Task indication processing system and use method

Abstract

The invention relates to the technical field of data processing, in particular to a task indication processing system and a use method, the automation degree of the task indication processing system is high, and the working efficiency and accuracy can be improved. The system can automatically analyze task indication information and decompose the task indication information into task content and task objects. The system can automatically select proper departments or personnel for processing according to task characteristics and department functions. The system can monitor the task processing progress in real time and feed back the task processing progress to the task in time, and work efficiency and accuracy are improved. The system can automatically record indication information and processing results, and data analysis and mining are facilitated. The system employs a variety of algorithms and techniques for data analysis and mining to discover potential laws and trends. The system can provide a data report and an analysis result to a decision maker according to the analysis result, so as to make accurate judgment and decision. According to the system and the method, automatic processing of task indication can be realized, and the working efficiency and accuracy are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a task instruction processing system and a method for using the system. The system and method can realize the automatic processing of task instructions and improve work efficiency and accuracy. Background Art

[0002] In many industries and organizations, task instructions are a common operation, which involves assigning tasks, indicating work directions and providing work guidance. Traditional task instructions usually use paper documents or simple electronic documents, which has many problems. First, paper documents are easy to lose and difficult to manage and track. Secondly, although simple electronic documents can be stored and searched, they lack an effective instruction processing mechanism, resulting in low work efficiency and high error rate. With the development of information technology, people have begun to seek to improve the efficiency and accuracy of task instructions through automated systems. However, existing task instruction processing systems usually only provide simple task allocation and tracking functions, and lack in-depth analysis and processing of instructions. This makes users still need to spend a lot of time and energy when processing tasks, and cannot fully utilize the advantages of automated systems. In order to solve the above problems, the present invention proposes a task instruction processing system and method. The system and method improve work efficiency and accuracy by automating the processing of task instructions, and provide users with a more efficient and reliable task management experience.

[0003] In the existing task approval process, paper documents or simple office software are usually used for approval and management. This method has the following problems: 1. Low processing efficiency: Paper documents need to be delivered and sorted manually, resulting in low processing efficiency.

[0004] 2. Error-prone: Errors are prone to occur during manual processing, such as misunderstanding of instruction content, delays in processing progress, etc.

[0005] 3. Lack of data analysis: It is impossible to analyze and mine the batch data, which is not conducive to decision-making and optimization.

[0006] Therefore, the present invention proposes a task instruction processing system and method, which can solve the deficiencies of the prior art. Summary of the invention

[0007] The purpose of the present invention is to provide a task approval processing system and method, which can realize the automatic processing of task approval and improve work efficiency and accuracy.

[0008] A task instruction processing system includes a receiving module, a parsing module, a distribution module, a reporting module, a monitoring module, a database module and an analysis module; Receiving module: used to receive task instruction information and store it in the database; the receiving module receives information in a variety of ways, such as importing through software interface and manual input; it uses natural language processing technology to automatically check and correct the input instruction information; and it uses rule-based methods to check and correct grammatical errors in text information; Parsing module: used to parse task instruction information and decompose the information into task content and task objects. The parsing module uses natural language processing and other technologies to automatically parse the instruction information and uses a natural language processing model based on deep learning to perform semantic analysis on the instruction information and extract the task content and task objects. Allocation module: used to allocate task objects to corresponding departments or personnel. The allocation module automatically selects appropriate departments or personnel for processing according to the characteristics of the task and the functions of the department. It uses machine learning algorithms to analyze and learn historical task data, and makes intelligent recommendations and allocations based on task characteristics and department functions. It uses collaborative filtering-based recommendation algorithms or reinforcement learning-based scheduling algorithms to allocate tasks. Reporting module: responsible for collecting the processing results of tasks and reporting them to the task approver, showing the task processing results, completion time, and feedback information to the task approver so that the task approver can evaluate and review the processing results of the tasks; Monitoring module: used to monitor the processing of tasks by departments or personnel, and provide timely feedback to the task instructor. The monitoring module uses real-time monitoring to provide real-time feedback on the progress of task processing; uses data mining technology to perform real-time analysis on task processing data, promptly discovers and handles abnormal situations, and uses anomaly detection algorithms to perform real-time analysis on task progress data. Once an abnormal situation is found, an alarm is issued and handled in a timely manner; Database module: used to store task instruction information and processing results, and to perform data analysis and mining. The database module uses relational database or big data storage, and is implemented using distributed database technology. It uses a NoSQL-based distributed database system to store and process large-scale instruction data. Analysis module: used to analyze task instruction information and processing results, explore potential rules and trends, use association rule mining algorithms to analyze instruction information and processing results to explore potential patterns and associations, use clustering algorithms to cluster similar instruction data to find commonalities and differences, and use time series analysis algorithms to analyze the temporal changes of instruction data to predict future trends and results.

[0009] Furthermore, the receiving module is an asynchronous programming model, which processes asynchronous tasks in the queue through an event loop; it utilizes the resources of a multi-core CPU combined with the optimization of stream processing, and implements batch or event processing of tasks in TaskProcessor; The parsing module uses the natural language processing model Spacy to parse the task instruction information. First, the input data is preprocessed to remove irrelevant characters and convert them to lowercase. The model Spacy is used to perform entity recognition on the preprocessed data to identify the task content and task object. Finally, the parsing result is returned, including the task content and task object. To parse the task instruction information in other languages, you need to select the corresponding natural language processing model or perform corresponding customization.

[0010] The assignment module uses a recommendation algorithm based on collaborative filtering to recommend tasks to users. First, the similarity between users is calculated based on their task ratings. Then, tasks are recommended based on user similarity. The user's ratings of different tasks are predicted by weighted average of other users' ratings. Tasks with higher ratings are selected for recommendation. Cosine similarity is used as a measure of user similarity. The collection module uses a dictionary to store the processing results of tasks. The collect_results method is used to collect the processing results of tasks, and the display_results method is used to display the processing results of tasks to the specified user. This module can update and display the processing results of tasks in real time according to user needs. The monitoring module uses real-time monitoring technology to monitor the task processing status and uses data mining technology to perform real-time analysis on the task processing data. Through the anomaly detection algorithm, once an abnormal situation is found, the module can immediately issue an alarm and handle it; The database module uses MongoDB as the storage engine, connects and operates through Python's pymongo library, and provides store_data and query_data for storing and querying task instruction information and processing results respectively; The analysis module includes three sub-modules: association rule mining, cluster analysis and time series analysis. It analyzes task instruction information and processing results to explore potential rules and trends.

[0011] A processing method of a task instruction processing system comprises the following steps: S1 Data Collection First, it is necessary to collect the relevant data of task approval, including the content of the approval, the time of approval, and the information of the person who approved the approval. These data can be automatically collected by the system or entered manually. S2 Data Preprocessing Remove duplicate data, fill missing values, and convert data into a unified format; S3 Data Exploration Conduct preliminary exploration and analysis of the preprocessed data to understand the distribution and characteristics of the data. This can be done through visualization charts, statistics, etc. S4 Association Rule Mining Analyze the associations and patterns between task instruction data through association rule mining algorithms; help discover the connections and impacts between various instructions, as well as potential business trends and patterns; S5 Cluster Analysis Through clustering algorithms, similar batch data are clustered into several groups to help discover the commonalities and differences between different batches; S6 Time Series Analysis Through time series analysis algorithms, the time series changes of batch data are analyzed and predicted; helping to predict future trends and results; S7 Model Evaluation and Optimization According to actual business needs and data characteristics, select appropriate algorithms and models for data analysis and mining. At the same time, the model needs to be evaluated and optimized; S8 Results Presentation Present the analysis results in the form of data reports and analytical charts to provide support for business development and management; S9 Continuous Monitoring and Updates Data is updated and monitored regularly to adapt to changes in business needs and upgrades in management models.

[0012] The beneficial effects of the present invention are: the automated processing technology of the system to avoid human errors and inefficiency, the data storage and analysis technology of the system to protect the integrity and security of the data, and the task allocation algorithm and technology of the system to protect the reasonable allocation and execution efficiency of tasks.

[0013] The task instruction processing system has a high degree of automation, which can improve work efficiency and accuracy. The system can automatically parse the task instruction information and decompose it into task content and task objects. The system can automatically select the appropriate department or personnel for processing according to the characteristics of the task and the functions of the department. The system can monitor the progress of task processing in real time and provide timely feedback to the task, improving work efficiency and accuracy. The system can automatically record instruction information and processing results to facilitate data analysis and mining. The system uses a variety of algorithms and technologies for data analysis and mining to discover potential patterns and trends. The system can provide data reports and analysis results to decision makers based on the analysis results to facilitate accurate judgments and decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A structural schematic diagram of a task instruction processing system of the present invention; Figure 2 It is a task instruction processing flow chart of the present invention; Figure 3 A flowchart of task monitoring of the present invention; Figure 4 Create a data analysis and mining flowchart for the task. DETAILED DESCRIPTION

[0015] A task instruction processing system includes the following modules: 1. Receiving module: This module is used to receive task instruction information and store it in the database. The receiving module can receive information in a variety of ways, such as importing through the software interface, manual entry, etc. In order to ensure the accuracy and completeness of the information, natural language processing technology can be used to automatically check and correct the input instruction information. For example, for text information, a rule-based method can be used to check and correct grammatical errors.

[0016] The following is a Python example of the receiving module: import asyncio import json from collections import deque class AsyncReceiverModule: def __init__(self): self.task_queue = deque() # Queue for storing asynchronous tasks self.task_processor = TaskProcessor() # Assume there is a predefined asynchronous task processor TaskProcessor self.loop = asyncio.get_event_loop() # Get the event loop def receive_input(self, input_data): """Receive external input task instruction information and process it asynchronously""" # Preprocess the input data, such as parsing JSON data, etc. processed_data = self._preprocess_input(input_data) # Put the task into the queue and trigger asynchronous processing self.task_queue.append((processed_data, self.loop)) self._process_tasks() def _preprocess_input(self, input_data): """Preprocess the input data, such as parsing JSON data, etc.""" # Add preprocessing logic here and perform corresponding processing according to actual needs # ... return processed_data # Returns preprocessed data def _process_tasks(self): """Process asynchronous tasks in the queue""" while self.task_queue: task_data, loop = self.task_queue.popleft() task = loop.create_task(self.task_processor.process(task_data)) task.add_done_callback(self._on_task_completed) async def _on_task_completed(self, task): """Callback function after the asynchronous task is completed""" # Here you can add further processing or logging of task results Pass The receiving module uses an asynchronous programming model to process asynchronous tasks in the queue through an event loop. This can fully utilize the resources of multi-core CPUs and improve the system's response speed and processing capabilities. At the same time, stream processing optimization measures can be achieved by processing tasks by batch or by event in TaskProcessor.

[0017] 2. Parsing module: This module is used to parse the task instruction information and decompose the information into task content and task object. The parsing module can use natural language processing and other technologies to automatically parse the instruction information. For example, a natural language processing model based on deep learning can be used to perform semantic analysis on the instruction information to extract the task content and task object.

[0018] The following is a Python example of parsing module: import json import spacy class TaskParser: def __init__(self): self.nlp = spacy.load('en_core_web_sm') # Load English natural language processing model def parse_input(self, input_data): """Parse the task instruction information and extract the task content and task object""" # Preprocess the input data, such as removing irrelevant characters, converting to lowercase, etc. preprocessed_data =self._preprocess_input(input_data) # Parsing using natural language processing models doc = self.nlp(preprocessed_data) # Extract task content and task objects task_content = [] task_objects = [] for token in doc.ents: if token.label_ == 'TASK': task_content.append(token.text) elif token.label_ == 'OBJECT': task_objects.append(token.text) # Return parsing results return task_content, task_objects def _preprocess_input(self, input_data): """Preprocess the input data, such as removing irrelevant characters, converting to lowercase, etc.""" # Add preprocessing logic here and perform corresponding processing according to actual needs # ... return preprocessed_data # Return preprocessed data The parsing module uses the English natural language processing model (Spacy) to parse task instruction information. First, the input data is preprocessed, such as removing irrelevant characters, converting to lowercase, etc. Then, the Spacy model is used to perform entity recognition (Named Entity Recognition, NER) on the preprocessed data to identify the task content and task object. Finally, the parsing result is returned, including the task content and task object. It should be noted that this example is only applicable to the parsing of English task instruction information. If you need to parse instruction information in other languages, you need to select the corresponding natural language processing model or perform corresponding customization.

[0019] 3. Allocation module: This module is used to allocate task objects to corresponding departments or personnel. The allocation module can automatically select appropriate departments or personnel for processing according to the characteristics of the task and the functions of the department. In order to achieve efficient allocation, machine learning algorithms can be used to analyze and learn historical task data, and intelligently recommend and allocate tasks according to task characteristics and department functions. For example, a recommendation algorithm based on collaborative filtering or a scheduling algorithm based on reinforcement learning can be used for task allocation.

[0020] Here is an example of assigning a module to Python: import pandas as pd from sklearn.metrics.pairwise import cosine_similarity class RecommendationBasedAssigner: def __init__(self, user_ratings): """Task allocator based on collaborative filtering recommendation algorithm""" self.user_ratings = user_ratings self.user_similarities = self._calculate_user_similarities() def _calculate_user_similarities(self): """Calculate the similarity between users""" # Assume user_ratings is a DataFrame containing user ratings for tasks # ... # Calculate the cosine similarity between users user_similarities = cosine_similarity(self.user_ratings) return user_similarities def recommend_tasks(self, user_id): """Recommend tasks for a specified user""" # Recommend tasks to users based on their similarity user_similarities = self.user_similarities[user_id] other_users = self.user_ratings.index[self.user_ratings.index!= user_id] task_scores = pd.Series(index=self.user_ratings.index) for other_user in other_users: if user_similarities[other_user]>0: task_scores += self.user_ratings.loc[other_user]*user_similarities[other_user] task_scores / = task_scores.sum() # Normalized score recommended_tasks = task_scores.sort_values(ascending=False).index[:10] # Recommend the top 10 tasks return recommended_tasks The assignment module uses a recommendation algorithm based on collaborative filtering to recommend tasks to users. First, it calculates the similarity between users based on their task ratings. Then, it recommends tasks to users based on their similarity, predicts the user's ratings of different tasks by taking the weighted average of other users' ratings, and selects tasks with higher ratings for recommendation. In this example, cosine similarity is used as a measure of user similarity.

[0021] 4. Reporting module: This module is responsible for collecting the processing results of tasks and reporting them to the task approver. It can show the task approver the processing results, completion time, feedback and other information through the system interface or interface, so that the task approver can evaluate and review the processing results of the task.

[0022] The following is a python example of the reporting module: class TaskResultCollector: def __init__(self): self.task_results = {} # Dictionary for storing task results def collect_results(self, task_id, result): """Collect the processing results of the task""" self.task_results[task_id] =result def display_results(self, user): """Display the task processing results to the specified user""" # Get the current user's task list user_tasks = self.task_results.keys()&user.tasks.keys() # Build result display list result_list = [] for task_id in user_tasks: result = self.task_results[task_id] result_list.append({ 'task_id': task_id, 'result': result, 'completion_time': user.tasks[task_id].completion_time, 'feedback': user.tasks[task_id].feedback }) # Return the result display list return result_list The collection module uses a dictionary to store the processing results of the task. The collect_results method is used to collect the processing results of the task, and the display_results method is used to display the processing results of the task to the specified user. This module can update and display the processing results of the task in real time according to the user's needs, and can be integrated with other processing modules to achieve automatic collection and organization. In addition, this module also takes into account the needs of data security and privacy protection to ensure that the collected and displayed information will not leak the user's sensitive information.

[0023] 5. Monitoring module: This module is used to monitor the processing of tasks by departments or personnel, and provide timely feedback to the task instructor. The monitoring module can use real-time monitoring and other technologies to provide real-time feedback on the progress of task processing. In order to improve the accuracy and timeliness of feedback, data mining technology can be used to analyze task processing data in real time to detect and handle abnormal situations in a timely manner. For example, anomaly detection algorithms can be used to analyze task progress data in real time, and once abnormal situations are found, alarms and processing can be carried out in a timely manner.

[0024] The following is a python example of the monitoring module: import pandas as pd from sklearn.ensemble import IsolationForest import plotly.express as px class TaskMonitor: def __init__(self, task_data): self.task_data = task_data self.isolation_forest = IsolationForest(contamination=0.1) #Anomaly detection model def monitor_tasks(self, user_id): """Monitor the tasks that the specified user is responsible for""" user_tasks = self.task_data[self.task_data['assigned_to']==user_id] if len(user_tasks) == 0: return "This user has no assigned tasks." # Real-time monitoring of task progress current_progress = user_tasks['progress'].values average_progress = user_tasks['progress'].mean() # Real-time analysis of abnormal situations anomalies = self.isolation_forest.predict(pd.DataFrame({'progress': current_progress})) anomalous_indices = anomalies == -1 if any(anomalous_indices): anomalous_task_id = user_tasks.index[anomalous_indices][0] return f"Warning: Abnormal progress for task {anomalous_task_id}." # Generate progress chart fig = px.bar(pd.DataFrame({'Task': user_tasks.index, 'Progress':current_progress}), x='Task', y='Progress', color='Task') fig.show() return f"Current average progress: {average_progress:.2%}" The monitoring module uses real-time monitoring technology to monitor the task processing status and uses data mining technology to analyze the task processing data in real time. Through the anomaly detection algorithm, once an abnormal situation is found, the module can immediately alarm and handle it. In addition, the module also provides a real-time task progress chart to help the person who approves the task intuitively understand the task processing status. Compared with other monitoring modules, this module has the advantages of real-time, anomaly detection, multi-dimensional monitoring, flexible customization and expansion, and data security and privacy protection.

[0025] 6. Database module: This module is used to store task instruction information and processing results, which is convenient for data analysis and mining. The database module can be implemented using relational database or big data storage technologies. In order to improve data storage and processing efficiency, distributed database technology can be used for implementation. For example, a NoSQL-based distributed database system can be used to store and process large-scale instruction data.

[0026] The following is a Python example of the database module: class DatabaseModule: def __init__(self, host, port, database_name): self.host = host self.port = port self.database_name = database_name self.client = None def connect(self): from pymongo import MongoClient self.client = MongoClient(self.host, self.port) self.client.database_name = self.database_name def store_data(self, data): collection_name = "task_data" collection = self.client[collection_name] result = collection.insert_one(data) return result.inserted_id def query_data(self, query): collection_name = "task_data" collection = self.client[collection_name] result = collection.find(query) return list(result) The database module uses MongoDB as the storage engine and connects and operates through Python's pymongo library. It provides two methods, store_data and query_data, which are used to store and query task instruction information and processing results respectively. Compared with other database modules, this module has the advantages of scalability, high performance, flexibility, data consistency and maintainability.

[0027] 7. Analysis module: This module is used to analyze task instruction information and processing results, and to explore potential rules and trends. The analysis module can use data mining and other technologies to provide data support and reference for decision makers. In order to improve the accuracy and comprehensiveness of the analysis, a variety of analysis methods and techniques can be used. For example, association rule mining algorithms can be used to analyze instruction information and processing results to explore potential patterns and associations. Clustering algorithms can be used to perform cluster analysis on similar instruction data to find commonalities and differences. Time series analysis algorithms can be used to analyze the time series changes of instruction data to predict future trends and results. By comprehensively using these methods and techniques, instruction data can be analyzed from different angles and dimensions to provide comprehensive and accurate data support for decision makers.

[0028] The following is an analysis module python example: from ml import apriori, kmeans, ts_predict class AnalysisModule: def __init__(self, data): self.data = data def association rule mining (self): rules = apriori(self.data, min_support=0.1, min_confidence=0.8) return rules def clustering analysis(self): clusters = kmeans(self.data, n_clusters=3) return clusters def time series analysis(self): predictions = ts_predict(self.data) return predictions The analysis module contains three submodules: association rule mining, cluster analysis, and time series analysis. By calling the methods of these submodules, task instruction information and processing results can be analyzed to mine potential rules and trends. Compared with other analysis modules, this module has the advantages of multi-dimensional analysis, high accuracy, real-time, customizability, and visual presentation.

[0029] Association rule mining submodule: Function: Used to mine association rules between task instructions and processing results. Use Apriori algorithm to mine frequent itemsets. Set minimum support and minimum confidence thresholds to filter out meaningful association rules. Visualize association rule network diagrams to show the association relationships between different task instructions.

[0030] Optimization points: Support dynamic adjustment of support and confidence thresholds to adapt to analysis requirements of different scales and data distributions. Use compression technology to reduce the storage and computational overhead of frequent itemsets.

[0031] Cluster analysis submodule: Function: Cluster similar task instruction data to find commonalities and differences. Use K-means algorithm for clustering. Visualize clustering results to show the distribution and characteristics of different clusters. Provide explanation and interpretation of clustering results to help users understand data distribution and patterns.

[0032] Optimization points: Support switching of multiple clustering algorithms to meet the clustering requirements of different types of data. Introduce soft clustering technology to handle data points with fuzzy boundaries. Provide clustering effect evaluation indicators, such as silhouette coefficient, to evaluate clustering quality.

[0033] Time series analysis submodule: Function: Analyze and predict the time series changes of task instruction data. Use time series analysis algorithms, such as ARIMA or LSTM. Provide visualization charts of time series data, such as line charts or time series charts. Predict future trends and results based on historical data.

[0034] Optimization points: Integrate multiple time series prediction models to select the best model for prediction. Introduce feature engineering to process and transform time series data to improve prediction accuracy. Provide verification and evaluation of prediction results, and use indicators such as mean square error to evaluate model performance.

[0035] In the task monitoring process, policy issues may have an impact in the following ways: 1. Task definition: Certain policy issues may make certain tasks particularly important or require special attention. For example, if the policy changes, some tasks that were not previously considered as priorities may become new monitoring priorities.

[0036] 2. Monitoring indicators: Changes in policies may require adjustments to monitoring indicators. For example, if the policy emphasizes performance in a particular area, then the relevant monitoring indicators also need to be adjusted accordingly.

[0037] 3. Status monitoring: During the task execution process, certain policy changes may cause the task to be delayed, terminated or difficult to execute. At this time, the system needs to focus on monitoring this part of the task and provide feedback.

[0038] 4. Result feedback: Policy changes may affect the final result of the task. For example, if the policy leads to a change in resource allocation, the completion result of the task will also be affected. When the system feedbacks the results, it needs to take into account the impact of these policy factors.

[0039] In the task instruction data analysis and mining process, the impact of policy issues is mainly reflected in the following aspects: 1. Data collection: Policy changes may affect the collection of approval data. For example, new policies may require more approval information to be recorded, or some approval data may not be collected due to privacy or confidentiality issues.

[0040] 2. Data preprocessing: During the data preprocessing stage, it is necessary to consider how to deal with missing or abnormal data caused by policy issues.

[0041] 3. Association rule mining and cluster analysis: In the association rule mining and cluster analysis stage, policy changes may affect the association and pattern between instruction data. For example, new policies may lead to changes in the frequency, content and pattern of instructions.

[0042] 4. Time series analysis: Policy changes may affect the time series changes of approval data. For example, if the policy leads to the acceleration or delay of the approval process, the results of the time series analysis will also be affected.

[0043] 5. Results presentation and continuous monitoring: In the results presentation stage, it is necessary to specifically point out the impact of policy issues on instructions and results. At the same time, in the continuous monitoring and updating stage, it is necessary to regularly evaluate the impact of policy changes on data analysis and mining results, and make corresponding adjustments and optimizations.

[0044] In order to reduce the impact of policy issues on task monitoring and instruction data analysis and mining, the following measures can be taken: 1. Establish a flexible system architecture: Make the system adaptable and flexible so that it can respond quickly to policy changes and data changes.

[0045] 2. Strengthen data management and updating: Regularly clean and update data to ensure data accuracy and timeliness.

[0046] 3. Continuous policy learning and research: Regularly study and analyze relevant policies, understand their impact on task monitoring and instructions, and make corresponding adjustments and optimizations.

[0047] The above system realizes the automated processing of task instructions, improves work efficiency and accuracy, reduces labor costs and error rates, and can automatically record and analyze instruction data, providing data support and reference basis for decision makers, which helps to optimize decision-making and improve management efficiency.

[0048] like Figure 1 The figure shows a structural block diagram of a task instruction processing system provided by an embodiment of the present application. As can be seen from the figure: a task instruction processing system includes a receiving module, a parsing module, an allocation module, a reporting module, a monitoring module, a database module, and an analysis module, wherein the receiving module is connected to the parsing module, the parsing module is connected to the allocation module, the allocation module is connected to the reporting module, the reporting module is connected to the monitoring module, the receiving module and the reporting module are connected to the database module, and the database module is connected to the analysis module.

[0049] A task instruction processing system is based on the internal functions and approval system of government agencies or enterprises. By integrating the approval processes of various internal departments and external collaborative departments, and using advanced computer technology and artificial intelligence algorithms, it receives, distributes, processes, monitors and analyzes task instructions, realizing online automatic processing and tracking management of the entire process of task instructions. The task instruction information and instruction processing process involved in the system process can be adjusted according to the actual situation of the user.

[0050] System initialization: When the system starts, it needs to be initialized, including loading system configuration, establishing database connection, starting services, etc. These configurations and connections can be achieved through preset parameters and data.

[0051] Receiving instruction information: The task authorizer can input instruction information through the system interface or interface, and the system will store the information in the database. At the same time, the system can also obtain instruction information from other data sources, such as emails, text messages, etc. After pre-processing, the obtained information can also be stored in the database.

[0052] Parsing of instruction information: The system uses natural language processing technology to parse instruction information and decompose it into task content and task object. For example, when the task instruction is "please investigate the sales data of the marketing department in the last month", the parsing module can decompose it into task content "investigate the sales data of the marketing department in the last month" and task object "marketing department". During the parsing process, the system can verify and correct the instruction information as needed to ensure the accuracy of the parsing.

[0053] Task allocation: Based on the task content and the functions of the department, the system uses an allocation algorithm to allocate tasks to the appropriate department or personnel. For example, for the above task, the system can allocate the task to the investigator in charge of sales data in the marketing department based on the functions of the department and the requirements of the task. The allocation algorithm can be implemented in many ways, such as rule-based matching, collaborative filtering-based recommendation, and reinforcement learning-based scheduling.

[0054] Task processing: After receiving a task, a department or person can view the task content and processing requirements through the system interface or interface, and promptly feedback the processing results to the task approver. For example, after receiving a task, an investigator can view the task content "investigate the sales data of the marketing department in the last month" and the processing requirements "accurate and timely" through the system interface, and then conduct an investigation and feedback the results to the task approver.

[0055] Task monitoring: Task monitoring monitors the processing of tasks by departments or personnel in real time and provides timely feedback to the task approver. For example, when the investigators provide feedback on the processing results, the task monitoring module can monitor the completion status of the task in real time and provide timely feedback to the task approver.

[0056] Database module storage: The database module stores task instruction information and processing results, and uses analysis algorithms to mine and analyze data to discover potential patterns and trends. For example, task instruction information, processing results, department and personnel responsibilities, and other information can be stored in the database for data analysis and mining. The analysis algorithm can use a variety of technologies, such as association rule mining, cluster analysis, time series analysis, etc.

[0057] Analysis module mining: The analysis module can analyze task instruction information and processing results, explore potential rules and trends, and provide data reports and analysis results to decision makers so that they can make accurate judgments and decisions. For example, the instruction data can be analyzed through association rule mining algorithms to discover association rules and patterns; similar instruction data can be clustered through clustering algorithms to discover commonalities and differences; and time series analysis algorithms can be used to analyze and predict the time series changes of instruction data to predict future trends and results.

[0058] like Figure 2The following is a flowchart of the task approval process provided by the embodiment of the present application. It can be seen that the task approval process is as follows: Task approval: The task approver enters the approval information through the system interface, or the system connects to other data sources such as email or SMS to receive task approval information. Task allocation: Based on the task content and departmental functions, the system uses an allocation algorithm to assign tasks to appropriate departments or personnel.

[0059] Task processing: After receiving a task, a department or person can view the task content and processing requirements through the system interface or interface, and promptly feedback the processing results to the task approver.

[0060] like Figure 3 The flowchart of the task monitoring provided by the embodiment of the present application is shown, and it can be seen that the process of system task monitoring is: 1. Define tasks: First, you need to clarify what tasks need to be monitored. This can include various types of tasks, such as key projects, important tasks, and supervision matters.

[0061] 2. Set monitoring indicators: According to the characteristics and requirements of the task, set corresponding monitoring indicators. These indicators can include the completion status, processing time, processing results, etc. of the task.

[0062] 3. Task monitoring: The system collects and processes task data in real time, and monitors and provides feedback based on the set indicators.

[0063] 4. Status monitoring: The system monitors the completion status of tasks in real time. Once an abnormal or unfinished task is found, feedback and reminders will be given immediately.

[0064] 5. Result feedback: When the processing personnel feedback the processing results, the system will feedback the results to the task instructor or other relevant personnel in real time. Feedback can include various forms, such as email notification, SMS reminder, report generation, etc.

[0065] like Figure 4 The figure is a flowchart of task instruction data analysis and mining provided by an embodiment of the present application, from which it can be seen that the processing flow of task instruction data analysis and mining is as follows: 1. Data collection: First, you need to collect the relevant data of the task instructions, including instruction content, instruction time, instruction personnel, etc. This data can be automatically collected by the system or manually entered.

[0066] 2. Data preprocessing: Clean, organize and standardize the collected data to ensure data quality and usability. This includes removing duplicate data, filling missing values, and converting data into a unified format.

[0067] 3. Data exploration: Conduct preliminary exploration and analysis of the preprocessed data to understand the distribution and characteristics of the data. This can be done through visualization charts, statistics, etc.

[0068] 4. Association rule mining: Through the association rule mining algorithm, the association and regularity between task instruction data are analyzed. This can help discover the connection and influence between various instructions, as well as potential business trends and patterns.

[0069] 5. Cluster analysis: Through clustering algorithms, similar batches of data are clustered into several groups. This can help discover the commonalities and differences between different batches and provide support for classification and decision-making.

[0070] 6. Time series analysis: Through the time series analysis algorithm, the time series changes of the batch data are analyzed and predicted. This can help predict future trends and results and provide reference and support for decision makers.

[0071] 7. Model evaluation and optimization: According to actual business needs and data characteristics, select appropriate algorithms and models for data analysis and mining. At the same time, the model needs to be evaluated and optimized to improve the accuracy and generalization ability of prediction.

[0072] 8. Results presentation: The analysis results are presented to decision makers in the form of data reports and analysis charts. This can help decision makers make accurate judgments and decisions, and provide support for business development and management.

[0073] 9. Continuous monitoring and updating: In order to maintain the accuracy and effectiveness of data analysis and mining, data needs to be updated and monitored regularly to adapt to changes in business needs and upgrades in management models. At the same time, models and algorithms need to be continuously optimized to improve the accuracy and generalization of predictions.

[0074] Through the above implementation, the automatic processing of task instructions can be realized, and the work efficiency and accuracy can be improved. At the same time, through the analysis and mining of data, more comprehensive and accurate data support can be provided to decision makers.

[0075] This patent solution implements a task instruction processing system and method, which can automatically process task instructions, greatly improving work efficiency and accuracy. By introducing advanced artificial intelligence technology, the system can automatically analyze task content, identify batch diagrams, and automatically generate instruction instructions according to preset rules and processes. This not only reduces manual intervention and errors, but also greatly speeds up task processing, making task allocation, execution and tracking more efficient and accurate. At the same time, the system also provides rich data analysis and visualization functions to help users better understand the execution and effects of task instructions, and further optimize workflow and efficiency.

[0076] 1. It can be applied to the scenario of processing instructions from leaders within the government. It can circulate, process and analyze various instructions from leaders within the government as a task instruction, and provide data support for leaders to assist in decision-making.

[0077] 2. It can be applied to scenarios such as internal leadership instructions and important task implementation, and continuously improve the work efficiency of various departments within the enterprise in implementing various key tasks.

Claims

1. A task instruction processing system, characterized in that It includes receiving module, parsing module, distribution module, reporting module, monitoring module, database module and analysis module; Receiving module: used to receive task instruction information and store the information in the database; The receiving module receives information in a variety of ways, including importing through software interfaces and manual input; Use natural language processing technology to automatically check and correct input instruction information; use rule-based methods to check and correct grammatical errors in text information; Parsing module: used to parse task instruction information and decompose the information into task content and task objects. The parsing module uses natural language processing and other technologies to automatically parse the instruction information and uses a natural language processing model based on deep learning to perform semantic analysis on the instruction information and extract the task content and task objects. Allocation module: used to allocate task objects to corresponding departments or personnel. The allocation module automatically selects appropriate departments or personnel for processing according to the characteristics of the task and the functions of the department. It uses machine learning algorithms to analyze and learn historical task data, and makes intelligent recommendations and allocations based on task characteristics and department functions. It uses collaborative filtering-based recommendation algorithms or reinforcement learning-based scheduling algorithms to allocate tasks. Reporting module: responsible for collecting the processing results of tasks and reporting them to the task approver, showing the task processing results, completion time, and feedback information to the task approver so that the task approver can evaluate and review the processing results of the tasks; Monitoring module: used to monitor the processing of tasks by departments or personnel, and provide timely feedback to the task instructor. The monitoring module uses real-time monitoring to provide real-time feedback on the progress of task processing; uses data mining technology to perform real-time analysis on task processing data, promptly discovers and handles abnormal situations, and uses anomaly detection algorithms to perform real-time analysis on task progress data. Once an abnormal situation is found, an alarm is issued and handled in a timely manner; Database module: used to store task instruction information and processing results, and to perform data analysis and mining. The database module uses relational database or big data storage, and is implemented using distributed database technology. It uses a NoSQL-based distributed database system to store and process large-scale instruction data. Analysis module: used to analyze task instruction information and processing results, explore potential rules and trends, use association rule mining algorithms to analyze instruction information and processing results to explore potential patterns and associations, use clustering algorithms to cluster similar instruction data to find commonalities and differences, and use time series analysis algorithms to analyze the temporal changes of instruction data to predict future trends and results.

2. The task batch processing system according to claim 1, characterized in that Said The receiving module is an asynchronous programming model that processes asynchronous tasks in the queue through an event loop. It uses the resources of multi-core CPUs combined with stream processing optimization to implement batch or event processing of tasks in TaskProcessor. The parsing module uses the natural language processing model Spacy to parse the task instruction information. First, the input data is preprocessed to remove irrelevant characters and convert them to lowercase. The model Spacy is used to perform entity recognition on the preprocessed data to identify the task content and task object. Finally, the parsing result is returned, including the task content and task object. To parse the task instruction information, if you need to parse instruction information in other languages, you need to select the corresponding natural language processing model or perform corresponding customization. The assignment module uses a recommendation algorithm based on collaborative filtering to recommend tasks to users. First, the similarity between users is calculated based on their task ratings. Then, tasks are recommended based on user similarity. The user's ratings of different tasks are predicted by weighted average of other users' ratings. Tasks with higher ratings are selected for recommendation. Cosine similarity is used as a measure of user similarity. The collection module uses a dictionary to store the processing results of tasks. The collect_results method is used to collect the processing results of tasks, and the display_results method is used to display the processing results of tasks to the specified user. This module can update and display the processing results of tasks in real time according to user needs. The monitoring module uses real-time monitoring technology to monitor the task processing status and uses data mining technology to perform real-time analysis on the task processing data. Through the anomaly detection algorithm, once an abnormal situation is found, the module can immediately issue an alarm and handle it; The database module uses MongoDB as the storage engine, connects and operates through Python's pymongo library, and provides store_data and query_data for storing and querying task instruction information and processing results respectively; The analysis module includes three sub-modules: association rule mining, cluster analysis and time series analysis. It analyzes task instruction information and processing results to explore potential rules and trends.

3. A processing method of a task batch processing system according to claim 1 or 2, characterized in that The steps include: S1 Data Collection First, it is necessary to collect the relevant data of task approval, including the content of the approval, the time of approval, and the information of the person who approved the approval. These data can be automatically collected by the system or entered manually. S2 Data Preprocessing Remove duplicate data, fill missing values, and convert data into a unified format; S3 Data Exploration Conduct preliminary exploration and analysis of the preprocessed data to understand the distribution and characteristics of the data; this is done through visualization charts, statistics, etc.; S4 Association Rule Mining Analyze the associations and patterns between task instruction data through association rule mining algorithms; help discover the connections and impacts between various instructions, as well as potential business trends and patterns; S5 Cluster Analysis Through clustering algorithms, similar batch data are clustered into several groups to help discover the commonalities and differences between different batches; S6 Time Series Analysis Analyze and predict the time series changes of batch data through time series analysis algorithm S7 Model Evaluation and Optimization According to actual business needs and data characteristics, select appropriate algorithms and models for data analysis and mining; at the same time, the model needs to be evaluated and optimized; S8 Results Presentation Present the analysis results in the form of data reports and analytical charts to provide support for business development and management; S9 Continuous Monitoring and Updates Data is updated and monitored regularly based on changes in business needs and upgrades to management models.

Images