Digital media online cooperation task distribution system

Abstract

The invention discloses a digital media online cooperation task distribution system, which belongs to the technical field of digital media and comprises an input storage module, a task demand analysis module, a task personnel screening module and a task distribution module. The input storage module is responsible for collecting and storing member and task information; the task demand analysis module calculates a personnel scheduling value according to task difficulty, urgency and error rate, and determines the number of personnel required by each sub-task; the task personnel screening module is used for obtaining personnel selection score values according to personnel task average consumption, random quality scores and task light load values, and screening out determined personnel; the task distribution module splits the sub-tasks into sub-skill tasks, distributes the sub-skill tasks according to the technical adaptation degree of the selected personnel and the priority of the sub-skill tasks, and dynamically adjusts the sub-skill tasks after distribution; the problems of low efficiency, lack of fairness and the like of traditional task allocation are solved, human resources can be scientifically allocated, the task completion quality and efficiency are improved, and fair and reasonable task allocation is realized.

Description

Technical Field

[0001] The present invention belongs to the field of digital media technology, and in particular relates to a digital media online collaborative task allocation system. Background Art

[0002] As the digital media industry continues to flourish, the scale and complexity of projects continue to rise. This makes digital media project development increasingly dependent on close collaboration among team members. With the popularization of internet technology, online collaboration has become a key method for executing digital media projects. Within this collaborative model, how to efficiently and reasonably allocate tasks, fully leverage the professional skills of team members, and improve overall project quality and delivery efficiency has become a key issue that needs to be addressed. The existing digital media online collaborative task allocation process has the following problems: Inefficient task allocation: When manually assigning tasks, project leaders need to spend a lot of time and energy to gain an in-depth understanding of the specific requirements of each project, the professional skills of team members, and the detailed characteristics of different tasks. This process is not only tedious and complex, but also prone to errors. Lack of fairness in task allocation: Traditional task allocation methods often lack scientific and reasonable measurement standards, and are mostly based on subjective experience or simple task lists. This leads to unfair phenomena in actual projects, where some team members are overloaded with tasks while others are underloaded. To this end, we have designed a digital media online collaborative task allocation system. Summary of the Invention

[0003] The purpose of the present invention is to provide a digital media online collaborative task allocation system to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a digital media online collaborative task allocation system, comprising: an input storage module, a task demand analysis module, a task personnel screening module, and a task allocation module: Input and storage module: used to collect, input and store member information and task information; Task demand analysis module: used to split the received digital media task into several subtasks, and comprehensively analyze the task urgency value, task difficulty value and task error rate corresponding to each subtask to obtain the personnel scheduling value, and match the corresponding number of scheduling personnel according to the personnel scheduling value corresponding to each subtask; Task Personnel Screening Module: For each subtask that requires personnel scheduling, preliminary personnel selection is carried out. A comprehensive analysis is conducted based on the task average consumption, task quality score, and task light load value of each preliminary candidate to obtain the candidate score. Based on the number of scheduling personnel corresponding to each subtask and the candidate score of each preliminary candidate under the corresponding position, scheduling personnel are selected to obtain the confirmed personnel for each subtask scheduling; The task assignment module splits each subtask into several sub-skill tasks, and assigns sub-skill tasks based on the technical suitability of each selected person for each skill task.

[0005] Preferably, the specific working process of the input storage module is: Storing member information and task information in the entry storage module; Member information includes: basic member information, skill positions, and task logs; Member basic information includes: name, gender, contact information; Skilled positions include: digital media designer, video editor, audio editor, creative planner, screenwriter, photographer, special effects artist; The task log includes: the number of tasks currently undertaken by the member, task progress and expected delivery time, the name of the tasks undertaken in the past, task creation time, task delivery time, and customer satisfaction; Task information includes: task name, project name to which the task belongs, task type, task-associated personnel, task creation time, and task completion time.

[0006] Preferably, the specific process of the task demand analysis module analyzing the task urgency value corresponding to each subtask is as follows: When receiving a new digital media project, break it down into multiple subtasks corresponding to different positions based on their professional skills, and clearly define the delivery time for each subtask; Subtasks include: digital media subtasks, video editing subtasks, audio editing subtasks, creative planning subtasks, screenwriting subtasks, photography subtasks, and special effects subtasks; For each subtask of a newly received digital media project task, among the personnel in the corresponding position, the average time taken by each person to complete the same type of task is calculated and recorded as the personnel time value; the personnel time values ​​of all personnel in this position are added up and then divided by the number of people in this position to obtain the average time value per person; the remaining time of the task is obtained by subtracting the delivery time of the subtask from the current time, and the ratio of the remaining time of the task to the average time value per person is recorded as the task urgency value RP.

[0007] Preferably, the specific process of the task requirement analysis module analyzing the task difficulty value and task error rate corresponding to each subtask is as follows: For different sub-task types, corresponding task skill disassembly libraries are established. Each task skill disassembly library covers all possible skills that can be disassembled under various circumstances for this sub-task type. Each skill in each task skill disassembly library is preset to correspond to a skill difficulty value. For each subtask of the newly received digital media project task, the task skills are decomposed, and the decomposed tasks are grouped into a set to obtain a task skill set. The task skill set is matched with the corresponding task skill decomposition library, and the skill difficulty value corresponding to each task skill in the task skill set is output. The skill difficulty value corresponding to each task skill in the task skill set is accumulated to obtain a task difficulty value RN; For different subtask types, the frequency of errors that occurred when completing subtasks of this type in the past is counted. Based on this, a subtask type error library is established. The library records the name of each subtask type, the number of past completions, the number of errors, and the corresponding error frequency; Each subtask after the newly received digital media project task is disassembled is matched with the subtask type error library respectively, and the error frequency corresponding to each subtask is output, which is recorded as the task error rate RC.

[0008] Preferably, the specific process of the task demand analysis module analyzing the personnel scheduling value and matching the corresponding number of scheduling personnel according to the personnel scheduling value corresponding to each subtask is as follows: By normalizing the task urgency value RP, task difficulty value RN, and task error rate RC of each subtask of the newly received digital media project task, the personnel scheduling value RDZ is obtained using the formula: RDZ = RP × a1 + RN × a2 + RC × a3, where a1, a2, and a3 are preset weight coefficients; For each type of subtask, several personnel scheduling value intervals are preset, and each personnel scheduling value interval corresponds to the corresponding number of scheduling personnel; When a new digital media project task is received and split, the corresponding personnel scheduling value is determined for each subtask. Then, the personnel scheduling value is matched with multiple personnel scheduling value intervals pre-set for this type of subtask, and the number of scheduled personnel corresponding to the subtask is output.

[0009] Preferably, the specific process of the task personnel screening module selecting preliminary candidates and analyzing the task average consumption, task quality score and task light load value of each preliminary candidate is as follows: For each subtask that requires personnel scheduling, all skilled personnel in the corresponding positions of the subtask are recorded as preliminary candidates; For each candidate, obtain all previously completed tasks and their corresponding creation and delivery times from the task log. For each previously completed task, subtract the creation time from the delivery time to obtain the task completion time. The average task RT is calculated by adding up the completion times of all tasks and dividing by the number of tasks. Obtain the customer satisfaction of each task completed by the preliminary candidates in the past, and score the customer satisfaction on a scale of 1 to 5. Record the tasks corresponding to customer satisfaction scores of 1-3 as unsatisfactory tasks, and record the tasks corresponding to customer satisfaction scores of 4 to 5 as satisfactory tasks. Count the total number of satisfactory tasks to obtain the number of satisfactory tasks RM. Record the total number of tasks completed by the preliminary candidates in the past as the total number of tasks RZ. Use the formula: RL = RZ × k1 + RM / RZ × k2 to obtain the task quality score RL, where k1 and k2 are preset weight coefficients. Check the number of tasks RS currently undertaken in the task log of the preliminary members, and find the minimum value Nmin and the maximum value Nmax of the current number of tasks among all preliminary members; use the formula: RF = (Nmax-RS) / (Nmax-Nmin) to obtain the task light load value RF.

[0010] Preferably, the task personnel screening module analyzes the candidate score and selects the dispatch personnel according to the number of dispatch personnel corresponding to each subtask and the candidate score corresponding to each preliminary candidate under the corresponding position. The specific process of obtaining the selected personnel for each subtask scheduling is as follows: After normalizing the task average consumption RT, task quality score RL, and task light load value RF, the formula is used: , get the candidate score value RXP, where w1, w2, and w3 are preset weight coefficients; For all the preliminary candidates, they are sorted from large to small according to the size of the candidate score corresponding to each preliminary candidate to obtain a personnel screening sorting sequence. Then, according to the number of dispatched personnel corresponding to the subtask, the corresponding number of staff members are selected from the front end of this sequence to determine the personnel who will finally participate in the subtask. The personnel selected to work on the subtask will be recorded as the confirmed personnel.

[0011] Preferably, the task assignment module splits each subtask into several sub-skill tasks and analyzes the technical suitability of each selected person for each skill task in the following specific process: For each subtask, break it down into multiple sub-skill tasks based on the corresponding task skill set. Each sub-skill task clearly corresponds to a specific skill requirement. For each selected person, calculate their technical suitability for each sub-skill task. The specific process is as follows: For each sub-skill task, count the number of times each selected person has performed similar sub-skill tasks in the past, and record it as the number of technical tasks performed JS. At the same time, count the total time each selected person has spent performing similar sub-skill tasks in the past and divide it by the corresponding number of technical tasks performed to obtain the average technical task time JT. After normalizing the number of technical tasks performed JS and the average technical task time JT, use the formula: , we get the technical proficiency JD, where JSmax and JSmin are the maximum and minimum values ​​of the number of technical tasks performed by all selected personnel, respectively; JTmax and JTmin are the maximum and minimum values ​​of the average time spent on technical tasks by all selected personnel, respectively; r1 and r2 are the preset weight coefficients; By normalizing the technical proficiency JD and task light load value RF corresponding to each selected personnel, and then entering the formula: JPD=JD×p1+RF×p2, the technical adaptability JPD is obtained, where p1 and p2 are preset weight coefficients.

[0012] Preferably, the specific process of the task assignment module performing sub-skill task assignment is as follows: Divide each sub-skill task into different priority levels based on the dependencies between them, and rank the sub-skill tasks within the priority level in order of importance. The specific process is as follows: Starting with sub-skill tasks that have no previous dependent tasks, set them to the first priority level; for sub-skill tasks that depend on tasks in the first priority level, set them to the second priority level, and so on; the first priority level is higher than the second priority level, and so on; For different priority levels, a corresponding importance scoring library is established. The importance scoring library contains all sub-skill tasks within this priority level, and each sub-skill task has a preset importance score. For each priority level, match the sub-skill tasks within the priority level with the corresponding importance score library, output the importance score corresponding to each sub-skill task, and sort them in descending order according to the importance score corresponding to each sub-skill task to obtain the importance ranking sequence of each sub-skill task within the priority level; Sub-skill tasks are assigned based on the priority levels, the importance ranking of each sub-skill task within the priority level, and the suitability of the selected personnel for each sub-skill task. The specific process is as follows: Starting from the highest priority level, sort the sequence according to the importance of the sub-skill tasks within that level. Starting from the front of the sequence, select executors for each sub-skill task one by one. Specifically, for each sub-skill task, find the confirmed person with the highest technical and service suitability for that sub-skill task among all confirmed persons, and assign this sub-skill task to him or her. And every time a sub-skill task is successfully assigned, the technical and service suitability of all confirmed persons for the remaining unassigned sub-skill tasks must be recalculated. Repeat this process until all sub-skill tasks in all priority levels have been successfully assigned.

[0013] Compared with the prior art, the present invention has the following beneficial effects: (1) This digital media online collaborative task allocation system can quickly decompose digital media projects into subtasks through the task demand analysis module, and calculate the personnel scheduling value based on the task urgency value, difficulty value and error rate, and accurately match the number of scheduling personnel required for each subtask. Compared with manual allocation, it avoids the tedious process of the project leader understanding the project requirements, member skills and task characteristics one by one, reduces the probability of error, significantly improves the efficiency of task allocation, ensures that the project can be quickly started and promoted within a tight time, avoids missing key time nodes, and can scientifically allocate human resources according to the actual needs of the subtasks, avoids the situation of redundant or insufficient personnel, makes full use of human resources, and improves overall production efficiency.

[0014] (2) This digital media online collaborative task allocation system uses a task personnel screening module to comprehensively evaluate the shortlisted personnel from multiple dimensions such as task average consumption, task quality score, and task light load value, comprehensively considers the personnel's work efficiency, task completion quality, and current task load, and selects the most suitable personnel to participate in the task; at the same time, the task allocation module further accurately allocates the selected personnel to each sub-skill task based on their technical and service adaptability, ensuring that each sub-skill task can be assigned to experienced and efficient personnel, thereby improving the completion quality of the entire project task.

[0015] (3) This digital media online collaborative task allocation system determines the personnel scheduling value according to the difficulty, urgency and error rate of the task, and determines the candidate score value according to the comprehensive performance of the preliminary candidates' average task consumption, task quality score and task light load value, thereby avoiding the unfair phenomenon that some team members have too heavy or too light tasks, making task allocation more fair and reasonable, helping to improve the work enthusiasm and satisfaction of team members, and enhancing the team's cohesion and collaboration ability.

[0016] (4) This digital media online collaborative task allocation system, through the task allocation module, divides the priority levels according to the dependency relationship between sub-skill tasks during the sub-skill task allocation process, and recalculates the technical suitability of the selected personnel for the remaining unassigned sub-skill tasks in real time based on the assigned tasks; this dynamic adjustment mechanism enables the system to adapt to changes in the task execution process in a timely manner, ensures that task allocation is always in the optimal state, and ensures that the project can be smoothly promoted without excessive interference from unexpected factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Example 1 See also Figure 1 ,The present invention provides a digital media online collaborative task allocation system, comprising: an input storage module, a task demand analysis module, a task personnel screening module, and a task allocation module; The input and storage module is used to collect, input and store member information and task information. The specific process is as follows: Member information includes: basic member information, skill positions, and task logs; Member basic information includes: name, gender, contact information; Skilled positions include: digital media designer, video editor, audio editor, creative planner, screenwriter, photographer, special effects artist; The task log includes: the number of tasks currently undertaken by the member, task progress and expected delivery time, the name of the tasks undertaken in the past, task creation time, task delivery time, and customer satisfaction; Task information includes: task name, task project name, task type, task associated personnel, task creation time, and task completion time; The member information and task information are stored in the entry storage module.

[0020] The task demand analysis module is used to split the received digital media task into several subtasks. Based on the task urgency value, task difficulty value, and task error rate corresponding to each subtask, a comprehensive analysis is performed to obtain the personnel scheduling value. The corresponding number of scheduling personnel is matched according to the personnel scheduling value corresponding to each subtask. The specific process is as follows: When receiving a new digital media project, break it down into multiple subtasks corresponding to different positions based on their professional skills, and clearly define the delivery time for each subtask. Subtasks include: digital media subtasks, video editing subtasks, audio editing subtasks, creative planning subtasks, screenwriting subtasks, photography subtasks, and special effects subtasks. For each subtask of a newly received digital media project task, calculate the average time it takes for each person in the corresponding position to complete similar tasks, and record it as the person's time value. Similar tasks refer to tasks of the same type as the current subtask and corresponding to the current position. Add up the time spent by all personnel in this position and divide it by the number of people in this position to get the average time spent per person. The remaining time of the task is obtained by subtracting the delivery time of the subtask from the current time. The ratio of the remaining time of the task to the average time spent per person is recorded as the task urgency value RP. The larger the task urgency value, the more urgent the task is to complete, and the more staff members are needed to work together. For different subtask types, corresponding task skill disassembly libraries are established. Each task skill disassembly library covers all possible skills that can be disassembled under various circumstances for this subtask type. For example, the skill disassembly library corresponding to the video editing subtask includes: material screening, material importing, editing and splicing, adding transitions, audio processing, adding subtitles, color grading, video output, etc.; each skill in each task skill disassembly library is preset to correspond to a skill difficulty value; For each subtask of the newly received digital media project task, the task skills are decomposed, and the decomposed tasks are grouped into a set to obtain a task skill set. The task skill set is matched with the corresponding task skill decomposition library, and the skill difficulty value corresponding to each task skill in the task skill set is output. The skill difficulty value corresponding to each task skill in the task skill set is accumulated to obtain a task difficulty value RN; For different subtask types, the frequency of errors that occurred when completing subtasks of that type in the past is counted. Based on this, a subtask type error library is established. The library records the name of each subtask type, the number of past completions, the number of errors, and the corresponding error frequency. For example, a video editing subtask has been completed 100 times in the past, of which 20 errors occurred, with an error frequency of 20%. Match each subtask after decomposing the newly received digital media project task with the subtask type error library, and output the error frequency corresponding to each subtask, which is recorded as the task error rate RC; By normalizing the task urgency value RP, task difficulty value RN, and task error rate RC of each subtask of the newly received digital media project task, the personnel scheduling value RDZ is obtained using the formula: RDZ = RP × a1 + RN × a2 + RC × a3, where a1, a2, and a3 are preset weight coefficients; For each type of subtask, several personnel scheduling value intervals are preset, and each personnel scheduling value interval corresponds to the corresponding number of scheduling personnel; When a new digital media project task is received and split, the corresponding staff scheduling value is determined for each subtask. Then, the staff scheduling value is matched with multiple staff scheduling value intervals pre-set for this type of subtask, and the number of scheduled staff corresponding to the subtask is output; By calculating the task urgency value, task difficulty value and task error rate, and normalizing and calculating the personnel scheduling value, the number of scheduled personnel is matched according to the preset personnel scheduling value range. This method can scientifically and rationally allocate human resources according to the actual needs of subtasks to avoid redundant or insufficient personnel. When the task is urgent and difficult, personnel input can be increased in a timely manner to ensure that the task is completed on time and with high quality. When the task is relatively easy, personnel arrangements can be reasonably reduced to reduce labor costs.

[0021] The task personnel screening module selects preliminary personnel for each subtask that requires personnel scheduling. It performs a comprehensive analysis based on the task average consumption, task quality score, and task light load value of each preliminary candidate to obtain the candidate score. It selects the scheduling personnel based on the number of scheduling personnel corresponding to each subtask and the candidate score corresponding to each preliminary candidate under the corresponding position, and obtains the confirmed personnel for each subtask scheduling. The specific process is as follows: For each subtask that requires personnel scheduling, all skilled personnel in the corresponding positions of the subtask are recorded as preliminary candidates; For each candidate, obtain all previously completed tasks and their corresponding creation and delivery times from the task log. For each previously completed task, subtract the creation time from the delivery time to obtain the task completion time. Add up the completion times of all tasks and divide by the number of tasks to obtain the average task RT. The larger the average task RT, the slower the candidate's task completion efficiency. Obtain the customer satisfaction of each task completed by the preliminary candidate in the past, and score the customer satisfaction from 1 to 5. Record the tasks corresponding to customer satisfaction scores of 1-3 as unsatisfactory tasks, and record the tasks corresponding to customer satisfaction scores of 4 to 5 as satisfactory tasks. Count the total number of satisfactory tasks to obtain the number of satisfactory tasks RM. Record the total number of tasks completed by the preliminary candidate in the past as the total number of tasks RZ. Use the formula: RL = RZ × k1 + RM / RZ × k2 to obtain the task quality score RL, where k1 and k2 are preset weight coefficients. The larger the task quality score RL, the higher the quality of the tasks completed by the preliminary candidate. Check the number of tasks RS currently undertaken by the preliminary members in their task logs, and find the minimum Nmin and maximum Nmax of the current number of tasks among all preliminary members. Calculate the task light load value RF using the formula: RF = (Nmax - RS) / (Nmax - Nmin). The larger the task light load value RF, the lighter the task load on the preliminary member. After normalizing the task average consumption RT, task quality score RL, and task light load value RF, the formula is used: , get the candidate score RXP, where w1, w2, and w3 are preset weight coefficients. The larger the candidate score RXP corresponding to the preliminary candidate, the more suitable the person is for this subtask. For all the preliminary candidates, sort them from large to small according to the candidate score corresponding to each preliminary candidate to obtain the personnel screening sorting sequence. Then, according to the number of dispatched personnel corresponding to the subtask, select the corresponding number of staff from the front of this sequence to determine the final personnel who will participate in the subtask. The personnel selected to work on the subtask are recorded as the selected personnel; The task assignment module splits each subtask into several sub-skill tasks and assigns sub-skill tasks based on the technical suitability of each selected person for each skill task. The specific process is as follows: For each subtask, we break it down into multiple sub-skill tasks based on the corresponding task skill set. Each sub-skill task clearly corresponds to a specific skill requirement. For example, taking the video editing subtask as an example, we break it down into sub-skill tasks such as material screening, material importing, editing and splicing, adding transitions, audio processing, adding subtitles, color grading, and video output. For each selected person, calculate their technical suitability for each sub-skill task. The specific process is as follows: For each sub-skill task, count the number of times each selected person has performed similar sub-skill tasks in the past, and record it as the number of technical tasks performed JS. At the same time, count the total time each selected person has spent performing similar sub-skill tasks in the past and divide it by the corresponding number of technical tasks performed to obtain the average technical task time JT. After normalizing the number of technical tasks performed JS and the average technical task time JT, use the formula: , we get the technical proficiency JD, where JSmax and JSmin are the maximum and minimum values ​​of the number of technical tasks performed by all selected personnel, respectively; JTmax and JTmin are the maximum and minimum values ​​of the average time spent on technical tasks by all selected personnel, respectively; r1 and r2 are preset weight coefficients; the greater the technical proficiency of a selected personnel for a sub-skill task, the better the overall performance of the personnel in performing the sub-skill task, that is, the more experienced and efficient the personnel, and the more suitable they are for undertaking the sub-skill task; By normalizing the technical proficiency JD and task light load value RF corresponding to each selected person, and then substituting them into the formula: JPD = JD × p1 + RF × p2, the technical suitability JPD is obtained, where p1 and p2 are preset weight coefficients. The larger the technical suitability JPD of the selected person for the sub-skill task, the more suitable it is to assign the sub-skill task to the selected person. Divide each sub-skill task into different priority levels based on the dependencies between them, and rank the sub-skill tasks within the priority level in order of importance. The specific process is as follows: Starting with sub-skill tasks that have no pre-dependent tasks, set them to the first priority level; for sub-skill tasks that depend on the first priority level tasks, set them to the second priority level, and so on; for example: in the video production process of a digital media project, "material shooting" is usually a task without pre-dependencies and is at the first priority level; "material screening" depends on "material shooting" to be completed, so "material screening" is at the second priority level; "editing and splicing" depends on "material screening", so it is at the third priority level; and the first priority level is higher than the second priority level, and so on; For different priority levels, a corresponding importance scoring library is established. The importance scoring library contains all sub-skill tasks within this priority level, and each sub-skill task has a preset importance score. For each priority level, match the sub-skill tasks within the priority level with the corresponding importance score library, output the importance score corresponding to each sub-skill task, and sort them in descending order according to the importance score corresponding to each sub-skill task to obtain the importance ranking sequence of each sub-skill task within the priority level; Sub-skill tasks are assigned based on the priority levels, the importance ranking of each sub-skill task within the priority level, and the suitability of the selected personnel for each sub-skill task. The specific process is as follows: Starting from the highest priority level (i.e. the first priority level), sort the sequence according to the importance of the sub-skill tasks within that level, and starting from the front of the sequence, select executors for the sub-skill tasks one by one. Specifically, for each sub-skill task, among all the selected personnel, find the selected personnel with the highest technical and service suitability for the sub-skill task and assign this sub-skill task to him; and every time a sub-skill task is successfully assigned, the technical and service suitability of all selected personnel for the remaining unassigned sub-skill tasks must be recalculated, and this process is repeated until all sub-skill tasks in all priority levels are successfully assigned.

[0022] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A digital media online collaborative task allocation system, comprising: The input storage module, task requirement analysis module, task personnel screening module, and task allocation module are characterized by: Input and storage module: used to collect, input and store member information and task information; Task demand analysis module: used to split the received digital media task into several subtasks, and comprehensively analyze the task urgency value, task difficulty value and task error rate corresponding to each subtask to obtain the personnel scheduling value, and match the corresponding number of scheduling personnel according to the personnel scheduling value corresponding to each subtask; Task Personnel Screening Module: For each subtask that requires personnel scheduling, preliminary personnel selection is carried out. A comprehensive analysis is conducted based on the task average consumption, task quality score, and task light load value of each preliminary candidate to obtain the candidate score. Based on the number of scheduling personnel corresponding to each subtask and the candidate score of each preliminary candidate under the corresponding position, scheduling personnel are selected to obtain the confirmed personnel for each subtask scheduling; The task assignment module splits each subtask into several sub-skill tasks, and assigns sub-skill tasks based on the technical suitability of each selected person for each skill task.

2. A digital media online collaborative task allocation system according to claim 1, characterized in that: The specific working process of entering the storage module is as follows: Storing member information and task information in the entry storage module; Member information includes: basic member information, skill positions, and task logs; Member basic information includes: name, gender, contact information; Skilled positions include: digital media designer, video editor, audio editor, creative planner, screenwriter, photographer, special effects artist; The task log includes: the number of tasks currently undertaken by the member, task progress and expected delivery time, the name of the tasks undertaken in the past, task creation time, task delivery time, and customer satisfaction; Task information includes: task name, project name to which the task belongs, task type, task-associated personnel, task creation time, and task completion time.

3. A digital media online collaborative task allocation system according to claim 2, characterized in that: The specific process of the task demand analysis module analyzing the task urgency value corresponding to each subtask is as follows: When receiving a new digital media project, break it down into multiple subtasks corresponding to different positions based on their professional skills, and clearly define the delivery time for each subtask; Subtasks include: digital media subtasks, video editing subtasks, audio editing subtasks, creative planning subtasks, screenwriting subtasks, photography subtasks, and special effects subtasks; For each subtask of a newly received digital media project task, among the personnel in the corresponding position, the average time taken by each person to complete the same type of task is calculated and recorded as the personnel time value; the personnel time values ​​of all personnel in this position are added up and then divided by the number of people in this position to obtain the average time value per person; the remaining time of the task is obtained by subtracting the delivery time of the subtask from the current time, and the ratio of the remaining time of the task to the average time value per person is recorded as the task urgency value RP.

4. A digital media online collaborative task allocation system according to claim 3, characterized in that: The specific process of the task requirement analysis module analyzing the task difficulty value and task error rate corresponding to each subtask is as follows: For different sub-task types, corresponding task skill disassembly libraries are established. Each task skill disassembly library covers all possible skills that can be disassembled under various circumstances for this sub-task type. Each skill in each task skill disassembly library is preset to correspond to a skill difficulty value. For each subtask of the newly received digital media project task, the task skills are decomposed, and the decomposed tasks are grouped into a set to obtain a task skill set. The task skill set is matched with the corresponding task skill decomposition library, and the skill difficulty value corresponding to each task skill in the task skill set is output. The skill difficulty value corresponding to each task skill in the task skill set is accumulated to obtain a task difficulty value RN; For different subtask types, the frequency of errors that occurred when completing subtasks of this type in the past is counted. Based on this, a subtask type error library is established. The library records the name of each subtask type, the number of past completions, the number of errors, and the corresponding error frequency; Each subtask after the newly received digital media project task is disassembled is matched with the subtask type error library respectively, and the error frequency corresponding to each subtask is output, which is recorded as the task error rate RC.

5. A digital media online collaborative task allocation system according to claim 4, characterized in that: The specific process of the task demand analysis module analyzing the personnel scheduling value and matching the corresponding number of scheduling personnel according to the personnel scheduling value corresponding to each subtask is as follows: By normalizing the task urgency value RP, task difficulty value RN, and task error rate RC of each subtask of the newly received digital media project task, the personnel scheduling value RDZ is obtained using the formula: RDZ = RP × a1 + RN × a2 + RC × a3, where a1, a2, and a3 are preset weight coefficients; For each type of subtask, several personnel scheduling value intervals are preset, and each personnel scheduling value interval corresponds to the corresponding number of scheduling personnel; When a new digital media project task is received and split, the corresponding personnel scheduling value is determined for each subtask. Then, the personnel scheduling value is matched with multiple personnel scheduling value intervals pre-set for this type of subtask, and the number of scheduled personnel corresponding to the subtask is output.

6. A digital media online collaborative task allocation system according to claim 5, characterized in that: The specific process of the task personnel screening module to select preliminary candidates and analyze each preliminary candidate's task consumption, task quality score, and task light load value is as follows: For each subtask that requires personnel scheduling, all skilled personnel in the corresponding positions of the subtask are recorded as preliminary candidates; For each candidate, obtain all previously completed tasks and their corresponding creation and delivery times from the task log. For each previously completed task, subtract the creation time from the delivery time to obtain the task completion time. Add up the completion times of all tasks and divide by the number of tasks to obtain the average task RT. Obtain the customer satisfaction of each task completed by the preliminary candidate in the past, and score the customer satisfaction on a scale of 1 to 5. Record the tasks corresponding to customer satisfaction scores of 1-3 as unsatisfactory tasks, and record the tasks corresponding to customer satisfaction scores of 4 to 5 as satisfactory tasks. Count the total number of satisfactory tasks to obtain the number of satisfactory tasks RM. Record the total number of tasks completed by the preliminary candidate in the past as the total number of tasks RZ. Use the formula: RL = RZ × k1 + RM / RZ × k2 to obtain the task quality score RL, where k1 and k2 are preset weight coefficients. Check the number of tasks RS currently undertaken in the task log of the preliminary members, and find the minimum value Nmin and the maximum value Nmax of the current number of tasks among all preliminary members; use the formula: RF = (Nmax-RS) / (Nmax-Nmin) to obtain the task light load value RF.

7. A digital media online collaborative task allocation system according to claim 6, characterized in that: The task personnel screening module analyzes the candidate scores and selects the dispatchers based on the number of dispatchers corresponding to each subtask and the candidate scores of the preliminary candidates for the corresponding positions. The specific process of obtaining the selected candidates for each subtask is as follows: After normalizing the task average consumption RT, task quality score RL, and task light load value RF, the formula is used: , get the candidate score value RXP, where w1, w2, and w3 are preset weight coefficients; For all the preliminary candidates, they are sorted from large to small according to the size of the candidate score corresponding to each preliminary candidate to obtain a personnel screening sorting sequence. Then, according to the number of dispatched personnel corresponding to the subtask, the corresponding number of staff members are selected from the front end of this sequence to determine the personnel who will finally participate in the subtask. The personnel selected to work on the subtask will be recorded as the confirmed personnel.

8. A digital media online collaborative task allocation system according to claim 7, characterized in that: The task assignment module splits each subtask into several sub-skill tasks and analyzes the technical suitability of each selected person for each skill task. The specific process is as follows: For each subtask, break it down into multiple sub-skill tasks based on the corresponding task skill set. Each sub-skill task clearly corresponds to a specific skill requirement. For each selected person, calculate their technical suitability for each sub-skill task. The specific process is as follows: For each sub-skill task, count the number of times each selected person has performed similar sub-skill tasks in the past, and record it as the number of technical tasks performed JS. At the same time, count the total time each selected person has spent performing similar sub-skill tasks in the past and divide it by the corresponding number of technical tasks performed to obtain the average technical task time JT. After normalizing the number of technical tasks performed JS and the average technical task time JT, use the formula: , we get the technical proficiency JD, where JSmax and JSmin are the maximum and minimum values ​​of the number of technical tasks performed by all selected personnel, respectively; JTmax and JTmin are the maximum and minimum values ​​of the average time spent on technical tasks by all selected personnel, respectively; r1 and r2 are the preset weight coefficients; By normalizing the technical proficiency JD and task light load value RF corresponding to each selected personnel, and then entering the formula: JPD=JD×p1+RF×p2, the technical adaptability JPD is obtained, where p1 and p2 are preset weight coefficients.

9. A digital media online collaborative task allocation system according to claim 8, characterized in that: The specific process of sub-skill task assignment in the task assignment module is as follows: Divide each sub-skill task into different priority levels based on the dependencies between them, and rank the sub-skill tasks within the priority level in order of importance. The specific process is as follows: Starting with sub-skill tasks that have no previous dependent tasks, set them to the first priority level; for sub-skill tasks that depend on tasks in the first priority level, set them to the second priority level, and so on; the first priority level is higher than the second priority level, and so on; For different priority levels, a corresponding importance scoring library is established. The importance scoring library contains all sub-skill tasks within this priority level, and each sub-skill task has a preset importance score. For each priority level, match the sub-skill tasks within the priority level with the corresponding importance score library, output the importance score corresponding to each sub-skill task, and sort them in descending order according to the importance score corresponding to each sub-skill task to obtain the importance ranking sequence of each sub-skill task within the priority level; Sub-skill tasks are assigned based on the priority levels, the importance ranking of each sub-skill task within the priority level, and the suitability of the selected personnel for each sub-skill task. The specific process is as follows: Starting from the highest priority level, sort the sequence according to the importance of the sub-skill tasks within that level, and starting from the front of the sequence, select executors for the sub-skill tasks one by one. Specifically, for each sub-skill task, among all the selected personnel, find the selected personnel with the highest technical and service suitability for that sub-skill task and assign this sub-skill task to him / her. And every time a sub-skill task is successfully assigned, the technical and service suitability of all selected personnel for the remaining unassigned sub-skill tasks must be recalculated. This process is repeated until all sub-skill tasks in all priority levels are successfully assigned.

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