Psychological assessment large model system and method, equipment, medium and program product

Abstract

The invention provides a psychological assessment large model system and method, equipment, a medium and a program product. The method comprises the following steps: collecting user data; preprocessing the collected data, and constructing a user psychological feature vector database; training a psychological assessment large model through the user psychological feature vector database; according to the current psychological state and historical data of the user, personalized psychological assessment suggestions are generated through calculation of the large psychological assessment model. According to the method, accurate psychological state recognition and personalized psychological assessment suggestion generation are realized by deeply mining user data; by utilizing an artificial intelligence technology, the response speed and the processing efficiency of psychological assessment service are greatly improved; and knowledge in multiple fields such as psychology and data science is integrated, so that the professionality and effectiveness of evaluation suggestions are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intersectional technology of artificial intelligence and psychological assessment, and in particular to a psychological assessment large model system and method, equipment, medium and program product. Background Art

[0002] With the accelerated pace of modern society and the increasing pressure of life, mental health issues are becoming increasingly prominent. Traditional psychological assessments are limited by factors such as time, location, and professional talent resources, and are difficult to meet the needs of a wide range of people. Although some existing online psychological assessment platforms provide convenience, they often lack personalized and accurate assessment services.

[0003] Specifically, most existing systems use universal assessment templates or assessment scales, which make it difficult to provide personalized services based on the user's specific circumstances; the traditional assessment model through psychological assessment scales has a single channel for obtaining the user's psychological state, and the assessment model is single, which cannot fully guide users to express their true thoughts and cannot obtain the emotions and psychological tendencies in free conversation content; it is difficult for ordinary users to directly obtain instant services from professional psychological assessors.

[0004] Therefore, there is an urgent need to develop a large model system and method that can communicate with users, conduct in-depth analysis based on the content of the user's conversation, and provide personalized psychological assessment suggestions. Summary of the invention

[0005] In order to achieve the above-mentioned purpose and other advantages of the present invention, the first purpose of the present invention is to provide a psychological evaluation method, comprising the following steps: Collect user data; Pre-process the collected data and build a user psychological feature vector database; Training the psychological assessment model through the user psychological feature vector database; According to the user's current psychological state and historical data, personalized psychological assessment suggestions are generated through the psychological assessment big model.

[0006] Furthermore, the step of collecting user data includes: Collect user data through user registration information, historical evaluation records, and online behavior data.

[0007] Furthermore, the step of preprocessing the collected data includes: The collected data is cleaned, labeled, and feature extracted to build a user psychological feature database.

[0008] Furthermore, the training of the psychological assessment model includes: Based on deep learning algorithms, labeled data is used to train large psychological assessment models.

[0009] Furthermore, the psychological assessment model is trained based on cognitive behavioral therapy counseling corpus, automatically generates data, and combines the automatically generated data with the labeled data, and continuously optimizes the model using supervised fine-tuning.

[0010] Furthermore, the fine-tuning data of the psychological assessment large model includes data obtained by quantitatively analyzing and calibrating a multimodal psychological assessment dataset.

[0011] Furthermore, the multimodal psychological assessment dataset is configured to generate each psychological assessment question by using a psychological scale and a psychological paradigm as the underlying question-answering strategy.

[0012] Furthermore, the multimodal psychological assessment dataset includes psychological scale assessment and feedback annotation, psychological interview audio and text records, cognitive behavioral therapy psychological consultation and empathy counseling professional corpus, and a corpus of common psychological problems and professional answers.

[0013] Furthermore, the fine-tuning data of the psychological assessment large model also includes the user's psychological scale data.

[0014] Furthermore, the psychological assessment model absorbs tens of millions of words of Chinese psychology books through incremental pre-training.

[0015] Furthermore, the base model used by the psychological assessment model is the QWen2 Chinese base model.

[0016] Furthermore, the method further comprises the steps of: Interact with users in real time to enable them to obtain personalized psychological assessment suggestions, feedback on usage effects, and customized knowledge bases.

[0017] Furthermore, the step of interacting with the user in real time so that the user can obtain personalized psychological assessment suggestions and provide feedback on the use effect includes: The hybrid psychological knowledge retrieval technology based on psychological knowledge graph and documents combines the psychological vector knowledge base and recall rearrangement retrieval technology during human-computer interaction to ensure the relevance and accuracy of the response.

[0018] The second object of the present invention is to provide a psychological assessment large model system, which applies the above method and includes a data acquisition module, a data processing module, a model training module, and an assessment suggestion generation module; wherein: The data collection module is used to collect user data; The data processing module is used to pre-process the collected data and construct a user psychological feature vector database; The model training module is used to train the psychological assessment model through the user psychological feature vector database; The evaluation suggestion generation module is used to generate personalized psychological evaluation suggestions based on the user's current psychological state and historical data through the psychological evaluation big model.

[0019] A third object of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0020] A fourth object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0021] A fifth object of the present invention is to provide a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0022] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a psychological assessment big model system and method, equipment, medium and program product, the method comprising the following steps: collecting user data; preprocessing the collected data to construct a user psychological feature vector database; training the psychological assessment big model through the user psychological feature vector database; and generating personalized psychological assessment suggestions through the psychological assessment big model calculation according to the user's current psychological state and historical data. The present invention realizes accurate psychological state recognition and personalized psychological assessment suggestion generation by deeply mining user data; utilizes artificial intelligence technology to greatly improve the response speed and processing efficiency of psychological assessment services; integrates knowledge in multiple fields such as psychology and data science to ensure the professionalism and effectiveness of assessment suggestions.

[0023] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of the psychological assessment method of Example 1; Figure 2This is a business flow chart of the psychological assessment large model system of Example 1; Figure 3 Develop a roadmap for the psychological assessment large model system of Example 1; Figure 4 This is the system architecture diagram of the psychological assessment large model of Example 1; Figure 5 This is the psychological assessment report diagram of Example 1; Figure 6 This is a schematic diagram of the psychological assessment large model system of Example 2; Figure 7 This is a schematic diagram of a computer device in Example 3; Figure 8 This is a schematic diagram of a computer-readable storage medium of Example 4. DETAILED DESCRIPTION

[0025] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. It should be noted that, under the premise of no conflict, the embodiments or technical features described below can be arbitrarily combined to form a new embodiment.

[0026] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0027] The figure numbers in this application are only used to distinguish the various steps in the scheme, and are not used to limit the execution order of the various steps. The specific execution order is subject to the description in the specification.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0029] Example 1 A psychological assessment method that aims to provide users with accurate and efficient psychological assessment services through intelligent means. Figure 1 , Figure 2 , Figure 4 As shown, the method comprises the following steps: S1. Collect user data; In some embodiments, the step of collecting user data includes: Collect user data through user registration information, historical evaluation records, online behavior data, etc.

[0030] S2, pre-processing the collected data to build a user psychological feature vector database; In some embodiments, the step of preprocessing the collected data includes: The collected data is preprocessed by cleaning, labeling, feature extraction, etc. to build a user psychological feature database.

[0031] S3. Training the psychological assessment big model through the user psychological feature vector database; wherein the psychological assessment big model is developed using Pytorch.

[0032] Specifically, the training of the psychological assessment model includes: Based on deep learning algorithms, a large amount of labeled data is used to train a large psychological assessment model, enabling it to accurately identify the user's psychological state and predict future development trends.

[0033] The introduction of the psychological assessment big model is a direct response to the demand for deep psychological expertise. After in-depth training and optimization in professional fields, the psychological assessment big model can not only accurately grasp industry terms and context, but also provide in-depth solutions based on psychological industry standards. In the mental health industry, this means that the model can more accurately identify user emotions and give more professional and personalized feedback.

[0034] In some embodiments, the base model used by the psychological assessment model is the QWen2 Chinese base model. Through incremental pre-training, the psychological assessment model absorbs tens of millions of words of Chinese psychology books, providing a rich professional knowledge base for the model, forming the "brain" of psychological counseling and assessment, and empowering Lingxi intelligent interactive hardware to provide users with reliable, effective, positive, and companion emotional interaction services.

[0035] In some embodiments, Figure 3 As shown, the psychological assessment large model is trained based on cognitive behavioral therapy counseling corpus, and uses advanced large language models' dialogue capabilities, prompt learning, context learning and other technologies to automatically generate data, improve the scale and diversity of psychological data, and combine the automatically generated data with manually labeled data, and use supervised fine-tuning to continuously optimize its own large language model to improve accuracy.

[0036] The fine-tuning data of the psychological assessment model includes data obtained by quantitatively analyzing and calibrating the multimodal psychological assessment data set. The data of the multimodal psychological assessment data set is quantitatively analyzed and calibrated, providing a scientific basis for the training of the psychological assessment model.

[0037] In addition, the fine-tuning data of the psychological assessment model also includes the user's psychological scale data, further enriching the training dimension of the model.

[0038] In some embodiments, the multimodal psychological assessment dataset is configured such that each psychological assessment question is generated by the system using a psychological scale and a psychological paradigm as an underlying question-answering strategy.

[0039] Specifically, the multimodal psychological assessment dataset includes psychological scale assessment and feedback annotation, psychological interview audio and text records, cognitive behavioral therapy psychological consultation and empathy counseling professional corpus, and a corpus of common psychological problems and professional answers.

[0040] Exemplarily, the multimodal psychological assessment dataset may be a multimodal psychological assessment dataset for adolescent anxiety and depression assessment, etc., as shown in Table 1.

[0041] Table 1 Multimodal psychological assessment dataset Number of rounds user Professional psychologist annotates the conversation 1 I am afraid of failure and I pursue perfection. The complicated formulas and the uncertainty of experiments put me under great pressure. You feel anxious because of your fear of failure and your need for perfection. Let's explore these feelings. 2 I often tried to solve problems on my own, but always felt frustrated and powerless. It's good to solve problems on your own, but it's also important to ask for help. Have you considered talking to someone about it? 3 I worry that asking for help will make others think I’m inadequate. Everyone has difficulties sometimes, and asking for help does not mean you are incapable. You can start by asking small questions. 4 OK, I feel like I spend most of my time studying and researching, and rarely have time to relax. It's important to balance study and rest. Try to plan some short breaks. 5 I don't know how to find a way to relax in the intense study. Try deep breathing exercises or simple stretching exercises, which can help you relax your body and mind in a short period of time. 6 I am worried that if I relax, my research progress will fall behind others. Progress can be adjusted, health is more important. Try to set realistic goals and move forward step by step. 7 I know, but how do I deal with this comparison mentality? Focus on your own progress rather than comparing yourself to others. Everyone's learning path is unique. 8 I'll give it a try, thank you. You're welcome, just remember to be patient with yourself. Change takes time and effort. S4. Based on the user's current psychological state and historical data, the psychological assessment model is used to calculate and generate personalized psychological assessment suggestions.

[0042] In some embodiments, the steps include: Interact with users in real time to enable them to obtain personalized psychological assessment suggestions, feedback on usage effects, and customized knowledge bases.

[0043] That is, this embodiment provides a friendly user interface, supports real-time interaction between users and the system, obtains evaluation suggestions and provides feedback on usage effects, continuously optimizes system performance and user experience, and provides users with the function of customizing knowledge bases. Users can build exclusive knowledge bases based on family culture and educational direction to guide children's further growth and development.

[0044] Specifically, the steps of interacting with the user in real time so that the user can obtain personalized psychological assessment suggestions and provide feedback on the use effect include: Based on the hybrid psychological knowledge retrieval technology of psychological knowledge graph and documents, in the human-computer interaction dialogue, combined with the psychological vector knowledge base and recall re-ranking retrieval technology, the evaluation suggestions are presented to the user in the form of text, charts, etc., and can ensure the relevance and accuracy of the response, reduce erroneous information, and create a professional and efficient mental health intelligent companion assistant to achieve effective psychological counseling. Figure 5 shown.

[0045] This embodiment provides a psychological assessment method, which includes the following steps: collecting user data; preprocessing the collected data to build a user psychological feature vector database; training a psychological assessment model through the user psychological feature vector database; and generating personalized psychological assessment suggestions through the psychological assessment model based on the user's current psychological state and historical data. This embodiment achieves accurate psychological state recognition and personalized psychological assessment suggestion generation by deeply mining user data; uses artificial intelligence technology to greatly improve the response speed and processing efficiency of psychological assessment services; integrates knowledge from multiple fields such as psychology and data science to ensure the professionalism and effectiveness of assessment suggestions.

[0046] Example 2 A psychological assessment model system is provided, which applies the above method. For a detailed description of the method, please refer to the corresponding description in the above method embodiment, which will not be repeated here. Figure 6 As shown, the system includes a data collection module 51, a data processing module 52, a model training module 53, and an evaluation suggestion generation module 54; wherein, The data collection module is used to collect user data; The data processing module is used to pre-process the collected data and construct a user psychological feature vector database; The model training module is used to train the psychological assessment model through the user psychological feature vector database; The evaluation suggestion generation module is used to generate personalized psychological evaluation suggestions based on the user's current psychological state and historical data through the psychological evaluation big model.

[0047] Based on the technical solution of the above embodiment, optionally, the step of collecting user data includes: Collect user data through user registration information, historical evaluation records, and online behavior data.

[0048] Based on the technical solution of the above embodiment, optionally, the step of preprocessing the collected data includes: The collected data is cleaned, labeled, and feature extracted to build a user psychological feature database.

[0049] Based on the technical solution of the above embodiment, optionally, the training of the psychological assessment large model includes: Based on deep learning algorithms, labeled data is used to train large psychological assessment models.

[0050] Based on the technical solution of the above embodiment, optionally, the psychological assessment model is trained based on cognitive behavioral therapy counseling corpus, automatically generates data, and combines the automatically generated data with the labeled data to continuously optimize the model using supervised fine-tuning.

[0051] Based on the technical solution of the above embodiment, optionally, the fine-tuning data of the psychological assessment large model includes data obtained by quantitatively analyzing and calibrating a multimodal psychological assessment data set.

[0052] Based on the technical solution of the above embodiment, optionally, the multimodal psychological assessment dataset is configured to generate each psychological assessment question by using a psychological scale and a psychological paradigm as the underlying question-answering strategy.

[0053] Based on the technical solution of the above embodiment, optionally, the multimodal psychological assessment dataset includes psychological scale assessment and feedback annotation, psychological interview audio and text records, cognitive behavioral therapy psychological counseling and empathy counseling professional corpus, and a corpus of common psychological problems and professional answers.

[0054] Based on the technical solution of the above embodiment, optionally, the fine-tuning data of the psychological assessment large model also includes the user's psychological scale data.

[0055] Based on the technical solution of the above embodiment, optionally, the psychological assessment model absorbs tens of millions of words of Chinese psychology books through incremental pre-training.

[0056] Based on the technical solution of the above embodiment, optionally, the base model used by the psychological assessment large model is the QWen2 Chinese base model.

[0057] Based on the technical solution of the above embodiment, optionally, the method further includes the following steps: Interact with users in real time to enable them to obtain personalized psychological assessment suggestions, feedback on usage effects, and customized knowledge bases.

[0058] Based on the technical solution of the above embodiment, optionally, the step of interacting with the user in real time so that the user can obtain personalized psychological assessment suggestions and provide feedback on the use effect includes: The hybrid psychological knowledge retrieval technology based on psychological knowledge graph and documents combines the psychological vector knowledge base and recall rearrangement retrieval technology during human-computer interaction to ensure the relevance and accuracy of the response.

[0059] This embodiment provides a psychological assessment model system, which includes a data collection module, a data processing module, a model training module, and an assessment suggestion generation module. This embodiment realizes accurate psychological state recognition and personalized psychological assessment suggestion generation by deeply mining user data; uses artificial intelligence technology to greatly improve the response speed and processing efficiency of psychological assessment services; integrates knowledge in multiple fields such as psychology and data science to ensure the professionalism and effectiveness of assessment suggestions.

[0060] Example 3 A computer device 600, such as Figure 7 As shown, it includes a memory 610, a processor 620, and a computer program 630 stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a psychological assessment method are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0061] Example 4 A computer readable storage medium such as Figure 8 As shown, a computer program is stored thereon, and when the computer program is executed by the processor, the steps of a psychological assessment method are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, and no further description is given here.

[0062] Example 5 A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of a psychological assessment method are implemented. For a detailed description of the method, reference may be made to the corresponding description in the above method embodiment, which will not be repeated here.

[0063] The number of devices and processing scales described here are used to simplify the description of the present invention. Applications, modifications and variations of the present invention will be obvious to those skilled in the art.

[0064] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

[0065] The apparatus, computer device, non-volatile computer storage medium and method provided in the embodiments of this specification correspond to each other, and therefore, the apparatus, computer device and non-volatile computer storage medium also have similar beneficial technical effects as the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding apparatus, computer device and non-volatile computer storage medium will not be repeated here.

[0066] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software units for implementing the method and structures within the hardware component.

[0067] The systems, devices or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions in various units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or more software and / or hardware.

[0068] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0069] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0073] The specification may be described in the general context of computer-executable instructions executed by a computer, such as program units. Generally, program units include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program units may be located in local and remote computer storage media, including storage devices.

[0074] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0075] The above description is only an embodiment of this specification and is not intended to limit one or more embodiments of this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of one or more embodiments of this specification.

Claims

1. A psychological assessment method, characterized in that: The following steps are involved: Collect user data; Pre-process the collected data and build a user psychological feature vector database; Training the psychological assessment model through the user psychological feature vector database; According to the user's current psychological state and historical data, personalized psychological assessment suggestions are generated through the psychological assessment big model.

2. A psychological assessment method as claimed in claim 1, characterized in that: The step of collecting user data comprises: Collect user data through user registration information, historical evaluation records, and online behavior data.

3. A psychological assessment method as claimed in claim 1, characterized in that: The step of preprocessing the collected data comprises: The collected data is cleaned, labeled, and feature extracted to build a user psychological feature database.

4. A psychological assessment method as claimed in claim 3, characterized in that: The training of the psychological assessment model includes: Based on deep learning algorithms, labeled data is used to train large psychological assessment models.

5. A psychological assessment method as claimed in claim 4, characterized in that: The psychological assessment model is trained based on cognitive behavioral therapy counseling corpus, automatically generates data, and combines the automatically generated data with labeled data, and continuously optimizes the model using supervised fine-tuning.

6. A psychological assessment method as claimed in claim 5, characterized in that: The fine-tuning data of the psychological assessment large model includes data obtained by quantitatively analyzing and calibrating a multimodal psychological assessment dataset.

7. A psychological assessment method as claimed in claim 6, characterized in that: The multimodal psychological assessment dataset is configured to generate each psychological assessment question by using a psychological scale and a psychological paradigm as the underlying question-answering strategy.

8. A psychological assessment method as claimed in claim 7, characterized in that: The multimodal psychological assessment dataset includes psychological scale assessment and feedback annotation, psychological interview audio and text records, cognitive behavioral therapy psychological consultation and empathy counseling professional corpus, and a corpus of common psychological questions and professional answers.

9. A psychological assessment method as claimed in claim 6, characterized in that: The fine-tuning data of the psychological assessment large model also includes the user's psychological scale data.

10. A psychological assessment method as claimed in claim 4, characterized in that: The psychological assessment model absorbs tens of millions of words of Chinese psychology books through incremental pre-training.

11. A psychological assessment method as claimed in claim 1, characterized in that: The base model used by the psychological assessment model is the QWen2 Chinese base model.

12. A psychological assessment method as claimed in claim 1, characterized in that: Also includes the steps: Interact with users in real time to enable them to obtain personalized psychological assessment suggestions, feedback on usage effects, and customized knowledge bases.

13. A psychological assessment method according to claim 12, characterized in that: The steps of interacting with the user in real time so that the user can obtain personalized psychological assessment suggestions and provide feedback on the use effect include: The hybrid psychological knowledge retrieval technology based on psychological knowledge graph and documents combines the psychological vector knowledge base and recall rearrangement retrieval technology during human-computer interaction to ensure the relevance and accuracy of the response.

14. A psychological assessment large model system, using the method according to any one of claims 1 to 13, characterized in that: It includes data collection module, data processing module, model training module and evaluation suggestion generation module; among them, The data collection module is used to collect user data; The data processing module is used to pre-process the collected data and construct a user psychological feature vector database; The model training module is used to train the psychological assessment model through the user psychological feature vector database; The evaluation suggestion generation module is used to generate personalized psychological evaluation suggestions based on the user's current psychological state and historical data through the psychological evaluation big model.

15. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 13 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

17. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

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