Online psychological counseling verbal skill reply method and system

Abstract

The invention relates to the technical field of online psychological counseling, in particular to a verbal skill reply method and system for online psychological counseling, and the method comprises the following steps: based on original data of emotional states and psychological counseling demands, carrying out symbol distribution on differentiated states and demands, carrying out initialization coding processing on the data, and generating a symbol set initial framework; according to the method, the verbal skill strategy is accurately mapped through initialization coding and symbol optimization, loss in the information transmission process is effectively avoided, information exchange in the consultation process is smoother and more accurate, continuous analysis and iterative adjustment of the information transmission efficiency are beneficial to continuously optimizing the reply strategy, the overall consultation quality is improved, and the consultation efficiency is improved. By simulating the psychological influence of the differentiated verbal skill strategy and drawing the influence map, potential psychological risks can be accurately predicted and avoided, it is ensured that the consultation service can better meet the actual needs of the help seeker, implementation of personalized services is enhanced, and psychological health of the user is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of online psychological consultation, and in particular to a speech reply method and system for online psychological consultation. Background Art

[0002] The field of online psychological counseling technology involves the use of Internet technology to provide mental health services and support, including psychotherapy and counseling services through video conferencing, instant messaging software, email and online platforms. The application of technology allows professionals to remotely contact those seeking help, breaking geographical and time constraints and providing more flexible and accessible mental health resources. With the development and popularization of Internet technology, online psychological counseling has become an important means to improve the level of public mental health, especially in densely populated or remote areas. It includes the development of related software and tools to enhance the efficiency and quality of counseling, such as using artificial intelligence to analyze user emotions or provide initial mental health assessments.

[0003] Among them, the speech response method of online psychological counseling refers to a specific communication technique or strategy used in the process of online psychological counseling. This method aims to enhance the communication effect through carefully designed response words, improve the responsiveness and personalization of counseling. The purpose is to more effectively support and guide the help seekers, help them feel understood and supported in the virtual environment, and promote psychological recovery and health improvement. It not only helps to improve the work efficiency of counselors, but also enhances the security and privacy of services. It is an important part of modern online psychological counseling services.

[0004] Existing online psychological counseling technologies provide flexible and extensive access methods, but they are deficient in personalized services and responsiveness. Existing technologies rely on standardized responses and procedures, lack in-depth analysis of individual differences and immediate emotional changes of clients, resulting in insufficient personalization and precision of counseling. Information transmission efficiency has not been finely optimized, resulting in the failure to fully understand and meet the actual needs of clients, affecting the effectiveness of counseling and client satisfaction. The deficiencies in existing technologies are particularly significant when dealing with complex emotions and psychological states, leading to misunderstandings and information transmission errors during the counseling process, affecting the client's trust and the overall quality of counseling. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for responding to online psychological consultations.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for replying words in online psychological consultation, comprising the following steps:

[0007] S1: Based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and coded to generate the initial framework of the symbol set;

[0008] S2: using the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining an optimized symbol set;

[0009] S3: construct a mapping relationship from symbols to speech strategies through the optimized symbol set, integrate the symbols and corresponding speech strategies, and generate a coding mapping table by checking them one by one;

[0010] S4: Analyze the information transmission efficiency of the speech reply by using the coding mapping table, avoid information loss by iteratively adjusting the mapping relationship in the mapping table, and generate an information transmission efficiency analysis result;

[0011] S5: Using the information transmission efficiency analysis results, according to the speech data and psychological risk assessment indicators, simulate the psychological impact of the differentiated speech strategy, draw a psychological impact map, and obtain a psychological impact prediction model;

[0012] S6: Analyze the psychological risks of the speech strategy through the psychological impact prediction model, compare the prediction results with the risk acceptance threshold, adjust the speech strategy in the mapping table, avoid psychological risks, and generate strategy adjustment results.

[0013] As a further solution of the present invention, the initial framework of the symbol set includes emoticons based on differentiated emotions, descriptive labels of psychological needs and associated initialization codes; the optimized symbol set includes symbols for distinguishing differentiated psychological states, hierarchical symbols of emotions and optimized behavior indicator symbols; the coding mapping table includes the degree of association between symbols and speech, the classification of application scenarios of speech and the expected feedback type of speech effects; the information transmission efficiency analysis results include the response time, information coverage and customer satisfaction index of speech; the psychological impact prediction model includes an emotion change prediction map, a psychological stability index and a predicted user behavior response pattern; the strategy adjustment results include a modified speech response strategy, risk threshold adjustment parameters and a newly introduced early warning mechanism.

[0014] As a further solution of the present invention, based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and encoded. The steps of generating the initial framework of the symbol set are specifically as follows:

[0015] S101: Collect raw data on emotional states and psychological counseling needs, assign unique identifiers to data items, record sources and timestamps, and generate a data identification framework;

[0016] S102: Based on the data identification framework, identify key variables in the data, including emotional fluctuations and consultation frequency, perform frequency and pattern analysis on the variables, extract key features, and generate a key feature index;

[0017] S103: Using the key feature index, assigning symbols to differentiated emotional states and psychological needs, marking and classifying the data, and generating an initial framework of the symbol set.

[0018] As a further solution of the present invention, the steps of adopting the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining the optimized symbol set are specifically as follows:

[0019] S201: using the initial framework of the symbol set, statistically analyzing the frequency and distribution of each symbol, recording the number of symbols and rare symbols, analyzing the application of symbols in differentiated emotional states and psychological needs, and generating symbol analysis results;

[0020] S202: Based on the symbol analysis results, evaluate the representativeness and discrimination of the symbols, merge the symbols with low discrimination, divide the high-frequency but low-representative symbols, check that each symbol has a definite and unique representational meaning, and generate a symbol optimization plan;

[0021] S203: Implement the symbol optimization scheme, readjust and encode the symbol set, and through iterative data encoding and symbol adjustment, check each symbol's impact on the target's emotional state and psychological counseling needs, and generate an optimized symbol set.

[0022] As a further solution of the present invention, the steps of constructing a mapping relationship between symbols and speech strategies through the optimized symbol set, integrating symbols and corresponding speech strategies, and checking them one by one to generate a coding mapping table are specifically as follows:

[0023] S301: using the optimized symbol set, associating each symbol with a target speech strategy, customizing the speech strategy according to the emotional state and psychological needs represented by the symbol, verifying that each symbol has a corresponding response speech, and generating an initialization mapping relationship;

[0024] S302: Integrate and optimize the initialized mapping relationship, pair the symbols with the corresponding speech words according to the real-time application scenario and logical relationship, check and resolve existing conflicts, verify the practicality of the mapping relationship, and generate a verified mapping relationship;

[0025] S303: According to the verified mapping relationship, each symbol and the corresponding speech strategy are recorded, and verification and adjustment are performed, including the definition and matching speech of each symbol, to generate a coding mapping table.

[0026] As a further solution of the present invention, the steps of using the coding mapping table to analyze the information transmission efficiency of the speech reply, and avoiding information loss by iteratively adjusting the mapping relationship in the mapping table to generate the information transmission efficiency analysis result are specifically as follows:

[0027] S401: Based on the coding mapping table, an initial information transmission efficiency test is performed to evaluate the response speed and user analysis degree of the differentiated speech strategy through simulation scenarios and user feedback, identify delays and ambiguous points in information transmission, and generate initial efficiency analysis results;

[0028] S402: According to the initialization efficiency analysis result, iteratively adjust the mapping relationship between symbols and speech in the mapping table, optimize the speech strategy that causes information delay and misunderstanding, adjust the speech content through real-time data and user behavior analysis, and generate an adjusted mapping relationship;

[0029] S403: Apply the adjusted mapping relationship, perform the information transmission efficiency test again, compare the improvement effects before and after, verify the information loss and optimize the efficiency of the speech reply, and generate the information transmission efficiency analysis results.

[0030] As a further solution of the present invention, the information transmission efficiency analysis results are used to simulate the psychological impact of differentiated speech strategies according to speech data and psychological risk assessment indicators, and a psychological impact map is drawn to obtain a psychological impact prediction model in the following steps:

[0031] S501: Using the information transmission efficiency analysis results, combined with the speech data and the psychological risk assessment index, setting simulation parameters to evaluate the psychological impact of the differentiated speech strategy in the real-time scenario, including the anxiety caused by the comfort and incentive effect, and generating psychological impact initialization simulation data;

[0032] S502: Based on the psychological impact initialization simulation data, using data visualization tools, evaluating the psychological impacts corresponding to various speech strategies, analyzing the intensity and range of the psychological impacts corresponding to differentiated speech strategies, checking the psychological effect of each strategy, and generating a psychological impact map;

[0033] S503: According to the psychological impact map, analyze the relationship between speech strategies and psychological reactions, predict the psychological impact of differentiated speech strategies, verify the real-time adjustment and optimization of the model to the speech, match the user's psychological state, and generate a psychological impact prediction model.

[0034] As a further solution of the present invention, the psychological risk of the speech strategy is analyzed by the psychological impact prediction model, the prediction result is compared with the risk acceptance threshold, the speech strategy in the mapping table is adjusted to avoid psychological risks, and the steps of generating the strategy adjustment result are specifically as follows:

[0035] S601: Using the psychological impact prediction model and a multiple linear regression algorithm, the psychological risks of the differentiated speech strategies are evaluated, including quantifying the changes in emotional instability and stress, and generating speech strategy risk assessment results;

[0036] S602: Compare the predicted risk value in the risk assessment result of the speech strategy with the pre-set risk acceptance threshold, identify the speech strategy that exceeds the threshold, analyze the risk source and impact of the strategy, and generate the analysis result of the speech strategy with excessive risk;

[0037] S603: Based on the analysis results of the risk-exceeding speech, adjust the speech strategy in the mapping table, optimize and replace the speech that causes psychological risks, avoid potential negative effects, rebalance the relationship between the speech strategy and the user's psychological safety, and generate a strategy adjustment result.

[0038] As a further solution of the present invention, the formula of the multivariate linear regression algorithm is as follows:

[0039]

[0040] Among them, R is the psychological risk score value, β0 represents the intercept of the regression model, β1 represents the weight parameter of emotional instability, E represents the measurement value of emotional instability, β2 represents the weight parameter of the change in sense of pressure, S represents the change in sense of pressure, T represents the duration of the speech, β3 represents the weight parameter of the complexity of the speech strategy, and C represents the complexity of the speech strategy.

[0041] A speech reply system for online psychological consultation, the speech reply system for online psychological consultation is used to execute the above-mentioned speech reply method for online psychological consultation, the system comprises:

[0042] The data coding module collects data on psychological counseling needs and emotional states, encodes the data, assigns target symbols, and generates an initial framework of symbol sets;

[0043] The symbol mapping module analyzes the representativeness and discrimination of the differentiated symbols based on the initial framework of the symbol set, merges the symbols of the same type and divides the symbols of different types, and obtains an optimized symbol set;

[0044] The speech strategy module uses the optimized symbol set to establish a mapping relationship from symbols to speech strategies, verifies the mapping relationship one by one, checks the adaptability of the speech, and constructs a speech mapping table;

[0045] The transmission efficiency processing module analyzes the efficiency of information transmission through the speech mapping table, iteratively adjusts the mapping table, verifies the speed and efficiency of information transmission through data analysis, and generates information efficiency analysis results;

[0046] The psychological risk assessment module simulates the psychological impact of differentiated speech strategies based on the information efficiency analysis results, combined with speech data and psychological risk assessment indicators, and generates strategy adjustment results.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by refining and encoding the emotional state and needs in psychological counseling, the uniqueness of the symbols is optimized, and the pertinence and efficiency of the speech response are significantly improved. The speech strategy is accurately mapped by initialization coding and symbol optimization, which effectively avoids the loss in the process of information transmission, making the information exchange in the consultation process smoother and more accurate. Continuous analysis and iterative adjustment of information transmission efficiency help to continuously optimize the response strategy and improve the overall consultation quality. By simulating the psychological impact of differentiated speech strategies and drawing an impact map, potential psychological risks can be accurately predicted and avoided, and the security of the service can be enhanced. The adjustment and optimization process of the system ensures that the consulting service can better meet the actual needs of the help seekers, strengthen the implementation of personalized services, and promote the mental health of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0050] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0051] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0052] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0053] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0054] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0055] Figure 7 This is a detailed flow chart of S6 of the present invention;

[0056] Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0058] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0059] See also Figure 1 The present invention provides a technical solution, a method for replying to online psychological consultation, comprising the following steps:

[0060] S1: Based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and coded to generate the initial framework of the symbol set;

[0061] S2: Using the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining the optimized symbol set;

[0062] S3: Use the optimized symbol set to build a mapping relationship from symbols to speech strategies, integrate symbols and corresponding speech strategies, check them one by one, and generate a coding mapping table;

[0063] S4: Use the coding mapping table to analyze the information transmission efficiency of the speech response, and iteratively adjust the mapping relationship in the mapping table to avoid information loss and generate information transmission efficiency analysis results;

[0064] S5: Using the information transmission efficiency analysis results, based on the speech data and psychological risk assessment indicators, simulate the psychological impact of differentiated speech strategies, draw a psychological impact map, and obtain a psychological impact prediction model;

[0065] S6: Analyze the psychological risks of the speech strategy through the psychological impact prediction model, compare the prediction results with the risk acceptance threshold, adjust the speech strategy in the mapping table, avoid psychological risks, and generate strategy adjustment results.

[0066] The initial framework of the symbol set includes emoticons based on differentiated emotions, descriptive labels of psychological needs and associated initialization codes. The optimized symbol set includes symbols for distinguishing differentiated psychological states, hierarchical symbols of emotions and optimized behavioral indicator symbols. The coding mapping table includes the degree of association between symbols and speech, the classification of application scenarios of speech and the expected feedback type of speech effects. The results of information transmission efficiency analysis include the response time of speech, information coverage and customer satisfaction indicators. The psychological impact prediction model includes the emotion change prediction map, the psychological stability index and the predicted user behavior response pattern. The strategy adjustment results include the modified speech response strategy, the risk threshold adjustment parameters and the newly introduced early warning mechanism.

[0067] See also Figure 2 Based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and coded. The steps to generate the initial framework of the symbol set are as follows:

[0068] S101: Collect the original data of emotional state and psychological counseling needs, assign unique identifiers to data items, record the source and timestamp, and generate the execution process of the data identification framework as follows;

[0069] In the process of collecting raw data on emotional states and psychological counseling needs, it is necessary to determine the source and type of data, such as obtaining data through questionnaires, online platform interactions, or direct face-to-face counseling sessions. The collected data should be classified in detail according to the specific content of emotional states and counseling needs, and a unique identifier should be assigned for future tracking and analysis. It is crucial to record the timestamp of the data because it can help researchers identify the specific time point of data collection and analyze the changing trends of emotional states or the timeliness of psychological counseling. The process involves formatting and standardizing the data output to ensure that the data can be correctly identified and used by the system in subsequent processing. The accuracy of data identification and classification directly affects the quality of data analysis and the effectiveness of data-driven decision-making, and generates a data identification framework.

[0070] S102: Based on the data identification framework, identify key variables in the data, including emotional fluctuations and consultation frequency, perform frequency and pattern analysis on the variables, extract key features, and generate the execution process of the key feature index as follows;

[0071] Identify key variables in the data and follow the formula Calculate the variance of emotional fluctuations. In the formula, x i represents the emotional state value recorded in a single counseling session, represents the average emotional state value, and N represents the total number of consultation sessions.

[0072] Detailed explanation of the formula and the process of formula calculation: Considering that the identification of emotional fluctuations and consultation frequency is achieved by analyzing the fluctuation pattern in time series data, the variance formula can be used to measure the degree of fluctuation of emotional state. By collecting emotional state data within a certain period of time, such as emotional records of each day in a week, if there are seven records, then N = 7. Let the emotional state of each record be x1, x2, ..., x7, and calculate the average value Each x i minus The sum of the squares of the differences is then divided by N to obtain the variance F of the emotion fluctuation.

[0073] For example, if the sentiment records for a week are 4, 5, 3, 4, 4, 5, 3, the average is The variance F is calculated as follows:

[0074]

[0075] The results showed that emotional states were relatively stable during the week and did not fluctuate much.

[0076] S103: Using the key feature index, assigning symbols to differentiated emotional states and psychological needs, marking and classifying the data, and generating the execution process of the initial framework of the symbol set are as follows;

[0077] Assign symbols to differentiated emotional states and psychological needs, and symbolize each type of data to improve data processing efficiency and accuracy, including the identification of each type of emotional state and psychological need. A variety of data classification techniques will be used in the process, such as cluster analysis, to ensure that each emotional state and need is properly identified and classified. The symbols assigned to each emotional state and psychological need need to have a certain logic and recognizability to facilitate subsequent data analysis and application. After classification, the data will be organized according to the symbol set and stored in the database. In subsequent data query and analysis, relevant data can be directly retrieved through symbols, which speeds up data processing, improves work efficiency, and generates the initial framework of the symbol set.

[0078] See also Figure 3 , using the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining the optimized symbol set in the following steps:

[0079] S201: Using the initial framework of the symbol set, statistically analyze the frequency and distribution of each symbol, record the number of symbols and rare symbols, analyze the application of symbols in differentiated emotional states and psychological needs, and generate the execution process of symbol analysis results as follows;

[0080] Using the initial framework of the symbol set, a statistical analysis of the frequency and distribution of each symbol is performed. The process includes collecting the number of times each symbol appears in the database, as well as its distribution in different emotional states and psychological needs. Symbols with high frequency but low representativeness or unclear specific meanings can be identified, as well as rare but significant symbols. The statistical analysis results will record the total number of symbols and the specific number of each symbol in order to provide decision support for subsequent symbol optimization in order to more accurately reflect different emotional states and psychological needs. The statistical analysis of symbols not only helps to understand the distribution characteristics of the data, but also provides a basis for further data processing and analysis. For example, in subsequent processing, the structure of the data set can be adjusted according to the distribution and frequency of the symbols to enhance the representativeness and discrimination of the data set and obtain an optimized symbol set.

[0081] S202: Based on the symbol analysis results, evaluate the representativeness and discrimination of the symbols, merge the symbols with low discrimination, divide the high-frequency but low-representative symbols, check that each symbol has a definite and unique representational meaning, and generate the execution process of the symbol optimization plan as follows;

[0082] Based on the results of symbol analysis, the symbols are optimized, including merging symbols with low discrimination and symbols with high segmentation frequency but low representativeness, to ensure that each symbol can accurately represent a specific emotional state or psychological need. The steps involve evaluating the functionality and practicality of the symbols. In this way, the efficiency and practicality of the symbol set can be improved. During the evaluation process, special attention will be paid to symbols that appear frequently in multiple categories but have ambiguous meanings. If the symbols cannot effectively distinguish different situations, it will lead to misunderstandings in data analysis. This includes redefining or adjusting the symbols to ensure that each symbol can play its due role in the analysis, improve the accuracy and efficiency of data processing, and generate symbol optimization plans.

[0083] S203: Implement the symbol optimization plan, readjust and encode the symbol set, and check each symbol for the target's emotional state and psychological counseling needs through iterative data encoding and symbol adjustment. The execution process of generating the optimized symbol set is as follows;

[0084] Implement the symbol optimization plan to ensure that each symbol can more accurately reflect the emotional state or psychological needs it represents by re-adjusting and re-encoding the existing symbol set. The process includes iterative data encoding and symbol adjustment, as well as testing and verification to ensure that each adjustment can improve the effectiveness of the symbol set, which will serve the emotional state and psychological counseling needs analysis more accurately and effectively. The technologies involved in the re-encoded symbol set include data classification, cluster analysis, and pattern recognition. The adjustment of each symbol is based on its performance in actual applications and the degree of matching with the emotional state and psychological needs of the target group. The optimization process ensures that each symbol has a unique and definite representational meaning, improves the performance and reliability of the entire data analysis system, and generates an optimized symbol set.

[0085] See also Figure 4 , through the optimized symbol set, construct the mapping relationship between symbols and speech strategies, integrate symbols and corresponding speech, check them one by one, and generate the coding mapping table in the following steps:

[0086] S301: Using the optimized symbol set, each symbol is associated with the target speech strategy. The speech strategy is customized according to the emotional state and psychological needs represented by the symbol. It is verified that each symbol has a corresponding response speech. The execution process of generating the initialization mapping relationship is as follows;

[0087] Using the optimized symbol set, a customized speech strategy is associated with each symbol. The strategy is carefully designed according to the emotional state and psychological needs represented by the symbol to ensure that each symbol has an appropriate response speech. The process includes the writing of the speech content and the corresponding setting of the symbol. The verification process ensures that the match between each symbol and the speech is accurate, and provides a preliminary symbol-to-speech linking framework. The framework provides the basis for subsequent optimization and application. The generation of the initialization mapping is a dynamic process involving repeated testing and adjustment to ensure that the speech can effectively reflect the emotions and needs represented by the symbols. Such a match is crucial for interaction and user satisfaction in actual applications, and an initialization mapping relationship is generated.

[0088] S302: Integrate and optimize the initial mapping relationship, pair the symbols with the corresponding words according to the real-time application scenario and logical relationship, check and resolve the existing conflicts, verify the practicality of the mapping relationship, and generate the verified mapping relationship. The execution process is as follows;

[0089] Integrate and optimize the initial mapping relationship. The steps involve effectively pairing symbols with corresponding speech strategies according to real-time application scenarios and logical relationships, checking and resolving existing conflicts to ensure that the mapping relationship can maintain consistency and practicality in various application scenarios. During the optimization process, pay special attention to adjusting the speech that is ineffective due to changes in the scenario to ensure that the speech of each symbol can work effectively in different situations. Systematic inspection and adjustment help improve the adaptability of the entire system and the quality of user interaction. It is a confirmation of the relationship between symbols and speech, ensuring that the relationship can achieve the best effect in actual use and generate a verified mapping relationship.

[0090] S303: According to the verified mapping relationship, each symbol and the corresponding speech strategy are recorded, and verification and adjustment are performed, including the definition and matching speech of each symbol, and the execution process of generating the coding mapping table is as follows;

[0091] According to the verified mapping relationship, record and adjust each symbol and the corresponding speech strategy, including detailed symbol definition and speech content writing. The adjustment process ensures that the definition of each symbol is accurate, and each speech can effectively express the meaning of the symbol and respond to user needs. It is used to guide future application and system development. Not only the correspondence between each symbol and speech is recorded, but also the instructions for using the symbols and speech in different situations. The recording and adjustment are to ensure that the system can efficiently and accurately reflect the user's emotions and needs during actual operation, improve the user experience and the application value of the system, and generate a coding mapping table.

[0092] See also Figure 5 ,Use the coding mapping table to analyze the information transmission efficiency of the speech reply, and avoid information loss by iteratively adjusting the mapping relationship in the mapping table. The specific steps for generating the information transmission efficiency analysis results are as follows:

[0093] S401: Based on the coding mapping table, an initial information transmission efficiency test is performed. By simulating scenarios and user feedback, the response speed and user analysis degree of the differentiated speech strategy are evaluated, and delays and ambiguities in information transmission are identified. The execution process of generating the initial efficiency analysis results is as follows;

[0094] Based on the coding mapping table, the initial information transmission efficiency test is implemented, including testing the response time of each symbol and speech strategy and the user's understanding level in a simulated scenario. Special attention will be paid to identifying factors that cause information delays and ambiguity in the evaluation. The test process evaluates the actual effect of the speech strategy by simulating the interaction between the user and the system. The problem points and causes found in the test are recorded in detail to provide a basis for subsequent adjustments. Efficiency testing is a key step to help determine the need for further optimization of the speech strategy in actual applications to improve the speed and accuracy of information transmission. It is not only related to the user experience, but also directly affects the operating efficiency of the system, generating initial efficiency analysis results.

[0095] S402: According to the initialization efficiency analysis result, the mapping relationship between symbols and speech in the mapping table is iteratively adjusted to optimize the speech strategy that causes information delay and misunderstanding. Through real-time data and user behavior analysis, the speech content is adjusted to generate the adjusted mapping relationship. The execution process is as follows;

[0096] According to the results of the initialization efficiency analysis, the mapping relationship from symbols to speech in the mapping table is iteratively adjusted, including analyzing the specific speech strategies that cause information delays and misunderstandings, and optimizing according to the in-depth analysis of real-time data and user behavior. The adjustment process not only focuses on improving the clarity and accuracy of the speech, but also includes adjusting the structure and expression of the speech content to adapt to different interaction scenarios and user needs, ensuring that information is transmitted more efficiently and without ambiguity. Iterative adjustment based on data is a key link in ensuring the adaptability and flexibility of the system. Through adjustment, the system can better serve users, improve the naturalness of interaction and user satisfaction, and generate adjusted mapping relationships.

[0097] S403: Apply the adjusted mapping relationship, perform the information transmission efficiency test again, compare the improvement effects before and after, verify the information loss and optimize the efficiency of the speech reply, and generate the information transmission efficiency analysis result. The execution process is as follows;

[0098] Apply the adjusted mapping relationship and perform the information transmission efficiency test again to compare the information transmission efficiency and user understanding before and after the adjustment. This step is intended to verify the actual effect of the adjustment measures and ensure that the optimization of the speech strategy can effectively reduce information loss and improve response speed. The test is carried out by simulating the user interaction scenario again. The evaluation results will record in detail the specific effects of each speech strategy improvement, including the reduction of response time and the improvement of user satisfaction, provide proof of the effectiveness of the adjustment measures, and ensure that each optimization is based on actual user feedback and data analysis. The iterative process is a key method to improve the overall performance of the system and user experience, and generate information transmission efficiency analysis results.

[0099] See also Figure 6, using the information transmission efficiency analysis results, according to the speech data and psychological risk assessment indicators, simulate the psychological impact of differentiated speech strategies, draw a psychological impact map, and obtain the psychological impact prediction model in the following steps:

[0100] S501: Using the information transmission efficiency analysis results, combined with the speech data and psychological risk assessment indicators, simulation parameters are set to evaluate the psychological impact of differentiated speech strategies in real-time scenarios, including the anxiety caused by comfort and incentive effects. The execution process of generating psychological impact initialization simulation data is as follows;

[0101] Using the results of information transmission efficiency analysis, combined with speech data and psychological risk assessment indicators, simulation parameters are set to evaluate the psychological impact of differentiated speech strategies in real-time scenarios, including anxiety or psychological reactions caused by speech strategies when providing comfort or motivation. The process of setting simulation parameters takes into account the characteristics of various speech strategies and the psychological characteristics of the target user group, providing a basic data framework for further analysis and optimization. The data set is an important tool for understanding the actual psychological impact of speech strategies, helping analysts and decision makers to foresee the specific psychological effects that different speech strategies may bring, and to more accurately adjust the speech content in actual applications to adapt to the actual needs of users, and generate psychological impact initialization simulation data.

[0102] S502: Based on the psychological impact initialization simulation data, using data visualization tools, evaluating the psychological impact corresponding to various speech strategies, analyzing the intensity and range of psychological impact corresponding to differentiated speech strategies, checking the psychological effect of each strategy, and generating the psychological impact map. The execution process is as follows;

[0103] Based on the simulation data initialized with psychological impact, data visualization tools are used to evaluate the psychological impact of different speech strategies. The intensity and range of the psychological impact of differentiated speech strategies are displayed through graphics and charts. The analysis helps identify which speech strategies are more effective in actual applications. At the same time, it points out that the strategies need further adjustment or improvement, and demonstrates the specific impact of various speech strategies on the user's psychological state. The map makes the design and optimization process of speech strategies more data-driven, ensuring that decisions are based on actual user psychological reactions, improving the overall effectiveness and applicability of speech strategies, and generating a psychological impact map.

[0104] S503: According to the psychological impact map, the relationship between the speech strategy and the psychological reaction is analyzed, the psychological impact of the differentiated speech strategy is predicted, the real-time adjustment and optimization of the speech strategy by the verification model is performed, and the psychological state of the user is matched. The execution process of generating the psychological impact prediction model is as follows;

[0105] According to the psychological impact map, the relationship between speech strategies and psychological reactions is analyzed, and the psychological impact of differentiated speech strategies is predicted. The process covers all stages from data to decision-making. Using the data in the psychological impact map, researchers can deeply understand how different speech strategies interact with the user's psychological state. Based on actual user feedback and psychological assessment data, a scientific basis is provided for the real-time adjustment and optimization of speech strategies, ensuring that the speech strategy can effectively match the user's psychological state in actual application, and used to guide future speech strategy design, ensuring that the speech content not only meets the needs of the situation, but also can positively affect the user's mental health and overall experience, and generate a psychological impact prediction model.

[0106] See also Figure 7 , through the psychological impact prediction model, analyze the psychological risks of the speech strategy, compare the prediction results with the risk acceptance threshold, adjust the speech strategy in the mapping table, avoid psychological risks, and generate the strategy adjustment results in the following steps:

[0107] S601: Using the psychological impact prediction model and the multivariate linear regression algorithm, the psychological risks of the differentiated speech strategies are evaluated, including the quantification of changes in emotional instability and sense of pressure. The execution process of generating the speech strategy risk assessment results is as follows;

[0108] The formula for the multiple linear regression algorithm is as follows:

[0109]

[0110] Among them, R is the psychological risk score value, β0 represents the intercept of the regression model, β1 represents the weight parameter of emotional instability, E represents the measurement value of emotional instability, β2 represents the weight parameter of the change in sense of pressure, S represents the change in sense of pressure, T represents the duration of the speech, β3 represents the weight parameter of the complexity of the speech strategy, and C represents the complexity of the speech strategy.

[0111] Detailed explanation of the formula and the process of formula calculation and derivation:

[0112] 1. Parameter definition and data acquisition:

[0113] β0 (intercept): obtained by analyzing historical data through a linear regression model and set to 2.5. The value represents the basic psychological risk score when there is no speech strategy.

[0114] β1 (emotional instability weight): Based on the correlation analysis between emotions and psychological risks, it is set to 1.2. The parameter changes greatly with emotional fluctuations.

[0115] β2 (weight of pressure change): reflects the impact of pressure change on psychological risk and is set to 0.8. The coefficient takes into account the adjustment effect of the logistic function form on the pressure change.

[0116] β3 (weight of speech complexity): referring to the actual effect of complexity on psychological influence, it is set to 0.5.

[0117] 2. Parameter quantization process:

[0118] E (emotional instability measurement): collected through emotion monitoring equipment, the emotion fluctuation index is set to 30 (quantifying the average daily fluctuation frequency of emotional stability).

[0119] S (change in pressure): measured by a psychological pressure monitor, set to 20 in this example (refers to the change in pressure index over a period of time).

[0120] T (duration of the conversation): When calculating the parameter, it is measured in minutes. In this example, it is set to 10 minutes.

[0121] C (complexity of the sales pitch strategy): The quantitative method is to count the number of variables and turning points in the implementation of the sales pitch, which is set to 4 in this example.

[0122] 3. Formula calculation derivation process:

[0123]

[0124] calculate As the T value increases, e -T is close to 0, the logic function approaches 1, so this part is calculated as

[0125] calculate Calculated

[0126] Comprehensive calculation formula: R=2.5+36+16+1.41=55.91.

[0127] The results show that according to the current settings of emotional instability, stress change, speech duration, and speech strategy complexity, the calculated psychological risk score is 55.91. The numerical value indicates that under the current speech strategy, the subject faces a medium or above psychological risk. Through quantitative evaluation, strategy makers can adjust speech strategies for high risk scores to reduce potential adverse psychological effects.

[0128] S602: Compare the predicted risk value in the risk assessment result of the speech strategy with the pre-set risk acceptance threshold, identify the speech strategy that exceeds the threshold, analyze the risk source and impact of the strategy, and generate the execution process of the risk-exceeding speech analysis result as follows;

[0129] Compare the predicted risk value in the speech strategy risk assessment result with the pre-set risk acceptance threshold to identify speech strategies that exceed the threshold. This step is to ensure that all speech strategies are within an acceptable risk range. Speech strategies that exceed the threshold need to be analyzed in detail to determine the source of risk and the extent of the impact caused, including the specific circumstances of each speech strategy that exceeds the threshold and the recommended treatment measures. This is a key safety assurance step to ensure that the speech strategy will not affect the user's mental health or cause adverse reactions due to excessive risks. In this way, the system can timely adjust or eliminate speech that causes user discomfort or negative effects, and generate risk-exceeding speech analysis results.

[0130] S603: Based on the analysis results of the risk-exceeding speech, the speech strategy in the mapping table is adjusted to optimize and replace the speech that causes psychological risks, avoid potential negative impacts, and rebalance the relationship between the speech strategy and the user's psychological safety. The execution process for generating the strategy adjustment results is as follows;

[0131] Based on the analysis results of risk-exceeding speech, the speech strategies in the mapping table are adjusted to optimize and replace speech that causes psychological risks, and to rebalance the relationship between speech strategies and user psychological safety. The process involves redesigning the content of the speech to ensure that all speech can effectively convey information while protecting the user's mental health, including the adjustment details and expected effects of each speech strategy. The adjustment is based on actual user feedback and professional psychological assessments. The purpose is to ensure that the speech strategy can maintain the user's psychological well-being while achieving its functions and avoid any negative impacts. Through optimization measures, the system's security and user satisfaction will be improved, generating strategy adjustment results.

[0132] See also Figure 8 , a speech reply system for online psychological counseling, the speech reply system for online psychological counseling is used to execute the above-mentioned speech reply method for online psychological counseling, and the system includes:

[0133] The data coding module collects data on psychological counseling needs and emotional states, encodes the data, assigns target symbols, and generates an initial framework of symbol sets;

[0134] The symbol mapping module analyzes the representativeness and discrimination of differentiated symbols based on the initial framework of the symbol set, merges symbols of the same type and divides symbols of differences, and obtains the optimized symbol set;

[0135] The speech strategy module uses the optimized symbol set to establish a mapping relationship between symbols and speech strategies, verifies the mapping relationship one by one, checks the adaptability of the speech, and constructs a speech mapping table;

[0136] The transmission efficiency processing module analyzes the efficiency of information transmission through the speech mapping table, iteratively adjusts the mapping table, verifies the speed and efficiency of information transmission through data analysis, and generates information efficiency analysis results;

[0137] The psychological risk assessment module is based on the information efficiency analysis results, combined with the speech data and psychological risk assessment indicators, to simulate the psychological impact of differentiated speech strategies and generate strategy adjustment results.

[0138] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for responding to online psychological consultation, characterized in that: The following steps are involved: Based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and coded to generate the initial framework of the symbol set; Using the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining an optimized symbol set; Through the optimized symbol set, a mapping relationship between symbols and speech strategies is constructed, symbols and corresponding speech strategies are integrated, and a coding mapping table is generated by checking each item one by one; Utilize the coding mapping table to analyze the information transmission efficiency of the speech reply, avoid information loss by iteratively adjusting the mapping relationship in the mapping table, and generate the information transmission efficiency analysis result; Using the information transmission efficiency analysis results, based on the speech data and psychological risk assessment indicators, simulate the psychological impact of the differentiated speech strategy, draw a psychological impact map, and obtain a psychological impact prediction model; Through the psychological impact prediction model, the psychological risks of the speech strategy are analyzed, the prediction results are compared with the risk acceptance threshold, the speech strategy in the mapping table is adjusted, the psychological risks are avoided, and the strategy adjustment results are generated.

2. The online psychological consultation speech reply method according to claim 1 is characterized in that: The initial framework of the symbol set includes emoticons based on differentiated emotions, descriptive labels of psychological needs and associated initialization codes; the optimized symbol set includes symbols for distinguishing differentiated psychological states, hierarchical symbols of emotions and optimized behavioral indication symbols; the coding mapping table includes the degree of association between symbols and speech, the classification of application scenarios of speech and the expected feedback type of speech effects; the information transmission efficiency analysis results include the response time of speech, information coverage and customer satisfaction indicators; the psychological impact prediction model includes an emotion change prediction map, a psychological stability index and a predicted user behavior response pattern; the strategy adjustment results include a modified speech response strategy, risk threshold adjustment parameters and a newly introduced early warning mechanism.

3. The online psychological consultation speech reply method according to claim 1 is characterized in that: Based on the original data of emotional state and psychological counseling needs, symbols are assigned to differentiated states and needs, and the data is initialized and coded. The steps to generate the initial framework of the symbol set are as follows: Collect raw data on emotional states and psychological counseling needs, assign unique identifiers to data items, record sources and timestamps, and generate a data identification framework; Based on the data identification framework, identify key variables in the data, including mood swings and consultation frequency, perform frequency and pattern analysis on the variables, extract key features, and generate a key feature index; The key feature index is used to assign symbols to differentiated emotional states and psychological needs, label and classify the data, and generate an initial framework of the symbol set.

4. The online psychological consultation speech reply method according to claim 1 is characterized in that: The steps of adopting the initial framework of the symbol set, analyzing the representativeness and distinctiveness of each symbol in the symbol set, dividing and merging the symbols, optimizing the uniqueness of the symbols, and obtaining the optimized symbol set are as follows: Using the initial framework of the symbol set, statistically analyze the frequency and distribution of each symbol, record the number of symbols and rare symbols, analyze the application of symbols in differentiated emotional states and psychological needs, and generate symbol analysis results; Based on the symbol analysis results, evaluate the representativeness and discrimination of the symbols, merge the symbols with low discrimination, divide the high-frequency but low-representative symbols, check that each symbol has a definite and unique representational meaning, and generate a symbol optimization plan; The symbol optimization scheme is implemented, the symbol set is readjusted and encoded, and through iterative data encoding and symbol adjustment, each symbol is checked for the target's emotional state and psychological counseling needs, and an optimized symbol set is generated.

5. The online psychological consultation speech reply method according to claim 1 is characterized in that: The steps of constructing a mapping relationship between symbols and speech strategies through the optimized symbol set, integrating symbols and corresponding speech strategies, and verifying them one by one to generate a coding mapping table are as follows: Using the optimized symbol set, each symbol is associated with a target speech strategy, the speech strategy is customized according to the emotional state and psychological needs represented by the symbol, and each symbol is verified to have a corresponding response speech, and an initialization mapping relationship is generated; Integrate and optimize the initialization mapping relationship, pair the symbols with the corresponding words according to the real-time application scenario and logical relationship, check and resolve existing conflicts, verify the practicality of the mapping relationship, and generate a verified mapping relationship; According to the verified mapping relationship, each symbol and the corresponding speech strategy are recorded, and verification and adjustment are performed, including the definition and matching speech of each symbol, to generate a coding mapping table.

6. The online psychological consultation speech reply method according to claim 1, characterized in that: The steps of using the coding mapping table to analyze the information transmission efficiency of the speech reply, iteratively adjusting the mapping relationship in the mapping table to avoid information loss, and generating the information transmission efficiency analysis result are as follows: Based on the coding mapping table, an initial information transmission efficiency test is performed to evaluate the response speed and user analysis degree of the differentiated speech strategy through simulation scenarios and user feedback, identify delays and ambiguous points in information transmission, and generate initial efficiency analysis results; According to the initialization efficiency analysis result, the mapping relationship between symbols and speech in the mapping table is iteratively adjusted to optimize the speech strategy that causes information delay and misunderstanding, and the speech content is adjusted through real-time data and user behavior analysis to generate an adjusted mapping relationship; Apply the adjusted mapping relationship, perform the information transfer efficiency test again, compare the improvement effects before and after, verify the information loss and optimize the efficiency of the speech reply, and generate the information transfer efficiency analysis results.

7. The online psychological consultation speech reply method according to claim 1, characterized in that: The steps of using the information transmission efficiency analysis results, simulating the psychological impact of differentiated speech strategies according to speech data and psychological risk assessment indicators, drawing a psychological impact map, and obtaining a psychological impact prediction model are as follows: Using the information transmission efficiency analysis results, combined with the speech data and psychological risk assessment indicators, setting simulation parameters to evaluate the psychological impact of differentiated speech strategies in real-time scenarios, including the anxiety caused by comfort and incentive effects, and generating psychological impact initialization simulation data; Based on the psychological impact initialization simulation data, using data visualization tools, evaluate the psychological impact corresponding to a variety of speech strategies, analyze the intensity and range of psychological impact corresponding to differentiated speech, check the psychological effect of each strategy, and generate a psychological impact map; According to the psychological impact map, the relationship between speech strategies and psychological reactions is analyzed, the psychological impact of differentiated speech strategies is predicted, the real-time adjustment and optimization of the dialogue by the verification model is carried out, the psychological state of the user is matched, and a psychological impact prediction model is generated.

8. The online psychological consultation speech reply method according to claim 1, characterized in that: The psychological risk of the speech strategy is analyzed by the psychological impact prediction model, the prediction result is compared with the risk acceptance threshold, the speech strategy in the mapping table is adjusted to avoid psychological risks, and the steps of generating the strategy adjustment result are as follows: The psychological impact prediction model is used to evaluate the psychological risks of differentiated speech strategies using a multivariate linear regression algorithm, including quantifying changes in emotional instability and stress, and generating speech strategy risk assessment results; Compare the predicted risk value in the risk assessment result of the speech strategy with the pre-set risk acceptance threshold, identify the speech strategy that exceeds the threshold, analyze the risk source and impact of the strategy, and generate the analysis result of the speech strategy that exceeds the risk limit; Based on the analysis results of the risk-exceeding speech, the speech strategies in the mapping table are adjusted, the speech that causes psychological risks is optimized and replaced, potential negative impacts are avoided, the relationship between the speech strategy and the user's psychological safety is rebalanced, and the strategy adjustment results are generated.

9. The online psychological consultation speech reply method according to claim 1, characterized in that: The formula of the multivariate linear regression algorithm is as follows: Among them, R is the psychological risk score value, β0 represents the intercept of the regression model, β1 represents the weight parameter of emotional instability, E represents the measurement value of emotional instability, β2 represents the weight parameter of the change in sense of pressure, S represents the change in sense of pressure, T represents the duration of the speech, β3 represents the weight parameter of the complexity of the speech strategy, and C represents the complexity of the speech strategy.

10. A speech response system for online psychological consultation, characterized in that: According to the online psychological consultation speech reply method according to any one of claims 1 to 9, the system comprises: The data coding module collects data on psychological counseling needs and emotional states, encodes the data, assigns target symbols, and generates an initial framework of symbol sets; The symbol mapping module analyzes the representativeness and discrimination of the differentiated symbols based on the initial framework of the symbol set, merges the symbols of the same type and divides the symbols of different types, and obtains an optimized symbol set; The speech strategy module uses the optimized symbol set to establish a mapping relationship from symbols to speech strategies, verifies the mapping relationship one by one, checks the adaptability of the speech, and constructs a speech mapping table; The transmission efficiency processing module analyzes the efficiency of information transmission through the speech mapping table, iteratively adjusts the mapping table, verifies the speed and efficiency of information transmission through data analysis, and generates information efficiency analysis results; The psychological risk assessment module simulates the psychological impact of differentiated speech strategies based on the information efficiency analysis results, combined with speech data and psychological risk assessment indicators, and generates strategy adjustment results.

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