Task path planning method and device for property management, equipment and storage medium

Abstract

The invention provides a task path planning method and device for property management, equipment and a storage medium, and the method comprises the steps: constructing a plurality of first tasks and a plurality of second tasks, dividing the first tasks needing to be executed by multiple persons into a plurality of third tasks after distribution, applying first graph nodes of the first tasks to the third tasks, and associating the second tasks with second graph nodes; determining the account allocated with the third task and the second task as the first account and the other accounts as the second accounts, applying a first pheromone concentration with a relatively large numerical value in a path formed by first graph nodes and applying a second pheromone concentration with a relatively small numerical value in other paths when path planning is performed on the first account by using the ant colony model, therefore, the first service path can be fused with the personal task according to the principle that the multi-person task is preferential, the first service path does not need to be split into multiple service paths, the reasonability of path planning is improved, and the efficiency of property service is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of path planning, and in particular to a task path planning method, device, equipment, and storage medium for property management. Background Art

[0002] In daily property management, property management companies need to assign tasks to employees in different lines, such as cleaning, maintenance, and security patrols, and each task is performed at a different location. Traditional path planning mainly relies on the personal experience of employees, and the rationality of the path cannot be guaranteed. With the development of artificial intelligence technology, the planning of property service paths has begun to introduce ant colony algorithms. First, a property area map is constructed based on the property service area. Corresponding graph nodes are generated in the property area map according to the execution locations of different tasks. The paths between the graph nodes are represented by connecting lines. Each employee is an ant in the ant colony, and the task movement path of each employee is planned through the ant colony algorithm.

[0003] However, tasks in the property sector usually include individual tasks and collective tasks, and some employees may be assigned to both individual tasks and collective tasks. In related technologies, the collective task is first used as the input of the ant colony algorithm to plan a collective path, and then the last node of the collective path is used as the starting point of the individual task. The ant colony algorithm is called again to plan the individual path. Each employee is assigned to two paths, and first completes the collective task based on the collective path, and then performs the individual task based on the individual path. It is likely that there will be a large overlap between the collective path and the individual path, and the rationality of the path planning is poor, which affects the efficiency of property services. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a task path planning method, device, equipment, and storage medium for property management, which can plan a fusion path based on multi-person tasks and personal tasks, improve the rationality of path planning, and improve the efficiency of property services.

[0005] In a first aspect, an embodiment of the present invention provides a task path planning method for property management, comprising: Constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second tasks to any of the employee accounts, wherein the first tasks are associated with a first graph node, the second tasks are associated with a second graph node, and the first graph node and the second graph node represent the geographical location of a property area map; Based on any of the first tasks, split the task into a plurality of third tasks according to the number of the assigned employee accounts, assign one employee account to each of the third tasks, and associate the first graph node with the third task; Determine a plurality of first accounts from the plurality of employee accounts, and construct a first node set corresponding to each of the first accounts, wherein the first account is assigned the third task and the second task, and the first node set includes the associated first graph node and the second graph node; Applying a preset first pheromone concentration in a first node path of the first node set, and applying a preset second pheromone concentration in a second node path, wherein the first node path is a path between two of the first graph nodes, the second node path is a path between the first graph node and the second graph node, and the first pheromone concentration is greater than the second pheromone concentration; A first service path of the first account is generated based on the first node set and a preset ant colony model, wherein a starting graph node of the first account is any of the first graph nodes.

[0006] According to some embodiments of the present invention, after generating the first service path of the first account based on the first node set and a preset ant colony model, the method further includes: Determine a plurality of second accounts from the plurality of employee accounts, wherein the second accounts are only assigned the second task or the third task; Constructing a second node set corresponding to each of the second accounts, wherein the second node set includes the associated first graph node or the second graph node; Applying the second pheromone concentration in each of the first node paths or third node paths of the second node set, wherein the third node path is a path between two of the second graph nodes; A second service path for the second account is generated based on the second node set and the ant colony model.

[0007] According to some embodiments of the present invention, constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second task to any of the employee accounts includes: Constructing a plurality of tasks to be assigned, wherein the tasks to be assigned are associated with task graph nodes and expected task durations; Determine the expected account duration of each employee account based on the task to be assigned, wherein the expected account duration is used to represent the duration for the corresponding employee account to complete the task to be assigned; Determine the to-be-allocated task whose expected duration is greater than or equal to the expected duration of at least one of the accounts as the second task, and determine the to-be-allocated task whose expected duration is less than the expected duration of any of the accounts as the first task; Determine the employee account whose expected account duration is less than or equal to the expected task duration as a second candidate account, and assign the second task to any second candidate account with the least number of assigned tasks; When all the second tasks are assigned, the employee accounts that have not been assigned tasks are determined as first candidate accounts, and each of the first tasks is assigned to a plurality of the first candidate accounts.

[0008] According to some embodiments of the present invention, allocating each of the first tasks to a plurality of the first candidate accounts includes: Obtaining a zoom sequence composed of a plurality of zoom coefficients arranged from small to large, wherein the zoom coefficient is an integer greater than 1; Selecting a target task from the plurality of first tasks, obtaining a target coefficient from the scaling sequence, and scaling the expected account duration of the first candidate account to a decomposed expected duration by using the target coefficient; Determine the first candidate account whose expected decomposition duration is less than or equal to the expected task duration as an allocatable account; When the number of the assignable accounts is greater than or equal to the target coefficient, assigning the target task to each of the assignable accounts; When the number of the assignable accounts is less than the target coefficient, obtaining the next target coefficient from the scaling sequence, scaling the account expected duration of the first candidate account to a new decomposed expected duration based on the next target coefficient, and matching the task expected duration based on the new decomposed expected duration; When the target task is assigned, assigning the next target task based on the remaining first candidate accounts; When the number of the first candidate accounts is less than the target coefficient for the first application of the target task, all the employee accounts are determined as the first candidate accounts and then the target task is continuously assigned.

[0009] According to some embodiments of the present invention, the expected duration of each account is also pre-associated with an expected cost value, and generating a first service path for the first account based on the first node set and a preset ant colony model includes: Based on each of the first tasks, the decomposed expected duration with the smallest associated value is determined as the lower limit of the task duration, the decomposed expected duration with the largest associated value is determined as the upper limit of the task duration, the lower limit of the task cost is determined based on the expected cost value with the smallest associated value and the target coefficient, and the upper limit of the task cost is determined based on the expected cost value with the largest associated value and the target coefficient; Based on the two first graph nodes of the first node path, the sum of the upper limit values ​​of the task duration is determined as the upper limit value of the path duration, the sum of the lower limit values ​​of the task duration is determined as the lower limit value of the path duration, the sum of the upper limit values ​​of the task cost is determined as the upper limit value of the path cost, and the sum of the lower limit values ​​of the task cost is determined as the lower limit value of the path cost; Based on the first graph node and the second graph node of the second node path, the sum of the corresponding upper limit of the task duration and the expected duration of the account is determined as the upper limit of the path duration, the sum of the lower limit of the task duration and the expected duration of the account is determined as the lower limit of the path duration, the sum of the upper limit of the task cost and the expected cost value is determined as the upper limit of the path cost, and the sum of the lower limit of the task cost and the expected cost value is determined as the lower limit of the path cost; Determine a first pheromone factor based on the path duration upper limit, the path duration lower limit, and a preset first weight, determine a second pheromone factor based on the path cost upper limit, the path cost lower limit, and a preset second weight, and determine the sum of the first pheromone factor and the second pheromone factor as a pheromone adjustment value, wherein the first weight and the second weight are both positive numbers, and the sum of the first weight and the second weight is equal to 1; The first pheromone concentration of the first node path is adjusted based on the pheromone adjustment value, or the second pheromone concentration of the second node path is adjusted, and the first service path is constructed based on the adjusted first pheromone concentration and the second pheromone concentration.

[0010] According to some embodiments of the present invention, after generating a second service path for the second account based on the second node set and the ant colony model, the method further includes: determining the first account that starts the first service path or the second account that starts the second service path as an activated account, and tracking in real time the real-time graph node where the activated account is currently located; When the activated account is assigned an exceptional task, determining a third graph node associated with the exceptional task; The ant colony model is input based on the real-time graph node, the third graph node, and the unreached first graph node and the second graph node corresponding to the activated account, and a third service path is reconstructed, wherein the node path between the real-time graph node and the third graph node is the first path of the third service path.

[0011] According to some embodiments of the present invention, generating a first service path of the first account based on the first node set and a preset ant colony model includes: Determining the task priority of each of the third tasks and each of the second tasks; When the third task assigned to the first account includes at least two levels of task priorities, splitting the first node set into a plurality of node subsets, wherein different node subsets correspond to different task priorities; generating a service subpath based on the node subset and the ant colony model; Based on the task priorities, multiple service sub-paths are spliced ​​into the first service path from high to low.

[0012] In a second aspect, an embodiment of the present invention provides a task path planning device for property management, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions executable by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the task path planning method for property management as described in the first aspect above.

[0013] In a third aspect, an embodiment of the present invention provides an electronic device, comprising the task path planning device for property management as described in the second aspect above.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the task path planning method for property management as described in the first aspect above.

[0015] The task path planning method for property management according to an embodiment of the present invention has at least the following beneficial effects: constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second tasks to any of the employee accounts, wherein the first task is associated with a first graph node, the second task is associated with a second graph node, and the first graph node and the second graph node represent the geographical location of the property area map; based on any of the first tasks, decomposing into a plurality of third tasks according to the number of the assigned employee accounts, assigning one of the third tasks to each of the third tasks, and associating the first graph node to the third task; determining a plurality of first accounts from the plurality of employee accounts, and constructing a plurality of first accounts for each of the first accounts; From the corresponding first node set, wherein the first account is assigned the third task and the second task, and the first node set includes the associated first graph node and the second graph node; applying a preset first pheromone concentration in the first node path of the first node set, and applying a preset second pheromone concentration in the second node path, wherein the first node path is a path between two of the first graph nodes, the second node path is a path between the first graph node and the second graph node, and the first pheromone concentration is greater than the second pheromone concentration; generating a first service path for the first account based on the first node set and the preset ant colony model, wherein the starting graph node of the first account is any of the first graph nodes. According to the technical solution of the embodiment of the present invention, the first task that needs to be performed by multiple people can be disassembled, and two pheromone concentrations with different values ​​are applied to the first account assigned with the third task for path planning, so that the first service path can be integrated with the personal task on the principle of multi-person task priority, without being split into multiple service paths, thereby improving the rationality of path planning and improving the efficiency of property services. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a task path planning method for property management provided by an embodiment of the present invention; Figure 2 is a schematic diagram of task path planning provided by an embodiment of the present invention; Figure 3 is a complete flow chart of a task path planning method for property management provided by another embodiment of the present invention; Figure 4 It is a structural diagram of a task path planning device for property management provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0019] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0020] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0021] The embodiment of the present invention provides a task path planning method, device, equipment, and storage medium for property management, wherein the task path planning method for property management includes: constructing multiple first tasks and multiple second tasks, assigning the first tasks to multiple employee accounts, and assigning the second tasks to any of the employee accounts, wherein the first task is associated with a first graph node, the second task is associated with a second graph node, and the first graph node and the second graph node represent the geographical location of a property area map; based on any of the first tasks, splitting into multiple third tasks according to the number of the assigned employee accounts, assigning one of the employee accounts to each of the third tasks, and associating the first graph node to the third task; determining multiple first accounts from the multiple employee accounts, Constructing a first node set corresponding to each of the first accounts, wherein the first account is assigned the third task and the second task, and the first node set includes the associated first graph node and the second graph node; applying a preset first pheromone concentration in the first node path of the first node set, and applying a preset second pheromone concentration in the second node path, wherein the first node path is a path between two of the first graph nodes, the second node path is a path between the first graph node and the second graph node, and the first pheromone concentration is greater than the second pheromone concentration; generating a first service path for the first account based on the first node set and the preset ant colony model, wherein the starting graph node of the first account is any of the first graph nodes. According to the technical solution of the embodiment of the present invention, the first task that needs to be performed by multiple people can be disassembled, and two pheromone concentrations with different values ​​are applied to the first account assigned with the third task for path planning, so that the first service path can be integrated with the personal task on the principle of multi-person task priority, without being split into multiple service paths, thereby improving the rationality of path planning and the efficiency of property services.

[0022] The technical solution of the embodiment of the present invention is further described below based on the accompanying drawings.

[0023] Reference Figure 1 , Figure 1 A flowchart of a task path planning method for property management provided by an embodiment of the present invention, the task path planning method for property management includes but is not limited to the following steps: S10, constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second tasks to any employee account, wherein the first tasks are associated with a first graph node, the second tasks are associated with a second graph node, and the first graph node and the second graph node represent the geographical location of the property area map; S20, based on any first task, split it into multiple third tasks according to the number of assigned employee accounts, assign an employee account to each third task, and associate the first graph node with the third task; S30, determining a plurality of first accounts from a plurality of employee accounts, and constructing a first node set corresponding to each of the first accounts, wherein the first account is assigned a third task and a second task, and the first node set includes associated first graph nodes and second graph nodes; S40, applying a preset first pheromone concentration in a first node path of the first node set, and applying a preset second pheromone concentration in a second node path, wherein the first node path is a path between two first graph nodes, the second node path is a path between a first graph node and a second graph node, and the first pheromone concentration is greater than the second pheromone concentration; S50: Generate a first service path of the first account based on the first node set and a preset ant colony model, wherein the starting graph node of the first account is any first graph node.

[0024] It should be noted that the first task and the second task of this embodiment can be common tasks in property management, such as cleaning tasks, inspection tasks, customer care tasks, etc. The difference between the first task and the second task is the number of people assigned. The two can be tasks of the same type. For example, the first task and the second task are both cleaning tasks. The cleaning task corresponding to the first task requires multiple people to complete, while the cleaning task corresponding to the second task only requires one person to complete. The subsequent third task is the same as the second task and only requires one person to complete. It will not be repeated later.

[0025] It should be noted that the first task and the second task can specify the number of people to be assigned when constructing the task, or the number of people to be assigned can be automatically calculated after the task is constructed, thereby distinguishing the first task from the second task. No further restrictions are made here.

[0026] It should be noted that before applying the ant colony algorithm, the property area is first converted into a graph structure, and the position corresponding to each first task or second task is marked in the graph structure. For example, if the first task is cleaning the gate of the community, the first graph node is generated at the gate of the community in the property area graph, and the second graph node is generated in the same way. I will not repeat it here.

[0027] It should be noted that in the field of property management, each employee account corresponds to a property employee, and each property employee can undertake multiple tasks. Therefore, each employee account can be assigned multiple tasks. When splitting the first task, it is not necessary to consider the distribution of all tasks, but only to split them according to their own execution needs. When splitting the first task, it is determined according to the number of people assigned, for example Figure 2As shown, the first task 1 is assigned to three employee accounts, and then it is decomposed into the third task 1, the third task 2 and the third task 3. Although the three employee accounts are all assigned the third tasks decomposed from the first task 1, when assigning the first task 2 and the first task 3, they are decomposed according to their own execution needs, and there is no need to consider how many third tasks the employee account has undertaken, ensuring that each first task can be decomposed into a granularity that can be undertaken.

[0028] It should be noted that the decomposition of the first task can be completed manually or automatically through the relevant task decomposition algorithm, and no further restrictions are made here. The multiple third tasks decomposed can be the same task, and are assigned to multiple employee accounts according to the required duration of the task. For example, the first task is to clean the lobby of the community, which needs to be completed within 1 hour, and the duration of the sweeping task is 1.5 hours. It is split into two third tasks representing sweeping, and then assigned to two different employee accounts. It will not be repeated later. It is worth noting that the upper limit of the number of decompositions of the first task is the upper limit of the number of employee accounts, which will not be repeated later.

[0029] It should be noted that since the third task is a subtask decomposed from the first task, the graph node of the third task inherits the corresponding first graph node, such as Figure 2 As shown, the first graph node of the first task 1 is graph node 1, and the graph nodes of the third task 1, the third task 2, and the third task 3 that are decomposed are all graph node 1.

[0030] It should be noted that the first account in this embodiment is an employee account assigned with the third task and the second task, and the second account is an employee account including only the second task or the third task. Figure 2 As shown, employee account 1 is assigned to the third task 1, the second task 1, the third task 4 and the third task 6, including the second task and the third task. Employee account 1 belongs to the first account, and similarly employee account 3 also belongs to the first account; employee account 2 is only assigned to the third task 2, the third task 5 and the third task 7, and belongs to the second account.

[0031] It should be noted that the first task is a task to be performed by multiple people, and the third tasks broken down can be completed independently. When each third task is completed, it can be determined that the first task is completed. Therefore, this embodiment does not limit the third tasks of each first task to be executed at the same time, which is obviously not in line with the rationality of path planning. Therefore, in this embodiment, the path between the first graph nodes is determined as the first node path, and the path between the first graph node and the second graph node is determined as the second node path. When planning the path for the first account, the first node path uses a larger first pheromone concentration as the initial pheromone concentration, and the second path node uses a smaller second pheromone concentration as the initial concentration. After executing the first first task, if there are other first tasks, the first account has a greater probability of going to the next first task in the next step. Of course, pheromone concentration is not the only parameter in the ant colony algorithm. For example, the path distance can also be used to determine parameters such as the heuristic factor. If there is a second graph node that is closer to the current first graph node, the second graph node may become the next graph node in the first service path after the ant colony algorithm. In this embodiment, by applying a larger first pheromone concentration to the first node path, each first graph node has a greater probability of being selected. Compared with the existing technology that must traverse the first graph node and then the second graph node, the planned first service path can integrate multi-person tasks and collective tasks, and the path planning is more reasonable, thereby improving the efficiency of property services.

[0032] For example Figure 2 As shown, the first task 1, the first task 2 and the first task 3 are multi-person tasks. The graph node 1 of the first task 1 is used as the path starting point, and the starting points of employee account 1, employee account 2 and employee account 3 are all graph node 1, which can ensure that the three third tasks corresponding to the first task 1 are completed synchronously. At this time, employee account 1 also includes graph node 2, graph node 4 and graph node 5. The first pheromone concentration is used between graph node 1 and graph node 5 and graph node 4, and the second pheromone concentration is used between graph node 1 and graph node 2. The distance between graph node 1 and graph node 5 and graph node 2 is close. For example, the first information degree concentration is used between graph node 5 and graph node 1, which has a greater probability of being selected. Therefore, the next graph node of graph node 1 is graph node 5. Graph node 5 is far away from graph node 4 and close to graph node 2. Even if the first pheromone concentration is used between graph node 5 and graph node 4, the heuristic factor determined based on the distance is larger. After combining the calculation with the second pheromone concentration, graph node 2 is selected as the next graph node, and finally graph node 4 is selected. The first service path obtained is graph node 1, graph node 5, graph node 2 and graph node 4. Taking employee account 3 as another example, graph node 1 is closer to graph node 3. Referring to the above principle, the first pheromone concentration is used between graph node 5 and graph node 1. The benefit of pheromone concentration after comprehensive calculation based on the ant colony model is greater, so the next graph node is calculated to be graph node 5.

[0033] It should be noted that the ant colony model of this embodiment is a prior art. This embodiment only involves the construction of graph nodes and the setting of initial pheromone concentrations, and the specific model principles are not elaborated here.

[0034] In addition, in one embodiment, referring to Figure 3 After executing step S50, the method further includes but is not limited to the following steps: S61, determining a plurality of second accounts from a plurality of employee accounts, wherein the second accounts are only assigned with the second task or the third task; S62, constructing a second node set corresponding to each second account, wherein the second node set includes the associated first graph node or second graph node; S63, applying the second pheromone concentration in each first node path or third node path of the second node set, wherein the third node path is a path between two second graph nodes; S64: Generate a second service path for the second account based on the second node set and the ant colony model.

[0035] It should be noted that according to the above definition of the second account, the second account only has the second task or the third task. Since there is no difference between single-person tasks and multi-person tasks, the second account only needs to use a uniform pheromone concentration when planning the path, and there is no need to set a probability biased guided ant colony model. I will not go into details here.

[0036] For example, Figure 2 As shown, employee account 2 is assigned the third task 3, the third task 5 and the third task 7, and therefore belongs to the second account. Even if each graph node is a first graph node, the second pheromone concentration is still used as the initial pheromone to ensure that the initial pheromone concentrations between each first node path are the same, and the optimal path planning is achieved through the ant colony model.

[0037] In addition, in one embodiment, referring to Figure 3 Step S10 specifically includes but is not limited to the following steps: S11, constructing a plurality of tasks to be assigned, wherein the tasks to be assigned are associated with task graph nodes and expected task durations; S12, determining the expected account duration of each employee account based on the task to be assigned, wherein the expected account duration is used to represent the duration for the corresponding employee account to complete the task to be assigned; S13, determining a task to be assigned whose expected duration is greater than or equal to the expected duration of at least one account as a second task, and determining a task to be assigned whose expected duration is less than the expected duration of any account as a first task; S14, determining an employee account whose expected account duration is less than or equal to the expected task duration as a second candidate account, and assigning the second task to any second candidate account with the least number of assigned tasks; S15, when all second tasks are assigned, employee accounts that have not been assigned tasks are determined as first candidate accounts, and each first task is assigned to a plurality of first candidate accounts.

[0038] It should be noted that, in this embodiment, only the expected duration of the task and the task graph node need to be input when constructing the task to be assigned. The location for executing the task can be determined in advance, so the task graph node can be obtained through simple configuration; the expected duration of the task can be set according to the actual needs of the task.

[0039] Exemplarily, the tasks to be assigned include cleaning the community gate and disposing of community garbage. The expected duration of the task of cleaning the community gate is set to 1 hour, and the task graph node is the community gate. The expected duration of the task of disposing of community garbage is set to 2 hours, and the task graph node is the community garbage station.

[0040] It should be noted that after completing the configuration of the tasks to be assigned, the expected time required for each employee account to complete the tasks to be assigned can be determined. Different employees have different work efficiency, so the expected time can be manually set according to the actual work efficiency of the employees. For example, if the configured task to be assigned is cleaning the community gate, the expected time for employee account 1 is 0.5 hours, and the expected time for employee account 2 is 1.5 hours.

[0041] It should be noted that this embodiment determines the task to be assigned as the first task or the second task based on the expected duration of the task. When the expected duration of the task can be met by the expected duration of at least one account, it can be determined as the second task. Otherwise, the task to be assigned requires multiple people to complete and is then determined as the first task.

[0042] Exemplarily, the tasks to be assigned include cleaning the gate of the community, community garbage disposal, and community patrol, and the expected duration of the tasks is 1 hour, 2 hours, and 3 hours, respectively. For the task of cleaning the gate of the community to be assigned, the account expected duration of employee account 1 is 0.5 hours, employee account 2 is 1.5 hours, and employee account 3 is 1 hour. Cleaning the gate of the community can be assigned to employee account 1 and employee account 3 and determined as the second task; for the task of community garbage disposal to be assigned, the account expected duration of employee account 1 is 1.5 hours, employee account 2 is 2.5 hours, and employee account 3 is 1.8 hours. Cleaning the gate of the community can be assigned to employee account 1 and employee account 3 and determined as the second task; for the task of patrolling the community to be assigned, the account expected duration of employee account 1 is 5 hours, employee account 2 is 4 hours, and employee account 3 is 6 hours. The expected duration of the task of patrolling the community is 3 hours, which is less than the expected duration of any account, and is determined as the first task.

[0043] It should be noted that multi-person tasks can satisfy more employee accounts by increasing the number of disassembly, while the number of employee accounts that can perform the second task alone is relatively limited, so this embodiment first allocates the second task. After referring to the principle of the above example, employee accounts whose expected account duration is less than or equal to the expected task duration are determined as second candidate accounts, the second tasks are allocated one by one, and the second tasks are allocated first to the second candidate accounts with the least number of tasks, which is conducive to improving the utilization rate of human resources. After completing the allocation of the second task, the remaining employee accounts are determined as first candidate accounts, and the third task is allocated. Of course, in the case of a small number of first candidate accounts, the second candidate accounts that can still undertake the task can also be determined as the first candidate accounts. This embodiment gives priority to the allocation of the second task, and does not limit employee accounts to only undertake the second task, and ensures that the first task can be allocated.

[0044] Exemplarily, the second candidate accounts for the second task 1 are employee account 1 and employee account 3. After employee account 1 is assigned the second task 1, the second task 2 is assigned to employee account 3. After the second task is assigned, employee account 2 is determined as the first candidate account.

[0045] In addition, in one embodiment, referring to Figure 3 Step S15 specifically includes but is not limited to the following steps: S151, obtaining a zoom sequence composed of a plurality of zoom coefficients arranged from small to large, wherein the zoom coefficient is an integer greater than 1; S152, selecting a target task from the plurality of first tasks, obtaining a target coefficient from the scaling sequence, and scaling the expected account duration of the first candidate account to the decomposed expected duration by the target coefficient; S153, determining a first candidate account whose expected decomposition duration is less than or equal to the expected task duration as an allocatable account; S154, when the number of allocatable accounts is greater than or equal to the target coefficient, allocating the target task to each allocatable account; S155, when the number of allocable accounts is less than the target coefficient, obtaining a next target coefficient from the scaling sequence, scaling the account expected duration of the first candidate account to a new decomposed expected duration based on the next target coefficient, and matching the task expected duration based on the new decomposed expected duration; S156, when the target task is assigned, the next target task is assigned based on the remaining first candidate accounts; S157, when the number of the first candidate accounts is less than the target coefficient of the first application of the target task, all employee accounts are determined as the first candidate accounts and then the target task is continued to be allocated.

[0046] It should be noted that after determining the first candidate account, it is necessary to ensure that the first task can be completed by each first candidate account after being disassembled. This embodiment compares the expected duration of the task and the expected duration of the account. When the expected duration of the task is less than the expected duration of the account, the task cannot be completed. The expected duration of the account is the duration required for the employee to perform the task alone. In this embodiment, the first task is assigned to multiple employees, so the expected duration of the account needs to be shortened before task matching. In this embodiment, a scaling sequence is pre-set, and the target coefficient is obtained from the scaling sequence to reduce the expected duration of the account. The target coefficient is used to characterize the number of disassemblies of the first task. For example, if the target coefficient is 2, the first task is assigned to two employee accounts. For the convenience of calculation, this embodiment divides the expected duration of the account by the target coefficient to obtain the decomposition expected duration. The expected duration of the task is matched based on the decomposition expected duration. When the number of first candidate accounts that meet the conditions is greater than or equal to the target coefficient, it can be determined that the target task can be completed after being disassembled. When the number of assignable accounts is less than the target coefficient, the next target coefficient is obtained for re-disassembly until the target task can be assigned.

[0047] Exemplarily, continuing to refer to the above example, the target task is to take community cleaning as an example. The expected duration of the task is 2 hours. The first candidate accounts include 3 employee accounts. The expected duration of employee account 1 is 3 hours, the expected duration of employee account 2 is 4 hours, and the expected duration of employee account 3 is 5 hours. The scaling series is {2,3,4,···}, and 2 is selected as the target coefficient. The decomposed expected duration of each first candidate account is 1.5 hours, 2 hours, and 2.5 hours. Employee account 1 and employee account 2 meet the conditions (the decomposed expected duration is less than or equal to the expected duration of the task) and are determined to be allocatable accounts. The community cleaning task is assigned to employee account 1 and employee account 2.

[0048] For example, taking community patrol as the target task, the expected duration of the task is 1.5 hours. The first candidate accounts continue to take 4 employee accounts as an example. The expected duration of employee account 1 is 3 hours, the expected duration of employee account 2 is 4 hours, the expected duration of employee account 3 is 4 hours, and the expected duration of employee account 4 is 4 hours. When 2 is selected as the target coefficient, the decomposed expected durations are 1.5 hours, 2 hours, 2 hours and 2 hours respectively. The only account that can be allocated is employee account 1, which does not meet the target coefficient. Continue to select 3 as the next target coefficient, and the decomposed expected durations are 1 hour, 1.3 hours, 1.3 hours and 1.3 hours. All 4 employee accounts are allocatable accounts, and the number is greater than the target coefficient. The target task can be assigned to any 3 employee accounts.

[0049] It should be noted that, in this embodiment, the target task is preferentially assigned to the first candidate account with the least number of tasks, thereby improving the utilization rate of human resources. When the number of the first candidate accounts is less than the target coefficient, all employee accounts can be determined as the first candidate accounts, and preferentially assigned to the first candidate account with the least number of tasks, and the remaining tasks can be arbitrarily assigned from other employee accounts, thereby improving the rationality of task allocation.

[0050] Exemplarily, the number of employee accounts is 4, employee account 1 and employee account 2 have been assigned the third task, the target coefficient of the target task is 3, and the first candidate accounts are only employee account 3 and employee account 4. Employee account 1 and employee account 2 are also determined as the first candidate accounts. After the third task decomposed from the target task is assigned to employee account 3 and employee account 4, the remaining third task is assigned to employee account 1 or employee account 2.

[0051] In addition, in one embodiment, referring to Figure 3 The expected duration of each account is also pre-associated with an expected cost value. Step S50 specifically includes but is not limited to the following steps: S51, based on each first task, determining the decomposed expected duration with the smallest associated value as the lower limit of the task duration, determining the decomposed expected duration with the largest associated value as the upper limit of the task duration, determining the lower limit of the task cost based on the expected cost value with the smallest associated value and the target coefficient, and determining the upper limit of the task cost based on the expected cost value with the largest associated value and the target coefficient; S52, based on the two first graph nodes of the first node path, determine the sum of the upper limit values ​​of the task duration as the upper limit value of the path duration, determine the sum of the lower limit values ​​of the task duration as the lower limit value of the path duration, determine the sum of the upper limit values ​​of the task cost as the upper limit value of the path cost, and determine the sum of the lower limit values ​​of the task cost as the lower limit value of the path cost; S53, based on the first graph node and the second graph node of the second node path, determine the sum of the corresponding task duration upper limit value and the account expected duration as the path duration upper limit value, determine the sum of the task duration lower limit value and the account expected duration as the path duration lower limit value, determine the sum of the task cost upper limit value and the expected cost value as the path cost upper limit value, and determine the sum of the task cost lower limit value and the expected cost value as the path cost lower limit value; S54, determining a first pheromone factor based on an upper limit value of the path time, a lower limit value of the path time and a preset first weight, determining a second pheromone factor based on an upper limit value of the path cost, a lower limit value of the path cost and a preset second weight, and determining the sum of the first pheromone factor and the second pheromone factor as the pheromone adjustment value, wherein the first weight and the second weight are both positive numbers, and the sum of the first weight and the second weight is equal to 1; S55, adjusting the first pheromone concentration of the first node path based on the pheromone adjustment value, or adjusting the second pheromone concentration of the second node path, and constructing a first service path based on the adjusted first pheromone concentration and second pheromone concentration.

[0052] It should be noted that in the ant colony model, the pheromone needs to be updated after each path is determined. The first task is decomposed into multiple third tasks and assigned to multiple first accounts. The expected decomposition time is the time it takes for the first account to complete the third task. In the above embodiment, the third task is obtained based on the average decomposition of the target coefficient, and the execution efficiency corresponding to each first account is different. Therefore, this embodiment first determines the upper limit and lower limit of the task duration based on the expected decomposition time of each first account assigned to the first task. For example, in the above-mentioned community cleaning example, the expected decomposition time of employee account 1 is 1.5 hours, and the expected decomposition time of employee account 2 is 2 hours. Therefore, the lower limit of the task duration is 1.5 hours, and the upper limit of the task duration is 2 hours. For the second task, since there is only one employee account, no relevant values ​​are involved.

[0053] It should be noted that the expected cost value is the cost required for each employee account to complete the task in the expected time of the account, such as the use of more expensive tools to speed up the completion of the task, or the labor cost caused by overtime, etc. The specific value is not limited here. The expected cost value is not necessarily positively correlated with the expected decomposition time. The proficiency of each employee in using tools may be different. Even if some employees use more expensive tools, but their proficiency is low, it will still lead to a longer expected decomposition time. In this embodiment, the preset cost value with the largest value among the multiple first accounts corresponding to the first task is determined as the upper limit of the task cost, and the smallest is determined as the lower limit of the task cost.

[0054] It should be noted that the first node path is composed of two first graph nodes, each of which corresponds to a first task, so the path duration upper limit can be obtained by calculating the task duration upper limit, and the same is true for other values. The second node path is composed of the first graph node and the second graph node, and the path duration upper limit is determined by the sum of the task duration upper limit of the first task of the first graph node and the expected duration of the account corresponding to the second task of the second graph node. The other values ​​are similar and will not be repeated here.

[0055] It should be noted that, when updating pheromones, the present embodiment sets a first weight and a second weight, the sum of which is 1, so that the calculated pheromone adjustment value can consider the weights of the task duration and the task cost in different proportions. The calculation formula of the pheromone adjustment value in step S54 is as follows: ; Among them, ij is used to represent the node path formed by the i-th graph node and the j-th graph node, j=i-1; is the expected duration of the task of the j-th graph node, i represents the i-th graph node, j is the j-th graph node, j=i-1, is the lower limit of the path duration, is the upper limit of the path duration; is the sum of the expected cost values ​​of the jth task, is the lower limit of the path cost, is the upper limit of the path cost, F(I) is the pheromone adjustment value from the i-th graph node to the j-th graph node, is the first weight, is the second weight.

[0056] It should be noted that the pheromone adjustment value can be used as the subtrahend of the first pheromone concentration, that is, the target pheromone concentration of the first node path is obtained by subtracting the pheromone adjustment value from the first pheromone concentration. After the target pheromone concentrations to each node are obtained at the i-th node, the next target node can be determined based on the ant colony model.

[0057] In addition, in one embodiment, referring to Figure 3 After executing step S64, the method further includes but is not limited to the following steps: S71, determining a first account that starts a first service path or a second account that starts a second service path as an activated account, and tracking in real time a real-time graph node where the activated account is currently located; S72, when the activated account is assigned an exceptional task, determining a third graph node associated with the exceptional task; S73, based on the real-time graph node, the third graph node and the unreached first graph node and the second graph node corresponding to the activated account, input the ant colony model to reconstruct the third service path, wherein the node path between the real-time graph node and the third graph node is the first path of the third service path.

[0058] It should be noted that after completing the planning of the first service path or the second service path, when the first account and the second account start to execute tasks, they are determined as activated accounts, and the current graph node of each first account and the second account is tracked in real time to track the task execution status of each employee account.

[0059] It should be noted that when an activated account is assigned an exceptional task, for example, a new emergency cleaning task temporarily appears and needs to be executed first, the graph node corresponding to the exceptional task is determined as the third graph node, the current node is the real-time graph node, and the path is replanned with the third graph node as the next graph node to achieve real-time modification of the service path. Of course, the third server path uses the real-time graph node and the third graph node as the first path, and the second path can be replanned according to the above-mentioned embodiment, for example, the position in the original path is adjusted according to the distance between the remaining first graph nodes or the second graph nodes and the third graph nodes. The path planning principle will not be repeated here.

[0060] For example, Figure 2 As shown, taking employee account 1 as the activation account as an example, the first service path includes graph node 1, graph node 5, graph node 2 and graph node 4 in sequence. When graph node 5 is executed, an exception task is assigned, and the corresponding task graph node is graph node 6. Then graph node 5 is the real-time graph node, and graph node 6 is the third graph node. If graph node 6 is close to graph node 4, graph node 2 and graph node 4 will be determined as the next graph node even if the corresponding pheromone concentrations are different. Therefore, the re-planned third service path includes graph node 5, graph node 6, graph node 4 and graph node 2 in sequence.

[0061] In addition, in one embodiment, referring to Figure 3 Step S50 also includes but is not limited to the following steps: S81, determining the task priority of each third task and each second task; S82, when the third task assigned to the first account includes at least two levels of task priorities, splitting the first node set into a plurality of node subsets, wherein different node subsets correspond to different task priorities; S83, generating a service sub-path based on the node subset and the ant colony model; S84, splicing multiple service sub-paths into a first service path based on the task priorities from high to low.

[0062] It should be noted that when performing task planning, task priorities can be set for each third task and second task. In the field of property management, if the task priority is higher, path planning can be prioritized without considering tasks with lower priority. Based on this, when the third task assigned to the first account includes at least two people's priorities, the first node set is split into multiple node subsets, and the corresponding service sub-paths are planned separately, and then the various task sub-paths are spliced ​​into the first service path to improve the flexibility of task planning.

[0063] It should be noted that after obtaining multiple node subsets, this embodiment first constructs a service sub-path based on the node subset with a higher task priority, and then takes the last graph node of the node subset with a higher task priority as the starting point to plan the service sub-path of the next level of task priority. When planning different task priorities, the position after the execution of the previous task priority can be considered, thereby improving the rationality of the first service path.

[0064] like Figure 4 As shown, Figure 4 : is a structural diagram of a task path planning device for property management provided by an embodiment of the present invention. The present invention also provides a task path planning device for property management, including: The processor 401 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 402, and the processor 401 calls and executes the task path planning method of property management in the embodiment of this application; Input / output interface 403, used to implement information input and output; Communication interface 404, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 405 that transmits information between various components of the device (e.g., processor 401, memory 402, input / output interface 403, and communication interface 404); The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via the bus 405 .

[0065] An embodiment of the present application also provides an electronic device, including the task path planning device for property management as described above.

[0066] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the task path planning method for property management is implemented.

[0067] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0068] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media generally include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0069] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions under the shared conditions without violating the spirit of the present invention. These equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A task path planning method for property management, characterized in that: include: Constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second tasks to any of the employee accounts, wherein the first tasks are associated with a first graph node, the second tasks are associated with a second graph node, and the first graph node and the second graph node represent the geographical location of a property area map; Based on any of the first tasks, split the task into a plurality of third tasks according to the number of the assigned employee accounts, assign one employee account to each of the third tasks, and associate the first graph node with the third task; Determine a plurality of first accounts from the plurality of employee accounts, and construct a first node set corresponding to each of the first accounts, wherein the first account is assigned the third task and the second task, and the first node set includes the associated first graph node and the second graph node; Applying a preset first pheromone concentration in a first node path of the first node set, and applying a preset second pheromone concentration in a second node path, wherein the first node path is a path between two of the first graph nodes, the second node path is a path between the first graph node and the second graph node, and the first pheromone concentration is greater than the second pheromone concentration; A first service path of the first account is generated based on the first node set and a preset ant colony model, wherein a starting graph node of the first account is any of the first graph nodes.

2. The task path planning method for property management according to claim 1, characterized in that: After generating the first service path of the first account based on the first node set and the preset ant colony model, the method further includes: Determine a plurality of second accounts from the plurality of employee accounts, wherein the second accounts are only assigned the second task or the third task; Constructing a second node set corresponding to each of the second accounts, wherein the second node set includes the associated first graph node or the second graph node; Applying the second pheromone concentration in each of the first node paths or third node paths of the second node set, wherein the third node path is a path between two of the second graph nodes; A second service path for the second account is generated based on the second node set and the ant colony model.

3. The task path planning method for property management according to claim 2 is characterized in that: Constructing a plurality of first tasks and a plurality of second tasks, assigning the first tasks to a plurality of employee accounts, and assigning the second tasks to any of the employee accounts, comprises: Constructing a plurality of tasks to be assigned, wherein the tasks to be assigned are associated with task graph nodes and expected task durations; Determine the expected account duration of each employee account based on the task to be assigned, wherein the expected account duration is used to represent the duration for the corresponding employee account to complete the task to be assigned; Determine the to-be-allocated task whose expected duration is greater than or equal to the expected duration of at least one of the accounts as the second task, and determine the to-be-allocated task whose expected duration is less than the expected duration of any of the accounts as the first task; Determine the employee account whose expected account duration is less than or equal to the expected task duration as a second candidate account, and assign the second task to any second candidate account with the least number of assigned tasks; When all the second tasks are assigned, the employee accounts that have not been assigned tasks are determined as first candidate accounts, and each of the first tasks is assigned to a plurality of the first candidate accounts.

4. The task path planning method for property management according to claim 3 is characterized in that: Allocating each of the first tasks to a plurality of the first candidate accounts comprises: Obtaining a zoom sequence composed of a plurality of zoom coefficients arranged from small to large, wherein the zoom coefficient is an integer greater than 1; Selecting a target task from the plurality of first tasks, obtaining a target coefficient from the scaling sequence, and scaling the expected account duration of the first candidate account to a decomposed expected duration by using the target coefficient; Determine the first candidate account whose expected decomposition duration is less than or equal to the expected task duration as an allocatable account; When the number of the assignable accounts is greater than or equal to the target coefficient, assigning the target task to each of the assignable accounts; When the number of the assignable accounts is less than the target coefficient, obtaining the next target coefficient from the scaling sequence, scaling the account expected duration of the first candidate account to a new decomposed expected duration based on the next target coefficient, and matching the task expected duration based on the new decomposed expected duration; When the target task is assigned, assigning the next target task based on the remaining first candidate accounts; When the number of the first candidate accounts is less than the target coefficient of the first application of the target task, all the employee accounts are determined as the first candidate accounts and then the target task is continuously assigned.

5. The task path planning method for property management according to claim 4 is characterized in that: The expected duration of each account is also pre-associated with an expected cost value, and the first service path of the first account is generated based on the first node set and a preset ant colony model, including: Based on each of the first tasks, the decomposed expected duration with the smallest associated value is determined as the lower limit of the task duration, the decomposed expected duration with the largest associated value is determined as the upper limit of the task duration, the lower limit of the task cost is determined based on the expected cost value with the smallest associated value and the target coefficient, and the upper limit of the task cost is determined based on the expected cost value with the largest associated value and the target coefficient; Based on the two first graph nodes of the first node path, the sum of the upper limit values ​​of the task duration is determined as the upper limit value of the path duration, the sum of the lower limit values ​​of the task duration is determined as the lower limit value of the path duration, the sum of the upper limit values ​​of the task cost is determined as the upper limit value of the path cost, and the sum of the lower limit values ​​of the task cost is determined as the lower limit value of the path cost; Based on the first graph node and the second graph node of the second node path, the sum of the corresponding upper limit of the task duration and the expected duration of the account is determined as the upper limit of the path duration, the sum of the lower limit of the task duration and the expected duration of the account is determined as the lower limit of the path duration, the sum of the upper limit of the task cost and the expected cost value is determined as the upper limit of the path cost, and the sum of the lower limit of the task cost and the expected cost value is determined as the lower limit of the path cost; Determine a first pheromone factor based on the path duration upper limit, the path duration lower limit, and a preset first weight, determine a second pheromone factor based on the path cost upper limit, the path cost lower limit, and a preset second weight, and determine the sum of the first pheromone factor and the second pheromone factor as a pheromone adjustment value, wherein the first weight and the second weight are both positive numbers, and the sum of the first weight and the second weight is equal to 1; The first pheromone concentration of the first node path is adjusted based on the pheromone adjustment value, or the second pheromone concentration of the second node path is adjusted, and the first service path is constructed based on the adjusted first pheromone concentration and the second pheromone concentration.

6. The task path planning method for property management according to claim 2, characterized in that: After generating a second service path for the second account based on the second node set and the ant colony model, the method further includes: determining the first account that starts the first service path or the second account that starts the second service path as an activated account, and tracking in real time the real-time graph node where the activated account is currently located; When the activated account is assigned an exceptional task, determining a third graph node associated with the exceptional task; The ant colony model is input based on the real-time graph node, the third graph node, and the unreached first graph node and the second graph node corresponding to the activated account, and a third service path is reconstructed, wherein the node path between the real-time graph node and the third graph node is the first path of the third service path.

7. The task path planning method for property management according to claim 1, characterized in that: Generating a first service path of the first account based on the first node set and a preset ant colony model includes: Determining the task priority of each of the third tasks and each of the second tasks; When the third task assigned to the first account includes at least two levels of task priorities, splitting the first node set into a plurality of node subsets, wherein different node subsets correspond to different task priorities; generating a service subpath based on the node subset and the ant colony model; Based on the task priorities, multiple service sub-paths are spliced ​​into the first service path from high to low.

8. A task path planning device for property management, characterized in that: comprising at least one control processor and a memory for communicatively coupling with the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the task path planning method for property management as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: Including the task path planning device for property management as described in claim 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the task path planning method for property management as described in any one of claims 1 to 7.

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