Online employment consultation record processing method and system for disabled helping service

Abstract

The invention discloses an online employment consultation record processing method and system for disabled-helping services, and relates to the technical field of disabled-helping services, and the method comprises the following specific steps: multivariate data collection and preprocessing: comprehensively collecting employment consultation of disabled people, enterprise post demands and related information through an online platform, and carrying out the preprocessing, the handicapped employment knowledge graph is constructed, monitored and updated in real time, so that the knowledge graph can accurately reflect the dynamic change of an employment market, and the intelligent matching and personalized recommendation module accurately extracts key features of handicapped persons and posts by utilizing the processed data and the knowledge graph, performs matching calculation and result sorting, and improves the accuracy of the handicapped employment. According to the technical scheme, a highly personalized post recommendation list is generated, and new employment information, skill improvement suggestions, related policies and other knowledge can be pushed, so that the employment service provided for the handicapped can closely fit own conditions and requirements, and the accuracy and personalized level of the employment service are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disability assistance services, and specifically to an online employment consultation record processing method and system for disability assistance services. Background Art

[0002] With the rapid development of Internet technology, it has been widely used in the field of employment services for people with disabilities. Online employment consultation has gradually become a key way for people with disabilities to obtain employment information and support. Online platforms have broken the limitations of time and space, providing people with disabilities with more convenient and efficient consultation channels, enabling them to have a broader understanding of employment market trends and job information. However, the existing online employment consultation record processing methods have gradually exposed many shortcomings in response to the growing consultation needs and the complex and changing employment market, and it is difficult to meet the requirements of precision and efficiency in employment services for people with disabilities.

[0003] The existing online employment consultation record processing methods have many defects. In terms of information processing, there is a lack of systematicity, which leads to scattered and messy data, low utilization efficiency, and inability to fully tap the potential value of the data. The semantic analysis is not in-depth enough, making it difficult to extract the implicit employment intentions and potential job adaptability of people with disabilities from the consultation records. The knowledge graph is not updated in a timely manner and cannot reflect the dynamic changes in the employment market in a timely manner, such as the emergence of new positions and adjustments to skill requirements. At the same time, the tracking and feedback mechanism is imperfect, and services cannot be effectively adjusted according to actual conditions. It is impossible to timely understand the problems and needs encountered by people with disabilities in the employment process, and it is difficult to evaluate the effectiveness of consulting services, which seriously affects the quality and effectiveness of providing accurate employment services for people with disabilities.

[0004] In response to the above problems, it is necessary to optimize the existing online employment consultation record processing method for disability assistance services. Through multi-step data processing and multi-module collaboration, various factors of people with disabilities can be fully considered to achieve accurate job matching and employment guidance. Therefore, it is of great significance to develop an online employment consultation record processing method and system for disability assistance services that can comprehensively realize the above characteristics. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an online employment consultation record processing method and system for disability assistance services. It can comprehensively and accurately collect relevant information of disabled people and enterprises through multi-dimensional data collection and preprocessing, and clean, standardize, semantically mine and fuse the data to provide a high-quality data foundation for subsequent analysis. The knowledge graph construction and dynamic update steps can construct a knowledge graph for disabled employment, and monitor and update employment market dynamic information in real time to ensure the accuracy and timeliness of the knowledge graph. Intelligent matching and personalized recommendations can be accurately matched and personalized based on the characteristics and job requirements of disabled people, thereby improving the pertinence and effectiveness of employment services. Intelligent consulting services use knowledge graphs to realize intelligent question and answer and personalized employment guidance, providing convenient and accurate services for disabled people and consultants. The tracking feedback and system optimization steps evaluate the effectiveness of consulting services by establishing a consulting record tracking mechanism and a feedback collection mechanism, and optimize the system based on the evaluation results to achieve continuous improvement of the system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a method for processing online employment consultation records for disability assistance services comprises the following specific steps: Multi-dimensional data collection and pre-processing: Through the online platform, comprehensive employment consultation information for people with disabilities, corporate job requirements, and related information are collected and pre-processed. Semantic enhancement technology is used to mine and integrate the implicit semantic information in the consultation records; Knowledge graph construction and dynamic updating: Identify various entities and their relationships from the received data to construct a knowledge graph on disability employment, monitor new developments in the employment field in real time and incorporate them into the graph, and regularly review and update the graph information; Intelligent matching and personalized recommendations: This system extracts key characteristics of individuals with disabilities and job positions, compares and calculates the degree of match between the two, integrates multiple factors to derive matching scores and sorts them, and generates a personalized job recommendation list. Furthermore, based on the updated knowledge graph, it pushes new knowledge and job information based on user behavior preferences. Intelligent consulting service implementation: semantic understanding of consultation questions from people with disabilities extracts key information, retrieves relevant knowledge from the knowledge graph, integrates reasoning to generate accurate and personalized career guidance answers, and provides real-time knowledge assistance to consultants; Tracking feedback and system optimization: Track the follow-up process of employment consultation for people with disabilities and record key node information, collect feedback from people with disabilities and consultants, evaluate the effectiveness of consulting services, and feedback the results to relevant personnel, so as to update and improve the knowledge graph and adjust and optimize the matching recommendation strategy.

[0007] Furthermore, in the multivariate data collection and preprocessing step, semantic enhancement technology is used to mine and fuse the implicit semantic information of the consultation records. The semantic information mining formula is: ,in, It represents the comprehensive score of the mined implicit semantic information, which is used to measure the importance and potential value of semantic information. It is the semantic information adjustment coefficient, which ranges from 0 to 1 and is used to adjust the mining results of semantic information according to the actual situation. Indicates the number of words or phrases used for semantic analysis in the consultation record. It is The weight of a word or phrase, It is The semantic feature value of a word or phrase is used to represent the characteristics of the semantic information carried by the word or phrase.

[0008] Furthermore, in the knowledge graph construction and dynamic update step, various entities and their relationships are identified from the received data to construct a knowledge graph for disability employment. Specifically, various entities are identified from the preprocessed data, including disability types, job positions, skill requirements, training resources, and employment policies. By analyzing and extracting text information in the data, the key features and attributes of each entity are determined, and the association relationships between entities are analyzed. Through comprehensive analysis and logical reasoning of the data, the type and strength of the relationship between entities are determined. The identified entities and extracted relationships are organized and stored in the form of a graph to construct a knowledge graph for disability employment. A real-time monitoring mechanism for dynamic information in the field of disability employment is established. New job positions, changes in skill requirements, online training resources, and employment policy adjustments are obtained through multiple information channels. When new information is monitored, it is automatically integrated into the knowledge graph, and the attributes and relationships of the entities are updated. The information in the knowledge graph is regularly reviewed and outdated or erroneous information is deleted to ensure the accuracy and timeliness of the knowledge graph.

[0009] Furthermore, in the knowledge graph construction and dynamic update step, the relationship type and strength between entities are determined through comprehensive analysis and logical reasoning of the data. The entity relationship strength calculation formula is: ,in, Representing an entity and entities The relationship strength between the two is between 0 and 1, and the larger the value, the stronger the relationship. It is the relationship strength adjustment coefficient, which ranges from 0 to 1 and is used to adjust the calculation results of relationship strength according to actual conditions. Representing an entity and entities The frequency of association between them, that is, the number of times the two appear at the same time in the data or the number of times related events occur, Representation and Entity The number of all entities associated.

[0010] Furthermore, in the intelligent matching and personalized recommendation steps, the key features of disabled persons and positions are extracted, and the degree of matching between the two is compared and calculated. Specifically, the key features of disabled persons and positions are extracted from the processed consultation records and knowledge graphs. For disabled persons, their disability type, skill level and employment intention characteristics are extracted. For positions, their job requirements and work environment adaptability characteristics are extracted. The characteristics of disabled persons are compared and calculated with the characteristics of positions, and the degree of matching between the two is evaluated. The matching score between each disabled person and each position is obtained, and the positions are sorted according to the matching score to generate a personalized job recommendation list, giving priority to recommending positions with a high degree of matching. Based on the updated knowledge graph, new employment information, skill improvement suggestions and relevant policy knowledge are pushed to consultants and disabled persons, and personalized knowledge push is performed according to their historical behavior and preferences.

[0011] Furthermore, in the intelligent matching and personalized recommendation step, the characteristics of the disabled person and the characteristics of the position are compared and calculated to evaluate the degree of match between the two. The matching degree calculation formula is: ,in, It indicates the matching score between disabled people and jobs. The higher the score, the better the matching degree. It is the matching adjustment coefficient, which ranges from 0 to 1 and is used to adjust the matching calculation results according to the actual situation. Indicates the number of characteristics of people with disabilities, It is The weight of each disabled person's feature reflects the importance of the feature in the matching process. It is The similarity score between the characteristics of disabled people and job requirements ranges from 0 to 1 and is obtained by comparing the characteristics of disabled people with the job requirements. Indicates the number of job characteristics, Indicates the The weight of each job feature reflects the importance of the feature in the matching process. It is The score of the fit between the job characteristics and the abilities of people with disabilities ranges from 0 to 1 and is obtained by comparing the job characteristics with the abilities of people with disabilities.

[0012] Furthermore, in the steps of implementing the intelligent consulting service, semantic understanding is performed on the consulting questions of persons with disabilities to extract key information. Specifically, when a person with disabilities raises a consulting question, the question is semantically analyzed and understood, and the key information and intention of the question are extracted. By comprehensively considering the context and background information of the question, the consulting needs of the person with disabilities are grasped. Based on the key information of the question, a rapid search is performed in the knowledge graph of disability employment to find relevant entities and relationships. The associated information of the knowledge graph is used to obtain comprehensive knowledge related to the question, and the retrieved knowledge is integrated and reasoned to generate accurate answers. In combination with the specific circumstances of the person with disabilities, personalized employment guidance suggestions are provided, and a knowledge assistance interface is provided for the consultant. When the consultant provides consulting services, the knowledge information related to the consulting question is displayed in real time.

[0013] Furthermore, in the tracking feedback and system optimization steps, feedback from people with disabilities and consultants is collected to evaluate the effectiveness of consulting services. The evaluation formula is: ,in, It indicates the evaluation score of the consulting service effect. The higher the score, the better the service effect. It is the service effect evaluation adjustment coefficient, with a value range of 0 to 1, and is used to adjust the calculation results of the evaluation score according to the actual situation. Indicates the number of indicators in the tracking record used to evaluate service performance. It is The weight of an evaluation indicator reflects the importance of the indicator in the evaluation of service effect. It is The score of each evaluation indicator ranges from 0 to 1 and is calculated by analyzing the tracking record data. Indicates the number of indicators used in feedback to evaluate service effectiveness. It is The weight of each feedback indicator reflects the importance of the indicator in the service effect evaluation. It is The score of each feedback indicator ranges from 0 to 1 and is obtained by analyzing and quantifying the feedback.

[0014] On the other hand, an online employment consultation record processing system for disability assistance services includes the following components: Data collection and preprocessing module: Responsible for receiving employment consultation information submitted by people with disabilities through the online platform, collecting enterprise job demand data and other relevant information about people with disabilities, storing this information in the database, and performing preprocessing, semantic mining and integration on it; Knowledge graph construction and update module: This module performs entity recognition, relationship extraction, and graph construction on pre-processed data to form a knowledge graph on disability employment. It also monitors dynamic changes in the disability employment field in real time, automatically integrates new information into the knowledge graph, and regularly reviews and updates the information in the knowledge graph. Intelligent matching and recommendation module: Based on the knowledge graph and processed consultation records, it extracts the characteristics of people with disabilities and positions, performs matching calculations and sorts the results, generates a personalized job recommendation list, and pushes new knowledge, new positions and other information to consultants and people with disabilities based on the updates of the knowledge graph; Intelligent consulting service module: This module uses the knowledge graph of disability employment to implement intelligent question-and-answer functions, understands, searches, and generates answers to consultation questions from people with disabilities, provides employment guidance and suggestions for people with disabilities, and provides a knowledge-assisted interface for consultants, supporting data interaction with other modules, thereby improving the professionalism and accuracy of consulting services. Tracking, feedback and optimization module: Track and manage the follow-up of consultation records, collect feedback from people with disabilities and consultants, evaluate the effectiveness of consultation services, feed back the evaluation results to system administrators and developers, and use feedback information to update and optimize the knowledge graph and intelligent matching and recommendation modules.

[0015] Compared with the existing technology, the online employment consultation record processing method and system for disability assistance services have the following beneficial effects: 1. The present invention constructs a knowledge graph of employment for people with disabilities and monitors and updates it in real time, so that the knowledge graph can accurately reflect the dynamic changes in the employment market. The intelligent matching and personalized recommendation module uses the processed data and knowledge graph to accurately extract the key features of people with disabilities and positions, perform matching calculations and sort the results, generate a highly personalized job recommendation list, and push new employment information, skill improvement suggestions, relevant policies and other knowledge, so that the employment services provided to people with disabilities can closely fit their own conditions and needs, greatly improving the accuracy and personalization of employment services, helping people with disabilities to find suitable jobs more efficiently and increase employment opportunities.

[0016] 2. The present invention updates and improves the knowledge graph through a perfect tracking feedback and system optimization mechanism, and adjusts and optimizes the strategies and rules of the intelligent matching and recommendation modules, so that the system can continuously adapt to changes in actual conditions, continuously improve its own performance and service quality, provide people with disabilities with better quality and more efficient employment consulting services, and promote the continuous development of employment services for people with disabilities.

[0017] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0019] Figure 1 A flowchart of a method for processing online employment consultation records for disability assistance services; Figure 2 This is a structural diagram of an online employment consultation record processing system for disability assistance services. DETAILED DESCRIPTION

[0020] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0021] Example 1 A disabled person with a second-level physical disability submitted an employment consultation through the web page and mobile APP of an online platform. The system comprehensively collected the basic information of the disabled person, including age, contact information, residential address, education level, etc. The disability was recorded in detail as a second-level physical disability, with limited mobility mainly reflected in walking and fine hand movements. The employment intention was clearly stated to be e-commerce related work, and the expectation was a relatively flexible working environment that could adapt to one's physical condition. The disabled person himself mastered basic computer operation skills, such as proficiency in using the Windows operating system, file management, etc., as well as simple image processing skills. The specific consultation question was "What e-commerce positions can a person with a second-level physical disability engage in, and the work intensity will not greatly affect the physical condition?"

[0022] At the same time, the system collects job requirements released by an e-commerce company from multiple channels, covering multiple positions such as e-commerce customer service, e-commerce graphic designer, e-commerce operation assistant and their corresponding job requirements. Among them, the e-commerce customer service position requires good communication skills (mainly through text communication) and familiarity with the customer service process of the e-commerce platform. The e-commerce graphic designer position requires proficiency in image processing software and certain creative design capabilities. The e-commerce operation assistant position requires understanding the basic links of e-commerce operations and being able to assist in completing data statistics and analysis.

[0023] After the collection was completed, the system carefully cleaned the data, corrected the errors in the educational information filled in by the disabled person, supplemented some of the content related to computer operation in his work experience, standardized his skill description, and standardized "proficient in using office software" to "proficient in the basic operations of Microsoft Office office software (Word, Excel, PowerPoint), and able to edit documents, process data and make presentations."

[0024] Using semantic enhancement technology, the formula Conduct semantic information mining, where represents the comprehensive score of the mined implicit semantic information, The semantic information adjustment coefficient is set by the system administrator based on the data quality assessment. , is the number of words or phrases used for semantic analysis in the consultation record, which is 30 after word segmentation. It is The weight of a word or phrase is obtained by training the TF-IDF algorithm on a large number of e-commerce employment consultation records. The weight of the word "telecommuting" is obtained by , It is The semantic feature value of a word or phrase is analyzed using the Word2Vec model. The semantic feature value of "telework" , deeply digging out the potential implicit information of the disabled person such as preference for remote working positions and demand for flexible working hours, and deeply integrating this information with other data to provide a richer and more accurate data foundation for subsequent processing.

[0025] The system accurately identifies entities such as "Level 2 Physical Disability", "E-commerce Customer Service", and "E-commerce Artist" from the pre-processed data. Through comprehensive analysis and logical reasoning of the data, the system uses the formula The analysis results in the entity relationship strength, where Representing an entity and entities The strength of the relationship between is the relationship strength adjustment coefficient, which is set by domain experts based on experience. , Representing an entity and entities The correlation frequency between "Level 2 Physical Disability" and "E-commerce Artist" is statistically analyzed. For 20 times, Representation and Entity The number of all associated entities, the number of entities associated with "Physical Disability Level 2" The value is 5, and it is concluded that there is an adaptation relationship between "Level 2 physical disability" and "e-commerce customer service" and "e-commerce graphic designer". The position of "e-commerce graphic designer" requires "image processing skills", and the position of "e-commerce operation assistant" requires "computer operation skills" and certain data analysis capabilities, thus constructing a detailed and accurate knowledge map of disability employment.

[0026] With the recent rapid development of the e-commerce industry, a new position has emerged: "E-commerce Live Broadcast Assistant (remote assistance available, mainly responsible for live broadcast backend operations and data monitoring, with moderate work intensity)". The system captures this information in a timely manner through multiple channels such as real-time monitoring of industry dynamics and information interaction with enterprises. It automatically integrates relevant information, including the specific responsibilities of the position, skill requirements, and adaptability to people with disabilities, into the knowledge graph to ensure that the knowledge graph can reflect the latest changes and employment opportunities in the industry in real time.

[0027] The system comprehensively extracts the characteristics of the disabled person, such as disability type, skill level, employment intention, special needs for work environment and intensity, as well as detailed characteristics of each position, including job content, skill requirements, work schedule, work intensity, etc., and uses the formula Calculate the degree of matching, where Indicates the matching score between people with disabilities and jobs. The matching adjustment coefficient is set by the system based on the current employment market situation. , is the number of characteristics of people with disabilities, which is 6 after sorting and counting. It is The weight of the disabled person feature, the weight of the “image processing skills” feature After training with logistic regression algorithm, It is The similarity scores between the characteristics of people with disabilities and job requirements, and the similarity scores between "image processing skills" and "e-commerce graphic designer" job requirements The cosine similarity algorithm is calculated to be 0.8. is the number of job characteristics, which is 7. It is The weight of the job characteristics, the weight of the "creative design ability" job characteristics , It is The fit score between the job characteristics and the abilities of the disabled person, and the fit score between "creative design ability" and the abilities of the disabled person The value is 0.4. Calculation shows that the skills and intentions of this disabled person are highly matched with the position of "e-commerce artist". The newly emerging position of "e-commerce live broadcast assistant" is also recommended to him. A personalized job recommendation list is generated according to the degree of match, detailing the advantages and possible challenges of each position. Based on the updated knowledge graph, the system pushes new skill improvement suggestions in the e-commerce industry to this disabled person according to his interests and needs. At the same time, these new job information and skill improvement suggestions are also pushed to the consultant responsible for consulting this disabled person, so that the consultant can better provide him with guidance and help.

[0028] The disabled person participated in an interview for the position of "e-commerce graphic designer". The system recorded in detail the interview time, company name, main questions during the interview and the performance of the disabled person. After the interview, the disabled person reported that the company valued dynamic graphics production skills and creative design capabilities more, which he lacked. At the same time, the working schedule provided by the company was relatively tight, which may cause great pressure on his physical condition.

[0029] Based on the feedback, the system uses the formula Comprehensively evaluate the effectiveness of this service, including: Indicates the evaluation score of the consulting service effect, The service effect evaluation adjustment coefficient is set to 0.6 based on the current service target. is the number of indicators used to evaluate service effectiveness in the tracking record, which is 5. It is The weight of the evaluation indicator, the weight of the "skill matching" evaluation indicator Training is 0.4, It is The score of the evaluation indicator "Skill Matching" , is the number of indicators used to evaluate service effectiveness in feedback, which is 4. It is The weight of the feedback indicator "reasonableness of work time arrangement" , It is The score of the feedback indicator "reasonableness of work time arrangement" The value is 0.4. The evaluation results show that there are deficiencies in skill matching, and the consideration of work intensity and time arrangement is not comprehensive enough. The feedback information will be promptly fed back to system administrators and developers, and the skill requirements for the "e-commerce artist" position in the knowledge graph will be updated, with more emphasis on dynamic image production skills and creative design capabilities, and supplemented with relevant information on the impact of working hours and intensity on people with physical disabilities. At the same time, the intelligent matching and recommendation module will be optimized. When recommending positions for people with similar disabilities, more attention will be paid to the precise matching of skills, as well as the comprehensive consideration of work intensity and time arrangement, to improve the accuracy and rationality of matching and better meet the actual needs of people with disabilities.

[0030] Example 2 A person with a first-level hearing disability submitted an employment consultation through various channels of the online platform, such as filling out online forms and leaving voice-to-text messages. The system collected their basic information in detail, including age, gender, ID number, and residential area. Their disability was clearly recorded as a first-level hearing disability, with almost complete loss of hearing and reliance on sign language or text to communicate. They firmly expressed their desire to engage in data entry-related work, believing that the job had relatively low hearing requirements and that they had a certain ability to concentrate. The skill level of this person with a disability was reflected in their proficiency in office software, such as the ability to quickly and accurately enter text in Word and use Excel for simple data organization and formula calculations. The specific consultation question was "What employment policy supports are there for people with hearing disabilities engaged in data entry work, and how are these policies implemented in their region?"

[0031] At the same time, the system collects data entry job requirements from multiple companies through data docking with government employment departments, corporate human resources management systems, and network information crawling. The information includes specific job responsibilities, skill requirements, salary and benefits, etc. It also obtains relevant employment policy information, such as the national-level employment security fund policy for people with disabilities, and subsidy policies and tax incentives for people with disabilities issued by local governments.

[0032] The system thoroughly cleans the collected data, corrects the incorrect address description in the residential area information filled in by the disabled person, supplements the computer-related courses learned in his educational background, standardizes his skill description, and further refines "proficient in the use of office software" to "proficient in the advanced operations of Microsoft Office office software (Word, Excel, PowerPoint), and can efficiently complete large amounts of text entry, complex data processing and presentation production." Using semantic enhancement technology, it deeply explores implicit information such as the disabled person's concern for employment stability and potential needs for specific procedures and safeguards for policy implementation. This information is closely integrated with other data to provide a richer and more accurate data foundation for subsequent analysis and services.

[0033] The system accurately identifies entities such as "first-level hearing disability", "data entry", "office software operation skills", "employment policy", "employment security fund for people with disabilities", "local subsidy policy", "tax preferential policy", "employment stability", and "policy implementation process" from the pre-processed data. Through in-depth analysis and logical reasoning of the data, it clearly concludes that "first-level hearing disability" is suitable for the "data entry" position (this position mainly relies on vision and manual operation, and has low hearing requirements), the "data entry" position requires "office software operation skills", the "employment security fund for people with disabilities" policy is applicable to enterprises that employ "first-level hearing disability" to engage in "data entry" work, "local subsidy policy" and "tax preferential policy" support people with "first-level hearing disability" to engage in "data entry" work, etc., thereby constructing a detailed, accurate and richly correlated knowledge map of disability employment.

[0034] When a new tax preferential policy for data entry for people with disabilities is introduced, the system monitors the government's official website, policy release platform and other information channels in real time to obtain the specific content, scope of application, implementation time and other information of the policy in a timely manner. At the same time, it interacts with local relevant departments to understand the specific implementation details and differences of the policy in different regions. The system automatically integrates this new information into the knowledge graph, updates the attributes and relationships of related entities, and ensures that the knowledge graph is always kept up to date and can accurately reflect the dynamic changes in employment policies.

[0035] The system comprehensively extracts characteristics such as the disabled person's disability type, skill level, employment intention, demand for employment stability, and concern for policy implementation, as well as detailed characteristics of each position, including job content, skill requirements, salary and benefits, employment stability, etc., as well as specific terms, scope of application, and application conditions of the employment policy. Through a complex matching degree calculation algorithm, it comprehensively considers multiple factors, such as skill matching, disability type adaptability, employment intention fit, policy applicability, etc., and recommends data entry positions in multiple companies to the disabled person, and lists in detail the policy-related benefits and advantages of each position, such as which companies can enjoy more tax incentives and thus may provide more stable salary benefits.

[0036] Based on the updated knowledge graph, the system pushes detailed interpretations of the new tax preferential policies, application process guidelines, and data entry skills improvement training resources (such as professional data entry skills training courses, office software advanced function training, etc.) to the disabled person and the consultant in charge according to the specific situation and needs of the disabled person. The push content also includes answers to frequently asked questions and precautions during the policy implementation process, helping the disabled person to better understand and utilize the policy and improve their employment competitiveness.

[0037] The disabled person inquired about the specific content of the newly introduced tax preferential policies, the application process, and the actual implementation in the area where he lives. The system conducted an in-depth semantic understanding of the question, accurately extracted key information and intentions, comprehensively searched for relevant knowledge in the knowledge graph, and generated detailed and accurate answers after integrated reasoning. At the same time, when the consultant provides services to the disabled person, the system displays the disabled person's relevant matching job information, skill improvement suggestions, employment policy knowledge, and policy implementation characteristics of the region in real time. It assists the consultant to provide more comprehensive and professional guidance to the disabled person from a professional perspective and in combination with actual conditions, such as helping him prepare application materials and analyze the policy advantages of different positions.

[0038] The disabled person successfully joined a company as a data entry worker. The system continuously tracks his employment status, including the time of joining, company name, specific distribution of work content, implementation of salary and benefits, etc. After working for a period of time, the disabled person reported that in the process of applying for tax preferential policies, the requirements of some application materials were different from the information provided by the system, and there were some unclear points in the company's implementation of the policy. At the same time, he hopes to further improve the efficiency and accuracy of data entry in his work.

[0039] The system collects the feedback and comprehensively evaluates the effectiveness of this service. It finds that there are problems with the accuracy of the policy information in the knowledge graph, the understanding of the actual implementation of the policy in the enterprise is not in-depth enough, and the skills improvement suggestions can be more personalized. The feedback information is promptly fed back to the relevant personnel, the policy information in the knowledge graph is updated and improved, and the detailed requirements for application materials and the specific specifications for the implementation of the enterprise policy are supplemented. At the same time, in cooperation with professional training institutions, more targeted skills improvement plans are provided according to the actual needs and work situation of the disabled person, and these plans are incorporated into the knowledge graph. In addition, the intelligent consulting service module is optimized, the accuracy and comprehensiveness of the answers to questions are improved, and the tracking and feedback mechanism of policy implementation is strengthened to ensure that the actual needs of the disabled person can be better met and the service quality and effectiveness of the system are improved.

[0040] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for processing online employment consultation records for disability assistance services, characterized in that: The method comprises the following specific steps: Multi-dimensional data collection and pre-processing: Through the online platform, comprehensive employment consultation information for people with disabilities, corporate job requirements, and related information are collected and pre-processed. Semantic enhancement technology is used to mine and integrate the implicit semantic information in the consultation records; Knowledge graph construction and dynamic updating: Identify various entities and their relationships from the received data to construct a knowledge graph on disability employment, monitor new developments in the employment field in real time and incorporate them into the graph, and regularly review and update the graph information; Intelligent matching and personalized recommendations: This system extracts key characteristics of individuals with disabilities and job positions, compares and calculates the degree of match between the two, integrates multiple factors to derive matching scores and sorts them, and generates a personalized job recommendation list. Furthermore, based on the updated knowledge graph, it pushes new knowledge and job information based on user behavior preferences. Intelligent consulting service implementation: semantic understanding of consultation questions from people with disabilities extracts key information, retrieves relevant knowledge from the knowledge graph, integrates reasoning to generate accurate and personalized career guidance answers, and provides real-time knowledge assistance to consultants; Tracking feedback and system optimization: Track the follow-up process of employment consultation for people with disabilities and record key node information, collect feedback from people with disabilities and consultants, evaluate the effectiveness of consulting services, and feedback the results to relevant personnel, so as to update and improve the knowledge graph and adjust and optimize the matching recommendation strategy.

2. The online employment consultation record processing method for disability assistance services according to claim 1 is characterized in that: In the multivariate data collection and preprocessing steps, semantic enhancement technology is used to mine and fuse the implicit semantic information of consultation records. The semantic information mining formula is: ,in, It represents the comprehensive score of the mined implicit semantic information, which is used to measure the importance and potential value of semantic information. It is the semantic information adjustment coefficient, which ranges from 0 to 1 and is used to adjust the mining results of semantic information according to the actual situation. Indicates the number of words or phrases used for semantic analysis in the consultation record. It is The weight of a word or phrase, It is The semantic feature value of a word or phrase is used to represent the characteristics of the semantic information carried by the word or phrase.

3. The online employment consultation record processing method for disability assistance services according to claim 1 is characterized in that: In the knowledge graph construction and dynamic update step, various entities and their relationships are identified from the received data to construct a knowledge graph for disability employment. Specifically, various entities are identified from the preprocessed data, including disability types, job positions, skill requirements, training resources, and employment policies. By analyzing and extracting text information in the data, the key features and attributes of each entity are determined, and the association relationships between entities are analyzed. Through comprehensive analysis and logical reasoning of the data, the relationship type and strength between entities are determined. The identified entities and extracted relationships are organized and stored in the form of a graph to construct a knowledge graph for disability employment. A real-time monitoring mechanism for dynamic information in the field of disability employment is established. New job positions, changes in skill requirements, online training resources, and employment policy adjustments are obtained through multiple information channels. When new information is monitored, it is automatically integrated into the knowledge graph, and the attributes and relationships of the entities are updated. The information in the knowledge graph is regularly reviewed and outdated or erroneous information is deleted to ensure the accuracy and timeliness of the knowledge graph.

4. The online employment consultation record processing method for disability assistance services according to claim 3 is characterized in that: In the knowledge graph construction and dynamic update steps, the relationship type and strength between entities are determined through comprehensive analysis and logical reasoning of the data. The entity relationship strength calculation formula is: ,in, Representing an entity and entities The relationship strength between the two is between 0 and 1, and the larger the value, the stronger the relationship. It is the relationship strength adjustment coefficient, which ranges from 0 to 1 and is used to adjust the calculation results of relationship strength according to actual conditions. Representing an entity and entities The frequency of association between them, that is, the number of times the two appear at the same time in the data or the number of times related events occur, Representation and Entity The number of all entities associated.

5. The online employment consultation record processing method for disability assistance services according to claim 1 is characterized in that: In the intelligent matching and personalized recommendation steps, key features of disabled persons and positions are extracted, and the degree of matching between the two is compared and calculated. Specifically, key features of disabled persons and positions are extracted from the processed consultation records and knowledge graphs. For disabled persons, their disability type, skill level and employment intention characteristics are extracted. For positions, their job requirements and work environment adaptability characteristics are extracted. The characteristics of disabled persons are compared and calculated with the characteristics of positions, and the degree of matching between the two is evaluated. The matching score between each disabled person and each position is obtained, and the positions are sorted according to the matching score to generate a personalized job recommendation list, with positions with high matching degrees being recommended first. Based on the updated knowledge graph, new employment information, skill improvement suggestions and relevant policy knowledge are pushed to consultants and disabled persons, and personalized knowledge push is performed based on their historical behavior and preferences.

6. The online employment consultation record processing method for disability assistance services according to claim 1 is characterized in that: In the intelligent matching and personalized recommendation steps, the characteristics of the disabled person and the characteristics of the position are compared and calculated to evaluate the degree of match between the two. The matching degree calculation formula is: ,in, It indicates the matching score between disabled people and jobs. The higher the score, the better the matching degree. It is the matching adjustment coefficient, which ranges from 0 to 1 and is used to adjust the matching calculation results according to the actual situation. Indicates the number of characteristics of people with disabilities, It is The weight of each disabled person's feature reflects the importance of the feature in the matching process. It is The similarity score between the characteristics of disabled people and job requirements ranges from 0 to 1 and is obtained by comparing the characteristics of disabled people with the job requirements. Indicates the number of job characteristics, Indicates the The weight of each job feature reflects the importance of the feature in the matching process. It is The score of the fit between the job characteristics and the abilities of people with disabilities ranges from 0 to 1 and is obtained by comparing the job characteristics with the abilities of people with disabilities.

7. The online employment consultation record processing method for disability assistance services according to claim 1 is characterized in that: In the steps of implementing the intelligent consulting service, semantic understanding is performed on the consulting questions of the disabled persons to extract key information. Specifically, when the disabled persons raise consulting questions, the questions are semantically analyzed and understood to extract the key information and intention of the questions. By comprehensively considering the context and background information of the questions, the consulting needs of the disabled persons are grasped. According to the key information of the questions, a rapid search is performed in the knowledge graph of disability employment to find relevant entities and relationships. The associated information of the knowledge graph is used to obtain comprehensive knowledge related to the questions, and the retrieved knowledge is integrated and reasoned to generate accurate answers. In combination with the specific circumstances of the disabled persons, personalized employment guidance suggestions are provided, and a knowledge assistance interface is provided for consultants. When consultants provide consulting services, knowledge information related to the consulting questions is displayed in real time.

8. The method for processing online employment consultation records for disability assistance services according to claim 1, characterized in that: In the tracking feedback and system optimization steps, feedback from people with disabilities and consultants is collected to evaluate the effectiveness of consulting services. The evaluation formula is: ,in, It indicates the evaluation score of the consulting service effect. The higher the score, the better the service effect. It is the service effect evaluation adjustment coefficient, with a value range of 0 to 1, and is used to adjust the calculation results of the evaluation score according to the actual situation. Indicates the number of indicators in the tracking record used to evaluate service performance. It is The weight of an evaluation indicator reflects the importance of the indicator in the evaluation of service effect. It is The score of each evaluation indicator ranges from 0 to 1 and is calculated by analyzing the tracking record data. Indicates the number of indicators used in feedback to evaluate service effectiveness. It is The weight of each feedback indicator reflects the importance of the indicator in the service effect evaluation. It is The score of each feedback indicator ranges from 0 to 1 and is obtained by analyzing and quantifying the feedback.

9. An online employment consultation record processing system for disability assistance services, the system being applicable to an online employment consultation record processing method for disability assistance services according to any one of claims 1 to 8, characterized in that: The system includes the following components: Data collection and preprocessing module: Responsible for receiving employment consultation information submitted by people with disabilities through the online platform, collecting enterprise job demand data and other relevant information about people with disabilities, storing this information in the database, and performing preprocessing, semantic mining and integration on it; Knowledge graph construction and update module: This module performs entity recognition, relationship extraction, and graph construction on pre-processed data to form a knowledge graph on disability employment. It also monitors dynamic changes in the disability employment field in real time, automatically integrates new information into the knowledge graph, and regularly reviews and updates the information in the knowledge graph. Intelligent matching and recommendation module: Based on the knowledge graph and processed consultation records, it extracts the characteristics of people with disabilities and positions, performs matching calculations and sorts the results, generates a personalized job recommendation list, and pushes new knowledge, new positions and other information to consultants and people with disabilities based on the updates of the knowledge graph; Intelligent consulting service module: This module uses the knowledge graph of disability employment to implement intelligent question-and-answer functions, understands, searches, and generates answers to consultation questions from people with disabilities, provides employment guidance and suggestions for people with disabilities, and provides a knowledge-assisted interface for consultants, supporting data interaction with other modules, thereby improving the professionalism and accuracy of consulting services. Tracking, feedback and optimization module: Track and manage the follow-up of consultation records, collect feedback from people with disabilities and consultants, evaluate the effectiveness of consultation services, feed back the evaluation results to system administrators and developers, and use feedback information to update and optimize the knowledge graph and intelligent matching and recommendation modules.

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