Whole-process electronic control method for quality management of pathology department

Abstract

The invention relates to a whole-process electronic control method for quality management of the pathology department, and the method comprises the steps: building and compiling a quality management file and related records in a control device based on a preset electronic template, building a digital file library, and enabling the control device to carry out the quality management according to a preset working process, an electronic instruction is automatically generated, a to-be-handled task is pushed to a corresponding user node, a related user executes electronic processing operation, file approval, equipment verification, environment monitoring, internal auditing and online examination through a control device, and the control device automatically records all processing operations, establishes a data association relationship, updates a task state and a related database in real time, and controls the task to be handled according to the data association relationship. A user monitors execution states of various tasks in real time through the control device, target data are rapidly positioned through multi-dimensional retrieval conditions, the control device automatically records all operation logs and data processing processes, an electronic auditing track which cannot be tampered is generated, and whole-process data backtracking is supported.

Description

[0001] Technical Field This application relates to the field of electronic digital data technology, specifically to a fully electronic control method for quality management in pathology departments. Background Technology

[0002] As an important component of medical laboratories, the standardization and completeness of the quality management system of the pathology department are directly related to the accuracy and reliability of diagnostic results, and are also a key prerequisite for passing ISO15189 international certification. Currently, most pathology departments still use traditional management models in their quality management processes, relying on a combination of paper-based records and scattered spreadsheets. This model has the following prominent problems: chaotic document management, difficulty in controlling system document versions, lack of effective tracking of the revision process, and easy occurrence of inconsistent document usage or misuse of expired documents; serious information silos, with modules such as personnel files, equipment management, reagent monitoring, and environmental data operating independently, lacking effective data linkage and integration mechanisms; and low process execution efficiency, with core quality management activities such as internal audits, management reviews, equipment validation, and personnel assessments relying on manual organization and recording, which is time-consuming, prone to omissions, and difficult to monitor in real time. While existing technologies include solutions related to electronic process quality control or diagnosis and monitoring of electronic control devices, they focus on "electronic quality inspection of paper documents" and "hardware fault diagnosis" respectively. They do not cover the full-process, multi-module integrated electronic control of the pathology department's quality management system, nor can they address the systematic, closed-loop management needs under the ISO15189 certification background. Therefore, there is an urgent need in this field for an electronic control method that can cover the entire process of quality management in pathology departments.

[0003] Summary of the Invention In order to solve the problems existing in the prior art, the purpose of this application is to provide a fully electronic control method for quality management in pathology departments.

[0004] The fully electronic control method for quality management in pathology departments described in this application includes the following steps: S101. Based on preset electronic templates, create and write quality management documents and related records in the control device to establish a digital document library; S102. The control device automatically generates electronic instructions and pushes the pending tasks to the corresponding user nodes according to the preset workflow. S103. Relevant users perform electronic processing operations through control devices, including document approval, equipment verification, environmental monitoring, internal auditing, and online assessment. S104. The control device automatically records all processing operations, establishes data association relationships, and updates the task status and related databases in real time. S105. Users can monitor the execution status of various tasks in real time through the control device and quickly locate target data based on multi-dimensional search conditions. The control device automatically records all operation logs and data processing processes, generates an unalterable electronic audit track, and supports full-process data backtracking.

[0005] Furthermore, in step S101, raw data is obtained from the control device through a preset electronic template and classified and organized to form a categorized data set. The data is compared with preset rules using a template matching method. If the data meets the requirements, a quality management document is generated and stored in the digital library. The storage path identifier is obtained. The system determines whether the relevant records need to be updated based on the identifier. If revision is required, the document is extracted from the library and the record content is updated in conjunction with the operation log. After the update, the system links the monitoring mechanism to periodically obtain the latest data to determine its consistency with the record version. For inconsistent data, the system re-executes the document generation process to update the documents and records in the library, thereby realizing the closed-loop processing of quality management.

[0006] Furthermore, in step S102, task data is obtained from the control device, classified according to its type and priority, and a preliminary allocation plan is obtained. Electronic instructions containing content and execution order are automatically generated. The push mechanism matches the instructions to available user nodes for transmission. The system then collects task status feedback to track the execution progress. For incomplete tasks, the process management module will readjust the instruction path, form an updated plan, and push it again until all tasks are issued. The system records complete task allocation and instruction transmission logs to the database.

[0007] Furthermore, in step S103, the control device verifies the user's identity and assigns operating permissions. The system processes the document approval flow according to the permissions, determines the priority according to the rules, and only allows data to be transmitted to the terminal after the device verification module confirms that the device status is safe. Abnormal environmental monitoring data will trigger internal review, and logs will be analyzed using a logistic regression model to identify potential risks. The online assessment system combines user responses and permission rules to obtain a process adjustment plan, and real-time feedback data is archived and stored in the database to form a complete operation record.

[0008] Furthermore, in step S104, the control device automatically captures and records each processing operation, forming a complete operation sequence data. The system constructs a data association mapping, matches the operation logs with the task status according to preset rules, establishes a corresponding relationship, monitors changes in task status in real time, and updates the tracking information immediately upon a status change, synchronously writing it to the database for persistent storage. The system extracts the latest status and logs from the data storage module, and if an association anomaly is detected, a verification mechanism is triggered. The system repairs the data by backtracking historical records through logs to ensure consistency, updates the monitoring records with the repaired data, re-completes the mapping between operations and status, and synchronizes it to the database.

[0009] Furthermore, in step S105, the control device collects the task execution status and timestamp, forms a structured record and stores it in the database, uses multi-dimensional retrieval to filter target data, and if the range is too large, it uses hierarchical indexing for secondary filtering to achieve rapid positioning. The system automatically records relevant operation logs to form a complete operation trajectory, extracts key fields and processing intermediate results, generates unalterable electronic audit records and stores them in distributed storage, constructs a full-process backtracking path, verifies that it covers all key nodes, associates and maps the path with the task status, and generates a structured full-process data view that can be viewed in real time.

[0010] The fully electronic control method for quality management in pathology departments described in this application has the advantage of achieving closed-loop electronic management throughout the entire process. By integrating more than ten core modules, including system documents, personnel, equipment, reagents, environment, internal audit, management audit, and examinations, an integrated quality management platform is constructed, completely eliminating information silos and realizing full-process digital closed-loop control from document creation, task assignment, execution monitoring to audit traceability. The system supports automated monitoring and intelligent early warning, such as reagent near-expiration reminders, temperature and humidity exceeding alarms, and equipment calibration due reminders, significantly reducing human error and improving the timeliness and accuracy of quality control. Optimize the efficiency of core management activities, enabling online organization and execution of internal audits, management reviews, and online examinations. The system automatically generates schedules, statistical results, and reports, significantly reducing paperwork and improving management efficiency. Enhance data association and traceability capabilities, with all operations automatically recorded and data relationships established by the system, supporting multi-dimensional retrieval and full-process audit backtracking, fully meeting the ISO15189 certification requirements for data integrity and traceability. It supports electronic signatures and version control. The system has built-in electronic signature and timestamp functions to ensure the legality, timeliness and immutability of documents and operations, further enhancing the standardization and credibility of the quality management system. Attached Figure Description

[0011] Figure 1 This application describes a fully electronic control method for quality management in pathology departments. Figure 1 ; Figure 2 This application describes a fully electronic control method for quality management in pathology departments. Figure 2 . Detailed Implementation

[0012] like Figures 1-2 As shown in this application, a fully electronic control method for quality management in pathology departments includes: like Figures 1-2As shown, S101, based on a preset electronic template, quality management documents and related records are created and written in the control device to establish a digital document library.

[0013] Furthermore, in step S101, the raw data required for quality management is obtained from the control device through a preset electronic template, and the data content is classified and organized to determine the classified data set; Based on the categorized dataset, template matching is used to compare the data with preset rules. If the data meets the rule requirements, the corresponding quality management document is generated. For the generated quality management documents, a file storage operation is performed within the control device to save the documents to the digital library and obtain the storage path identifier; By identifying the storage path, we can obtain the update requirements for relevant records, match the update requirements with the management process, and determine whether the records need to be revised. If a record needs to be revised, the corresponding file is extracted from the digital library, and the relevant record content is updated in conjunction with the device operation log to determine the updated record version. Based on the updated record version, the monitoring mechanism in the linkage management process is linked to periodically obtain the latest data from the control device to determine the consistency between the data and the record version. Based on the consistency assessment results, a document generation process is executed for inconsistent data to update the documents and records in the digital library, thus completing the closed-loop process of quality management.

[0014] Specifically, in step S101, the method for creating and writing quality management documents and related records in the control device based on a preset electronic template, and establishing a digital document library, can be fully automated through information technology. The system will automatically generate quality management documents based on preset electronic templates. The templates contain standardized fields such as document number, version number, and creation date. Assuming the document numbering rule is "QM-2023-001", where "2023" represents the year and "001" is the serial number, the system automatically increments the serial number using a built-in algorithm to ensure uniqueness. At the same time, the creation date is accurate to the second, such as "2023-10-15, 14:30:25", and embedded in the document metadata. The system uses the data acquisition module in the control device to acquire quality data in real time during the production process, such as the defect rate of a certain batch of products. Assuming that 25 defective products are collected out of 1,000 products, the system automatically calculates the defect rate as 25 / 1000×100%=2.5%, and compares this data with the preset threshold of 3.0%. The analysis result shows that the current batch is qualified, and a quality record file is automatically generated. The record contains the defect rate of 2.5%, the comparison result of "qualified", and a timestamp. The system intelligently categorizes files using a built-in classification algorithm. Based on file type and content keywords, it assigns files to the corresponding directories in the digital file library, such as "Quality Records / 2023 / 10". It also uses a hash algorithm to generate a unique identifier for each file, such as a SHA-256 value, to ensure that the files cannot be tampered with. At the same time, it records access logs. If a file is accessed 5 times in 24 hours, the system automatically analyzes the access frequency to determine if it is abnormal. If it exceeds a preset threshold of 10 times / day, it triggers an early warning mechanism and generates an anomaly report. The process forms a closed-loop logic, with seamless integration of document creation, data analysis, classification and storage, and security monitoring. If cross-departmental business is involved, the system can synchronize documents to the supply chain management system through an interface to ensure information sharing. For example, data with a defect rate of 2.5% can be pushed to the supplier evaluation module to automatically update the supplier score. The scoring algorithm is: score = 100 - defect rate × 10 = 100 - 2.5 × 10 = 75 points. If the score is below 80 points, it is marked as needing improvement, thereby extending the business chain and ensuring the linkage between quality management and supply chain management.

[0015] like Figures 1-2 As shown in Figure S102, the control device automatically generates electronic instructions and pushes the pending tasks to the corresponding user nodes according to the preset workflow.

[0016] Furthermore, in step S102, the task data to be processed is obtained from the control device through a preset process, and the task data is classified according to the task type and priority to determine a preliminary task allocation scheme. Based on the categorized task data, corresponding electronic instructions are automatically generated to obtain the instruction content and execution order, taking into account the specific requirements of the task. Through the push mechanism, the generated electronic instructions are matched with the corresponding user nodes. If the node status is available, the instructions are transmitted to the target node to complete the task push. Obtain task status feedback after push notification, collect execution progress information from user nodes, organize the feedback data, and determine the real-time results of task tracking; Based on the task tracking results, the completion status of pending tasks is analyzed. If there are incomplete tasks, the instruction transmission path is readjusted through the process management module to obtain an updated allocation scheme. By using the updated allocation scheme and node matching logic, the tasks to be done are re-pushed to the corresponding user nodes, and it is determined whether all tasks have been distributed to obtain the final push confirmation information. Based on the final push confirmation information, a complete log of task allocation and instruction transmission is recorded and stored in the database of the control device to determine the closed-loop processing result of the business process.

[0017] Specifically, in step S102, the control device automatically generates electronic instructions according to the preset workflow and pushes the pending tasks to the corresponding user nodes. The specific implementation method can be achieved through the following integration method. Based on a pre-set workflow database, the system automatically extracts the priority and time limit of the current task. Assuming that the priority of a task is 5 (out of 10) and the deadline is 24 hours, the system calculates the task urgency score using an algorithm = priority × time weight, where the time weight is 1 / remaining hours, i.e. 5 × (1 / 24) = 0.2083. The analysis shows that the urgency of the task is low, but it still needs to be assigned. The system invokes the task allocation algorithm to compare the skill matching degree between the task and the user node. Assuming that user A's skill matching degree is 85% and user B's is 60%, through weighted calculation (matching degree × 0.7 + urgency × 0.3), the comprehensive score of user A is 85 × 0.7 + 0.2083 × 0.3 = 59.5625, and that of user B is 42.0625. Therefore, the system automatically generates an electronic instruction and allocates the task to user A's node. The system pushes pending tasks to user A's terminal in the form of data packets through an internal message queue mechanism. The data packets contain fields such as task ID, deadline of 24 hours, and task description, ensuring that the push delay does not exceed 500 milliseconds and the push success rate reaches more than 99.9%. The analysis log shows that the average push time is 320 milliseconds, which meets the performance requirements. To form a logical chain, the system also includes a feedback mechanism after the task is completed. If the task is not completed within 24 hours, the system will automatically trigger a reminder and push a reminder every 6 hours until the task status is updated to "completed", thus ensuring a closed loop in the process. Using the above methods, the system achieves fully automated processing from task generation and allocation to push notifications, ensuring both efficiency and accuracy.

[0018] like Figures 1-2 As shown in S103, relevant users perform electronic processing operations through the control device, including document approval, equipment verification, environmental monitoring, internal auditing, and online assessment.

[0019] Further, in step S103, based on the above business content and the extracted relevant attributes, the following business solution is generated, focusing on core business processes such as document approval, equipment verification, environmental monitoring, internal audit, and online assessment. Combining attributes such as control device, electronic operation, user management, permission allocation, data recording, and real-time feedback, the specific technical implementation steps are as follows: user identity information is obtained through the control device, permission allocation is determined based on the identity data, and if the permission meets the preset access conditions, access to the electronic operation interface is allowed to obtain the user's operation permission range. Based on the user's operation permission scope, obtain the data stream related to document approval, use pre-established classification rules to perform preliminary screening of document content, and determine the priority order of document approval; The device verification module obtains the current device's operating status data. If the device status meets the preset security standards, the document approval data is allowed to be transmitted to the designated terminal, and the transmission process is judged to be complete. The system acquires data streams collected by environmental monitoring sensors, performs real-time analysis on environmental parameters, and triggers an internal audit mechanism to determine the source and scope of the abnormal data if abnormal fluctuations are detected. Based on the source range of the abnormal data, internal audit log records are obtained, and logistic regression models are used to classify the log data to determine whether there are potential risk factors. By using the online assessment system, we can obtain users' response data to risk factors, compare and analyze the response content in conjunction with the permission allocation rules, and determine the final business process adjustment plan. Based on the business process adjustment plan, obtain real-time feedback data streams, archive the feedback content, and store it in a designated database through the data recording module to obtain complete operation records.

[0020] Specifically, in step S103, relevant users perform electronic processing operations through control devices. The specific implementation methods covering multiple stages such as document approval, equipment verification, environmental monitoring, internal audit, and online assessment are integrated as follows: In the document approval stage, the system automatically loads the documents to be approved and uses built-in intelligent algorithms to extract keywords and perform compliance checks on the document content. For example, it checks whether the document contains sensitive words. If the proportion of sensitive words exceeds 0.5%, it is automatically marked as high risk and pushed to the second-level review. At the same time, a risk report is generated, including specific proportion data such as 0.7% and a list of related words. The analysis process is based on a natural language processing model, ensuring that the approval efficiency is improved by 30%. During the equipment verification process, IoT technology is used to collect equipment operating parameters in real time, such as CPU utilization should be below 80%. If a device's utilization is detected to be 85%, the system will automatically trigger a load reduction command and record an abnormal log, analyze possible causes such as too many background processes, and obtain optimization suggestions. In the environmental monitoring stage, a sensor network is used to collect temperature and humidity data every 5 minutes. If the temperature exceeds 28.5 degrees, the system automatically adjusts the air conditioner setting to 25 degrees and predicts the temperature trend for the next hour through time series analysis, with the error controlled within 0.3 degrees to ensure environmental stability. The internal audit process uses data mining algorithms to detect anomalies in operation logs. For example, if a user's operation frequency exceeds the average by 3 times, the system automatically generates an audit report, including the number of operations, such as 50 times / hour, and an anomaly score of 0.9, analyzes its potential risks, and links them to the access control module to adjust the strategy. The online assessment uses an adaptive testing algorithm that dynamically adjusts the difficulty of questions based on the user's accuracy rate. If the accuracy rate is below 60%, the system automatically pushes a basic question bank, records the answering time (e.g., an average of 3.2 minutes per question), analyzes the learning curve, and generates personalized improvement plans to ensure that the assessment results match the user's abilities. The overall logic forms a closed loop through data flow and system linkage.

[0021] like Figures 1-2 As shown in S104, the control device automatically records all processing operations, establishes data association relationships, and updates the task status and related databases in real time.

[0022] Furthermore, in step S104, each processing operation is captured by the control device, and the operation log is automatically recorded to obtain complete operation sequence data; For operation sequence data, a data association mapping is constructed, and operation logs are matched with task status using preset rules to determine the correspondence between operations and status; Based on the matched relationship, the task status changes are monitored in real time. If the task status changes, the status tracking information is updated immediately. Obtain status tracking information, synchronously update the corresponding database, write the changed task status to the data storage module, and complete the data persistence process. The data storage module extracts the latest task status and operation logs. If an abnormal data association is detected, a verification mechanism is triggered to determine the data integrity. Based on the results of the verification mechanism, abnormal data is repaired by using log backtracking to obtain historical operation records and determine the consistency of the repaired data. By updating the real-time monitoring records with the repaired data, the processing operations and task status are remapped and synchronized to the corresponding database, forming a closed-loop management system.

[0023] Specifically, in step S104, the process of automatically recording all processing operations, establishing data associations, and updating task status and related databases in real time by the control device can be automated through the following specific methods. The control device automatically generates an operation log for each operation. For example, if a device performs a temperature adjustment operation at 10:00:00 on October 1, 2023, the operation type is recorded as "temperature adjustment", the adjustment value is adjusted from 25.5 degrees Celsius to 26.8 degrees Celsius, and the operation takes 3.2 seconds. This data is automatically collected and stored in the operation log database through the built-in sensor and timestamp module, and the log ID is OP202310011000. The system utilizes an association algorithm to establish data relationships and employs a time- and device ID-based matching mechanism to bind the operation log with device ID DEV001 and task ID TASK20231001, forming a triplet data structure (OP202310011000, DEV001, TASK20231001). A hash table index is used to improve query efficiency. Analysis results show that the association process takes only 0.01 seconds with an accuracy rate of 99.9%. The system updates the task status in real time. Assuming that the initial status of task TASK20231001 is "in progress", after the temperature adjustment operation is detected to be completed, the state machine algorithm determines that the task progress has reached 80% and automatically updates the status to "near completion". At the same time, the progress data is synchronized to the task database. The update frequency is once per second to ensure that the data delay does not exceed 0.5 seconds. Related databases are synchronized and updated. A distributed transaction management mechanism is used to ensure consistent updates of operation logs, task status, and device data across multiple database nodes. For example, data synchronization is completed through a two-phase commit protocol (2PC). Analysis shows that the synchronization failure rate is less than 0.001%. If synchronization fails, an automatic rollback mechanism is triggered and error logs are recorded for subsequent analysis. Through the above automated processes, the system achieves closed-loop management of the entire chain from operation records to data association and status updates. At the same time, combined with equipment maintenance business, it associates equipment operation data with maintenance cycles. If the equipment operation time exceeds 1000 hours, maintenance reminder tasks are automatically generated, further enhancing the system's logic and business value.

[0024] like Figures 1-2 As shown in S105, the user monitors the execution status of various tasks in real time through the control device, and quickly locates the target data based on multi-dimensional search conditions. The control device automatically records all operation logs and data processing processes, generates an unalterable electronic audit track, and supports full-process data backtracking.

[0025] Further, in step S105, based on the above business content and the extracted relevant attributes, the following business solution is generated, focusing on the business objectives of task status monitoring and data backtracking, and generating specific technical process steps as follows: the status information during task execution is collected through the control device, the running data and timestamp of each task are obtained, and stored in the pre-established database to obtain a structured task status record; Based on the task status records, multidimensional search conditions are used to filter the data in the database, extract the records that meet the conditions, and determine the preliminary range of the target data. If the initial range of the target data exceeds the preset threshold, the data will be filtered a second time through a hierarchical indexing mechanism to obtain a more accurate set of target data and determine whether the requirements for rapid positioning are met. For the target data set after rapid positioning, the operation logs related to it are automatically recorded, including operation time, operation type and operation object, to obtain complete operation trajectory information; Key fields are extracted from the operation trajectory information and combined with the intermediate results of the data processing process to form an unalterable electronic audit record, which is then stored in a distributed storage system to ensure the integrity of the audit data. By using electronic audit records in a distributed storage system, a full-process data backtracking path is constructed to obtain the complete chain from task execution to operation logs, and to determine whether the backtracking path covers all key nodes; If the backtracking path covers all key nodes, the path information is associated with and mapped to the task status records to generate a structured backtracking index, resulting in a full-process data view that can be viewed in real time.

[0026] Specifically, in step S105, in the process of enabling users to monitor the task execution status in real time and quickly locate the target data through the control device, the system collects 1,000 task execution data per second through the built-in real-time data acquisition module, including task progress percentage, execution time and resource utilization rate, etc. The data is stored in a distributed database with timestamp as index, and a consistent hashing algorithm is used to ensure balanced data distribution and reduce query latency to within 50 milliseconds. To enable rapid location of multi-dimensional search criteria, the system is designed with a multi-level index structure that supports three-dimensional queries based on task type, time range, and execution status. For example, if a user sets the search criteria to "task type = data processing, time range = past 24 hours, status = failure", the system can filter out 500 records that meet the criteria within 1 second using a B+ tree index, calculate the analysis results with a failure rate of 10.2%, and automatically generate a visual report for decision-making reference. The control device automatically records operation logs and data processing procedures. Blockchain technology is used to ensure that the logs are tamper-proof. Each operation log generates a unique hash value (SHA-256 algorithm generates a 64-bit hash) and is synchronized to distributed nodes at a frequency of 1,000 logs per minute to ensure data integrity and verify that the consistency error is controlled below 0.01%. To support end-to-end data backtracking, the system builds a time-series database to store all operation and processing records. Combined with a time window sliding algorithm (window size of 1 hour), it automatically analyzes historical data trends. For example, it finds that the execution time of a certain task has increased by 15.3% in the past 7 days, and traces back to the specific resource bottleneck node, automatically adjusting the allocation of CPU resources from 30% to 45% to optimize execution efficiency. These processes are automated through the system, forming a complete closed loop from data collection and retrieval analysis to log recording and backtracking optimization, ensuring the efficiency and reliability of the technology implementation.

[0027] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A fully electronic control method for quality management in pathology departments, characterized in that, include: S101. Based on preset electronic templates, create and write quality management documents and related records in the control device to establish a digital document library; S102. The control device automatically generates electronic instructions and pushes the pending tasks to the corresponding user nodes according to the preset workflow. S103. Relevant users perform electronic processing operations through control devices, including document approval, equipment verification, environmental monitoring, internal auditing, and online assessment. S104. The control device automatically records all processing operations, establishes data association relationships, and updates the task status and related databases in real time. S105. Users can monitor the execution status of various tasks in real time through the control device and quickly locate target data based on multi-dimensional search conditions. The control device automatically records all operation logs and data processing processes, and generates an unalterable electronic audit track to support full-process data backtracking.

2. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, S101 includes: Using a pre-set electronic template, the raw data required for quality management is obtained from the control device, classified and organized, and a classified data set is formed. The categorized data set is compared with preset rules. If the data meets the rule requirements, a corresponding quality management file is generated. The generated quality management documents are stored in a digital document library, and the storage path identifier is obtained; Based on the storage path identifier, determine whether the relevant record needs to be updated. If so, extract the corresponding file from the digital file library and update the record content in conjunction with the operation log. Based on the updated record version, the latest data is periodically obtained from the control device to determine its consistency with the record version; If the data is inconsistent, the file generation process is re-executed to update the files and records in the digitized file library.

3. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, S102 includes: The task data to be processed is obtained from the control device and classified according to the task type and priority to determine a preliminary task allocation plan. Based on the categorized task data, electronic instructions containing instruction content and execution order are automatically generated; The electronic instructions are matched to the corresponding user nodes that are in an available state and transmitted through a push mechanism. Collect task status feedback information from user nodes to track task execution progress; If there are any unfinished tasks, the instruction transmission path will be readjusted and the allocation scheme will be updated until all tasks are distributed and completed. Record a complete log of task assignments and command transmissions to the control unit's database.

4. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, S103 includes: The system obtains user identity information through a control device and determines the scope of user operation permissions based on the identity data. Within the scope of user operation permissions, obtain the document approval data stream and determine the priority order of document approval according to the classification rules; The device verification module acquires device operating status data and allows document approval data to be transmitted to the designated terminal when the device status meets safety standards. Environmental parameter data is collected through environmental monitoring sensors. If abnormal fluctuations are detected, an internal audit mechanism is triggered. Based on internal audit log records, log data is analyzed using a logistic regression model to identify potential risk factors; The system obtains user response data to risk factors through an online assessment system, and determines business process adjustment plans based on permission allocation rules. Real-time feedback data is archived and stored in a designated database to form a complete operation record.

5. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, S104 includes: Automatically capture and process operations and generate operation sequence data; Establish a mapping between operation logs and task status; Monitor task status changes in real time and update tracking information; Write the status changes to the database synchronously; A verification mechanism is triggered when abnormal data associations are detected; data consistency is ensured by backtracking through logs to repair the data.

6. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, S105 includes: The control device collects task execution status information and timestamps, forms a structured task status record, and stores it in the database; The data in the database is filtered based on multi-dimensional search criteria to determine the initial scope of the target data; If the target data range exceeds the preset threshold, a secondary filtering is performed through a hierarchical indexing mechanism to quickly locate the precise target data set. Automatically record operation logs related to the target dataset to form complete operation trajectory information; extract key fields from the operation trajectory information, combine them with intermediate data processing results, generate unalterable electronic audit records, and store them in a distributed storage system; Based on the aforementioned electronic audit records, a full-process data backtracking path is constructed, and it is verified whether it covers all key nodes; The backtracking path is mapped to the task status record to generate a full-process data view that can be viewed in real time.

7. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, The electronic audit trail is ensured to be tamper-proof through digital signatures and hash algorithms.

8. The fully electronic control method for quality management in pathology departments according to claim 1, characterized in that, The control device is also used to synchronize quality management data to the supply chain management system via an interface.

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