Task monitoring method, apparatus, device, and medium

Abstract

The present application relates to the technical field of task management, and discloses a task monitoring method, device, equipment and medium, comprising: obtaining initial task execution information of a task team, the task team comprising task members, the initial task execution information comprising task member information and member-assigned tasks; querying member task historical behavior data of the task members based on the task member information; generating a member task reminder strategy based on the member task historical behavior data and the member-assigned tasks; sending a task reminder to the task members based on the member task reminder strategy, and determining a member task execution progress according to response information of the task members to the task reminder; updating the initial task execution information to task execution update information based on the member task execution progress; and finally, visualizing the task execution update information to obtain visualized task progress information. The present application can be applied to the fields of financial technology and health care, and improves the accuracy of task monitoring.

Description

Technical Field

[0001] This invention relates to the field of task management technology, and in particular to a task monitoring method, apparatus, equipment and medium. Background Technology

[0002] Task monitoring is used to comprehensively supervise the team's task execution process and overall progress, in order to judge the efficiency and quality of task execution, thereby ensuring the orderly operation of various business tasks. Taking relevant business areas as an example, by monitoring team tasks in insurance business processing or medical service delivery, the entire process status of task execution can be controlled, thereby improving the standardization of industry business management and the overall work efficiency.

[0003] Currently, task monitoring typically involves managers manually compiling and analyzing data based on information reported by staff. However, this method relies heavily on the experience and data processing skills of managers, and is also prone to issues such as delays in manually reported information and omissions in data processing. Therefore, using manual analysis for task monitoring can easily lead to inaccurate monitoring results. As a result, improving the accuracy of task monitoring has become an urgent technical problem to be solved. Summary of the Invention

[0004] This invention provides an artificial intelligence task monitoring method, apparatus, computer equipment, and medium to improve the accuracy of task monitoring.

[0005] Firstly, a task monitoring method is provided, including: Obtain initial task execution information of the task team, wherein the task team includes one or more task members, and the initial task execution information includes task member information and the tasks assigned to the members; Based on the task member information, historical data is queried to obtain the member's historical task behavior data; Based on the member's historical task behavior data and the member's assigned tasks, a member task reminder strategy is generated; Based on the member task reminder strategy, task reminders are sent to the task members, and the task execution progress is determined according to the task members' response information to the task reminders. Based on the progress of the member tasks, the initial task execution information is updated to obtain task execution update information; Based on the task execution update information, visualization processing is performed to obtain visualized task progress information.

[0006] Secondly, a task monitoring device is provided, comprising: The information acquisition module is used to acquire the initial task execution information of the task team, wherein the task team includes one or more task members, and the initial task execution information includes task member information and the tasks assigned to the members; The data query module is used to perform historical data queries based on the task member information to obtain the member's historical task behavior data; The strategy generation module is used to generate member task reminder strategies based on the member's historical task behavior data and the member's assigned tasks; The progress monitoring module is used to send task reminders to the task members based on the member task reminder strategy, and to determine the task execution progress based on the task members' response information to the task reminders. The task update module is used to update the initial task execution information based on the execution progress of the member tasks, so as to obtain task execution update information; The task visualization module is used to perform visualization processing based on the task execution update information to obtain visualized task progress information.

[0007] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the task monitoring method described above.

[0008] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the task monitoring method described above.

[0009] In the aforementioned task monitoring method, device, computer equipment, and storage medium, the historical data of task members is queried to obtain their historical task behavior data. Based on this data and the assigned tasks, a task reminder strategy is generated, ensuring that the reminder strategy is highly adapted to the work habits of each task member. This effectively improves the accuracy and effectiveness of task reminders and reduces the interference of invalid reminders on the normal work of task members. Furthermore, according to the task reminder strategy, task members performing tasks are given regular reminders, and the task execution progress of each member is collected in real time. Based on this progress, the initial task execution information is updated to obtain the task execution progress. Updating task execution information ensures its timeliness and accuracy, preventing work continuity issues caused by information lag. It also improves the accuracy of task monitoring. Furthermore, visualizing task execution updates transforms abstract task progress data into intuitive views, allowing task team managers to monitor the overall task and sub-task progress in real time. This enables timely identification and adjustment of problems during task execution, optimizing team task management processes and improving overall team efficiency. It also allows each task member to clearly understand their own and the team's task progress, promoting collaboration among team members. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram of an application environment for a task monitoring method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a task monitoring method according to an embodiment of the present invention; Figure 3 yes Figure 2 A schematic diagram of a specific implementation method for step S20; Figure 4 yes Figure 2 A schematic diagram of a specific implementation method for step S30; Figure 5 yes Figure 4 A flowchart illustrating a specific implementation of step S31; Figure 6 This is a schematic diagram of the structure of a task monitoring device in one embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 8 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The task monitoring method provided in this embodiment of the invention can be applied to, for example... Figure 1 In this application environment, the client communicates with the server via a network. The server can obtain the initial task execution information of the task team, which includes one or more task members. The initial task execution information includes task member information and assigned tasks. Based on the task member information, the server performs historical data queries to obtain historical task behavior data of the members. Then, based on the historical task behavior data and the assigned tasks of the members, the server generates a member task reminder strategy. Subsequently, based on the member task reminder strategy, the server sends task reminders to the clients corresponding to the task members via the network, and determines the task execution progress of the members based on the response information of the task members to the task reminders from the clients. Based on the task execution progress, the server updates the initial task execution information to obtain task execution update information. Finally, based on the task execution update information, the server performs visualization processing to obtain visualized task progress information, and sends the visualized task progress information to the clients of relevant managers for display. This invention addresses team task management in scenarios such as insurance and medical services. By mining the historical work behavior characteristics of task members, it generates personalized task reminder strategies and achieves dynamic updates and visual displays of progress. This effectively improves the accuracy and effectiveness of task reminders, avoids information lag, and thus greatly enhances the accuracy of task monitoring and the overall work efficiency of the team. The client can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a dedicated server or a server cluster consisting of multiple servers. The invention will be described in detail below through specific embodiments.

[0014] Please see Figure 2 As shown, Figure 2 A flowchart illustrating the task monitoring method provided in this embodiment of the invention includes the following steps: S10: Obtain the initial task execution information of the task team. The task team includes one or more task members. The initial task execution information includes task member information and the tasks assigned to the members. S20: Based on the task member information, perform historical data queries to obtain the member's historical task behavior data; S30: Generate member task reminder strategies based on member task history behavior data and member assigned tasks; S40: Based on the member task reminder strategy, send task reminders to task members and determine the task execution progress based on the task members' response information to the task reminders; S50: Based on the progress of member task execution, update the initial task execution information to obtain task execution update information; S60: Based on the task execution update information, perform visualization processing to obtain visualized task progress information.

[0015] Steps S10 to S60, as illustrated in this embodiment, involve querying historical data of task members to obtain their historical task behavior data. Based on this data and the assigned tasks, a task reminder strategy is generated, ensuring that the reminder strategy is highly compatible with the work habits of each task member. This effectively improves the accuracy and effectiveness of task reminders and reduces the interference of invalid reminders on the normal work of task members. Furthermore, according to the task reminder strategy, task members performing tasks are given regular reminders, and the task execution progress of each member is collected in real time. Based on this progress, the initial task execution information is updated to obtain task execution update information. This information ensures the timeliness and accuracy of task execution updates, avoiding work continuity problems caused by information lag. It also improves the accuracy of task monitoring. Furthermore, visualizing task execution updates transforms abstract task progress data into an intuitive view, allowing task team managers to monitor the overall task and sub-task progress in real time, promptly identify and adjust problems during execution, optimize team task management processes, and improve overall team efficiency. It also allows each task member to clearly understand their own and the team's task progress, promoting collaboration among team members.

[0016] In step S10 of some embodiments, the initial task execution information refers to the basic information related to the task to be executed that is collected and organized in advance before the task monitoring work. It should be noted that the initial task execution information can be obtained by sorting and summarizing according to the actual work arrangement of the task team.

[0017] Task members refer to relevant personnel who participate in the execution of specific tasks.

[0018] Task member information consists of basic information about the task members, such as their identity identifier, workstation number, job position, and professional skills.

[0019] Member assignment tasks refer to specific sub-tasks assigned to each task member based on the overall task breakdown results, matching their job functions. For example, in the insurance policy review scenario, the member assignment task could be the verification of information on a single policy assigned to task member A. In the medical record organization and archiving scenario, the member assignment task could be the classification and information improvement of a single patient's medical record assigned to task member B.

[0020] In this embodiment, the task management terminal can first sort out the overall tasks to be executed according to the actual work needs of specific business scenarios, and break down the overall tasks according to job functions and task difficulty to determine the specific staff involved in the execution of the overall task, i.e., the task team. Generally speaking, the task team includes one or more task members. Then, based on the task member information such as identity identifier, work position number, job position, and professional ability, corresponding specific sub-tasks are assigned to each task member, thus forming the member allocation task. Finally, the task member information and the corresponding member allocation task information are structurally integrated to form complete initial task execution information. For example, in the scenario of insurance claims processing, a complex car insurance claim case can be broken down into sub-tasks such as document review, on-site inspection, loss assessment and calculation, and claims calculation. After assigning corresponding sub-tasks to the claims specialist, investigator, and accountant respectively, the task member information of the claims specialist, investigator, and accountant is integrated with the corresponding member allocation task information to form the initial task execution information.

[0021] In step S20 of some embodiments, member task history behavior data refers to various behavior record data generated by task members in the past when performing various tasks. For example, in the insurance claims calculation task scenario, member task history behavior data may be the duration and preferences of task member A in the past when handling claims calculation tasks. In the medical surgery preoperative preparation task scenario, member task history behavior data may be the efficiency of task member B in completing preoperative preparation work and the time of responding to task reminders.

[0022] This application embodiment first extracts the unique identifier of each task member. Then, based on this unique identifier, a targeted search is performed in a pre-built user behavior database to obtain search results. It's important to know that this user behavior database stores all past behavior records of each task member for various tasks. Next, similar behavior data related to the type of task assigned to each member is filtered from the search results. This filtered similar behavior data undergoes deduplication, completion, and cleaning processes to ultimately obtain standardized member task history behavior data. For example, in the surgical instrument preparation task in a medical department, after retrieving the identifier of the nurse participating in the task, past behavior data of that nurse preparing similar surgical instruments can be filtered from the user behavior database. This includes, but is not limited to, preparation time and instrument verification accuracy. Finally, by integrating the nurse's preparation time and instrument verification accuracy in past similar tasks, the nurse's member task history behavior data can be formed.

[0023] In detail, such as Figure 3 As shown, step S20, which involves querying historical data based on task member information to obtain member task historical behavior data, includes the following steps: S21: Extract the identity identifiers from the task member information to obtain the unique identity identifiers of the members; S22: Based on the member's unique identity identifier, perform a targeted search on the preset user behavior database to obtain the member's historical task behavior data.

[0024] In step S21 of some embodiments, the unique member identifier refers to the exclusive identification information used to uniquely distinguish each task member and deeply bound to the task member's personal work information, such as the system-specific employee number, the account code in the business system, etc.

[0025] This application embodiment can extract a unique identity identifier pre-assigned to each task member by the task system from the collected task member information. This unique identifier is unique within the task system, will not be duplicated, and is deeply bound to the task member's job position, business permissions, and other information. This unique identity identifier serves as the unique member identifier for each task member. For example, in monitoring insurance auto insurance claims, the unique business ID of each staff member can be extracted from the basic work information of the surveyors and accountants participating in the claims task, serving as the unique member identifier for each task member. In monitoring multidisciplinary joint surgeries in medicine, the system account codes of the doctors and nurses participating in the surgery can be extracted from their basic work information to obtain the corresponding unique member identifier.

[0026] In step S22 of some embodiments, the user behavior database refers to a database that continuously collects and stores data, specifically used to save all behavioral records generated by task members in the past when performing various work tasks. It should be noted that the user behavior database can be used to store data such as task processing time, reminder response efficiency, and task completion quality of task members.

[0027] In this embodiment, after obtaining the unique identifier of each task member, the unique identifier can be used as the core search keyword to initiate a targeted search command in the pre-built and data-stored user behavior database. The search module of the user behavior database will filter out all past task behavior records that are precisely bound to the unique identifier of the member from the full data based on the core search keyword. Subsequently, all the retrieved past task behavior records will undergo preliminary filtering to remove redundant data that is irrelevant to the current task type assigned to the member, retaining only the behavior data of the same type of task, thus obtaining standardized and targeted member task historical behavior data. For example, in the task monitoring of insurance customer maintenance business, the unique employee number of the maintenance business specialist can be used as the keyword to conduct a targeted search in the user behavior database, filtering out data such as the past task response time and business completion time of the maintenance business specialist, thus forming their member task historical behavior data.

[0028] Steps S21 to S22 as illustrated in this embodiment of the application involve a progressive process: first, extracting the identity identifier from the task member information to obtain a unique and exclusive member identifier; then, performing a targeted retrieval of a preset user behavior database based on this identifier. This enables accurate and rapid querying of historical behavior data of task members in relevant business scenarios. Relying on the unique identifier effectively avoids retrieval result deviations caused by confusion of identity information. At the same time, the targeted retrieval method can directly filter out exclusive data matching the member, eliminating interference from irrelevant and redundant data, ensuring the accuracy and relevance of the obtained member task historical behavior data. This provides reliable and effective data support for subsequently generating personalized member task reminder strategies based on member task assignments. Furthermore, targeted retrieval significantly improves data query efficiency compared to full data retrieval, reduces the time cost of database retrieval, and allows the task management end to quickly obtain the required historical data, ensuring the smooth connection and progress of each step of the overall task monitoring method.

[0029] In step S30 of some embodiments, the member task reminder strategy refers to a personalized task execution reminder scheme formulated for each task member by combining the historical behavioral characteristics of the task members with the specific tasks assigned. It should be noted that the member task reminder strategy is usually a combination scheme of reminder time and reminder channel that adapts to the member's work habits.

[0030] This application first performs feature mining on the historical behavior data of members' tasks to extract core behavioral features such as high-frequency task processing times and reminder channel preferences. Then, it analyzes the attributes of the tasks assigned to members to clarify key attributes such as task priority, deadline, and urgency. Subsequently, it fuses and matches the core behavioral features of the task members with the key attributes of the tasks to determine the personalized reminder time, frequency, and channel suitable for each member. Finally, it integrates these personalized reminder parameters to generate a member task reminder strategy tailored to each member. For example, in an insurance policy information review task, if a review specialist's historical behavior data shows that their high-frequency task processing time is between 9 and 10 AM, they prefer in-app message reminders, and their assigned review task is high priority, then a member task reminder strategy can be formulated to send an in-app message reminder at 8:50 AM and a second reminder four hours before the task deadline.

[0031] In detail, such as Figure 4 As shown, step S30, which generates a member task reminder strategy based on member task history behavior data and member assigned tasks, includes the following steps: S31: Extract features from the historical behavior data of members' tasks to obtain the characteristics of members' task processing habits; S32: Based on the characteristics of members' task processing habits and the tasks assigned to members, reminder information is matched to obtain task reminder information; S33: Based on the task reminder information, generate a strategy to obtain the member task reminder strategy.

[0032] In step S31 of some embodiments, the member task processing habit characteristics refer to the characteristic information in the member task history behavior data that can reflect the daily work task processing behavior patterns and preferences of the task members. For example, the member task processing habit characteristics may be the high-frequency periods when the task members process similar tasks, their preference for various reminder channels, etc.

[0033] This application embodiment can rely on data mining algorithms to perform multi-dimensional analysis of members' historical task behavior data. Then, it focuses on mining relevant content that reflects the work behavior patterns of task members, such as the high-frequency time intervals during which task members handle similar tasks, and the channel selection preferences of task members when responding to various task reminders. Next, the mined core information is organized and summarized to obtain the member task processing habit characteristics that comprehensively reflect the work habits of task members. For example, in the management of auto insurance claims tasks, feature extraction is performed on the historical behavior data of surveyors. It is found that surveyors have a high-frequency time period for handling survey tasks from 9:00 to 11:00 AM every day, and surveyors prefer to receive in-app message reminders. Therefore, the 9:00 to 11:00 AM time period and channel preference can be summarized as the member task processing habit characteristics of that surveyor.

[0034] In detail, such as Figure 5 As shown, step S31, which involves extracting features from the member's historical task behavior data to obtain the member's task processing habit features, includes the following steps: S311: Min the task processing time periods from the member's historical task behavior data to obtain the high-frequency task processing time periods of the member; S312: Min the reminder channels from the historical behavior data of members' tasks to obtain reminder channel preference characteristics; S313: Summarize the characteristics of members' high-frequency task processing time periods and reminder channel preferences to obtain the characteristics of members' task processing habits.

[0035] In step S311 of some embodiments, the high-frequency task processing period of a member refers to a specific time interval in the member's task history behavior data where the task member processes tasks more frequently and has relatively better work efficiency in daily work. For example, in the scenarios of insurance policy review and medical record organization and archiving, the high-frequency task processing period of a member can be the time when task members concentrate on processing work, such as from 9:00 to 11:00 in the morning and from 2:00 to 4:00 in the afternoon.

[0036] This application embodiment first extracts all record data related to task processing time from the member's historical task behavior data, including but not limited to the start time, completion time, and concentrated response time of each task. Then, it performs multi-dimensional statistical analysis on these time record data to calculate the task processing volume, task completion efficiency, and response frequency of task members in different time intervals. This allows for the selection of the target time interval with the highest task processing volume, highest completion efficiency, and most concentrated response frequency. Finally, this target time interval can be determined as the member's high-frequency task processing period. For example, in the management of insurance auto insurance claims investigation tasks, the time records of past investigation tasks handled by investigators are extracted. Analysis reveals that the investigators have the highest investigation task processing volume and the most timely on-site response between 9:00 AM and 12:00 PM daily. Therefore, the time interval between 9:00 AM and 12:00 PM daily is determined as the investigator's high-frequency task processing period.

[0037] In step S312 of some embodiments, the reminder channel preference feature refers to the feature information that can reflect the task member's preference for and acceptance of various task reminder channels. The reminder channels include, but are not limited to, in-app messages, SMS or email.

[0038] This application embodiment first extracts all task reminder channel records related to task reminders from the member's task history behavior data, including the types of reminders received in the past, the reminder response time under different channels, the response completion rate, and records of non-response situations. Then, it performs statistical analysis on these task reminder channel record data to calculate the average response time and response completion rate under various reminder channels. Next, it eliminates reminder channels with low response rates and excessively long response times, and filters out one or more reminder channels with the most timely response and the highest completion rate, obtaining the filtering results. Then, the filtering results can be determined as the member's reminder channel preference characteristics. For example, in insurance underwriting task management, after analyzing the member task history behavior data of underwriting specialists, it is found that the underwriting specialist's average response time for in-app message reminders is five minutes and the response completion rate is 100%, which is much higher than that for email and SMS reminders. In this case, in-app messages can be determined as the specialist's reminder channel preference characteristics.

[0039] In step S313 of some embodiments, the high-frequency task time periods of the members obtained are first standardized and organized to clarify the specific start and end times of the high-frequency task time periods of the members, forming standardized time period feature information. Then, the reminder channel preference features are sorted out to clarify the reminder channel types and priorities preferred by members, which facilitates the formation of standardized channel feature information. Subsequently, the standardized time period feature information and channel feature information are integrated and systematically summarized according to the preset feature format. The time period feature information and channel feature information can be integrated into a complete feature set, which can comprehensively and accurately reflect the task processing behavior patterns and preferred task processing habits of task members.

[0040] Steps S311 to S313 of this embodiment involve a progressive process: first, mining the task processing time periods of members' historical task behavior data to obtain the high-frequency task processing time periods; then, mining the reminder channels of this data to obtain reminder channel preference features; and finally, summarizing the two types of features to obtain the member's task processing habit features. This process can accurately mine and extract the key work habit features of task members from the two core dimensions of time and channel in relevant business scenarios. It eliminates irrelevant and redundant information in historical behavior data, ensuring the relevance and accuracy of the extracted feature information. The resulting member task processing habit features can truly and comprehensively reflect the actual work behavior patterns and preferences of members, providing specific and reliable feature basis for subsequent reminder information matching based on member-assigned tasks. This avoids reminder information matching deviations caused by fuzzy feature extraction, improves the adaptability and accuracy of subsequent reminder strategy generation, and the refined feature extraction steps are logically clear, highly operable, and adaptable to the diverse work characteristics of task members in relevant business scenarios. This provides solid technical support for the implementation of the intelligent reminder link in the overall task monitoring method, helping to improve the effectiveness of task reminders in relevant business scenarios.

[0041] In step S32 of some embodiments, the task reminder information refers to information that is matched with the work habits of the task members and the specific tasks assigned, in order to ensure that the tasks are carried out on time. It should be noted that the task reminder information usually includes task reminder time that is adapted to the task members and reminder channels that are in line with the task members' work habits.

[0042] This application embodiment first performs attribute analysis on the tasks assigned to task members, clarifying key attributes such as priority, deadline, and urgency of each task. Then, it combines these key attributes with extracted task processing habit characteristics of the members to match suitable task reminder times for each member. These reminder times align with the task members' high-frequency work hours and allow sufficient processing time. Simultaneously, it can also incorporate task members' reminder channel preferences to select appropriate reminder channels. Finally, by integrating the matched reminder times and channels, complete task reminder information can be obtained. For example, in an insurance underwriting task, if an underwriter's processing habits include high-frequency work in the morning and a preference for email reminders, and the key attributes of their assigned tasks are high priority and completion before 5 PM, then a task reminder time of 10 AM and an email reminder channel can be matched for them.

[0043] In detail, in step S32, the characteristics of member task processing habits include member task processing time and member communication channels. Based on the characteristics of member task processing habits and the tasks assigned to members, reminder information is matched to obtain task reminder information, including the following steps: Based on the member's task processing time and the tasks assigned to the member, the reminder time is matched to obtain the task reminder time; Based on member communication channels and assigned tasks, reminder channels are matched to obtain task reminder channels; Information on task reminder times and channels is collected to obtain task reminder information.

[0044] In some embodiments, member task processing time refers to the high-frequency time intervals in which task members process daily work tasks, mined from member task historical behavior data, and is a key feature reflecting the work rhythm of task members.

[0045] Member communication channels refer to the information transmission channels that task members use frequently and with high efficiency when receiving information and responding to notifications in their daily work.

[0046] The task reminder time refers to the specific time at which a task reminder is sent to the task members, determined by combining the members' task processing time and the attributes of the tasks assigned to the members, in order to ensure the smooth progress of the task. Generally speaking, the task reminder time can be a specific moment within the high-frequency working period before the task deadline.

[0047] Task reminder channels refer to the information delivery channels selected to send task reminders to task members by combining the member's communication channels and the attributes of the tasks assigned to the members, in order to ensure that the reminder information is effectively delivered. Generally speaking, task reminder channels can be in-app messages, SMS or email channels that are adapted to the work habits of task members.

[0048] In this embodiment, after clarifying that the current task processing habits of the members are specifically defined as two core elements—task processing time and communication channels—the actual attributes of the tasks assigned to members can be considered, including task priority, urgency, deadline, and estimated processing time. Based on the task processing time, a suitable specific time can be selected within the high-frequency working hours of the task members, while reserving sufficient task processing time. This allows for the matching of task reminder times. For example, in a large-amount auto insurance claim task, a surveyor's task processing time is from 9:00 AM to 12:00 PM. If the task assigned to a member is high priority, needs to be completed that afternoon, and has an estimated processing time of three hours, the task reminder time can be matched to any time point between 9:00 AM and 10:00 AM. Next, based on the member's communication channels and the urgency of the assigned tasks, a corresponding task reminder channel can be selected. For example, if the task is highly urgent, a channel preferred by the member can be selected as the task reminder channel; if the task is of a regular level, any core channel can be selected. Finally, the matched task reminder time and task reminder channel are integrated to form complete task reminder information.

[0049] This application implements a progressive process: first, it clarifies the specific characteristics of members' task processing habits; then, it matches task reminder times with members' task processing time and assigned tasks; next, it matches task reminder channels with members' communication channels and assigned tasks; and finally, it summarizes the two matching results to obtain task reminder information. This process ensures that task reminder times are determined in accordance with members' actual work rhythms, avoiding information omissions caused by sending reminders during non-high-frequency work periods. Simultaneously, it ensures that the selection of task reminder channels aligns with members' information receiving preferences, guaranteeing effective delivery of reminder information. Furthermore, the matching process is conducted throughout, taking into account the attributes of members' assigned tasks, ensuring that the determination of reminder times and channels is adapted to the actual needs of the tasks, guaranteeing the relevance and rationality of the matching results. The final summarized task reminder information is complete and accurate, providing specific and reliable information for generating personalized member task reminder strategies. This effectively improves the accuracy and effectiveness of subsequent task reminder work, adapts to different types and urgency levels of task needs in relevant business scenarios, reduces the interference of invalid reminders on staff, and helps ensure the orderly progress of insurance and medical work.

[0050] In step S33 of some embodiments, after obtaining the appropriate task reminder information, the frequency of reminders can be determined based on the attribute characteristics of the tasks assigned to the members. For example, if the task member's task is of high priority and high urgency, multiple progressive reminders can be set. If the task member's task is a regular task, one or two reminders can be set. Then, the matched reminder time, reminder channel and determined reminder frequency are integrated and sorted according to the preset strategy format to clarify the specific time, corresponding channel and key points of each reminder. Finally, by summarizing the aforementioned reminder frequency, specific time of each reminder, corresponding channel and key points of the reminder content, a member task reminder strategy that is exclusive to the task member and can be directly executed can be formed.

[0051] Steps S31 to S33 of this application embodiment involve a progressive process: first, extracting features from members' historical task behavior data to identify processing habit features that align with their work characteristics; then, accurately matching these features with the tasks assigned to members to generate reminder information; and finally, generating a personalized member task reminder strategy based on the matched reminder information. This approach ensures that the generated reminder strategy is tailored to the work behavior patterns of each task member in relevant business scenarios, making the matched reminder information highly compatible with members' work habits. This avoids the ineffective interference caused by uniform reminders and ensures that the generated member task reminder strategy matches the specific task attributes assigned to members, guaranteeing the relevance and practicality of the reminder strategy. This effectively improves the accuracy and effectiveness of task reminders, laying a solid foundation for subsequent member task execution reminders and accurate task execution progress collection. It adapts to diverse task types and staff work characteristics in relevant business scenarios, optimizes the intelligent reminder management process for team tasks, and facilitates the orderly advancement of insurance and medical work.

[0052] In step S40 of some embodiments, the response information is a set of various data returned by the task member's terminal device to the task monitoring system, which characterizes the feedback action taken by the task member in response to the task reminder. The response information may include, but is not limited to, the task member's task completion rate, problems in task execution, etc.

[0053] Member task execution progress refers to the actual situation and progress of task completion during the execution of assigned tasks by task members. For example, in the scenario of carrying out insurance claims, member task execution progress can be the completion rate of on-site information collection for the claims task. In the scenario of implementing medical rehabilitation treatment plans, member task execution progress can be the achievement of the phase goals of rehabilitation treatment.

[0054] This application embodiment can send task reminders to each task member at a preset reminder time through a selected reminder channel according to the generated member task reminder strategy. Simultaneously, a progress feedback entry is built on the task management terminal, allowing task members to report the task execution status in real time. This status includes, but is not limited to, pending, processing, and completed. The task management terminal then receives, records, and summarizes the reported information from each member in real time. Combined with the actual completion status of the task, the task completion rate of each member can be calculated, i.e., the complete member task execution progress. For example, in the follow-up task for patients with chronic diseases, after sending SMS reminders to the follow-up specialists according to the reminder strategy, the specialists promptly report to the management terminal after completing the follow-up of one patient. The management terminal summarizes the reported information from all specialists and calculates the overall completion rate of the follow-up task and the individual completion progress of each specialist.

[0055] In some embodiments, key parameters such as reminder time, reminder channel, and core task requirements can be extracted from the member task reminder strategy to generate a member task reminder instruction containing the task name, specific execution requirements, task deadline, and progress reporting entry. This task reminder will adjust its display format according to different reminder channels to adapt to the information transmission characteristics of each channel. For example, in an insurance auto insurance claims investigation task, an in-app message reminder instruction containing the investigation case number, accident scene address, and investigation data preparation requirements can be generated for the investigator based on the member task reminder strategy. In a medical surgical instrument preparation task, an in-app message reminder instruction containing the surgical name, instrument list, and preparation completion time can be generated for the nurse based on the member task reminder strategy. The system sends SMS reminders, and then, based on the generated member task reminders, sends information to the corresponding task members' work terminals through the selected reminder channels at the specified reminder time. At the same time, it relies on the task management system to build an online progress feedback portal, allowing task members to report the execution status of the task in real time, including pending, in progress, completed, and paused, based on the actual progress of the work. It can also report the task completion percentage to form response information. Finally, the task management terminal receives the response information of each task member to the task reminder in real time, and then classifies, statistically analyzes, and summarizes these response information. Combined with the overall requirements of the task, it calculates the task completion rate of each member and finally obtains the complete task execution progress of each member.

[0056] In step S50 of some embodiments, task execution update information refers to task execution information formed by combining the collected member task execution progress with the initial task execution information to complete dynamic adjustment and data update. It should be noted that task execution update information can reflect the latest execution status of the overall task and each subtask in real time.

[0057] This application embodiment can accurately associate and match the task execution progress of each task member with the corresponding tasks assigned to the members in the initial task execution information, clarify the current execution status of each sub-task, and then adjust and update the task node status, task completion rate, remaining working time, and other data in the initial task execution information in real time based on the matching results. At the same time, by integrating the updated progress information of each sub-task with the overall task progress data and organizing it in a structured manner, complete task execution update information can be obtained. For example, in the scenario of insurance customer policy maintenance business processing, if eight out of ten policy maintenance information modification tasks assigned by a specialist have been completed, the specialist's task completion rate in the initial information will be updated from 0 to 80%, and the overall policy maintenance business completion progress will be updated simultaneously to form task execution update information.

[0058] Specifically, in step S50, that is, based on the progress of member task execution, the initial task execution information is updated to obtain task execution update information, including the following steps: The task matching relationship is obtained by associating the task execution progress of members with the tasks assigned to them. Based on the task matching relationship and the task execution progress of members, the initial task execution information is adjusted to obtain updated task execution information.

[0059] In some embodiments, the task matching relationship refers to the association between progress and task established after accurately matching the task execution progress of a task member with the tasks assigned to that member. For example, in the scenario of handling insurance claims, this relationship may be the correspondence between the completion progress of a claims specialist's investigation task and the car insurance investigation sub-task assigned by that specialist.

[0060] In this embodiment, the collected and summarized task execution progress of each task member is first precisely associated with the tasks assigned to that member in the initial task execution information. This clarifies the specific sub-tasks corresponding to the execution progress of each task member, thereby establishing a clear task matching relationship and avoiding confusion between progress and task correspondence. For example, in an auto insurance claims task, the progress of an investigator completing 80% of the on-site investigation is matched with the on-site investigation sub-task of a specific auto insurance case assigned to that investigator; similarly, the progress of an accountant completing 50% of the claims calculation is matched with the claims calculation sub-task of the same auto insurance case assigned to that accountant. Subsequently, based on the established task matching relationship and combined with the actual task execution progress of each task member, The initial task execution information is comprehensively adjusted, including updating the completion status, completion rate, and remaining work of each member's assigned tasks. Simultaneously, the overall task completion progress and the progress of each task node are adjusted. For example, in a multidisciplinary surgical task, if the surgeon's surgical plan development task is completed, the status of that sub-task in the initial information is updated from "processing" to "completed." If the anesthesiologist's anesthesia plan preparation task is 60% complete, the completion rate of that sub-task in the initial information is updated to 60%. Based on the updated data of each sub-task, the overall completion progress of the surgical preparation task is adjusted, ultimately forming task execution update information that reflects the task execution status in real time.

[0061] This application embodiment employs a progressive step-by-step approach: first, it correlates and matches member task execution progress with assigned tasks to obtain a task matching relationship; then, based on this matching relationship and member task execution progress, it adjusts the initial task execution information to obtain updated task execution information. This ensures that member task execution progress accurately corresponds to their assigned sub-tasks, avoiding information update deviations caused by mismatches between progress and tasks. Then, based on the precise matching relationship, targeted information adjustments are made, making the updates to the initial task execution information more targeted and accurate. This ensures that the obtained task execution update information accurately, comprehensively, and in real-time reflects the latest execution status of each sub-task and the overall task in the relevant business scenario, effectively guaranteeing the timeliness and accuracy of task information. This provides a reliable and accurate data foundation for subsequent visualization processing of task execution update information, and allows insurance team managers and medical department heads to promptly grasp the real-time progress of tasks through updated information. This facilitates timely identification of problems in task execution and makes reasonable adjustment decisions, preventing information lag from affecting the orderly progress of insurance business and medical work, and optimizing the team task progress management process in relevant business scenarios.

[0062] In step S60 of some embodiments, visualized task progress information refers to task progress data presented in an intuitive form after the task execution update information has been professionally processed. It should be noted that visualized task progress information can be a task progress data display result in the form of a dashboard or a chart.

[0063] This application's embodiments first classify task execution update information into different dimensions, such as task status classification data, member progress statistics data, and task node completion data. Then, relying on lightweight visualization technology, corresponding visualization views are generated based on the data of different dimensions. Specifically, a Kanban view can be generated based on task status classification data, a Gantt chart view can be generated based on task node completion data, and a statistical report view can be generated based on member progress statistics data. Finally, the various visualization views are integrated to form intuitive and easy-to-view visualized task progress information. For example, in a multi-departmental joint diagnosis and treatment task, the updated progress data of each stage of diagnosis and treatment can be converted into a Gantt chart, which can clearly display the planned and actual progress of each department's task. At the same time, the task completion status of each department can be displayed in Kanban form. Integrating these two visualization views forms visualized task progress information.

[0064] Specifically, based on the task execution update information, visualization processing is performed to obtain visualized task progress information, including: The task execution update information is parsed to obtain the current progress value and task status characteristics of the task. Visualize and map the current progress value of the task to obtain the parameters of the graphical interface elements; Visualize and map the state features to obtain the target icon identifier; Based on the graphical interface element parameters and target icon identifiers, the preset visualization chart template is rendered to obtain visualized task progress information.

[0065] In some embodiments, the current progress value of a task refers to numerical data that quantifies the overall degree of task completion, and is usually presented as a percentage or proportion.

[0066] Task status characteristics refer to the characteristic identification information that represents the execution stage, running status, or anomaly type of a task.

[0067] Graphical interface element parameters refer to configuration parameters used to define the appearance and size ratio of visual charts, including information such as size, ratio, color, and position.

[0068] A target icon identifier is a unique identifier corresponding to a standardized icon used to visually represent the characteristics of a task's status. For example, a target icon identifier can correspond to a processing icon, a success icon, or a failure icon.

[0069] Visual chart templates are pre-designed visual display templates with pre-defined structural frameworks, basic styles, and layout rules. They are used to display progress and status information. Visual chart templates can be progress bar templates or pie chart templates.

[0070] In this embodiment, a preset task information parsing rule base can be loaded first to structurally decompose the obtained task execution update information. At the same time, redundant and invalid data can be filtered out to identify the progress-related fields and status-related fields in the task execution update information. The quantitative current progress value of the task can be obtained through data conversion, and the corresponding task status features can be extracted by matching status keywords.

[0071] Next, preset numerical parameter mapping rules can be invoked to establish the correspondence between progress values ​​and graphic element attributes. Based on the size range of the progress value, the corresponding size ratio, color value, transparency and other configuration information are matched and integrated to form complete graphical interface element parameters.

[0072] Simultaneously, a pre-built status icon association library is retrieved, and the identified task status features are accurately matched with the status labels in the association library. The icon identifier code that uniquely corresponds to the status feature is then selected and output as the target icon identifier.

[0073] Finally, the graphical interface element parameters are assigned to the basic style attributes of the visualization chart template. The corresponding icon resources are loaded according to the target icon identifier and positioned in the specified position of the template. After the template style is filled and the elements are overlaid, the final displayable visualization task progress information is generated.

[0074] This application first extracts task progress values ​​and status features accurately through information parsing, realizing the quantification and feature processing of abstract task information, providing an accurate and reliable data foundation for subsequent visualization. Then, it adapts the numerical display and status identification requirements through dual visualization mapping, ensuring that both progress quantification information and status feature information can adapt to the graphical expression logic. Then, it is rendered based on a preset visualization chart template, simplifying the development and generation process of the visualization interface, improving the efficiency of information display, and finally generating visualized task progress information that combines progress quantification display and intuitive status identification. This effectively solves the problems of abstract and difficult-to-understand task progress information and unclear status distinction, significantly improving the readability and intuitiveness of task information, reducing user understanding costs, and optimizing the information interaction experience in the task management process.

[0075] As can be seen, in the above solution, historical data of task members can be queried to obtain their historical task behavior data. Based on this data and the assigned tasks, a task reminder strategy can be generated, ensuring that the reminder strategy is highly adapted to the work habits of each task member. This effectively improves the accuracy and effectiveness of task reminders, reduces the interference of invalid reminders on the normal work of task members, and provides timed reminders to task members performing tasks according to the strategy. Furthermore, the task execution progress of each member is collected in real time, and the initial task execution information is updated based on the progress, resulting in updated task execution information. This ensures that the task execution progress is maintained. This ensures the timeliness and accuracy of task execution update information, avoiding work continuity problems caused by information lag. It also improves the accuracy of task monitoring. Furthermore, visualizing task execution update information to obtain visualized task progress information transforms abstract task progress data into an intuitive view, allowing task team managers to monitor the progress of the overall task and its sub-tasks in real time. This enables timely identification and adjustment of problems during task execution, optimizing the team's task management process and improving overall team efficiency. It also allows each task member to clearly understand their own and the team's task progress, promoting collaboration among team members.

[0076] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0077] In one embodiment, a task monitoring device is provided, which corresponds one-to-one with the task monitoring methods described in the above embodiments. For example... Figure 6 As shown, the task monitoring device includes an information acquisition module 101, a data query module 102, a strategy generation module 103, a progress monitoring module 104, a task update module 105, and a task visualization module 106. Detailed descriptions of each functional module are as follows: The information acquisition module 101 is used to acquire the initial task execution information of the task team. The task team includes one or more task members, and the initial task execution information includes task member information and the tasks assigned to the members. The data query module 102 is used to perform historical data queries based on task member information to obtain the member's historical task behavior data; The strategy generation module 103 is used to generate member task reminder strategies based on member task history behavior data and member assigned tasks; The progress monitoring module 104 is used to send task reminders to task members based on the member task reminder strategy, and determine the task execution progress based on the task members' response information to the task reminders. The task update module 105 is used to update the initial task execution information based on the execution progress of member tasks, so as to obtain task execution update information; The task visualization module 106 is used to perform visualization processing based on task execution update information to obtain visualized task progress information.

[0078] This invention provides a task monitoring device. First, it queries historical data of task members to obtain their historical task behavior data. Based on this data and the assigned tasks, it generates a task reminder strategy that is highly adapted to the work habits of each task member, thereby effectively improving the accuracy and effectiveness of task reminders and reducing interference from invalid reminders. Second, according to the task reminder strategy, it provides timed reminders to task members performing tasks and collects the task execution progress of each member in real time. Then, based on the progress, it updates the initial task execution information to obtain updated task execution information. It ensures the timeliness and accuracy of task execution updates, avoiding work continuity problems caused by information lag. It also improves the accuracy of task monitoring. Furthermore, by visualizing task execution updates, it provides visualized task progress information, transforming abstract task progress data into an intuitive view. This allows task team managers to monitor the progress of the overall task and its sub-tasks in real time, promptly identify and adjust any issues during task execution, optimize the team's task management process, and improve overall team efficiency. It also allows each task member to clearly understand their own and the team's task progress, promoting collaboration among team members.

[0079] Specific limitations regarding the task monitoring device can be found in the limitations of the intelligent question-answering method described above, and will not be repeated here. Each module in the aforementioned task monitoring device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0080] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a task monitoring method on the server side.

[0081] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a task monitoring method on the client side. In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain the initial task execution information of the task team. The task team includes one or more task members. The initial task execution information includes task member information and the tasks assigned to each member. Based on the task member information, historical data can be queried to obtain the member's historical task behavior data; Based on members' historical task behavior data and assigned tasks, generate member task reminder strategies; Based on the member task reminder strategy, task reminders are sent to task members, and the task execution progress is determined according to the task members' response information to the task reminders. Based on the progress of member task execution, the initial task execution information is updated to obtain task execution update information; Based on the task execution update information, visualization processing is performed to obtain visualized task progress information.

[0082] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0083] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0085] It should be noted that any AI models, software tools, or components not belonging to this company appearing in the embodiments of this application are merely illustrative examples and do not represent actual use. All user personal information involved in the embodiments of this application has been authorized (with the knowledge and consent) by the relevant parties or has been fully authorized by all parties, and the executing entity may obtain it through various legal and compliant means. The collection, storage, use, processing, transmission, provision, and disclosure of the information, data, and signals involved all comply with relevant laws and regulations and do not violate public order and good morals.

[0086] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A task monitoring method, characterized in that, The method includes: Obtain initial task execution information of the task team, wherein the task team includes one or more task members, and the initial task execution information includes task member information and the tasks assigned to the members; Based on the task member information, historical data is queried to obtain the member's historical task behavior data; Based on the member's historical task behavior data and the member's assigned tasks, a member task reminder strategy is generated; Based on the member task reminder strategy, task reminders are sent to the task members, and the task execution progress is determined according to the task members' response information to the task reminders. Based on the progress of the member tasks, the initial task execution information is updated to obtain task execution update information; Based on the task execution update information, visualization processing is performed to obtain visualized task progress information.

2. The task monitoring method as described in claim 1, characterized in that, The step of generating a member task reminder strategy based on the member's historical task behavior data and the member's assigned tasks includes: Feature extraction is performed on the member's historical task behavior data to obtain member task processing habit features; Based on the member's task processing habits and assigned tasks, reminder information is matched to obtain task reminder information; Based on the task reminder information, a strategy is generated to obtain the member task reminder strategy.

3. The task monitoring method as described in claim 2, characterized in that, The step of extracting features from the member's historical task behavior data to obtain member task processing habit features includes: By mining the task processing time periods of the member's historical task behavior data, the high-frequency task processing time periods of the member can be obtained; By mining the reminder channels from the historical behavior data of the members' tasks, reminder channel preference characteristics can be obtained; The characteristics of the member's task processing habits are obtained by summarizing the characteristics of the member's high-frequency task processing time periods and reminder channel preferences.

4. The task monitoring method as described in claim 2, characterized in that, The characteristics of members' task processing habits include members' task processing time and members' communication channels; The step of matching reminder information based on the member's task processing habits and the member's assigned tasks to obtain task reminder information includes: Based on the member's task processing time and the member's assigned tasks, the reminder time is matched to obtain the task reminder time; Based on the member's communication channels and the tasks assigned to the member, reminder channels are matched to obtain task reminder channels; The task reminder information is obtained by summarizing the task reminder time and the task reminder channel.

5. The task monitoring method as described in claim 1, characterized in that, The step of performing visualization processing based on the task execution update information to obtain visualized task progress information includes: The task execution update information is parsed to obtain the current progress value and task status characteristics of the task; Visualize and map the current progress value of the task to obtain the graphical interface element parameters; The state features are visualized and mapped to obtain the target icon identifier; Based on the graphical interface element parameters and the target icon identifier, the preset visualization chart template is rendered to obtain the visualization task progress information.

6. The task monitoring method according to any one of claims 1-5, characterized in that, The step of querying historical data based on the task member information to obtain member task historical behavior data includes: The task member information is used to extract the unique identity identifier of each member. Based on the member's unique identifier, a targeted search is performed on a preset user behavior database to obtain the member's historical task behavior data.

7. The task monitoring method according to any one of claims 1-5, characterized in that, The step of updating the initial task execution information based on the member task execution progress to obtain task execution update information includes: The task execution progress of the member is associated with the task assigned to the member to obtain the task matching relationship; Based on the task matching relationship and the execution progress of the member tasks, the initial task execution information is adjusted to obtain the task execution update information.

8. A task monitoring device, characterized in that, include: The information acquisition module is used to acquire the initial task execution information of the task team, wherein the task team includes one or more task members, and the initial task execution information includes task member information and the tasks assigned to the members; The data query module is used to perform historical data queries based on the task member information to obtain the member's historical task behavior data; The strategy generation module is used to generate member task reminder strategies based on the member's historical task behavior data and the member's assigned tasks; The progress monitoring module is used to send task reminders to the task members based on the member task reminder strategy, and to determine the task execution progress based on the task members' response information to the task reminders. The task update module is used to update the initial task execution information based on the execution progress of the member tasks, so as to obtain task execution update information; The task visualization module is used to perform visualization processing based on the task execution update information to obtain visualized task progress information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the task monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the task monitoring method as described in any one of claims 1 to 7.

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