Employee welfare consultation method and system based on intelligent customer service

Abstract

The invention relates to the field of artificial intelligence, and discloses an employee welfare consultation method and system based on intelligent customer service, and the method comprises the steps: firstly obtaining welfare consultation content inputted by an employee, extracting a query element, analyzing a threshold value, and recognizing an information type; retrieving associated entries, analyzing the request queue, extracting response features and calculating an associated response index; then, staff access data is collected, an access intention scene is determined, load data is extracted, and a consultation load value is calculated; generating and verifying a reply template, and analyzing and outputting; and finally, generating a push time sequence and a response instruction, and outputting a welfare consultation result. According to the invention, the welfare consultation experience degree of each enterprise employee can be improved.

Description

Technical Field

[0001] This invention relates to an employee welfare consultation method and system based on intelligent customer service, belonging to the field of artificial intelligence. Background Technology

[0002] Employee benefits consultation is an indispensable part of corporate human resource management, covering a variety of information such as salary subsidies, leave policies, and insurance coverage. It is usually supported by the human resources department through human customer service or static question and answer databases.

[0003] Currently, most companies still rely on traditional methods, such as telephone inquiries, email replies, or pre-set FAQ pages, for answering employee welfare questions. These methods are inefficient, have delayed information updates, and struggle to handle peak inquiries, resulting in long employee wait times, inconsistent responses, and insufficient personalized support. This is especially true in large enterprises with complex and diverse welfare policies. Traditional methods cannot dynamically respond and allocate resources based on real-time inquiry load and employee specific query intentions, easily leading to service congestion and a degraded experience. Therefore, an intelligent customer service-based employee welfare consultation method is needed to improve the welfare consultation experience for every employee in every company. Summary of the Invention

[0004] This invention provides a method and system for employee welfare consultation based on intelligent customer service, the main purpose of which is to improve the welfare consultation experience of each enterprise's employees.

[0005] To achieve the above objectives, the present invention provides an employee benefits consultation method based on intelligent customer service, comprising: Obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold; Based on the welfare information type, retrieve information-related entries from the preset welfare information database, analyze the consultation request queues corresponding to the information-related entries, extract queue response features from the consultation request queues, and calculate the association response index corresponding to the information-related entries based on the queue response features. Based on the correlation response index, collect employee access data corresponding to the employee to be consulted, query the access intent scenario of the employee access data, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform. Based on the consultation load value, a consultation response template in the preset welfare consultation platform is generated. The consultation response template is verified to obtain the verified response content. The content output stage corresponding to the verified response content is analyzed. Generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

[0006] Optionally, identifying the welfare information type corresponding to the content query element based on the consultation concurrency threshold includes: Analyze the request concurrency patterns within the consultation concurrency threshold; Based on the request concurrency mode, identify the hot query dimensions among the content query elements; Map the welfare classification system corresponding to the aforementioned hot query dimensions; Extract the dominant information features from the welfare classification system; Based on the dominant information features, the welfare information type corresponding to the content query element is identified.

[0007] Optionally, identifying hot query dimensions among the content query elements based on the request concurrency mode includes: Analyze the concurrent request traffic under the aforementioned concurrent request mode; Extract the characteristic peak data from the concurrent request traffic; Based on the aforementioned peak feature data, match the set of query entries in the content query elements; Label the hot search tags in the query item set; Based on the hotspot query tags, identify the hotspot query dimensions in the content query elements.

[0008] Optionally, the step of retrieving information-related entries from a preset welfare information database based on the welfare information type includes: Parse the information category code corresponding to the welfare information type; Analyze the library retrieval identifier corresponding to the information category code; Based on the database retrieval identifier, query the potential item set in the preset welfare information database; Calculate the entry association value corresponding to each entry in the potential entry set; Based on the aforementioned entry association values, information association entries in a pre-defined welfare information database are retrieved.

[0009] Optionally, parsing the information category code corresponding to the welfare information type includes: Extract the type identifier field corresponding to the welfare information type; Based on the type identifier field, match the set of rule entries in the preset encoding rule base; Verify the valid rule information in the rule information set; Reconstruct the information encoding process corresponding to the effective rule information; Based on the aforementioned information encoding process, the information category code corresponding to the welfare information type is parsed.

[0010] Optionally, calculating the association response index corresponding to the information association item based on the queue response characteristics includes: Analyze the feature association sequence corresponding to the queue response features; The feature-related sequences are quantized to obtain the quantization correlation index; Query the correlation response threshold corresponding to the quantitative correlation index; Analyze the correlation load factor corresponding to the correlation response threshold; Based on the aforementioned association load factor, the association response index corresponding to the information association item is calculated using the following formula: ; in, This represents the association response index corresponding to the information association item. This indicates the number of dimensions corresponding to the consultation dimension of the information association entry. Index representing the number of consultation dimensions. Indicates the first The dimension weights corresponding to each consultation dimension Indicates the first The corresponding correlation response threshold for each consultation dimension Indicates the first Correlation load factors corresponding to each consultation dimension

[0011] Optionally, extracting the scenario load data from the access intent scenario includes: Identify the scene access sequence in the access intent scenario; Analyze the concurrent request volume corresponding to the access sequence of the scenario; Query the concurrency baseline value in the aforementioned concurrent request volume; Analyze the resource load index corresponding to the concurrency benchmark value; Based on the resource load index, extract the scenario load data in the access intent scenario; The consultation load value of the scenario load data on the preset welfare consultation platform is calculated using the following formula: ; in, This indicates the consultation load value of the scenario load data in the preset welfare consultation platform. This indicates the current resource load index in the preset welfare consultation platform. This indicates the total number of time series intervals corresponding to the scenario load data. Index representing the number of time series intervals. Indicates the first Concurrent request volume within a time series interval This represents the concurrency baseline value.

[0012] Optionally, generating a pre-defined consultation response template for the welfare consultation platform based on the consultation load value includes: Based on the consultation load value, analyze the platform load status corresponding to the preset welfare consultation platform; Analyze the status classification indicators corresponding to the platform load status; Determine the hierarchical presentation content corresponding to the state hierarchical index; Query the content structure details of the hierarchical presentation content; Based on the content structure details, a consultation response template for a preset welfare consultation platform is generated.

[0013] Optionally, the step of verifying the consultation response template to obtain the verified response content includes: Identify the response information points in the consultation response template; Iterate through the welfare information responses covered by the aforementioned response information points; Verify that the welfare information response corresponds to the relevant regulations and policy objectives; Based on the policy objectives of the aforementioned regulations, the core response units in the welfare information response are verified; Based on the core response unit, the consultation response template is validated to obtain the validated response content.

[0014] To address the above problems, the present invention also provides an employee welfare consultation system based on intelligent customer service, the system comprising: The type recognition module is used to obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold. The index calculation module is used to retrieve information association entries in a preset welfare information database based on the welfare information type, analyze the consultation request queues corresponding to the information association entries, extract queue response features from the consultation request queues, and calculate the association response index corresponding to the information association entries based on the queue response features. The load value calculation module is used to collect employee access data corresponding to the employee to be consulted based on the correlation response index, query the access intent scenario in which the employee access data is located, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform. The phase analysis module is used to generate a consultation response template in the preset welfare consultation platform based on the consultation load value, perform response verification on the consultation response template, obtain the verified response content, and analyze the content output phase corresponding to the verified response content. The result output module is used to generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

[0015] Compared to the problems described in the background technology, this invention, by acquiring the welfare consultation content input by the employee seeking consultation and extracting the content query elements from that content, can quickly focus on the core needs of the employee's consultation, avoiding repeated communication due to ambiguous information. This lays the foundation for accurate matching of welfare information subsequently, ensuring the accuracy and efficiency of the consultation service. Based on the welfare information type, this invention retrieves information-related entries from a preset welfare information database, enabling the system to accurately locate welfare content directly related to the employee's consultation needs, avoiding blind searching through massive amounts of information, significantly improving the efficiency and accuracy of information matching, and promoting the efficient and orderly progress of the overall consultation process. Furthermore, based on the correlation response index, this invention collects employee access data corresponding to the employee seeking consultation and queries the access intent scenario of the employee's access data, making data collection more targeted and ensuring that the acquired access data is accurate and relevant. The data is highly correlated with consultation needs; it can accurately pinpoint the specific intent scenarios of employees in welfare consultations, providing a precise basis for subsequent extraction of scenario load data and calculation of consultation load values. Furthermore, based on the consultation load values, this invention generates preset consultation response templates in the welfare consultation platform, which can accurately match response resources according to the load situation, avoiding more consultations caused by non-standard responses during high loads; it can also predict consultation hotspots through load data, optimize template content in advance, making responses more targeted, and enhancing the overall stability of the platform service. Finally, by generating the content push sequence corresponding to the content output stage and constructing the output response instructions corresponding to the content push sequence, this invention can ensure that the verified response content is pushed accurately at a reasonable time rhythm, avoiding information piling up or push delays, ensuring that employees efficiently obtain key information, and improving the automation and accuracy of the overall service. Therefore, the employee welfare consultation method and system based on intelligent customer service provided by this invention can improve the welfare consultation experience of employees in every enterprise. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an employee benefits consultation method based on intelligent customer service, provided as an embodiment of the present invention. Figure 2 This is a schematic diagram of the core logic framework of an employee welfare consultation method based on intelligent customer service, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of a module for implementing an employee welfare consultation system based on intelligent customer service, provided as an embodiment of the present invention.

[0017] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an employee benefits consultation method based on intelligent customer service. The executing entity of this intelligent customer service-based employee benefits consultation method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application embodiment: a server, a terminal, etc. In other words, the intelligent customer service-based employee benefits consultation method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating an employee benefits consultation method based on intelligent customer service, according to an embodiment of the present invention. In this embodiment, the employee benefits consultation method based on intelligent customer service includes: S1. Obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold.

[0021] This invention obtains the welfare consultation content input by the employee seeking consultation and extracts the content query elements from the welfare consultation content. It can quickly focus on the core needs of the employee's consultation, avoid repeated communication caused by ambiguous information, lay the foundation for accurate matching of welfare information in the future, and ensure the accuracy and efficiency of consultation services.

[0022] The "employees seeking consultation" refers to current employees within the company who have questions about benefits and proactively initiate consultation requests. This includes both new and existing employees, employees in different positions (such as technical and administrative roles), and employees at various levels. Their core need is to obtain benefit information related to their own rights to solve practical problems. For example, an employee who has been with the customer service department of an e-commerce company for six months, unfamiliar with the application materials and deadlines for maternity allowance, plans to initiate a consultation through the company's benefit consultation system. This employee qualifies as an employee seeking consultation, as their consultation stems from a need for information on the benefit application process, requiring the system to provide targeted answers. The "benefit consultation content" refers to the specific questions entered by the employees seeking consultation, in text, voice, or other forms, concerning company benefit policies, benefit details, application processes, and disbursement progress. These questions cover dimensions such as salary subsidies, leave rules, and insurance reimbursement, directly reflecting the employee's specific needs for benefit information. For example, a construction worker in a construction company might enter on the consultation platform, "My high-temperature allowance for the third quarter of 2024 should be 1200 yuan, but currently only..." The message, "Received 800 yuan, want to know the reason why the difference hasn't been paid and whether it should be paid in cash or transferred," falls under the category of welfare consultation. It clearly includes the type of welfare, the difference in amount, and the direction of the question. The content query elements refer to the key information extracted from the welfare consultation content that precisely addresses the core needs of the consultation, including welfare type, time frame, specific data, and focus of the question. These are the core basis for the system to match welfare information and generate responses, helping the system quickly pinpoint the key points of the consultation. For example, from a teacher's consultation at an educational institution stating, "My application for supplementary housing provident fund payment submitted in October 2024 has not yet been approved. I want to know the review progress and whether supplementary materials are required," the extracted elements are: welfare type (supplementary housing provident fund payment), time (October 2024), and core issues (review progress, whether supplementary materials are required), clearly defining the core of the consultation. Optionally, obtaining the welfare consultation content input by the employee seeking consultation can be achieved through online form collection methods, such as using Google. The Forms tool is used to design welfare consultation questionnaires, which employees fill out and submit online to obtain welfare consultation content. The content query elements extracted from the welfare consultation content can be achieved through natural language processing algorithms, such as using the BERT model to extract key information from the text and identify elements such as welfare types and conditions in employee consultations, thereby obtaining content query elements.

[0023] Furthermore, by analyzing the consultation concurrency thresholds corresponding to the query elements, this invention can accurately identify the real-time request popularity of different welfare consultation types, avoid system resource allocation imbalance caused by a concentrated influx of certain types of consultations, and ensure the overall stability of the platform operation; at the same time, it can allow the system to predict high concurrency risks in advance, and improve the efficiency and quality of the overall consultation service.

[0024] The consultation concurrency threshold refers to the maximum number of consultation requests that the system can stably process per unit time (usually minutes or hours) for different welfare consultation query elements (such as "salary subsidy inquiry" and "annual leave application inquiry") in a preset welfare consultation platform. It is a critical value set based on historical consultation data, system hardware capacity, and service quality standards. Once the real-time consultation volume exceeds this threshold, the system will trigger a dynamic adjustment mechanism (such as traffic diversion and capacity expansion) to avoid service degradation. For example, for the query element "monthly salary details inquiry", the consultation concurrency threshold is set to 500 times per hour based on past data. If the number of such requests reaches 520 times in a certain hour, the system will start temporary resource expansion to ensure that subsequent requests can still be responded to within 3 seconds. Optionally, the consultation concurrency threshold corresponding to the query element can be obtained by queuing theory modeling methods, such as using the M / M / c queue model to analyze the historical consultation request arrival rate and service processing capacity to obtain the consultation concurrency threshold.

[0025] Furthermore, based on the consultation concurrency threshold, the present invention identifies the welfare information type corresponding to the content query element, enabling the system to quickly distinguish the demand and processing priority of different welfare consultations, avoiding the delay in response to high-demand consultations due to the average allocation of resources, ensuring the service efficiency of core welfare consultations, and ensuring that the overall consultation service is more in line with the actual needs of employees.

[0026] The welfare information type refers to the final definition of the welfare category to which the content query element belongs based on the dominant information features. It is a specific welfare category in the welfare classification system, which can directly correspond to the welfare field consulted by the employee, and provide a clear direction for subsequent information retrieval and response generation. For example, from the employee's inquiry "inquire about the housing provident fund contribution details for the second half of 2024", after feature matching, the corresponding welfare information type is identified as "insurance protection - housing provident fund - contribution details inquiry", accurately locating the specific welfare sub-item.

[0027] As an embodiment of the present invention, the step of identifying the welfare information type corresponding to the content query element based on the consultation concurrency threshold includes: parsing the request concurrency pattern in the consultation concurrency threshold; identifying the hot query dimension in the content query element according to the request concurrency pattern; mapping the welfare classification system corresponding to the hot query dimension; extracting the dominant information features in the welfare classification system; and identifying the welfare information type corresponding to the content query element based on the dominant information features.

[0028] The request concurrency mode refers to the frequency, distribution pattern, and fluctuation characteristics of consultation requests corresponding to a certain type of content query element within a unit of time, within a set consultation concurrency threshold. This includes key characteristics such as peak request periods, peak intervals, and duration, reflecting the real-time traffic pattern of this type of consultation. For example, for the query element "monthly salary payment," the request concurrency mode often shows a surge in requests between 9-11 AM on the 5th-8th of each month, peaking at 420 requests per hour, while the average is only 60 requests per hour during other times, exhibiting a clear periodicity and temporal variation. The segmented and concentrated characteristics; the hot topic query dimension refers to the core information direction that employees are generally concerned about, extracted from the query item set by combining hot topic query tags. It is a dimensional summary of similar hot topic consultation content, which can clarify the focus area of ​​a certain type of welfare consultation. For example, in the query item set of "maternity leave application", the items marked with "high popularity" tag are mostly related to "maternity leave duration" and "salary payment standard during maternity leave". The hot topic query dimension identified based on this is "maternity leave rights protection", which covers the two core questions that employees are most concerned about; the welfare classification system This refers to a structured classification system established by enterprises based on their welfare policy framework, according to the nature of the welfare, the target beneficiaries, and the type of rights. It covers major categories such as salary subsidies, leave systems, insurance protection, and employee care, as well as their subcategories. This provides a standardized classification basis for matching consulting needs. For example, in a company's welfare classification system, the "insurance protection" category includes subcategories such as "five social insurances and one housing fund," "supplementary commercial insurance," and "major illness medical subsidies." "Supplementary commercial insurance" is further subdivided into "children's medical supplementary insurance" and "accidental disability insurance," with clear hierarchical levels covering all welfare scenarios. The dominant information features refer to key information identifiers extracted from the welfare classification system that accurately represent the core attributes of a certain type of welfare. These include unique attributes such as the scope of benefits, applicable conditions, and application rules. They are the core markers distinguishing different welfare categories. For example, in the "housing provident fund" welfare classification, the dominant information features include "maximum contribution base (e.g., 3 times the local average wage)," "contribution ratio range (5%-12%)," and "withdrawal conditions (purchase of housing, rent, retirement, etc.)." These features clearly define the differences between this welfare and other insurance or subsidy-type welfare benefits.

[0029] Furthermore, the parsing of the request concurrency pattern in the consultation concurrency threshold can be achieved through time series clustering analysis methods, such as using the K-means algorithm to cluster and group historical request volume time series data to obtain the request concurrency pattern; the identification of hot query dimensions in the content query elements can be achieved through frequency statistics algorithms, such as using the FP-growth algorithm frequent itemset mining technique to count the co-occurrence relationship of high-frequency keywords to obtain hot query dimensions; the mapping of the welfare classification system corresponding to the hot query dimensions can be achieved through knowledge graph construction methods, such as using the Neo4j graph database to establish a semantic association network between dimensions and welfare clauses to obtain the welfare classification system; the extraction of dominant information features in the welfare classification system can be achieved through principal component analysis methods, such as using PCA dimensionality reduction technology to extract the feature combination with the highest variance contribution rate in the classification system to obtain the dominant information features; the identification of the welfare information type corresponding to the content query elements can be achieved through deep learning classification algorithms, such as using the TextCNN text classification model to perform multi-label classification prediction on query elements to obtain the welfare information type.

[0030] As another embodiment of the present invention, the step of identifying the hot query dimension in the content query element according to the request concurrency mode includes: parsing the concurrent request traffic in the request concurrency mode; extracting the characteristic peak data in the concurrent request traffic; matching the query item set in the content query element based on the characteristic peak data; labeling the hot query tags in the query item set; and identifying the hot query dimension in the content query element based on the hot query tags.

[0031] The concurrent request traffic refers to the total data traffic generated by consultation requests related to a certain type of content query element within a unit of time (e.g., hourly, daily) corresponding to the concurrent request mode. It includes information such as request initiation time, quantity, and source channel, and can intuitively reflect the real-time access intensity and traffic change trend of this type of consultation. For example, regarding the query element "annual physical examination appointment," the concurrent request traffic in a certain week showed an average of 820 requests per day from Monday to Friday, with a peak of 156 requests at 10:00 AM on Wednesday, and an average of only 98 requests per day on weekends, exhibiting an overall traffic distribution characteristic of high on weekdays and low on weekends. The characteristic peak data refers to representative traffic peak information extracted from the concurrent request traffic, including the time of peak occurrence, the total number of requests during the peak period, and the difference between the peak and average traffic. It is a key data basis for judging whether a certain type of consultation has become a hot topic. For example, regarding the query element "high-temperature subsidy distribution," the characteristic peak data for concurrent request traffic in July was from 2:00 PM to 4:00 PM on July 15th, with 320 requests during this period, far exceeding the daily average of 85 requests per 2 hours. Traffic, with peak request volume accounting for 28% of the total daily requests; the query item set refers to the collection of all specific consultation request items initiated by employees related to the content query elements. Each item contains information such as the specific content of the consultation, the initiation time, and the employee identifier. It is the basic data set for filtering hot consultation content. For example, the query item set related to "Mid-Autumn Festival welfare distribution" contains 120 specific consultation items such as "Is the Mid-Autumn Festival welfare a physical item or a shopping card?", "Is the welfare distribution time one week before the festival?", and "Can newly hired employees receive Mid-Autumn Festival welfare?", covering various questions about the welfare from different employees; the hot query tag refers to the classification label marked on consultation items with high access volume and high frequency of occurrence in the query item set based on characteristic peak data. It is used to quickly distinguish between hot and non-hot consultation content. The tag is usually related to the core needs of the consultation. For example, in the query item set of "year-end bonus calculation", the items "year-end bonus tax calculation method" and "performance-year-end bonus linkage ratio" are marked as "high popularity - core question" hot query tags because the access volume during the peak period reaches 45%.

[0032] Furthermore, the parsing of concurrent request traffic under the concurrent request mode can be achieved through network traffic analysis methods, such as using Wireshark to capture and parse real-time data packet throughput to obtain concurrent request traffic; the extraction of feature peak data from the concurrent request traffic can be achieved through signal processing algorithms, such as using wavelet transform peak detection technology to identify extreme points in the traffic sequence to obtain feature peak data; the matching of query item sets in the content query elements can be achieved through semantic similarity calculation methods, such as using the BERT model to generate query text embedding vectors and performing cosine similarity matching to obtain query item sets; the labeling of hot query tags in the query item set can be achieved through text labeling tools, such as using the LabelStudio platform to manually label and classify high-frequency query items to obtain hot query tags; the identification of hot query dimensions in the content query elements can be achieved through association rule mining algorithms, such as using the Apriori algorithm to analyze frequent itemsets and association relationships between query elements to obtain hot query dimensions.

[0033] S2. Based on the welfare information type, retrieve information association entries in the preset welfare information database, analyze the consultation request queue corresponding to the information association entries, extract the queue response features in the consultation request queue, and calculate the association response index corresponding to the information association entries based on the queue response features.

[0034] Based on the aforementioned welfare information type, this invention retrieves related information entries from a preset welfare information database, enabling the system to accurately pinpoint welfare content directly related to employee consultation needs. This avoids blindly searching through massive amounts of information, significantly improving the efficiency and accuracy of information matching and promoting the efficient and orderly progress of the overall consultation process.

[0035] The information related entries refer to information entries selected from the potential entry set whose entry correlation value reaches a preset threshold (such as 0.7). These are core contents that are highly matched with the current welfare information type and can directly provide accurate and relevant information support for employee inquiries. For example, for the welfare information type of "salary subsidy - year-end bonus - tax calculation method", two entries with correlation values ​​≥ 0.7, namely "year-end bonus separate tax calculation rules" and "year-end bonus combined tax calculation example", are selected from the potential entry set. These two entries are the information related entries that can be used to answer questions.

[0036] As an embodiment of the present invention, the step of retrieving information-related entries in a preset welfare information database based on the welfare information type includes: parsing the information category code corresponding to the welfare information type; analyzing the database retrieval identifier corresponding to the information category code; querying a potential entry set in the preset welfare information database based on the database retrieval identifier; calculating the entry association value corresponding to each entry in the potential entry set; and retrieving information-related entries in the preset welfare information database based on the entry association value.

[0037] The information category code refers to a structured code obtained through information coding process parsing, used to uniquely identify the type of welfare information. It is typically composed of symbols of different dimensions and can quickly associate with corresponding data in a pre-set welfare information database. For example, after parsing, the information category code for "salary subsidy - high-temperature subsidy - summer (June-August) payment" is "01-GWB-0608", where "01" represents the major category of salary subsidy, "GWB" represents the sub-category of high-temperature subsidy, and "0608" represents the summer payment period. This code directly identifies the relevant information. The database contains relevant information; the database retrieval identifier refers to a specific marker bound to the information category code, used to trigger a retrieval in a preset welfare information database, including keywords, index IDs, data tags, etc. It serves as a bridge connecting welfare information types and data within the database, shortening the information retrieval path. For example, for the information category code "XC001-JT004-BX006" for "salary subsidy - transportation subsidy - monthly reimbursement", its corresponding database retrieval identifiers are "monthly transportation subsidy reimbursement", "JT-BX-YUE" and index ID "892541". The relevant data storage location can be quickly located using any identifier. The potential entry set refers to the set of all information entries that are likely to be associated with the welfare information type, which are initially retrieved from the preset welfare information database based on the database retrieval identifier. It includes directly and indirectly related entries and is the basic data pool for filtering information-related entries. For example, after querying the database retrieval identifier "Holiday System - Annual Leave - Calculation of Days", the potential entry set contains 15 entries such as "Rules for Calculating Annual Leave Days", "Standards for the Correlation between Annual Leave and Seniority", and "Compensation Methods for Unused Annual Leave", covering various rule descriptions related to annual leave. The entry association value refers to the numerical value calculated by the algorithm to determine the degree of association between each entry in the potential entry set and the current welfare information type. It usually takes a value range of 0-1, with a higher value indicating a stronger association. The calculation basis includes the content matching degree of the entry and the frequency of historical consultation citations. For example, in the potential entry set of "Insurance Protection - Supplementary Medical Care - Reimbursement Scope", the content matching degree of "Supplementary Medical Care Reimbursement Drug List" reaches 92%, and the historical citation frequency accounts for 85% of this type of consultation. The calculated entry association value is 0.91, which is significantly higher than other entries.

[0038] Furthermore, the parsing of the information category code corresponding to the welfare information type can be achieved through classification coding rule methods, such as using the United Nations Standard Product and Service Coding (UNSPSC) system to hierarchically encode welfare types to obtain information category codes; the analysis of the library retrieval identifier corresponding to the information category code can be achieved through hash mapping algorithms, such as using the MD5 hash function to convert the classification code into a fixed-length digital fingerprint to obtain the library retrieval identifier; the querying of the potential item set in the preset welfare information library can be achieved through database query languages, such as writing SQL statements to perform multi-condition joint retrieval in a MySQL database to obtain the potential item set; the calculation of the item association value corresponding to each item in the potential item set can be achieved through similarity calculation algorithms, such as using the cosine similarity algorithm to measure the semantic association strength between the item and the query requirement to obtain the item association value; the retrieval of information association items in the preset welfare information library can be achieved through graph database traversal methods, such as using Neo4j's Cypher query language to perform relationship-based path search to obtain information association items.

[0039] As another embodiment of the present invention, the step of parsing the information category code corresponding to the welfare information type includes: extracting the type identifier field corresponding to the welfare information type; matching the rule entry set in the preset encoding rule base based on the type identifier field; verifying the valid rule information in the rule information set; reconstructing the information encoding process corresponding to the valid rule information; and parsing the information category code corresponding to the welfare information type based on the information encoding process.

[0040] The type identifier field refers to the key information field extracted from the welfare information type to distinguish its core attributes. It typically includes features such as welfare category, sub-item, and service scenario, serving as the basis for matching coding rules. It is unique and representative. For example, the welfare information type "Insurance Protection - Supplementary Commercial Insurance - Child Medical Supplementary Insurance" has type identifier fields including "Welfare Category: Insurance Protection," "Sub-item: Supplementary Commercial Insurance," and "Specific Item: Child Medical Supplementary Insurance." These fields accurately locate the type's position in the coding system. The rule entry set refers to the set of all coding rule entries in the preset coding rule library that match the type identifier field. Each entry includes coding structure, field correspondence, and format requirements, providing rule support for parsing information category coding. For example, for the type identifier field of "salary subsidy" welfare, the rule entry set includes eight specific rule entries such as "major category code uses 2 digits (e.g., 01 represents salary subsidy)," "medium category code uses 3 letters (e.g., TRA represents transportation subsidy)," and "minor category code uses 4 digits (e.g., 0001 represents monthly transportation subsidy)." The valid rule information refers to information extracted from the rule entries. The selected rules, which conform to the current welfare information type attributes and can be used normally for code parsing, must exclude expired, conflicting, or inapplicable rules to ensure the accuracy of code parsing. For example, when parsing the information category code for "Leave System - Maternity Leave - Salary Payment," rules applicable to personal leave and maternity leave duration codes abolished in 2023 are excluded from the rule set. Ultimately, four valid rules are retained: "Maternity leave related codes use 3 digits (e.g., 002 represents maternity leave)" and "Salary payment related suffixes use 2 letters (e.g., SA represents salary)." The information coding stream... The process refers to the standardized steps for reconstructing the corresponding codes of welfare information types based on valid rule information. It covers fields such as matching, code combination, and format verification to ensure that the code parsing process is standardized and traceable. For example, the process for parsing the code of the information category "Employee Care - Holiday Benefits - Mid-Autumn Festival Benefits" is as follows: First, use the 2-digit code "03" to correspond to the "Employee Care" category; second, use the 3-letter code "FES" to correspond to the "Holiday Benefits" subcategory; third, use the 4-digit code "0008" to correspond to the "Mid-Autumn Festival Benefits" subcategory; fourth, combine the codes and verify the format to finally generate the complete code.

[0041] Furthermore, the extraction of the type identifier field corresponding to the welfare information type can be achieved through regular expression matching methods, such as using the Python re module to match specific pattern strings in the information text to obtain the type identifier field; the matching of the rule entry set in the preset encoding rule library can be achieved through semantic similarity calculation methods, such as using the Word2Vec word vector model to calculate the semantic distance between the field and the rule entry to obtain the rule entry set; the verification of the valid rule information in the rule information set can be achieved through rule conflict detection algorithms, such as using the CLIPS expert system to detect logical contradictions and redundancies between rules to obtain valid rule information; the reconstruction of the information encoding process corresponding to the valid rule information can be achieved through directed acyclic graph modeling methods, such as using the Graphviz tool to visualize and construct the encoding process dependency graph to obtain the information encoding process; the parsing of the information category code corresponding to the welfare information type can be achieved through decision tree classification algorithms, such as using the C4.5 algorithm to automatically generate hierarchical classification codes based on type features to obtain the information category code.

[0042] This invention analyzes the consultation request queue corresponding to the information association items and extracts the queue response characteristics in the consultation request queue. This allows the system to clearly understand the distribution and processing progress of current similar welfare consultation requests, avoid resource misallocation caused by lack of understanding of the queue status, ensure orderly processing of consultation requests, and reduce employee dissatisfaction caused by response issues.

[0043] The consultation request queue refers to a set of pending consultation requests sorted by request initiation time or priority around a specific information-related item (such as "housing provident fund withdrawal process"). It includes information such as the initiation time, employee ID, consultation content, and current processing status of each request. This queue serves as the foundation for the system to coordinate the processing of similar consultations. For example, for the information-related item "medical insurance reimbursement progress inquiry," a consultation request queue formed between 10-11 am on a certain workday contains 32 requests, of which 20 are in the "awaiting allocation" state, 8 are in the "processing" state, and 4 are in the "awaiting feedback" state. The queue is sorted according to the principle of "initiation time + priority for senior employees" to ensure orderly processing. The queue response characteristics refer to key indicator features extracted from the consultation request queue that reflect the queue's processing efficiency and service quality, including average waiting time, peak processing rate, and the percentage of unresponsive requests. Manual transfer rate, etc., can intuitively reflect the current response capability and existing problems of the queue. For example, the response characteristics of a certain "year-end bonus tax consultation" queue are: average waiting time of 8 minutes, peak processing rate of 15 messages / hour, unresponsive request rate of 5%, and manual transfer rate of 12%. These characteristics indicate that the queue has moderate processing efficiency and a small backlog of requests. Resources need to be adjusted appropriately to shorten the waiting time. Optionally, the analysis of the consultation request queue corresponding to the information association entries can be achieved through message queue monitoring methods, such as using the RabbitMQ management interface to track the message backlog status and processing rate in real time, thereby obtaining the consultation request queue. The extraction of queue response characteristics from the consultation request queue can be achieved through time series feature extraction algorithms, such as using the TSFresh library to automatically extract features such as queue length volatility and response delay period, thereby obtaining the queue response characteristics.

[0044] Furthermore, based on the queue response characteristics, the present invention calculates the association response index corresponding to the information association item, which can transform the fragmented characteristics of the queue response efficiency, processing quality, etc. into a quantifiable unified indicator, allowing the system to quickly judge the consultation service level corresponding to the item and avoid judgment bias caused by feature fragmentation.

[0045] The correlation response index refers to a comprehensive indicator that ultimately reflects the service response capability of information correlation items, calculated by combining the correlation load factor and the quantitative correlation index. The value ranges from 0 to 100, with higher values ​​indicating stronger response capabilities. For example, when the quantitative correlation index is 72 points and the correlation load factor is 0.2, the correlation response index = 72 × (1 + 0.2) = 86.4 points, comprehensively reflecting the actual response level of the item under the current load.

[0046] As an embodiment of the present invention, the step of calculating the association response index corresponding to the information association item based on the queue response characteristics includes: parsing the feature association sequence corresponding to the queue response characteristics; performing sequence quantization on the feature association sequence to obtain the quantized association index; querying the association response threshold corresponding to the quantized association index; parsing the association load factor corresponding to the association response threshold; and calculating the association response index corresponding to the information association item based on the association load factor.

[0047] The feature association sequence refers to an ordered sequence formed by sorting queue response features according to association logic. It includes the influence relationships and weight percentages between features, reflecting the comprehensive effect of each response feature on the overall service quality. For example, a sequence consisting of "average waiting time (3 minutes) → peak processing rate (20 messages / hour) → non-response rate (2%) → manual transfer rate (8%)" has a waiting time weight of 30% and a processing rate weight of 40%, reflecting that processing efficiency has a more significant impact on service quality. The quantitative association index refers to a comprehensive index obtained by weighting the features in the feature association sequence after numerical conversion. The value range is typically 0-100, with higher values ​​indicating better queue response performance. For example, "waiting time 3 minutes" is quantified as 85 points, "processing rate 20 messages / hour" as 90 points, and the quantitative association index calculated by weighting is 88 points, intuitively reflecting the performance of the queue. The overall response level of the sequence; the correlation response threshold refers to the preset critical value used to judge whether the quantitative correlation index meets the standard, including the upper threshold (excellent standard) and the lower threshold (qualified standard), which is the baseline for evaluating service quality. For example, for "insurance reimbursement consultation", the correlation response threshold is set as follows: upper limit 85 points (representing excellent service) and lower limit 60 points (representing qualified service). If the quantitative correlation index is 72 points, it is in the qualified but needs optimization range; the correlation load factor refers to the coefficient that reflects the current service load pressure calculated based on the difference between the correlation response threshold and the quantitative correlation index. The value is usually between -1 and 1. A positive number indicates that the load is lower than the threshold (service has spare capacity), and a negative number indicates that the load exceeds the threshold (service is under pressure). For example, when the quantitative correlation index is 72 points and the lower threshold is 60 points, the correlation load factor is (72-60) / 60=0.2, indicating that the current service load is within a controllable range.

[0048] Furthermore, the parsing of the feature association sequence corresponding to the queue response features can be achieved through association rule mining methods, such as using the FP-Growth algorithm to analyze the frequent co-occurrence patterns between features, thereby obtaining the feature association sequence; the sequence quantization of the feature association sequence can be achieved through information entropy calculation methods, such as using the Shannon entropy formula to calculate the uncertainty measure of feature values ​​in the sequence, thereby obtaining the quantized association index; the querying of the association response threshold corresponding to the quantized association index can be achieved through threshold optimization algorithms, such as using the Otsu adaptive threshold method to determine the optimal split point of the index distribution, thereby obtaining the association response threshold; the parsing of the association load factor corresponding to the association response threshold can be achieved through load balancing calculation methods, such as using a weighted round-robin algorithm to dynamically calculate the system load factor based on the threshold, thereby obtaining the association load factor; the calculation of the association response index corresponding to the information association item can be achieved through the following formula.

[0049] In another embodiment of the present invention, the association response index corresponding to the information association item is calculated based on the association load factor using the following formula: ; in, This represents the association response index corresponding to the information association item. This indicates the number of dimensions corresponding to the consultation dimension of the information association entry. Index representing the number of consultation dimensions. Indicates the first The dimension weights corresponding to each consultation dimension Indicates the first The corresponding correlation response threshold for each consultation dimension Indicates the first The correlation load factor corresponding to each consultation dimension.

[0050] In detail, the correlation response index can be a quantitative result reflecting the overall response level of information-related items, calculated by weighting multiple consultation dimensions. For example, in the scenario of corporate welfare consultation, by integrating dimensions such as "response speed" and "answer accuracy" to calculate R, the comprehensive service capability of the welfare consultation service can be clearly understood. In the formula, it is the final calculation target, obtained by weighted summation of data from various dimensions. Calculating the correlation response index for employee medical insurance and welfare consultation can guide the optimization of service processes. The consultation dimension can be a classification of the consultation content of information-related items, covering aspects such as response speed, answer quality, and demand matching degree, where n is... The total number of dimensions, where i is the index of a single dimension. Taking employee benefit "annual leave application" consultation as an example, it can be broken down into dimensions such as "clarity of application process (i=1)", "approval time (i=2)", and "satisfaction with Q&A (i=3)", with n=3. Different dimensions describe the consultation service from multiple perspectives, making the calculation more detailed. The dimension weights can be the proportion of importance of each consultation dimension in the overall evaluation, reflecting the magnitude of the influence of different dimensions on the correlation response index. The values ​​are determined based on business needs, experience, or data analysis, and the sum of the weights of all dimensions is 1. For example, for "employee training registration consultation", if "convenience of registration process" is considered to have a large impact, let its weight be 1. =0.4; "Course Suitability" is slightly lower. =0.3; "Customer service response speed" =0.3, using weights to differentiate the importance of dimensions, making the calculation more aligned with actual business priorities; the associated response threshold can be the ideal response standard value expected to be achieved by the i-th consultation dimension, representing the optimal or qualified level that the service of that dimension should achieve, such as the "timeliness of result feedback" dimension for "employee physical examination arrangement" consultation, set to... =24 hours (i.e., the physical examination results must be returned within 24 hours), serving as a reference for measuring whether the service in this dimension meets the standard. If the actual return time is 36 hours, analysis and improvement are needed. The associated load factor can reflect the actual operating load pressure of the i-th consultation dimension, reflecting the gap between the current service capacity and the ideal threshold. It can be calculated by comparing the actual value with the threshold (e.g., ...). =Actual load / threshold load), using the "material preparation speed" dimension of "employee birthday benefit distribution" consultation. It was supposed to be prepared in 3 days, but if it actually took 4 days, =4 / 3≈1.33. A value greater than 1 indicates that the service in this dimension is under pressure and resource guarantee efficiency needs to be adjusted. In the formula, it is used to measure the impact of the actual load of the dimension on the response index.

[0051] S3. Based on the correlation response index, collect the employee access data corresponding to the employee to be consulted, query the access intent scenario in which the employee access data is located, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform.

[0052] Based on the aforementioned correlation response index, this invention collects employee access data corresponding to the employee to be consulted and queries the access intent scenario of the employee access data, making data collection more targeted and ensuring that the acquired access data is highly correlated with the consultation needs; it can accurately locate the specific intent scenario of the employee in welfare consultation, providing a precise basis for subsequent extraction of scenario load data and calculation of consultation load value.

[0053] The employee access data refers to the data set generated when employees perform welfare-related operations on the pre-set welfare consultation platform. This data can be used to analyze their consultation behavior and needs, covering dimensions such as access time, operation path, dwell time, interactive actions (such as clicks, input, and jumps), and historical consultation records. For example, when an employee consults about "housing subsidies" on the welfare platform, their access data includes: logging into the platform at 9:15 on the same day, clicking on the "Welfare Center - Subsidy Application - Housing Subsidy" pages in sequence (dwelling on each page for 2 minutes, 3 minutes, and 5 minutes respectively), entering the keyword "subsidy amount" to search, and viewing 3 historical consultation records. This data can intuitively reflect the employee's access trajectory and demand tendency. The access intent scenario refers to the scenario types that reflect the specific welfare consultation purpose of employees, based on employee access data and combined with the welfare consultation business logic. Each scenario corresponds to a clear consultation demand direction and behavioral characteristics, which is a concrete classification of the intent behind the employee's access behavior. For example, based on employee access data, scenarios such as "subsidy application consultation," "annual leave rule inquiry," and "physical examination appointment consultation" can be identified. If employee access data shows that they have continuously browsed the "list of medical examination institutions" and "medical examination appointment process" pages and clicked the "appointment entrance" multiple times, it can be determined that they are in a "medical examination appointment consultation scenario." This scenario clearly indicates the employee's consultation needs regarding medical examination appointment-related benefits. Optionally, the collection of employee access data corresponding to the employee seeking consultation can be achieved through user behavior tracking technology, such as using Apache Kafka to collect real-time data on user page clicks and dwell time in the benefits system, thereby obtaining employee access data. The query of the access intent scenario of the employee access data can be achieved through scenario classification algorithms, such as using an SVM classifier to identify the consultation scenario type based on the access path and operation characteristics, thereby obtaining the access intent scenario.

[0054] Furthermore, by extracting scenario load data from the access intent scenario and calculating the consultation load value of the scenario load data in the preset welfare consultation platform, the present invention enables the system to accurately grasp the service pressure distribution under different intent scenarios, ensuring that the platform can still operate stably when multiple scenarios are concurrent, reducing service lag or interruption caused by sudden high load, and improving the stability of employee consultation experience.

[0055] The scenario load data refers to a dataset that integrates information such as scenario access sequence, concurrent request volume, and resource load index to comprehensively reflect the pressure of access intent scenarios on platform resources. Taking the employee physical examination and welfare scenario as an example, the scenario load data includes "daily access sequence from 9-10 am (appointment registration - report query)," "peak concurrent request volume of 150," and "resource load index of 0.9," providing a comprehensive basis for assessing the impact of scenarios on platform load and formulating response strategies. The pre-set welfare consultation platform refers to a digital service platform that enterprises or organizations have built and configured in advance to handle employee welfare-related consultation needs. This platform will pre-set the business scope of welfare consultation (such as social security payment, annual leave rules, physical examination arrangements, subsidy application, etc.), response process (such as automatic Q&A, manual transfer, work order allocation, etc.), data statistics dimensions (such as consultation volume, response time, resolution rate, etc.), and service standards (such as response time limit, satisfaction threshold, etc.), while integrating corresponding information databases and interactive functions to ensure that employees can conveniently submit welfare consultations.

[0056] As an embodiment of the present invention, the step of extracting scenario load data in the access intent scenario includes: identifying scenario access sequence in the access intent scenario; parsing the concurrent request volume corresponding to the scenario access sequence; querying the concurrent baseline value in the concurrent request volume; parsing the resource load index corresponding to the concurrent baseline value; and extracting scenario load data in the access intent scenario based on the resource load index.

[0057] The scenario access sequence refers to an ordered sequence of user access behaviors formed by time or logical order under the access intent scenario, recording access steps, interaction nodes, etc. For example, in the employee welfare consultation scenario of "welfare policy inquiry - online Q&A consultation - application process download", the access sequence clearly presents the user's behavioral trajectory from understanding the policy to interactive consultation and then to obtaining information. By analyzing the sequence, we can gain insight into user operating habits and provide a basis for optimizing service paths. The concurrent request volume refers to the number of access requests initiated at the same moment (or a very short time interval) in the scenario access sequence. Taking the welfare subsidy application scenario as an example, applications are submitted in a concentrated manner from the 1st to the 5th of each month. If 300 employees submit subsidy application requests at the same time within a certain minute, this 300 is the concurrent request volume for that period, reflecting the concentration of scenario access and instantaneous pressure, which is a key data for measuring platform load. The concurrent benchmark value refers to a preset reference value used to measure whether the concurrent request volume is normal. It is set based on historical data or business needs. For example, for the holiday welfare consultation scenario, the historical average concurrent request volume for the same period is 200, which is set as the concurrent benchmark value. When the actual concurrent request volume is 250, comparing it with the baseline value can determine whether the current load exceeds the normal range, assisting in resource allocation decisions. The resource load index refers to a quantified resource consumption indicator that combines the concurrent request volume with the system resource (such as server computing power, bandwidth, etc.) usage. Assuming that the welfare platform requires 1 unit of resource to process 1 concurrent request, when the concurrent request volume is 200, if the resource load index is 0.8, it means that the actual resource usage is 80% of the theoretical value; if the index reaches 1.2, it represents that the resources are overloaded and need to be expanded or optimized, reflecting the matching status of resources and requests.

[0058] Furthermore, the identification of the scene access sequence in the access intent scenario can be achieved through user behavior pattern mining methods, such as using the PrefixSpan sequence pattern mining algorithm to analyze frequent paths in user operation logs to obtain the scene access sequence; the parsing of the concurrent request volume corresponding to the scene access sequence can be achieved through time series prediction methods, such as using the ARIMA model to predict the request volume at future time points based on historical sequence data to obtain the concurrent request volume; the querying of the concurrent baseline value in the concurrent request volume can be achieved through statistical analysis methods, such as using box plot analysis to calculate the upper and lower quartiles and median of the request volume data to obtain the concurrent baseline value; the parsing of the resource load index corresponding to the concurrent baseline value can be achieved through load assessment models, such as using the weighted moving average method to comprehensively calculate the load coefficients of CPU, memory, and network IO to obtain the resource load index; the extraction of scene load data in the access intent scenario can be achieved through log parsing tools, such as using the Logstash component in the ELK technology stack to collect and filter system performance indicators in real time to obtain scene load data.

[0059] In another embodiment of the present invention, the consultation load value of the scenario load data in the preset welfare consultation platform is calculated using the following formula: ; in, This indicates the consultation load value of the scenario load data in the preset welfare consultation platform. This indicates the current resource load index in the preset welfare consultation platform. This indicates the total number of time series intervals corresponding to the scenario load data. Index representing the number of time series intervals. Indicates the first Concurrent request volume within a time series interval This represents the concurrency baseline value.

[0060] In detail, the consultation load value is a quantitative measure of the pressure exerted on the welfare consultation platform by a scenario load that comprehensively considers factors such as platform resources and concurrent requests within a time interval. For example, for a corporate welfare consultation platform, calculating the consultation load value for the "annual leave application" scenario clearly indicates the level of pressure this scenario brings to the platform, guiding the platform to prepare resources in advance to cope with peak periods. For instance, if the ZL value is high, more customer service seats can be added and servers expanded. The current resource load index reflects the degree of utilization of the current resources (such as server computing power, bandwidth, customer service manpower, etc.) of the preset welfare consultation platform. It assumes that when the platform resources are fully loaded... =1, when handling welfare inquiries, the server's computing power was used by 60% and customer service manpower by 70%, based on comprehensive calculations. =0.65, substituting this into the formula can reflect the impact of current resource status on consulting workload. A large number indicates that resources are already strained, and the ZL (Zero Load Level) generated by the same scenario load may be even higher. The time series interval can include parameters m and j, where m is the total number of time intervals corresponding to the scenario load data, and j is the index of a single interval. By dividing continuous time into smaller segments for analysis, taking welfare consultation from 9:00 to 18:00 on a weekday as an example, each hour is set as one time series interval, m=9, and j from 1 to 9 corresponds to each hour. By statistically analyzing the concurrent requests within each j-interval, the load changes at different times can be captured in detail, making the ZL calculation more consistent with actual fluctuations. The number of concurrent requests can represent the number of welfare consultation requests initiated at the same time (or in a very short time) within the j-th time series interval. For example, in the scenario of "holiday welfare distribution rules" consultation, from 10:00 to 11:00 (j=2 interval), 80 employees consult simultaneously. =80, which is the basic data for calculating ZL, reflecting the instantaneous access pressure within each time interval; the concurrency benchmark value can represent a preset reference value for measuring whether the number of concurrent requests is normal, set based on historical normal periods or platform capacity, for example, the average normal concurrent request volume for "employee health check benefits" consultations in the same period in history is 50, let B=50, when =60, / B=1.2 reflects the concurrent exceedance of the benchmark at this interval. It is used as the denominator in the formula to standardize the concurrent data of different intervals, allowing ZL calculation to compare with the benchmark to judge the load level.

[0061] S4. Based on the consultation load value, generate a consultation response template in the preset welfare consultation platform, verify the consultation response template, obtain the verified response content, and analyze the content output stage corresponding to the verified response content.

[0062] Based on the consultation load value, this invention generates a consultation response template for a preset welfare consultation platform. It can accurately match response resources according to the load, avoiding more consultations caused by non-standard responses when the load is high. It can also predict consultation hotspots through load data, optimize template content in advance, make responses more targeted, and enhance the overall stability of the platform service.

[0063] The consultation response template refers to a standardized and reusable welfare consultation response framework built based on content structure details, combined with platform load status, grading indicators, etc. It includes fixed scripts, information filling positions, and layout formats, for customer service or intelligent systems to reply to employee inquiries. For example, for medium load "health checkup welfare consultation", the generated template is "[Core Policy] Health Checkup Institution / Time → [Frequently Asked Questions] Process / Item Q&A → [Self-Service Inquiry] Report Download Guide". Customer service can quickly fill in the content according to this, improving the efficiency and standardization of the response.

[0064] As an embodiment of the present invention, generating a consultation response template in a preset welfare consultation platform based on the consultation load value includes: analyzing the platform load status corresponding to the preset welfare consultation platform based on the consultation load value; parsing the status grading index corresponding to the platform load status; determining the graded presentation content corresponding to the status grading index; querying the content structure details in the graded presentation content; and generating a consultation response template in the preset welfare consultation platform based on the content structure details.

[0065] The platform load status refers to a comprehensive description of the operational status of a pre-set welfare consultation platform under specific time periods or scenarios, based on the consultation load value, including resource consumption and request processing pressure, reflecting the actual load level of the platform in handling consultation services. For example, when the consultation load value reaches 80 (assuming a maximum score of 100), the platform may be in a "high load state," characterized by a server computing power utilization rate exceeding 70% and an average customer service response waiting time of 5 minutes, clearly showing the current operational pressure on the platform. The status grading index refers to a quantitative or qualitative standard for classifying the platform load status, including indicator parameters corresponding to different load ranges, used to accurately define the load status level. For example, a consultation load value of 0-30 corresponds to "low load," 31-70 to "medium load," and 71-100 to "high load," with each range accompanied by indicators such as server resource utilization and consultation response timeliness. For instance, a response timeliness rate of no less than 60% is required during high load, serving as the basis for judging the status grading. The graded presentation content refers to the information that needs to be presented in the consultation response according to the status grading index and corresponding to different platform load status levels. The content collection includes core points and expression styles. For example, under low load conditions, the tiered content can include full details of welfare policies and extended illustrative cases; under high load conditions, it focuses on key processes and the simplest Q&A language, such as "the high-load response template highlights the core steps of the 'three-step subsidy application' and omits unnecessary background information," adapting to the service needs of different loads. The content structure details refer to the detailed breakdown of the organizational structure, information hierarchy, and module composition of the tiered content, clarifying the arrangement order, relationship, and presentation format of each part. Taking the response to "annual leave welfare consultation" under high load conditions as an example, the content structure details are "first list 'annual leave day calculation rules (first-level heading)' → then attach 'common job day comparison table (second-level list)' → finally mark 'application entry link (quick guide)'", making the content arrangement of the response template clear and orderly, facilitating the rapid generation of usable templates.

[0066] Furthermore, the analysis of the platform load status corresponding to the preset welfare consultation platform can be achieved through system monitoring tools, such as using Prometheus to collect server CPU utilization and memory usage data in real time to obtain the platform load status; the parsing of the status grading index corresponding to the platform load status can be achieved through clustering analysis methods, such as using the K-means algorithm to perform multi-dimensional clustering of the load data to obtain the status grading index; the determination of the graded presentation content corresponding to the status grading index can be achieved through rule engine methods, such as using the Drools rule engine to automatically generate graded description text based on index thresholds to obtain the graded presentation content; the querying of the content structure details in the graded presentation content can be achieved through natural language processing technology, such as using the BERT model to parse the key information and logical structure in the text to obtain the content structure details; the generation of consultation response templates in the preset welfare consultation platform can be achieved through template engine technology, such as using Apache FreeMarker to dynamically generate a standard response framework containing variable placeholders to obtain the consultation response template.

[0067] This invention verifies the consultation response template to obtain the verified response content, which can preemptively check whether there are any errors in the template content, outdated information, or logical loopholes, thus ensuring the quality of the response. It also allows the template to adapt to consultation needs under different load scenarios, ensuring that it is concise and efficient under high load and detailed and accurate under low load, thereby improving the consultation experience for employees.

[0068] The "verified response content" refers to standardized content that, after verifying the consultation response template, confirms that the content aligns with the policy objectives of the regulations, that the core response units are accurate and effective, and that can be directly used to respond to actual consultations. For example, when verifying the "high-temperature subsidy consultation template," if it is confirmed that the "subsidy distribution standard (300 yuan per month, distributed from June to September)" and "recipient eligibility criteria (outdoor workers, employees working in high-temperature environments)" are compliant and complete, the generated "verified response content" can be directly used to reply to employee inquiries, ensuring the quality of the response.

[0069] As an embodiment of the present invention, the step of verifying the consultation response template to obtain the verified response content includes: identifying response information points in the consultation response template; traversing the welfare information responses covered by the response information points; verifying the regulations and policy objectives corresponding to the welfare information responses; verifying the core response units in the welfare information responses based on the regulations and policy objectives; and verifying the consultation response template based on the core response units to obtain the verified response content.

[0070] The "response information points" refer to the key information particles in the consultation response template used to respond to employee welfare inquiries. These particles cover specific content such as welfare rules, procedures, and standard conditions, and are the basic elements constituting the response. For example, in the "annual leave welfare consultation template," "annual leave days calculation method (divided by length of service: 1-5 years of service, 5 days / year; 5-10 years of service, 7 days / year)" and "application submission deadline (December 20th each year)" are the response information points, clearly defining the core content that the response must cover. The "welfare information response" refers to the specific response content regarding welfare policies, procedures, and standards based on the response information points, addressing employee welfare inquiries. It is a collection of information output to answer employee questions. Taking "maternity welfare consultation" as an example, the welfare information response includes "maternity leave duration (98 days for normal delivery, 15 days additional for difficult delivery)" and "maternity allowance application process (online submission of materials → departmental preliminary review → medical insurance bureau review → fund disbursement)." By integrating these information points, a complete response is formed to meet the employee's consultation needs. The policy objectives mentioned above refer to the management and protection goals that the welfare-related regulations and policies aim to achieve when they are formulated, such as standardizing welfare distribution, protecting employee rights, and improving the efficiency of welfare utilization. These objectives serve as the basis for verifying whether the responses to welfare information are compliant and reasonable. For example, the policy objective of "employee health check-up welfare policy" is "to protect employee health, enhance employee welfare perception, and promote the construction of a healthy corporate culture." When verifying the response, it is necessary to check whether the content such as "selection criteria for health check-up institutions" and "coverage of health check-up items" aligns with this objective. The core response unit refers to the information module in the welfare information response that is directly related to the policy objectives and plays a key role in answering core questions. This includes the most core policy interpretations and key process nodes. For example, for "housing provident fund withdrawal consultation," the core response units are "withdrawal conditions (applicable situations such as home purchase / rental / resignation)" and "withdrawal amount calculation (a certain percentage of the house price for home purchase, and a monthly rent limit for rental)." These units determine whether the response accurately addresses employee needs and aligns with policy objectives.

[0071] Furthermore, the identification of response information points in the consultation response template can be achieved through keyword extraction algorithms, such as using the TextRank algorithm to automatically identify core information nodes in the template, thereby obtaining response information points; the traversal of welfare information responses covered by the response information points can be achieved through graph traversal algorithms, such as using a depth-first search algorithm to traverse all policy clauses associated with the information points, thereby obtaining welfare information responses; the verification of the regulations and policy objectives corresponding to the welfare information responses can be achieved through rule matching methods, such as using the Rete algorithm to match the response content with compliance rules in the policy database, thereby obtaining the regulations and policy objectives; the verification of the core response units in the welfare information responses can be achieved through semantic consistency detection methods, such as using the BERT model to calculate the semantic similarity between the response units and the original policy text, thereby obtaining the core response units; the verification of the consultation response template can be achieved through an automated testing framework, such as using the JUnit framework to build template logical integrity verification test cases, thereby obtaining the verified response content.

[0072] This invention analyzes the content output stage corresponding to the verification response content, which can clearly distinguish the service focus and operation standards of different stages, avoid the confusion of the output process due to the ambiguity of stage positioning, ensure the orderly transmission of the response content, and ensure that the verified response content reaches employees accurately and efficiently.

[0073] The content output stage refers to the entire process of delivering the verification response content to the consulting staff in the welfare consultation platform. It is divided into different stages according to service logic and time sequence. Each stage corresponds to a specific output goal, operation method, and resource allocation, covering the complete link from content preparation to final delivery to the employee. For example, for the verification response content of "employee medical insurance reimbursement consultation," its output stage can be divided into: 1. Content waking stage (0-10 seconds after the consultation is initiated, the system automatically retrieves and loads the verification response content); 2. Initial output stage (10-30 seconds, pushing the core response points to the employee, such as "reimbursement ratio 70%)"; 3. Supplementary output stage (after 30 seconds, if the employee has not closed the page, pushing detailed content such as "reimbursement material list" and "online submission portal"). Each stage is sequentially connected to adapt to the response needs of different consultation scenarios. Optionally, the analysis of the content output stage corresponding to the verification response content can be achieved through a lifecycle state machine modeling method, such as using the Stateflow tool to construct a response content generation-review-release state transition model to obtain the content output stage.

[0074] Specifically, for a more intuitive understanding of the execution logic and data flow relationships corresponding to the consultation response template generation and verification process in this solution, please refer to [link / reference]. Figure 2 .Should Figure 2As the core process framework of the welfare consultation platform service system, it clearly presents the complete link from initialization and resource loading to content output stage analysis: The input layer focuses on model loading (load analysis model), configuration file initialization (state classification rules), and data loading (consultation load value input), which is the basis for subsequent analysis; The processing layer transforms the input data into core information that can support decision-making through the collaborative logic of "topology graph rendering (content structure details query), node traversal and path analysis (state classification index parsing), performance bottleneck detection (classification of content presentation), and topology analysis (platform load status analysis)"; The decision and strategy center outputs a complete welfare consultation service strategy through a step-by-step process of "optimized processing area marking (consultation response template generation) → rule matching (response verification and compliance verification) → strategy application (content output stage analysis)". It should be noted that the connections between the various links in the flowchart are essentially an abstraction of the business logic of welfare consultation. In actual scenarios, the complexity of model calculations (such as the dynamic correlation between consultation load value and platform load status) and the diversity of link adaptation (differentiated response template generation rules corresponding to different platform load statuses) are far greater than what is shown in the diagram. This architecture is only a concise display of the core logic to provide an intuitive reference for understanding the operation of the welfare consultation service system.

[0075] S5. Generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

[0076] This invention generates a content push sequence corresponding to the content output stage and constructs an output response instruction corresponding to the content push sequence. This enables the verification response content to be pushed accurately at a reasonable time rhythm, avoiding information piling up or push delays, ensuring that employees can efficiently obtain key information, and improving the automation and accuracy of the overall service.

[0077] The content push sequence refers to the planned push nodes and durations set according to the content output stages, advancing chronologically for the verification response content. This clarifies the order, triggering conditions, and intervals for content pushes at different stages, ensuring that information pushes align with employee reception habits and consultation scenario needs. For example, for the verification response content regarding "employee year-end bonus tax calculation consultation," the push sequence is set as follows: 5 seconds after the consultation request is submitted (triggering condition: system identifies consultation type), a "Tax Calculation Method Selection Guide" is pushed; 15 seconds later (triggering condition: employee has not clicked "View Details"), a "Comparison Table of Two Tax Calculation Methods" is pushed; 30 seconds later (triggering condition: employee remains on the page and has not left), a "Tax Calculator Entry" is pushed. This timing control achieves layered and orderly information delivery. The output response instruction refers to a standardized instruction generated by a pre-set welfare consultation platform based on the content push sequence, used to trigger content push actions at each stage. This instruction includes the instruction type, execution target, and operation parameters (such as push time, content identifier, and target employee ID). Information such as these ensures the platform can automatically and accurately execute push tasks. For example, regarding the content push sequence for "Annual Leave Application Consultation," the output response instructions can be divided into: Instruction 1 (Type: Instant Push, Execution Target: Platform Message Module, Parameter: Push Time = 8 seconds after consultation initiation, Content Identifier = "Core Steps of Annual Leave Application Process", Employee ID = 20240512); Instruction 2 (Type: Delayed Push, Execution Target: Platform Message Module, Parameter: Push Time = 20 seconds after Instruction 1 execution, Content Identifier = "Frequently Asked Questions about Annual Leave Application", Employee ID = 20240512). These instructions clarify operational details, ensuring accurate implementation of the push action. Optionally, the generation of the content push sequence corresponding to the content output stage can be achieved through a dynamic scheduling algorithm, such as using the earliest deadline priority algorithm to calculate the optimal push time sequence for each stage, thereby obtaining the content push sequence. The construction of the output response instructions corresponding to the content push sequence can be achieved through an instruction template generation method, such as using the Apache Velocity template engine to automatically generate JSON format instruction messages based on the time sequence data, thereby obtaining the output response instructions.

[0078] Furthermore, based on the output response command, the present invention outputs the welfare consultation results required by the employee to be consulted, enabling the platform to push information strictly according to the preset time sequence and operation standards, ensuring that the employee receives an accurate answer at the appropriate time, and avoiding mis-sending or omission of information; and it can also rely on the command to realize the automated output of consultation results, reduce manual intervention, significantly shorten the employee waiting time, and improve consultation response efficiency.

[0079] The welfare consultation result refers to the complete set of information that is highly matched to the employee's welfare consultation needs. This information is presented by the preset welfare consultation platform according to the output response instructions, after the content has been verified and accurately delivered to the employee seeking consultation in the order of content push. It includes core answers, operation instructions, supplementary explanations, etc., and the content conforms to the policy objectives of welfare regulations and can directly solve the employee's questions. For example, when an employee inquires about "the distribution of summer high temperature subsidies in 2024", the welfare consultation result includes: "Distribution standard: 320 yuan per month, distribution period is from June 1 to September 30 (a total of 4 months, totaling 1280 yuan)", "Distribution recipients: employees working outdoors and indoor employees whose workplace temperature is ≥33℃", "Query path: Enterprise OA - 'Employee Welfare' section - 'Subsidy Inquiry' - enter the employee number to view the distribution progress". All information is accurate and complete and can directly meet the employee's consultation needs. Optionally, the output of the welfare consultation result required by the employee seeking consultation can be achieved through natural language generation technology, such as using the GPT model to convert structured data into personalized natural language descriptions to obtain the welfare consultation result.

[0080] Compared to the problems described in the background technology, this invention, by acquiring the welfare consultation content input by the employee seeking consultation and extracting the content query elements from that content, can quickly focus on the core needs of the employee's consultation, avoiding repeated communication due to ambiguous information. This lays the foundation for accurate matching of welfare information subsequently, ensuring the accuracy and efficiency of the consultation service. Based on the welfare information type, this invention retrieves information-related entries from a preset welfare information database, enabling the system to accurately locate welfare content directly related to the employee's consultation needs, avoiding blind searching through massive amounts of information, significantly improving the efficiency and accuracy of information matching, and promoting the efficient and orderly progress of the overall consultation process. Furthermore, based on the correlation response index, this invention collects employee access data corresponding to the employee seeking consultation and queries the access intent scenario of the employee's access data, making data collection more targeted and ensuring that the acquired access data is accurate and relevant. The data is highly correlated with consultation needs; it can accurately pinpoint the specific intent scenarios of employees in welfare consultations, providing a precise basis for subsequent extraction of scenario load data and calculation of consultation load values. Furthermore, based on the consultation load values, this invention generates preset consultation response templates in the welfare consultation platform, which can accurately match response resources according to the load situation, avoiding more consultations caused by non-standard responses during high loads; it can also predict consultation hotspots through load data, optimize template content in advance, making responses more targeted, and enhancing the overall stability of the platform service. Finally, by generating the content push sequence corresponding to the content output stage and constructing the output response instructions corresponding to the content push sequence, this invention can ensure that the verified response content is pushed accurately at a reasonable time rhythm, avoiding information piling up or push delays, ensuring that employees efficiently obtain key information, and improving the automation and accuracy of the overall service. Therefore, the employee welfare consultation method and system based on intelligent customer service provided by this invention can improve the welfare consultation experience of employees in every enterprise.

[0081] like Figure 3 The diagram shown is a functional block diagram of an employee welfare consultation system based on intelligent customer service according to the present invention.

[0082] The employee welfare consultation system 200 based on intelligent customer service described in this invention can be installed in an electronic device. Depending on the functions implemented, the employee welfare consultation system based on intelligent customer service may include a type identification module 201, an index calculation module 202, a load value calculation module 203, a stage analysis module 204, and a result output module 205. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0083] In this embodiment of the invention, the functions of each module / unit are as follows: The type identification module 201 is used to obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold. The index calculation module 202 is used to retrieve information association entries in a preset welfare information database based on the welfare information type, analyze the consultation request queue corresponding to the information association entries, extract the queue response features in the consultation request queue, and calculate the association response index corresponding to the information association entries based on the queue response features. The load value calculation module 203 is used to collect employee access data corresponding to the employee to be consulted based on the correlation response index, query the access intent scenario where the employee access data is located, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform. The stage analysis module 204 is used to generate a consultation response template in the preset welfare consultation platform based on the consultation load value, perform response verification on the consultation response template, obtain the verification response content, and analyze the content output stage corresponding to the verification response content. The result output module 205 is used to generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

[0084] In detail, the modules in the employee welfare consultation system 200 based on intelligent customer service described in this embodiment of the invention adopt the same approach as described above when in use. Figure 1 The method used is the same as the intelligent customer service-based employee welfare consultation method described in the article, and can produce the same technical effect, so it will not be elaborated here.

[0085] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. In the above multiple embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for providing employee benefits consultation based on intelligent customer service, characterized in that, The method includes: Obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold; Based on the welfare information type, retrieve information-related entries from the preset welfare information database, analyze the consultation request queues corresponding to the information-related entries, extract queue response features from the consultation request queues, and calculate the association response index corresponding to the information-related entries based on the queue response features. Based on the correlation response index, collect employee access data corresponding to the employee to be consulted, query the access intent scenario of the employee access data, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform. Based on the consultation load value, a consultation response template in the preset welfare consultation platform is generated. The consultation response template is verified to obtain the verified response content. The content output stage corresponding to the verified response content is analyzed. Generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

2. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The step of identifying the welfare information type corresponding to the content query element based on the consultation concurrency threshold includes: Analyze the request concurrency patterns within the consultation concurrency threshold; Based on the request concurrency mode, identify the hot query dimensions among the content query elements; Map the welfare classification system corresponding to the aforementioned hot query dimensions; Extract the dominant information features from the welfare classification system; Based on the dominant information features, the welfare information type corresponding to the content query element is identified.

3. The employee welfare consultation method based on intelligent customer service as described in claim 2, characterized in that, The step of identifying hot query dimensions among the content query elements based on the request concurrency mode includes: Analyze the concurrent request traffic under the aforementioned concurrent request mode; Extract the characteristic peak data from the concurrent request traffic; Based on the aforementioned peak feature data, match the set of query entries in the content query elements; Label the hot search tags in the query item set; Based on the hotspot query tags, identify the hotspot query dimensions in the content query elements.

4. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The step of retrieving information-related entries from a preset welfare information database based on the welfare information type includes: Parse the information category code corresponding to the welfare information type; Analyze the library retrieval identifier corresponding to the information category code; Based on the database retrieval identifier, query the potential item set in the preset welfare information database; Calculate the entry association value corresponding to each entry in the potential entry set; Based on the entry association value, information association entries in the preset welfare information database are retrieved.

5. The employee welfare consultation method based on intelligent customer service as described in claim 4, characterized in that, The parsing of the information category code corresponding to the welfare information type includes: Extract the type identifier field corresponding to the welfare information type; Based on the type identifier field, match the set of rule entries in the preset encoding rule base; Verify the valid rule information in the rule information set; Reconstruct the information encoding process corresponding to the effective rule information; Based on the aforementioned information encoding process, the information category code corresponding to the welfare information type is parsed.

6. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The step of calculating the association response index corresponding to the information association item based on the queue response characteristics includes: Analyze the feature association sequence corresponding to the queue response features; The feature-related sequences are quantized to obtain the quantization correlation index; Query the correlation response threshold corresponding to the quantitative correlation index; Analyze the correlation load factor corresponding to the correlation response threshold; Based on the aforementioned association load factor, the association response index corresponding to the information association item is calculated using the following formula: ； in, This represents the association response index corresponding to the information association item. This indicates the number of dimensions corresponding to the consultation dimension of the information association entry. Index representing the number of consultation dimensions. Indicates the first The dimension weights corresponding to each consultation dimension Indicates the first The corresponding correlation response threshold for each consultation dimension Indicates the first The correlation load factor corresponding to each consultation dimension.

7. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The extraction of scenario load data from the access intent scenario includes: Identify the scene access sequence in the access intent scenario; Analyze the concurrent request volume corresponding to the access sequence of the scenario; Query the concurrency baseline value in the aforementioned concurrent request volume; Analyze the resource load index corresponding to the concurrency benchmark value; Based on the resource load index, extract the scenario load data in the access intent scenario; The consultation load value of the scenario load data on the preset welfare consultation platform is calculated using the following formula: ； in, This indicates the consultation load value of the scenario load data in the preset welfare consultation platform. This indicates the current resource load index in the preset welfare consultation platform. This indicates the total number of time series intervals corresponding to the scenario load data. Index representing the number of time series intervals. Indicates the first Concurrent request volume within a time series interval This represents the concurrency baseline value.

8. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The step of generating a pre-defined consultation response template for the welfare consultation platform based on the consultation load value includes: Based on the consultation load value, analyze the platform load status corresponding to the preset welfare consultation platform; Analyze the status classification indicators corresponding to the platform load status; Determine the hierarchical presentation content corresponding to the state hierarchical index; Query the content structure details of the hierarchical presentation content; Based on the content structure details, a consultation response template for a preset welfare consultation platform is generated.

9. The employee welfare consultation method based on intelligent customer service as described in claim 1, characterized in that, The step of verifying the consultation response template to obtain the verified response content includes: Identify the response information points in the consultation response template; Iterate through the welfare information responses covered by the aforementioned response information points; Verify that the welfare information response corresponds to the relevant regulations and policy objectives; Based on the policy objectives of the aforementioned regulations, the core response units in the welfare information response are verified; Based on the core response unit, the consultation response template is validated to obtain the validated response content.

10. An employee benefits consultation system based on intelligent customer service, characterized in that, The system includes: The type recognition module is used to obtain the welfare consultation content input by the employee to be consulted, extract the content query elements in the welfare consultation content, parse the consultation concurrency threshold corresponding to the query elements, and identify the welfare information type corresponding to the content query elements based on the consultation concurrency threshold. The index calculation module is used to retrieve information association entries in a preset welfare information database based on the welfare information type, analyze the consultation request queues corresponding to the information association entries, extract queue response features from the consultation request queues, and calculate the association response index corresponding to the information association entries based on the queue response features. The load value calculation module is used to collect employee access data corresponding to the employee to be consulted based on the correlation response index, query the access intent scenario in which the employee access data is located, extract the scenario load data in the access intent scenario, and calculate the consultation load value of the scenario load data in the preset welfare consultation platform. The phase analysis module is used to generate a consultation response template in the preset welfare consultation platform based on the consultation load value, perform response verification on the consultation response template, obtain the verified response content, and analyze the content output phase corresponding to the verified response content. The result output module is used to generate the content push sequence corresponding to the content output stage, construct the output response instruction corresponding to the content push sequence, and output the welfare consultation result required by the employee to be consulted based on the output response instruction.

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