Method and system for assigning surveying tasks under multi-person cooperative working condition

Abstract

This invention proposes a method and system for allocating surveying tasks under multi-person collaborative working conditions. The method includes: S1: obtaining the partition information and edge information of the target surveying task; S2: obtaining the number of operators n; S3: constructing a weighted undirected graph based on the partition information and edge information; S4: dividing the weighted undirected graph into n subsets using a graph equalization partitioning algorithm, and obtaining the final survey area corresponding to each operator based on each subset. This method can automatically divide the task area into multiple final survey areas with roughly the same workload based on the partition information, edge information, and number of operators, while minimizing the edge processing work between different operators, thereby improving work efficiency.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping technology, and in particular to a method and system for allocating surveying and mapping tasks under conditions of multi-person collaborative work. Background Technology

[0002] To resolve the contradiction between "limited carriers" and "unlimited terrain" and to achieve standardized splicing and management, surveying and mapping products require generation by map sheet. This breaks down the task into thousands of standard "map sheet units," which are then assigned to different work groups for parallel production. This facilitates product quality control and data update and maintenance, but it also requires additional work to handle the edge connection issues between adjacent map sheets. Ensuring that the transition of features, landforms, coordinates, and elevations at the boundaries between adjacent map sheets is natural and without abnormal abrupt changes is crucial for surveying and mapping and data entry.

[0003] In the current operation process, the allocation of specific map sheet tasks relies more on the experience of management personnel and lacks optimal planning. This may result in too many border areas in the division results, increasing the workload. In addition, the differences in the difficulty of different map sheets are easily overlooked during the task division process, resulting in large differences in the completion time of different areas. The problem of waiting for other operators to complete the border processing work also reduces the work efficiency. Summary of the Invention

[0004] This invention provides a method and system for allocating surveying tasks under multi-person collaborative working conditions. Its main purpose is to minimize the number of edges when dividing surveying tasks, thereby effectively improving the work efficiency of operators.

[0005] In a first aspect, embodiments of the present invention provide a method for allocating surveying and mapping tasks under conditions of multi-person collaborative work, including:

[0006] S1: Obtain the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas.

[0007] S2: Obtain the number of workers, n;

[0008] S3: Construct a weighted undirected graph based on the partition information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node.

[0009] S4: Using a graph equalization partitioning algorithm, the weighted undirected graph is divided into n subsets, and the final survey area corresponding to each operator is obtained based on each subset.

[0010] Furthermore, step S4 is followed by:

[0011] S5: Calculate the weight balance degree and edge connection task consistency rate for all subsets, and determine whether the partitioning result is qualified based on the weight balance degree and edge connection task consistency rate. If it is not qualified, readjust the parameters of the graph equalization partitioning algorithm and re-partition the weighted undirected graph into n subsets until the partitioning result is qualified.

[0012] Furthermore, in step S3 Obtained by following these steps:

[0013] Each initial survey area is divided into multiple levels according to the difficulty of the task;

[0014] Based on the level of each initial survey area, obtain the workload of each initial survey area;

[0015] The weight of each node is obtained based on the workload of each initial survey area.

[0016] Further, in step S4, the weighted undirected graph is divided into n subsets using a graph equalization partitioning algorithm, following the principles below:

[0017] The sum of weights for each subset is such that the difference between any two sums of weights is minimized.

[0018] The number of edges spanning subsets is minimized;

[0019] Each initial survey area is assigned to only one operator.

[0020] Furthermore, the weight balance in step S5 is calculated as follows:

[0021] .

[0022] Furthermore, the consistency rate of the edge connection task mentioned in step S5 is calculated using the following formula:

[0023] .

[0024] Furthermore, the graph equalization partitioning algorithm includes a greedy region growing algorithm, a multi-level partitioning algorithm, and a two-dimensional balanced partitioning algorithm.

[0025] Secondly, embodiments of the present invention provide a surveying task allocation system under multi-person collaborative working conditions, comprising:

[0026] The information acquisition module is used to acquire the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas.

[0027] The personnel acquisition module is used to obtain the number of workers, n.

[0028] The undirected graph module is used to construct a weighted undirected graph based on the partitioning information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node.

[0029] The partitioning module is used to divide the weighted undirected graph into n subsets according to each subset using a graph equalization partitioning algorithm, so as to obtain the final survey area corresponding to each operator.

[0030] Thirdly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for allocating surveying tasks under conditions of multi-person collaborative work.

[0031] Fourthly, embodiments of the present invention provide a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for allocating surveying tasks under conditions of multi-person collaborative work.

[0032] This invention proposes a method and system for allocating surveying tasks under multi-person collaborative working conditions. This method can automatically divide the task area into multiple final survey areas with basically the same workload based on zoning information, edge information and the number of operators, and ensure that the edge processing work between different operators is minimized, thereby improving work efficiency. Attached Figure Description

[0033] Figure 1 A flowchart illustrating a method for allocating surveying tasks under multi-person collaborative working conditions, provided by an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of an initial survey area division provided in an embodiment of the present invention;

[0035] Figure 3 This is a schematic diagram of the task allocation for a survey area provided in an embodiment of the present invention;

[0036] Figure 4 This is a schematic diagram illustrating the result of dividing a large-area, complex survey area into its task, as provided in an embodiment of the present invention.

[0037] Figure 5 This is a schematic diagram of a surveying task allocation system under multi-person collaborative working conditions provided in an embodiment of the present invention.

[0038] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0039] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0040] To enable those skilled in the art to better understand the solutions of this application, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0041] In the embodiments of this application, "at least one" refers to one or more; "multiple" refers to two or more. In the description of this application, terms such as "first," "second," and "third" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or order. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0042] References such as “one embodiment” or “some embodiments” as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the terms “comprising,” “including,” “having,” and variations thereof, as used in this specification, mean “including, but not limited to,” unless otherwise specifically emphasized.

[0043] Figure 1 A flowchart of a surveying task allocation method under multi-person collaborative working conditions provided by an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0044] S1: Obtain the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas.

[0045] In this embodiment of the invention, the surveying task is densely divided according to the standard map sheet. Each initial survey area is a square, and all initial survey areas have the same side length. The two sides of the square that share a vertex point to due south / north and due west / east, respectively. Adjacent and resolvable sides are squares sharing a common side, and the specific form is similar. Figure 2 .

[0046] S2: Obtain the number of workers, n;

[0047] In this embodiment of the invention, it is assumed that the work efficiency of all operators is approximately the same, that is, the time required for each operator to complete the same initial survey area is approximately equal. This assumption is basically consistent with the actual situation. For skilled operators, the amount of work that can be completed each day to complete the same task is approximately the same.

[0048] S3: Construct a weighted undirected graph based on the partition information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node.

[0049] like Figure 2 As shown, in this embodiment of the invention, each initial survey area represents a node, denoted as... ,but The relationship between adjacent initial survey areas forms an edge of the graph, denoted as . ,in , Let each represent the number of an adjacent initial survey area, then the undirected graph... The set of edges can be denoted as .

[0050] Adjacent initial survey areas refer to initial survey areas that share a common edge, such as... Figure 2 middle and All are adjacent initial survey areas, but and They are not considered to be adjacent initial survey areas because There are no intersecting areas, and There are only common vertices, no common edges. It is easy to see that once the initial test areas with common edges have all completed edge connection optimization, the initial test areas with only common vertices have also automatically completed edge connection optimization. Therefore, there is no need to perform edge connection optimization on the test areas with only common vertices.

[0051] Specifically, for Figure 2 For the initial survey area task, a total of 17 edges are formed, including 9 longitudinal edges: , , , , , , , And 8 horizontal joints: , , , , , , , .

[0052] In addition, each node has a corresponding weight, representing the amount of work required to complete that node, denoted as . The weight set can then be denoted as .

[0053] When the difficulty level of different initial test areas has been assessed in advance, or when the average work efficiency of different types of initial test areas has been understood through trial production, the weight of the initial test area can be assigned according to the time required to complete it. The specific method is as follows:

[0054] First, the initial survey area is divided into multiple levels according to the difficulty of the task. For example, for the task of repairing a real-world 3D model, the survey area can be divided into three levels: urban area, suburbs, and non-urban area, based on the building density, with corresponding difficulty levels of 1, 2, and 3, respectively. For topographic map quality inspection, the difficulty level can be divided according to the number of vector patches in the initial survey area.

[0055] Next, for each level, based on statistics of the first product, test production area, or historical completion status, the number of initial test areas completed by one worker per unit of time is determined. ,in This represents the difficulty level of the initial survey area. For example, based on experience, for a realistic 3D model repair task, if each worker can complete 24 non-urban area repairs, or 16 suburban area repairs, or 12 urban area repairs per day, then... , , .

[0056] The final weight of the node corresponding to each initial test area of ​​each difficulty level is calculated as follows: ,in For the first The corresponding difficulty level for each initial test area, for example, for Figure 2 The initial test areas shown are assumed to be: areas 1-4 are non-urban areas with a difficulty level of 3; areas 5-8 are urban areas with a difficulty level of 1; and areas 9-12 are suburban areas with a difficulty level of 2. The weights of each initial test area are then:

[0057] ;

[0058] ;

[0059] .

[0060] S4: Using a graph equalization partitioning algorithm, the weighted undirected graph is divided into n subsets, and the final survey area corresponding to each operator is obtained based on each subset.

[0061] It follows the following principles:

[0062] The weights of each subset are summed, and the difference between any two weight sums is minimized, meaning that the weight sums of each subset should be as equal as possible.

[0063] The number of edges spanning subsets is minimized.

[0064] Each initial survey area is assigned to only one operator.

[0065] Since the graph equilibrium partitioning problem is an NP-hard problem, it is usually relaxed to optimize the weight balance while minimizing the number of edge cuts. It can be approximated by using greedy region growing algorithms, iterative improvement algorithms (such as Kernighan-Lin (KL) algorithm), multi-level methods, or two-dimensional partitioning algorithms.

[0066] Among them, the greedy region growing algorithm has the simplest idea, essentially "local optimum leading to global approximate optimum". This algorithm calculates an edge-friendliness score (the more edges a worker already has with the unassigned region, the better) and a balance penalty score (the penalty score for exceeding the weight that should be assigned) for each worker in each unassigned region. It then combines these two scores to select the optimal worker. This algorithm is efficient and fast, but it is prone to getting trapped in local optima and is sensitive to initial random assignment, requiring multiple trials of the initial assignment strategy to obtain good partitioning results.

[0067] Iterative improvement algorithms (such as the Kernighan-Lin (KL) algorithm) reduce the number of edges by iteratively swapping survey areas already assigned to different workers to obtain the optimal solution. This algorithm first requires a weighted initial solution (which can use a greedy algorithm as input or a random assignment result). By swapping survey areas in two different groups (workers), the "profit" after the swap (i.e., the number of reduced edges minus the number of increased edges) is calculated. A series of swaps with the maximum profit are performed until further optimization is impossible or a balance constraint is reached. This algorithm can escape local optima and significantly reduce the number of cross-worker edges, but the number of iterations is uncontrollable, the result is highly dependent on the initial partitioning result, and for very large survey areas, the iterative search time is long.

[0068] Multi-level methods eliminate the dependence on the initial solution. This method shrinks the large graph into smaller graphs, then quickly finds an approximate solution on the smaller graphs, and then maps the approximate solution from the smaller graphs back to the large graph. This allows the results to better reflect the overall structure, and the algorithm is faster and more suitable for large-scale computation.

[0069] After completing the balanced partitioning of the graph, the partitioning results need to be evaluated to check whether they can be used as the final partitioning scheme and issued to the operators. This is done through the following steps:

[0070] S5: Calculate the weight balance degree and edge connection task consistency rate of each subset, and determine whether the partitioning result of each subset is qualified based on the weight balance degree and edge connection task consistency rate. If it is not qualified, readjust the parameters of the graph equalization partitioning algorithm and re-partition the weighted undirected graph into n subsets until the partitioning result is qualified.

[0071] The weight balance is used to evaluate whether the surveying tasks are distributed relatively evenly among each worker. The calculation method is as follows: first, calculate the sum of the weights of each subset in each step S4; find the subsets with the maximum and minimum weights; then calculate the average weight of all subsets; finally, calculate the weight balance using the following formula:

[0072] .

[0073] Edge matching consistency rate is used to evaluate whether the subset partitioning results minimize cross-operation personnel subsets as much as possible. The calculation method is as follows: first, count the total number of edges in all initial survey areas, and then count the number of edges across operations in the subset partitioning results. Finally, calculate using the following formula:

[0074] .

[0075] For both of the above results, under acceptable conditions, the weight balance should be greater than 0.95, and the edge task consistency rate should be greater than 0.8. For partitioning results that do not meet either of the above two indicators, the algorithm initialization parameters need to be readjusted, and the tasks need to be partitioned again until the evaluation results meet the requirements.

[0076] For example, for the greedy region growing algorithm, the grid traversal method and the initial seed and initial test area selection can be modified, and multiple attempts can be made to improve the evaluation index; for the iterative improvement algorithm, the initial allocation algorithm, the number of iterations and the iteration stopping criteria can be adjusted to improve the evaluation index; for the multi-level partitioning algorithm, the algorithm parameters, such as the clustering algorithm, random number seed, optimization of the number of iterations, minimum connectivity, etc., can be adjusted to improve the partitioning results.

[0077] It is particularly important to note that for certain survey areas, due to the nature of the area itself, it may be impossible to meet both indicators under any circumstances, such as... Figure 3 As shown in the figure, when the 12 survey areas are assigned to 3 operators, under the condition of equal workload, the optimal survey division still results in 6 edges that need to be optimized across operators. At this point, the edge task consistency rate is 0.65, which is lower than the threshold. However, after visual inspection, the survey area division is reasonable and the work distribution is balanced, so the tasks can be assigned to different operators accordingly.

[0078] The advantage of this invention is that, according to the method described herein, for large-scale complex task areas, when it is necessary to assign tasks to a large number of operators, manual assignment is difficult to find the optimal solution in a short time. Usually, the entire row or column of the test area is divided into equal parts, resulting in a large number of edges and additional workload for subsequent edge assignment. However, the method of this invention can quickly find the optimal assignment strategy and significantly improve work efficiency.

[0079] like Figure 4 As shown, a surveying task includes 900 initial survey areas, which need to be assigned to 10 workers to work simultaneously. If the work is assigned using the traditional method, each worker is assigned 3 rows (90 initial survey areas) to work on, and the number of edges to be connected across workers is 270. However, the allocation scheme calculated by this invention still ensures that each worker is assigned 90 initial survey areas, but the number of edges to be connected is reduced to 153, which reduces the edge connection work by 43% and can significantly improve the work efficiency.

[0080] Figure 5 This is a structural diagram of a surveying task allocation system under multi-person collaborative working conditions provided by an embodiment of the present invention, as shown below. Figure 5 As shown, the system includes:

[0081] The information acquisition module 510 is used to acquire the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas.

[0082] The personnel acquisition module 520 is used to obtain the number of workers, n.

[0083] Undirected graph module 530 is used to construct a weighted undirected graph based on the partitioning information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node.

[0084] The partitioning module 540 is used to divide the weighted undirected graph into n subsets according to each subset using a graph equalization partitioning algorithm, so as to obtain the final survey area corresponding to each operator.

[0085] This embodiment is a system embodiment corresponding to the above method embodiment. Its specific implementation process is the same as that of the above method embodiment. For details, please refer to the above method embodiment. This system embodiment will not repeat the details.

[0086] The modules in the surveying task allocation system under the aforementioned multi-person collaborative working conditions can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0087] This invention provides a computer device, which may be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a computer storage medium and internal memory. The computer storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the computer storage medium. The database stores data generated or acquired during the execution of a surveying task allocation method under multi-user collaborative working conditions. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a surveying task allocation method under multi-user collaborative working conditions.

[0088] This invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a surveying task allocation method under multi-person collaborative working conditions as described in the above embodiments. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the embodiment of a surveying task allocation system under multi-person collaborative working conditions.

[0089] This invention provides a computer storage medium storing a computer program. When executed by a processor, the computer program implements the steps of the surveying task allocation method under multi-person collaborative working conditions described in the above embodiments. Alternatively, when executed by a processor, the computer program implements the functions of each module / unit in the above embodiment of the surveying task allocation system under multi-person collaborative working conditions.

[0090] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0091] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0092] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for allocating surveying tasks under multi-person collaborative working conditions, characterized in that, include: S1: Obtain the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas. S2: Obtain the number of workers, n; S3: Construct a weighted undirected graph based on the partition information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node. S4: Using a graph equalization partitioning algorithm, the weighted undirected graph is divided into n subsets, and the final survey area corresponding to each operator is obtained based on each subset.

2. The method for allocating surveying and mapping tasks under multi-person collaborative working conditions according to claim 1, characterized in that, Step S4 is followed by: S5: Calculate the weight balance degree and edge connection task consistency rate for all subsets, and determine whether the partitioning result is qualified based on the weight balance degree and edge connection task consistency rate. If it is not qualified, readjust the parameters of the graph equalization partitioning algorithm and re-partition the weighted undirected graph into n subsets until the partitioning result is qualified.

3. The method for allocating surveying and mapping tasks under multi-person collaborative working conditions according to claim 1, characterized in that, In step S3 Obtained by following these steps: Each initial survey area is divided into multiple levels according to the difficulty of the task; Based on the level of each initial survey area, obtain the workload of each initial survey area; The weight of each node is obtained based on the workload of each initial survey area.

4. The method for allocating surveying and mapping tasks under multi-person collaborative working conditions according to claim 1, characterized in that, In step S4, the weighted undirected graph is divided into n subsets using a graph equalization partitioning algorithm, following the principles below: The sum of weights for each subset is such that the difference between any two sums of weights is minimized. The number of edges spanning subsets is minimized; Each initial survey area is assigned to only one operator.

5. The method for allocating surveying tasks under multi-person collaborative working conditions according to claim 2, characterized in that, The weight balance in step S5 is calculated as follows: 。 6. The method for allocating surveying and mapping tasks under multi-person collaborative working conditions according to claim 2, characterized in that, The consistency rate of the edge connection task mentioned in step S5 is calculated using the following formula: 。 7. The method for allocating surveying tasks under multi-person collaborative working conditions according to any one of claims 1 to 6, characterized in that, The graph equilibrium partitioning algorithm includes a greedy region growth algorithm, a multi-level partitioning algorithm, and a two-dimensional equilibrium partitioning algorithm.

8. A surveying task allocation system for multi-person collaborative work, characterized in that, include: The information acquisition module is used to acquire the partition information and edge information of the target mapping task. The partition information includes multiple square initial survey areas of the same size, and the edge information includes the common edge of adjacent initial survey areas. The personnel acquisition module is used to obtain the number of workers, n. The undirected graph module is used to construct a weighted undirected graph based on the partitioning information and the edge information. ,in, This represents the nodes of the weighted undirected graph, with one node corresponding to each initial test area. The edges of the weighted undirected graph represent the connection relationships between all initial survey areas. The weight of the node represents the workload of the initial survey area corresponding to the node. The partitioning module is used to divide the weighted undirected graph into n subsets according to each subset using a graph equalization partitioning algorithm, so as to obtain the final survey area corresponding to each operator.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the surveying task allocation method under multi-person collaborative working conditions as described in any one of claims 1 to 7.

10. A computer storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the surveying task allocation method under the condition of multi-person collaborative work as described in any one of claims 1 to 7.

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