Semantic-based big data analysis method and business intelligence system

Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a semantic-based big data analysis method and a business intelligence system.The method comprises the steps that firstly, a dynamic task undertaking portrait pool is constructed by using an integrated model by obtaining a historical task completion state and undertaking team and individual attribute data; secondly, carrying out attribute analysis on the published task, and decomposing the published task into sub-tasks by utilizing a rule splitting model; according to the dynamic task undertaking portrait pool and subtask attributes, calculating matching scores between teams and tasks and between members and subtasks and team task coverage through a dual matching model; according to a preset matching rule, tasks are intelligently allocated to teams or individuals, and interface configuration among the sub-tasks is optimized; finally, the task progress is monitored in real time, the evaluation model is used for scoring the completion condition, feedback is used for dynamically adjusting or updating the task undertaking portrait pool, intelligent management of task allocation and completion state evaluation is achieved, and the task allocation efficiency and the completion quality are improved.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence technology, and in particular relates to a semantic big data analysis method and a business intelligence system. Background Art

[0002] With the acceleration of digital transformation, more and more companies are beginning to rely on a flexible labor market to meet their changing human resource needs; especially in the IT field, a large number of freelancers and contractors have become an indispensable part of the company; in this context, business intelligence (BI) technology is widely used to optimize human resource management and task allocation processes. At present, there are platforms such as "Freelancers" on the market, which provide a bridge for companies and freelancers through the "platform + technology + service" model. Such platforms can support various types of human resource needs including software development, IT consulting, project management, testing, database and big data analysis, Internet operation and promotion, etc.; however, these platforms cannot split the published tasks, which leads to low matching of corresponding teams or talents;

[0003] For example, the patent with authorization announcement number CN115577983B discloses a blockchain-based enterprise task matching method, server and storage medium, which includes: obtaining the first standard professional information of the target task; for each recommended node, obtaining the second standard professional information of the recommended node personnel; calculating the spatial distance between the first standard professional information and the second standard professional information, and determining the task matching degree between the personnel and the target task based on the spatial distance; selecting personnel whose task matching degree is within a preset matching degree range to form a target task team to complete the target task.

[0004] For example, the patent with publication number CN115269771A discloses a semantic-based big data analysis system, including a data collection unit, a data identification unit, a data analysis unit and a data visualization unit. The data collection unit is used to store and update big data in real time, the data identification unit is used to identify and preliminarily filter the information required by the user, the data analysis unit is used to integrate, classify and correlate the big data information, form analysis results, and provide real-time data required for analysis, and the data visualization unit is used to present the data analysis results in graphics and voice that can be recognized by the user.

[0005] The above existing technologies have the following problems: the existing platforms lack sufficient intelligent means for task splitting, which may lead to unreasonable splitting results, which makes the matching degree of corresponding talents or teams not high. For this reason, the present invention provides a semantic big data analysis method and a business intelligence system. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention proposes a semantic big data analysis method and a business intelligence system. The method first obtains the historical task completion status and the attribute data of the undertaking team and individuals, and uses an integrated model to build a dynamic task undertaking portrait pool; secondly, the published tasks are analyzed for attributes, and are decomposed into subtasks using a rule splitting model; based on the dynamic task undertaking portrait pool and the subtask attributes, the matching scores of the team and tasks, members and subtasks, and the team task coverage are calculated through a double matching model; according to the preset matching rules, tasks are intelligently assigned to teams or individuals, and the interface configuration between subtasks is optimized; finally, the task progress is monitored in real time, the completion status is scored using an evaluation model, and the feedback is used to dynamically adjust or update the task undertaking portrait pool, so as to realize the intelligent management of task allocation and completion status evaluation, and improve the task allocation efficiency and completion quality.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Based on the semantic big data analysis method, the steps include:

[0009] S1. Obtain the completion status data of historically released tasks and the corresponding attribute distribution status data of the undertaking team and individuals. Based on the acquired data, obtain the dynamic task undertaking portrait pool through the configured integration model;

[0010] S2. Obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the list of subtasks after splitting and the corresponding attribute data;

[0011] S3. According to the dynamic task acceptance portrait pool, the list of split subtasks and the corresponding attribute data, the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the team's coverage of the task are obtained through the configured double matching model;

[0012] S4. Set matching rules. Match the corresponding teams according to the matching rules and the obtained matching scores and coverage. If all teams do not meet the matching rules, match the split subtasks with a single person, and configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with the individual. At the same time, continue to match the remaining subtasks with the teams until all subtasks are assigned;

[0013] S5. Obtain the matching team or individual task completion status data in real time, and evaluate the team or individual task completion status through the configured evaluation model to obtain the completion status evaluation score, and feed back the obtained evaluation score to the dynamic task acceptance portrait pool to adjust or delete the team or individual attribute status in the pool.

[0014] Specifically, the dynamic task acceptance portrait pool includes a team task acceptance portrait sub-pool and a personal task acceptance portrait sub-pool; the steps of constructing the team task acceptance portrait sub-pool include:

[0015] S101, obtaining the types and proficiency of professional skills of members of different teams, the number of complete or partial tasks undertaken in historical unit time, the maximum task load, the number of remaining tasks at the current moment, the types of tasks undertaken, the time proportion of task completion, the task undertaking fee, the initial delivery success rate of different types of tasks, the delivery success rate of different types of tasks, and text information on satisfaction with delivered tasks;

[0016] S102, inputting the acquired text information into the configured entity extraction algorithm, obtaining team member skill attribute keyword information and task attribute keyword information in sequence, and automatically annotating the acquired team member skill attribute keyword information and task attribute keyword information with score tags through the trained automatic annotation model;

[0017] S103, inputting the annotated team member skill attribute keyword information into the evaluation algorithm 1 to obtain the team's overall skill proficiency evaluation score and the corresponding skill proficiency evaluation score of each member;

[0018] S104, inputting the annotated task attribute keyword information into the evaluation algorithm 2 to obtain the team's task acceptance evaluation score and the team's task satisfaction score;

[0019] S105. Obtain a comprehensive team assessment score based on the obtained team overall skill proficiency score, team task capability assessment score, and team task satisfaction score;

[0020] S106. According to the obtained keyword information of different teams and the corresponding evaluation scores input into the graph database, a team task undertaking portrait sub-pool is constructed, and the team name is used as the first-level node in the team task undertaking portrait sub-pool. At the same time, the obtained comprehensive evaluation score of each team is used as the sorting label of the first-level node to sort the teams in descending order.

[0021] S107, using the professional skill type and proficiency keyword information of the corresponding team members as the secondary node, and using the obtained team overall skill proficiency score as the secondary node sorting label, to sort the team skill levels in descending order;

[0022] S108. The keyword information of the attributes of the historical tasks undertaken by the corresponding team is used as the third-level node, and the obtained evaluation score of the team's ability to undertake tasks is used as the first-level sorting label of the third-level node, and the task satisfaction score is used as the second-level sorting label.

[0023] Specifically, the steps for constructing the sub-pool for accepting personal tasks include:

[0024] S109, obtaining the S101 text information corresponding to the non-team individual, and obtaining the corresponding skill proficiency assessment score and personal task satisfaction score of the non-team individual through the S102-S104 process;

[0025] S110, inputting the acquired non-team individual corresponding skill proficiency into the graph database, constructing a sub-pool of individual task acceptance portraits, using the corresponding individual name keywords as the first-level nodes in the sub-pool of individual task acceptance portraits, and using the acquired individual corresponding skill proficiency assessment scores as the first-level sorting labels;

[0026] S111, mapping the occupational type and proficiency attribute information of the team members in the team task acceptance portrait sub-pool to the first-level node in the individual task acceptance portrait sub-pool, and using the obtained skill proficiency assessment score of the individual member as the corresponding first-level sorting label;

[0027] S112. Arrange the personal information in the first-level nodes in the personal task acceptance portrait sub-pool in descending order according to the obtained first-level ranking labels of personal skill proficiency, and use the task attribute information of non-team individuals as the second-level nodes, and use the corresponding personal task satisfaction scores as the ranking labels in the second-level nodes;

[0028] S113, mapping the team task satisfaction score to the individual task satisfaction score corresponding to the team member as the ranking label of the team member in the secondary node in the individual task acceptance portrait sub-pool;

[0029] S114. Sort the task attribute information of team members and non-team members according to the obtained sorting labels in the secondary nodes, and obtain a constructed sub-pool of personal task acceptance portraits.

[0030] Specifically, the steps for building a dynamic task acceptance profile pool include:

[0031] S115. According to the mapping relationship between the team members and the personal task acceptance portrait sub-pool in S113, the personal task acceptance portrait sub-pool and the team task acceptance portrait sub-pool are connected to obtain a constructed dynamic task acceptance portrait pool;

[0032] S116. Set a team comprehensive evaluation score threshold and an individual task satisfaction score threshold, and embed the team comprehensive evaluation score threshold into the team task acceptance portrait sub-pool, and embed the individual task satisfaction score threshold into the individual task acceptance portrait sub-pool. When the corresponding team comprehensive evaluation score is lower than the team comprehensive evaluation score threshold, the corresponding team will be deleted from the team task acceptance portrait sub-pool;

[0033] S117. If the personal task satisfaction score of a non-team member is lower than the personal task satisfaction score threshold, the corresponding individual will be deleted from the personal task acceptance portrait sub-pool. If the personal task satisfaction score of an individual in the team is lower than the personal task satisfaction score threshold, an alarm will be issued to the team as a whole.

[0034] Specifically, the steps of obtaining the split subtask list in S2 include:

[0035] S201, obtaining overall information of the released task, and extracting the task cycle information and overall required skill type information corresponding to the released task through the configured entity extraction algorithm;

[0036] S202, setting a cycle splitting threshold. If the acquired task cycle is greater than the cycle splitting threshold, inputting the overall information of the released task into the comprehensive fuzzy evaluation algorithm to evaluate the urgency score of each stage of the subtask in the overall task;

[0037] S203, dividing the overall task into an early subtask set, a mid-term subtask set, and a late subtask set according to the obtained urgency scores of the subtasks at each stage;

[0038] S204, for the obtained early subtask set, mid-term subtask set and late subtask set, obtain the occupational skill type required for the early subtask set, the occupational skill type required for the mid-term subtask set and the occupational skill type required for the late subtask set through the configured skill recognition model;

[0039] S205. Functionally split the early subtask set, mid-term subtask set and late subtask set according to the required occupational skill types obtained to obtain the early functional subtask set, mid-term functional subtask set and late functional subtask set, and use the obtained early functional subtask set, mid-term functional subtask set and late functional subtask set to construct a subtask list.

[0040] Specifically, the steps of obtaining the split subtask list in S2 include:

[0041] S206: If the acquired task period is less than or equal to the period splitting threshold, directly repeat S204-S205 to functionally split the overall task to obtain a functional subtask set and a subtask list;

[0042] S207. If, through the configured skill recognition model, only one type of professional skill is required for the overall task, the task will not be split, and the corresponding task will be marked for individual matching.

[0043] Specifically, the steps of task matching in S3 and S4 include:

[0044] S301, matching the subtask list after the corresponding task is split with the team task undertaking portrait subpool, and obtaining the matching score 1 between each team as a whole and the overall task through matching algorithm 1, and obtaining the matching score 2 between each team member and the required professional skill type in the subtask list through matching algorithm 2;

[0045] S302, calculating the coverage of the professional skills required in the subtask list by each team according to the intersection of the professional skill types required in the subtask list and the professional skill types corresponding to the team members and the professional skill types required in the subtask list;

[0046] S303, setting a matching score 1 threshold, a matching score 2 threshold, and a coverage threshold. If the number of teams that satisfy the matching score 1, matching score 2, and coverage that are all greater than the corresponding thresholds is greater than one, then according to the task initial delivery success rate of the corresponding team, the task is matched to the team with the highest task initial delivery success rate;

[0047] S304. If the initial task delivery success rate of all teams that meet the conditions in S303 is 0, the task will be matched to the team with the lowest task undertaking fee;

[0048] S305: If all teams do not meet the coverage threshold, then compare the matching score 1 and the matching score 2 with the corresponding thresholds. If there are more than one team that meets both the matching score 1 threshold and the matching score 2 threshold, match the functional subtask set covered by the team to the team with the largest coverage.

[0049] S306. If there is more than one team with the largest coverage, S303 and S304 will be repeated to match the team and the task.

[0050] Specifically, the task matching steps in S3 and S4 also include:

[0051] S307. If there is more than one uncovered functional subtask left after matching in process S306, the remaining functional subtasks are matched with the unmatched teams that meet the conditions in S305. If there is a team whose matching score 1, matching score 2 and coverage are all greater than the corresponding threshold, the remaining functional subtasks are assigned to the team that meets the conditions, and the interface attributes of the functional subtasks assigned in process S306 adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned in S307.

[0052] S308. If there is no team that satisfies the matching score 1, matching score 2 and coverage that are all greater than the corresponding thresholds, the remaining functional subtasks are matched with individuals in the personal task undertaking portrait subpool. If the number of individuals with matching scores greater than the matching score 2 threshold for the same remaining functional subtask is greater than one, the corresponding remaining functional subtask is matched to the individual with the highest personal task satisfaction score until the remaining functional subtasks are matched;

[0053] S309, if the number of individuals with the highest individual task satisfaction scores is greater than 1, the task is matched to the individual with the highest corresponding skill proficiency assessment score, and the interface attributes of the functional subtasks assigned by the S306 process adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned by S307;

[0054] S310: If the acquired task period is less than or equal to the period splitting threshold, then repeat S206 and S301-S303. If there is a team that meets the condition of S303 and the number of teams is greater than 1, then match the task to the team with the smallest proportion of task completion time.

[0055] S311. If there is no team that meets the conditions of S303, repeat the process of S305-S309 to assign tasks;

[0056] S312. When there is only one type of professional skill required for the overall task, repeat S308-S309 to match individuals with tasks from the personal task acceptance portrait sub-pool, and match the task to the individual who meets the conditions in S308-S309.

[0057] Business intelligence system based on semantic big data analysis, including: portrait pool construction module, task splitting module and matching module;

[0058] The portrait pool construction module includes a data processing unit, a portrait unit and a portrait pool unit;

[0059] The data processing unit is used to obtain the completion status data of historical release tasks and the corresponding attribute distribution status data of the undertaking team and individuals, and perform standardized preprocessing on the obtained data;

[0060] The portrait unit constructs hierarchical team portraits and individual portraits based on the pre-processed team and individual attribute distribution status data through the configured natural language algorithm; the portrait pool unit uses the constructed hierarchical team portraits and individual portraits to construct a dynamic task acceptance portrait pool through the graph database;

[0061] The task splitting module is used to obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the differential subtask list and corresponding attribute data;

[0062] The matching module includes a team matching unit, a matching judgment unit, and a matching optimization unit;

[0063] The team matching unit, based on the dynamic task acceptance profile pool, the list of split subtasks and the corresponding attribute data, obtains the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the team's coverage of the task through the configured dual matching model;

[0064] The matching judgment unit matches the corresponding team by setting matching rules according to the obtained matching scores and coverage. If all teams do not meet the matching rules, the split subtasks are matched with a single person.

[0065] The matching optimization unit is used to configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with individuals, and continue to match the remaining subtasks with the team until all subtasks are assigned.

[0066] A computer-readable storage medium stores computer instructions, which, when executed, execute the semantic big data analysis method.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] In view of the deficiencies in the prior art, the present invention, through the configured rule splitting model, can scientifically and reasonably split the task into multiple subtasks according to the complexity of the task and the required skill type, and generate a detailed subtask list and corresponding attribute data, so as to ensure that each subtask maintains both independence and the overall consistency of the task; secondly, in the matching stage, by using the dynamic task acceptance portrait pool and the dual matching model, it is possible to accurately evaluate the matching score between the team and the task, the matching score between the team members and the subtasks, and the team's coverage of the task, thereby greatly improving the matching accuracy and efficiency, effectively solving the deficiencies of the existing platform in task splitting and talent matching, ensuring the rationality and efficiency of task allocation, and thus significantly improving the overall execution quality of the project and the management level of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a flow chart of the semantic big data analysis method of the present invention;

[0070] Figure 2 This is a module diagram of the business intelligence system based on semantic big data analysis of the present invention. DETAILED DESCRIPTION

[0071] Example 1

[0072] See also Figure 1 , an embodiment provided by the present invention: based on the semantic big data analysis method, the steps include:

[0073] S1. Obtain the completion status data of historically released tasks and the corresponding attribute distribution status data of the undertaking team and individuals. Based on the acquired data, obtain the dynamic task undertaking portrait pool through the configured integration model;

[0074] Furthermore, in this embodiment, the dynamic task undertaking portrait pool includes a team task undertaking portrait sub-pool and a personal task undertaking portrait sub-pool;

[0075] Furthermore, in this embodiment, the steps of constructing a team task acceptance portrait sub-pool include:

[0076] S101, obtaining the types and proficiency of professional skills of members of different teams, the number of complete or partial tasks undertaken in historical unit time, the maximum task load, the number of remaining tasks at the current moment, the types of tasks undertaken, the time proportion of task completion, the task undertaking fee, the initial delivery success rate of different types of tasks, the delivery success rate of different types of tasks, and text information on satisfaction with delivered tasks;

[0077] Furthermore, in this embodiment, the proficiency of professional skills is measured by the number of years that the corresponding team members have been engaged in the current professional skills; for example, in the IT field, professional skill types include front-end, back-end, data analysis, operation and maintenance, testing, UG, art, product, etc.; the professional skills corresponding to different fields are set by technical personnel in this field;

[0078] Furthermore, in this embodiment, the time ratio of task completion is the actual time length of the team completing the task after accepting the task and the task completion cycle length specified in the task release; the type of task accepted in this embodiment is the industry type corresponding to the task as a whole, such as IT industry, painting industry, etc.; the initial delivery success rate in this embodiment is the success rate of the task being passed after the first delivery to Party A after the task is completed; the task delivery success rate in this embodiment is the success rate of task delivery after multiple modifications, excluding tasks that were successfully delivered for the first time;

[0079] S102, inputting the acquired text information into the configured entity extraction algorithm, obtaining team member skill attribute keyword information and task attribute keyword information in sequence, and automatically annotating the acquired team member skill attribute keyword information and task attribute keyword information with score tags through the trained automatic annotation model;

[0080] In this embodiment, the team member skill attribute keyword information, such as member A, working in the backend, and having been in the industry for ten years; task attribute keyword information, such as task cycle length, industry field, quotation, etc.;

[0081] S103, inputting the annotated team member skill attribute keyword information into the evaluation algorithm 1 to obtain the team's overall skill proficiency evaluation score and the corresponding skill proficiency evaluation score of each member;

[0082] Furthermore, the evaluation algorithm in this embodiment is a comprehensive fuzzy evaluation algorithm;

[0083] Furthermore, in this embodiment, the skill proficiency assessment score of a single member is marked by the number of years each team member has been engaged in the corresponding skill type; for example, if member A has been engaged in backend work for 5 years, the score given to member A is 5 points;

[0084] Furthermore, the overall skill proficiency assessment score of the team is the sum of the product of the skill proficiency assessment score of each member of the team and the probability of successfully completing the corresponding skill subtask in the task they undertake in one go; the probability of successfully completing the corresponding skill subtask in the task in one go is the ratio of the number of corresponding skill subtasks successfully completed in one go in all the historical tasks of each member of the team to the total number;

[0085] S104, inputting the annotated task attribute keyword information into the evaluation algorithm 2 to obtain the team's task acceptance evaluation score and the team's task satisfaction score;

[0086] Furthermore, in this embodiment, the steps of obtaining the evaluation score of the task that the team can undertake include:

[0087] The comprehensive fuzzy algorithm evaluation factors and evaluation indicators are constructed based on the number of complete or partial tasks undertaken in the historical unit time, the maximum task load, the remaining tasks at the current moment, and the types of tasks undertaken;

[0088] Input the constructed evaluation factors and evaluation indicators into the comprehensive fuzzy algorithm to obtain the evaluation score of the tasks that the team can undertake;

[0089] Similarly, the team task satisfaction score is obtained through a comprehensive fuzzy algorithm using the time percentage of task completion, task undertaking costs, initial delivery success rate of different types of tasks, delivery success rate of different types of tasks, and text information of satisfaction with the delivered tasks.

[0090] S105. Obtain a comprehensive team assessment score based on the obtained team overall skill proficiency score, team task capability assessment score, and team task satisfaction score;

[0091] S106. According to the obtained keyword information of different teams and the corresponding evaluation scores input into the graph database, a team task undertaking portrait sub-pool is constructed, and the team name is used as the first-level node in the team task undertaking portrait sub-pool. At the same time, the obtained comprehensive evaluation score of each team is used as the sorting label of the first-level node to sort the teams in descending order.

[0092] S107, using the professional skill type and proficiency keyword information of the corresponding team members as the secondary node, and using the obtained team overall skill proficiency score as the secondary node sorting label, to sort the team skill levels in descending order;

[0093] S108. The keyword information of the attributes of the historical tasks undertaken by the corresponding team is used as the third-level node, and the obtained evaluation score of the team's ability to undertake tasks is used as the first-level sorting label of the third-level node, and the task satisfaction score is used as the second-level sorting label.

[0094] In the process of constructing the team task acceptance portrait sub-pool, the process comprehensively evaluates the professional skill types, proficiency and other key indicators of team members (such as the proportion of task completion time, task acceptance costs, delivery success rate, etc.), and uses the configured entity extraction algorithm and automatic labeling model to ensure the high accuracy of the team's overall skill proficiency score and the corresponding skill proficiency assessment score of individual members; secondly, the task attributes are evaluated through evaluation algorithm 2 to obtain the team's task acceptance assessment score and task satisfaction score, further optimizing the rationality of task allocation; finally, by inputting these evaluation scores into the graph database to construct the team task acceptance portrait sub-pool, and sorting them according to the comprehensive evaluation scores, dynamic monitoring and optimization of the team's ability to undertake tasks is achieved; this process not only improves the scientificity and rationality of task allocation, but also enhances the transparency and controllability of team management.

[0095] Furthermore, in this embodiment, the steps of constructing a sub-pool of personal task acceptance portraits include:

[0096] S109, obtaining the S101 text information corresponding to the non-team individual, and obtaining the corresponding skill proficiency assessment score and personal task satisfaction score of the non-team individual through the S102-S104 process;

[0097] S110, inputting the acquired non-team individual corresponding skill proficiency into the graph database, constructing a sub-pool of individual task acceptance portraits, using the corresponding individual name keywords as the first-level nodes in the sub-pool of individual task acceptance portraits, and using the acquired individual corresponding skill proficiency assessment scores as the first-level sorting labels;

[0098] S111, mapping the occupational type and proficiency attribute information of the team members in the team task acceptance portrait sub-pool to the first-level node in the individual task acceptance portrait sub-pool, and using the obtained skill proficiency assessment score of the individual member as the corresponding first-level sorting label;

[0099] S112. Arrange the personal information in the first-level nodes in the personal task acceptance portrait sub-pool in descending order according to the obtained first-level ranking labels of personal skill proficiency, and use the task attribute information of non-team individuals as the second-level nodes, and use the corresponding personal task satisfaction scores as the ranking labels in the second-level nodes;

[0100] S113, mapping the team task satisfaction score to the individual task satisfaction score corresponding to the team member as the ranking label of the team member in the secondary node in the individual task acceptance portrait sub-pool;

[0101] S114. Sort the task attribute information of team members and non-team members according to the obtained sorting labels in the secondary nodes, and obtain a constructed sub-pool of personal task acceptance portraits.

[0102] Furthermore, in this embodiment, the steps of constructing a dynamic task acceptance portrait pool include:

[0103] S115. According to the mapping relationship between the team members and the personal task acceptance portrait sub-pool in S113, the personal task acceptance portrait sub-pool and the team task acceptance portrait sub-pool are connected to obtain a constructed dynamic task acceptance portrait pool;

[0104] S116. Set a team comprehensive evaluation score threshold and an individual task satisfaction score threshold, and embed the team comprehensive evaluation score threshold into the team task acceptance portrait sub-pool, and embed the individual task satisfaction score threshold into the individual task acceptance portrait sub-pool. When the corresponding team comprehensive evaluation score is lower than the team comprehensive evaluation score threshold, the corresponding team will be deleted from the team task acceptance portrait sub-pool;

[0105] S117. If the personal task satisfaction score of a non-team member is lower than the personal task satisfaction score threshold, the corresponding individual will be deleted from the personal task acceptance portrait sub-pool. If the personal task satisfaction score of an individual in the team is lower than the personal task satisfaction score threshold, an alarm will be issued to the team as a whole.

[0106] The dynamic task acceptance portrait pool constructed by this process ensures the objectivity and accuracy of the evaluation results by comprehensively considering multi-dimensional information such as the professional skill proficiency, task completion history, etc. of team members and non-team individuals; secondly, by setting score thresholds to screen teams and individuals, entities with weak task acceptance capabilities are effectively eliminated, ensuring the scientificity and rationality of task allocation; thirdly, the team task acceptance portrait sub-pool is connected with the individual task acceptance portrait sub-pool to achieve seamless connection between teams and individuals, making it easier to quickly match the most suitable team or individual according to project requirements; finally, this process not only optimizes resource allocation, but also improves the level of refinement of team management, providing a strong guarantee for the smooth completion of tasks, thereby achieving a dual improvement in the efficiency and quality of task allocation.

[0107] S2. Obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the list of subtasks after splitting and the corresponding attribute data;

[0108] Furthermore, in this embodiment, the step of obtaining the split subtask list includes:

[0109] S201, obtaining overall information of the released task, and extracting the task cycle information and overall required skill type information corresponding to the released task through the configured entity extraction algorithm;

[0110] Furthermore, in this embodiment, the entity extraction algorithm includes a Bert model pre-trained in Chinese and English;

[0111] S202, setting a cycle splitting threshold. If the acquired task cycle is greater than the cycle splitting threshold, inputting the overall information of the released task into the comprehensive fuzzy evaluation algorithm to evaluate the urgency score of each stage of the subtask in the overall task;

[0112] Furthermore, in this embodiment, each stage subtask is a stage division of the overall task according to Party A's improvement requirements for a certain part of the overall task;

[0113] Furthermore, in this embodiment, the period splitting threshold is determined according to the length of time to complete the task setting. For example, tasks with a period of more than one month are split, and tasks within one month are not split.

[0114] S203, according to the obtained urgency scores of the subtasks in each stage, the overall task is divided into an early subtask set, a mid-term subtask set and a late subtask set; further, the urgency scores of the subtasks in each stage are divided according to the deadlines corresponding to each sub-function in the overall task; sub-functions with short deadlines are placed in the early subtask set, for example, a task has an overall cycle of two months, including six functional subtasks, but the cycle lengths of two of the functional subtasks are only 15 days, one subtask cycle is one month, and the other three cycle lengths are two months, then the 15-day subtask is placed in the early subtask set, the one-month subtask is placed in the mid-term subtask set, and the two-month subtask is placed in the late subtask set;

[0115] S204, for the obtained early subtask set, mid-term subtask set and late subtask set, obtain the occupational skill type required for the early subtask set, the occupational skill type required for the mid-term subtask set and the occupational skill type required for the late subtask set through the configured skill recognition model;

[0116] Furthermore, in this embodiment, the skill recognition model is constructed by a pre-trained RoBERTa model to identify the types of skills required for each subtask. For example, to build a data platform, the types of skills required include: front-end, back-end, database construction and other skill types;

[0117] S205. Functionally split the early subtask set, mid-term subtask set and late subtask set according to the required occupational skill types obtained to obtain the early functional subtask set, mid-term functional subtask set and late functional subtask set, and use the obtained early functional subtask set, mid-term functional subtask set and late functional subtask set to construct a subtask list.

[0118] Furthermore, in this embodiment, each functional subtask in the corresponding early functional subtask set, mid-term functional subtask set and late functional subtask set in the subtask list is numbered in the order of logical implementation;

[0119] S206: If the acquired task period is less than or equal to the period splitting threshold, directly repeat S204-S205 to functionally split the overall task to obtain a functional subtask set and a subtask list;

[0120] S207. If, through the configured skill recognition model, only one type of professional skill is required for the overall task, the task will not be split, and the corresponding task will be marked for individual matching.

[0121] This process extracts task cycle information and required skill type information through the configured entity extraction algorithm, ensuring the accuracy of basic task information; secondly, a cycle splitting threshold is set to split tasks with a task cycle of more than one month, and the urgency score of each stage subtask is evaluated through the evaluation algorithm, dividing the task into early, mid-term and late subtask sets; this process determines the urgency of each subtask by considering its deadline, ensuring the rationality of task arrangement; thirdly, the type of professional skills required for each subtask is identified through the configured skill recognition model, further refining the task splitting; finally, the subtask set is functionally split according to the required skill type, and a subtask list is constructed; for tasks with a task cycle less than or equal to the cycle splitting threshold, functional splitting is performed directly, while for tasks with a single required skill type, no splitting is performed and personal matching and labeling is performed directly. This process not only improves the accuracy of task splitting, but also ensures the rationality and efficiency of task allocation.

[0122] S3. According to the dynamic task acceptance portrait pool, the list of split subtasks and the corresponding attribute data, the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the team's coverage of the task are obtained through the configured double matching model;

[0123] S4. Set matching rules. Match the corresponding teams according to the matching rules and the obtained matching scores and coverage. If all teams do not meet the matching rules, match the split subtasks with a single person, and configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with the individual. At the same time, continue to match the remaining subtasks with the teams until all subtasks are assigned;

[0124] Furthermore, the steps of performing task matching in this embodiment include:

[0125] S301, matching the subtask list after the corresponding task is split with the team task undertaking portrait subpool, and obtaining the matching score 1 between each team as a whole and the overall task through matching algorithm 1, and obtaining the matching score 2 between each team member and the required professional skill type in the subtask list through matching algorithm 2;

[0126] S302, calculating the coverage of the professional skills required in the subtask list by each team according to the intersection of the professional skill types required in the subtask list and the professional skill types corresponding to the team members and the professional skill types required in the subtask list;

[0127] S303, setting a matching score 1 threshold, a matching score 2 threshold, and a coverage threshold. If the number of teams that satisfy the matching score 1, matching score 2, and coverage that are all greater than the corresponding thresholds is greater than one, then according to the task initial delivery success rate of the corresponding team, the task is matched to the team with the highest task initial delivery success rate;

[0128] S304. If the initial task delivery success rate of all teams that meet the conditions in S303 is 0, the task will be matched to the team with the lowest task undertaking fee;

[0129] S305: If all teams do not meet the coverage threshold, then compare the matching score 1 and the matching score 2 with the corresponding thresholds. If there are more than one team that meets both the matching score 1 threshold and the matching score 2 threshold, match the functional subtask set covered by the team to the team with the largest coverage.

[0130] S306. If there is more than one team with the largest coverage, S303 and S304 will be repeated to match the team and the task.

[0131] S307. If there is more than one uncovered functional subtask left after matching in process S306, the remaining functional subtasks are matched with the unmatched teams that meet the conditions in S305. If there is a team whose matching score 1, matching score 2 and coverage are all greater than the corresponding threshold, the remaining functional subtasks are assigned to the team that meets the conditions, and the interface attributes of the functional subtasks assigned in process S306 adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned in S307.

[0132] Further, in this embodiment, the interface attributes of the functional subtasks assigned by the S306 process are configured to the remaining functional subtasks assigned by S307. The interface attributes here are the early functional subtask set, the mid-term functional subtask set and the late functional subtask set obtained after the overall task is split, and each functional subtask is numbered in the order of logical implementation; here, the functional subtasks assigned to the corresponding team of S306 and the corresponding team of S307 respectively and with adjacent numbers are configured with relevant information for the adjacent functional subtasks according to the information of the other functional subtask, so that even if they are assigned to two teams, they can be fully integrated in the later stage; illustratively, the functional subtask 1 in the early functional subtask set is assigned to the corresponding team of S306, and the functional subtask 2 is assigned to the corresponding team of S307. Before the assignment, the task information corresponding to the functional subtask 1 is made into an information interface configured to the functional subtask 2, and the task information corresponding to the functional subtask 2 is made into an information interface configured to the functional subtask 1, so that even if they are assigned to two different teams, when the final functional subtasks are integrated, they can be matched and integrated without distinction, and there will be no failure in the splicing of the functional subtasks.

[0133] S308. If there is no team that satisfies the matching score 1, matching score 2 and coverage that are all greater than the corresponding thresholds, the remaining functional subtasks are matched with individuals in the personal task undertaking portrait subpool. If the number of individuals with matching scores greater than the matching score 2 threshold for the same remaining functional subtask is greater than one, the corresponding remaining functional subtask is matched to the individual with the highest personal task satisfaction score until the remaining functional subtasks are matched;

[0134] S309, if the number of individuals with the highest individual task satisfaction scores is greater than 1, the task is matched to the individual with the highest corresponding skill proficiency assessment score, and the interface attributes of the functional subtasks assigned by the S306 process adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned by S307;

[0135] S310: If the acquired task period is less than or equal to the period splitting threshold, then repeat S206 and S301-S303. If there is a team that meets the condition of S303 and the number of teams is greater than 1, then match the task to the team with the smallest proportion of task completion time.

[0136] Furthermore, in step S310, the functional subtask set obtained by functional splitting that meets the conditions in S206 is allocated;

[0137] S311. If there is no team that meets the conditions of S303, repeat the process of S305-S309 to assign tasks;

[0138] S312. When there is only one type of professional skill required for the overall task, repeat S308-S309 to match individuals with tasks from the personal task acceptance portrait sub-pool, and match the task to the individual who meets the conditions in S308-S309.

[0139] The process ensures the quality and efficiency of task execution by breaking down the task into multiple subtasks and matching the specific professional skills required by the subtasks with the professional skills of the team or individuals. Secondly, through the dual matching model and coverage calculation, not only the overall capabilities of the team and the matching degree of the professional skills of individual members are considered, but also the comprehensive coverage of the skills required by the team for the subtasks is taken into account, thereby improving the reliability and success rate of task execution. In addition, the process sets clear matching rules and thresholds, and can dynamically adjust the matching strategy based on multiple dimensions such as the success rate of the first delivery of the task, the cost of task acceptance, and the coverage of skills, making the task allocation more reasonable. When the team cannot fully cover the subtasks, the process also provides a mechanism to match the remaining subtasks with individuals to ensure that all tasks can be handled in a timely and effective manner. Finally, by considering the task cycle, the time management of task allocation is further optimized to ensure that resources can be reasonably arranged within a tight time frame.

[0140] S5. Obtain the matching team or individual task completion status data in real time, and evaluate the team or individual task completion status through the configured evaluation model to obtain the completion status evaluation score, and feed back the obtained evaluation score to the dynamic task acceptance portrait pool to adjust or delete the team or individual attribute status in the pool.

[0141] Example 2

[0142] See also Figure 2 , another embodiment provided by the present invention: a business intelligence system based on semantic big data analysis, comprising: a portrait pool construction module, a task splitting module, a matching module and a feedback module;

[0143] The portrait pool construction module is used to construct a dynamic task undertaking portrait pool; the portrait pool construction module includes a data processing unit, a portrait unit and a portrait pool unit;

[0144] The data processing unit is used to obtain the completion status data of historical release tasks and the corresponding attribute distribution status data of the undertaking team and individuals, and perform standardized preprocessing on the obtained data;

[0145] The portrait unit constructs hierarchical team portraits and individual portraits based on the pre-processed team and individual attribute distribution status data through the configured natural language algorithm; the portrait pool unit uses the constructed hierarchical team portraits and individual portraits to construct a dynamic task acceptance portrait pool through the graph database;

[0146] The task splitting module is used to obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the differential subtask list and corresponding attribute data;

[0147] The matching module is used to match the published tasks with the teams or individuals in the dynamic task acceptance profile pool; the matching module includes a team matching unit, a matching judgment unit, and a matching optimization unit;

[0148] The team matching unit, based on the dynamic task acceptance profile pool, the list of split subtasks and the corresponding attribute data, obtains the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the team's coverage of the task through the configured dual matching model;

[0149] The matching judgment unit matches the corresponding team by setting matching rules according to the obtained matching scores and coverage. If all teams do not meet the matching rules, the split subtasks are matched with a single person.

[0150] The matching optimization unit is used to configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with individuals, and to continue matching the remaining subtasks with the team until all subtasks have been assigned;

[0151] The feedback module is used to match the team or individual task completion status for evaluation, and adjust or delete the team or individual attribute status in the dynamic task acceptance portrait pool based on the evaluation results.

[0152] Example 3

[0153] A computer-readable storage medium stores computer instructions, which, when executed, execute a semantic big data analysis method.

[0154] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements a semantic big data analysis method when executing the computer program.

[0155] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are within the protection of the present invention.

[0156] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A semantic big data analysis method, characterized in that the steps include: S1. Obtain the completion status data of historically released tasks and the corresponding attribute distribution status data of the undertaking team and individuals. Based on the acquired data, obtain the dynamic task undertaking portrait pool through the configured integration model; S2. Obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the list of subtasks after splitting and the corresponding attribute data; S3. According to the dynamic task acceptance portrait pool, the list of split subtasks and the corresponding attribute data, the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the team's coverage of the task are obtained through the configured double matching model; S4. Set matching rules. Match the corresponding teams according to the matching rules and the obtained matching scores and coverage. If all teams do not meet the matching rules, match the split subtasks with a single person, and configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with the individual. At the same time, continue to match the remaining subtasks with the teams until all subtasks are assigned; S5. Obtain the matching team or individual task completion status data in real time, and evaluate the team or individual task completion status through the configured evaluation model to obtain the completion status evaluation score, and feed back the obtained evaluation score to the dynamic task acceptance portrait pool to adjust or delete the team or individual attribute status in the pool.

2. The semantic big data analysis method according to claim 1, characterized in that: The dynamic task acceptance portrait pool includes a team task acceptance portrait sub-pool and a personal task acceptance portrait sub-pool; the steps of constructing the team task acceptance portrait sub-pool include: S101, obtaining the types and proficiency of professional skills of members of different teams, the number of complete or partial tasks undertaken in historical unit time, the maximum task load, the number of remaining tasks at the current moment, the types of tasks undertaken, the time proportion of task completion, the task undertaking fee, the initial delivery success rate of different types of tasks, the delivery success rate of different types of tasks, and text information on satisfaction with delivered tasks; S102, inputting the acquired text information into the configured entity extraction algorithm, obtaining team member skill attribute keyword information and task attribute keyword information in sequence, and automatically annotating the acquired team member skill attribute keyword information and task attribute keyword information with score tags through the trained automatic annotation model; S103, inputting the annotated team member skill attribute keyword information into the evaluation algorithm 1 to obtain the team's overall skill proficiency evaluation score and the corresponding skill proficiency evaluation score of each member; S104, inputting the annotated task attribute keyword information into the evaluation algorithm 2 to obtain the team's task acceptance evaluation score and the team's task satisfaction score; S105. Obtain a comprehensive team assessment score based on the obtained team overall skill proficiency score, team task capability assessment score, and team task satisfaction score; S106. According to the obtained keyword information of different teams and the corresponding evaluation scores input into the graph database, a team task undertaking portrait sub-pool is constructed, and the team name is used as the first-level node in the team task undertaking portrait sub-pool. At the same time, the obtained comprehensive evaluation score of each team is used as the sorting label of the first-level node to sort the teams in descending order. S107, using the professional skill type and proficiency keyword information of the corresponding team members as the secondary node, and using the obtained team overall skill proficiency score as the secondary node sorting label, to sort the team skill levels in descending order; S108. The keyword information of the attributes of the historical tasks undertaken by the corresponding team is used as the third-level node, and the obtained evaluation score of the team's ability to undertake tasks is used as the first-level sorting label of the third-level node, and the task satisfaction score is used as the second-level sorting label.

3. The semantic big data analysis method according to claim 2, characterized in that: The steps of constructing the personal task undertaking portrait sub-pool include: S109, obtaining the text information of S101 corresponding to the non-team individual, and obtaining the skill proficiency assessment score and personal task satisfaction score of the non-team individual through the process of S102-S104; S110, inputting the acquired non-team individual corresponding skill proficiency into the graph database, constructing a sub-pool of individual task acceptance portraits, using the corresponding individual name keywords as the first-level nodes in the sub-pool of individual task acceptance portraits, and using the acquired individual corresponding skill proficiency assessment scores as the first-level sorting labels; S111, mapping the occupational type and proficiency attribute information of the team members in the team task undertaking portrait sub-pool to the first-level node in the individual task undertaking portrait sub-pool, and using the obtained skill proficiency assessment score of the individual member as the corresponding first-level sorting label; S112. Arrange the personal information in the first-level nodes in the personal task acceptance portrait sub-pool in descending order according to the obtained first-level ranking labels of personal skill proficiency, and use the task attribute information of non-team individuals as the second-level nodes, and use the corresponding personal task satisfaction scores as the ranking labels in the second-level nodes; S113, mapping the team task satisfaction score to the individual task satisfaction score corresponding to the team member as the ranking label of the team member in the secondary node in the individual task acceptance portrait sub-pool; S114. Sort the task attribute information of team members and non-team members according to the obtained sorting labels in the secondary nodes, and obtain a constructed sub-pool of personal task acceptance portraits.

4. The semantic big data analysis method according to claim 3, characterized in that: The steps of constructing the dynamic task acceptance portrait pool include: S115. According to the mapping relationship between the team members and the personal task acceptance portrait sub-pool in S113, the personal task acceptance portrait sub-pool and the team task acceptance portrait sub-pool are connected to obtain a constructed dynamic task acceptance portrait pool; S116. Set a team comprehensive evaluation score threshold and an individual task satisfaction score threshold, and embed the team comprehensive evaluation score threshold into the team task acceptance portrait sub-pool, and embed the individual task satisfaction score threshold into the individual task acceptance portrait sub-pool. When the corresponding team comprehensive evaluation score is lower than the team comprehensive evaluation score threshold, the corresponding team will be deleted from the team task acceptance portrait sub-pool; S117. If the personal task satisfaction score of a non-team member is lower than the personal task satisfaction score threshold, the corresponding individual will be deleted from the personal task acceptance portrait sub-pool. If the personal task satisfaction score of an individual in the team is lower than the personal task satisfaction score threshold, an alarm will be issued to the team as a whole.

5. The semantic big data analysis method according to claim 4, characterized in that: The step of obtaining the split subtask list in S2 includes: S201, obtaining overall information of the released task, and extracting the task cycle information and overall required skill type information corresponding to the released task through the configured entity extraction algorithm; S202, setting a cycle splitting threshold. If the acquired task cycle is greater than the cycle splitting threshold, inputting the overall information of the released task into the comprehensive fuzzy evaluation algorithm to evaluate the urgency score of each stage of the subtask in the overall task; S203, dividing the overall task into an early subtask set, a mid-term subtask set, and a late subtask set according to the obtained urgency scores of the subtasks at each stage; S204, for the obtained early subtask set, mid-term subtask set and late subtask set, obtain the occupational skill type required for the early subtask set, the occupational skill type required for the mid-term subtask set and the occupational skill type required for the late subtask set through the configured skill recognition model; S205. Functionally split the early subtask set, mid-term subtask set and late subtask set according to the required occupational skill types obtained to obtain the early functional subtask set, mid-term functional subtask set and late functional subtask set, and use the obtained early functional subtask set, mid-term functional subtask set and late functional subtask set to construct a subtask list.

6. The semantic big data analysis method according to claim 5, characterized in that: The step of obtaining the split subtask list in S2 further includes: S206: If the acquired task period is less than or equal to the period splitting threshold, directly repeat S204-S205 to functionally split the overall task to obtain a functional subtask set and a subtask list; S207. If, through the configured skill recognition model, only one type of professional skill is required for the overall task, the task will not be split, and the corresponding task will be marked for individual matching.

7. The semantic big data analysis method according to claim 6, characterized in that: The steps of performing task matching in S3 and S4 include: S301. If the acquired task cycle is greater than the cycle splitting threshold, the subtask list after the corresponding task splitting is matched with the team task undertaking portrait subpool, and the matching score 1 between each team as a whole and the overall task is obtained through matching algorithm 1. At the same time, the matching score 2 between each team member and the required professional skill type in the subtask list is obtained through matching algorithm 2; S302, calculating the coverage of the professional skills required in the subtask list by each team according to the intersection of the professional skill types required in the subtask list and the professional skill types corresponding to the team members and the professional skill types required in the subtask list; S303, setting a matching score 1 threshold, a matching score 2 threshold, and a coverage threshold. If the number of teams that satisfy the matching score 1, matching score 2, and coverage that are all greater than the corresponding thresholds is greater than one, then according to the task initial delivery success rate of the corresponding team, the task is matched to the team with the highest task initial delivery success rate; S304. If the initial task delivery success rate of all teams that meet the conditions in S303 is 0, the task will be matched to the team with the lowest task undertaking fee; S305: If all teams do not meet the coverage threshold, then compare the matching score 1 and the matching score 2 with the corresponding thresholds. If there are more than one team that meets both the matching score 1 threshold and the matching score 2 threshold, match the functional subtask set covered by the team to the team with the largest coverage. S306. If there is more than one team with the largest coverage, repeat S303 and S304 to match the team and the task.

8. The semantic big data analysis method according to claim 7, characterized in that: The steps of performing task matching in S3 and S4 further include: S307. If there is more than one uncovered functional subtask left after matching in process S306, the remaining functional subtasks are matched with the unmatched teams that meet the conditions in S305. If there is a team whose matching score 1, matching score 2 and coverage are all greater than the corresponding threshold, the remaining functional subtasks are assigned to the team that meets the conditions, and the interface attributes of the functional subtasks assigned in process S306 adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned in process S307. S308. If there is no team that satisfies the matching score 1, matching score 2 and coverage that are all greater than the corresponding thresholds, the remaining functional subtasks are matched with individuals in the personal task undertaking portrait subpool. If the number of individuals with matching scores greater than the matching score 2 threshold for the same remaining functional subtask is greater than one, the corresponding remaining functional subtask is matched to the individual with the highest personal task satisfaction score until the remaining functional subtasks are matched; S309, if the number of individuals with the highest individual task satisfaction scores is greater than 1, the task is matched to the individual with the highest corresponding skill proficiency assessment score, and the interface attributes of the functional subtasks assigned by the S306 process adjacent to the remaining functional subtasks are configured to the remaining functional subtasks assigned by S307; S310: If the acquired task period is less than or equal to the period splitting threshold, then repeat S206 and S301-S303. If there is a team that meets the condition of S303 and the number of teams is greater than 1, then match the task to the team with the smallest proportion of task completion time. S311. If there is no team that meets the conditions of S303, repeat the process of S305-S309 to assign tasks; S312. When there is only one type of professional skill required for the overall task, repeat S308-S309 to match individuals with tasks from the personal task acceptance portrait sub-pool, and match the task to the individual who meets the conditions in S308-S309.

9. A business intelligence system based on semantic big data analysis, which is used to implement the semantic big data analysis method according to any one of claims 1 to 8, characterized in that: include: Portrait pool building module, task splitting module and matching module; The portrait pool construction module includes a data processing unit, a portrait unit and a portrait pool unit; The data processing unit is used to obtain the completion status data of historically released tasks and the corresponding attribute distribution status data of the undertaking team and individuals, and perform standardized preprocessing on the obtained data; The portrait unit constructs hierarchical team portraits and personal portraits through a configured natural language algorithm according to the pre-processed team and personal attribute distribution state data; the portrait pool unit constructs a dynamic task acceptance portrait pool through a graph database using the constructed hierarchical team portraits and personal portraits; The task splitting module is used to obtain the attribute data of the published task, split the published task through the configured rule splitting model, and obtain the differential subtask list and corresponding attribute data; The matching module includes a team matching unit, a matching determination unit and a matching optimization unit; The team matching unit obtains the matching score between the team and the task, the matching score between the team members and the corresponding subtasks, and the coverage of the team to the task through the configured dual matching model according to the dynamic task acceptance portrait pool, the split subtask list and the corresponding attribute data; The matching determination unit matches the corresponding teams by setting matching rules according to the obtained matching scores and coverage. If all teams do not meet the matching rules, the split subtasks are matched with a single person. The matching optimization unit is used to configure the upstream and downstream subtask interface attributes for the subtasks that have been matched with the individuals, and to continue matching the remaining subtasks with the teams until all the subtasks have been assigned.

10. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the semantic big data analysis method described in any one of claims 1 to 8 is executed.

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