A method and apparatus for intelligent matching and tracking feedback

Abstract

The application relates to the technical field of information processing, and specifically provides a suggestion intelligent matching and tracking feedback method and device, which has the following steps: S1, a suggestion acquisition module is a multi-channel access port, integrates multiple channels, and realizes unified reception of suggestions; S2, an intelligent preprocessing module processes the collected suggestion content based on natural language processing (NLP) technology; S3, a multi-dimensional matching module constructs a multi-dimensional matching model to realize docking of suggestions and undertaking departments; S4, a tracking and supervision module realizes tracking and management of the whole process of suggestion handling; S5, a feedback interaction module builds an interactive bridge between the suggesters and the departments, realizes timely feedback and exchange of handling information; and S6, a data security and analysis module guarantees system data security and mines data value. Compared with the prior art, the application can realize efficient collection, accurate matching, whole-process tracking and timely feedback of suggestions.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically providing a method and apparatus for intelligent matching and tracking feedback. Background Technology

[0002] While suggestions were solicited, many pain points still exist in actual work: (1) Low sorting efficiency: A large number of suggestions need to be sorted and distributed manually. Faced with thousands or even tens of thousands of suggestions per day, manual sorting is not only time-consuming and laborious, but also prone to sorting errors due to differences in the subjective judgment of staff.

[0003] (2) Inaccurate department matching: Some suggestions involve multiple overlapping fields, making it difficult for manual staff to quickly and accurately locate the core demands and match them with the corresponding responsible departments. This often results in "multiple assignments" and "passing the buck," which prolongs the processing period.

[0004] (3) Lack of follow-up and supervision: The process of making suggestions lacks a transparent follow-up mechanism, making it difficult for management departments to keep track of the progress of each stage in real time. When problems such as overdue processing or poor processing quality occur, they cannot intervene in time. At the same time, the person making the suggestion cannot know the processing status of their suggestion in a timely manner.

[0005] (4) Ineffective feedback mechanism: The results of the suggestions are mostly fed back through a single channel. The feedback content is too formalized and lacks interaction with the suggesters, which reduces the public’s enthusiasm and satisfaction.

[0006] (5) Data value wasted: Historical data was not effectively mined and utilized, making it impossible to extract common problems and predict people's needs through data analysis, and making it difficult to formulate data support.

[0007] While some existing technologies involve suggestion collection functions, they mostly remain at the basic information reception level and lack intelligent semantic analysis, multi-dimensional accurate matching, and full-process closed-loop management capabilities for suggestions. As a result, they cannot meet the needs of efficient processing and improved service quality in the new era.

[0008] Therefore, developing a suggestion processing system with intelligent matching and tracking feedback functions has become an urgent need for current information technology construction. Summary of the Invention

[0009] This invention addresses the shortcomings of the prior art by providing a highly practical intelligent matching and tracking feedback method for suggestions.

[0010] A further technical objective of this invention is to provide a rationally designed, safe, and applicable intelligent matching and tracking feedback device.

[0011] The technical solution adopted by this invention to solve its technical problem is: A method for intelligent matching and tracking feedback includes the following steps: S1. It is suggested that the collection module be a multi-channel access port, integrating multiple channels to achieve unified reception of suggestions; S2. The intelligent preprocessing module processes the collected suggestions based on Natural Language Processing (NLP) technology. S3. The multi-dimensional matching module constructs a multi-dimensional matching model to connect suggestions with the responsible departments; S4, the tracking and supervision module enables the tracking and management of the entire process of handling suggestions; S5. The feedback and interaction module builds an interactive bridge between the person making the suggestion and the department, enabling timely feedback and exchange of processing information. S6, the data security and analysis module, ensures system data security and unlocks data value.

[0012] Furthermore, in step S1, the input consists of suggestion content in various formats, where voice content is converted into text using speech recognition technology, and text information in images and videos is extracted using OCR technology. The metadata of the suggester is automatically recorded to form standardized suggestion data entries.

[0013] Furthermore, step S2 includes: Semantic parsing unit: performs word segmentation, part-of-speech tagging, and syntactic analysis on the suggested text to identify core demands, relevant fields, geographical scope, and urgency information; Tag extraction unit: Based on a preset tag system and combined with semantic analysis results, it automatically adds tags to suggestions to achieve standardized classification of suggestions; Quality screening unit: Identifies and filters invalid suggestions to improve the efficiency of subsequent processing; Data standardization unit: Converts the processed suggestion information and metadata into a unified format and stores it in the system database.

[0014] Furthermore, step S3 includes: a basic database construction unit: establishing and updating in real time the departmental function list database, key work task database, historical processing database, and expert knowledge base; Matching Model Training Unit: Using historical processing data as training samples, the matching model is trained using machine learning algorithms. The model input includes suggested label information, core demands, and geographical characteristics, and the output is the matching score for each responsible department. The core matching score is calculated using a weighted summation formula to accurately quantify the matching relationship. ; In the formula, The representative suggests a matching score with the i-th responsible department. The weights of the k-th class features are... For the proposed k-th class feature vector, Let be the standard vector of the i-th department on the k-th type of feature. This is the cosine similarity calculation function, with a value range of 0-1, used to measure the degree of similarity between two vectors; Intelligent matching unit: Input the preprocessed suggestion features into the trained model, calculate the matching degree with each department, and automatically assign the suggestion to the department when the score of the department with the highest matching degree exceeds the preset threshold; if there are multiple departments with similar matching degrees or involving cross-domain areas, a matching suggestion list is generated and submitted to the management department for manual review and confirmation. Dynamic optimization unit: Adjusts model parameters in real time based on new processing results to improve matching accuracy.

[0015] Furthermore, step S4 includes: Process node setting unit: Preset suggested process nodes and set standard processing time limits for each node; Progress tracking unit: By connecting with the government systems of various departments, it collects processing progress information in real time and forms a visual process ledger in the system background. Government management departments can view the current node, processing personnel and time consumption of each suggestion in real time. Early warning and reminder unit: When the suggested processing time is approaching or has exceeded the deadline, an early warning and reminder will be sent to the responsible department and person in charge; for those that have not been completed within the deadline and have no reasonable reason, a supervision notice will be sent to the government affairs management department. Quality assessment unit: The handling quality of the responsible department is automatically scored based on the processing time, the degree to which the handling result matches the suggestion, and the satisfaction of the suggester. The scoring results are included in the department's performance evaluation.

[0016] Furthermore, step S5 includes: Multi-channel feedback unit: Based on the contact information provided by the person making the suggestion, provide real-time feedback on the allocation results, processing progress and final results of the suggestion; Satisfaction evaluation unit: After the suggestion is completed, a satisfaction evaluation questionnaire will be sent to the person who made the suggestion to collect their evaluation opinions and suggestions for improvement on the handling results; Interactive Communication Unit: Supports online communication between the proposer and the responsible department through the system. When the responsible department needs to further verify the details of the proposal or the proposer has questions about the handling results, two-way interaction can be achieved through leaving messages and online consultation. Results Disclosure Unit: Suggestions without privacy information and their handling results are publicly disclosed on the government affairs platform.

[0017] Furthermore, step S6 includes: Data security unit: Employs data encryption, access control, or privacy information desensitization technologies to protect the personal information of the person making the suggestion and the content of the suggestion, and establishes a data backup mechanism; Data Analysis Unit: Utilizes big data analytics to perform statistical analysis on historical recommendation data and generate data analysis reports; Decision support unit: Pushes data analysis reports to relevant departments to provide data support for strategy formulation, resource allocation, and work deployment.

[0018] A suggested intelligent matching and tracking feedback device includes: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to invoke the machine-readable program to execute a suggested intelligent matching and tracking feedback method.

[0019] Compared with the prior art, the proposed intelligent matching and tracking feedback method and apparatus of the present invention have the following outstanding advantages: (1) This invention significantly reduces the workload of manual sorting and allocation by automatically collecting data through multiple channels, intelligent preprocessing and precise matching, shortening the recommended average allocation time from the traditional 2-3 working days to a few hours, thus significantly improving the overall processing efficiency.

[0020] (2) Based on the multi-dimensional matching model and dynamic optimization mechanism, the matching accuracy of the suggestion with the responsible department can reach more than 90%, which effectively solves the problems of cross-assignment and buck-passing, and shortens the processing cycle.

[0021] (3) The tracking and supervision module realizes full-process visual monitoring of the handling of suggestions from receipt to completion. Combined with the early warning and reminder mechanism, the overdue handling rate is reduced by more than 60%, and the handling quality is significantly improved.

[0022] (4) Diversified feedback channels and interactive communication functions allow the proposers to keep track of the progress of the proposal in real time. The satisfaction evaluation mechanism has improved the transparency of the proposal process. The public's satisfaction with the proposal process has increased to over 85%, which has stimulated the public's enthusiasm for participating in government affairs.

[0023] (5) The analysis of historical recommendation data provides data support for accurately identifying pain points in people's livelihoods and scientifically formulating strategies, promotes the transformation of services from "passive response" to "proactive prediction", and improves the modernization level of governance capabilities.

[0024] (6) The sound data security mechanism effectively protects the privacy information of the proposers, avoids the risk of data leakage, and enhances the public's trust in the solicitation work. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a flowchart illustrating a suggested intelligent matching and tracking feedback method. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The following is a preferred embodiment: like Figure 1 As shown, the intelligent matching and tracking feedback method in this embodiment includes the following steps: S1. The suggestion collection module is a multi-channel access port, integrating various collection channels such as government hotlines, government apps, official websites, WeChat official accounts, email, and offline terminals to achieve unified reception of suggestions.

[0029] It supports input of suggestion content in various formats, including text, voice, images, and videos. Voice content is converted into text using speech recognition technology, and text information in images and videos is extracted using OCR technology, ensuring complete collection of suggestion information. Simultaneously, the module automatically records metadata such as the suggester's basic information (anonymity is optional), submission time, and content category, forming standardized suggestion data entries.

[0030] S2, the intelligent preprocessing module processes the collected suggestions based on Natural Language Processing (NLP) technology, specifically including: Semantic parsing unit: performs word segmentation, part-of-speech tagging, and syntactic analysis on the suggested text to identify key information such as core demands, relevant fields, geographical scope, and urgency. Tag extraction unit: Based on the preset tag system (including primary tags such as people's livelihood security, urban management, and economic development, and secondary tags such as education, medical care, and transportation), and combined with semantic analysis results, it automatically adds tags to suggestions to achieve standardized classification of suggestions; Quality screening unit: Identifies and filters invalid suggestions (such as duplicate submissions, empty content, malicious requests, etc.) to improve the efficiency of subsequent processing; Data standardization unit: Converts the processed suggestion information and metadata into a unified format and stores it in the system database.

[0031] S3. The multi-dimensional matching module constructs a multi-dimensional matching model to connect suggestions with the responsible departments; Specifically, it includes: Basic database construction unit: Establish and update in real time the departmental function list database (clarifying the responsibilities and areas of responsibility of each department), the key work task database (entering key tasks and livelihood projects in stages), the historical processing database (storing data such as the responsible departments, processing results, and matching accuracy of past suggestions), and the expert knowledge base (including professional opinions from various fields); Matching Model Training Unit: Using historical processing data as training samples, the matching model is trained using machine learning algorithms (such as support vector machines and neural networks). The model input includes suggested label information, core demands, geographical scope, and other features. The output is the matching score for each responsible department. The core matching score is calculated using a weighted summation formula to accurately quantify the matching relationship. ; In the formula, The representative suggests a matching score with the i-th responsible department. The weight of the k-th feature (obtained through training and optimization using historical data, with the core objective being to assign the highest weight, and a value range of 0.1-0.5). For the proposed k-th class feature vector, Let be the standard vector of the i-th department on the k-th type of feature. This is the cosine similarity calculation function, with a value range of 0-1, used to measure the degree of similarity between two vectors; Intelligent matching unit: Input the preprocessed suggestion features into the trained model, calculate the matching degree with each department, and automatically assign the suggestion to the department with the highest matching degree when the score exceeds the preset threshold; if there are multiple departments with similar matching degrees or involving overlapping fields, a matching suggestion list is generated and submitted to the government affairs management department for manual review and confirmation. Dynamic optimization unit: Adjusts model parameters in real time based on new processing results to continuously improve matching accuracy.

[0032] S4, the tracking and supervision module enables the tracking and management of the entire process of handling suggestions; Specifically, it includes: Process node setting unit: Preset suggested process nodes (such as receiving, accepting, processing, reviewing, and completing), and set standard processing time limits for each node; Progress tracking unit: By connecting with the government systems of various departments, it collects processing progress information in real time and forms a visual process ledger in the system background. Government management departments can view the current node, processing personnel and time consumption of each suggestion in real time. Early warning and reminder unit: When the suggested processing is nearing or has exceeded the deadline, an early warning and reminder will be automatically sent to the responsible department and person in charge via SMS, system message, etc.; for those that have not been completed within the deadline and have no reasonable reason, a supervision notice will be sent to the government affairs management department. Quality assessment unit: Based on indicators such as processing time, the degree to which the processing result matches the suggestion, and the satisfaction of the suggester, the unit automatically scores the processing quality of the responsible department, and the scoring results are included in the department's performance evaluation.

[0033] S5. The feedback and interaction module builds an interactive bridge between the person making the suggestion and the department, enabling timely feedback and exchange of processing information. Specifically, it includes: Multi-channel feedback unit: Based on the contact information provided by the proposer, feedback on the allocation results, processing progress and final results of the proposal will be provided in real time via SMS, telephone, APP push, email and other means. Satisfaction evaluation unit: After the suggestion is completed, a satisfaction evaluation questionnaire will be sent to the person who made the suggestion to collect their evaluation opinions on the handling results (such as very satisfied, satisfied, basically satisfied, dissatisfied) and suggestions for improvement; Interactive Communication Unit: Supports online communication between the proposer and the responsible department through the system. When the responsible department needs to further verify the details of the proposal or the proposer has questions about the handling results, two-way interaction can be achieved through leaving a message, online consultation and other means. Results Disclosure Unit: Suggestions without privacy information and their handling results are publicly disclosed on the government affairs platform for public supervision.

[0034] S6. The data security and analysis module ensures system data security and unlocks data value. Specifically, it includes: Data security unit: Employs technologies such as data encryption, access control, and privacy information anonymization to protect the personal information of the person making the suggestion and the content of the suggestion, preventing data leakage; establishes a data backup mechanism to ensure data integrity; Data Analysis Unit: Utilizes big data analytics to statistically analyze historical suggestion data, uncovering patterns and characteristics in high-frequency complaint areas, regional livelihood hotspots, and seasonal issues, and generating data analysis reports. Decision support unit: Pushes data analysis reports to relevant departments to provide data support for strategy formulation, resource allocation, and work deployment, enabling precise policy implementation.

[0035] The system management module enables daily maintenance and management of the system, including user permission management (assigning different operation permissions to government departments, responsible departments, and proposers), system parameter configuration (such as processing time limits and matching threshold settings), log management (recording system operations and data changes), and system fault early warning.

[0036] Based on the above method, a proposed intelligent matching and tracking feedback device in this embodiment includes: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to invoke the machine-readable program to execute a suggested intelligent matching and tracking feedback method.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent matching and tracking feedback, characterized in that, It has the following steps: S1. It is suggested that the collection module be a multi-channel access port, integrating multiple channels to achieve unified reception of suggestions; S2. The intelligent preprocessing module processes the collected suggestions based on Natural Language Processing (NLP) technology. S3. The multi-dimensional matching module constructs a multi-dimensional matching model to connect suggestions with the responsible departments; S4, the tracking and supervision module enables the tracking and management of the entire process of handling suggestions; S5. The feedback and interaction module builds an interactive bridge between the person making the suggestion and the department, enabling timely feedback and exchange of processing information. S6, the data security and analysis module, ensures system data security and unlocks data value.

2. The intelligent matching and tracking feedback method according to claim 1, characterized in that, In step S1, the input consists of suggestion content in various formats. The voice content is converted into text using speech recognition technology, and the text information in the images and videos is extracted using OCR technology. The metadata of the person making the suggestion is automatically recorded to form standardized suggestion data entries.

3. The intelligent matching and tracking feedback method according to claim 2, characterized in that, Step S2 includes: Semantic parsing unit: performs word segmentation, part-of-speech tagging, and syntactic analysis on the suggested text to identify core demands, relevant fields, geographical scope, and urgency information; Tag extraction unit: Based on a preset tag system and combined with semantic analysis results, it automatically adds tags to suggestions to achieve standardized classification of suggestions; Quality screening unit: Identifies and filters invalid suggestions to improve the efficiency of subsequent processing; Data standardization unit: Converts the processed suggestion information and metadata into a unified format and stores it in the system database.

4. The intelligent matching and tracking feedback method according to claim 3, characterized in that, Step S3 includes: Basic database construction unit: establishing and updating in real time the departmental function list database, key work task database, historical processing database and expert knowledge base; Matching Model Training Unit: Using historical processing data as training samples, the matching model is trained using machine learning algorithms. The model input includes suggested label information, core demands, and geographical characteristics, and the output is the matching score for each responsible department. The core matching score is calculated using a weighted summation formula to accurately quantify the matching relationship. ； In the formula, The representative suggests a matching score with the i-th responsible department. The weights of the k-th class features are... For the proposed k-th class feature vector, Let be the standard vector of the i-th department on the k-th type of feature. This is the cosine similarity calculation function, with a value range of 0-1, used to measure the degree of similarity between two vectors; Intelligent matching unit: Input the preprocessed suggestion features into the trained model, calculate the matching degree with each department, and automatically assign the suggestion to the department when the score of the department with the highest matching degree exceeds the preset threshold; if there are multiple departments with similar matching degrees or involving cross-domain areas, a matching suggestion list is generated and submitted to the management department for manual review and confirmation. Dynamic optimization unit: Adjusts model parameters in real time based on new processing results to improve matching accuracy.

5. The intelligent matching and tracking feedback method according to claim 4, characterized in that, Step S4 includes: Process node setting unit: Preset suggested process nodes and set standard processing time limits for each node; Progress tracking unit: By connecting with the government systems of various departments, it collects processing progress information in real time and forms a visual process ledger in the system background. Government management departments can view the current node, processing personnel and time consumption of each suggestion in real time. Early warning and reminder unit: When the suggested processing time is approaching or has exceeded the deadline, an early warning and reminder will be sent to the responsible department and person in charge; for those that have not been completed within the deadline and have no reasonable reason, a supervision notice will be sent to the government affairs management department. Quality assessment unit: The handling quality of the responsible department is automatically scored based on the processing time, the degree to which the handling result matches the suggestion, and the satisfaction of the suggester. The scoring results are included in the department's performance evaluation.

6. The intelligent matching and tracking feedback method according to claim 5, characterized in that, Step S5 includes: Multi-channel feedback unit: Based on the contact information provided by the person making the suggestion, provide real-time feedback on the allocation results, processing progress and final results of the suggestion; Satisfaction evaluation unit: After the suggestion is completed, a satisfaction evaluation questionnaire will be sent to the person who made the suggestion to collect their evaluation opinions and suggestions for improvement on the handling results; Interactive Communication Unit: Supports online communication between the proposer and the responsible department through the system. When the responsible department needs to further verify the details of the proposal or the proposer has questions about the handling results, two-way interaction can be achieved through leaving messages and online consultation. Results Disclosure Unit: Suggestions without privacy information and their handling results are publicly disclosed on the government affairs platform.

7. The intelligent matching and tracking feedback method according to claim 6, characterized in that, Step S6 includes: Data security unit: Employs data encryption, access control, or privacy information desensitization technologies to protect the personal information of the person making the suggestion and the content of the suggestion, and establishes a data backup mechanism; Data Analysis Unit: Utilizes big data analytics to perform statistical analysis on historical recommendation data and generate data analysis reports; Decision support unit: Pushes data analysis reports to relevant departments to provide data support for strategy formulation, resource allocation, and work deployment.

8. A suggested intelligent matching and tracking feedback device, characterized in that, include: At least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is configured to invoke the machine-readable program to perform the method according to any one of claims 1 to 7.

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