Reconnaissance robot task distribution system and method based on optimization algorithm

Abstract

The invention discloses a reconnaissance robot task distribution system and method based on an optimization algorithm, and relates to the field of robot task distribution, and the system comprises a basic data obtaining module which is used for obtaining the basic condition of a reconnaissance region and the basic condition of a reconnaissance robot, and obtaining the basic data of a target reconnaissance task; the preliminary analysis module is used for performing preliminary analysis according to the basic data of the target investigation task to obtain a task robot preliminary allocation result corresponding to the target investigation task; and the comprehensive analysis module is used for performing comprehensive analysis according to the basic data of the target investigation task and the task robot preliminary allocation result corresponding to the target investigation task to obtain a comprehensive allocation result corresponding to the target investigation task. The task allocation method has the effect of improving the task allocation efficiency of the investigation robot and the task allocation flexibility.

Description

Technical Field

[0001] The present application relates to the field of robot task allocation, and in particular to a reconnaissance robot task allocation system and method based on an optimization algorithm. Background Art

[0002] Robots have been widely used in various fields due to their low cost, small size and high flexibility, such as civil search and rescue, military reconnaissance and detection, etc. Robots are used to replace manual labor to ensure the safety of personnel performing tasks.

[0003] Generally speaking, when a robot performs a reconnaissance task, the corresponding instructions are issued manually, and then the reconnaissance robot performs the corresponding reconnaissance task according to the instructions. In the prior art, the allocation of reconnaissance tasks is still based on the traditional mechanical allocation method. Or directly through manual control, the reconnaissance task is manually allocated and manually performed.

[0004] In this way, the reconnaissance efficiency of the reconnaissance robot is low when performing reconnaissance tasks. At the same time, when the traditional task allocation model faces more difficult reconnaissance tasks, it may increase the difficulty of task allocation. Summary of the invention

[0005] The purpose of the present invention is to provide a reconnaissance robot task allocation system and method based on an optimization algorithm to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a reconnaissance robot task allocation system based on an optimization algorithm, comprising: Basic data acquisition module: used to acquire the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot, and obtain the basic data of the target reconnaissance task; Preliminary analysis module: used to perform preliminary analysis based on the basic data of the target reconnaissance task and obtain the preliminary allocation result of the task robot corresponding to the target reconnaissance task; Comprehensive analysis module: used to conduct comprehensive analysis based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task, and obtain the comprehensive allocation results corresponding to the target reconnaissance task.

[0007] In the preferred embodiment of this scheme, the specific implementation method of the basic data acquisition module is as follows: Establish a data extraction module between the basic data acquisition module and the database to extract the functional parameters corresponding to each type of reconnaissance robot stored in the database, wherein the functional parameters include the power consumption corresponding to the reconnaissance equipment in the reconnaissance robot, the maximum reconnaissance radius of the reconnaissance equipment, the standard driving speed and the power consumption corresponding to the standard driving speed; Obtaining basic information of the investigation area, wherein the basic information of the investigation area includes the area, geographical location and geological features of the investigation area; Obtain the model and remaining power of each reconnaissance robot that performs each task, record each reconnaissance robot that performs each task as each task robot, perform data matching on the model and remaining power of each task robot with the functional parameters corresponding to each model of reconnaissance robot, and obtain the maximum driving distance of each task robot, the power consumption corresponding to the reconnaissance equipment, and the maximum reconnaissance radius of the reconnaissance equipment; The maximum driving distance of each task robot, the corresponding power consumption of the reconnaissance equipment, the maximum reconnaissance radius of the reconnaissance equipment and the area of ​​the reconnaissance area are recorded as the basic data of the target reconnaissance task.

[0008] In the preferred embodiment of this scheme, the specific implementation method of the preliminary analysis module is as follows: Measure the maximum distance between the starting point and the boundary of the detection area, mark the boundary coordinate point with the maximum distance from the starting point, connect the starting point and the boundary coordinate point to obtain a connecting line segment, mark the minimum distance coincidence point connecting the starting point and the detection area, set segmentation points in an equidistant manner for the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance, record the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance as the target line segment, make a perpendicular line between each segmentation point and the target line segment according to each segmentation point in the target line segment, divide the detection area into task sub-areas of each level, and obtain the area of ​​each task sub-area by statistics; Measure the distance between the starting point and the point where the minimum distance in the reconnaissance area coincides, and record it as the initial distance; Measure the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area, and record twice the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area as the reference distance of each level of task sub-area corresponding to the starting point; A preliminary screening is performed according to the maximum driving distance of each task robot and the reference distance of each level of task sub-area corresponding to the starting point, and the task robots corresponding to each level of task sub-area are obtained. The task robots corresponding to each level of task sub-area are recorded as the preliminary allocation results of the task robots corresponding to the target reconnaissance task.

[0009] In the preferred embodiment of this scheme, the specific implementation method of the comprehensive analysis module is as follows: According to the calculation formula: the overflow driving distance of each task robot corresponding to each task sub-area = the maximum driving distance of each task robot - the reference distance of each task sub-area, the overflow driving distance of each task robot corresponding to each task sub-area is matched with the functional parameters of each task robot to obtain the patrol remaining power of each task robot corresponding to each task sub-area; By calculating the formula , calculate the theoretical inspection time of each task robot ; By calculating the formula , calculate the theoretical inspection path length of each task robot ,in It represents the remaining power of each task robot during patrol. It is represented by the power consumption of the detection equipment of each task robot. It is expressed as the power consumption corresponding to the standard driving speed of each task robot. It is represented by the standard driving speed corresponding to each task robot. It represents the number of each task robot; The preliminary task area of ​​each task robot is divided according to the theoretical inspection path length of each task robot and the preliminary task sub-area corresponding to each task robot, so that the theoretical inspection paths of each task robot are connected end to end, and the ideal inspection task area corresponding to each task robot is obtained; Establish a data extraction relationship between the preliminary analysis module and the database, extract the driving difficulty coefficient and the detection difficulty coefficient corresponding to each geological feature, perform data matching based on the geological feature information of the ideal patrol task area and the detection area corresponding to each task robot, obtain the geological feature information of the ideal patrol task area corresponding to each task robot, and screen and obtain the driving difficulty coefficient and the detection difficulty coefficient of the terrain corresponding to each task robot; Obtain meteorological information of the geographical location corresponding to the reconnaissance area through the meteorological platform, including wind speed, rainfall and air particle concentration; By calculating the formula , calculate the weather driving difficulty coefficient of the ideal patrol task area corresponding to each task robot ; By calculating the formula , calculate the meteorological detection difficulty coefficient of the ideal patrol task area corresponding to each task robot ,in , , They are respectively represented by the preset ideal wind speed, ideal rainfall and ideal air particle concentration. , , They represent the wind speed, rainfall and air particle concentration in the ideal inspection task area corresponding to each task robot. Expressed as a natural constant; By calculating the formula , calculate the actual patrol path length corresponding to each task robot ,in , They are respectively represented as the terrain driving difficulty coefficient and terrain reconnaissance difficulty coefficient corresponding to each task robot; According to the actual patrol path length corresponding to each task robot and the task sub-areas of each level corresponding to each task robot, the preliminary task area corresponding to each task robot is divided to obtain the actual patrol task area corresponding to each task robot, and the area of ​​the actual patrol task area corresponding to each task robot is calculated; The areas of the actual patrol task areas of each task robot corresponding to each level of task sub-area are statistically calculated and recorded as the detection areas corresponding to each level of task sub-area; The overflow detection area corresponding to each level of task sub-area is calculated based on the detection area corresponding to each level of task sub-area and the area of ​​each level of task sub-area. The overflow detection area corresponding to each level of task sub-area is analyzed from far to near. If the overflow detection area corresponding to the highest-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the highest-level task sub-area is divided into the second-level task sub-area. The task robot combination divided into the second-level task sub-area is analyzed to obtain the actual overflow detection area corresponding to the second-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the second-level task sub-area is divided into the next-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is less than 0, the task robot combination in the next-level task sub-area is divided into the next-level task sub-area. The task robots are divided into sub-high-level task sub-areas in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the sub-high-level task sub-area is greater than and infinitely close to 0. If the overflow detection area of ​​the next-level task sub-area is greater than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robot combination corresponding to the overflow detection area that is smaller than and closest to the next-level task sub-area is divided into the next-level task sub-area. If the overflow detection area of ​​the next-level task sub-area is less than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robots in the next-level task sub-area are divided into the next-level task sub-area in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the next-level task sub-area is greater than and infinitely close to 0; The task robots corresponding to the task sub-areas at each level are statistically obtained, and the actual patrol task areas of the task robots corresponding to the task sub-areas at each level are recorded as the comprehensive allocation results corresponding to the target reconnaissance tasks.

[0010] To achieve the above object, the present invention also provides the following technical solution: a reconnaissance robot task allocation method based on an optimization algorithm, comprising the following steps: Obtain the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot to obtain the basic data of the target reconnaissance mission; Perform a preliminary analysis based on the basic data of the target reconnaissance mission to obtain the preliminary allocation results of the task robots corresponding to the target reconnaissance mission; A comprehensive analysis is performed based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task to obtain the comprehensive allocation results corresponding to the target reconnaissance task.

[0011] Compared with the prior art, the present invention has the following beneficial effects: By acquiring the basic data of the target reconnaissance task and analyzing the basic data, the preliminary task allocation results are obtained. Based on the basic data and the preliminary task allocation results, the driving difficulty coefficient and reconnaissance difficulty coefficient corresponding to each geological feature extracted, as well as the meteorological information in the reconnaissance area are comprehensively analyzed to obtain the total allocation results corresponding to the reconnaissance task. This improves the efficiency and flexibility of the reconnaissance robot in task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0013] Figure 1 This is a schematic diagram of module connection according to an embodiment of the present invention.

[0014] Figure 2 It is a schematic diagram of the connection steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] See also Figure 1 The present invention provides a reconnaissance robot task allocation system based on an optimization algorithm, which includes a basic data acquisition module, a preliminary analysis module and a comprehensive analysis module.

[0017] The basic data acquisition module is connected with the preliminary analysis module, and the preliminary analysis module is connected with the comprehensive analysis module.

[0018] The basic data acquisition module is used to acquire the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot, and obtain the basic data of the target reconnaissance task; Furthermore, the specific implementation of the basic data acquisition module is as follows: Establish a data extraction module between the basic data acquisition module and the database to extract the functional parameters corresponding to each type of reconnaissance robot stored in the database, wherein the functional parameters include the power consumption corresponding to the reconnaissance equipment of the reconnaissance robot, the maximum reconnaissance radius of the reconnaissance equipment, the standard driving speed and the power consumption corresponding to the standard driving speed; Obtaining basic information of the investigation area, wherein the basic information of the investigation area includes the area, geographical location and geological features of the investigation area; Obtain the model and remaining power of each reconnaissance robot that performs each task, record each reconnaissance robot that performs each task as each task robot, perform data matching on the model and remaining power of each task robot with the functional parameters corresponding to each model of reconnaissance robot, and obtain the maximum driving distance of each task robot, the power consumption corresponding to the reconnaissance equipment, and the maximum reconnaissance radius of the reconnaissance equipment; The maximum driving distance of each task robot, the corresponding power consumption of the reconnaissance equipment, the maximum reconnaissance radius of the reconnaissance equipment and the area of ​​the reconnaissance area are recorded as the basic data of the target reconnaissance task.

[0019] The preliminary analysis module is used to perform preliminary analysis based on the basic data of the target reconnaissance task and obtain the preliminary allocation result of the task robot corresponding to the target reconnaissance task; Furthermore, the specific execution method of the preliminary analysis module is as follows: Measure the maximum distance between the starting point and the boundary of the detection area, mark the boundary coordinate point with the maximum distance from the starting point, connect the starting point and the boundary coordinate point to obtain a connecting line segment, mark the minimum distance coincidence point connecting the starting point and the detection area, set segmentation points in an equidistant manner for the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance, record the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance as the target line segment, make a perpendicular line between each segmentation point and the target line segment according to each segmentation point in the target line segment, divide the detection area into task sub-areas of each level, and obtain the area of ​​each task sub-area by statistics; It should be noted that the classification method of task sub-areas at each level refers to recording the perpendicular bisector of the minimum distance coincidence point and the perpendicular bisector of the nearest segmentation point as the first-level preliminary task sub-area, and so on to obtain task sub-areas at each level.

[0020] Measure the distance between the starting point and the point where the minimum distance in the reconnaissance area coincides, and record it as the initial distance; Measure the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area, and record twice the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area as the reference distance of each level of task sub-area corresponding to the starting point; A preliminary screening is performed according to the maximum driving distance of each task robot and the reference distance of each level of task sub-area corresponding to the starting point, and the task robots corresponding to each level of task sub-area are obtained. The task robots corresponding to each level of task sub-area are recorded as the preliminary allocation results of the task robots corresponding to the target reconnaissance task.

[0021] The comprehensive analysis module is used to conduct a comprehensive analysis based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task, so as to obtain the comprehensive allocation results corresponding to the target reconnaissance task.

[0022] Furthermore, the specific implementation of the comprehensive analysis module is as follows: According to the calculation formula: the overflow driving distance of each task robot corresponding to each task sub-area = the maximum driving distance of each task robot - the reference distance of each task sub-area, the overflow driving distance of each task robot corresponding to each task sub-area is matched with the functional parameters of each task robot to obtain the patrol remaining power of each task robot corresponding to each task sub-area; By calculating the formula , get the theoretical inspection time of each task robot ; By calculating the formula , get the theoretical inspection path length of each task robot ,in It represents the remaining power of each task robot during patrol. It is represented by the power consumption of the detection equipment of each task robot. It is expressed as the power consumption corresponding to the standard driving speed of each task robot. It is represented by the standard driving speed corresponding to each task robot. It represents the number of each task robot; The preliminary task area of ​​each task robot is divided according to the theoretical inspection path length of each task robot and the preliminary task sub-area corresponding to each task robot, so that the theoretical inspection paths of each task robot are connected end to end, and the ideal inspection task area corresponding to each task robot is obtained; Establish a data extraction relationship between the preliminary analysis module and the database, extract the driving difficulty coefficient and the detection difficulty coefficient corresponding to each geological feature, perform data matching based on the geological feature information of the ideal patrol task area and the detection area corresponding to each task robot, obtain the geological feature information of the ideal patrol task area corresponding to each task robot, and screen and obtain the driving difficulty coefficient and the detection difficulty coefficient of the terrain corresponding to each task robot; Obtain meteorological information of the geographical location corresponding to the reconnaissance area through the meteorological platform, including wind speed, rainfall and air particle concentration; By calculating the formula , calculate the weather driving difficulty coefficient of the ideal patrol task area corresponding to each task robot ; By calculating the formula , calculate the meteorological detection difficulty coefficient of the ideal patrol task area corresponding to each task robot ,in , , They are respectively represented by the preset ideal wind speed, ideal rainfall and ideal air particle concentration. , , They represent the wind speed, rainfall and air particle concentration in the ideal inspection task area corresponding to each task robot. Expressed as a natural constant; By calculating the formula , calculate the actual patrol path length corresponding to each task robot ,in , They are respectively represented as the terrain driving difficulty coefficient and terrain reconnaissance difficulty coefficient corresponding to each task robot; According to the actual patrol path length corresponding to each task robot and the task sub-areas of each level corresponding to each task robot, the preliminary task area corresponding to each task robot is divided to obtain the actual patrol task area corresponding to each task robot, and the area of ​​the actual patrol task area corresponding to each task robot is calculated; The areas of the actual patrol task areas of each task robot corresponding to each level of task sub-area are statistically calculated and recorded as the detection areas corresponding to each level of task sub-area; The overflow detection area corresponding to each level of task sub-area is calculated based on the detection area corresponding to each level of task sub-area and the area of ​​each level of task sub-area. The overflow detection area corresponding to each level of task sub-area is analyzed from far to near. If the overflow detection area corresponding to the highest-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the highest-level task sub-area is divided into the second-level task sub-area. The task robot combination divided into the second-level task sub-area is analyzed to obtain the actual overflow detection area corresponding to the second-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the second-level task sub-area is divided into the next-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is less than 0, the task robot combination in the next-level task sub-area is divided into the next-level task sub-area. The task robots are divided into sub-high-level task sub-areas in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the sub-high-level task sub-area is greater than and infinitely close to 0. If the overflow detection area of ​​the next-level task sub-area is greater than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robot combination corresponding to the overflow detection area that is smaller than and closest to the next-level task sub-area is divided into the next-level task sub-area. If the overflow detection area of ​​the next-level task sub-area is less than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robots in the next-level task sub-area are divided into the next-level task sub-area in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the next-level task sub-area is greater than and infinitely close to 0; It should be noted that the farther the task sub-area is from the starting point, the higher the level.

[0023] The task robots corresponding to the task sub-areas at each level are statistically obtained, and the actual patrol task areas of the task robots corresponding to the task sub-areas at each level are recorded as the comprehensive allocation results corresponding to the target reconnaissance tasks.

[0024] See also Figure 2 To achieve the above purpose, the present invention also provides the following technical solution: a reconnaissance robot task allocation method based on an optimization algorithm, comprising the following steps: Obtain the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot to obtain the basic data of the target reconnaissance mission; Perform a preliminary analysis based on the basic data of the target reconnaissance mission to obtain the preliminary allocation results of the task robots corresponding to the target reconnaissance mission; A comprehensive analysis is performed based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task to obtain the comprehensive allocation results corresponding to the target reconnaissance task.

[0025] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A reconnaissance robot task allocation system based on an optimization algorithm, characterized by: include: Basic data acquisition module: used to acquire the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot, and obtain the basic data of the target reconnaissance task; Preliminary analysis module: used to perform preliminary analysis based on the basic data of the target reconnaissance task and obtain the preliminary allocation result of the task robot corresponding to the target reconnaissance task; Comprehensive analysis module: used to conduct comprehensive analysis based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task, and obtain the comprehensive allocation results corresponding to the target reconnaissance task.

2. The reconnaissance robot task allocation system based on optimization algorithm according to claim 1 is characterized in that: The specific implementation method of the basic data acquisition module is as follows: Establish a data extraction module between the basic data acquisition module and the database to extract the functional parameters corresponding to each type of reconnaissance robot stored in the database, wherein the functional parameters include the power consumption corresponding to the reconnaissance equipment in the reconnaissance robot, the maximum reconnaissance radius of the reconnaissance equipment, the standard driving speed and the power consumption corresponding to the standard driving speed; Obtaining basic information of the investigation area, wherein the basic information of the investigation area includes the area, geographical location and geological features of the investigation area; Obtain the model and remaining power of each reconnaissance robot that performs each task, record each reconnaissance robot that performs each task as each task robot, perform data matching on the model and remaining power of each task robot with the functional parameters corresponding to each model of reconnaissance robot, and obtain the maximum driving distance of each task robot, the power consumption corresponding to the reconnaissance equipment, and the maximum reconnaissance radius of the reconnaissance equipment; The maximum driving distance of each task robot, the corresponding power consumption of the reconnaissance equipment, the maximum reconnaissance radius of the reconnaissance equipment and the area of ​​the reconnaissance area are recorded as the basic data of the target reconnaissance task.

3. The reconnaissance robot task allocation system based on optimization algorithm according to claim 2 is characterized in that: The specific implementation method of the preliminary analysis module is as follows: Measure the maximum distance between the starting point and the boundary of the detection area, mark the boundary coordinate point with the maximum distance from the starting point, connect the starting point and the boundary coordinate point to obtain a connecting line segment, mark the minimum distance coincidence point connecting the starting point and the detection area, set segmentation points in an equidistant manner for the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance, record the line segment between the minimum distance coincidence point and the boundary coordinate point with the maximum distance as the target line segment, make a perpendicular line between each segmentation point and the target line segment according to each segmentation point in the target line segment, divide the detection area into task sub-areas of each level, and obtain the area of ​​each task sub-area by statistics; Measure the distance between the starting point and the point where the minimum distance in the reconnaissance area coincides, and record it as the initial distance; Measure the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area, and record twice the distance between the starting point and the midpoint of the line segment between two segment points in each level of task sub-area as the reference distance of each level of task sub-area corresponding to the starting point; A preliminary screening is performed according to the maximum driving distance of each task robot and the reference distance of each level of task sub-area corresponding to the starting point, and the task robots corresponding to each level of task sub-area are obtained. The task robots corresponding to each level of task sub-area are recorded as the preliminary allocation results of the task robots corresponding to the target reconnaissance task.

4. The reconnaissance robot task allocation system based on optimization algorithm according to claim 3 is characterized in that: The specific implementation method of the comprehensive analysis module is as follows: According to the calculation formula: the overflow driving distance of each task robot corresponding to each task sub-area = the maximum driving distance of each task robot - the reference distance of each task sub-area, the overflow driving distance of each task robot corresponding to each task sub-area is matched with the functional parameters of each task robot to obtain the patrol remaining power of each task robot corresponding to each task sub-area; By calculating the formula , get the theoretical inspection time of each task robot ; By calculating the formula , get the theoretical inspection path length of each task robot ,in It represents the remaining power of each task robot during patrol. It is represented by the power consumption of the detection equipment of each task robot. It is expressed as the power consumption corresponding to the standard driving speed of each task robot. It is represented by the standard driving speed corresponding to each task robot. It is represented by the number of each task robot; The preliminary task area of ​​each task robot is divided according to the theoretical inspection path length of each task robot and the preliminary task sub-area corresponding to each task robot, so that the theoretical inspection paths of each task robot are connected end to end, and the ideal inspection task area corresponding to each task robot is obtained; Establish a data extraction relationship between the preliminary analysis module and the database, extract the driving difficulty coefficient and the detection difficulty coefficient corresponding to each geological feature, perform data matching based on the geological feature information of the ideal patrol task area and the detection area corresponding to each task robot, obtain the geological feature information of the ideal patrol task area corresponding to each task robot, and screen and obtain the driving difficulty coefficient and the detection difficulty coefficient of the terrain corresponding to each task robot; Obtain meteorological information of the geographical location corresponding to the reconnaissance area through the meteorological platform, including wind speed, rainfall and air particle concentration; By calculating the formula , calculate the weather driving difficulty coefficient of the ideal patrol task area corresponding to each task robot ; By calculating the formula , calculate the meteorological detection difficulty coefficient of the ideal patrol task area corresponding to each task robot ,in , , They are respectively represented by the preset ideal wind speed, ideal rainfall and ideal air particle concentration. , , They represent the wind speed, rainfall and air particle concentration in the ideal inspection task area corresponding to each task robot. Expressed as a natural constant; By calculating the formula , calculate the actual patrol path length corresponding to each task robot ,in , They are respectively represented as the terrain driving difficulty coefficient and terrain reconnaissance difficulty coefficient corresponding to each task robot; According to the length of the actual patrol path corresponding to each task robot and the task sub-areas of each level corresponding to each task robot, the preliminary task area corresponding to each task robot is divided, the actual patrol task area corresponding to each task robot is obtained, and the area of ​​the actual patrol task area corresponding to each task robot is calculated; The areas of the actual patrol task areas of each task robot corresponding to each level of task sub-area are statistically calculated and recorded as the detection areas corresponding to each level of task sub-area; The overflow detection area corresponding to each level of task sub-area is calculated based on the detection area corresponding to each level of task sub-area and the area of ​​each level of task sub-area. The overflow detection area corresponding to each level of task sub-area is analyzed from far to near. If the overflow detection area corresponding to the highest-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the highest-level task sub-area is divided into the second-level task sub-area. The task robot combination divided into the second-level task sub-area is analyzed to obtain the actual overflow detection area corresponding to the second-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is greater than 0, the task robot combination smaller than and closest to the overflow detection area in the second-level task sub-area is divided into the next-level task sub-area. If the actual overflow detection area corresponding to the second-level task sub-area is less than 0, the task robot combination in the next-level task sub-area is divided into the next-level task sub-area. The task robots are divided into sub-high-level task sub-areas in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the sub-high-level task sub-area is greater than and infinitely close to 0. If the overflow detection area of ​​the next-level task sub-area is greater than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robot combination corresponding to the overflow detection area that is smaller than and closest to the next-level task sub-area is divided into the next-level task sub-area. If the overflow detection area of ​​the next-level task sub-area is less than 0 after the next-level task sub-area receives or divides the task robot into the sub-high-level task sub-area, then the task robots in the next-level task sub-area are divided into the next-level task sub-area in the order of the actual patrol task area from large to small, until the actual overflow detection area corresponding to the next-level task sub-area is greater than and infinitely close to 0; The task robots corresponding to the task sub-areas at each level are statistically obtained, and the actual patrol task areas of the task robots corresponding to the task sub-areas at each level are recorded as the comprehensive allocation results corresponding to the target reconnaissance tasks.

5. A reconnaissance robot task allocation method based on an optimization algorithm, applied to a reconnaissance robot task allocation system based on an optimization algorithm as claimed in any one of claims 1 to 4, characterized in that: include: Obtain the basic conditions of the reconnaissance area and the basic conditions of the reconnaissance robot to obtain the basic data of the target reconnaissance mission; Perform a preliminary analysis based on the basic data of the target reconnaissance mission to obtain the preliminary allocation results of the task robots corresponding to the target reconnaissance mission; A comprehensive analysis is performed based on the basic data of the target reconnaissance task and the preliminary allocation results of the task robots corresponding to the target reconnaissance task to obtain the comprehensive allocation results corresponding to the target reconnaissance task.

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