Instrument detection task processing method and device, storage medium and electronic equipment

Abstract

The invention discloses an instrument detection task processing method, which comprises the steps of determining a task priority of a target detection task and a team score of a candidate detection team based on task description data of the target detection task and team description data of the candidate detection team, the resource adaptation degree and the time adaptation degree of a candidate detection team to the target detection task are determined; determining whether the target detection task meets a task splitting condition or not based on the resource adaptation degree, the time adaptation degree and the task priority of the target detection task; if the target detection task meets the task splitting condition, splitting the target detection task based on the resource adaptation degree or the time adaptation degree to obtain to-be-allocated detection sub-tasks; and determining a target detection team from the candidate detection teams based on the team scores and the resource adaptation degrees of the candidate detection teams to the to-be-allocated detection sub-tasks, and issuing the to-be-allocated detection sub-tasks to the target detection team. According to the invention, the flexibility and adaptability of task allocation are enhanced.

Description

Technical Field

[0001] This application relates to the field of instrument testing, and specifically to an instrument testing task processing method, apparatus, storage medium, and electronic device.

[0002] Background technology instrument testing refers to the technical means of automatically detecting, identifying, and measuring specific objects using specialized equipment or systems. It has wide applications in fields such as power testing, environmental monitoring, and medical testing. As testing scenarios become increasingly complex and task scales continue to expand, how to efficiently and rationally allocate testing tasks to execution teams has become the key to improving overall system efficiency.

[0003] Currently, most related technologies employ fixed matching or simple rule-based allocation methods, such as assigning tasks solely based on team geolocation or static load. These methods lack consideration for the dynamic adaptation between task characteristics and execution capabilities, failing to assess differences in resource requirements and time constraints, and unable to flexibly adjust based on team real-time status. When faced with large-scale, high-concurrency, or highly heterogeneous detection tasks, existing mechanisms struggle to achieve flexible resource allocation and adaptive task distribution, easily leading to some teams being overloaded while others remain idle. Furthermore, the lack of assessment of task decomposability makes it difficult for the system to reasonably break down complex tasks and distribute them across multiple teams for collaborative execution, further limiting the system's throughput and responsiveness in dynamic environments. Summary of the Invention

[0004] This application provides an instrument detection task processing method, apparatus, storage medium, and electronic device, which can improve the flexibility and adaptability of task allocation.

[0005] According to a first aspect of this application, an instrument detection task processing method is provided, the method comprising:

[0006] Based on the task description data of the target detection task and the team description data of the candidate detection teams, the task priority of the target detection task, the team score of the candidate detection teams, and the resource suitability and time suitability of the candidate detection teams to the target detection task are determined.

[0007] Based on the resource adaptability, the time adaptability, and the task priority of the target detection task, determine whether the target detection task meets the task splitting conditions;

[0008] If the target detection task meets the task splitting condition, the target detection task is split based on the resource adaptability or the time adaptability to obtain the detection subtasks to be assigned.

[0009] Based on the team score and the resource suitability of the candidate detection teams for the detection sub-tasks to be assigned, a target detection team is determined from the candidate detection teams, and the detection sub-tasks to be assigned are distributed to the target detection team.

[0010] According to a second aspect of this application, an instrument detection task processing apparatus is provided, the apparatus comprising:

[0011] The information determination module is used to determine the task priority of the target detection task, the team score of the candidate detection team, and the resource suitability and time suitability of the candidate detection team to the target detection task based on the task description data of the target detection task and the team description data of the candidate detection team.

[0012] The splitting judgment module is used to determine whether the target detection task meets the task splitting conditions based on the resource adaptability, the time adaptability, and the task priority of the target detection task.

[0013] The task splitting module is used to split the target detection task based on the resource adaptability or the time adaptability if the target detection task meets the task splitting conditions, so as to obtain the detection sub-tasks to be assigned.

[0014] The task allocation module is used to determine the target detection team from the candidate detection teams based on the team score and the resource suitability of the candidate detection teams for the detection sub-task to be allocated, and to assign the detection sub-task to the target detection team.

[0015] According to a third aspect of the present invention, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the instrument detection task processing method as described in embodiments of this application.

[0016] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the instrument detection task processing method as described in the embodiments of the present application.

[0017] According to a fifth aspect of this application, an embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements the instrument detection task processing method as described in the embodiment of this application.

[0018] This technical solution achieves intelligent and optimized task allocation by comprehensively calculating task priority, team score, resource suitability, and time suitability based on task description data of target detection tasks and team description data of candidate detection teams. First, by evaluating resource suitability and time suitability in conjunction with task priority, it dynamically determines whether a task meets the splitting conditions. This allows for reasonable splitting when a task is too complex or has excessively high resource requirements, generating sub-tasks to be assigned. This effectively avoids inefficiency caused by insufficient resources or time for individual teams, improves the feasibility of task execution and system throughput, and ensures that highly important tasks can be reliably executed through the splitting mechanism. Second, after task splitting, target detection teams are selected based on team score and resource suitability, ensuring that sub-tasks are assigned to teams with matching capabilities and sufficient resources. This not only optimizes resource utilization and reduces the risk of idleness or overload but also ensures the quality and timeliness of high-importance tasks through priority and scoring mechanisms. Overall, this solution enhances the flexibility and adaptability of task allocation, enabling efficient and reliable task execution in changing environments, thereby improving the overall performance and scalability of the detection system.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the instrument detection task processing method provided in Embodiment 1;

[0022] Figure 2 This is a flowchart of the instrument detection task processing method provided in Embodiment 2;

[0023] Figure 3 This is a schematic diagram of the instrument detection task processing device provided in Embodiment 3 of this application;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," "target," and "candidate," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This is a flowchart of the instrument testing task processing method provided in Embodiment 1. This embodiment is applicable to situations where instrument testing tasks are assigned. The method can be executed by an instrument testing task processing device, which is implemented in hardware and / or software and can be integrated into the electronic device running this system.

[0029] like Figure 1 As shown, the method includes:

[0030] S110. Based on the task description data of the target detection task and the team description data of the candidate detection teams, determine the task priority of the target detection task, the team score of the candidate detection teams, and the resource and time adaptability of the candidate detection teams to the target detection task.

[0031] S120. Based on the resource adaptability, the time adaptability, and the task priority of the target detection task, determine whether the target detection task meets the task splitting conditions.

[0032] S130. If the target detection task meets the task splitting condition, the target detection task is split based on the resource adaptability or the time adaptability to obtain the detection subtasks to be assigned.

[0033] S140. Based on the team score and the resource suitability of the candidate detection teams for the detection sub-tasks to be assigned, a target detection team is determined from the candidate detection teams, and the detection sub-tasks to be assigned are assigned to the target detection team.

[0034] In this context, target detection tasks refer to specific exploration and identification work that needs to be completed using detection equipment or systems. The fields involved in target detection tasks and the types of instruments to be tested are not limited here; they are determined based on actual business needs. For example, the fields involved in target detection tasks could be power testing or medical testing. Correspondingly, the types of instruments to be tested could be oscilloscopes or medical CT scanners. Task description data refers to the data set used to define the attributes and requirements of the target detection task, including parameters such as customer priority, task completion deadline, task scale, required detection resources, task complexity, and task location. The role of task description data is to provide the system with the input basis needed to calculate task priority, resource suitability, and time suitability.

[0035] A candidate detection team refers to a set of detection teams available for scheduling; each detection team is a task execution unit. Team description data refers to a dataset used to describe the status and capability characteristics of candidate detection teams. This includes parameters such as available detection resources, processing capacity limit, team response speed, current team load, team capability score, historical experience score, team location, and the priority score of the target detection task within the candidate detection team. The role of candidate detection teams is to provide the system with the quantitative basis needed to evaluate team scores, resource suitability, time suitability, and team priority.

[0036] The task priority of object detection is related not only to the object detection task itself but also to the candidate detection teams. The object detection task itself provides the inherent objective requirement for determining its priority, which determines the priority level that the object detection task should be treated under ideal conditions. The candidate detection teams, on the other hand, calibrate and modify the basic priority of the object detection task from the perspective of feasibility.

[0037] Task priority refers to the importance of a task, which is used to determine the order and method of processing.

[0038] The team score for candidate detection teams is determined based on the task description data of the target detection task and the team description data of the candidate detection teams. The task description data provides a specific context and reference system for determining the team score. This ensures that the team score is not an abstract, absolute capability value, but a relative evaluation specific to the current target detection task, guaranteeing a high degree of correlation between the team score and the task scenario. The team description data provides all the intrinsic basis and quantitative foundation for calculating the team score, constituting an objective portrait of the team's overall strength and serving as a core element in evaluating its competence.

[0039] Resource fit refers to the degree of matching between the candidate detection team's resources and the requirements of the target detection task. Task description data clearly defines the hardware, software, and other resource thresholds and specifications necessary for successful execution of the task, providing an objective benchmark for evaluation. Team description data displays the list of resources that the candidate team currently possesses and can allocate. Time fit refers to the degree of matching between the candidate detection team's schedule and the time requirements of the target detection task. Task description data defines the task's time constraints. Team description data provides the capability parameters to meet these time requirements.

[0040] Resource fit and time fit measure whether an object detection task meets the task splitting criteria from the perspective of execution feasibility. Resource fit and time fit directly reflect the objective gap between the capabilities of the current candidate detection teams and the task requirements: low resource fit means that a single team cannot meet the resource consumption of the task; low time fit means that the probability of independently completing the task within the time limit is very small. Task priority measures whether an object detection task meets the task splitting criteria from the perspective of business importance. Task priority can be used to constrain resource fit or as an independent judgment condition to determine whether an object detection task meets the task splitting criteria.

[0041] If an object detection task meets the aforementioned task splitting conditions, it means that the object detection task is either objectively difficult to execute due to insufficient resource or time adaptability, or the task has a very high priority and is too important for detection.

[0042] If the target detection task meets the task splitting conditions, it means that the target detection task must adopt a more reliable distributed execution scheme, which involves splitting the target detection task based on time adaptability or resource adaptability. When splitting based on resource adaptability, the operation logic is resource-oriented splitting, and the direct goal of splitting is to resolve resource bottlenecks. When splitting based on time adaptability, the operation logic is time-oriented splitting, and the core goal of splitting is to reduce the total execution time.

[0043] Decomposing object detection tasks based on resource suitability or time suitability is not an arbitrary choice, but a problem-oriented and precise decision. This ensures that the decomposition operation is not simply about breaking the task down into smaller parts, but rather about purposefully reconstructing large and difficult object detection tasks into a set of sub-tasks that are resource-capable or time-capable for allocation.

[0044] The subtask to be assigned is the smallest independent task unit generated by splitting the target detection task. This subtask inherits the core detection objectives and attributes of the original target detection task, such as task priority, but its task size, required resources, or spatiotemporal scope has been divided into smaller units that can be independently undertaken and completed by a single detection team.

[0045] Resource fit measures the degree of match between the specific resource requirements of a given detection subtask and the available detection resources of a detection team. Team score is a quantitative evaluation of the long-term stability, professional capabilities, and historical reputation of candidate detection teams. Optionally, candidate detection teams can be initially screened based on resource fit, and then the detection team with the highest team score can be prioritized as the target detection team.

[0046] Optionally, after the target inspection team is determined, the number and type of inspection equipment, as well as the team's capability score, are reviewed to ensure that the equipment resources and capability score of the target inspection team meet the basic requirements of the target inspection team.

[0047] Once the target detection team is determined, the sub-tasks to be assigned can be distributed to the target detection team based on the resource suitability of the sub-tasks to be assigned.

[0048] This technical solution achieves intelligent and optimized task allocation by comprehensively calculating task priority, team score, resource suitability, and time suitability based on task description data of target detection tasks and team description data of candidate detection teams. First, by evaluating resource suitability and time suitability in conjunction with task priority, it dynamically determines whether a task meets the splitting conditions. This allows for reasonable splitting when a task is too complex or has excessively high resource requirements, generating sub-tasks to be assigned. This effectively avoids inefficiency caused by insufficient resources or time for individual teams, improves the feasibility of task execution and system throughput, and ensures that highly important tasks can be reliably executed through the splitting mechanism. Second, after task splitting, target detection teams are selected based on team score and resource suitability, ensuring that sub-tasks are assigned to teams with matching capabilities and sufficient resources. This not only optimizes resource utilization and reduces the risk of idleness or overload but also ensures the quality and timeliness of high-importance tasks through priority and scoring mechanisms. Overall, this solution enhances the flexibility and adaptability of task allocation, enabling efficient and reliable task execution in changing environments, thereby improving the overall performance and scalability of the detection system.

[0049] In an optional embodiment, splitting the target detection task based on the resource adaptability or the time adaptability to obtain the detection subtasks to be assigned includes: determining the number of task splits for the target detection task based on the resource adaptability or the time adaptability; and splitting the target detection task based on the number of task splits to obtain the detection subtasks to be assigned.

[0050] The task splitting quantity refers to the total number of independent, unassigned sub-tasks resulting from decomposing the original target detection task. The task splitting quantity is determined based on either resource adaptability or time adaptability. When determining the task splitting quantity based on resource adaptability, the operation follows resource capacity decomposition logic to ensure that the resource requirements of the unassigned sub-tasks match the team's available resources. When determining the task splitting quantity based on time adaptability, the operation follows time window decomposition logic to ensure that the total workload can be completed within the time limit.

[0051] Optionally, when the target detection task meets the task splitting condition based on time adaptability, the number of task splits for the target detection task is determined based on time adaptability. For example, if the time adaptability is less than a first preset value, the number of task splits is set to 4 or 5. If the time adaptability is between the first and second preset values, the number of task splits is set to 3. If the time adaptability is greater than the second preset value, the number of task splits is set to 2. The first and second preset values ​​are not limited here and are determined based on actual business needs. For example, the first preset value could be 0.3 and the second preset value could be 0.6.

[0052] When determining that a target detection task meets the task splitting condition based on resource suitability, the maximum suitability is determined from the resource suitability of the candidate detection teams, and the number of task splits for the target detection task is determined based on the maximum suitability. For example, if the maximum suitability is less than a third preset value, the number of task splits is set to 4 or 5. If the maximum suitability is between the third and fourth preset values, the number of task splits is set to 2 or 3. The third and fourth preset values ​​are not limited here and are determined based on actual business needs. For example, the third preset value could be 0.5 and the second preset value could be 1.

[0053] The above technical solution achieves optimal decomposition of complex tasks by accurately determining the number of task splits based on resource adaptability or time adaptability. When a task cannot be executed due to excessive resource requirements, it can be split into resource-capable subtasks according to resource capacity, thereby eliminating resource bottlenecks and improving system throughput. When a task faces time pressure, it can be split into parallel-executable subtasks according to processing capacity, significantly shortening the total completion time through parallel processing and ensuring task timeliness. This ensures that each subtask achieves a balance between scale and execution constraints, laying the foundation for efficient subsequent allocation, thereby comprehensively improving the system's adaptability and overall efficiency in handling complex detection tasks.

[0054] In an optional embodiment, the process of assigning the detection subtask to the target detection team based on the team score and the resource suitability of the candidate detection team for the subtask to be assigned includes: determining the team priority of the target detection team, and determining the task allocation order for the target detection team based on the team priority and the team score; determining the number of tasks to be allocated for the target detection team based on the resource suitability of the target detection team for the subtask to be assigned; and assigning the subtask to the target detection team based on the task allocation order and the number of tasks allocated. The team priority of the target detection team is determined based on the resource suitability of the target detection team for the subtask to be assigned, the task priority of the target detection task, and the load throughput of the target detection team. The load throughput is determined based on the team response speed and the current load of the team in the team description data.

[0055] The calculation of team priority integrates three key factors: resource adaptability, task priority, and load throughput. This makes team priority a comprehensive indicator that reflects both task matching and the real-time load status of the system. Resource adaptability reflects the team's specific capability matching in executing the sub-task, task priority ensures the strategic orientation of system resources towards important tasks, and load throughput, determined by the team's response speed and current load, reflects the team's real-time processing capability.

[0056] Optional, adopt . For the first The team priority of each testing team For the team's response speed, The current load of the team is based on the number of assigned tasks and equipment utilization. For the first Resource suitability of each testing team refers to the resource suitability of the assigned testing sub-tasks. The task priority for the target detection task. , and All are weights. in, , For the first Available testing resources for each testing team The required detection resources for the sub-tasks to be assigned. Team response time is determined based on historical task completion times, and the team's current load is determined based on the number of assigned tasks and equipment utilization.

[0057] When determining the order of task allocation, the principles of strategic priority and capability assurance are followed. Allocation is not simply based on team scores, but rather a combination of team priority and team scores to jointly determine the order. This ensures that high-importance tasks are prioritized for teams that possess the corresponding capabilities and are currently in optimal condition, achieving a balance between strategic orientation and execution efficiency.

[0058] When determining the number of tasks to be assigned, the capacity boundary principle is followed. Resource suitability directly determines the upper limit of the number of specific sub-tasks that a single testing team can undertake, preventing team overload and decreased execution quality due to over-allocation.

[0059] Optional, adopt Determine the first The task allocation ratio for each testing team, and then, based on The total number of tasks to be assigned, along with the total number of subtasks to be assigned, determines the number of tasks to be assigned to the target detection team. Among these, This ensures that all subtasks to be assigned can be fully assigned.

[0060] By integrating the task allocation order and the number of tasks, the sub-tasks to be assigned are distributed to the target detection teams. This not only ensures that individual sub-tasks can be executed efficiently and reliably, but also achieves load balancing, enabling high-priority tasks to receive priority and sufficient resource guarantees. At the same time, it maximizes the potential capabilities of all teams, thereby significantly improving the timeliness, reliability, and overall throughput performance of task execution.

[0061] The aforementioned technical solution determines team priority by comprehensively considering resource adaptability, task priority, and load throughput. This ensures that task allocation simultaneously considers execution matching, task importance, and real-time system load status. It guarantees that high-priority tasks are assigned to the most suitable teams while effectively preventing team overload through load throughput, thus achieving system load balancing. Secondly, when determining the task allocation order, team priority is combined with team scores reflecting long-term capabilities. This ensures that tasks are assigned to teams with high real-time comprehensive weights while also considering historical reliability, thereby improving response speed while guaranteeing task execution quality. Finally, by precisely controlling the number of tasks assigned to a single team based on resource adaptability, the solution fully taps into the team's execution potential while preventing resource bottlenecks caused by over-allocation, ensuring that each subtask executes under optimal resource conditions. Overall, this multi-layered, adaptive allocation mechanism significantly improves the accuracy of task allocation and the overall system throughput efficiency, while also enhancing the system's responsiveness and stability in dynamic environments.

[0062] In an optional embodiment, after the sub-task to be assigned is distributed to the target detection team, the method further includes: obtaining at least two instrument detection data points fed back by the target detection team for the sub-task to be assigned; the at least two instrument detection data points are collected from at least two detection points respectively; each instrument detection data point corresponds to at least two indicator dimensions; obtaining the dimension weights corresponding to the indicator dimensions and the point weights corresponding to the detection points; wherein, the dimension weights corresponding to the indicator dimensions are related to the indicator criticality of the indicator dimension and the task priority of the target detection task; the at least two instrument detection data points are weighted using the dimension weights and the point weights to obtain the instrument performance score corresponding to the sub-task to be assigned, and an instrument detection report is generated based on the instrument performance score.

[0063] Acquire multi-dimensional instrument testing data from multiple testing locations to ensure comprehensive data collection and spatial coverage. Instrument testing data refers to the raw data collected by the target testing team using testing instruments after performing the assigned testing sub-tasks; testing locations refer to the specific spatial positions where the testing tasks are performed; and indicator dimensions refer to parameters that measure the testing effectiveness from different technical perspectives. The indicator dimensions are related to the field involved in the assigned sub-tasks and the type of instrument being tested. For example, for medical CT scanners in the medical testing field, indicator dimensions could be radiation dose, image clarity, and temperature. The specific number of testing locations is not limited here and is determined based on actual business needs.

[0064] Obtain the dimension weights corresponding to the indicator dimensions and the point weights corresponding to the detection points. Dimension weights refer to the importance coefficients assigned to each indicator dimension based on indicator criticality and task priority; indicator criticality refers to the inherent influence of the indicator on the accuracy of the detection results. The linkage between dimension weights and task priority allows the evaluation criteria to dynamically adapt to the importance requirements of different tasks. Higher task priority will automatically increase the weight of key indicators, thereby strengthening the control over core quality.

[0065] After obtaining the dimensional and location weights, at least two sets of instrument detection data are weighted using these weights to obtain the instrument performance score corresponding to the sub-task to be assigned. An instrument detection report is then generated based on this score. Location weight refers to the influence coefficient assigned to each detection point based on its spatial importance and representativeness; instrument performance score is a quantitative evaluation of detection quality obtained through weighted calculation; and the instrument detection report is a formatted document generated based on the score. Location weights differentiate the value of data from different spatial locations, ensuring that detection data from key areas have a greater impact on the final score. Finally, a dual-weighting fusion process is used to integrate the scattered multidimensional data into a unified instrument performance score. This score reflects both the overall quality of the detection results and the strategic emphasis guided by task priorities.

[0066] Continuing with the example of a medical CT scanner in the medical testing field, we will explain how to determine the performance rating of the instrument. For instance, a medical CT scanner is tested using five detectors. The instrument test data provided by each detector includes three dimensions: radiation dose, image clarity, and equipment temperature. Each detector corresponds to one test point.

[0067] Radiation dose was defined as a target indicator dimension, and the following was adopted: Radiation dose is processed, among which, For the first Radiation dose at each detection point This is the average radiation dose. This represents the standard deviation of the radiation dose. The dimensional weights for radiation dose are used. Calculate the radiation dose score corresponding to the sub-task to be assigned. Among them, For the first The point weights of each detection data point are given. n is the total number of detection points. Based on the above method for determining radiation dose scores, the image sharpness score and equipment temperature score corresponding to the sub-task to be assigned can be determined. Then, by combining the radiation dose score, image sharpness score, and equipment temperature score, the instrument performance score of the sub-task to be assigned is determined.

[0068] Regarding dimensional weights, since radiation dose and image sharpness are more critical than device temperature, the dimensional weights for radiation dose and image sharpness can be set between 0.3 and 0.5, and the dimensional weight for device temperature can be set between 0.15 and 0.2. For tasks with high priority, such as those exceeding a priority threshold, the weights for radiation dose and image sharpness can be increased by 10%.

[0069] Regarding the point weights, since the detection point corresponding to detector 1 is more critical than the detection point corresponding to detector 2, the point weight corresponding to detector 1 can be set higher than the point weight corresponding to detector 2.

[0070] The aforementioned technical solution achieves precise quantitative evaluation of detection quality by acquiring multi-dimensional data from multiple detection points and introducing a dynamic weighting system linked to task priorities. This ensures the comprehensiveness and representativeness of the evaluation results, reflecting the detection characteristics of different spatial locations and covering multiple key technical indicators. By dynamically linking indicator weights to task priorities, the evaluation criteria can intelligently adapt to the importance requirements of different tasks, automatically increasing the assessment requirements of key indicators for high-priority tasks, thereby effectively guaranteeing the execution quality of important tasks. Through weighted fusion to generate a unified performance score and detection report, complex multi-source data is transformed into an intuitive and reliable evaluation basis. This not only achieves objective measurement of the performance of a single task but also provides data support for the continuous optimization of subsequent task allocation strategies, thereby comprehensively improving the quality management level and continuous improvement capability of the detection system.

[0071] Example 2

[0072] Figure 2 This is a flowchart of the instrument detection task processing method provided in Embodiment 2. This embodiment is a further optimization based on the above embodiments.

[0073] like Figure 2 As shown, the method includes:

[0074] S210. Based on the task description data of the target detection task and the team description data of the candidate detection teams, determine the task priority of the target detection task, the team score of the candidate detection teams, and the resource and time adaptability of the candidate detection teams to the target detection task.

[0075] S220. Based on the task priority of the target detection task, determine the resource adaptation threshold for the resource adaptation.

[0076] The resource suitability threshold refers to the minimum value used to determine whether resources are sufficient. This threshold is not fixed but dynamically determined based on task priority. This means that higher-priority tasks will have stricter resource requirements to ensure their execution reliability.

[0077] S230. Obtain the time adaptation threshold preset for the time adaptation and the task priority threshold preset for the task priority.

[0078] The time fit threshold is the baseline for judging whether the time meets the requirements, and it is usually preset based on the general performance of the system. The task priority threshold is the key dividing point for classifying the importance level of tasks.

[0079] S240. If the resource adaptability is less than the resource adaptability threshold, the time adaptability is less than the time adaptability threshold, or the task priority is greater than the task priority threshold, then the target detection task is determined to meet the task splitting condition.

[0080] When resource fit is below the resource fit threshold, it indicates that the available detection resources of a single detection team cannot meet the requirements of the target detection task, and the resource pressure needs to be alleviated by splitting the task. When time fit is below the time fit threshold, it indicates that a single detection team cannot complete the task independently within the time limit, and parallel processing needs to be achieved by splitting the task.

[0081] When a task's priority exceeds its threshold, the target detection task will be proactively split regardless of current resource or time constraints. This pre-defined risk diversification mechanism for critical tasks ensures high execution success rates by breaking them down into smaller, manageable parts.

[0082] S250. If the target detection task meets the task splitting condition, the target detection task is split based on the resource adaptability or the time adaptability to obtain the detection subtasks to be assigned.

[0083] S260. Based on the team score and the resource suitability of the candidate detection teams for the detection sub-tasks to be assigned, a target detection team is determined from the candidate detection teams, and the detection sub-tasks to be assigned are assigned to the target detection team.

[0084] Optional, based on Determine the parallel processing efficiency of the target detection team; among which, For the first The response speed of each target detection team. For the first The current load of each target detection team. and These are the response speed weight and the device compatibility weight, respectively. For the first The resource suitability of each target detection team for the assigned detection subtasks can be assessed. Based on parallel processing efficiency, the optimal team combination can be selected to maximize task completion efficiency.

[0085] Optionally, after assigning the subtasks to be distributed to the target detection teams, the team status of the target detection teams is monitored during the execution of the subtasks. The team status of the target detection teams can be reflected through the team description data. If an abnormal team status is detected in a target detection team, it is identified as an abnormal detection team, and the subtasks assigned to the abnormal detection team are also identified as abnormal subtasks.

[0086] An auxiliary detection team is selected from the backup detection teams (excluding the target detection team) of the candidate detection teams, and the abnormal sub-tasks are assigned to the auxiliary detection team. The selection logic for the auxiliary detection team is similar to that for the target detection team, and will not be elaborated here.

[0087] This application's technical solution dynamically links resource suitability thresholds with task priorities, subjecting high-priority tasks to stricter resource review standards and effectively ensuring the reliability of critical task execution. Employing a three-pronged judgment logic—resource suitability, time suitability, and task priority—it effectively identifies execution bottlenecks caused by insufficient resources or time constraints, and proactively triggers task splitting based on task importance to mitigate risk. This multi-dimensional evaluation mechanism overcomes the limitations of traditional single-threshold judgments, enabling the system to intelligently differentiate task requirements based on different priorities. While ensuring high-priority tasks receive sufficient resource guarantees, it effectively improves the accuracy of system resource allocation and the robustness of task execution, thereby achieving synergistic optimization of overall task processing efficiency and quality.

[0088] In an optional embodiment, determining the team score of the candidate detection team based on the task description data of the target detection task and the team description data of the candidate detection team includes: extracting the current team load, team capability score, historical experience score, team location, and priority score of the target detection task in the candidate detection team from the team description data; extracting the task location of the target detection task from the task description data; determining the distance score based on the task location and the team location; and determining the team score of the candidate detection team based on the current team load, team capability score, historical experience score, distance score, and priority score of the target detection task in the candidate detection team.

[0089] Among them, the team score refers to the overall evaluation of the candidate team obtained by combining the team's current load, team capability score, historical experience score, distance score and priority score, which reflects the degree to which the candidate detection team is suitable for performing the target detection task.

[0090] The team's current load, team capability score, historical experience score, and priority score of the object detection task among candidate detection teams are all extracted from the team description data. The distance score is a quantified value of geographical proximity calculated based on the location of the task and the location of the team. The task's location is extracted from the task description data.

[0091] The team's current load ensures its real-time availability and avoids overload allocation; the team's capability score and historical experience score assess its long-term reliability from technical and experiential perspectives; the distance score optimizes response time and execution cost; and the priority score reflects the strategic importance of the task within the team. This multi-dimensional integrated evaluation not only reflects the absolute capability of the candidate detection team but also dynamically considers both task-specific requirements and the team's real-time status. Optionally, the team score of a candidate detection team can be determined using the following formula based on the team's current load, team capability score, historical experience score, distance score, and the priority score of the target detection task within the candidate detection team. ;

[0092] in, This refers to the team score of the candidate testing teams; This refers to the team's current load. It can be determined based on the number of assigned tasks and equipment utilization; This refers to the team's ability rating. The assessment is based on factors such as the team's professional capabilities and the compatibility of the testing equipment. This refers to the team's experience rating. It can be determined based on the team's past performance in missions; This refers to the distance score; This refers to the priority score of the object detection task within the candidate detection team. Determined based on the urgency of the target detection task and customer needs; , , , and These are weights. The values ​​of these weights are not limited here; they can be determined based on the type of object detection task and business requirements. For example, =0.2, = 0.25, = 0.2, = 0.15, = 0.2.

[0093] The aforementioned technical solution, by constructing a multi-dimensional integrated evaluation system, achieves precise quantification of the comprehensive capabilities of candidate detection teams, realizes optimal matching between tasks and execution teams, and significantly improves system resource utilization efficiency, task execution success rate, and overall scheduling efficiency. Specifically, by integrating multiple parameters such as current team load, team capability score, historical experience score, distance score, and priority score of target detection tasks among candidate detection teams, the team score can simultaneously reflect the team's real-time status, technical capabilities, experience accumulation, and task adaptability. This effectively overcomes the limitations of traditional single-indicator evaluation, avoiding the risk of team overload due to neglecting real-time load, and ensuring the reliability of task execution through historical experience scores. In particular, by introducing a distance score calculated based on the location of tasks and teams, response efficiency and execution costs are significantly optimized. Simultaneously, the priority of tasks within teams is taken into consideration, ensuring that high-importance tasks are preferentially matched with teams that have higher priority.

[0094] In an optional embodiment, the task priority of the target detection task, and the resource and time adaptability of the candidate detection teams to the target detection task are determined based on the task description data of the target detection task and the team description data of the candidate detection teams. This includes: extracting customer priority, task completion time limit, task size, required detection resources, and task complexity from the task description data of the target detection task; extracting available detection resources, processing capacity limit, and team response speed from the team description data of the candidate detection teams; determining the task priority of the target detection task based on the customer priority, task completion time limit, task size, team response speed, required detection resources, available detection resources, task complexity, and processing capacity limit; determining the resource adaptability of the candidate detection teams to the target detection task based on the required detection resources and available detection resources; and determining the time adaptability of the candidate detection teams to the target detection task based on the task completion time limit, task size, and team response speed.

[0095] Among them, task priority is used to determine the importance level of the target detection task.

[0096] Optionally, the task priority of the object detection task can be determined using the following formula:

[0097] in, Prioritizing customers Determined based on contract terms or customer level; For the remaining time, , The time limit for task completion.

[0098] For the current time, The estimated completion time is... The determination is based on the task size and the team's response speed. The equipment resources required for target detection tasks. It can be determined based on the number of testing items or the type of instrument; This represents the maximum available equipment resources among the candidate testing teams, reflecting the degree of resource scarcity. For task complexity, It can be determined based on the number of testing items and accuracy requirements. The upper limit of processing capacity refers to the maximum complexity that a candidate detection team can support. , and The weights for customer priority, time margin, and equipment requirements are respectively optimized using machine learning models (such as linear regression) or expert rules.

[0099] Among them, the resource suitability of the candidate detection team for the target detection task refers to the degree of matching between the required detection resources and the available detection resources. Optionally, a method can be adopted... Determine the resource suitability of the candidate detection teams for the target detection task. For the first Available equipment resources for candidate testing teams. The equipment resources required for target detection tasks.

[0100] Time fit refers to the degree of alignment between the task completion deadline and the response capabilities of candidate testing teams after considering the task scale. Time fit comprehensively considers the time window stipulated by the task completion deadline, the theoretical workload derived from the task scale, and the processing capacity per unit time represented by the team's response speed to determine whether the team can complete the task before the deadline.

[0101] Optional, adopt Determine the time fit of candidate detection teams for the target detection task. Among these, ; The time limit for task completion. For the current time, The estimated completion time is... The determination is based on the task size and the team's response speed.

[0102] The aforementioned technical solution, by incorporating team response speed into task priority calculation, upgrades task importance assessment from a static attribute to a dynamic feasibility judgment. This ensures that high-priority tasks are automatically matched with the resources possessing the strongest execution capabilities, significantly improving the ability to guarantee the completion of critical tasks. Secondly, by employing a precise demand and supply comparison mechanism to calculate resource suitability, a strict technical feasibility threshold is established, fundamentally preventing task execution failures due to resource mismatch. Simultaneously, by comprehensively verifying time suitability by considering task completion deadlines, task scale, and team response speed, precise matching of time windows and execution capabilities is achieved, effectively preventing the risk of task overdue. This overcomes the shortcomings of traditional methods that rely on a single element, comprehensively improving system resource utilization efficiency, task execution success rate, and the scientific nature of scheduling decisions.

[0103] Example 3

[0104] Figure 3 This is a schematic diagram of the instrument testing task processing device provided in Embodiment 3 of this application. This embodiment is applicable to situations where instrument testing tasks are assigned. The device can be implemented by software and / or hardware and can be integrated into electronic devices such as smart terminals.

[0105] like Figure 3 As shown, the instrument detection task processing device 300 may include:

[0106] The information determination module 310 is used to determine the task priority of the target detection task, the team score of the candidate detection team, and the resource suitability and time suitability of the candidate detection team to the target detection task based on the task description data of the target detection task and the team description data of the candidate detection team.

[0107] The splitting judgment module 320 is used to determine whether the target detection task meets the task splitting conditions based on the resource adaptability, the time adaptability and the task priority of the target detection task;

[0108] The task splitting module 330 is used to split the target detection task based on the resource adaptability or the time adaptability if the target detection task meets the task splitting conditions, so as to obtain the detection sub-tasks to be assigned.

[0109] The task allocation module 340 is used to determine the target detection team from the candidate detection teams based on the team score and the resource suitability of the candidate detection teams to the detection sub-task to be allocated, and to issue the detection sub-task to be allocated to the target detection team.

[0110] This technical solution achieves intelligent and optimized task allocation by comprehensively calculating task priority, team score, resource suitability, and time suitability based on task description data of target detection tasks and team description data of candidate detection teams. First, by evaluating resource suitability and time suitability in conjunction with task priority, it dynamically determines whether a task meets the splitting conditions. This allows for reasonable splitting when a task is too complex or has excessively high resource requirements, generating sub-tasks to be assigned. This effectively avoids inefficiency caused by insufficient resources or time for individual teams, improves the feasibility of task execution and system throughput, and ensures that highly important tasks can be reliably executed through the splitting mechanism. Second, after task splitting, target detection teams are selected based on team score and resource suitability, ensuring that sub-tasks are assigned to teams with matching capabilities and sufficient resources. This not only optimizes resource utilization and reduces the risk of idleness or overload but also ensures the quality and timeliness of high-importance tasks through priority and scoring mechanisms. Overall, this solution enhances the flexibility and adaptability of task allocation, enabling efficient and reliable task execution in changing environments, thereby improving the overall performance and scalability of the detection system.

[0111] Optionally, the information determination module 310 includes: a task information extraction submodule, used to extract customer priority, task completion time limit, task size, required detection resources, and task complexity from the task description data of the target detection task; a team information extraction submodule, used to extract available detection resources, processing capacity limit, and team response speed from the team description data of the candidate detection teams; a task priority determination submodule, used to determine the task priority of the target detection task based on the customer priority, the task completion time limit, the task size, the team response speed, the required detection resources, the available detection resources, the task complexity, and the processing capacity limit; a resource suitability determination submodule, used to determine the resource suitability of the candidate detection team for the target detection task based on the required detection resources and the available detection resources; and a time suitability determination submodule, used to determine the time suitability of the candidate detection team for the target detection task based on the task completion time limit, the task size, and the team response speed.

[0112] Optionally, the split judgment module 320 includes: a first threshold determination submodule, used to determine a resource adaptability threshold for the resource adaptability based on the task priority of the target detection task;

[0113] The second threshold acquisition submodule is used to acquire a time adaptation threshold preset for the time adaptation and a task priority threshold preset for the task priority; the split judgment submodule is used to determine that the target detection task meets the task splitting condition if the resource adaptation is less than the resource adaptation threshold, the time adaptation is less than the time adaptation threshold, or the task priority is greater than the task priority threshold.

[0114] Optionally, the task splitting module 330 includes: a splitting quantity determination submodule, used to determine the task splitting quantity of the target detection task based on the resource adaptability or the time adaptability; and a task splitting submodule, used to split the target detection task based on the task splitting quantity to obtain the detection subtasks to be assigned.

[0115] Optionally, the task allocation module 340 includes: an allocation order determination submodule, used to determine the team priority of the target detection team and determine the task allocation order for the target detection team based on the team priority and the team score; an allocation quantity determination submodule, used to determine the task allocation quantity for the target detection team based on the resource adaptability of the target detection team to the detection sub-tasks to be allocated; and a task allocation submodule, used to distribute the detection sub-tasks to be allocated to the target detection team based on the task allocation order and the task allocation quantity; wherein, the team priority of the target detection team is determined based on the resource adaptability of the target detection team to the detection sub-tasks to be allocated, the task priority of the target detection task, and the load throughput of the target detection team; and the load throughput is determined based on the team response speed and the current load of the team in the team description data.

[0116] Optionally, the information determination module 310 includes: a team priority determination submodule, used to extract from the team description data the current load, team capability score, historical experience score, team location, and priority score of the target detection task in the candidate detection team; a task location determination submodule, used to extract the task location of the target detection task from the task description data; a distance score determination submodule, used to determine the distance score based on the task location and the team location; and a team score determination submodule, used to determine the team score of the candidate detection team based on the current load, team capability score, historical experience score, distance score, and priority score of the target detection task in the candidate detection team.

[0117] Optionally, the device further includes: a detection data acquisition module, configured to acquire at least two instrument detection data points fed back by the target detection team for the sub-task to be assigned after the sub-task to be assigned is issued to the target detection team; the at least two instrument detection data points are collected from at least two detection points respectively; each instrument detection data point corresponds to at least two indicator dimensions; a point weight acquisition submodule, configured to acquire the dimension weight corresponding to the indicator dimension and the point weight corresponding to the detection point; wherein the dimension weight corresponding to the indicator dimension is related to the indicator criticality of the indicator dimension and the task priority of the target detection task; and a performance score determination submodule, configured to perform weighted processing on the at least two instrument detection data points using the dimension weight and the point weight to obtain the instrument performance score corresponding to the sub-task to be assigned, and generate an instrument detection report based on the instrument performance score.

[0118] The instrument testing task processing device provided in the embodiments of the invention can execute the instrument testing task processing method provided in any embodiment of this application, and has the corresponding performance modules and beneficial effects for executing the instrument testing task processing method.

[0119] Example 4

[0120] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0121] Figure 4 A schematic diagram of an electronic device 410, which can be implemented using an embodiment, is shown. The electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412, a random access memory (RAM) 413, etc., communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0122] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0123] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as instrument detection task processing methods.

[0124] In some embodiments, the instrument detection task processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the instrument detection task processing method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the instrument detection task processing method by any other suitable means (e.g., by means of firmware).

[0125] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, special-purpose computer, or other programmable instrument detection task processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0127] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0129] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as an instrument testing task processing server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0130] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0131] This application also discloses a computer program product, which includes a computer program that, when executed by a processor, implements the instrument detection task processing method provided in any embodiment of this application. This program product shares the same inventive concept as the instrument detection task processing methods disclosed in the embodiments of this application, and therefore will not be described in detail here.

[0132] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0133] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An apparatus for detecting a task processing method, characterized by, The method comprises: determining a task priority of the target detection task, a team score of the candidate detection team, and a resource adaptation degree and a time adaptation degree of the candidate detection team to the target detection task based on task description data of the target detection task and team description data of the candidate detection team; determining whether the target detection task meets a task splitting condition based on the resource adaptation degree, the time adaptation degree, and the task priority of the target detection task; if the target detection task meets the task splitting condition, splitting the target detection task based on the resource adaptation degree or the time adaptation degree to obtain a to-be-assigned detection subtask; determining a target detection team from the candidate detection team based on the team score and the resource adaptation degree of the candidate detection team to the to-be-assigned detection subtask, and assigning the to-be-assigned detection subtask to the target detection team.

2. The method of claim 1, wherein, determining a task priority of the target detection task, and a resource adaptation degree and a time adaptation degree of the candidate detection team to the target detection task based on task description data of the target detection task and team description data of the candidate detection team, comprises: extracting a customer priority, a task completion deadline, a task size, required detection resources, and a task complexity from the task description data of the target detection task; extracting available detection resources, a processing capability upper limit, and a team response speed from the team description data of the candidate detection team; determining the task priority of the target detection task based on the customer priority, the task completion deadline, the task size, and the team response speed, the required detection resources and the available detection resources, and the task complexity and the processing capability upper limit; determining the resource adaptation degree of the candidate detection team to the target detection task based on the required detection resources and the available detection resources; determining the time adaptation degree of the candidate detection team to the target detection task based on the task completion deadline, the task size, and the team response speed.

3. The method of claim 1, wherein, The determination of whether the target detection task meets a task splitting condition based on the resource adaptation degree, the time adaptation degree, and the task priority of the target detection task comprises: determining a resource adaptation degree threshold value for the resource adaptation degree based on the task priority of the target detection task; obtaining a time adaptation degree threshold value pre-set for the time adaptation degree, and a task priority threshold value pre-set for the task priority; if the resource adaptation degree is less than the resource adaptation degree threshold value, the time adaptation degree is less than the time adaptation degree threshold value, or the task priority is greater than the task priority threshold value, it is determined that the target detection task meets the task splitting condition.

4. The method of claim 1, wherein, The splitting of the target detection task based on the resource adaptation degree or the time adaptation degree to obtain a to-be-assigned detection subtask comprises: determining a task splitting number of the target detection task according to the resource adaptation degree or the time adaptation degree; splitting the target detection task based on the task splitting number to obtain the to-be-assigned detection subtask.

5. The method of claim 1, wherein, The method comprises the following steps: determining a team priority of the target detection team, and determining a task allocation sequence for the target detection team based on the team priority and the team score; determining a task allocation quantity for the target detection team based on the resource adaptation degree of the target detection team to the to-be-allocated detection subtask; determining a task allocation quantity for the target detection team based on the resource adaptation degree of the target detection team to the to-be-allocated detection subtask; determining a task allocation quantity for the target detection team based on the task allocation sequence and the task allocation quantity; 6. The method of claim 1, wherein, wherein the team priority of the target detection team is determined based on the resource adaptation degree of the target detection team to the to-be-allocated detection subtask, the task priority of the target detection task, and the load throughput of the target detection team; and the load throughput is determined based on a team response speed and a team current load in the team description data. The method comprises the following steps: extracting a team current load, a team capability score, a historical experience score, a team location, and a priority score of the target detection task in the candidate detection team from the team description data; extracting a task location of the target detection task from the task description data; determining a distance score based on the task location and the team location; 7. The method of claim 1, wherein, determining the team score of the candidate detection team based on the team current load, the team capability score, the historical experience score, the distance score, and the priority score of the target detection task in the candidate detection team. After the to-be-allocated detection subtask is allocated to the target detection team, the method further comprises the following steps: obtaining at least two pieces of instrument detection data fed back by the target detection team for the to-be-allocated detection subtask; the at least two pieces of instrument detection data are respectively collected from at least two detection points; each piece of instrument detection data corresponds to at least two index dimensions; obtaining a dimension weight corresponding to the index dimension and a point weight corresponding to the detection point; wherein the dimension weight corresponding to the index dimension is related to an index key degree of the index dimension and a task priority of the target detection task; 8. An apparatus for detecting a task processing device, characterized by comprising: performing weighted processing on the at least two pieces of instrument detection data by using the dimension weight and the point weight to obtain an instrument performance score corresponding to the to-be-allocated detection subtask, and generating an instrument detection report based on the instrument performance score. The device comprises: an information determination module configured to determine a task priority of a target detection task, a team score of a candidate detection team, and a resource adaptation degree and a time adaptation degree of the candidate detection team to the target detection task based on task description data of the target detection task and team description data of the candidate detection team; The splitting judgment module is configured to determine whether the target detection task meets a task splitting condition based on the resource adaptation degree, the time adaptation degree, and a task priority of the target detection task. The task splitting module is configured to split the target detection task based on the resource adaptation degree or the time adaptation degree to obtain a to-be-assigned detection subtask if the target detection task meets the task splitting condition. The task assignment module is configured to determine a target detection team from the candidate detection teams based on the team score and a resource adaptation degree of the to-be-assigned detection subtask to the candidate detection teams, and assign the to-be-assigned detection subtask to the target detection team.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the instrument detection task processing method of any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the instrument detection task processing method of any one of claims 1-7.

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