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

Abstract

The present invention proposes a task path planning method, device, equipment, and storage medium for property management. The method includes: constructing multiple first tasks and multiple second tasks, and splitting the first task that needs to be performed by multiple people into multiple third tasks after allocation, the third task uses the first graph node of the first task, and the second task is associated with the second graph node; the account assigned with the third task and the second task is determined as the first account, and the other accounts are determined as the second account. When using the ant colony model to plan the path of the first account, a first pheromone concentration with a larger value is applied in the path formed by the first graph node, and a second pheromone concentration with a smaller value is applied to the other paths, so that the first service path can be integrated with personal tasks based on the principle of multi-person task priority, without having to be split into multiple service paths, thereby improving the rationality of path planning and improving the efficiency of property services.

Description

Technical Field

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

[0002] In daily property management, companies need to assign tasks to employees in different areas, such as cleaning, maintenance, and security patrols. Each task is performed in a different location. Traditional path planning relies primarily on individual employee experience, which can't guarantee the correctness of the path. With the development of artificial intelligence (AI), ant colony algorithms are being introduced to property service path planning. First, a property area map is constructed based on the property service area. Based on the location of each task, corresponding graph nodes are generated within the map. Pathways are represented by connecting lines between the graph nodes. Each employee is like an ant in the ant colony, and the ant colony algorithm is used to plan each employee's task movement path.

[0003] However, tasks in the property management sector typically include both individual and collective tasks, and some employees may be assigned to both. In related technologies, the collective task is first used as input to an ant colony algorithm to plan a collective path. The last node of the collective path is then used as the starting point for the individual task, and the ant colony algorithm is again used to plan an individual path. Each employee is assigned to both paths, completing the collective task based on the collective path before completing their individual task based on the individual path. This can lead to significant overlap between the collective and individual paths, making path planning less rational and impacting the efficiency of property management 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 provides a task path planning method, apparatus, device, and storage medium for property management. The method is capable of planning a fused path based on multi-person tasks and individual tasks, thereby improving the rationality of path planning and 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:

[0006] Constructing multiple first tasks and multiple second tasks, assigning the first tasks to multiple employee accounts, and assigning the second task 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 geographic locations of a property area map;

[0007] Based on any of the first tasks, split the task into multiple third tasks according to the number of the assigned employee accounts, assign one of the employee accounts to each of the third tasks, and associate the first graph node with the third task;

[0008] Determining a plurality of first accounts from the plurality of employee accounts, and constructing a first node set corresponding to each of the first accounts, wherein the first accounts are assigned the third task and the second task, and the first node set includes the associated first graph nodes and the second graph nodes;

[0009] 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;

[0010] 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.

[0011] 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:

[0012] Determining a plurality of second accounts from the plurality of employee accounts, wherein the second accounts are assigned only the second task or the third task;

[0013] 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;

[0014] applying the second pheromone concentration in each of the first node paths or third node paths in the second node set, wherein the third node path is a path between two of the second graph nodes;

[0015] A second service path for the second account is generated based on the second node set and the ant colony model.

[0016] According to some embodiments of the present invention, constructing multiple first tasks and multiple second tasks, assigning the first tasks to multiple employee accounts, and assigning the second task to any of the employee accounts includes:

[0017] Constructing a plurality of tasks to be assigned, wherein the tasks to be assigned are associated with task graph nodes and expected task durations;

[0018] 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;

[0019] Determine the to-be-assigned 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-assigned task whose expected duration is less than the expected duration of any of the accounts as the first task;

[0020] 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;

[0021] 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 first candidate accounts.

[0022] According to some embodiments of the present invention, assigning each of the first tasks to a plurality of the first candidate accounts includes:

[0023] Obtaining a scaling sequence composed of a plurality of scaling coefficients arranged from small to large, wherein the scaling coefficients are integers greater than 1;

[0024] 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 using the target coefficient;

[0025] Determine the first candidate account whose expected decomposition duration is less than or equal to the expected task duration as an allocatable account;

[0026] 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;

[0027] When the number of assignable accounts is less than the target coefficient, obtaining the next target coefficient from the scaling sequence, scaling the expected account duration of the first candidate account to a new decomposed expected duration based on the next target coefficient, and matching the expected task duration based on the new decomposed expected duration;

[0028] After the target task is assigned, assigning the next target task based on the remaining first candidate accounts;

[0029] 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 the target task is then continuously assigned.

[0030] 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:

[0031] Based on each of the first tasks, determining the decomposed expected duration with the smallest associated value as a lower limit of the task duration, determining the decomposed expected duration with the largest associated value as an 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;

[0032] Based on the two first graph nodes of the first node path, the sum of the task duration upper limits is determined as the path duration upper limit, the sum of the task duration lower limits is determined as the path duration lower limit, the sum of the task cost upper limits is determined as the path cost upper limit, and the sum of the task cost lower limits is determined as the path cost lower limit;

[0033] Based on the first graph node and the second graph node of the second node path, the sum of the corresponding task duration upper limit and the account expected duration is determined as the path duration upper limit, the sum of the task duration lower limit and the account expected duration is determined as the path duration lower limit, the sum of the task cost upper limit and the expected cost value is determined as the path cost upper limit, and the sum of the task cost lower limit and the expected cost value is determined as the path cost lower limit;

[0034] determining a first pheromone factor based on the path duration upper limit, the path duration lower limit, and a preset first weight, determining a second pheromone factor based on the path cost upper limit, the path cost lower limit, and a preset second weight, and determining 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;

[0035] 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.

[0036] 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:

[0037] 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;

[0038] When the activated account is assigned an exception task, determining a third graph node associated with the exception task;

[0039] 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.

[0040] According to some embodiments of the present invention, generating a first service path for the first account based on the first node set and a preset ant colony model includes:

[0041] determining a task priority of each of the third tasks and each of the second tasks;

[0042] 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;

[0043] generating a service subpath based on the node subset and the ant colony model;

[0044] Based on the task priorities from high to low, multiple service sub-paths are spliced ​​into the first service path.

[0045] In the 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 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 the first aspect above.

[0046] 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.

[0047] 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.

[0048] According to the task path planning method for property management of an embodiment of the present invention, there are at least the following beneficial effects: 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 the 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 employee account 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, and constructing a list of 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 the path between two of the first graph nodes, the second node path is the 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 a 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 an embodiment of the present invention, it is possible to disassemble the first task that requires multiple people to perform, and apply two pheromone concentrations with different values ​​to the first account assigned the third task for path planning, so that the first service path can be integrated with individual tasks based on the principle of multi-person task priority, without having to be split into multiple service paths, thereby improving the rationality of path planning and the efficiency of property services. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of a task path planning method for property management provided by one embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of task path planning provided by one embodiment of the present invention;

[0051] Figure 3 This is a complete flow chart of a task path planning method for property management provided by another embodiment of the present invention;

[0052] Figure 4It is a structural diagram of a task path planning device for property management provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following describes embodiments of the present invention in detail. Examples of the embodiments 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 intended only to explain the present invention and are not to be construed as limiting the present invention.

[0054] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They 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. Therefore, they cannot be understood as limitations on the present invention.

[0055] In the description of the present invention, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0056] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting 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.

[0057] An embodiment of the present invention provides a task path planning method, apparatus, device, 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 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, decomposing 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 tasks; determining multiple first accounts from the multiple employee accounts, Constructing a first node set corresponding to each first account, wherein the first account is assigned the third task and the second task, and the first node set includes the associated first graph nodes and the second graph nodes; 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 of the first node set, wherein the first node path is the path between two first graph nodes, the second node path is the 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 a 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 an embodiment of the present invention, it is possible to decompose the first task that requires multiple people to perform, and apply two pheromone concentrations with different values ​​for path planning in the first account assigned the third task, so that the first service path can be integrated with individual tasks based on the principle of multi-person task priority, without having to be split into multiple service paths, thereby improving the rationality of path planning and the efficiency of property services.

[0058] The technical solutions of the embodiments of the present invention are further described below based on the accompanying drawings.

[0059] Reference Figure 1 , Figure 1 A flowchart of a task path planning method for property management provided by an embodiment of the present invention includes but is not limited to the following steps:

[0060] S10, constructing multiple first tasks and multiple second tasks, assigning the first tasks to multiple employee accounts, and assigning the second tasks to any employee account, wherein the first tasks are associated with a first graph node, and the second tasks are associated with a second graph node, and the first graph node and the second graph node represent geographic locations of the property area map;

[0061] 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;

[0062] S30, determining a plurality of first accounts from the 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;

[0063] 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;

[0064] S50: Generate a first service path for 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.

[0065] 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 only 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 together, and the cleaning task corresponding to the second task only needs to be completed by one person. The subsequent third task is the same as the second task, and only needs to be completed by one person, and will not be repeated later.

[0066] It should be noted that the number of people assigned to the first task and the second task can be specified when the task is constructed, or the number of people required can be automatically calculated after the task is constructed to distinguish the first task from the second task. No further restrictions will be made here.

[0067] 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 to clean the gate of the community, the first graph node is generated at the gate of the community in the property area graph. The second graph node is generated in the same way, and will not be repeated here.

[0068] 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. It is only necessary to split it according to its own execution needs. When splitting the first task, it is determined according to the number of people assigned, for example Figure 2As shown in the figure, if the first task 1 is assigned to three employee accounts, it will be split 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 split from the first task 1, when assigning the first task 2 and the first task 3, they are split according to their own execution needs, without considering how many third tasks the employee account has already undertaken. This ensures that each first task can be split into a granularity that can be undertaken.

[0069] It should be noted that the decomposition of the first task can be completed manually or automatically through the relevant task decomposition algorithm, without further limitation here. The multiple third tasks decomposed can be the same task and assigned to multiple employee accounts according to the required task duration. For example, the first task is to clean the community lobby and needs to be completed within 1 hour. The floor sweeping task takes 1.5 hours to execute. It is split into two third tasks representing floor 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. It will not be repeated later.

[0070] 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 are all graph node 1.

[0071] 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 both the second and third tasks. 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.

[0072] It should be noted that the first task is a task 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, this embodiment defines the path between the first graph nodes as the first node path, and the path between the first graph node and the second graph node as the second node path. When planning a path for the first account, the first node path uses the larger first pheromone concentration as the initial pheromone concentration, and the second path node uses the smaller second pheromone concentration as the initial concentration. After completing the first first task, if there are other first tasks, the first account has a higher probability of proceeding to the next first task. Of course, pheromone concentration is not the only parameter in the ant colony algorithm. For example, parameters such as path distance can also be used to determine heuristic factors. If a second graph node is closer to the current first graph node, this second graph node may become the next graph node in the first service path after the ant colony algorithm. This embodiment uses the larger first pheromone concentration in the first node path to increase the selection probability of each first graph node. Compared to existing technologies that require traversing the first graph node before traversing the second graph node, the planned first service path can integrate multi-person and collective tasks, making path planning more efficient and improving property service efficiency.

[0073] For example Figure 2 As shown, the first task 1, the first task 2 and the first task 3 are multi-person tasks. Taking the graph node 1 of the first task 1 as the starting point of the path, 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 concentration is used between graph nodes 5 and 1, resulting in a higher probability of selection. Therefore, the next graph node after graph node 1 is graph node 5. However, graph node 5 is farther away from graph node 4 and closer to graph node 2. Even if the first pheromone concentration is used between graph nodes 5 and 4, the heuristic factor determined by distance is greater. Combined with the second pheromone concentration, graph node 2 is selected as the next graph node, and finally graph node 4 is selected. The resulting first service path is graph node 1, graph node 5, graph node 2, and graph node 4. For employee account 3, graph nodes 1 and 3 are relatively close. Referring to the above principle, the first pheromone concentration is used between graph nodes 5 and 1. After comprehensive calculation based on the ant colony model, the pheromone concentration benefit is greater, so the next graph node is calculated to be graph node 5.

[0074] It should be noted that the ant colony model of this embodiment is an existing technology. 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.

[0075] 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:

[0076] S61, determining a plurality of second accounts from a plurality of employee accounts, wherein the second accounts are assigned only the second task or the third task;

[0077] S62: Construct a second node set corresponding to each second account, wherein the second node set includes the associated first graph node or second graph node;

[0078] 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;

[0079] S64: Generate a second service path for the second account based on the second node set and the ant colony model.

[0080] 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 distinction between single-player tasks and multi-player 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 guidance ant colony model. I will not elaborate on this here.

[0081] 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.

[0082] In addition, in one embodiment, referring to Figure 3 Step S10 specifically includes but is not limited to the following steps:

[0083] S11, constructing multiple tasks to be assigned, wherein the tasks to be assigned are associated with task graph nodes and expected task durations;

[0084] S12, determining the expected account duration for each employee account based on the task to be assigned, wherein the expected account duration is used to represent the time required for the corresponding employee account to complete the task to be assigned;

[0085] S13, determining the to-be-assigned task whose expected duration is greater than or equal to the expected duration of at least one account as the second task, and determining the to-be-assigned task whose expected duration is less than the expected duration of any account as the first task;

[0086] 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;

[0087] 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.

[0088] 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.

[0089] 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.

[0090] It should be noted that after completing the configuration of the tasks to be assigned, the account expected time required for each employee account to complete the assigned tasks can be determined. Different employees have different work efficiency, so the account 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 account expected time for employee account 1 is 0.5 hours, and the account expected time for employee account 2 is 1.5 hours.

[0091] 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.

[0092] For example, the tasks to be assigned include community entrance cleaning, community garbage disposal, and community patrol, with expected task durations of 1 hour, 2 hours, and 3 hours, respectively. For the community entrance cleaning task, the expected duration for employee account 1 is 0.5 hours, for employee account 2 is 1.5 hours, and for employee account 3 is 1 hour. Therefore, community entrance cleaning can be assigned to employee account 1 and employee account 3 and determined as the second task. For the community garbage disposal task, the expected duration for employee account 1 is 1.5 hours, for employee account 2 is 2.5 hours, and for employee account 3 is 1.8 hours. Therefore, community entrance cleaning can be assigned to employee account 1 and employee account 3 and determined as the second task. For the community patrol task, the expected duration for employee account 1 is 5 hours, for employee account 2 is 4 hours, and for employee account 3 is 6 hours. The expected duration of the community patrol task, 3 hours, is less than the expected duration of any account and is determined as the first task.

[0093] It should be noted that multi-person tasks can satisfy more employee accounts by increasing the number of splits, while the number of employee accounts that can perform the second task alone is relatively limited. Therefore, this embodiment first allocates the second task. After referring to the principle of the above example, the 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 preferentially allocated 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 the employee accounts to only being able to undertake the second task. It is sufficient to ensure that the first task can be completed.

[0094] For example, the second candidate accounts for the second task 1 are employee account 1 and employee account 3. After employee account 1 is assigned to 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.

[0095] In addition, in one embodiment, referring to Figure 3 Step S15 specifically includes but is not limited to the following steps:

[0096] S151, obtaining a scaling sequence consisting of a plurality of scaling coefficients arranged from small to large, wherein the scaling coefficient is an integer greater than 1;

[0097] 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 a decomposed expected duration using the target coefficient;

[0098] S153, determining a first candidate account whose expected decomposition duration is less than or equal to the expected task duration as an allocable account;

[0099] S154, when the number of allocable accounts is greater than or equal to the target coefficient, allocating the target task to each allocable account;

[0100] S155: When the number of allocable accounts is less than the target coefficient, obtain the next target coefficient from the scaling sequence, scale the expected duration of the account of the first candidate account to a new decomposed expected duration based on the next target coefficient, and match the expected duration of the task based on the new decomposed expected duration;

[0101] S156, after the target task is assigned, the next target task is assigned based on the remaining first candidate accounts;

[0102] 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 the target task is continued to be assigned.

[0103] 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 of the first candidate accounts after being broken down. This embodiment compares the expected task duration with the expected account duration. If the expected task duration is less than the expected account duration, the task cannot be completed. The expected account duration is the time required for an employee to perform the task independently. In this embodiment, the first task is assigned to multiple employees, so the expected account duration needs to be shortened before task matching. This embodiment pre-sets a scaling sequence and obtains a target coefficient from the scaling sequence to reduce the expected account duration. The target coefficient represents the number of subdivisions of the first task. For example, if the target coefficient is 2, the first task is assigned to two employee accounts. For ease of calculation, this embodiment divides the expected account duration by the target coefficient to obtain the subdivision expected duration. The subdivision expected duration is then matched to the expected task duration. If 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 broken down. If the number of assignable accounts is less than the target coefficient, the next target coefficient is obtained and the task is broken down again until the target task can be assigned.

[0104] For example, continuing to refer to the above example, the target task is community cleaning. The expected duration of the task is 2 hours. The first candidate accounts include 3 employee accounts. The account expected duration of employee account 1 is 3 hours, the account expected duration of employee account 2 is 4 hours, and the account 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 allocable accounts. The community cleaning task is assigned to employee account 1 and employee account 2.

[0105] For example, the target task takes community patrol as an example, and the expected duration of the task is 1.5 hours. The first candidate account continues 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 assigned is employee account 1, which does not meet the target coefficient. 3 is selected 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 assignable accounts, and the number is greater than the target coefficient. The target task can be assigned to any 3 employee accounts.

[0106] It should be noted that this embodiment prioritizes assigning target tasks to the first candidate account with the fewest tasks, thereby improving human resource utilization. If the number of first candidate accounts is less than the target coefficient, all employee accounts can be designated as first candidate accounts and preferentially assigned to the first candidate account with the fewest tasks. The remaining tasks can be randomly assigned from other employee accounts, thereby improving the rationality of task allocation.

[0107] For example, 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.

[0108] 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:

[0109] S51, based on each first task, determining the decomposed expected duration with the smallest associated value as a lower limit of the task duration, determining the decomposed expected duration with the largest associated value as an 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 a 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;

[0110] S52, based on the two first graph nodes of the first node path, determining the sum of the task duration upper limits as the path duration upper limit, determining the sum of the task duration lower limits as the path duration lower limit, determining the sum of the task cost upper limits as the path cost upper limit, and determining the sum of the task cost lower limits as the path cost lower limit;

[0111] 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 and the account expected duration as the path duration upper limit, determine the sum of the task duration lower limit and the account expected duration as the path duration lower limit, determine the sum of the task cost upper limit and the expected cost value as the path cost upper limit, and determine the sum of the task cost lower limit and the expected cost value as the path cost lower limit;

[0112] S54, determining a first pheromone factor based on the path duration upper limit, the path duration lower limit, and a preset first weight, determining a second pheromone factor based on the path cost upper limit, the path cost lower limit, 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;

[0113] S55: Adjust the first pheromone concentration of the first node path based on the pheromone adjustment value, or adjust the second pheromone concentration of the second node path, and construct a first service path based on the adjusted first pheromone concentration and second pheromone concentration.

[0114] 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.

[0115] It should be noted that the expected cost value is the cost required for each employee account to complete the task within the expected timeframe of the account. This includes, for example, the use of more expensive tools to expedite task completion, or the labor costs incurred by overtime work. The specific value is not specifically defined here. The expected cost value is not necessarily positively correlated with the expected decomposition timeframe. Each employee may have different proficiency in using tools. Even if some employees use more expensive tools with lower proficiency, this may still result in a longer expected decomposition timeframe. In this embodiment, the largest preset cost 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.

[0116] It should be noted that the first-node path consists of two first-node graph nodes, each of which corresponds to a first task. Therefore, the path duration limit can be calculated by calculating the task duration limit. The same applies to other values. The second-node path consists of a first graph node and a second graph node. The path duration limit is determined by the sum of the task duration 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.

[0117] It should be noted that, when performing pheromone updates, this embodiment sets a first weight and a second weight, the sum of which is 1, so that the calculated pheromone adjustment value can take into account 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:

[0118] ; 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 j-th 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.

[0119] 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 for each node are obtained at the i-th node, the next target node can be determined based on the ant colony model.

[0120] 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:

[0121] S71, 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;

[0122] S72, when the activated account is assigned an exception task, determining a third graph node associated with the exception task;

[0123] S73, based on the real-time graph node, the third graph node, and the unreached first and second graph nodes corresponding to the activated account, the ant colony model is input to reconstruct a 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.

[0124] 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.

[0125] It should be noted that when an activated account is assigned an exception task, such as a new emergency cleaning task that temporarily appears and needs to be executed first, the graph node corresponding to the exception task is determined to be the third graph node, the current node is the real-time graph node, and the path is re-planned 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 re-planned according to the method of the above embodiment, for example, according to the distance between the remaining first graph nodes or the second graph node and the third graph node, the position in the original path is adjusted. The path planning principle will not be repeated here.

[0126] For example, Figure 2 As shown, taking employee account 1 as the activated 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 taken as 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.

[0127] In addition, in one embodiment, referring to Figure 3 Step S50 also includes but is not limited to the following steps:

[0128] S81, determining the task priority of each third task and each second task;

[0129] S82, when the third task assigned to the first account includes at least two levels of task priority, splitting the first node set into a plurality of node subsets, wherein different node subsets correspond to different task priorities;

[0130] S83, generating service subpaths based on node subsets and ant colony model;

[0131] S84: Combine multiple service sub-paths into a first service path based on the task priorities from high to low.

[0132] 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.

[0133] It should be noted that after obtaining multiple node subsets, this embodiment first constructs a service sub-path based on the node subset with higher task priority, and then uses the last graph node of the node subset with 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.

[0134] like Figure 4 As shown, Figure 4 This is a structural diagram of a task path planning device for property management provided by one embodiment of the present invention. The present invention also provides a task path planning device for property management, comprising:

[0135] The processor 401 may be implemented as a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0136] 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 solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 402 and is called by the processor 401 to execute the task path planning method for property management in the embodiments of this application.

[0137] Input / output interface 403, used to implement information input and output;

[0138] Communication interface 404, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0139] Bus 405 , which transmits information between various components of the device (e.g., processor 401 , memory 402 , input / output interface 403 , and communication interface 404 );

[0140] 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 a bus 405 .

[0141] An embodiment of the present application also provides an electronic device, including the task path planning device for property management as described above.

[0142] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned property management task path planning method.

[0143] The memory, as a non-transient computer-readable storage medium, 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 device. In some embodiments, the memory optionally includes a memory remotely located relative to the processor, and these remote memories can 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 located in one place, or may 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.

[0144] Those skilled in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media 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 includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes 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.

[0145] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above implementation. Those skilled in the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present invention. These equivalent modifications 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 multiple first tasks and multiple second tasks, assigning the first tasks to multiple employee accounts, and assigning the second task 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 geographic locations of a property area map; Based on any of the first tasks, split the task into multiple third tasks according to the number of the assigned employee accounts, assign one of the employee accounts to each of the third tasks, and associate the first graph node with the third task; Determining a plurality of first accounts from the plurality of employee accounts, and constructing a first node set corresponding to each of the first accounts, wherein the first accounts are assigned the third task and the second task, and the first node set includes the associated first graph nodes and the second graph nodes; 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; generating a first service path for the first account based on the first node set and a preset ant colony model, wherein a starting graph node of the first account is any first graph node; Generating a first service path for the first account based on the first node set and a preset ant colony model includes: Determine the path duration upper limit, path duration lower limit, path cost upper limit, and path cost lower limit of the first node path and the second node path based on the task duration lower limit, task duration upper limit, task cost lower limit, and task cost upper limit of each of the first tasks; determining a first pheromone factor based on the path duration upper limit, the path duration lower limit, and a preset first weight, determining a second pheromone factor based on the path cost upper limit, the path cost lower limit, and a preset second weight, and determining 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.

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 a preset ant colony model, the method further includes: Determining a plurality of second accounts from the plurality of employee accounts, wherein the second accounts are assigned only 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 in 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, characterized in that: Constructing multiple first tasks and multiple second tasks, assigning the first tasks to multiple 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; 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; Determine the to-be-assigned 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-assigned 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 first candidate accounts.

4. The task path planning method for property management according to claim 3, characterized in that: Allocating each of the first tasks to a plurality of the first candidate accounts includes: Obtaining a scaling sequence composed of a plurality of scaling coefficients arranged from small to large, wherein the scaling coefficients are integers 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 using the target coefficient; Determine the first candidate account, wherein the 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 assignable accounts is less than the target coefficient, obtaining the next target coefficient from the scaling sequence, scaling the expected account duration of the first candidate account to a new decomposed expected duration based on the next target coefficient, and matching the expected task duration based on the new decomposed expected duration; After 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 the target task is then continuously assigned.

5. The task path planning method for property management according to claim 4, characterized in that: The expected duration of each account is also pre-associated with an expected cost value. Based on the lower limit of the task duration, the upper limit of the task duration, the lower limit of the task cost, and the upper limit of the task cost of each of the first tasks, the upper limit of the path duration, the lower limit of the path duration, the upper limit of the path cost, and the lower limit of the path cost of the first node path and the second node path are determined, including: Based on each of the first tasks, determining the decomposed expected duration with the smallest associated value as a lower limit of the task duration, determining the decomposed expected duration with the largest associated value as an 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; Based on the two first graph nodes of the first node path, the sum of the task duration upper limits is determined as the path duration upper limit, the sum of the task duration lower limits is determined as the path duration lower limit, the sum of the task cost upper limits is determined as the path cost upper limit, and the sum of the task cost lower limits is determined as the path cost lower limit; Based on the first graph node and the second graph node of the second node path, the corresponding sum of the 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.

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 exception task, determining a third graph node associated with the exception 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 for the first account based on the first node set and a preset ant colony model includes: determining a 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 from high to low, multiple service sub-paths are spliced ​​into the first service path.

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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