CN113723936B - Quality supervision and management method and system for electric power engineering - Google Patents
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Abstract
The application discloses a quality supervision and management method and system for electric power engineering, wherein the method comprises the following steps: receiving a quality supervision task plan uploaded by a client; determining whether the quality supervision task schedule has a matched quality supervision task item to be processed; if yes, all sub-tasks to be distributed in the quality supervision task schedule are distributed to at least one task execution client respectively; receiving subtask execution data uploaded by a task execution client, and executing an approval process according to a preset subtask approval process; the application improves the quality supervision work efficiency of the power engineering by intelligently auditing the quality supervision task schedule and efficiently distributing a plurality of subtasks in the quality supervision task schedule.
Description
Technical Field
The application relates to the technical field of power engineering quality supervision, in particular to a power engineering quality supervision management method and system.
Background
The electric power engineering relates to social public safety and benefits, namely the electric power engineering has the characteristics of safety, applicability, stability and the like, is particularly important to ensure that the quality management and control of electric power engineering project are well ensured along with the continuous promotion of the construction of the electric network in China, has higher requirements on supervision and management departments and inspection professionals, and most working links still adopt manual treatment at present. For the supervision and management work, the work development cannot quickly obtain response, each flow link is connected with hysteresis, and the distribution and execution condition management and supervision efficiency of each subtask is low, so that the quality supervision work of the power engineering cannot be completed timely and efficiently.
Disclosure of Invention
Aiming at the problems existing in the prior art, the application provides a quality supervision and management method and a system for electric power engineering, which are used for realizing the improvement of the quality supervision work efficiency of the electric power engineering by intelligently auditing a quality supervision task schedule and efficiently distributing a plurality of subtasks in the quality supervision task schedule. The technical scheme is as follows:
in a first aspect, a method for quality supervision and management of electric power engineering is provided, the method comprising:
receiving a quality supervision task plan uploaded by a client;
determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
if yes, all sub-tasks to be distributed in the quality supervision task schedule are distributed to at least one task execution client respectively;
receiving subtask execution data uploaded by a task execution client, and executing an approval process according to a preset subtask approval process;
and entering a next sub-task node in the sub-task or judging whether to enter the next sub-task node in the sub-task based on the approval result.
In one possible implementation manner, the allocating all sub-tasks to be allocated in the quality supervision task schedule to at least one task execution client respectively includes:
acquiring a plurality of matched task execution clients according to the type of the subtasks to be distributed;
determining the matching degree of the task execution client and the subtasks to be distributed based on historical task execution data of the task execution client and the task data to be processed;
and determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree.
In a possible implementation manner, the determining, based on the matching degree size, the allocation priority of the subtasks to be allocated to each task execution client, further includes:
when a task execution client corresponding to the current priority does not accord with the execution constraint condition of the subtask to be allocated, determining the task execution client for executing the subtask to be allocated in the next priority of the current priority;
and when determining that all the task execution clients with the priorities do not meet the execution constraint conditions of the subtasks to be distributed, starting a task distribution standby mode, and re-determining the distribution priorities of the subtasks to be distributed to each task execution client.
In one possible implementation manner, the determining that the task execution client corresponding to the current priority cannot execute the subtask includes:
acquiring the starting time of executing the subtask to be distributed by the task execution client corresponding to the current priority, and determining that the subtask to be distributed cannot be executed by the task execution client corresponding to the current priority when the limited execution time of the subtask to be distributed is earlier than the starting time.
In one possible implementation manner, the starting the task allocation standby mode, redefining allocation priorities of the subtasks to be allocated to each task execution client, includes:
determining an execution time period of a task to be processed of a task execution client based on historical task execution data of the task execution client;
determining the order of the subtasks to be allocated in the tasks to be processed according to the importance level of the subtasks to be allocated and the importance level of the tasks to be processed of the task execution client;
predicting an execution time period of the subtasks to be distributed based on the redetermined execution sequence of the subtasks to be processed;
and re-determining the allocation priority of the subtasks to each task execution client based on the predicted execution time period of the subtasks to be allocated.
In one possible implementation manner, the determining, based on the historical task execution data of the task execution client and the task data to be processed, the matching degree of the task execution client and the subtasks to be allocated includes:
predicting task execution data in a second time period in the future based on task execution data in the first time period in the history;
and determining task execution data of the task execution client for the task data to be processed based on the task execution data in the second time period, and determining the matching degree of the task execution client and the subtasks based on the task execution data of the task data to be processed.
In one possible implementation manner, the task execution data in the second time period is predicted based on the task execution data in the first time period, and the task execution data prediction model is completed by training, and the training of the task execution data prediction model includes:
acquiring actual task execution data of a third time period and actual task execution data of a fourth time period based on the historical task execution data;
extracting each subtask execution duration characteristic and execution starting time characteristic of the task execution client based on actual task execution data of a third time period;
inputting an input feature vector formed by the execution duration feature and the execution start time feature of the third time period into a task execution data prediction model to obtain predicted task execution data of a fourth time period output by the model;
and updating the model parameters based on the actual task execution data and the predicted task execution data in the fourth time period to obtain a task execution data prediction model with completed training.
In a possible implementation manner, the determining, based on the task execution data in the second period of time, task execution data of the task execution client for the task data to be processed, and determining, based on the task execution data of the task data to be processed, a matching degree between the task execution client and the subtasks to be allocated, includes:
determining the execution duration and the execution starting time of each subtask in a second time period in the future based on the task execution data in the second time period;
determining the execution ending time of each task to be processed based on the execution duration and the execution starting time of each subtask, the number of the tasks to be processed and the execution sequence in the second time period;
and determining the matching degree of the task execution client and the subtasks based on the sequence of the final end time of all the tasks to be processed.
In one possible implementation manner, the determining, based on the historical task execution data of the task execution client and the task data to be processed, the matching degree between the task execution client and the subtasks to be allocated further includes: according to the historical task execution data of each task execution client, determining the time length and task execution stability of each task execution client for executing the completed subtasks, and determining the matching degree of the task execution client and the subtasks to be distributed based on the task execution stability, wherein the determining the execution stability of each task execution client comprises:
based on task execution data in a historical first time period, determining whether each executed task is in a completion state and is completed independently according to each executed task in a task execution sequence, and acquiring an execution time period and an execution sequence number of the executed task which is completed independently;
for the independently completed executed tasks, acquiring a time length value of an execution time period of each task, determining the maximum value of all the time length values based on the time length value, and determining the first task execution stability of each task execution client according to the ratio of the maximum value to the time length value of each task execution time period;
determining a second task execution stability of each task execution client based on the distribution dispersion of the execution sequence numbers of the independently completed executed tasks;
and determining the task execution stability of each task execution client based on the first task execution stability and the second task execution stability.
In a second aspect, there is provided a power engineering quality supervision and management system, comprising:
the quality supervision task schedule receiving module is used for receiving the quality supervision task schedule uploaded by the client;
the quality supervision task schedule auditing module is used for determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
the quality supervision subtask allocation module is used for allocating all subtasks to be allocated in the quality supervision task schedule to at least one task execution client respectively;
the quality supervision subtask execution auditing module is used for receiving the subtask execution data uploaded by the task execution client and executing an approval process according to a preset subtask approval process;
and the quality supervision subtask execution module is used for controlling a next subtask node entering the subtask or controlling whether to enter the next subtask node in the subtask based on an approval result.
The method and the system for monitoring and managing the quality of the power engineering have the following beneficial effects:
1. after the quality supervision task schedule is determined to have the matched quality supervision task items to be processed, all subtasks to be distributed in the quality supervision task schedule are distributed to at least one task execution client respectively, subtask execution data uploaded by the task execution client are received, an approval process is executed according to a preset subtask approval process, a next subtask node in the subtasks is entered, or whether the next subtask node in the subtasks is entered is judged based on an approval result. The method realizes the auditing of the quality supervision task schedule and the unified management of the distribution, execution and auditing flow of all tasks to be distributed in the quality supervision task schedule.
2. According to the application, all subtasks to be distributed in a quality supervision task schedule are distributed to a task execution client, and the matching degree of the task execution client and the subtasks to be distributed is determined based on historical task execution data of the task execution client and the task data to be processed; and determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree. The method and the device realize matching and distributing the subtasks to be distributed according to the task execution capacity of each task execution client so as to realize the distribution balance of all the subtasks to be distributed in the quality supervision task schedule and avoid the problem of low execution efficiency of each subtask to be distributed caused by unreasonable distribution of the subtasks to be distributed.
Drawings
FIG. 1 is a flow chart of a method for quality supervision and management of electrical power engineering in an embodiment of the application;
FIG. 2 is a flowchart of a method for determining a matching degree between a task execution client and a subtask according to an embodiment of the present application;
FIG. 3 is a flow chart of a method for determining task execution stability of each task execution client in an embodiment of the present application;
fig. 4 is a block diagram of a quality supervision and management system for electric power engineering according to an embodiment of the present application.
Detailed Description
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
The embodiment of the application provides a quality supervision and management method for electric power engineering, which comprises the following steps:
receiving a quality supervision task plan uploaded by a client;
determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
if yes, all sub-tasks to be distributed in the quality supervision task schedule are distributed to at least one task execution client respectively;
receiving subtask execution data uploaded by a task execution client, and executing an approval process according to a preset subtask approval process;
and entering a next sub-task node in the sub-task or judging whether to enter the next sub-task node in the sub-task based on the approval result.
Further, the determining whether the quality supervision task schedule has the matched quality supervision task item to be processed includes:
and if the quality supervision task schedule does not have the matched quality supervision task project to be processed, feeding back the quality supervision task schedule to the client.
In the embodiment of the application, the task schedule uploaded by the client is checked and confirmed firstly, and the uploaded task schedule is determined to be designed for the task project with the quality supervision to be processed in advance, so that the sub-task allocation of the invalid task schedule is avoided, and the management efficiency of the sub-task allocation and execution is improved.
Further, the foregoing allocating all sub-tasks to be allocated in the quality supervision task schedule to at least one task execution client respectively includes:
acquiring a plurality of matched task execution clients according to the type of the subtasks to be distributed;
determining the matching degree of the task execution client and the subtasks to be distributed based on historical task execution data of the task execution client and the task data to be processed;
and determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree.
In the embodiment of the application, after a plurality of decomposed subtasks are acquired based on a task schedule uploaded by a client, the subtasks are matched and distributed to a plurality of task execution clients, whether the subtasks are arranged to each task execution client is determined according to task execution history data, current execution conditions and current task conditions of each task execution client.
Further, the determining, based on the matching degree, the allocation priority of the subtasks to be allocated to each task execution client, further includes:
when a task execution client corresponding to the current priority does not accord with the execution constraint condition of the subtask to be allocated, determining the task execution client for executing the subtask to be allocated in the next priority of the current priority;
and when determining that all the task execution clients with the priorities do not meet the execution constraint conditions of the subtasks to be distributed, starting a task distribution standby mode, and re-determining the distribution priorities of the subtasks to be distributed to each task execution client.
In the embodiment of the application, the task of each task execution client is allocated, the priority of the task execution client for executing the subtasks is determined according to the historical task execution data and the task data to be processed of each task execution client, and further the task execution clients meeting the conditions are searched successively according to the priority of each subtask according to the execution constraint condition of each subtask, so that the allocation efficiency and the task execution efficiency of each subtask for monitoring the quality of the electric power engineering are improved, and the management efficiency of monitoring the quality of the electric power engineering is improved.
Further, the determining that the task execution client corresponding to the current priority cannot execute the subtask includes:
acquiring the starting time of executing the subtask to be distributed by the task execution client corresponding to the current priority, and determining that the subtask to be distributed cannot be executed by the task execution client corresponding to the current priority when the limited execution time of the subtask to be distributed is earlier than the starting time.
The subtask execution constraint conditions may be execution qualification, execution difficulty, execution location distance between a subtask to be allocated and other subtasks, and the like, in the embodiment of the present application, a subtask execution preset time period is used as a first priority subtask execution constraint condition, further, a time period of executing the subtask by the subtask execution client is determined according to the historical task execution data of each task execution client and the to-be-processed task data, when the starting time of executing the subtask by the task execution client is later than the limited execution starting time of the subtask to be allocated, it is determined that the task execution client corresponding to the current priority cannot execute the subtask to be allocated, further, in the task execution clients whose starting time of executing the subtask to be allocated meets the limited execution starting time of the subtask to be allocated, according to the historical task execution data of each task execution client, the time length and the task execution stability of each task execution client of executing the subtask are determined, and the ending time of executing the subtask by each task execution client is predicted, and whether the ending time of executing the subtask to be allocated is met or not is determined according to the predicted ending time of the ending time of executing the task execution client.
Furthermore, on the basis of taking the subtask execution preset time period as a first priority subtask execution constraint condition, taking the execution qualification as a second priority subtask execution constraint condition, namely on the basis of meeting the first priority subtask execution constraint condition, determining whether the execution qualification of each task execution client meets the execution qualification requirement of the subtask to be allocated.
Further, the above-mentioned starting task allocation standby mode, redetermining the allocation priority of the subtasks to be allocated to each task execution client, includes:
determining an execution time period of a task to be processed of a task execution client based on historical task execution data of the task execution client;
determining the order of the subtasks to be allocated in the tasks to be processed according to the importance level of the subtasks to be allocated and the importance level of the tasks to be processed of the task execution client;
predicting an execution time period of the subtasks to be distributed based on the redetermined execution sequence of the subtasks to be processed;
and re-determining the allocation priority of the subtasks to each task execution client based on the predicted execution time period of the subtasks to be allocated.
In the embodiment of the application, a task allocation standby mode is started, namely, the subtasks to be allocated are inserted into an original to-be-processed task queue of a task execution client according to the importance level of the subtasks to be allocated, the sequence of each subtask to be allocated in the to-be-processed task queue of the task execution client is determined, and the allocation priority of the subtasks to be allocated to each task execution client is redetermined. Further, based on the redetermined allocation priority of the subtasks to be allocated, whether the task execution client corresponding to the current priority accords with the subtask execution constraint condition can be judged one by one, specifically, when the subtask execution preset time period to be allocated is taken as the subtask execution constraint condition, whether the task execution client corresponding to the current priority accords with the subtask execution constraint condition can be determined according to the predicted execution time period of each task execution client for executing the subtask to be allocated.
The determining the matching degree between the task execution client and the subtasks to be allocated based on the historical task execution data and the task data to be processed of the task execution client includes:
predicting task execution data in a second time period in the future based on task execution data in the first time period in the history;
and determining task execution data of the task execution client for the task data to be processed based on the task execution data in the second time period, and determining the matching degree of the task execution client and the subtasks based on the task execution data of the task data to be processed.
In the embodiment of the application, the task execution data in the historical first time period comprises the attribute of each task, the execution start time, the execution end time and the like of the task execution client to each task, the task execution condition of the task execution client in a future second time period is predicted according to the task execution data in the historical first time period, the task execution condition comprises the number of tasks which can be executed in the future second time period, the time period of task execution on each execution sequence and the like, the task execution condition of the task execution client to the task to be processed can be determined based on the task execution data in the second time period, and the matching degree of the task execution client to execute the subtasks to be allocated outside the task to be processed is further determined.
Further, the task execution data in the second time period is predicted based on the task execution data in the first time period, and the task execution data prediction model is used for completing training, wherein the training of the task execution data prediction model comprises the following steps:
acquiring actual task execution data of a third time period and actual task execution data of a fourth time period based on the historical task execution data;
extracting each subtask execution duration characteristic and execution starting time characteristic of the task execution client based on actual task execution data of a third time period;
inputting an input feature vector formed by the execution duration feature and the execution start time feature of the third time period into a task execution data prediction model to obtain predicted task execution data of a fourth time period output by the model;
and updating the model parameters based on the actual task execution data and the predicted task execution data in the fourth time period to obtain a task execution data prediction model with completed training.
Further, the determining task execution data of the task execution client for the task data to be processed based on the task execution data in the second time period, and determining the matching degree between the task execution client and the subtask to be allocated based on the task execution data of the task data to be processed, includes:
determining the execution duration and the execution starting time of each subtask in a second time period in the future based on the task execution data in the second time period;
determining the execution ending time of each task to be processed based on the execution duration and the execution starting time of each subtask, the number of the tasks to be processed and the execution sequence in the second time period;
and determining the matching degree of the task execution client and the subtasks based on the sequence of the final end time of all the tasks to be processed.
Further, the determining the matching degree between the task execution client and the subtask to be allocated based on the historical task execution data of the task execution client and the task data to be processed further includes: according to the historical task execution data of each task execution client, determining the time length and task execution stability of each task execution client for executing the completed subtasks, and determining the matching degree of the task execution client and the subtasks to be distributed based on the task execution stability, wherein the determining the execution stability of each task execution client comprises:
based on task execution data in the historical time period, determining whether each executed task is in a completion state and is completed independently according to each executed task in the task execution sequence, and acquiring the execution time period and the execution sequence number of the executed task which is completed independently;
for the independently completed executed tasks, acquiring a time length value of an execution time period of each task, determining the maximum value of all the time length values based on the time length value, and determining the first task execution stability of each task execution client according to the ratio of the maximum value to the time length value of each task execution time period;
determining a second task execution stability of each task execution client based on the distribution dispersion of the execution sequence numbers of the independently completed executed tasks;
and determining the task execution stability of each task execution client based on the first task execution stability and the second task execution stability.
Specifically, in the embodiment of the present application, task execution data in a second time period in the future is predicted for each task execution client by using a task execution data prediction model after training, so as to determine a time period in which the task execution client executes the subtask to be allocated, and the matching degree between the task execution client and the subtask to be allocated is determined in a combined manner according to the time period in which the task execution client executes the subtask to be allocated and the task execution stability of the task execution client.
The embodiment of the application also provides a power engineering quality supervision and management system, which comprises:
the quality supervision task schedule receiving module is used for receiving the quality supervision task schedule uploaded by the client;
the quality supervision task schedule auditing module is used for determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
the quality supervision subtask allocation module is used for allocating all subtasks to be allocated in the quality supervision task schedule to at least one task execution client respectively;
the quality supervision subtask execution auditing module is used for receiving the subtask execution data uploaded by the task execution client and executing an approval process according to a preset subtask approval process;
and the quality supervision subtask execution module is used for controlling a next subtask node entering the subtask or controlling whether to enter the next subtask node in the subtask based on an approval result.
The specific limitation regarding a power engineering quality supervision and management system may be referred to above as limitation regarding a power engineering quality supervision and management method, and will not be described herein. Each module in the above-mentioned power engineering quality supervision and management system may be implemented in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above units.
The present application is not limited to the above-described specific embodiments, and various modifications may be made by those skilled in the art without inventive effort from the above-described concepts, and are within the scope of the present application.
Claims (8)
1. The quality supervision and management method for the electric power engineering is characterized by comprising the following steps of:
receiving a quality supervision task plan uploaded by a client;
determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
if yes, all sub-tasks to be distributed in the quality supervision task schedule are distributed to at least one task execution client respectively;
receiving subtask execution data uploaded by a task execution client, and executing an approval process according to a preset subtask approval process;
entering a next sub-task node in the sub-task or judging whether to enter the next sub-task node in the sub-task based on an approval result;
the step of respectively distributing all the subtasks to be distributed in the quality supervision task schedule to at least one task execution client comprises the following steps:
acquiring a plurality of matched task execution clients according to the type of the subtasks to be distributed;
determining the matching degree of the task execution client and the subtasks to be distributed based on historical task execution data of the task execution client and the task data to be processed;
determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree;
the determining the matching degree between the task execution client and the subtasks to be distributed based on the historical task execution data of the task execution client and the task data to be processed further comprises: according to the historical task execution data of each task execution client, determining the time length and task execution stability of each task execution client for executing the completed subtasks, and determining the matching degree of the task execution client and the subtasks to be distributed based on the task execution stability, wherein the determining the execution stability of each task execution client comprises:
based on task execution data in a historical first time period, determining whether each executed task is in a completion state and is completed independently according to each executed task in a task execution sequence, and acquiring an execution time period and an execution sequence number of the executed task which is completed independently;
for the independently completed executed tasks, acquiring a time length value of an execution time period of each task, determining the maximum value of all the time length values based on the time length value, and determining the first task execution stability of each task execution client according to the ratio of the maximum value to the time length value of each task execution time period;
determining a second task execution stability of each task execution client based on the distribution dispersion of the execution sequence numbers of the independently completed executed tasks;
and determining the task execution stability of each task execution client based on the first task execution stability and the second task execution stability.
2. The power engineering quality supervision and management method according to claim 1, wherein the determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree size further comprises:
when a task execution client corresponding to the current priority does not accord with the execution constraint condition of the subtask to be allocated, determining the task execution client for executing the subtask to be allocated in the next priority of the current priority;
and when determining that all the task execution clients with the priorities do not meet the execution constraint conditions of the subtasks to be distributed, starting a task distribution standby mode, and re-determining the distribution priorities of the subtasks to be distributed to each task execution client.
3. The method for quality supervision and management of electrical power engineering according to claim 2, wherein the determining that the task execution client corresponding to the current priority cannot execute the subtask includes:
acquiring the starting time of executing the subtask to be distributed by the task execution client corresponding to the current priority, and determining that the subtask to be distributed cannot be executed by the task execution client corresponding to the current priority when the limited execution time of the subtask to be distributed is earlier than the starting time.
4. The power engineering quality supervision and management method according to claim 2, wherein the starting the task allocation standby mode to redetermine allocation priorities of the sub-tasks to be allocated to each task execution client includes:
determining an execution time period of a task to be processed of a task execution client based on historical task execution data of the task execution client;
determining the order of the subtasks to be allocated in the tasks to be processed according to the importance level of the subtasks to be allocated and the importance level of the tasks to be processed of the task execution client;
predicting an execution time period of the subtasks to be distributed based on the redetermined execution sequence of the subtasks to be processed;
and re-determining the allocation priority of the subtasks to each task execution client based on the predicted execution time period of the subtasks to be allocated.
5. The method for quality supervision and management of electrical engineering according to claim 1, wherein determining the matching degree of the task execution client and the subtasks to be allocated based on the historical task execution data and the task data to be processed of the task execution client comprises:
predicting task execution data in a second time period in the future based on task execution data in the first time period in the history;
and determining task execution data of the task execution client for the task data to be processed based on the task execution data in the second time period, and determining the matching degree of the task execution client and the subtasks based on the task execution data of the task data to be processed.
6. The method according to claim 5, wherein the task execution data in the second time period is predicted based on task execution data in the first time period, and the task execution data prediction model is trained by using a task execution data prediction model that is completed, and the training of the task execution data prediction model includes:
acquiring actual task execution data of a third time period and actual task execution data of a fourth time period based on the historical task execution data;
extracting each subtask execution duration characteristic and execution starting time characteristic of the task execution client based on actual task execution data of a third time period;
inputting an input feature vector formed by the execution duration feature and the execution start time feature of the third time period into a task execution data prediction model to obtain predicted task execution data of a fourth time period output by the model;
and updating the model parameters based on the actual task execution data and the predicted task execution data in the fourth time period to obtain a task execution data prediction model with completed training.
7. The method of claim 5, wherein determining task execution data of the task execution client for the task data to be processed based on the task execution data in the second period of time, determining a matching degree of the task execution client and the sub-task to be allocated based on the task execution data of the task data to be processed, includes:
determining the execution duration and the execution starting time of each subtask in a second time period in the future based on the task execution data in the second time period;
determining the execution ending time of each task to be processed based on the execution duration and the execution starting time of each subtask, the number of the tasks to be processed and the execution sequence in the second time period;
and determining the matching degree of the task execution client and the subtasks based on the sequence of the final end time of all the tasks to be processed.
8. A power engineering quality supervision and management system, characterized by comprising:
the quality supervision task schedule receiving module is used for receiving the quality supervision task schedule uploaded by the client;
the quality supervision task schedule auditing module is used for determining whether the quality supervision task schedule has a matched quality supervision task item to be processed;
the quality supervision subtask allocation module is used for allocating all subtasks to be allocated in the quality supervision task schedule to at least one task execution client respectively;
the quality supervision subtask execution auditing module is used for receiving the subtask execution data uploaded by the task execution client and executing an approval process according to a preset subtask approval process;
the quality supervision subtask execution module is used for controlling a next subtask node entering the subtask or controlling whether the next subtask node entering the subtask based on an approval result;
in the quality supervision subtask allocation module, all subtasks to be allocated in the quality supervision task schedule are respectively allocated to at least one task execution client, and the quality supervision subtask allocation module comprises:
acquiring a plurality of matched task execution clients according to the type of the subtasks to be distributed;
determining the matching degree of the task execution client and the subtasks to be distributed based on historical task execution data of the task execution client and the task data to be processed;
determining the allocation priority of the subtasks to be allocated to each task execution client based on the matching degree;
the determining the matching degree between the task execution client and the subtasks to be distributed based on the historical task execution data of the task execution client and the task data to be processed further comprises: according to the historical task execution data of each task execution client, determining the time length and task execution stability of each task execution client for executing the completed subtasks, and determining the matching degree of the task execution client and the subtasks to be distributed based on the task execution stability, wherein the determining the execution stability of each task execution client comprises:
based on task execution data in a historical first time period, determining whether each executed task is in a completion state and is completed independently according to each executed task in a task execution sequence, and acquiring an execution time period and an execution sequence number of the executed task which is completed independently;
for the independently completed executed tasks, acquiring a time length value of an execution time period of each task, determining the maximum value of all the time length values based on the time length value, and determining the first task execution stability of each task execution client according to the ratio of the maximum value to the time length value of each task execution time period;
determining a second task execution stability of each task execution client based on the distribution dispersion of the execution sequence numbers of the independently completed executed tasks;
and determining the task execution stability of each task execution client based on the first task execution stability and the second task execution stability.
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