Integrated volunteer intelligent scheduling system

Abstract

The invention discloses an integrated volunteer intelligent scheduling system, and relates to the technical field of intelligent scheduling, the integrated volunteer intelligent scheduling system comprises an information acquisition module, a task merging module and a scheduling execution module, the information acquisition module acquires and structurally stores basic information of micro tasks and real-time state information of volunteers, and forms a corresponding information set; the task merging module is used for automatically aggregating into candidate merging task groups based on spatial position relevance of the microtasks, generating a minimum closed space boundary and screening volunteers in the boundary to form an exclusive volunteer pool; and the scheduling execution module synchronously executes cumulative duration matching, moving line coherence and service object accessibility verification on the volunteers, and rejects unqualified volunteers from the exclusive volunteer pool to obtain a final schedulable volunteer pool. According to the method, accurate adaptation of the microtask and the volunteer is realized, and the invalid execution risk is reduced while the scheduling efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent scheduling technology, specifically to an integrated intelligent volunteer scheduling system. Background Technology

[0002] With the increasingly refined development of volunteer services, intelligent volunteer scheduling systems have become a core support for improving service efficiency and optimizing resource allocation. However, existing intelligent volunteer scheduling technologies still have the following shortcomings: First, the merging of micro-tasks often relies on manual grouping or simple distance sorting, without fully considering the spatial adjacency and continuity between micro-tasks. This can easily lead to uneven distribution of the merged task groups and increase the time spent by volunteers in executing their tasks. Secondly, the volunteer selection relies solely on preliminary matching of basic information, without accurately screening based on the task's spatial scope. Furthermore, there is a lack of simultaneous verification of time matching, route rationality, and service recipient accessibility, leading to problems such as time conflicts, route detours, or temporary unavailability of service recipients. Third, many methods for generating task space boundaries use simple coordinate extrema or fixed shape fitting, resulting in a high proportion of invalid space and affecting the accuracy of volunteer matching. Therefore, there is an urgent need for an integrated intelligent volunteer dispatch system. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an integrated intelligent volunteer scheduling system, which solves the problems of unscientific micro-task merging, inaccurate volunteer matching, and poor service accessibility.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an integrated intelligent volunteer dispatch system, comprising: The information collection module and the scheduling system collect basic information of all micro-tasks to be executed and real-time status information of volunteers in the same area, and store the collected information in a structured manner to form a micro-task information set and a volunteer status set. The task merging module reads the location coordinates of all micro-tasks to be executed from the micro-task information set, automatically aggregates them into candidate merged task groups based on the spatial correlation between the micro-tasks to be executed, extracts the location coordinates of all micro-task nodes in the group, generates the minimum closed spatial boundary of the task group, and then reads the current location coordinates of all volunteers in the volunteer status set, filters out volunteers whose current location is within the corresponding minimum closed spatial boundary, and assigns them to the exclusive volunteer pool of the candidate merged task group. The scheduling and execution module performs cumulative duration matching, route connectivity, and service object reachability checks on volunteers in the dedicated volunteer pool. If any one of these checks fails, the corresponding volunteer is removed from the volunteer pool, resulting in the final dedicated volunteer pool that can be scheduled for the candidate merged task group.

[0005] As a further aspect of the present invention, the basic information of the micro-task includes location coordinates, minimum execution time per instance, and real-time reachability status of the service object; the real-time status information of the volunteer includes current geographical location, declared free time interval, and historical time records of movement routes for similar tasks in the same area.

[0006] As a further aspect of the present invention, the specific operation of automatically aggregating candidate merging task groups based on spatial location correlation is as follows: The scheduling system reads the location coordinates of all microtasks to be executed from the microtask information set, treats each microtask to be executed as an independent spatial node, and for any two microtask nodes, determines whether the two nodes are within each other's direct spatial radiation range. If they are, the two microtask nodes are determined to form a bidirectional adjacent node pair; otherwise, they are determined to be non-adjacent nodes. After the scheduling system performs bidirectional adjacency determination on all micro-task nodes to be executed, it generates a list of micro-task adjacency relationships and selects an initial set of adjacency node clusters based on the list of micro-task adjacency relationships. For each node cluster in the initial set of adjacent node clusters, the scheduling system obtains the position coordinates of all nodes in the cluster, calculates the spatial distance between any two nodes, and performs a spatial continuity check: if the maximum spatial distance between all nodes in the cluster is less than or equal to the maximum distance between the physical space units to which the corresponding microtask execution location belongs, then the cluster is determined to pass the check and is directly locked as a candidate merge task group; If the verification fails, the cluster will be split into the smallest sub-clusters centered on the core node and satisfying spatial continuity. Each sub-cluster will be locked as a candidate merge task group; isolated nodes will be locked as candidate merge task groups separately.

[0007] As a further aspect of the present invention, the microtask node M is determined. i Is it in another microtask node M? j The direct space radiation range specifically includes: For microtask M j Match the physical space unit to which its execution location belongs, collect the coordinates of all vertices of that physical space unit, extract the extreme values ​​of the coordinates, and determine M. j The boundary of the direct spatial radiation range is specifically expressed as: x j,min ≤x≤x j,max And y j,min ≤y≤y j,max , where xj,min x j,max M respectively j The minimum and maximum values ​​of the x-axis coordinates corresponding to the physical space unit, and the y-axis coordinates. j,min y j,max M respectively j The minimum and maximum values ​​of the y-axis coordinates of the corresponding physical space unit; Get microtask node M i coordinates (x) i ,y i Substitute this into the boundary expression above, and if x satisfies... j,min ≤x i ≤x j,max And y j,min ≤y i ≤y j,max Then determine M i In M j If it is within the direct spatial radiation range, then M is determined. i Not in M j Within the direct spatial radiation range.

[0008] As a further aspect of the present invention, the specific operation for selecting the initial set of adjacent node clusters is as follows: The scheduling system reads the micro-task adjacency list, counts the number of bidirectional adjacency nodes for each node, and generates a statistical list containing the node identifier and the number of bidirectional adjacency nodes. Initialize the set of unprocessed nodes and the set of processed nodes, and set the initial set of adjacent node clusters to empty; From the set of unprocessed nodes, select the node with the largest number of bidirectional adjacent nodes and determine it as the core node of this round. If there are multiple nodes with the same number of bidirectional adjacent nodes and all of them are the maximum value, then randomly select one of them as the core node of this round. From the microtask adjacency list, select all nodes that are marked as bidirectionally adjacent to the core node of this round, and form the core node of this round and these nodes together into the initial adjacency node cluster of this round, and add this cluster to the initial adjacency node cluster set; Remove all nodes from the unprocessed node set from the initial adjacent node cluster of this round, and add them to the processed node set. Continue repeating the above steps until the unprocessed node set is empty.

[0009] As a further aspect of the present invention, the specific operation for generating the minimum closed space boundary of the candidate merging task group is as follows: The scheduling system extracts the location coordinates of all microtask nodes within the candidate merge task group, and for each node P i (X i ,Y i), calculate the Euclidean distance from the node to all other nodes in the candidate merging task group, and sum them to obtain the path density value S of the node. i ; Press S to connect all nodes i Sort the nodes from smallest to largest, select the top 50% as the core cluster node subset, and calculate the mean coordinates of the core cluster node subset. Define this coordinate point as the core aggregation center Q; With Q as the origin, for each node P in the candidate merging task group i Calculate its azimuth angle relative to Q. The azimuth range is [0, 2π); Azimuth angles of all nodes Sort in ascending order, and then process each azimuth angle after sorting. compute node P i Euclidean distance r to Q i And define this distance as Radius of the azimuth; Connect each one in azimuth order. Radiation radius endpoints in azimuth This forms a closed polygon. If all microtask nodes are inside this closed polygon, then this closed polygon is the minimum closed spatial boundary of the candidate merged task group.

[0010] As a further aspect of the present invention, the specific operation for performing cumulative duration matching verification is as follows: The scheduling system extracts the minimum execution time of each subtask within the candidate merged task group, and sums up the minimum execution time of all subtasks to obtain the total basic execution time of the subtasks. The scheduling system retrieves the volunteer's historical movement time records, extracts the average movement time between adjacent sub-tasks when the volunteer performs the same type of task in the same area as the candidate merged task group, and sums up the average movement time between adjacent sub-tasks in the order of spatial distribution of sub-tasks in the merged task group to obtain the total movement time. The scheduling system adds the total basic time of the subtasks to the total time of the movement route to obtain the total time of the candidate merged task group. It then retrieves the volunteer's declared free time intervals and checks whether the total time of the candidate merged task group is completely within the interval. That is, the start time of the declared free time interval is less than or equal to the start time of the candidate merged task group, and the end time of the candidate merged task group is less than or equal to the end time of the declared free time interval.

[0011] As a further aspect of the present invention, the specific operation for performing the flow connectivity verification is as follows: The scheduling system obtains the current location coordinates of the volunteer and the location coordinates of all subtasks within the candidate merged task group. Taking the current location coordinates of the volunteer as the starting node, it calculates the straight-line distance from the starting node to all subtask nodes, selects the subtask node with the closest distance as the next execution node, and uses this execution node as the new reference node. The above distance calculation and node selection operations are repeated to determine the subsequent execution nodes in sequence. The estimated execution time of each subtask is marked on the corresponding node in sync, forming a moving node chain with execution time. For the generated chain of moving nodes, both no backtracking and full coverage must be satisfied simultaneously: For the no-return verification, extract the coordinates of two adjacent nodes in the movement node chain to generate a path vector, and calculate the angle between every two consecutive path vectors. If all angles are greater than 90°, the path is determined to be without reversal. If any angle is less than or equal to 90°, the path is determined to have a reversal or detour. For coverage integrity verification, count the number of subtask nodes in the movement node chain. If the number is completely consistent with the total number of subtasks in the candidate merged task group and there are no duplicate nodes, then the movement line is determined to cover all subtasks.

[0012] As a further aspect of the present invention, the specific operation for performing service object reachability verification is as follows: The scheduling system extracts the core information of each subtask node in the movement node chain, including the subtask identifier, the corresponding service object identifier, and the estimated arrival time T. arrive-i Expected execution time T exec-i And calculate the reachability decision time window W for each subtask. i =[T arrive-i -△t,T arrive-i +T exec-i ], where △t is the advance judgment buffer time; W of each subtask node i The unique identifier of the service object and the identifier of the subtask are bound one by one to generate a time window-service object association table. The order of this association table is completely consistent with the order of the movement node chain. The scheduling system initiates a real-time status retrieval mechanism according to the operation order of the association table, and for the real-time status data of each subtask, it needs to verify whether the following two conditions are met simultaneously: Condition 1: The service recipient reports that the service is currently reachable, and the expected duration of reachability is ≥ T. exec-i ; Condition 2: During the execution of the subtask, the scheduling system schedules every T... exec-i For a duration of / 3, refresh the status of the service object once in real time to confirm that the status has not changed to unreachable; If both conditions are met, the service object of the subtask is determined to be reachable; if either condition is not met, the scheduling system immediately triggers exception handling: backtracking to the node chain with execution duration, skipping the current subtask, rescheduling the time window of subsequent nodes, and marking the current subtask as pending execution.

[0013] As a further aspect of the present invention, the activation of the real-time status retrieval mechanism specifically includes: When the scheduling system time arrives at each subtask W i Start time T arrive-i When -△t occurs, a status retrieval request is automatically sent to the service object. The system receives real-time status data from the service recipient. This real-time status data includes two core pieces of information: whether the service is currently reachable and the estimated duration of reachability. If no feedback is received within Δt / 2, the scheduling system initiates a second fetch. If there is still no feedback, the service is marked as temporarily unreachable.

[0014] This invention provides an integrated intelligent volunteer scheduling system, which has the following advantages compared with the prior art: (1) This invention automatically aggregates candidate merged task groups by determining spatial adjacency and verifying continuity, and generates the minimum closed spatial boundary by combining path density and azimuth radiation method, ensuring that the spatial distribution of task groups is compact, reducing invalid areas, and shortening the time spent by volunteers in executing the movement path. (2) This invention filters a dedicated volunteer pool through spatial boundaries and simultaneously performs three checks: cumulative duration, mobility connection, and accessibility of service recipients, to comprehensively ensure the suitability of volunteers and tasks. (3) Based on the time window of the moving node chain, the present invention accurately matches the real-time status of the service object through the real-time status retrieval and dynamic refresh mechanism, and with the abnormal backtracking correction logic, avoids invalid execution caused by static verification. Attached Figure Description

[0015] Figure 1 This is the system principle block diagram of the present invention; Figure 2 A flowchart illustrating the steps involved in constructing the minimum closed spatial boundary for this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] like Figure 1This invention provides an integrated intelligent volunteer scheduling system; As an embodiment of this application, the specific steps include the following: The information collection module and the scheduling system collect basic information of all micro-tasks to be executed and real-time status information of volunteers in the same area, and store the collected information in a structured manner to form a micro-task information set and a volunteer status set. The task merging module reads the location coordinates of all micro-tasks to be executed from the micro-task information set, automatically aggregates them into candidate merged task groups based on the spatial correlation between the micro-tasks to be executed, extracts the location coordinates of all micro-task nodes in the group, generates the minimum closed spatial boundary of the task group, and then reads the current location coordinates of all volunteers in the volunteer status set, filters out volunteers whose current location is within the corresponding minimum closed spatial boundary, and assigns them to the exclusive volunteer pool of the candidate merged task group. The scheduling and execution module performs cumulative duration matching, route connectivity, and service object reachability checks on volunteers in the dedicated volunteer pool. If any one of these checks fails, the corresponding volunteer is removed from the volunteer pool, resulting in the final dedicated volunteer pool that can be scheduled for the candidate merged task group.

[0018] As a second embodiment of this application, it is implemented based on the first embodiment, except that this embodiment includes: The information collection module and the scheduling system collect basic information of all micro-tasks to be executed and real-time status information of volunteers in the same area, and store the collected information in a structured manner to form a micro-task information set and a volunteer status set. In the context of intelligent volunteer dispatch, the micro-task refers to a small volunteer service task with an independent execution goal, a single service target, and can be completed in a short time. This task can be used as an independent dispatch unit, such as delivering meals to elderly people living alone, conducting single-point inspections of community facilities, or delivering small public welfare materials. The basic information of the microtask includes location coordinates, minimum execution time per session, and real-time reachability of the service object. Location coordinates refer to the precise geographical coordinates of the execution location of each micro-task, such as latitude and longitude, and the specific building unit number within the community, rather than a vague and general community. Minimum execution time per session refers to the shortest time required to complete a single microtask, excluding travel time. This value needs to be calculated based on historical execution data of similar tasks. The real-time reachability status of the service object refers to whether the service object corresponding to the micro-task is currently at the task execution location and can receive services. This can be synchronized in real time through the service object's terminal (such as a smart bracelet). The real-time status information of volunteers includes their current geographical location, the reported available time intervals, and historical time records for similar tasks in the same area. Current geographic location refers to the precise geographic coordinates of the volunteer's real-time location, which can be located via mobile phone GPS. The declared available time slots refer to the specific time periods that volunteers have declared in advance in the dispatch system when they can participate in volunteer services, such as 11:00-11:40 on October 26, 2024. The slots can be updated in real time and can be modified if something comes up. Historical movement time records for similar tasks in the same area refer to the actual time spent by volunteers moving between different micro-tasks when performing similar micro-tasks in the same area in the past. For example, the movement time from Building 1 to Building 5 is 3 minutes, and the movement time from the community entrance to Building 2 is 2 minutes.

[0019] The task merging module, through which the scheduling system reads the location coordinates of all microtasks to be executed from the microtask information set, automatically aggregates them into candidate merged task groups based on the spatial correlation between the microtasks. The specific operation is as follows: The scheduling system reads the precise location coordinates of all microtasks to be executed from the microtask information set, treats each microtask to be executed as an independent spatial node, and for any two microtask nodes, determines whether the two nodes are within each other's direct spatial radiation range. If they are, the two microtask nodes are determined to form a bidirectional adjacent node pair; otherwise, they are determined to be non-adjacent nodes. Determine the microtask node M i Is it in another microtask node M? j Methods within the direct space radiation range specifically include: For microtask M j Match the physical space unit to which the execution location belongs, such as community buildings, park functional areas, etc.; Collect the precise coordinates of all vertices of this physical space unit, extract the extreme values ​​of the coordinates, and determine M. j The boundary of the direct spatial radiation range is specifically expressed mathematically as: x j,min ≤x≤x j,max And y j,min ≤y≤y j,max , where x j,min x j,max M respectively j The minimum and maximum values ​​of the x-axis coordinates corresponding to the physical space unit, and the y-axis coordinates. j,min y j,max M respectively j The minimum and maximum values ​​of the y-axis coordinates of the corresponding physical space unit; Get microtask node M i precise coordinates (x i,y i Substitute this into the boundary expression above, and if x satisfies... j,min ≤x i ≤x j,max And y j,min ≤y i ≤y j,max Then determine M i In M j If it is within the direct spatial radiation range, then M is determined. i Not in M j Within the direct spatial radiation range; After the scheduling system performs bidirectional adjacency determination on all microtask nodes to be executed, it generates a list of microtask adjacency relationships, which includes pairs of bidirectional adjacent nodes and non-adjacent nodes. Based on the microtask adjacency list, an initial set of adjacency node clusters is selected. The specific operation is as follows: The scheduling system reads the micro-task adjacency list, counts the number of bidirectional adjacency nodes for each node, and generates a statistical list containing the node identifier and the number of bidirectional adjacency nodes. Initialize the set of unprocessed nodes (containing all microtask nodes) and the set of processed nodes (initially empty), and the set of adjacent node clusters is also initially empty; From the set of unprocessed nodes, select the node with the largest number of bidirectional adjacent nodes and determine it as the core node of this round. If there are multiple nodes with the same number of bidirectional adjacent nodes and all of them are the maximum value, then randomly select one of them as the core node of this round. From the microtask adjacency list, select all nodes that are marked as bidirectionally adjacent to the core node of this round, and form the core node of this round and these nodes together into the initial adjacency node cluster of this round, and add this cluster to the initial adjacency node cluster set; Remove all nodes from the unprocessed node set in the initial adjacent node cluster of this round and add them to the processed node set. Continue to repeat the above steps until the unprocessed node set is empty. For nodes in the processed node set that have not been assigned to any initial adjacent node cluster, i.e. isolated nodes with zero bidirectional adjacent nodes, each node is assigned to a separate initial adjacent node cluster and added to the initial adjacent node cluster set. For example, if a community has 5 micro-task nodes, denoted as M1 to M5, the number of bidirectional adjacent nodes for each micro-task node is: M1=2, M2=1, M3=2, M4=1, M5=0. Initially, the set of unprocessed nodes is {M1, M2, M3, M4, M5}, and the set of processed nodes is ∅. In the first round: the unprocessed nodes with the largest number are M1 and M3. M1 is randomly selected as the core node, and its bidirectional adjacent nodes are selected to form the cluster C1={M1, In the first round, M1, M2, and M4 are moved into the processed set, and the unprocessed set becomes {M3, M5}. In the second round, M3 is the largest number of unprocessed nodes, so it is selected as the core node. Its bidirectional adjacent nodes are selected to form the cluster C2{M3} for this round. M3 is then moved into the processed set, and the unprocessed set becomes {M5}. In the third round, only M5 (an isolated node) remains in the unprocessed set, and it is clustered separately as C3{M5}. The final initial set of adjacent node clusters is {C1, C2, C3}. For each node cluster in the initial set of adjacent node clusters, the scheduling system obtains the precise location coordinates of all nodes in the cluster, calculates the spatial distance between any two nodes, and performs a spatial continuity check: if the maximum spatial distance between all nodes in the cluster is less than or equal to the maximum distance between the physical space units to which the corresponding microtask execution location belongs, then the cluster is determined to pass the check and is directly locked as a candidate merge task group. If the verification fails, the cluster will be split into the smallest sub-clusters centered on the core node and satisfying spatial continuity. Each sub-cluster will be locked as a candidate merge task group; isolated nodes will be locked as candidate merge task groups separately. For each candidate merged task group, the scheduling system extracts the position coordinates of all micro-task nodes within the group and generates the minimum closed spatial boundary of the task group. Then, the current location coordinates of all volunteers in the volunteer status set are read, and volunteers whose current location is within the boundary of the minimum closed space are selected and included in the exclusive volunteer pool of the candidate merging task group. If no volunteers are within the boundary of the minimum closed space, the distance between the volunteers and the boundary of the minimum closed space is calculated, and volunteers whose distance is less than the preset distance threshold are included in the exclusive volunteer pool of the candidate merging task group.

[0020] The scheduling and execution module performs cumulative duration matching, path connectivity, and service object reachability checks on the candidate merging task group and the volunteers in the corresponding volunteer pool. If any one of the checks fails, the corresponding volunteer is removed from the volunteer pool. The specific operation for performing the cumulative duration matching and verification is as follows: The scheduling system extracts the minimum execution time of each subtask within the candidate merged task group, and sums up the minimum execution time of all subtasks to obtain the total basic execution time of the subtasks. The scheduling system retrieves the volunteer's historical movement time records, extracts the average movement time between adjacent sub-tasks when the volunteer performs the same type of task in the same area as the candidate merged task group, and sums up the average movement time between adjacent sub-tasks in the order of spatial distribution of sub-tasks in the merged task group to obtain the total movement time. For example, if the subtasks in the candidate merge task group are distributed in the order of M1→M2→M3, and the average time taken for volunteers to move from M1 to M2 in the same area is 3 minutes, and the average time taken to move from M2 to M3 is 5 minutes, then the total time taken to move is 8 minutes. The scheduling system adds the total basic time of the subtasks to the total time of the movement route to obtain the total time of the candidate merged task group. It then retrieves the volunteer's declared free time intervals and checks whether the total time of the candidate merged task group is completely within the interval. That is, the start time of the declared free time interval is less than or equal to the start time of the candidate merged task group, and the end time of the candidate merged task group is less than or equal to the end time of the declared free time interval. The specific steps for verifying the connectivity of traffic flow are as follows: The scheduling system obtains the current location coordinates of the volunteer and the location coordinates of all subtasks within the candidate merged task group. Taking the current location coordinates of the volunteer as the starting node, it calculates the straight-line distance from the starting node to all subtask nodes, selects the subtask node with the closest distance as the next execution node, and uses this execution node as the new reference node. The above distance calculation and node selection operations are repeated to determine the subsequent execution nodes in sequence. The estimated execution time of each subtask is marked on the corresponding node in sync, forming a moving node chain with execution time. For the generated chain of moving nodes, both no backtracking and full coverage must be satisfied simultaneously: For the no-return verification, extract the coordinates of two adjacent nodes in the movement node chain to generate a path vector, and calculate the angle between every two consecutive path vectors. If all angles are greater than 90°, the path is determined to be without reversal. If any angle is less than or equal to 90°, the path is determined to have a reversal or detour. For coverage integrity verification, count the number of subtask nodes in the movement node chain. If the count is completely consistent with the total number of subtasks in the candidate merged task group and there are no duplicate nodes, then the movement is determined to cover all subtasks. The specific steps for service object reachability verification are as follows: The scheduling system extracts the core information of each subtask node in the movement node chain, including the subtask identifier, the corresponding service object identifier, and the estimated arrival time T. arrive-i Expected execution time T exec-i And calculate the reachability decision time window W for each subtask. i =[T arrive-i-△t,T arrive-i +T exec-i ], where △t is the advance judgment buffer time; W of each subtask node i The unique identifier of the service object and the identifier of the subtask are bound one by one to generate a time window-service object association table. The order of this association table is completely consistent with the order of the movement node chain. For example, the node chain with execution time is: G0→M1(10)→M2(5)→M3(15), where the estimated arrival time T of M1 is... arrive-1 =10:00, estimated execution time T exec-1 =10min, △t=5min, then the corresponding W1 for M1 is [9:55,10:10], and the associated table record is: M1-Service Object A-W1=[9:55,10:10]; The scheduling system initiates a real-time status retrieval mechanism according to the operation sequence of the association tables: When the scheduling system time arrives at each subtask W i Start time T arrive-i When -△t occurs, a status retrieval request is automatically sent to the service object. The system receives real-time status data from the service object. The real-time status data includes two core pieces of information: whether the service is currently reachable and the estimated duration of reachability. If no feedback is received within △t / 2, the scheduling system initiates a second fetch. If there is still no feedback, the service is marked as temporarily unreachable. Dynamically fetching status data by time window ensures that the acquired status data is synchronized with the actual execution time of the subtask, avoiding the problem of status invalidation caused by fetching the full data in advance. The secondary fetching mechanism can eliminate objective interference such as network latency and improve the reliability of status data. For the real-time status data of each subtask, it is necessary to check whether the following two conditions are met simultaneously: Condition 1: The service recipient reports that the service is currently reachable, and the expected duration of reachability is ≥ T. exec-i To ensure that the status is not interrupted during execution; Condition 2: During the execution of the subtask, the scheduling system schedules every T... exec-i For a duration of / 3, refresh the status of the service object once in real time to confirm that the status has not changed to unreachable; If both conditions are met, the service object of the subtask is determined to be reachable; if either condition is not met, the scheduling system immediately triggers exception handling: backtracking to the node chain with execution duration, skipping the current subtask, rescheduling the time window of subsequent nodes, and marking the current subtask as to be executed later. Simply verifying whether the arrival time is reachable cannot guarantee the stability of the state during execution. By adding state refresh during the execution process, task interruption caused by the service object's temporary absence or unexpected situations can be avoided. At the same time, the exception handling is directly linked with the movement node chain to ensure that the verification results can be fed back to the movement planning in real time, forming a closed loop.

[0021] As a third embodiment of this application, this embodiment further discloses a method for generating the minimum closed space boundary of candidate merging task groups based on embodiments one and two, such as... Figure 2 As shown, the specific content includes: The scheduling system extracts the location coordinates of all microtask nodes within the candidate merge task group, and for each node P i (X i ,Y i ), calculate the Euclidean distance from the node to all other nodes in the candidate merging task group, and sum them to obtain the path density value S of the node. i The smaller this value, the closer the node is to the core clustering area of ​​the nodes in the group; Press S to connect all nodes i Sort the nodes from smallest to largest, select the top 50% as the core cluster node subset, and calculate the mean coordinates of the core cluster node subset. Define this coordinate point as the core aggregation center Q; Taking the core aggregation center Q as the origin, for each node P in the candidate merging task group i Calculate its azimuth angle relative to Q. The azimuth range is [0, 2π); Azimuth angles of all nodes Sort in ascending order to ensure coverage of the entire azimuth interval [0, 2π), and then sort each azimuth angle in the sorted order. compute node P i Euclidean distance r to Q i And define this distance as Radius of the azimuth; Connect each one in azimuth order. Radiation radius endpoints in azimuth This forms a closed polygon. If all microtask nodes are inside this closed polygon, then this closed polygon is the minimum closed space boundary of the candidate merged task group. Existing technologies can only connect the outer vertices of the convex hull, and OBB only generates rectangles along the principal component direction. Neither can achieve full-range node fitting. However, this step uses full-range angular radiation from the core center, and the boundary of each direction is filled by the farthest node in that direction, ensuring that the boundary completely fits the distribution contour of the nodes.

[0022] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0023] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An integrated intelligent volunteer dispatch system, characterized in that, include: The information collection module and the scheduling system collect basic information of all micro-tasks to be executed and real-time status information of volunteers in the same area, and store the collected information in a structured manner to form a micro-task information set and a volunteer status set. The task merging module reads the location coordinates of all micro-tasks to be executed from the micro-task information set, automatically aggregates them into candidate merged task groups based on the spatial correlation between the micro-tasks to be executed, extracts the location coordinates of all micro-task nodes in the group, generates the minimum closed spatial boundary of the task group, and then reads the current location coordinates of all volunteers in the volunteer status set, filters out volunteers whose current location is within the corresponding minimum closed spatial boundary, and assigns them to the exclusive volunteer pool of the candidate merged task group. The scheduling and execution module performs cumulative duration matching, route connectivity, and service object reachability checks on volunteers in the dedicated volunteer pool. If any one of these checks fails, the corresponding volunteer is removed from the volunteer pool, resulting in the final dedicated volunteer pool that can be scheduled for the candidate merged task group.

2. The integrated intelligent volunteer dispatching system according to claim 1, characterized in that, The basic information of the micro-task includes location coordinates, minimum execution time per instance, and real-time availability of the service recipient; the real-time status information of the volunteer includes current geographical location, declared free time interval, and historical time records of movement routes for similar tasks in the same area.

3. The integrated intelligent volunteer dispatching system according to claim 1, characterized in that, The specific steps for automatically aggregating candidate merging task groups based on spatial location correlation are as follows: The scheduling system reads the location coordinates of all microtasks to be executed from the microtask information set, treats each microtask to be executed as an independent spatial node, and for any two microtask nodes, determines whether the two nodes are within each other's direct spatial radiation range. If they are, the two microtask nodes are determined to form a bidirectional adjacent node pair; otherwise, they are determined to be non-adjacent nodes. After the scheduling system performs bidirectional adjacency determination on all micro-task nodes to be executed, it generates a list of micro-task adjacency relationships and selects an initial set of adjacency node clusters based on the list of micro-task adjacency relationships. For each node cluster in the initial set of adjacent node clusters, the scheduling system obtains the position coordinates of all nodes in the cluster, calculates the spatial distance between any two nodes, and performs a spatial continuity check: if the maximum spatial distance between all nodes in the cluster is less than or equal to the maximum distance between the physical space units to which the corresponding microtask execution location belongs, then the cluster is determined to pass the check and is directly locked as a candidate merge task group; If the verification fails, the cluster will be split into the smallest sub-clusters centered on the core node and satisfying spatial continuity. Each sub-cluster will be locked as a candidate merge task group; isolated nodes will be locked as candidate merge task groups separately.

4. The integrated intelligent volunteer dispatching system according to claim 3, characterized in that, Determine the microtask node M i Is it in another microtask node M? j The direct space radiation range specifically includes: For microtask M j Match the physical space unit to which its execution location belongs, collect the coordinates of all vertices of that physical space unit, extract the extreme values ​​of the coordinates, and determine M. j The boundary of the direct spatial radiation range is specifically expressed as: x j,min ≤x≤x j,max And y j,min ≤y≤y j,max , where x j,min x j,max M respectively j The minimum and maximum values ​​of the x-axis coordinates corresponding to the physical space unit, and the y-axis coordinates. j,min y j,max M respectively j The minimum and maximum values ​​of the y-axis coordinates of the corresponding physical space unit; Get microtask node M i coordinates (x) i ,y i Substitute this into the boundary expression above, and if x satisfies... j,min ≤x i ≤x j,max And y j,min ≤y i ≤y j,max Then determine M i In M j If it is within the direct spatial radiation range, then M is determined. i Not in M j Within the direct spatial radiation range.

5. The integrated intelligent volunteer dispatching system according to claim 3, characterized in that, The specific operation for selecting the initial set of adjacent node clusters is as follows: The scheduling system reads the micro-task adjacency list, counts the number of bidirectional adjacency nodes for each node, and generates a statistical list containing the node identifier and the number of bidirectional adjacency nodes. Initialize the set of unprocessed nodes and the set of processed nodes, and set the initial set of adjacent node clusters to empty; From the set of unprocessed nodes, select the node with the largest number of bidirectional adjacent nodes and determine it as the core node of this round. If there are multiple nodes with the same number of bidirectional adjacent nodes and all of them are the maximum value, then randomly select one of them as the core node of this round. From the microtask adjacency list, select all nodes that are marked as bidirectionally adjacent to the core node of this round, and form the core node of this round and these nodes together into the initial adjacency node cluster of this round, and add this cluster to the initial adjacency node cluster set; Remove all nodes from the unprocessed node set in the initial adjacent node cluster of this round, and add them to the processed node set. Continue repeating the above steps until the unprocessed node set is empty.

6. The integrated intelligent volunteer dispatching system according to claim 1, characterized in that, The specific operation for generating the minimum closed space boundary of the candidate merging task group is as follows: The scheduling system extracts the location coordinates of all microtask nodes within the candidate merge task group, and for each node P i (X i ,Y i ), calculate the Euclidean distance from the node to all other nodes in the candidate merging task group, and sum them to obtain the path density value S of the node. i ; Press S to connect all nodes i Sort the nodes from smallest to largest, select the top 50% as the core cluster node subset, and calculate the mean coordinates of the core cluster node subset. Define this coordinate point as the core aggregation center Q; With Q as the origin, for each node P in the candidate merging task group i Calculate its azimuth angle relative to Q. The azimuth range is [0, 2π); Azimuth angles of all nodes Sort in ascending order, and then process each azimuth angle after sorting. compute node P i Euclidean distance r to Q i And define this distance as Radius of the azimuth; Connect each one in azimuth order. Radiation radius endpoints in azimuth This forms a closed polygon. If all microtask nodes are inside this closed polygon, then this closed polygon is the minimum closed spatial boundary of the candidate merged task group.

7. The integrated intelligent volunteer dispatching system according to claim 1, characterized in that, The specific steps for performing cumulative duration matching and verification are as follows: The scheduling system extracts the minimum execution time of each subtask within the candidate merged task group, and sums up the minimum execution time of all subtasks to obtain the total basic execution time of the subtasks. The scheduling system retrieves the volunteer's historical movement time records, extracts the average movement time between adjacent sub-tasks when the volunteer performs the same type of task in the same area as the candidate merged task group, and sums up the average movement time between adjacent sub-tasks in the order of spatial distribution of sub-tasks in the merged task group to obtain the total movement time. The scheduling system adds the total basic time of the subtasks to the total time of the movement route to obtain the total time of the candidate merged task group. It then retrieves the volunteer's declared free time intervals and checks whether the total time of the candidate merged task group is completely within the interval. That is, the start time of the declared free time interval is less than or equal to the start time of the candidate merged task group, and the end time of the candidate merged task group is less than or equal to the end time of the declared free time interval.

8. The integrated intelligent volunteer dispatching system according to claim 1, characterized in that, The specific steps for performing the traffic flow connectivity check are as follows: The scheduling system obtains the current location coordinates of the volunteer and the location coordinates of all subtasks within the candidate merged task group. Taking the current location coordinates of the volunteer as the starting node, it calculates the straight-line distance from the starting node to all subtask nodes, selects the subtask node with the closest distance as the next execution node, and uses this execution node as the new reference node. The above distance calculation and node selection operations are repeated to determine the subsequent execution nodes in sequence. The estimated execution time of each subtask is marked on the corresponding node in sync, forming a moving node chain with execution time. For the generated chain of moving nodes, both no backtracking and full coverage must be satisfied simultaneously: For the no-return verification, extract the coordinates of two adjacent nodes in the movement node chain to generate a path vector, and calculate the angle between every two consecutive path vectors. If all angles are greater than 90°, the path is determined to be without reversal. If any angle is less than or equal to 90°, the path is determined to have a reversal or detour. For coverage integrity verification, count the number of subtask nodes in the movement node chain. If the number is completely consistent with the total number of subtasks in the candidate merged task group and there are no duplicate nodes, then the movement line is determined to cover all subtasks.

9. The integrated intelligent volunteer dispatch system according to claim 1, characterized in that, The specific steps for performing service object reachability verification are as follows: The scheduling system extracts the core information of each subtask node in the movement node chain, including the subtask identifier, the corresponding service object identifier, and the estimated arrival time T. arrive-i Expected execution time T exec-i And calculate the reachability decision time window W for each subtask. i =[T arrive-i -△t,T arrive-i +T exec-i ], where △t is the advance judgment buffer time; W of each subtask node i The unique identifier of the service object and the identifier of the subtask are bound one by one to generate a time window-service object association table. The order of this association table is completely consistent with the order of the movement node chain. The scheduling system initiates a real-time status retrieval mechanism according to the operation order of the association table, and for the real-time status data of each subtask, it needs to verify whether the following two conditions are met simultaneously: Condition 1: The service recipient reports that the service is currently reachable, and the expected duration of reachability is ≥ T. exec-i ; Condition 2: During the execution of the subtask, the scheduling system schedules every T... exec-i For a duration of / 3, refresh the status of the service object once in real time to confirm that the status has not changed to unreachable; If both conditions are met, the service object of the subtask is determined to be reachable; if either condition is not met, the scheduling system immediately triggers exception handling: backtracking to the node chain with execution duration, skipping the current subtask, rescheduling the time window of subsequent nodes, and marking the current subtask as to be executed later.

10. The integrated intelligent volunteer dispatching system according to claim 9, characterized in that, Enabling the real-time status retrieval mechanism specifically includes: When the scheduling system time arrives at each subtask W i Start time T arrive-i When -△t occurs, a status retrieval request is automatically sent to the service object. The system receives real-time status data from the service recipient. This real-time status data includes two core pieces of information: whether the service is currently reachable and the estimated duration of reachability. If no feedback is received within Δt / 2, the scheduling system initiates a second fetch. If there is still no feedback, the service is marked as temporarily unreachable.

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