Method for scheduling authentication audit tasks

Abstract

This invention relates to the field of task scheduling technology, and more particularly to a method for scheduling authentication and auditing tasks. The method includes: constructing a first directed graph, which includes vertices and a first type of edge; using the time distribution, geographical distribution, and personnel matching rules of the authentication and auditing tasks as constraints, generating auditing tasks corresponding to auditors within a specified time interval based on a simulated annealing algorithm, and updating the first directed graph; the calculation process of the simulated annealing algorithm includes probabilistically generating a second type of edge based on the first directed graph and the auditors' historical behavior information, where the starting vertex of the second type of edge is the location where the auditing task occurs. This invention solves the technical problem that traditional methods struggle to cope with real-time changing dynamic environments. By introducing a second type of edge, this invention allows the model to consider not only physical distance but also human behavior patterns, making the scheduling results closer to actual operational situations. This invention is mainly used for auditor task scheduling.

Description

Technical Field

[0001] This invention relates to the field of task scheduling technology, and in particular to a scheduling method for authentication and auditing tasks. Background Technology

[0002] The certification audit workload is large and distributed across a wide geographical area. Each task has a specific time window and skill requirements, and each auditor has different qualifications, work locations, and statuses. Certification audit task scheduling is a typical complex scheduling problem that combines vehicle routing, personnel scheduling, and optimization of time and space constraints.

[0003] Traditional methods treat auditing tasks as nodes, which auditors need to access and find the optimal path under specified constraints. However, the computational complexity of traditional methods increases exponentially with the problem size, making it difficult to cope with dynamic, real-time environments and limiting its ability to handle uncertainty. Summary of the Invention

[0004] At least one aspect and advantage of the invention will be set forth in part in the description which follows, or may be apparent from the description, or may be obtained by practicing the subject matter of this disclosure.

[0005] According to a first aspect of the present invention, a method for scheduling authentication and auditing tasks is provided, comprising:

[0006] Construct a first directed graph, which includes vertices and a first type of edge, where the first type of edge includes traffic cost and time cost attributes;

[0007] Using the time distribution, geographical distribution, and personnel matching rules of certification audit tasks as constraints, the algorithm generates audit tasks corresponding to auditors within a specified time interval and updates the first directed graph based on simulated annealing.

[0008] Furthermore, the calculation process of the simulated annealing algorithm includes probabilistically generating a second type of edge based on the first directed graph and the auditor's historical behavior information. The starting vertex of the second type of edge is the location where the audit task occurs, and the endpoint of the second type of edge is selected from a second preset vertex set. The second type of edge includes time or cost attributes.

[0009] Optionally, the constraints may further include preset detection points, which include personnel information, location information, and dwell time.

[0010] Optionally, allocating review tasks based on the simulated annealing algorithm includes:

[0011] Create a second directed graph, and randomly generate second-type edges for each candidate reviewer;

[0012] Iterate through the set of certification audit tasks to be assigned, and determine the candidate auditors for each certification audit task based on the time, region, personnel qualification requirements and current status of the personnel.

[0013] Traverse the certification audit tasks, randomly select an auditor from the candidate auditors of the certification audit tasks, and remove the corresponding auditors from the candidate auditors of other certification audit tasks. Determine the third type of edge connecting the current position of the auditor to the location of the certification audit task based on the position of the auditor, and create a loop edge based on the auditor and the certification audit task.

[0014] And stop the assignment when at least one certification audit task has no matching auditor, and calculate the first value to determine whether to accept the current assignment scheme;

[0015] The second directed graph is a copy of the first directed graph;

[0016] The first value is the sum of the weights of the edges involved by the auditor.

[0017] Optionally, after determining the candidate auditors, the method further includes determining a fourth type of edge based on the auditor's previous auditor; the starting point of the fourth type of edge is the vertex corresponding to the location of the previous audit task, and the ending point of the fourth type of edge is selected from a second preset vertex set.

[0018] Optionally, the probability of generating the second type of edge is calculated based on the first probability and the second probability;

[0019] The first probability is the percentage of time during which the auditor is unable to perform an audit action within the corresponding time period;

[0020] The second probability is the ratio of the duration of consecutive audit actions performed by the auditors within the first directed graph.

[0021] Optionally, after an assignment scheme for an audit task is accepted, the second directed graph is updated based on the relationship between the audit task and the auditor corresponding to the assignment scheme.

[0022] The process of updating the second directed graph includes:

[0023] A loop edge and a fifth type edge are created based on the auditor and the audit task; the starting point of the fifth type edge is the location of the audit task, and the ending point of the fifth type edge is a preset vertex that corresponds to the region where the audit task is located but is different from the location of the audit task.

[0024] The vertex corresponding to the loop edge is the location of the review task.

[0025] Optionally, based on the updated second directed graph, the simulated annealing algorithm is used to assign other unassigned authentication auditing tasks.

[0026] Optionally, the first directed graph is updated based on the second directed graph, and the time cost or transportation cost is calculated based on the updated first directed graph.

[0027] According to a second aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory being coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the scheduling method for the authentication and auditing task described in the first aspect of the present invention.

[0028] According to a third aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the scheduling method for authentication and auditing tasks as described in the first aspect of the present invention.

[0029] The beneficial effects of this invention are as follows: By introducing a second type of edge, the model not only considers physical distance but also incorporates human behavioral patterns, making the scheduling results closer to actual operational conditions. It also optimizes time and transportation costs and ensures the feasibility of the solution through constraints. Furthermore, this method can handle dynamically added tasks; simply add them as new vertices to the directed graph and restart the optimization process. Attached Figure Description

[0030] Figure 1 This is a flowchart of the scheduling method for authentication and auditing tasks in this application embodiment. Detailed Implementation

[0031] The present disclosure will now be discussed with reference to several exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and thus implement the present disclosure, and are not intended to imply any limitation on the scope of the disclosure.

[0032] like Figure 1 As shown in the figure, this embodiment introduces a method for scheduling authentication and auditing tasks, including:

[0033] Construct a first directed graph, which includes vertices and a first type of edge, where the first type of edge includes traffic cost and time cost attributes;

[0034] Using the time distribution, geographical distribution, and personnel matching rules of certification audit tasks as constraints, the algorithm generates audit tasks corresponding to auditors within a specified time interval and updates the first directed graph based on simulated annealing.

[0035] Furthermore, the calculation process of the simulated annealing algorithm includes probabilistically generating a second type of edge based on the first directed graph and the auditor's historical behavior information. The starting vertex of the second type of edge is the location where the audit task occurs, and the endpoint of the second type of edge is selected from a second preset vertex set. The second type of edge includes time or cost attributes.

[0036] The first directed graph is the foundational data model of the entire system, representing the entire business environment. The vertices in the first directed graph represent various locations, such as the location where the audit task occurs, the location of the auditor, and other key geographical locations.

[0037] The first type of edge represents a path connecting these vertices. Each edge has two attributes: traffic cost and time cost. The traffic cost can be fuel costs, toll fees, etc., while the time cost can be the travel time. The first type of edge is used to calculate the cost of traveling from one vertex to another.

[0038] The time distribution of certification audit tasks indicates that the tasks must be completed within a specified time; the geographical distribution indicates the geographical restrictions of the tasks and auditors; and the personnel matching rules indicate that specific types of audit tasks require auditors with specific skills or qualifications.

[0039] The starting vertex of the second type of edge is the location where the audit task occurs, and the ending vertex is selected from the second preset vertex set, which includes other unassigned task points, the auditor's ending point, or the starting point of other auditors.

[0040] The second type of edge is generated probabilistically based on the auditor's historical behavior information. For example, if historical data shows that an auditor has a high probability of going to location B after completing a task at location A, then the likelihood of generating a second type edge from location A to location B is greater. This probability can be modeled based on historical path frequency, regional preferences, time preferences, etc.

[0041] Simulated annealing is a biomimetic algorithm that allows for a certain probability of accepting a worse solution during the search process, thereby helping to escape local optima and ultimately find the global optimum.

[0042] This embodiment introduces a second type of edge, enabling the model to consider not only physical distance but also human behavioral patterns, making the scheduling results closer to actual operational conditions. It also optimizes time and transportation costs, and ensures the feasibility of the solution through constraints. Furthermore, this method can handle dynamically added tasks; simply add them as new vertices to the directed graph and restart the optimization process.

[0043] In this embodiment, the constraints also include preset detection points, which include personnel information, location information, and dwell time.

[0044] The personnel information for a pre-defined checkpoint indicates that the checkpoint is associated with a specific auditor; not all auditors need to go to this point. Location information indicates the fixed location of the checkpoint, which will become a vertex in the first directed graph. Dwell time indicates the specified time an auditor must stay at the checkpoint after arriving; this is different from the time cost of movement and is a mandatory task time expenditure.

[0045] Each preset detection point is added as a vertex to the first directed graph. The system needs to calculate the first type of edges between this detection point and other task points. Preset detection points introduce a new set of mandatory constraints: the path generated for a certain auditor must contain its corresponding preset detection point.

[0046] By adding preset inspection points, a wider range of business scenarios can be handled. For example, a simulated auditor can pick up equipment at a designated location every morning, submit a preliminary report or hand over materials at a fixed site at noon, conduct a mandatory inspection of the auditor's vehicle or equipment at a specific location, or complete a short online training session while on the go.

[0047] The constraint of pre-set detection points has enabled this scheduling model to take a crucial step from an ideal, simplified theoretical model towards a mature, implementable, industrial-grade solution capable of handling the complexity and irregularities of the real world. Through the powerful expressive capabilities of graph models and metaheuristic algorithms, it seamlessly integrates fixed schedules, variable tasks, and personnel behavior patterns within the same optimization framework, aiming to generate truly executable scheduling schemes that conform to all company operational procedures.

[0048] In this embodiment, the allocation of review tasks based on the simulated annealing algorithm includes:

[0049] Create a second directed graph, randomly generating second-type edges for each candidate reviewer; the second directed graph is a copy of the first directed graph.

[0050] Iterate through the set of certification audit tasks to be assigned, and determine the candidate auditors for each certification audit task based on the time, region, personnel qualification requirements and current status of the personnel.

[0051] Traverse the certification audit tasks, randomly select an auditor from the candidate auditors of the certification audit tasks, and remove the corresponding auditors from the candidate auditors of other certification audit tasks. Determine the third type of edge connecting the current position of the auditor to the location of the certification audit task based on the position of the auditor, and create a loop edge based on the auditor and the certification audit task.

[0052] The assignment process stops when at least one certification audit task has no matching auditor, and a first value is calculated to determine whether to accept the current assignment scheme; the first value is the sum of the weights of the edges involved by the auditor.

[0053] The second directed graph is a copy of the first directed graph, meaning that all vertices and first-class edges of the first directed graph are inherited. The optimization process will be performed on this copy of the second directed graph without destroying the original base graph data.

[0054] The third type of edge is dynamically created during the allocation process. It connects the auditor's current location to the location of the new task assigned to them, representing the specific movement resulting from the scheduling decision. A loop edge represents the path an auditor takes back to their destination from the last task point after completing all assigned tasks; this is a necessary condition for a closed path.

[0055] Each pending certification audit task is iterated through. For each task, a set of candidate auditors qualified to handle the task is selected based on its time (whether it falls within the auditor's working hours), location (whether the auditor can arrive in time), personnel qualifications (whether the auditor has the corresponding skills), and the auditor's current status (whether they have already been assigned a task, their current location, etc.). This step ensures that any allocation scheme must meet basic business constraints.

[0056] The system iterates through the set of certification audit tasks in a certain order. For each task currently being traversed, an auditor is randomly selected from its set of candidate auditors. Once an auditor is selected and assigned to the current task, the system immediately removes that auditor from the candidate set of all other unassigned tasks. This ensures that an auditor can be assigned to at most one task at a time, resolving resource conflicts.

[0057] In the second directed graph, an edge is created from the reviewer's current location to the location of the new task. This edge carries the movement cost of this scheduling. The reviewer's current location is then updated to the location of the new task.

[0058] The process of randomly selecting auditors for the next task continues until at least one task has an empty candidate set and no matching auditor can be found, at which point the assignment process stops. This means that the current random assignment attempt produced an incomplete solution, with some tasks not being assigned. This is allowed in the simulated annealing algorithm because the algorithm can accept temporarily worsened solutions.

[0059] For each auditor who has been assigned a task, create an edge from their last task location to their final destination, thus forming a complete path for each auditor who has been assigned a task.

[0060] The first value is the objective function, which is the sum of the weights of all edges traversed on the auditor's path. This includes first-type edges representing the basic movement cost, second-type edges representing the possible costs based on historical behavior, third-type edges representing the movement cost generated by the current allocation decision, and loop edges representing the return cost. This first value is the standard used by the simulated annealing algorithm to evaluate the quality of the current allocation scheme.

[0061] This newly generated allocation scheme (and its corresponding first value) is submitted to the Metropolis criterion of simulated annealing for evaluation. The algorithm compares it with the previous scheme and, based on the cost difference and the current temperature, decides with a certain probability whether to accept the new scheme (even if it is worse or has no assigned task) or reject it and roll back to the previous scheme. Subsequently, the temperature decreases, and the next iteration begins, generating new random allocations.

[0062] This embodiment focuses on the decision of who does what task during the allocation phase. A large number of different task-person matching schemes are quickly generated using a random greedy algorithm, and conflict handling ensures the feasibility of each scheme. In the evaluation phase, the focus is on assessing the effectiveness of each scheme. The total cost of each allocation scheme is precisely quantified by calculating the cost of the complete path (first value) on a graph model. This separation allows the simulated annealing algorithm to efficiently search the space of allocation strategies, and each search yields a relatively accurate cost assessment, making it well-suited for solving real-time scheduling problems with complex constraints.

[0063] In this embodiment, after determining the candidate auditor, a fourth type of edge is determined based on the auditor's previous auditor; the starting point of the fourth type of edge is the vertex corresponding to the location of the previous audit task, and the ending point of the fourth type of edge is selected from the second preset vertex set.

[0064] The starting point of the fourth type of edge is the vertex corresponding to the location of the previous review task, which means that it is state-dependent and changes dynamically as the allocation process proceeds.

[0065] With the introduction of the fourth type of edge, the assignment process is no longer simply about assigning tasks to reviewers, but rather about building a continuously growing task chain for each reviewer. After identifying a candidate reviewer, the system examines that candidate's current path, finds the last vertex of the path (the location of the previous review task, or, if it was the first task), its initial position. From this last vertex, the system creates a fourth type edge pointing to the location of the current new task.

[0066] Add the newly created fourth-type edge and the new task vertex to the candidate reviewer's path, update the candidate reviewer's current position to the location of this new task, and then remove the reviewer from the candidate set of other tasks.

[0067] The calculation of the first value now incorporates the cost of the fourth type of edge, which is the core movement cost of this allocation scheme because it directly represents the scheduling decision from one task location to the next.

[0068] With the introduction of the fourth type of edge, the system no longer statically assigns tasks to personnel. Instead, it dynamically weaves a task chain for each auditor in each iteration of the simulated annealing algorithm. The cost of each step (the fourth type of edge) in this chain is calculated and optimized in real time, resulting in not just a list of what each person should do, but a series of specific, feasible, and near-optimal daily execution roadmaps. This greatly enhances the practical feasibility and overall efficiency of the solution.

[0069] In this embodiment, the probability of generating the second type of edge is calculated based on the first probability and the second probability; the first probability is the percentage of time during which the auditor is unable to perform the audit behavior within the corresponding time period; the second probability is the percentage of time during which the auditor continuously performs the audit behavior within the first directed graph.

[0070] The first probability represents the proportion of an auditor who is unable to work due to external coercive factors within a specific time period. This time period can be one day, one week, or any other time period. External coercive factors can include various factors such as meetings, training, lunch breaks, equipment malfunctions, and administrative tasks. The first probability is the ratio of the time an auditor is unable to work to the total working time.

[0071] When the first probability is relatively high, it means that the reviewer's time is severely fragmented, with many unusable time slots. Therefore, when generating the second type of edge probabilistically, its endpoint is less likely to point to a new review task, but more likely to point to a vertex representing an interruption or rest, such as returning to the office or heading to a rest stop.

[0072] When the first probability is relatively low, it means that the auditor has a continuous and complete working time. Therefore, the algorithm will more actively generate second-type edges pointing to the vertex of the new task, exploring paths that allow for continuous work.

[0073] The second probability is the ratio of the duration an auditor performs auditing actions continuously. This measures an auditor's tendency or ability to continue auditing without interruption once they begin. For example, some auditors are highly efficient and can work continuously for 4 hours, while others may need to take a break every 2 hours.

[0074] A higher probability of the second type of edge indicates that the reviewer is adept at working long hours. The algorithm tends to generate long chains of second-type edges, connecting one task point to another more distant task point, thus constructing a compact schedule.

[0075] When the second probability is relatively low, it indicates that the reviewer needs to take frequent breaks. The algorithm will generate shorter second-type edges, with closer distances between connected vertices, or will probabilistically insert rest points into the path.

[0076] In this embodiment, the probability of generating the second type of edge is calculated based on the first probability and the second probability. Historical data is used to guide the simulated annealing algorithm to search for areas that are more in line with the reviewer's personal work habits, which greatly reduces the time wasted on obviously unreasonable solutions and allows the algorithm to converge faster.

[0077] In this embodiment, after an assignment scheme for an audit task is accepted, the second directed graph is updated based on the relationship between the audit task and the auditor corresponding to the assignment scheme.

[0078] The process of updating the second directed graph includes: creating a loop edge and a fifth type edge based on the auditor and the audit task; the starting point of the fifth type edge is the location of the audit task, and the ending point of the fifth type edge is a preset vertex corresponding to the region where the audit task is located but different from the location of the audit task; the vertex corresponding to the loop edge is the location of the audit task.

[0079] After an assignment scheme is accepted by the simulated annealing algorithm, the second directed graph, which is a working copy of the current optimization process, is updated. This demonstrates that successful experiences are immediately used to guide the optimization process of subsequent algorithms.

[0080] The fifth type of edge starts at the location of the newly assigned review task and ends at a preset vertex that is related to the region where the task is located but is different from the task location. This represents a kind of regional experience transfer.

[0081] For example, if an auditor is successfully assigned a specific audit task (vertex A) located in the East City Auto City, the system will find a preset vertex P representing the entire East City area based on the Auto City. This vertex P is not a specific task point, but a regional hub. Then the system creates a fifth-type edge from the specific task vertex (vertex A) to the regional representative vertex P.

[0082] The purpose of creating fifth-type edges is to abstract and generalize experience. The system learns not just "the auditor went to point A," but "the auditor performed the task in the Chengdong District." This fifth-type edge abstracts specific success cases into regional experience. In subsequent iterations of the simulated annealing algorithm, when the algorithm probabilistically generates second-type edges, these newly added fifth-type edges become candidates.

[0083] For example, another auditor completes a task in the north of the city, and the algorithm is looking for their next task. Since a fifth-type edge was previously created from the East City Auto City to the East District, this pattern might be referenced. The system might generate a second-type edge from this auditor's current location to the East District.

[0084] This approach encourages cross-regional scheduling searches, breaking down geographical limitations and potentially leading to the discovery of globally superior allocation schemes.

[0085] In this embodiment, based on the updated second directed graph, the simulated annealing algorithm is used to assign other unassigned authentication and auditing tasks.

[0086] In this embodiment, the first directed graph is updated based on the second directed graph, and the time cost or transportation cost is calculated based on the updated first directed graph.

[0087] The temporary experience learned from the second directed graph is permanently embedded into the base first directed graph. The updated first directed graph becomes the new knowledge benchmark for all future cost calculation and scheduling tasks.

[0088] The updates include edge weight updates, edge existence updates, and vertex attribute updates.

[0089] If an edge is frequently used and proven efficient across multiple scheduling iterations, the system can dynamically reduce its cost weight. Conversely, if an edge consistently leads to timeouts or high costs, its weight can be increased.

[0090] Fifth-class edges, created empirically in the second directed graph, can be permanently added to the first directed graph if they prove extremely effective, for example, by repeatedly guiding the algorithm to find high-quality solutions. This allows future scheduling to leverage this successful pattern from the outset. Edges that are not used for a long time or always result in high costs can be marked or removed to simplify the graph structure and improve search efficiency.

[0091] Updating vertex attributes includes updating task vertices and reviewer vertices, such as more accurately estimating the average review time for a location based on historical data.

[0092] When calculating costs, the system starts with the updated first directed graph that incorporates all historical experience. The calculation of time and transportation costs is based on the updated edge weights, which reflect real, verified traffic conditions rather than the initial theoretical estimates, making cost calculations more accurate.

[0093] This embodiment introduces an electronic device, including a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the scheduling method for authentication and auditing tasks according to any embodiment of the present invention.

[0094] This embodiment introduces a computer-readable storage medium storing a computer program that, when executed by a processor, implements a scheduling method for authentication and auditing tasks as described in any embodiment of the present invention.

[0095] While specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention.

[0096] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0097] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0098] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0099] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0100] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0101] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0102] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0103] It should be understood that the sequence numbers of the steps in the invention's content and embodiments do not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The foregoing description of embodiments of this disclosure has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this disclosure to the exact form disclosed. Various modifications and variations may exist based on the foregoing teachings, or various modifications and variations may be derived from the practice of this disclosure. These embodiments were chosen and described to illustrate the principles of this disclosure and its practical application, so that those skilled in the art can utilize this disclosure in various implementations and modifications suitable for the specific purpose of the concept.

Claims

1. A method for scheduling authentication audit tasks, characterized in that, include: Construct a first directed graph, which includes vertices and a first type of edge, where the first type of edge includes traffic cost and time cost attributes; Using the time distribution, geographical distribution, and personnel matching rules of certification audit tasks as constraints, the algorithm generates audit tasks corresponding to auditors within a specified time interval and updates the first directed graph based on simulated annealing. Furthermore, the calculation process of the simulated annealing algorithm includes probabilistically generating a second type of edge based on the first directed graph and the auditor's historical behavior information. The starting vertex of the second type of edge is the location where the audit task occurs, and the endpoint of the second type of edge is selected from a second preset vertex set. The second type of edge includes time or cost attributes. The allocation of audit tasks based on the simulated annealing algorithm includes: Create a second directed graph; Iterate through the set of certification audit tasks to be assigned, and determine the candidate auditors for each certification audit task based on the time, region, personnel qualification requirements, and current status of the personnel. Traverse the certification audit tasks, randomly select an auditor from the candidate auditors of the certification audit tasks, and remove the corresponding auditors from the candidate auditors of other certification audit tasks. Determine the third type of edge connecting the current position of the auditor to the location of the certification audit task based on the position of the auditor, and create a loop edge based on the auditor and the certification audit task. And stop the assignment when at least one certification audit task has no matching auditor, and calculate the first value to determine whether to accept the current assignment scheme; The second directed graph is a copy of the first directed graph; The first value is the sum of the weights of the edges involved by the auditor; The probability of generating the second type of edge is calculated based on the first probability and the second probability; The first probability is the percentage of time during which the auditor is unable to perform an audit action within the corresponding time period; The second probability is the ratio of the duration of consecutive audit actions performed by the auditors within the first directed graph.

2. The scheduling method for certification audit tasks as described in claim 1, characterized in that, The constraints also include preset detection points, which include personnel information, location information, and dwell time.

3. The method of claim 1, wherein the authentication of the audit task is performed by a server. After determining the candidate auditors, the process also includes determining a fourth type of edge based on the auditor's previous auditor; the starting point of the fourth type of edge is the vertex corresponding to the location of the previous audit task, and the ending point of the fourth type of edge is selected from a second preset vertex set.

4. The method of claim 1, wherein, Once an assignment scheme for an audit task is accepted, the second directed graph is updated based on the relationship between the audit tasks and auditors corresponding to that assignment scheme. The process of updating the second directed graph includes: Create a loop edge and a fifth-class edge based on the auditor and the audit task; The starting point of the fifth type of edge is the location of the review task, and the ending point of the fifth type of edge is a preset vertex that corresponds to the region where the review task is located but is different from the location of the review task. The vertex corresponding to the loop edge is the location of the review task.

5. The method of claim 4, wherein, Based on the updated second directed graph, the simulated annealing algorithm is used to assign other unassigned authentication and auditing tasks.

6. The method of claim 1, wherein, The first directed graph is updated based on the second directed graph, and the time cost or transportation cost is calculated based on the updated first directed graph.

Images