection submodule calculates the straight-line distance between the two points based on the coordinate value between the path starting point and the real-time position of the robot recorded in the path connection curve data, extracts the coordinates of the obstacle points in the connection area, counts the number of obstacle points within the coverage range of the path segment, and generates the distribution value of the obstacle points in the connection area.

[0059] The starting point coordinates of the target path segment are extracted from the path connection curve data, and the path segment number is set to C015, and its starting point coordinates are (45.6, 20.3). Then, the current position coordinates are obtained through the robot navigation system, such as (44.9, 19.8). The system calculates the straight-line distance between the starting point and the current position. The distance value reflects the spatial proximity between the robot and the starting point of the connection path. At the same time, combined with the physical position of the path segment, the obstacle point coordinate data within the area is extracted. The obstacle point information comes from the real-time point cloud data. The system screens the obstacle points according to the width of the path segment and the set buffer area. The path segment width is set to 1.5 meters, and 0.5 meters is extended on both sides as the perception boundary. The path segment detection width is 2.5 meters, and the path segment is collected within the rectangular area from the starting point to the end point. Collect all the point cloud coordinate points belonging to the area and mark them as obstacle points. Within this range, the system counts the number of points. For example, if 14 obstacle points are detected, the distribution coordinates and density index of the obstacle points are recorded at the same time. The density can be calculated by the number of points per unit area. If there are 14 obstacle points in a 2.5m×3m area, the obstacle point distribution value is 14, and this value is marked as the obstacle density index of the current connection area. When executing this module, the system analyzes each connectable path segment one by one, and outputs its connection distance and the number of obstacle points respectively, which are used to evaluate whether the traffic environment of the connection path segment meets the minimum standard. If there are multiple optional path segments, the system will compare the connection distance and the number of obstacles of each segment as the input basis for path deviation judgment and task recovery, and generate the number of obstacle point distribution values ​​in the connection area.

[0060] The direction offset measurement submodule calls the number of obstacle point distribution values ​​in the connection area, extracts the data direction of the path connection curve and the direction data of the original inspection path, calculates the direction vector angle between the two, identifies the path connection angle offset interval, and generates the path direction angle offset value.

[0061] After receiving the number of obstacle points in the connection area, the system further extracts the direction parameters in the connection path segment curve and the direction information of the original inspection path. The system confirms the data direction of the current connection path segment, sets the direction record in the path connection curve parameters to 15 degrees east of north, and the direction of the original inspection path segment is recorded as due east. The direction offset is measured by analyzing the angle between the two direction vectors. After the system standardizes the direction information into an angle representation, it calculates the angle difference between the two vectors, which is 15 degrees in this case, that is, the path connection angle is 15 degrees offset from the original path direction. At the same time, the system sets the offset angle interval according to the actual usage scenario. When the offset angle is less than 3 When the angle is 0 degrees, it is considered that the path still has directional continuity. If the offset angle is greater than 60 degrees, it is marked as a significant direction change and it is not recommended to connect. The judgment interval can be input by the operator in the system parameter setting, and is determined according to the operation accuracy, robot structure size and steering ability. For example, if the maximum acceptable direction offset angle set for a certain model of wheeled inspection robot is 45 degrees, then the current 15-degree offset is within the acceptable range and is used together with the number of obstacle point distribution values ​​in the connection area as input parameters for the next step of path status judgment. The direction offset analysis of all path segments will form a direction change statistics table, which is used to dynamically schedule and control the inspection robot and generate path direction angle offset values.

[0062] The execution judgment submodule compares the obstacle point distribution value with the path pass critical value based on the path direction angle offset value and the obstacle point distribution value in the connection area. When the two values ​​are within the range of continuing execution conditions, the path connection status is restored and the original inspection task identifier is activated to generate a path status recovery result.

[0063] After obtaining the path direction angle offset value and the number of obstacle points in the connection area, the two key values ​​need to be jointly determined. The system sets the critical standard value for path passage, for example, the obstacle point number threshold is set to 20, and the direction offset angle threshold is set to 30 degrees. The system compares the data of the current path segment with these two critical values. If the number of obstacle points in a connection segment is 14, which is lower than the number threshold of 20, and the direction offset angle is 15 degrees, which is also lower than the offset threshold of 30 degrees, then both items meet the conditions for continuing execution. The system restores the path connection status accordingly and marks the current connection path segment status as "Connected", and activate the original inspection task identification at the same time, that is, reset the task number in the suspended state to "activated" or "running", restore the inspection logic of this section of the task path and update the task progress data, generate the path status recovery result, which includes the recovery path number, recovery timestamp, number of obstacles, offset angle and task status identification. The system will synchronously write this information into the task control and push it to the scheduling interface for real-time monitoring by the operation and maintenance personnel. If the connection conditions are not met, the system will keep the original task suspended and record the reason for failure to ensure that the logical judgment process is complete and reliable and generate the path status recovery result.

[0064] like Figure 3 As shown, the present invention also provides a patrol robot control method based on patrol information, which is executed based on the above-mentioned patrol robot control system based on patrol information, and includes the following steps: S1: Obtain the two-dimensional coordinate point set, the robot's starting and ending positions, and the initial inspection path data in the inspection map, calculate the cumulative distance value of adjacent coordinate points in the path and the direction angle change value between consecutive points, perform regional mapping and segment identification allocation according to the spatial coordinate point positions within the two-dimensional map range, and generate a path number spatial data table; S2: Call the path number space data table, collect the obstacle center coordinate value detected by the inspection robot, measure the straight-line distance value between the obstacle center coordinate value and the real-time point of the path, compare the distance value with the inspection robot stop distance threshold, and determine whether it is less than the stop distance threshold. If so, mark the real-time path segment as interrupted to obtain a temporary obstacle avoidance path data set; S3: According to the temporary obstacle avoidance path data set, the obstacle coverage area status under the corresponding path segment number is continuously updated, the distance value between the obstacle boundary coordinate value and the connection point coordinate value of the path segment point set is obtained, the total value of the connected distance of the feasible connection segment number is calculated, and whether the reachable number meets the pass condition is determined, and the regressed reachable path identification is generated; S4: Using the regressive reachable path identification, extract the coordinate values ​​of the two points between the target number path point and the real-time robot position, calculate the direction angle difference, distance value and angle deflection between the two points, construct a connection curve segment with the direction angle difference as the reference angle and the distance as the radius, and obtain the path connection curve data; S5: Utilize the path connection curve data to count the number of obstacle points in the connection path area, calculate the angle offset between the connection path direction angle and the original path direction angle, and determine whether the number of obstacle points is less than the obstacle density. If so, continue the task by connecting to the original path segment and generate a path state recovery result.

[0065] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention; therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A patrol robot control system based on patrol information, characterized in that: The system comprises: The path planning module obtains the two-dimensional coordinate point set of the inspection map, the robot's starting and ending positions, and the starting inspection path data, segments the path according to the degree of change in the direction of the path, assigns a unique logical segment identifier and a spatial block number to each path segment, completes the number assignment through the spatial projection mapping of the two-dimensional map coordinate range, and generates a path number spatial data table; The obstacle detection module collects obstacle point information detected by the inspection robot based on the path number space data table, measures the straight-line distance between the obstacle center and the real-time point of the path, and determines whether the straight-line distance is shorter than the stopping distance. If so, the real-time path execution is suspended to obtain a temporary obstacle avoidance path data set; The regional assessment module continuously updates the real-time occupancy status corresponding to the temporary obstacle avoidance path number area according to the temporary obstacle avoidance path data set, calculates the connection distance and the safety distance value from the obstacle boundary point to the path segment point set, and generates a regressed reachable path identifier; The path reconstruction module uses the target path number recorded in the regressive reachable path identifier and the position of the real-time inspection robot to verify the continuity of the direction, speed and angle change of the inspection path and obtain the path connection curve data.

2. The inspection robot control system based on inspection information according to claim 1, characterized in that: The path number space data table includes a logical segment identifier, a spatial block number, and a coordinate mapping relationship; the temporary obstacle avoidance path data set includes a path segment interruption identifier, a position coordinate, and an interruption status; the regressive reachable path identifier includes an occupancy status, a connectivity distance, and a safety distance value; The path connection curve data includes direction difference, distance value, and deflection angle.

3. The inspection robot control system based on inspection information according to claim 2 is characterized in that: The path planning module includes: The path point recognition submodule obtains the two-dimensional coordinate point set, the robot's starting and ending positions, and the starting inspection path data in the inspection map, extracts the coordinate change value and coordinate point sequence between consecutive points in the path, and selects the coordinate point sequence that meets the path connectivity conditions according to the arrangement order of the consecutive coordinate points and the path connectivity between the robot's starting and ending positions, and generates the path point sequence quantity value; The path segment identification submodule extracts a multi-segment path point sequence based on the path point sequence quantity value, assigns continuous logical numbers to the path point segments in sequence order, evaluates the association between the logical segment identification value and the path segment, and obtains a path logical segment number record; The spatial block number mapping submodule calculates the coordinate projection value of the logical segment path point according to the path logical segment number record and the two-dimensional coordinate value range of the path point in the path segment, and assigns the corresponding spatial number to the logical segment according to the map area grid division and matching spatial block number where the projection value is located, and generates a path number spatial data table.

4. The inspection robot control system based on inspection information according to claim 3 is characterized in that: The obstacle detection module comprises: The obstacle information extraction submodule collects the two-dimensional coordinate information of the obstacle points detected by the inspection robot and the coordinates of the center position of the obstacle points, extracts the path point coordinate range and the path segment identification value in the path number space data table, performs regional comparison on the obstacle point coordinates and selects the center coordinates of the obstacle points within the path point coordinate range, and generates the obstacle point path attribution quantity value; The path distance measurement submodule calls the obstacle point path attribution quantity value, obtains the coordinate value of the center position of each obstacle point and the real-time execution point of the corresponding path segment, calculates the Euclidean distance value between the center of the obstacle point and the real-time path point, counts the distance value between the obstacle point and the path point in the path segment, and generates the path segment obstacle distance value; The path interruption judgment submodule makes a difference judgment between the obstacle distance value and the stop distance threshold in each path segment according to the obstacle distance value of the path segment and the stop distance threshold set by the inspection robot. When the distance value is shorter than the stop distance threshold, the logical number of the real-time path segment, the coordinate position of the path point and the path execution interruption status mark are recorded, and the interruption point information of the path segment is integrated to obtain a temporary obstacle avoidance path data set.

5. The inspection robot control system based on inspection information according to claim 4 is characterized in that: The regional assessment module includes: The status update submodule extracts the path segment number and the interruption status mark in the path number area based on the temporary obstacle avoidance path data set, updates the occupancy status of the interruption status path segment number in real time, and obtains the occupancy status record of the path number area by referring to the mapping relationship between the occupancy status and the path number; The boundary distance calculation submodule calls the path number area occupancy status record, extracts the coordinate values ​​of the obstacle boundary point set and the path segment point set in the corresponding area, calculates the distance value between the boundary point and the path segment point, compares the safety critical distance value of each set of boundary points and the path segment point set, obtains the safety distance judgment result under each path segment, and generates the path segment boundary safety distance value; The formula for calculating the distance value between the boundary point and the path segment point is as follows: ； in, Represents the boundary point set and path segment point set The average distance between Represents the boundary point set Middle The x-coordinate of the point, Represents a set of path segment points Middle The x-coordinate of the point, Represents the boundary point set Middle The y coordinate of the point, Represents a set of path segment points Middle The y coordinate of the point, Represents the boundary point set Middle The z coordinate of the point, Represents a set of path segment points Middle The z coordinate of the point, For the Directional weight coefficient for coordinate difference, For the Adjustment parameters for the effect of z-coordinate differences on distance, is the total number of point pairs involved in the calculation; The path feasibility judgment submodule performs interval judgment on the numerical relationship between the path connectivity distance value and the safety distance value according to the path segment boundary safety distance value and the connectivity distance value of each path segment in the path numbering area. When the connectivity distance exceeds the safety distance threshold from the path interruption point to the boundary point, the path is marked as a regressible path, and the logical numbers of the regressible path segments are integrated to generate a regressive reachable path identifier.

6. The inspection robot control system based on inspection information according to claim 5, characterized in that: The path reconstruction module comprises: The coordinate extraction submodule extracts the starting point coordinate value of the target path segment and the position coordinate of the robot during the actual inspection based on the target path number recorded in the regressive reachable path identifier and the real-time position of the inspection robot, calculates the coordinate difference between the two points and extracts the coordinate attribute combination of the two points to generate the logarithmic value of the path start and end coordinates; The connection curve construction submodule calls the path start and end coordinate pairs, obtains the direction difference, straight line distance and deflection angle between each set of coordinate pairs, arranges the direction difference, distance and deflection angle as curve parameters in order, and generates a path segment connection curve parameter group; The continuity verification submodule extracts the direction value, speed value and angle change value of the real-time segment of the inspection path according to the path segment connection curve parameter group, and performs sequence comparison and continuous change trend judgment on the three parameters of the connection curve and the real-time path segment direction, speed and angle change values. When the change range of the three is within the continuous change threshold interval, it is marked as a successful connection, and the path connection curve data is obtained.

7. The inspection robot control system based on inspection information according to claim 6, characterized in that: To calculate the coordinate difference between two points, the formula used is: ； in, Representative The path difference feature value between the group path points, The first The horizontal coordinate value, Represents the number of times the inspection robot actually inspects The horizontal coordinate value, The first The vertical coordinate value, Represents the number of inspections performed by the inspection robot. The vertical coordinate value, Represents the total number of coordinate pairs within the target path segment.

8. The inspection robot control system based on inspection information according to claim 7, characterized in that: The system also includes a state recovery module: The state recovery module detects the number of obstacle points in the connection area according to the straight-line distance between the path starting point and the real-time position of the robot recorded in the path connection curve data, and measures the angle offset value between the path direction and the original inspection path direction, and determines whether the value meets the conditions for continuing to execute. If so, the original inspection task is continued to be executed to generate a path state recovery result; The path status recovery result includes the number of obstacle point distribution, path direction angle offset value, and path executable status.

9. The inspection robot control system based on inspection information according to claim 8, characterized in that: The state recovery module comprises: The distance detection submodule calculates the straight-line distance between the two points based on the coordinate value between the path starting point and the real-time position of the robot recorded in the path connection curve data, extracts the coordinates of the obstacle points in the connection area, counts the number of obstacle points within the coverage range of the path segment, and generates a distribution value of obstacle points in the connection area; The direction deviation measurement submodule calls the obstacle point distribution quantity value of the connection area, extracts the data direction of the path connection curve and the direction data of the original inspection path, calculates the direction vector angle between the two, identifies the path connection angle deviation interval, and generates the path direction angle deviation value; The execution judgment submodule compares the obstacle point distribution value with the path pass critical value based on the path direction angle offset value and the obstacle point distribution value in the connection area. When the two values ​​are within the range of continuing execution conditions, the path connection state is restored and the original inspection task identifier is activated to generate a path state recovery result.

10. A patrol robot control method based on patrol information, characterized in that: The control system of an inspection robot based on inspection information according to any one of claims 1 to 9 is implemented, comprising the following steps: S1: Obtain the two-dimensional coordinate point set, the robot's starting and ending positions, and the initial inspection path data in the inspection map, calculate the cumulative distance value of adjacent coordinate points in the path and the direction angle change value between consecutive points, perform regional mapping and segment identification allocation according to the spatial coordinate point positions within the two-dimensional map range, and generate a path number spatial data table; S2: calling the path number space data table, collecting the obstacle center coordinate value detected by the inspection robot, measuring the straight-line distance value between the obstacle center coordinate value and the real-time point of the path, comparing the distance value with the inspection robot stop distance threshold, and judging whether it is less than the stop distance threshold. If so, marking the real-time path segment as an interrupted state, and obtaining a temporary obstacle avoidance path data set; S3: According to the temporary obstacle avoidance path data set, continuously update the obstacle coverage area status under the corresponding path segment number, obtain the distance value between the obstacle boundary coordinate value and the connection point coordinate of the path segment point set, calculate the total value of the connected distance of the feasible connection segment number, determine whether the reachable number meets the pass condition, and generate a regressed reachable path identifier; S4: using the regressive reachable path identification, extracting the coordinate values ​​of two points between the target numbered path point and the real-time robot position, calculating the direction angle difference, distance value and angle deflection between the two points, constructing a connection curve segment with the direction angle difference as the reference angle and the distance as the radius, and obtaining the path connection curve data; S5: Using the path connection curve data, count the number of obstacle points in the connection path area, calculate the angle offset value between the connection path direction angle and the original path direction angle, and determine whether the number of obstacle points is less than the obstacle density. If so, continue the task by connecting to the original path segment and generate a path state recovery result.

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