Information filtering methods, devices, terminal equipment, and storage media

Abstract

This application discloses an information filtering method, apparatus, terminal device, and storage medium, relating to the field of information processing. The method includes: acquiring participant information and task information; acquiring a participant set based on the participant information; formulating a filtering strategy based on the task information and participant information; and filtering a task executor information set from the participant set based on the filtering strategy. This invention rationally filters task executors, improving the rationality of task processing.

Description

Technical Field

[0001] This application relates to the field of information processing, and more particularly to an information filtering method, apparatus, terminal equipment, and storage medium. Background Technology

[0002] With the continuous development of workflow technology, various open-source process engine frameworks on the market (such as osworkflow, jbpm, activiti, flowable, and camunda) are becoming more mature in terms of node state control and process flow.

[0003] However, assigning appropriate participants to process activities remains a key issue that workflow engines need to address. Current workflow standards organizations only suggest that when multiple participants are assigned to a specific task, participants can be assigned to that task, but they do not select the actual executor from the given task participants, which is precisely a crucial part of improving the rationality of task processing. Summary of the Invention

[0004] The main purpose of this application is to provide an information filtering method, apparatus, terminal equipment, and storage medium, which aims to reasonably filter task executors and improve the rationality of task processing.

[0005] To achieve the above objectives, this application provides an information filtering method, which includes:

[0006] Obtain participant and task information;

[0007] Based on the participant information, obtain the participant set;

[0008] A screening strategy is formulated based on the task information and participant information;

[0009] Based on the filtering strategy, a set of task executor information is selected from the set of participants.

[0010] Optionally, the screening strategy includes an assignment strategy, a restriction strategy, and a robot strategy, and the step of screening the task executor information set from the participant set based on the screening strategy includes:

[0011] Based on the assignment strategy, candidates who meet the requirements are selected from the set of participants, and candidates who do not meet the requirements are removed from the set of participants based on the restriction strategy, thus obtaining a candidate set.

[0012] Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the candidate set to obtain the task executor information set.

[0013] Optionally, the task information includes a participation mode, the filtering strategy further includes a selection strategy, and the steps of filtering qualified candidates from the participant set based on the assignment strategy and removing unqualified candidates from the participant set based on the restriction strategy to obtain a candidate set further include:

[0014] Based on the participation mode, the required number of participants for the task is determined;

[0015] If the number of people subject to constraint is less than the number of people in the candidate set, then the candidate set is selected according to the selection strategy to obtain the selected candidate set.

[0016] The step of analyzing the number of robots required to process the task based on the robot strategy, adding the corresponding number of robots to the candidate set, and obtaining the task executor information set includes:

[0017] Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the selected candidate set to obtain the task executor information set.

[0018] Optionally, the selection strategy includes an automatic selection mode, and the step of selecting from the candidate set according to the selection strategy to obtain the selected candidate set includes:

[0019] The candidate set is selected by using preset automatic selection rules to obtain the selected candidate set.

[0020] Optionally, the automatic selection rules include equal competition rules, and the step of filtering the candidate set according to the preset automatic selection rules to obtain the selected candidate set includes:

[0021] The selected candidate set is obtained by randomly selecting candidates from the candidate set using the fair competition rules.

[0022] Optionally, the participant information includes their busy / idle status, the automatic selection rule includes a balanced competition rule, and the step of filtering the candidate set using the preset automatic selection rule to obtain the selected candidate set includes:

[0023] By analyzing the busy / idle status using the equilibrium competition rule, a selection is made from the candidate set to obtain the selected candidate set.

[0024] Optionally, the participant information includes experience value and ability value, the automatic selection rule includes a priority competition rule, and the step of filtering the candidate set according to the preset automatic selection rule to obtain the selected candidate set includes:

[0025] By analyzing the experience value and the ability value through the priority competition rule, the selected candidate set is obtained by filtering from the candidate set.

[0026] Optionally, the selection strategy further includes a manual selection mode, wherein the step of selecting from the candidate set according to the selection strategy to obtain the selected candidate set includes:

[0027] Get manually selected information;

[0028] Based on the manually selected information, the candidate set is selected to obtain the selected candidate set.

[0029] This application also proposes an information filtering device, which includes:

[0030] The information acquisition module is used to acquire participant information and task information;

[0031] The set selection module is used to obtain a set of participants based on the participant information;

[0032] The strategy formulation module is used to formulate a screening strategy based on the task information and participant information.

[0033] The filtering module is used to filter out a set of task executor information from the set of participants based on the filtering strategy.

[0034] This application also proposes a terminal device, which includes a memory, a processor, and an information filtering program stored in the memory and executable on the processor. When the information filtering program is executed by the processor, it implements the steps of the information filtering method described above.

[0035] This application also proposes a computer-readable storage medium storing an information filtering program, which, when executed by a processor, implements the steps of the information filtering method described above.

[0036] The information filtering method, apparatus, terminal device, and storage medium proposed in this application involve: acquiring participant information and task information; obtaining a participant set based on the participant information; formulating a filtering strategy based on the task information and participant information; and filtering a task executor information set from the participant set based on the filtering strategy. Specifically, by acquiring participant information, the corresponding participant set, and task information, a filtering strategy is formulated based on the participant information and task information. The filtering strategy includes dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing. Then, suitable task executors are selected from the participant set using the filtering strategy to obtain a task executor information set, thereby reasonably filtering task executors and improving the rationality of task processing. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the functional modules of the terminal device to which the information filtering device of this application belongs;

[0038] Figure 2 This is a flowchart illustrating a first exemplary embodiment of the information filtering method of this application;

[0039] Figure 3 This is a flowchart illustrating a second exemplary embodiment of the information filtering method of this application;

[0040] Figure 4 This is a schematic diagram of the screening strategy involved in this application;

[0041] Figure 5 This is a schematic diagram of the information screening process involved in this application.

[0042] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0043] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0044] The main solution of this application embodiment is as follows: First, obtain participant information and task information. Second, obtain a participant set based on the participant information. Third, formulate a screening strategy based on the task information and participant information. Fourth, based on the screening strategy, select a task executor information set from the participant set. Specifically, by obtaining participant information, the corresponding participant set, and task information, a screening strategy is formulated based on the participant information and task information. The screening strategy includes dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing. Then, suitable task executors are selected from the participant set using the screening strategy to obtain a task executor information set, thereby reasonably selecting task executors and improving the rationality of task processing.

[0045] This application embodiment takes into account that, with the continuous development of workflow technology, various open-source process engine frameworks (such as osworkflow, jbpm, activiti, flowable and camunda) on the market are becoming more mature in terms of node state control and process flow.

[0046] However, assigning appropriate participants to process activities remains a key issue that workflow engines need to address. Current workflow standards organizations only suggest that when multiple participants are assigned to a specific task, participants can be assigned to that task, but they do not select the actual executor from the given task participants, which is precisely a crucial part of improving the rationality of task processing.

[0047] Based on this, this application proposes a solution, which specifically involves obtaining participant information and the corresponding participant set and task information, formulating a screening strategy based on the participant information and task information, wherein the screening strategy includes dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing, and then using the screening strategy to select suitable task executors from the participant set to obtain a task executor information set.

[0048] Specifically, refer to Figure 1 , Figure 1 This is a schematic diagram of the functional modules of the terminal device to which the information filtering device of this application belongs. The information filtering device can be a data processing device independent of the terminal device, or it can be carried on the terminal device in the form of hardware or software.

[0049] In this embodiment, the terminal device to which the information filtering device belongs includes at least an output module 110, a processor 120, a memory 130, and a communication module 140.

[0050] The memory 130 stores the operating system and information filtering program to obtain participant information and task information; obtain a participant set based on the participant information; formulate a filtering strategy based on the task information and participant information; and, based on the filtering strategy, filter out a task executor information set from the participant set and store it in the memory 130. The output module 110 can be a display screen, speaker, etc. The communication module 140 can include a WIFI module, a mobile communication module, and a Bluetooth module, etc., and communicates with external devices or servers through the communication module 140.

[0051] When the information filtering program in memory 130 is executed by the processor, it performs the following steps:

[0052] Obtain participant information and task information; obtain a participant set based on the participant information; formulate a screening strategy based on the task information and participant information;

[0053] Based on the filtering strategy, a set of task executor information is selected from the set of participants.

[0054] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0055] Based on the assignment strategy, candidates who meet the requirements are selected from the set of participants, and candidates who do not meet the requirements are removed from the set of participants based on the restriction strategy, thus obtaining a candidate set.

[0056] Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the candidate set to obtain the task executor information set.

[0057] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0058] Based on the participation mode, the required number of participants for the task is determined;

[0059] If the number of people subject to constraint is less than the number of people in the candidate set, then the candidate set is selected according to the selection strategy to obtain the selected candidate set.

[0060] The step of analyzing the number of robots required to process the task based on the robot strategy, adding the corresponding number of robots to the candidate set, and obtaining the task executor information set includes:

[0061] Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the selected candidate set to obtain the task executor information set.

[0062] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0063] The candidate set is selected by using preset automatic selection rules to obtain the selected candidate set.

[0064] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0065] The selected candidate set is obtained by randomly selecting candidates from the candidate set using the fair competition rules.

[0066] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0067] By analyzing the busy / idle status using the equilibrium competition rule, a selection is made from the candidate set to obtain the selected candidate set.

[0068] Furthermore, when the information filtering program in memory 130 is executed by the processor, it also performs the following steps:

[0069] By analyzing the experience value and the ability value through the priority competition rule, the selected candidate set is obtained by filtering from the candidate set.

[0070] This embodiment, through the above-described scheme, obtains participant information and task information; obtains a participant set based on the participant information; formulates a screening strategy based on the task information and participant information; and selects a task executor information set from the participant set based on the screening strategy. Specifically, it obtains participant information, the corresponding participant set, and task information; formulates a screening strategy based on the participant information and task information, wherein the screening strategy includes dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing; and then selects suitable task executors from the participant set using the screening strategy to obtain a task executor information set, thereby reasonably selecting task executors and improving the rationality of task processing.

[0071] Based on, but not limited to, the terminal device architecture described above, this application proposes method embodiments.

[0072] Reference Figure 2 , Figure 2 This is a flowchart illustrating a first exemplary embodiment of the information filtering method of this application.

[0073] An embodiment of the present invention provides an information filtering method, the method comprising:

[0074] Step S10: Obtain participant information and task information;

[0075] Currently popular open-source workflow engines only suggest what to do when multiple participants are assigned to a specific task, but they do not explain how to select the final executor from the given task participants, which is a crucial part of improving the rationality of task processing.

[0076] This embodiment establishes a mapping relationship between personnel organization description and workflow process description at the system level, and selects appropriate participants for workflow tasks and activities.

[0077] Specifically, the process involves obtaining participant information and the corresponding participant set and task information, formulating a screening strategy based on the participant and task information, including dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing, and then using the screening strategy to select suitable task executors from the participant set to obtain a task executor information set.

[0078] First, it is necessary to obtain participant information and task information. Specifically, participant information may include: Name: the participant's full name or surname; Contact information: the participant's phone number, email address or other contact information; Position or role: the participant's position or role in the organization or project, such as manager, developer, designer, etc.; Company or organization name: the name of the company, organization or team to which the participant belongs.

[0079] Specifically, task information may include: Task Name: a brief description or title of the task; Task Description: a detailed description and requirements of the task, including the task's objectives, content, scope, and expected results; Task Deadline: the deadline or end date by which participants must complete the task; Task Priority: the importance or urgency of the task, used to determine the priority order of tasks; Designated Executor: the person who assigns the task to the participants, i.e., the person responsible for assigning and delegating the task; and the authority to process the task.

[0080] Step S20: Obtain the participant set based on the participant information;

[0081] The participant set is obtained based on the participant information. This participant set includes all participants, which is a relatively large range. The purpose of this embodiment is to gradually narrow down from a large set to a smaller set, and then select the most suitable executor from among them.

[0082] Step S30: Develop a screening strategy based on the task information and participant information;

[0083] Step S40: Based on the filtering strategy, filter out the task executor information set from the participant set.

[0084] Specifically, appropriate screening strategies are developed based on task information and participant information.

[0085] First, a screening strategy needs to be developed based on the task information. This involves assessing each participant's skills, experience, and abilities according to the task's requirements and objectives. Their expertise and skill level in the relevant field, as well as their past performance on similar tasks, should be considered. This will help determine which participants are best suited to perform the specific task and possess the necessary abilities and qualities to complete it.

[0086] Secondly, a selection strategy needs to be developed based on participant information, taking into account participant availability and resource load. Understanding each participant's schedule, workload, and other tasks is crucial to ensuring they have sufficient time and energy to perform their assigned tasks. Avoid assigning too many tasks to any one participant to prevent over-concentration of resources or delays in task completion.

[0087] Understandably, the collaboration and communication needs among participants should also be considered when developing a selection strategy. Depending on the nature and complexity of the task, determine whether teamwork or cross-departmental collaboration is required. Ensure cooperation and coordination among different participants in task allocation to facilitate information sharing, problem-solving, and smooth task progress.

[0088] In addition, task prioritization strategies can be developed based on task priority and urgency. Specifically, high-priority or urgent tasks can be assigned to participants with higher skill levels or more experience to ensure timely and high-quality completion.

[0089] Furthermore, before assigning tasks, communication and negotiation can be conducted with participants to specify manual selection rules, allowing suitable participants to be manually selected as the final task executors. This ensures that the final executors understand the task requirements, objectives, and expected results, and confirms that they have sufficient resources and support to execute the task. Clear task instructions and feedback mechanisms are provided so that participants clearly understand the task's execution standards and expected outcomes.

[0090] Understandably, after the above steps, the task executor information set can be selected from the participant set based on the screening strategy.

[0091] The information filtering method proposed in this application involves obtaining participant information and task information; obtaining a participant set based on the participant information; formulating a filtering strategy based on the task information and participant information; and filtering a task executor information set from the participant set based on the filtering strategy. Specifically, it involves obtaining participant information, the corresponding participant set, and task information; formulating a filtering strategy based on the participant information and task information; and then using the filtering strategy to filter suitable task executors from the participant set to obtain a task executor information set, thereby reasonably filtering task executors and improving the rationality of task processing.

[0092] Reference Figure 3 , Figure 3 This is a flowchart illustrating a second exemplary embodiment of the information filtering method of this application.

[0093] Based on the first embodiment, a second embodiment of this application is proposed. The difference between the second embodiment and the first embodiment is that the screening strategy includes an assignment strategy, a restriction strategy, and a robot strategy.

[0094] Step S40, which involves selecting the task executor information set from the participant set based on the filtering strategy, is further refined. The refined steps may include:

[0095] In this embodiment, step S40, the step of filtering the task executor information set from the participant set based on the filtering strategy, includes:

[0096] Step S41: Based on the assignment strategy, select qualified candidates from the participant set; based on the restriction strategy, remove unqualified candidates from the participant set to obtain a candidate set.

[0097] To improve screening efficiency and reduce the complexity of participant selection, a batch assignment model for task activity participants needs to be established to determine the candidate set for the task activity. Specifically, a selection of qualified candidates can be screened from the participant set using an assignment strategy.

[0098] More specifically, the batch assignment mode can be used to consider multiple participants simultaneously and quickly select suitable candidates based on specific rules and conditions.

[0099] When establishing a batch assignment model, the requirements and conditions of the task activity must first be clearly defined. This may include specific rules regarding the participants' roles, departments, skills, etc. Based on these rules, participants who meet the conditions can be selected to form a candidate set.

[0100] Furthermore, automated tools or algorithms can be used to perform batch assignments. These tools can automatically match candidates with task activities based on predefined rules and conditions, selecting the most suitable participants from the set of participants.

[0101] The advantage of an assignment strategy is that it simplifies the participant selection process and provides greater efficiency and consistency. By using clearly defined rules, a candidate set can be generated quickly, reducing the need for manual intervention and improving overall work efficiency.

[0102] Furthermore, the restriction strategy is as follows: Each participant usually has multiple permissions, so it is necessary to remove participants who do not meet the hard constraints based on task information or participant information. For example, participants who do not meet the requirements can be removed based on the product permissions of the task processing or the mutual exclusion of nodes. Specifically, in a unified process instance, adjacent nodes cannot select the same participant for processing.

[0103] Understandably, combining assignment and constraint strategies can determine the candidate set, thereby obtaining a candidate set that meets the task requirements and participant requirements.

[0104] Step S44: Based on the robot strategy, analyze the number of robots required to process the task, add the corresponding number of robots to the candidate set, and obtain the task executor information set.

[0105] Understandably, some tasks require robots or can be automated without human intervention. Therefore, adding robots can meet task requirements and improve efficiency. Specifically, a robot strategy can be used to analyze the number of robots needed to process the task and then add the corresponding number of robots to the candidate set. Alternatively, a robot strategy can be used to replace a certain number of candidates, thereby reducing labor costs and increasing the level of task automation.

[0106] Specifically, after determining the candidate set, it is necessary to determine whether the task can be processed automatically. If it can be processed automatically, the corresponding processing robot needs to be placed into the candidate set.

[0107] Apply the robot strategy to the task execution set. If the task needs to be handled by a robot, the corresponding robot number needs to be added to the candidate set.

[0108] Furthermore, if no robot processing is required, the current candidate set becomes the final task execution set.

[0109] The information filtering method proposed in this application involves filtering qualified candidates from the participant set based on the assignment strategy, removing unqualified candidates from the participant set based on the restriction strategy, and obtaining a candidate set. Based on the robot strategy, the number of robots required to process the task is analyzed, and a corresponding number of robots are added to the candidate set to obtain the task executor information set. By using the filtering strategy, suitable task executors are reasonably selected from the participant set, thereby improving the rationality of task processing.

[0110] Based on the second embodiment, a third embodiment of this application is proposed. The difference between the third embodiment and the second embodiment is that the task information includes a participation mode, and the filtering strategy further includes a selection strategy.

[0111] Step S41, after obtaining the candidate set, involves filtering qualified candidates from the participant set based on the assignment strategy and removing unqualified candidates from the participant set based on the restriction strategy. This supplementary step may include:

[0112] In this embodiment, after step S41, which involves selecting qualified candidates from the participant set based on the assignment strategy and removing unqualified candidates from the participant set based on the restriction strategy to obtain the candidate set, the method further includes:

[0113] Step S42: Based on the participation mode, obtain the number of participants required for the task.

[0114] Specifically, the number of participants is the number of people required for the task, and the participation modes include full participation, exclusive participation, and partial participation. Full participation means that all candidates in the candidate set need to participate in the execution of the task; exclusive participation means that only one candidate needs to participate in the execution of the task; and partial participation means that only a portion of the candidate set needs to participate.

[0115] In cases of exclusive participation and partial participation, the number of participants may be less than the number of participants in the candidate set. In such cases, a selection strategy is needed to select candidates from the candidate set who meet the number of participants requirement.

[0116] Step S43: If the number of people subject to constraint is less than the number of people in the candidate set, then the candidate set is selected according to the selection strategy to obtain the selected candidate set.

[0117] When the number of people subject to constraints is less than the number of people in the candidate set, the candidate set needs to be reduced to the number of people subject to constraints according to the selection strategy, until the selected candidate set is reached.

[0118] The selection strategy can include two modes: one is that the system selects automatically according to the rules; the other is that business personnel select manually.

[0119] The choice of selection strategy depends on the specific situation and needs. In most cases, the automatic selection mode can provide efficient, fast, and accurate results. However, for certain special tasks or specific requirements, the manual selection mode may be more suitable in order to fully consider the factors of subjective judgment and professional knowledge.

[0120] Whether selected automatically or manually, the resulting candidate pool will meet the headcount requirements and include the most suitable candidates. This will ensure the smooth progress of the task and provide a foundation for the success of the organization or project.

[0121] The information filtering method proposed in this application obtains the required number of participants for a task based on the participation mode. If the required number of participants is less than the number of participants in the candidate set, the candidate set is selected according to the selection strategy to obtain a selected candidate set. The step of analyzing the number of robots required to process the task based on the robot strategy and adding a corresponding number of robots to the candidate set to obtain the task executor information set includes: analyzing the number of robots required to process the task based on the robot strategy and adding a corresponding number of robots to the selected candidate set to obtain the task executor information set. Through a suitable selection strategy, the selected candidate set will meet the requirement of the required number of participants and include the most suitable candidates. This will ensure the smooth progress of the task and provide a foundation for the success of the organization or project.

[0122] Based on the third embodiment, a fourth embodiment of this application is proposed. The difference between the fourth embodiment and the third embodiment is that the selection strategy includes an automatic selection mode.

[0123] Step S43, which involves selecting candidates from the candidate set according to the selection strategy, is further refined. The refined steps may include:

[0124] In this embodiment, step S43, selecting candidates from the candidate set according to the selection strategy to obtain the selected candidate set, includes:

[0125] Step S431: Select candidates from the candidate set using preset automatic selection rules to obtain the selected candidate set.

[0126] Specifically, the candidate selection process can be completed quickly and accurately using automated selection rules, resulting in a selected candidate set. By setting clear rules, such as priority competition rules, balanced competition rules, and equal competition rules, the system can automatically select the most suitable candidates based on the task requirements and conditions. This not only saves time and human resources but also reduces the impact of subjective factors on the selection process.

[0127] Furthermore, the automatic selection rules can be customized based on the characteristics of the task and the needs of the organization. For example, specific skill requirements, experience levels, educational backgrounds, etc., can be set as screening criteria. The system will then evaluate and rank candidates based on these criteria to determine the most suitable set of candidates.

[0128] The information filtering method proposed in this application selects candidates from the candidate set using preset automatic selection rules, thus obtaining the selected candidate set. This automatic selection method not only saves time and human resources but also reduces the influence of subjective factors on the selection process.

[0129] Based on the fourth embodiment, a fifth embodiment of this application is proposed. The difference between the fifth embodiment and the fourth embodiment is that the automatic selection rule includes a fair competition rule.

[0130] Step S431, which involves selecting candidates from the candidate set using preset automatic selection rules, can be further refined. The refined steps may include:

[0131] In this embodiment, step S431, which involves selecting candidates from the candidate set using preset automatic selection rules to obtain the selected candidate set, includes:

[0132] Step S4311: Randomly select from the candidate set according to the equal competition rule to obtain the selected candidate set;

[0133] Under the equal competition rule, all candidates have an equal chance of being selected, without any bias or special treatment. This rule ensures fairness and impartiality, avoids the possibility of subjective preference or unfair treatment, and thus fairly selects the final set of candidates.

[0134] The selection process can be conducted through random sampling, where each candidate has an equal chance of being selected. Systems or tools can use random number generators to implement this process, ensuring that each candidate has an equal probability of being selected. This ensures the objectivity and unbiasedness of the selection process.

[0135] The information filtering method proposed in this application randomly selects candidates from the candidate set through the equal competition rule to obtain the selected candidate set. The equal competition rule can ensure the objectivity and unbiasedness of the selection process.

[0136] Based on the fourth embodiment, a sixth embodiment of this application is proposed. The difference between the sixth embodiment and the fourth embodiment is that the automatic selection rule includes a fair competition rule.

[0137] Step S431, which involves selecting candidates from the candidate set using preset automatic selection rules, can be further refined. The refined steps may include:

[0138] In this embodiment, step S431, which involves selecting candidates from the candidate set using preset automatic selection rules to obtain the selected candidate set, includes:

[0139] Step S4312: Analyze the busy / idle status using the equilibrium competition rule, and select from the candidate set to obtain the selected candidate set.

[0140] The balanced competition rule allows for selection based on the current workload of each candidate in the pool. The principle of this rule is to prioritize assigning tasks to individuals with fewer current tasks, thereby achieving a balanced workload and ultimately resulting in a reasonably selected candidate pool.

[0141] Specifically, candidates are evaluated and compared based on their task load and workload. Everyone in the candidate pool is considered, and their current workload level is an important reference factor in the selection process. Priority is given to those with fewer tasks to ensure a more balanced allocation of tasks and avoid overburdening the candidates.

[0142] The information filtering method proposed in this application analyzes the busy / idle status through the balanced competition rule, selects candidates from the candidate set, and obtains the selected candidate set, so that the task allocation is more balanced and avoids the situation of excessive workload.

[0143] Based on the fourth embodiment, a seventh embodiment of this application is proposed. The difference between the seventh embodiment and the fourth embodiment is that the participant information includes experience value and ability value.

[0144] Step S431, which involves selecting candidates from the candidate set using preset automatic selection rules, can be further refined. The refined steps may include:

[0145] In this embodiment, step S431, which involves selecting candidates from the candidate set using preset automatic selection rules to obtain the selected candidate set, includes:

[0146] Step S4313: Analyze the experience value and the ability value using the priority competition rule, and filter from the candidate set to obtain the selected candidate set.

[0147] By employing a priority competition rule, selection can be made based on each individual's experience and ability level for a specific task within the candidate set, prioritizing participants with high experience and ability levels. The effectiveness of this algorithm depends on a reasonable assessment of the participants' abilities, thereby ensuring a well-balanced selection of the final candidate set.

[0148] Specifically, this selection method involves evaluating and comparing candidates' experience and abilities, prioritizing participants with extensive experience and high-level skills in the task area to ensure high-quality task execution and achievement of results.

[0149] To achieve a reasonable assessment of participants' abilities, various methods can be employed. For example, a candidate's work history, professional background, and skills certifications can be used to determine their experience and skill level. The system can then rank and sort candidates through data analysis and comprehensive evaluation, selecting the most suitable participants.

[0150] The information filtering method proposed in this application analyzes the experience value and the ability value through the priority competition rule, and filters from the candidate set to obtain the selected candidate set. The priority competition rule ensures high-quality execution of the task and achievement of results.

[0151] Based on the third embodiment, an eighth embodiment of this application is proposed. The difference between the eighth embodiment and the third embodiment is that the selection strategy further includes a manual selection mode.

[0152] Step S43, which involves selecting candidates from the candidate set according to the selection strategy, is further refined. The refined steps may include:

[0153] In this embodiment, step S43, selecting candidates from the candidate set according to the selection strategy to obtain the selected candidate set, includes:

[0154] Step S432: Obtain manual selection information;

[0155] Step S433: Select candidates from the candidate set according to the manually selected information to obtain the selected candidate set.

[0156] Specifically, business personnel manually select candidates based on their judgment and professional knowledge, thereby obtaining manually selected information. Then, based on the manually selected information, they select from the candidate set to obtain the selected candidate set.

[0157] This model is typically suitable for complex tasks or special situations where the system cannot fully automate the selection process. Business personnel can manually specify candidates, taking into account factors such as their abilities, experience, and interests, to ensure the optimal candidate combination. This ensures that the final selected candidate set possesses the necessary skills and adaptability, improving task execution effectiveness and success rate.

[0158] The information filtering method proposed in this application obtains manually selected information; and selects candidates from the candidate set based on the manually selected information to obtain the selected candidate set. By manually selecting, it can ensure that the final selected candidate set has the required capabilities and adaptability, thereby improving the execution effect and success rate of the task.

[0159] Figure 4 This is a schematic diagram of the screening strategy involved in this application;

[0160] Figure 5 This is a schematic diagram of the screening process involved in this application.

[0161] Furthermore, embodiments of this application also propose an information filtering device, the information filtering device comprising:

[0162] The information acquisition module is used to acquire participant information and task information;

[0163] The set selection module is used to obtain a set of participants based on the participant information;

[0164] The strategy formulation module is used to formulate a screening strategy based on the task information and participant information.

[0165] The filtering module is used to filter out a set of task executor information from the set of participants based on the filtering strategy.

[0166] The principle and implementation process of information filtering in this embodiment are explained in the above embodiments and will not be repeated here.

[0167] Furthermore, this application also proposes a terminal device, which includes a memory, a processor, and an information filtering program stored in the memory and executable on the processor. When the information filtering program is executed by the processor, it implements the steps of the information filtering method described above.

[0168] Since this information filtering program employs all the technical solutions of all the foregoing embodiments when executed by the processor, it has at least all the beneficial effects brought about by all the technical solutions of all the foregoing embodiments, which will not be elaborated here.

[0169] Furthermore, embodiments of this application also propose a computer-readable storage medium storing an information filtering program, which, when executed by a processor, implements the steps of the information filtering method described above.

[0170] Since this information filtering program employs all the technical solutions of all the foregoing embodiments when executed by the processor, it has at least all the beneficial effects brought about by all the technical solutions of all the foregoing embodiments, which will not be elaborated here.

[0171] Compared to existing technologies, the information filtering method, apparatus, terminal device, and storage medium proposed in this application obtain participant information and task information; obtain a participant set based on the participant information; formulate a filtering strategy based on the task information and participant information; and filter a task executor information set from the participant set based on the filtering strategy. Specifically, by obtaining participant information and the corresponding participant set and task information, a filtering strategy is formulated based on the participant information and task information. The filtering strategy includes dimensions such as assignment method, assignment constraints, participation mode, selection rules, competition strategy, and robot processing. Then, suitable task executors are selected from the participant set using the filtering strategy to obtain a task executor information set, thereby reasonably filtering task executors and improving the rationality of task processing.

[0172] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0173] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0174] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the methods of each embodiment of this application.

[0175] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An information filtering method, characterized in that, The information filtering method includes: Obtain participant and task information; Based on the participant information, obtain the participant set; A screening strategy is formulated based on the task information and participant information; Based on the filtering strategy, a set of task executor information is filtered out from the set of participants; The screening strategy includes an assignment strategy, a restriction strategy, and a robot strategy. The step of selecting the task executor information set from the participant set based on the screening strategy includes: Based on the assignment strategy, candidates who meet the requirements are selected from the set of participants, and candidates who do not meet the requirements are removed from the set of participants based on the restriction strategy, thus obtaining a candidate set. Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the candidate set to obtain the task executor information set. The step of removing candidates who do not meet the requirements from the set of participants based on the restriction strategy includes: Candidates who do not meet the requirements are removed based on the product permissions and node mutual exclusion for task processing. The node mutual exclusion means that adjacent nodes of the process instance cannot select the same participant for processing. The task information includes a participation mode, and the screening strategy further includes a selection strategy. Following the steps of selecting qualified candidates from the participant set based on the assignment strategy and removing unqualified candidates from the participant set based on the restriction strategy to obtain the candidate set, the process further includes: Based on the participation mode, the number of participants required for the task is obtained, wherein the number of participants required for the task refers to the number of participants required for the task, and the participation mode includes full participation, exclusive participation, and partial participation. If the number of people subject to constraint is less than the number of people in the candidate set, then the candidate set is selected according to the selection strategy to obtain the selected candidate set.

2. The information filtering method according to claim 1, characterized in that, The step of analyzing the number of robots required to process the task based on the robot strategy, adding the corresponding number of robots to the candidate set, and obtaining the task executor information set includes: Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the selected candidate set to obtain the task executor information set.

3. The information filtering method according to claim 2, characterized in that, The selection strategy includes an automatic selection mode, and the step of selecting from the candidate set according to the selection strategy to obtain the selected candidate set includes: The candidate set is selected by using preset automatic selection rules to obtain the selected candidate set.

4. The information filtering method according to claim 3, characterized in that, The automatic selection rules include equal competition rules. The step of filtering the candidate set according to the preset automatic selection rules to obtain the selected candidate set includes: The selected candidate set is obtained by randomly selecting candidates from the candidate set using the fair competition rules.

5. The information filtering method according to claim 3, characterized in that, The participant information includes their busy / idle status, the automatic selection rules include a balanced competition rule, and the step of filtering the candidate set using preset automatic selection rules to obtain the selected candidate set includes: By analyzing the busy / idle status using the equilibrium competition rule, a selection is made from the candidate set to obtain the selected candidate set.

6. The information filtering method according to claim 3, characterized in that, The participant information includes experience and ability values, the automatic selection rules include priority competition rules, and the step of filtering the candidate set using preset automatic selection rules to obtain the selected candidate set includes: By analyzing the experience value and the ability value through the priority competition rule, the selected candidate set is obtained by filtering from the candidate set.

7. The information filtering method according to claim 1, characterized in that, The selection strategy also includes a manual selection mode. The step of selecting candidates from the candidate set according to the selection strategy to obtain the selected candidate set includes: Get manually selected information; Based on the manually selected information, the candidate set is selected to obtain the selected candidate set.

8. An information filtering device, characterized in that, The information filtering device includes: The information acquisition module is used to acquire participant information and task information; The set selection module is used to obtain a set of participants based on the participant information; The strategy formulation module is used to formulate a screening strategy based on the task information and participant information. The filtering module is used to filter out a set of task executor information from the set of participants based on the filtering strategy. The screening strategy includes assignment strategy, restriction strategy, and robot strategy. The step of selecting the task executor information set from the participant set based on the screening strategy includes: Based on the assignment strategy, candidates who meet the requirements are selected from the set of participants, and candidates who do not meet the requirements are removed from the set of participants based on the restriction strategy, thus obtaining a candidate set. Based on the robot strategy, the number of robots required to process the task is analyzed, and the corresponding number of robots are added to the candidate set to obtain the task executor information set. The process of removing candidates who do not meet the requirements from the set of participants based on the restriction strategy includes: Candidates who do not meet the requirements are removed based on the product permissions and node mutual exclusion for task processing. The node mutual exclusion means that adjacent nodes of the process instance cannot select the same participant for processing. The task information includes a participation mode, and the screening strategy further includes a selection strategy. After selecting qualified candidates from the participant set based on the assignment strategy and removing unqualified candidates from the participant set based on the restriction strategy, the process of obtaining the candidate set further includes: Based on the participation mode, the number of participants required for the task is obtained. The number of participants refers to the number of people required for the task. The participation modes include full participation, exclusive participation, and partial participation. If the number of people subject to constraint is less than the number of people in the candidate set, then the candidate set is selected according to the selection strategy to obtain the selected candidate set.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and an information filtering program stored in the memory and executable on the processor. When the information filtering program is executed by the processor, it implements the steps of the information filtering method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an information filtering program, which, when executed by a processor, implements the steps of the information filtering method as described in any one of claims 1-7.

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