A business task intelligent tracking method and system based on BI report

Abstract

The application relates to the field of business intelligence, in particular to a business task intelligent tracking method and system based on a BI report. The method comprises the following steps: real-time monitoring of a warehouse environment, triggering a task reminder when detecting a stagnant product; creating a new task record in a task schedule based on an API, and storing task information to the corresponding task record; based on the task information, assigning the task to the corresponding user end, and outputting a task assignment result; real-time tracking of the task state, updating the task state when the task is completed, collecting task execution data, and generating a task feedback report; based on the task feedback report, formulating and distributing improvement measures to the user end, and feeding back the improvement effect to the BI report after evaluating the improvement effect. The application has the effects of fast real-time updating of data indexes and intelligent analysis.

Description

Technical Field

[0001] This application relates to the field of business intelligence, and in particular to a business task intelligent tracking method and system based on BI reports. Background Technology

[0002] Currently, enterprises widely use BI reporting systems and data analytics tools to monitor business metrics and issue alerts when abnormalities such as obsolete inventory are detected. These systems support task allocation and closed-loop management, while data-driven decision-making and real-time monitoring technologies are helping management make more accurate decisions.

[0003] However, existing technologies have limitations in real-time data metric updates and intelligent analysis, directly impacting a company's ability to promptly identify potential problems and seize market opportunities. The inability to quickly identify and respond to market changes may cause companies to miss key business opportunities or fail to mitigate risks in a timely manner, thus affecting overall competitiveness and decision-making quality. Furthermore, this deficiency leads to delays in business process optimization, as real-time data and analytics are crucial for identifying and resolving bottlenecks and inefficiencies in business processes.

[0004] Therefore, improvements are needed. Summary of the Invention

[0005] To address the above technical issues, this application provides a business task intelligent tracking method and system based on BI reports.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A method for intelligent tracking of business tasks based on BI reports includes the following steps:

[0008] Real-time monitoring of the warehouse environment; triggering task alerts when stagnant or obsolete goods are detected.

[0009] Create new task records in the task scheduler based on the API, and store the task information in the corresponding task record;

[0010] Based on the task information, assign tasks to the corresponding user terminals and output the task assignment results;

[0011] Track task status in real time, update task status when task is completed, collect task execution data, and generate task feedback reports;

[0012] Based on the task feedback report, develop and assign improvement measures to the user end, evaluate the improvement effect, and then feed it back to the BI report.

[0013] In a preferred embodiment, the step of real-time monitoring of the warehouse environment and triggering a task reminder when obsolete items are detected includes the following steps:

[0014] Based on predefined warehouse environment monitoring parameters and IoT sensor network, warehouse environment data is collected in real time.

[0015] Based on BI reports, when obsolete items are detected, task reminders are output according to task priority.

[0016] In a preferred embodiment, the step of outputting a task reminder based on task priority when an obsolete item is detected, based on a BI report, includes the following steps:

[0017] Based on RFID, key information of items is obtained, including warehousing time, shelf life, and physical volume parameters.

[0018] Based on the sales data from the ERP system and the aforementioned key information, calculate the inventory turnover days, remaining shelf life days, and warehouse space occupancy rate.

[0019] When inventory turnover days > preset threshold, remaining shelf life days < shelf life warning value, and warehouse space occupancy rate > warehouse planning threshold, obsolete products are detected.

[0020] Based on image recognition, detect whether there are any abnormalities;

[0021] The abnormal situations include deformation of the outer packaging and abnormal data from the temperature and humidity sensors.

[0022] When an anomaly is detected, the task priority is increased.

[0023] In a preferred embodiment, the step of allocating tasks to corresponding user terminals based on task information and outputting task allocation results includes the following steps:

[0024] Based on the task information, output a task allocation plan;

[0025] The task information includes task ID, task type, task priority, and task status;

[0026] When a task with a priority higher than the preset priority threshold L1 is detected, a preemptive scheduling mechanism is triggered to interrupt the currently executing low-priority task.

[0027] Output the task allocation result to the corresponding user terminal. The task allocation result includes the assigned user terminal identifier, task allocation time, task priority, and task status.

[0028] In a preferred embodiment, the steps of real-time tracking of task status, updating task status when task completion, collecting task execution data, and generating a task feedback report include the following steps:

[0029] The task execution data includes personnel efficiency and task completion time;

[0030] Compare the task execution data with the preset evaluation indicators to determine whether the task has met the standards;

[0031] When the task fails to meet the target, analyze the reasons for the failure, including equipment failure and human error.

[0032] Upon completion of the judgment, a task feedback report is output to the user's terminal.

[0033] The task feedback report includes the judgment result, the reason for failure, and improvement suggestions.

[0034] In a preferred embodiment, the step of formulating and allocating improvement measures to the user based on the task feedback report, evaluating the improvement effect, and then feeding back the results to the BI report includes the following steps:

[0035] Based on machine learning algorithms, analyze task feedback reports and formulate corresponding improvement measures;

[0036] Assign the improvement measures to the corresponding user terminals and set completion deadlines;

[0037] Based on IoT technology, the implementation of improvement measures can be tracked in real time;

[0038] Based on big data analytics, the performance is evaluated in multiple dimensions to generate personalized optimization suggestions.

[0039] Based on blockchain technology, multi-dimensional evaluation results and personalized optimization suggestions are recorded and fed back to BI reports.

[0040] The second objective of this invention is achieved through the following technical solution:

[0041] A business task intelligent tracking system based on BI reports includes:

[0042] Trigger module: Monitors the warehouse environment in real time and triggers task reminders when stagnant or obsolete items are detected;

[0043] Storage module: Based on the API, create new task records in the task scheduling table and store the task information in the corresponding task record;

[0044] Output module: Based on task information, assign tasks to the corresponding user terminals and output the task assignment results;

[0045] Collection module: Tracks task status in real time, updates task status when task is completed, collects task execution data, and generates task feedback reports;

[0046] Feedback module: Based on task feedback reports, formulate and assign improvement measures to users, evaluate the effectiveness of the improvements, and then provide feedback to BI reports.

[0047] The above-mentioned objective three of this application is achieved through the following technical solution:

[0048] A computer device includes 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 above-described intelligent tracking method for business tasks based on BI reports.

[0049] The fourth objective of this application is achieved through the following technical solution:

[0050] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent tracking method for business tasks based on BI reports.

[0051] In summary, this application includes at least one of the following beneficial technical effects:

[0052] First, by monitoring the warehouse environment in real time, the system can quickly identify obsolete items and automatically trigger task reminders, ensuring that problems are addressed promptly. Next, using API technology, new task records are created and stored in the task scheduling table, standardizing and automating task information processing. Then, the system intelligently assigns tasks to corresponding user terminals based on the task information and outputs the assignment results, ensuring reasonable task allocation and execution. During task execution, the system tracks the task status in real time. Once a task is completed, it updates the status, collects execution data, and generates a task feedback report, providing detailed data support for subsequent analysis. Finally, based on the task feedback report, the system formulates and distributes improvement measures to user terminals. After evaluating the effectiveness of the improvements, feedback is sent to BI reports, forming a self-optimizing management loop. Overall, this process not only improves the efficiency of task management but also enhances the enterprise's decision-making capabilities and operational efficiency through continuous data analysis and feedback. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating an embodiment of a business task intelligent tracking method based on BI reports according to this application.

[0054] Figure 2 This is a flowchart of step S10 in an embodiment of a business task intelligent tracking method based on BI reports in this application;

[0055] Figure 3 This is a flowchart of step S102 in an embodiment of a business task intelligent tracking method based on BI reports in this application;

[0056] Figure 4This is another implementation flowchart of step S30 in an embodiment of a business task intelligent tracking method based on BI reports in this application;

[0057] Figure 5 This is a flowchart of step S40 in an embodiment of a business task intelligent tracking method based on BI reports according to this application;

[0058] Figure 6 This is a flowchart of step S50 in an embodiment of a business task intelligent tracking method based on BI reports according to this application;

[0059] Figure 7 This is a schematic block diagram of a computer device according to this application. Detailed Implementation

[0060] The following is in conjunction with the appendix Figure 1-7 This application will be described in further detail.

[0061] In one embodiment, such as Figure 1 As shown, this application discloses a business task intelligent tracking method based on BI reports, which specifically includes the following steps:

[0062] S10: Real-time monitoring of the warehouse environment; triggers task alerts when obsolete or stagnant goods are detected.

[0063] S20: Create a new task record in the task scheduling table based on the API, and store the task information in the corresponding task record;

[0064] S30: Based on the task information, assign tasks to the corresponding user terminals and output the task assignment results;

[0065] S40: Tracks task status in real time, updates task status when task is completed, collects task execution data, and generates task feedback reports;

[0066] S50: Based on the task feedback report, formulate and allocate improvement measures to the user end, evaluate the improvement effect and feed it back to the BI report.

[0067] In this embodiment, firstly, by monitoring the warehouse environment in real time (S10), the system can quickly identify stagnant goods and automatically trigger task reminders, ensuring that problems can be handled promptly. Next, new task records are created and stored in the task scheduling table using API technology (S20), a step that standardizes and automates task information processing. Subsequently, the system intelligently allocates tasks to corresponding user terminals based on task information and outputs the allocation results (S30), ensuring reasonable task allocation and execution. During task execution, the system tracks task status in real time (S40). Once a task is completed, it updates the status, collects execution data, and generates a task feedback report, providing detailed data support for subsequent analysis. Finally, based on the task feedback report (S50), the system formulates and allocates improvement measures to user terminals, evaluates the improvement effects, and feeds back to BI reports, forming a self-optimizing management loop. Overall, this process not only improves the efficiency of task management but also enhances the enterprise's decision-making capabilities and operational efficiency through continuous data analysis and feedback.

[0068] Figure 2 Step S10 includes the following steps:

[0069] S101: Based on predefined warehouse environment monitoring parameters and IoT sensor network, collect warehouse environment data in real time;

[0070] S102: Based on BI reports, when obsolete items are detected, output task reminders based on task priority.

[0071] In this embodiment, in step S10, the system first collects warehouse environment data in real time based on predefined monitoring parameters and an IoT sensor network through sub-step S101, ensuring the accuracy and timeliness of the data. Then, in sub-step S102, the system utilizes the analytical capabilities of BI reports to output task alerts based on task priority once obsolete items are detected. This process not only accelerates problem identification but also ensures that critical tasks are prioritized. Overall, the implementation of this method makes warehouse management more intelligent and efficient, reduces resource waste, improves inventory turnover, and provides enterprises with more accurate data support to optimize inventory management and improve decision-making quality.

[0072] Figure 3 Step S102 includes the following steps:

[0073] SB1: Based on RFID, acquire key information about the items, including the time of entry into the warehouse, shelf life, and physical volume parameters;

[0074] SB2: Based on the sales data from the ERP system and the aforementioned key information, calculate the inventory turnover days, remaining shelf life days, and warehouse space occupancy rate;

[0075] SB3: Obsolete products are detected when inventory turnover days > preset threshold, remaining shelf life days < shelf life warning value, and warehouse space occupancy rate > warehouse planning threshold;

[0076] SB4: Based on image recognition, detect whether there are any abnormal situations;

[0077] SB5: The abnormal situations mentioned include deformation of the outer packaging and abnormal data from the temperature and humidity sensor;

[0078] SB6: When an anomaly is detected, increase the task priority.

[0079] In this embodiment, in step S102, the key information of the items, including the warehousing time, shelf life, and physical volume parameters, is first obtained using RFID technology through sub-step SB1, providing basic data for subsequent analysis. Next, in sub-step SB2, the inventory turnover days, remaining shelf life days, and warehouse space occupancy rate are calculated by combining sales data from the ERP system and the key information obtained from RFID. These indicators are important criteria for judging obsolete inventory. In sub-step SB3, when these indicators exceed preset thresholds, the system detects obsolete inventory, achieving early detection of potential inventory problems. In sub-steps SB4 and SB5, image recognition technology is used to detect anomalies such as deformed outer packaging and abnormal temperature and humidity sensor data, further enhancing the accuracy of problem identification. Finally, in sub-step SB6, when anomalies are detected, the system prioritizes the task to ensure that these problems are resolved promptly. Overall, this method not only improves the intelligence level of warehouse management but also reduces inventory risk, optimizes inventory structure, and improves operational efficiency and customer satisfaction by timely identifying and handling obsolete inventory.

[0080] Figure 4 Step S30 includes the following steps:

[0081] S301: Output a task allocation scheme based on task information;

[0082] S302: The task information includes task ID, task type, task priority, and task status;

[0083] S303: When a task with a priority greater than the preset priority threshold L1 is detected, a preemptive scheduling mechanism is triggered to interrupt the currently executing low-priority task.

[0084] S304: Output the task allocation result to the corresponding user terminal. The task allocation result includes the assigned user terminal identifier, task allocation time, task priority, and task status.

[0085] In this embodiment, in step S30, the system first generates a task allocation scheme based on task information, including task ID, type, priority, and status, through sub-step S301. In sub-step S302, the specific content of the task information is clarified, providing detailed data support for task allocation. Next, in sub-step S303, when a task priority exceeds a preset priority threshold L1, the system triggers a preemptive scheduling mechanism. This allows the system to interrupt currently executing low-priority tasks to ensure the immediate execution of high-priority tasks, significantly improving the response speed of critical tasks. Finally, in sub-step S304, the system outputs the task allocation result to the corresponding user terminal, including user terminal identifier, task allocation time, and task status, making task execution more transparent and facilitating effective task management by the user terminal based on the allocation results. Overall, this method improves the flexibility and efficiency of task execution through intelligent task allocation and priority management, ensuring the smooth operation of critical business processes, thereby enhancing overall operational efficiency and customer satisfaction.

[0086] Figure 5 Step S40 includes the following steps:

[0087] S401: The task execution data includes personnel efficiency and task completion time;

[0088] S402: Compare the task execution data with the preset evaluation indicators to determine whether the task has met the standards;

[0089] S403: When the task fails to meet the target, analyze the reasons for the failure, including equipment failure and human error.

[0090] S404: When the judgment is completed, output a task feedback report to the user terminal;

[0091] S405: The task feedback report includes the judgment result, the reason for failure, and improvement suggestions.

[0092] In this embodiment, in step S40, task execution data, including key indicators such as personnel efficiency and task completion time, is first collected through sub-step S401. Next, in sub-step S402, the system compares this data with preset evaluation indicators to determine whether the task meets the expected standards. In sub-step S403, if the task fails to meet the standards, the system further analyzes the reasons for failure, such as equipment malfunction or human error; this step helps to gain a deeper understanding of the problem. Then, in sub-step S404, once the judgment is completed, the system outputs a task feedback report to the user, ensuring timely information delivery. Finally, in sub-step S405, the task feedback report includes the judgment result, the reasons for failure, and improvement suggestions, providing the user with clear solutions and improvement directions. Overall, this method, through a closed-loop feedback mechanism, not only improves the transparency and traceability of task execution but also promotes the timely discovery and resolution of problems, thereby improving work efficiency and task completion quality, bringing continuous business optimization and competitiveness enhancement to the enterprise.

[0093] Figure 6 S50 steps include the following steps:

[0094] S501: Analyze task feedback reports based on machine learning algorithms and formulate corresponding improvement measures;

[0095] S502: Assign the improvement measures to the corresponding user terminals and set a completion deadline;

[0096] S503: Based on IoT technology, track the implementation status of improvement measures in real time;

[0097] S504: Based on big data analytics, the performance is evaluated in multiple dimensions, and personalized optimization suggestions are generated.

[0098] S505: Based on blockchain technology, multi-dimensional evaluation results and personalized optimization suggestions are recorded and fed back to BI reports.

[0099] In this embodiment, in step S50, sub-step S501 first utilizes machine learning algorithms to deeply analyze the task feedback report, thereby formulating targeted improvement measures. This step ensures the accuracy and effectiveness of the improvement measures. Next, in sub-step S502, the system assigns these improvement measures to the corresponding user terminals and sets completion deadlines to promote timely execution. In sub-step S503, IoT technology is used to track the execution of the improvement measures in real time, ensuring transparency and real-time monitoring of the execution process. Then, in sub-step S504, big data analytics is used to conduct a multi-dimensional evaluation of the execution status and generate personalized optimization suggestions. This step helps to continuously optimize business processes. Finally, in sub-step S505, blockchain technology is used to record the multi-dimensional evaluation results and personalized optimization suggestions and feed them back to the BI report. The immutability of blockchain ensures the authenticity and reliability of the data. Overall, this approach enables the intelligent formulation and execution of improvement measures through technological integration. It not only improves enterprises' ability to monitor task execution, but also enhances their continuous improvement and risk management capabilities through data-driven decision support, ultimately improving overall operational efficiency and customer satisfaction.

[0100] 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 this application.

[0101] In one embodiment, a business task intelligent tracking system based on BI reports is provided, which corresponds to the business task intelligent tracking method based on BI reports described in the above embodiment. The business task intelligent tracking system based on BI reports includes:

[0102] Trigger module: Monitors the warehouse environment in real time and triggers task reminders when stagnant or obsolete items are detected;

[0103] Storage module: Based on the API, create new task records in the task scheduling table and store the task information in the corresponding task record;

[0104] Output module: Based on task information, assign tasks to the corresponding user terminals and output the task assignment results;

[0105] Collection module: Tracks task status in real time, updates task status when task is completed, collects task execution data, and generates task feedback reports;

[0106] Feedback module: Based on task feedback reports, formulate and assign improvement measures to users, evaluate the effectiveness of the improvements, and then provide feedback to BI reports.

[0107] Optional, also includes:

[0108] Data Acquisition Module: Based on predefined warehouse environment monitoring parameters and IoT sensor network, it collects warehouse environment data in real time.

[0109] First output module: Based on BI reports, when stagnant products are detected, task reminders are output based on task priority.

[0110] Optional, also includes:

[0111] The first module includes: based on RFID, acquiring key information of the items, including warehousing time, shelf life, and physical volume parameters;

[0112] Calculation module: Based on sales data from the ERP system and the aforementioned key information, calculate inventory turnover days, remaining shelf life days, and warehouse space occupancy rate;

[0113] Judgment module: When inventory turnover days > preset threshold, remaining shelf life days < shelf life warning value, and warehouse space occupancy rate > warehouse planning threshold, obsolete products are detected;

[0114] Detection module: Based on image recognition, it detects whether there are any abnormalities;

[0115] The second module includes: the abnormal situations include outer packaging deformation and abnormal temperature and humidity sensor data;

[0116] Module 1: When an anomaly is detected, the task priority is increased.

[0117] Optional, also includes:

[0118] The third output module outputs a task allocation scheme based on task information.

[0119] The third module includes: the task information includes task ID, task type, task priority, and task status;

[0120] First detection module: When a task with a priority greater than the preset priority threshold L1 is detected, a preemptive scheduling mechanism is triggered to interrupt the currently executing low-priority task.

[0121] The fourth module outputs the task allocation result to the corresponding user terminal. The task allocation result includes the assigned user terminal identifier, task allocation time, task priority, and task status.

[0122] Optional, also includes:

[0123] The fifth module includes: the task execution data includes personnel efficiency and task completion time;

[0124] First judgment module: Compare the task execution data with preset evaluation indicators to determine whether the task meets the standards;

[0125] Failure Reason Module: When a task fails to meet its objectives, the module analyzes the reasons for failure, including equipment malfunction and human error.

[0126] Fourth output module: When the judgment is completed, output a task feedback report to the user terminal;

[0127] The sixth module includes: the task feedback report, which includes the judgment result, the reason for failure, and improvement suggestions.

[0128] Optional, also includes:

[0129] Improvement Measures Module: Analyzes task feedback reports based on machine learning algorithms and formulates corresponding improvement measures;

[0130] Configuration module: Assigns improvement measures to the corresponding user terminals and sets completion deadlines;

[0131] Tracking module: Based on IoT technology, it tracks the implementation status of improvement measures in real time;

[0132] Multi-dimensional evaluation module: Based on big data analytics, it performs multi-dimensional evaluation of the execution and generates personalized optimization suggestions;

[0133] The second module: Based on blockchain technology, multi-dimensional evaluation results and personalized optimization suggestions are recorded and fed back to BI reports.

[0134] For specific limitations regarding a business task intelligent tracking system based on BI reports, please refer to the limitations of a business task intelligent tracking method based on BI reports mentioned above, which will not be repeated here. Each module in the aforementioned business task intelligent tracking system based on BI reports 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 a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0135] 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 storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores task information. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a business task intelligent tracking method based on BI reports.

[0136] 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 implement a business task intelligent tracking method based on BI reports.

[0137] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, is a business task intelligent tracking method based on BI reports.

[0138] 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, and when executed, it 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 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various 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.

[0139] 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.

Claims

1. A business task intelligent tracking method based on BI reports, characterized in that, Including the following steps: Real-time monitoring of the warehouse environment; triggering task alerts when stagnant or obsolete goods are detected. Based on predefined warehouse environment monitoring parameters and IoT sensor network, warehouse environment data is collected in real time. Based on BI reports, when obsolete products are detected, task reminders are output according to task priority; Based on RFID, key information of items is obtained, including warehousing time, shelf life, and physical volume parameters. Based on the sales data from the ERP system and the aforementioned key information, calculate the inventory turnover days, remaining shelf life days, and warehouse space occupancy rate. When inventory turnover days > preset threshold, remaining shelf life days < shelf life warning value, and warehouse space occupancy rate > warehouse planning threshold, obsolete products are detected. Based on image recognition, detect whether there are any abnormalities; The abnormal situations include deformation of the outer packaging and abnormal data from the temperature and humidity sensors. When an anomaly is detected, the task priority is increased; Create new task records in the task scheduler based on the API, and store the task information in the corresponding task record; Based on the task information, assign tasks to the corresponding user terminals and output the task assignment results; Based on task information, output a task allocation plan; The task information includes task ID, task type, task priority, and task status; When a task with a priority higher than the preset priority threshold L1 is detected, a preemptive scheduling mechanism is triggered to interrupt the currently executing low-priority task. Output the task allocation result to the corresponding user terminal. The task allocation result includes the allocated user terminal identifier, task allocation time, task priority, and task status. Track task status in real time, update task status when task is completed, collect task execution data, and generate task feedback reports; Based on the task feedback report, formulate and assign improvement measures to the user end, evaluate the improvement effect and then feed it back to the BI report; Based on machine learning algorithms, analyze task feedback reports and formulate corresponding improvement measures; Assign the improvement measures to the corresponding user terminals and set completion deadlines; Based on IoT technology, the implementation of improvement measures can be tracked in real time; Based on big data analytics, the performance is evaluated in multiple dimensions to generate personalized optimization suggestions. Based on blockchain technology, multi-dimensional evaluation results and personalized optimization suggestions are recorded and fed back to BI reports.

2. The intelligent tracking method for business tasks based on BI reports according to claim 1, characterized in that, The steps of real-time tracking of task status, updating task status when the task is completed, collecting task execution data, and generating a task feedback report include the following steps: The task execution data includes personnel efficiency and task completion time; Compare the task execution data with the preset evaluation indicators to determine whether the task has met the standards; When the task fails to meet the target, analyze the reasons for the failure, including equipment failure and human error. Upon completion of the judgment, a task feedback report is output to the user's terminal. The task feedback report includes the judgment result, the reason for failure, and improvement suggestions.

3. A business task intelligent tracking system based on BI reports, characterized in that, include: Trigger module: Monitors the warehouse environment in real time and triggers task reminders when stagnant or obsolete items are detected; Data Acquisition Module: Based on predefined warehouse environment monitoring parameters and IoT sensor network, it collects warehouse environment data in real time. First output module: Based on BI reports, when stagnant products are detected, a task reminder is output based on task priority; The first module includes: based on RFID, acquiring key information of the item, including the warehousing time, shelf life, and physical volume parameters; Calculation module: Based on sales data from the ERP system and the aforementioned key information, calculate inventory turnover days, remaining shelf life days, and warehouse space occupancy rate; Judgment module: When inventory turnover days > preset threshold, remaining shelf life days < shelf life warning value, and warehouse space occupancy rate > warehouse planning threshold, obsolete products are detected; Detection module: Based on image recognition, it detects whether there are any abnormalities; The second module includes: the abnormal situations include outer packaging deformation and abnormal temperature and humidity sensor data; Module 1: When an anomaly is detected, the task priority is increased; Storage module: Based on the API, create new task records in the task scheduling table and store the task information in the corresponding task record; Output module: Based on task information, assign tasks to the corresponding user terminals and output the task assignment results; The third output module outputs a task allocation scheme based on task information. The third module includes: the task information includes task ID, task type, task priority, and task status; First detection module: When a task with a priority greater than the preset priority threshold L1 is detected, a preemptive scheduling mechanism is triggered to interrupt the currently executing low-priority task. The fourth module includes: outputting task allocation results to the corresponding user terminal, wherein the task allocation results include the assigned user terminal identifier, task allocation time, task priority, and task status; Collection module: Tracks task status in real time, updates task status when task is completed, collects task execution data, and generates task feedback reports; Feedback module: Based on task feedback reports, formulate and assign improvement measures to users, evaluate the effectiveness of the improvements, and then provide feedback to BI reports; Improvement Measures Module: Analyzes task feedback reports based on machine learning algorithms and formulates corresponding improvement measures; Configuration module: Assigns improvement measures to the corresponding user terminals and sets completion deadlines; Tracking module: Based on IoT technology, it tracks the implementation status of improvement measures in real time; Multi-dimensional evaluation module: Based on big data analytics, it performs multi-dimensional evaluation of the execution and generates personalized optimization suggestions; The second module: Based on blockchain technology, multi-dimensional evaluation results and personalized optimization suggestions are recorded and fed back to BI reports.

4. 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 business task intelligent tracking method based on BI reports as described in any one of claims 1 to 2.

5. 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 business task intelligent tracking method based on BI reports as described in any one of claims 1 to 2.

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