A task delegation processing system and method of use

Abstract

The present application relates to the technical field of data processing, and particularly relates to a task review processing system and a use method. The task review processing system has high automation degree, and can improve work efficiency and accuracy. The system can automatically analyze task review information and decompose it into task content and task object. The system can automatically select appropriate departments or personnel for processing according to task characteristics and department functions. The system can monitor task processing progress in real time and feed back to the task in time, improving work efficiency and accuracy. The system can automatically record review information and processing results, facilitating data analysis and mining. The system uses various algorithms and technologies for data analysis and mining to discover potential laws and trends. The system can provide data reports and analysis results to decision makers according to analysis results, so as to make accurate judgments and decisions. The system and method can realize automatic processing of task review, improve work efficiency and accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a task assignment processing system and method, which can realize automatic processing of task assignment and improve work efficiency and accuracy. BACKGROUND

[0002] In many industries and organizations, task assignment is a common operation that involves assigning tasks, indicating work direction, and providing work guidance. Traditional task assignment usually adopts paper documents or simple electronic documents, which has many problems. First, paper documents are easy to lose and difficult to manage and track. Second, although simple electronic documents can be stored and searched, they lack effective assignment processing mechanisms, 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 assignment through automated systems. However, existing task assignment processing systems usually only provide simple task assignment and tracking functions, lacking in-depth analysis and processing of assignments. This makes users still need to spend a lot of time and effort in processing tasks, and cannot fully utilize the advantages of automated systems. In order to solve the above problems, the present application proposes a task assignment processing system and method. The system and method improve work efficiency and accuracy by automatically processing task assignment, providing users with a more efficient and reliable task management experience.

[0003] In existing task assignment processing, paper documents or simple office software are usually used for assignment and management, which has the following problems:

[0004] 1. Low processing efficiency: paper documents need to be manually delivered and sorted, resulting in low processing efficiency.

[0005] 2. prone to errors: manual processing is prone to errors, such as misinterpretation of assignment content, delay in processing progress, etc.

[0006] 3. Lack of data analysis: unable to analyze and mine assignment data, which is not conducive to decision-making and optimization.

[0007] Therefore, the present application proposes a task assignment processing system and method that can solve the deficiencies of the prior art. SUMMARY

[0008] The purpose of the present application is to provide a task assignment processing system and method that can realize automatic processing of task assignment and improve work efficiency and accuracy.

[0009] A task assignment processing system, comprising a receiving module, an analysis module, an assignment module, a reporting module, a monitoring module, a database module, and an analysis module;

[0010] Receiving module: for receiving task review information and storing the information into the database; the receiving module receives information in multiple ways, such as importing through software interface and manual input; natural language processing technology is used to automatically check and correct the input review information; for text information, rule-based methods are used to check and correct grammatical errors;

[0011] Parsing module: for parsing task review information and decomposing the information into task content and task object; the parsing module uses natural language processing technology to automatically parse the review information, uses a natural language processing model based on deep learning to analyze the semantics of the review information, and extracts the task content and task object;

[0012] Distribution module: for distributing task objects to corresponding departments or personnel; the distribution module automatically selects appropriate departments or personnel for processing according to the characteristics of the task and the functions of the departments; machine learning algorithms are used to analyze and learn historical task data, and intelligent recommendation and distribution are made according to task characteristics and department functions; task distribution is made using a recommendation algorithm based on collaborative filtering or a scheduling algorithm based on reinforcement learning;

[0013] Reporting module: responsible for collecting the processing results of the task and reporting them to the task reviewer, showing the processing results, completion time, and feedback information to the task reviewer, so that the task reviewer can evaluate and audit the processing results of the task;

[0014] Monitoring module: for monitoring the processing of tasks by departments or personnel and feeding back to the task reviewer in a timely manner; the monitoring module uses real-time monitoring to provide real-time feedback on task processing progress; data mining techniques are used to analyze task processing data in real time, and abnormal situations are discovered and handled in a timely manner; abnormal detection algorithms are used to analyze task progress data in real time, and once an abnormal situation is found, an alarm and handling are performed in a timely manner;

[0015] Database module: for storing task review information and processing results, conducting data analysis and mining; the database module uses a relational database or big data storage, uses distributed database technology to implement, and uses a distributed database system based on NoSQL to store and process large-scale review data;

[0016] Analysis module: for analyzing task review information and processing results, mining potential patterns and trends; association rule mining algorithms are used to analyze and mine potential patterns and association relationships in review information and processing results; clustering algorithms are used to cluster similar review data to find commonalities and differences; time series analysis algorithms are used to analyze the time series changes of review data to predict future trends and results.

[0017] Further, the receiving module is an asynchronous programming model, which processes asynchronous tasks in the queue through an event loop; by utilizing the resources of multi-core CPU combined with the optimization of streaming processing, the tasks are processed by batch or event in the TaskProcessor;

[0018] The parsing module is a natural language processing model Spacy, which parses the task assignment information. First, the input data is preprocessed to remove irrelevant characters and converted to lowercase. The preprocessed data is then subjected to entity recognition using the Spacy model to identify the task content and task object. Finally, the parsing results, including the task content and task object, are returned. If other languages need to be parsed, the corresponding natural language processing model needs to be selected or customized.

[0019] The allocation module uses a recommendation algorithm based on collaborative filtering to recommend tasks for users. First, the similarity between users is calculated based on their ratings of tasks. Then, tasks are recommended to users based on their similarity. The scores of different tasks are predicted by weighted average of other users' scores, and the tasks with higher scores are selected for recommendation. Cosine similarity is used as a measure of user similarity.

[0020] 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 the user's needs.

[0021] The monitoring module uses real-time monitoring technology to monitor the task processing situation, and uses data mining technology to analyze the task processing data in real time. Through the abnormal detection algorithm, once the abnormal situation is found, the module can immediately alarm and handle;

[0022] The database module uses MongoDB as the storage engine, connects and operates through the Python pymongo library, and provides store_data and query_data for storing and querying task assignment information and processing results, respectively.

[0023] The analysis module includes three sub-modules: association rule mining, clustering analysis, and time series analysis, which analyze the task assignment information and processing results to mine potential patterns and trends.

[0024] A processing method of a task assignment processing system, comprising the following steps:

[0025] S1 Data collection

[0026] First, the relevant data of the task review needs to be collected, including the review content, review time, and reviewer information. These data are automatically collected or manually entered through the system;

[0027] S2 data preprocessing

[0028] The collected data is processed to remove duplicate data, fill in missing values, and convert the data to a unified format;

[0029] S3 data exploration

[0030] The preprocessed data is preliminarily explored and analyzed to understand the distribution and characteristics of the data. Visualization charts, statistical quantities, and other methods are used;

[0031] S4 association rule mining

[0032] Through association rule mining algorithms, the associations and rules between task review data are analyzed. It helps to discover the connections and influences between various reviews, as well as potential business trends and patterns;

[0033] S5 clustering analysis

[0034] Through clustering algorithms, similar review data is clustered into several groups, helping to discover the similarities and differences between different reviews;

[0035] S6 time series analysis

[0036] Through time series analysis algorithms, the time series changes of review data are analyzed and predicted. It helps to predict future trends and results;

[0037] S7 model evaluation and optimization

[0038] According to the actual business needs and data characteristics, appropriate algorithms and models are selected for data analysis and mining. At the same time, the model needs to be evaluated and optimized;

[0039] S8 result presentation

[0040] The analysis results are presented in the form of data reports and analysis charts to support business development and management;

[0041] S9 continuous monitoring and updating

[0042] Regular data updates and monitoring are needed to adapt to changes in business needs and upgrades in management modes.

[0043] The beneficial effects of the present invention are: the automatic processing technology of the system to avoid human errors and low efficiency. The data storage and analysis technology of the system to protect the integrity and security of the data. The task allocation algorithm and technology of the system to protect the reasonable allocation and execution efficiency of the task.

[0044] The task review processing system has high automation degree, which can improve work efficiency and accuracy. The system can automatically analyze task review information and decompose it into task content and task object. The system can automatically select appropriate departments or personnel for processing according to task characteristics and department functions. 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 review information and processing results, making it easy to analyze and mine data. The system uses various 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, so as to make accurate judgments and decisions. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The structure diagram of the task review processing system of the present application is shown in the figure.

[0046] Figure 2 The task review processing flowchart of the present application is shown in the figure.

[0047] Figure 3 The task monitoring flowchart of the present application is shown in the figure.

[0048] Figure 4 The task review data analysis and mining flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] A task review processing system includes the following modules:

[0050] 1. Receiving module: This module is used to receive task review information and store the information in the database. The receiving module can receive information in various ways, such as through software interface import, manual input, 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 review information. For example, for text information, rule-based methods can be used to check and correct grammatical errors.

[0051] The following is a python example of the receiving module:

[0052] import asyncio

[0053] import json

[0054] from collections import deque

[0055] class AsyncReceiverModule:

[0056] def __init__(self):

[0057] self.task_queue = deque() # Queue for storing asynchronous tasks

[0058] self.task_processor = TaskProcessor() # Assume there is a predefined asynchronous task processor TaskProcessor

[0059] self.loop = asyncio.get_event_loop() # Get the event loop

[0060] def receive_input(self, input_data):

[0061] """Receive task submission information for external input and process asynchronously"""

[0062] # Preprocess the input data, such as parsing JSON data, etc.

[0063] processed_data = self._preprocess_input(input_data)

[0064] # Put the task into the queue and trigger asynchronous processing

[0065] self.task_queue.append((processed_data, self.loop)

[0066] self._process_tasks()

[0067] def _preprocess_input(self, input_data):

[0068] """Preprocess the input data, such as parsing JSON data, etc."""

[0069] # Add preprocessing logic here, according to actual needs for corresponding processing

[0070] #...

[0071] return processed_data # Return the preprocessed data

[0072] def _process_tasks(self):

[0073] """Process asynchronous tasks in the queue"""

[0074] while self.task_queue:

[0075] task_data, loop = self.task_queue.popleft()

[0076] task = loop.create_task(self.task_processor.process(task_data))

[0077] task.add_done_callback(self._on_task_completed)

[0078] async def _on_task_completed(self, task):

[0079] Callback function after asynchronous task completion.

[0080] # Here you can add further processing of task results or log recording, etc.

[0081] Pass

[0082] The receiving module uses an asynchronous programming model, processing asynchronous tasks in the queue through an event loop. This fully utilizes the resources of multi-core CPUs and improves the system's response speed and processing capacity. Furthermore, stream processing optimizations can be implemented by batching or processing tasks in the TaskProcessor.

[0083] 2. Parsing Module: This module parses task instruction information, breaking it down into task content and task objects. The parsing module can employ technologies such as natural language processing to automatically parse the instruction information. For example, a deep learning-based natural language processing model can be used to perform semantic analysis on the instruction information, extracting the task content and task objects.

[0084] Here is a Python example of a parsing module:

[0085] import json

[0086] import spacy

[0087] class TaskParser:

[0088] def __init__(self):

[0089] self.nlp = spacy.load('en_core_web_sm') # Load English natural language processing model

[0090] def parse_input(self, input_data):

[0091] """Parse task assignment information and extract task content and task objects"""

[0092] # Preprocess input data, such as removing irrelevant characters, converting to lowercase, etc.

[0093] preprocessed_data =self._preprocess_input(input_data)

[0094] # Use natural language processing model for parsing

[0095] doc = self.nlp(preprocessed_data)

[0096] # Extract task content and task objects

[0097] task_content = []

[0098] task_objects = []

[0099] for token in doc.ents:

[0100] if token.label_ == 'TASK':

[0101] task_content.append(token.text)

[0102] elif token.label_ == 'OBJECT':

[0103] task_objects.append(token.text)

[0104] # Return parsing results

[0105] return task_content, task_objects

[0106] def _preprocess_input(self, input_data):

[0107] """Preprocess the input data, such as removing irrelevant characters, converting to lowercase, etc."""

[0108] # Add preprocessing logic here, according to actual needs

[0109] #...

[0110] return preprocessed_data # Return the preprocessed data

[0111] The parsing module uses an English natural language processing model (Spacy) to parse the task assignment information. First, preprocess the input data, such as removing irrelevant characters, converting to lowercase, etc. Then, use the Spacy model to perform entity recognition (Named Entity Recognition, NER) on the preprocessed data to identify the task content and task object. Finally, return the parsing result, including the task content and task object. Note that this example is only applicable to English task assignment information parsing. If you need to parse assignment information in other languages, you need to choose the corresponding natural language processing model or make appropriate customizations.

[0112] 3. Assignment module: This module is used to assign task objects to corresponding departments or personnel. The assignment module can automatically select appropriate departments or personnel for processing based on the characteristics of the task and the functions of the department. In order to achieve efficient assignment, machine learning algorithms can be used to analyze and learn historical task data, and intelligent recommendation and assignment can be made based on 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 assignment.

[0113] The following is a python example of the assignment module:

[0114] import pandas as pd

[0115] from sklearn.metrics.pairwise import cosine_similarity

[0116] class RecommendationBasedAssigner:

[0117] def __init__(self, user_ratings):

[0118] """Task assigner based on collaborative filtering recommendation algorithm"""

[0119] self.user_ratings = user_ratings

[0120] self.user_similarities = self._calculate_user_similarities()

[0121] def _calculate_user_similarities(self):

[0122] """Calculate the similarity between users"""

[0123] # Assume user_ratings is a DataFrame containing user ratings for tasks

[0124] #...

[0125] # Calculate cosine similarity between users

[0126] user_similarities = cosine_similarity(self.user_ratings)

[0127] return user_similarities

[0128] def recommend_tasks(self, user_id):

[0129] """Recommend tasks for a given user"""

[0130] # Recommend tasks based on user similarity

[0131] user_similarities = self.user_similarities[user_id]

[0132] other_users = self.user_ratings.index[self.user_ratings.index!= user_id]

[0133] task_scores = pd.Series(index=self.user_ratings.index)

[0134] for other_user in other_users:

[0135] if user_similarities[other_user] > 0:

[0136] task_scores += self.user_ratings.loc[other_user] * user_similarities[other_user]

[0137] task_scores / = task_scores.sum() # normalize scores

[0138] recommended_tasks = task_scores.sort_values(ascending=False).index[:10] # recommend top 10 tasks

[0139] return recommended_tasks

[0140] The assignment module uses a collaborative filtering-based recommendation algorithm to recommend tasks to users. First, it calculates the similarity between users based on their ratings of tasks. Then, it recommends tasks to users based on their similarity, predicts users' ratings of different tasks by weighted averaging other users' ratings, and selects tasks with higher ratings for recommendation. In this example, cosine similarity is used as a measure of user similarity.

[0141] 4. Reporting module: This module is responsible for collecting the processing results of tasks and reporting them to the task approver. It can display the processing results, completion time, feedback information, etc. of the task to the task approver through the system interface or interface, so that the task approver can evaluate and audit the processing results of the task.

[0142] Here is the python example of the reporting module:

[0143] class TaskResultCollector:

[0144] def __init__(self):

[0145] self.task_results = {} # dictionary for storing task results

[0146] def collect_results(self, task_id, result):

[0147] """Collect the processing results of the task"""

[0148] self.task_results[task_id] =result

[0149] def display_results(self, user):

[0150] "Display the task processing results to the designated user."

[0151] # Get the current user's task list

[0152] user_tasks = self.task_results.keys()&user.tasks.keys()

[0153] # Build Results Display List

[0154] result_list = []

[0155] for task_id in user_tasks:

[0156] result = self.task_results[task_id]

[0157] result_list.append({

[0158] 'task_id': task_id,

[0159] 'result': result,

[0160] 'completion_time': user.tasks[task_id].completion_time,

[0161] 'feedback': user.tasks[task_id].feedback

[0162] })

[0163] # Return to the results display list

[0164] return result_list

[0165] The collection module uses a dictionary to store the task processing results. The `collect_results` method collects the task processing results, and the `display_results` method displays the results to the specified user. This module can update and display task processing results in real time according to user needs and can be integrated with other processing modules to achieve automated collection and organization. Furthermore, this module considers data security and privacy protection requirements, ensuring that the collected and displayed information does not disclose sensitive user information.

[0166] 5. Monitoring Module: This module monitors the handling of tasks by departments or personnel and provides timely feedback to the task approver. The monitoring module can employ real-time monitoring technologies to provide real-time feedback on task progress. To improve the accuracy and timeliness of feedback, data mining techniques can be used to analyze task processing data in real time, promptly identifying and addressing anomalies. For example, anomaly detection algorithms can be used to analyze task progress data in real time, triggering alarms and prompting appropriate action upon detecting anomalies.

[0167] The following is a Python example of a monitoring module:

[0168] import pandas as pd

[0169] from sklearn.ensemble import IsolationForest

[0170] import plotly.express as px

[0171] class TaskMonitor:

[0172] def __init__(self, task_data):

[0173] self.task_data = task_data

[0174] self.isolation_forest = IsolationForest(contamination=0.1) # Anomaly detection model

[0175] def monitor_tasks(self, user_id):

[0176] "Monitor tasks assigned to designated users"

[0177] user_tasks = self.task_data[self.task_data['assigned_to']==user_id]

[0178] if len(user_tasks) == 0:

[0179] The system returned the message: "This user has not been assigned any tasks."

[0180] # Real-time monitoring of task progress

[0181] current_progress = user_tasks['progress'].values

[0182] average_progress = user_tasks['progress'].mean()

[0183] # Real-time analysis of anomalies

[0184] anomalies = self.isolation_forest.predict(pd.DataFrame({'progress': current_progress}))

[0185] anomalous_indices = anomalies == -1

[0186] if any(anomalous_indices):

[0187] anomalous_task_id = user_tasks.index[anomalous_indices][0]

[0188] The system returned the warning: "Warning: Progress of task {anomalous_task_id} is abnormal."

[0189] # Generate progress chart

[0190] fig = px.bar(pd.DataFrame({'Task': user_tasks.index, 'Progress':current_progress}),

[0191] x='Task', y='Progress', color='Task')

[0192] fig.show()

[0193] return f"Current average progress: {average_progress:.2%}"

[0194] The monitoring module uses real-time monitoring techniques to monitor task processing and uses data mining techniques to analyze task processing data in real time. Through an anomaly detection algorithm, the module can immediately alert and handle once an abnormal situation is detected. In addition, the module also provides real-time task progress charts to help supervisors intuitively understand task processing. Compared with other monitoring modules, this module has advantages such as real-time, anomaly detection, multi-dimensional monitoring, flexible customization and expansion, data security and privacy protection, etc.

[0195] 6. Database module: This module is used to store task review information and processing results, facilitating data analysis and mining. The database module can be implemented using relational databases or big data storage technologies. To improve data storage and processing efficiency, distributed database technology can be used. For example, a distributed database system based on NoSQL can be used to store and process large-scale review data.

[0196] Here is a Python example of the database module:

[0197] class DatabaseModule:

[0198] def __init__(self, host, port, database_name):

[0199] self.host = host

[0200] self.port = port

[0201] self.database_name = database_name

[0202] self.client = None

[0203] def connect(self):

[0204] from pymongo import MongoClient

[0205] self.client = MongoClient(self.host, self.port)

[0206] self.client.database_name = self.database_name

[0207] def store_data(self, data):

[0208] collection_name = "task_data"

[0209] collection = self.client[collection_name]

[0210] result = collection.insert_one(data)

[0211] return result.inserted_id

[0212] def query_data(self, query):

[0213] collection_name = "task_data"

[0214] collection = self.client[collection_name]

[0215] result = collection.find(query)

[0216] return list(result)

[0217] The database module uses MongoDB as the storage engine, connecting and operating through the pymongo library in Python. It provides two methods, store_data and query_data, for storing and querying task submission information and processing results. Compared with other database modules, this module has advantages such as scalability, high performance, flexibility, data consistency, and maintainability.

[0218] 7. Analysis Module: This module analyzes task instructions and processing results to uncover potential patterns and trends. It can employ data mining and other techniques to provide decision-makers with data support and reference. To improve the accuracy and comprehensiveness of the analysis, various methods and techniques can be used. For example, association rule mining algorithms can be used to analyze instructions and processing results to uncover potential patterns and relationships; clustering algorithms can be used to cluster similar instructions to discover commonalities and differences; and time series analysis algorithms can be used to analyze the time-series changes of instructions to predict future trends and outcomes. By comprehensively utilizing these methods and techniques, instructions can be analyzed from different angles and dimensions, providing decision-makers with comprehensive and accurate data support.

[0219] The following is a Python example of an analysis module:

[0220] from ml import apriori, kmeans, ts_predict

[0221] class AnalysisModule:

[0222] def __init__(self, data):

[0223] self.data = data

[0224] def Association Rule Mining(self):

[0225] rules = apriori(self.data, min_support=0.1, min_confidence=0.8)

[0226] return rules

[0227] def clustering analysis(self):

[0228] clusters = kmeans(self.data, n_clusters=3)

[0229] return clusters

[0230] def time series analysis(self):

[0231] predictions = ts_predict(self.data)

[0232] return predictions

[0233] The analysis module includes three sub-modules: association rule mining, clustering analysis, and time series analysis. By calling the methods of these sub-modules, the task assignment information and processing results can be analyzed to uncover potential patterns and trends. Compared with other analysis modules, this module has advantages such as multi-dimensional analysis, high accuracy, real-time performance, customizability, and visual presentation.

[0234] Association rule mining sub-module:

[0235] Function: Used to mine the association rules between task assignment information and processing results. The Apriori algorithm is used for frequent item set mining. Minimum support and minimum confidence thresholds are set to filter out meaningful association rules. Visualize the association rule network graph to show the association relationships between different task assignments.

[0236] Optimization points: Support dynamic adjustment of support and confidence thresholds to adapt to analysis needs of different scales and data distributions. Use compression techniques to reduce the storage and computation overhead of frequent item sets.

[0237] Clustering analysis sub-module:

[0238] Function: Cluster similar task assignment data to find commonalities and differences. Use the K-means algorithm for clustering. Visualize the clustering results to show the distribution and characteristics of different clusters. Provide explanations and interpretations of clustering results to help users understand data distribution and patterns.

[0239] Optimization points: Support switching between multiple clustering algorithms to meet the clustering needs of different types of data. Introduce soft clustering techniques to handle data points with fuzzy boundaries. Provide clustering quality evaluation indicators such as silhouette coefficients to evaluate clustering quality.

[0240] Time series analysis sub-module:

[0241] Function: Analyze and predict the time series changes of task assignment data. Use time series analysis algorithms such as ARIMA or LSTM. Provide visual charts of time series data such as line charts or time series charts. Predict future trends and results based on historical data.

[0242] Optimization points: Integrate multiple time series prediction models to select the best model for prediction. Introduce feature engineering to process and convert time series data to improve prediction accuracy. Provide validation and evaluation of prediction results using mean square error and other indicators for model performance evaluation.

[0243] In the task monitoring process, policy issues may have an impact through the following aspects:

[0244] 1. Task Definition: Certain policy changes may make certain tasks particularly important or require special attention. For example, if a policy changes, certain tasks that were not previously considered a priority may become a new focus for monitoring.

[0245] 2. Monitoring Indicators: Policy changes may require adjustments to monitoring indicators. For example, if a policy emphasizes performance in a particular area, related monitoring indicators may need to be adjusted accordingly.

[0246] 3. Status Monitoring: During task execution, certain policy changes may cause delays, suspensions, or difficulties in executing tasks, which require special attention and feedback from the system.

[0247] 4. Result Feedback: Policy changes may affect the final results of tasks. For example, if a policy leads to changes in resource allocation, the completion of tasks will also be affected. When the system provides feedback, it needs to consider the impact of these policy factors.

[0248] In the process of task review data analysis and mining, the impact of policy issues mainly manifests in the following aspects:

[0249] 1. Data Collection: Policy changes may affect the collection of review data. For example, new policies may require more review information to be recorded, or certain review data may not be collected due to privacy or security concerns.

[0250] 2. Data Preprocessing: In the data preprocessing stage, we need to consider how to handle missing or abnormal data caused by policy issues.

[0251] 3. Association Rule Mining and Cluster Analysis: In the association rule mining and cluster analysis stage, policy changes may affect the association and patterns between review data. For example, new policies may lead to changes in the frequency, content, and patterns of reviews.

[0252] 4. Time Series Analysis: Policy changes may affect the timing of review data. For example, if a policy leads to acceleration or delay of the review process, the results of time series analysis will also be affected.

[0253] 5. Result Presentation and Continuous Monitoring: In the result presentation stage, we need to highlight the impact of policy issues on reviews and effects. In the continuous monitoring and updating stage, we need to regularly assess the impact of policy changes on data analysis and mining results, and make appropriate adjustments and optimizations.

[0254] To reduce the impact of policy issues on task monitoring and review data analysis and mining, the following measures can be taken:

[0255] 1. Establish a flexible system architecture: make the system have certain adaptability and flexibility, and be able to quickly respond to policy changes and data changes.

[0256] 2. Strengthen data management and update: regularly clean up and update data to ensure the accuracy and timeliness of data.

[0257] 3. Continuous policy learning and research: regularly study and analyze relevant policies, understand their impact on task monitoring and review, and make corresponding adjustments and optimizations.

[0258] Through the above system, the automatic processing of task review is realized, which improves work efficiency and accuracy, reduces labor cost and error rate, and can automatically record and analyze review data, providing data support and reference for decision-makers, which helps to optimize decision-making and improve management efficiency.

[0259] As Figure 1 shown is a structural block diagram of a task review processing system provided by an embodiment of the present application, as shown in the figure: a task review processing system, comprising a receiving module, an analysis 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 analysis module, the analysis 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.

[0260] A task review processing system is based on government agencies or internal functions and approval systems, by integrating the approval processes of internal departments and external collaboration departments, using advanced computer technology and artificial intelligence algorithms, the task review work is received, allocated, processed, monitored, and analyzed, realizing the whole process online automatic processing and tracking management of task review. The task review information and review processing process involved in the system process can be adjusted according to the actual situation of the user.

[0261] System initialization: when the system starts, system initialization is needed, including loading system configuration, establishing database connection, starting service, etc. These configurations and connections can be realized by preset parameters and data.

[0262] Review information receiving: the task reviewer can input the review 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 review information from other data sources, such as email, SMS, etc. The obtained information can also be stored in the database after preprocessing.

[0263] Batch information analysis: The system uses natural language processing techniques to analyze the batch information, breaking it down into task content and task object. For example, when the task batch is "Please investigate the sales data of the marketing department in the last month," the analysis module can break it down into the task content "investigate the sales data of the marketing department in the last month" and the task object "marketing department." During the analysis process, the system can check and correct the batch information as needed to ensure the accuracy of the analysis.

[0264] Task allocation: According to the task content and the functions of the departments, the system uses allocation algorithms to allocate tasks to appropriate departments or personnel. For example, for the above task, the system can allocate the task to the marketing department responsible for sales data investigation according to the functions of the departments and the requirements of the task. The allocation algorithm can use various methods, such as rule-based matching, collaborative filtering-based recommendation, reinforcement learning-based scheduling, etc.

[0265] Task processing: After receiving the task, the department or personnel can view the task content and processing requirements through the system interface or interface, and timely feedback the processing results to the task batch person. For example, when the investigation personnel receive the task, they 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 the investigation and feedback the results to the task batch person.

[0266] Task monitoring: Task monitoring monitors the processing of tasks by departments or personnel in real time and timely feedback to the task batch person. For example, when the investigation personnel feedback the processing results, the task monitoring module can monitor the completion status of the task in real time and timely feedback the results to the task batch person.

[0267] Database module storage: The database module stores the task batch information and processing results, and uses analysis algorithms to mine and analyze the data to discover potential laws and trends. For example, the task batch information, processing results, department and personnel responsibilities, etc. can be stored in the database for data analysis and mining. Analysis algorithms can use various techniques, such as association rule mining, clustering analysis, time series analysis, etc.

[0268] Analysis module mining: The analysis module can analyze the task batch information and processing results to mine potential laws and trends, and provide data reports and analysis results to decision makers to facilitate accurate judgment and decision making. For example, association rule mining algorithms can be used to analyze batch data to discover association rules and patterns; clustering algorithms can be used to cluster similar batch data to discover commonalities and differences; time series analysis algorithms can be used to analyze and predict the time series changes of batch data to predict future trends and results.

[0269] For example, Figure 2As shown in the task review processing flowchart provided by the embodiments of the present application, it can be known that the task review processing flow is:

[0270] Task review: The task reviewer inputs the review information through the system interface, or the system receives the task review information from other data sources such as emails or short messages.

[0271] Task allocation: According to the task content and the functions of the departments, the system uses an allocation algorithm to allocate the task to the appropriate department or personnel.

[0272] Task processing: After receiving the task, the department or personnel can view the task content and processing requirements through the system interface or interface, and timely feedback the processing result to the task reviewer.

[0273] As Figure 3 shown is the task monitoring flowchart provided by the embodiments of the present application, it can be known that the system task monitoring flow is:

[0274] 1. Define the task: First, it is necessary to clarify what task needs to be monitored. This can include various types of tasks, such as key projects, important work, supervision items, etc.

[0275] 2. Set monitoring indicators: According to the characteristics and needs of the task, set the corresponding monitoring indicators. These indicators can include the completion status of the task, processing time, processing results, etc.

[0276] 3. Task monitoring: The system collects and processes task data in real time, and monitors and feeds back according to the set indicators.

[0277] 4. State monitoring: The system monitors the completion status of the task in real time, and once it finds an abnormal or incomplete task, it will immediately feedback and remind.

[0278] 5. Result feedback: When the processing personnel feedback the processing result, the system will feedback the result to the task reviewer or other related personnel in real time. Feedback can include various forms, such as email notification, SMS reminder, report generation, etc.

[0279] As Figure 4 shown is the task review data analysis and mining flowchart provided by the embodiments of the present application, it can be known that the task review data analysis and mining processing flow is:

[0280] 1. Data collection: First, the relevant data of the task review needs to be collected, including review content, review time, review personnel, etc. These data can be collected automatically by the system or manually entered.

[0281] 2. Data preprocessing: Clean, organize and standardize the collected data to ensure the quality and availability of the data. This includes removing duplicate data, filling in missing values, converting data to a unified format, etc.

[0282] 3. Data exploration: Preliminary exploration and analysis of pre-processed data to understand the distribution and characteristics of the data. This can be done through visualization charts, statistical quantities, etc.

[0283] 4. Association rule mining: Through association rule mining algorithms, analyze the association and rules between task approval data. This can help discover the relationship and influence between various approvals, as well as potential business trends and patterns.

[0284] 5. Clustering analysis: Through clustering algorithms, cluster similar approval data into several groups. This can help discover the commonalities and differences between different approvals, providing support for classification and decision-making.

[0285] 6. Time series analysis: Through time series analysis algorithms, analyze and predict the time series changes of approval data. This can help predict future trends and results, providing reference and support for decision-makers.

[0286] 7. Model evaluation and optimization: According to the 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 the prediction.

[0287] 8. Result presentation: Present the analysis results in the form of data reports and analysis charts to decision-makers. This can help decision-makers make accurate judgments and decisions, providing support for business development and management.

[0288] 9. Continuous monitoring and updating: In order to maintain the accuracy and effectiveness of data analysis and mining, regular data updating and monitoring are needed to adapt to changes in business needs and upgrades in management mode. At the same time, models and algorithms need to be continuously optimized to improve the accuracy and generalization ability of the prediction.

[0289] Through the above implementation, the automatic processing of task approval can be realized, improving work efficiency and accuracy. At the same time, through the analysis and mining of data, more comprehensive and accurate data support can be provided for decision-makers.

[0290] The patent scheme realizes a task review processing system and method, which can automatically process task reviews, greatly improving work efficiency and accuracy. By introducing advanced artificial intelligence technology, the system can automatically analyze task content, identify review intentions, and automatically generate review instructions according to pre-set rules and processes. This not only reduces manual intervention and errors, but also greatly speeds up the task processing speed, 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 effect of task reviews, further optimizing the work process and efficiency.

[0291] 1. It can be applied to the processing scene of internal leadership review in government, and various types of internal leadership review information in government can be processed and analyzed as a task review, and data support can be provided for leadership decision-making assistance.

[0292] 2. It can be applied to the scene of internal leadership instruction review and important task implementation in enterprises, continuously improving the work efficiency of various departments in enterprises in implementing key work.

Claims

1. A task assignment processing system characterized by The system comprises a receiving module, an analyzing module, an assigning module, a reporting module, a monitoring module, a database module and an analyzing module. The receiving module is used for receiving task review information and storing the information in the database. The receiving module receives information in multiple ways, such as through software interface import and manual input. The natural language processing technology is used to automatically check and correct the input review information. The analyzing module is used for analyzing task review information and decomposing the information into task content and task object. The assigning module is used for assigning task objects to corresponding departments or personnel. The reporting module is responsible for collecting task processing results and reporting them to the task reviewer. The monitoring module is used for monitoring the processing of tasks by departments or personnel and feeding back to the task reviewer in a timely manner. The database module is used for storing task review information and processing results, and performing data analysis and mining. The analyzing module is used for analyzing task review information and processing results, and mining potential rules and trends.

2. The task briefing processing system according to claim 1, characterized by The receiving module is an asynchronous programming model, which processes asynchronous tasks in the queue through event loop. The receiving module utilizes the resources of multi-core CPU and the optimization of streaming processing to process tasks by batch or event in TaskProcessor. The parsing module is a natural language processing model Spacy to parse the task briefing information. First, the input data is preprocessed to remove irrelevant characters and converted to lowercase. The preprocessed data is then processed by the Spacy model to identify entities, including task content and task objects. Finally, the parsing results, including task content and task objects, are returned. If other languages need to be parsed, the corresponding natural language processing model or customization needs to be selected. The allocation module uses a recommendation algorithm based on collaborative filtering to recommend tasks to users. First, the similarity between users is calculated based on their ratings of tasks. Then, tasks are recommended to users based on their similarity. The ratings of different tasks are predicted by weighting the ratings of other users, and 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 the user's needs. The monitoring module uses real-time monitoring technology to monitor the task processing situation, 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. The database module uses MongoDB as the storage engine, connects and operates through the pymongo library of Python, and provides store_data and query_data for storing and querying task briefing information and processing results, respectively. The analysis module includes three sub-modules: association rule mining, clustering analysis, and time series analysis, which analyze task briefing information and processing results to mine potential rules and trends.

3. The processing method of a task assignment processing system according to claim 1 or 2, characterized by The steps include: S1 Data collection First, collect the relevant data of task briefing, including briefing content, briefing time, and briefing personnel information. These data are collected automatically by the system or manually entered; S2 Data preprocessing Remove duplicate data, fill in missing values, and convert data to a unified format; S3 Data exploration Preliminary exploration and analysis of preprocessed data to understand the distribution and characteristics of data. Visualization charts and statistical methods are used; S4 Association rule mining Through association rule mining algorithm, analyze the association and rules between task briefing data. Help discover the relationship and influence between various briefings, as well as potential business trends and patterns; S5 Clustering analysis Through clustering algorithm, similar briefing data is clustered into several groups to help discover the commonalities and differences between different briefings; S6 Time series analysis Through time series analysis algorithm, analyze and predict the time series changes of briefing data S7 Model evaluation and optimization According to the actual business needs and data characteristics, choose the appropriate algorithm and model for data analysis and mining; at the same time, the model needs to be evaluated and optimized; S8 result presentation Present the analysis results in the form of data reports and analysis charts to support business development and management; S9 continuous monitoring and updating Regularly update and monitor the data, according to the changes of business needs and the upgrading of management mode.

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