Cognitive thinking digital training method and device, terminal and storage medium

Abstract

The application discloses a cognitive thinking digital training method and device, a terminal and a storage medium. The method comprises the following steps: receiving individual basic information and environmental state information input by a user and storing the information in a preset database; clarifying the demand of the user and grading the user; analyzing the thinking characteristic grading of the user according to the user characteristic data to determine the training type; matching the training dimension plan and setting the training index target; generating a multi-dimensional and multi-modal training plan; wherein the multi-dimensional and multi-modal training plan comprises a thinking training dimension and a modal dimension sequence; executing single-dimensional cognitive thinking training; calculating and evaluating a thinking characteristic balance index to determine whether the index meets the standard; and outputting a training report. The present application integrates scattered psychological training techniques into a complete system with data input, plan generation, effect evaluation and feedback adjustment, ensures the scientificity and replicability of the intervention method, and greatly improves the pertinence and effect of the intervention.

Description

Technical Field

[0001] This invention relates to the field of psychological intervention technology, and in particular to a digital training method, device, terminal and storage medium for cognitive thinking. Background Technology

[0002] Currently, facing over 70% of the population in a sub-healthy state, and over 20% of those exhibiting significant psychological sub-health, poor health, or social maladjustment, existing psychological intervention techniques, including manual psychological intervention, counseling, psychotherapy, and electronic measurement systems, are all limited to specific local and temporal points. Their impact on cognitive rationalization and personality reconstruction is significantly insufficient in both depth and efficiency. Especially for the general public, manual psychological intervention is costly and insufficiently available, while up to 20% of the total population suffering from psychological distress lacks scientifically sound methods and tools for self-help healing.

[0003] Essentially, individual growth and strength are centered on the rationalization and balancing of underlying thought processes and the enhancement of adaptability. Cognitive thinking training can help those struggling to overcome their psychological barriers correct their underlying logic, enhancing the rationality, stability, and psychological balance of their personality's thought system. Looking at the treatment methods of various major schools of psychology, they all essentially change and improve the underlying logic of an individual's psychological structure through various conceptual dimensions and intervention methods, thereby resolving psychological obstacles.

[0004] In the field of mental health and cognitive enhancement, traditional interventions primarily rely on human psychological counseling, therapy, or electronic self-help tools based on simple logic. These existing technologies typically follow a basic "assessment-advice-practice" model, but generally suffer from fragmented methods, static processes, and insufficient personalization. Specifically, existing technologies usually involve human counselors conducting a one-time assessment and providing advice based on experience, or electronic systems providing standardized, general training content. They fail to dynamically construct and execute a systematic, structured, and closed-loop cognitive training plan based on the user's real-time state, environmental context, and multidimensional individual characteristics. This makes it difficult for existing self-help methods to achieve deep, adaptive, and efficient cognitive reshaping, and also fails to meet the urgent needs of a large population for low-cost, highly private, and professional self-help services.

[0005] Current psychological intervention techniques mainly fall into two categories: one is the manual intervention model led by psychological counselors; the other is standardized electronic self-help tools, primarily using pre-set questionnaires, audio-visual courses, or simple interactive games. While the manual model has a certain degree of targeting, it is costly, inaccessible, difficult to standardize and replicate, and heavily reliant on the counselor's subjective experience, failing to guarantee the systematic and scientific nature of the intervention. Existing electronic self-help tools, although widely used, are essentially "content players" or "fixed-process executors." Therefore, existing cognitive intervention methods suffer from the following fundamental flaws: (1) Data isolation and inability to integrate systematically: Existing tools often only collect single types of user data (such as questionnaire scores), and cannot match the user's basic information, environmental status, individual characteristics with the training content in a multi-dimensional and systematic way (i.e., lack of collaborative matching process of "context, environment, individual characteristics, modality"), resulting in a serious disconnect between the training plan and the user's real needs and context. (2) The plan is static and lacks dynamic generation: The training content is usually pre-set and linear, and does not dynamically "generate" a personalized training plan that includes specific dimension ranking and modality combination based on the results of multi-source data matching. Users can only passively accept fixed content, the training process is rigid and has poor adaptability; (3) The assessment is disconnected from the training and lacks closed-loop regulation: The assessment of existing tools is usually independent and phased, and is separated from the training process. The assessment results are only used for classification or simple recommendation. They do not combine the real-time data in the training process with the result data to calculate and assess the "thinking characteristic balance index" and make dynamic decisions based on the index to end the training or return to a specific step to continue reinforcement, so as to achieve an intelligent closed loop of training-assessment-adjustment. (4) Existing psychological interventions usually only target the psychological symptoms of mental patients and do not involve the deep training intervention of patients, resulting in "treating the symptoms but not the root cause" and poor training intervention effect.

[0006] Furthermore, the evaluation of existing technologies largely relies on the subjective experience of consultants or simple questionnaire scores, lacking publicly available, quantifiable, and replicable specific rules for determining "needs classification" (e.g., clear thresholds) and specific calculation models for "balance indices" that comprehensively evaluate training effectiveness (e.g., the specific form of the normalization function, the source and calculation method of the one-dimensional index). This keeps existing technological solutions at the functional description level, preventing those skilled in the art from directly implementing a stable and accurate automated cognitive thinking training system based on its publicly available content.

[0007] Therefore, it is necessary to provide a cognitive thinking digital training method, device, terminal, and storage medium to overcome the above-mentioned defects. Summary of the Invention

[0008] The purpose of this invention is to provide a digital training method, device, terminal, and storage medium for cognitive thinking, aiming to solve the problems of insufficient personalization, poor adaptability, and lack of deep-level systemic intervention capabilities in existing psychological self-help techniques. It simulates the systematic and structural nature of expert intervention, while possessing the scalability and privacy of digital technology, ultimately achieving efficient, low-cost, and deep-level cognitive thinking self-help training.

[0009] To achieve the above objectives, the first aspect of the present invention provides a cognitive thinking digital training method, comprising the following steps: Step S101: Receive the user's basic individual information and environmental status information, and store them in a preset database; Step S102: Conduct a needs clarification and grading assessment for users. Based on the user's feedback data on the preset standardized psychological scale, calculate the total score of the scale and compare it with the preset judgment threshold to identify the user's needs classification. After the needs are confirmed, user characteristic data is generated and stored in the preset database. Step S103: Perform a cognitive characteristic classification and grading analysis based on the user characteristic data to determine the type of subsequent training: if the cognitive characteristics are poor, proceed to step S104; if the adaptation strategy is poor, execute coping strategy training; if the cognitive characteristics are balanced, proceed to step S108. Step S104: Based on the user feature data, perform scenario data matching, environment data matching, individual feature matching, and modal data matching in the preset database respectively, and then combine the target value and reward mode to perform training dimension plan matching and training indicator target setting; Step S105: Generate a multidimensional multimodal training plan based on the matched training dimension plan and the set training indicator target; wherein, the multidimensional multimodal training plan includes the order of thinking training dimensions and modal dimensions; Step S106: Perform single-dimensional cognitive thinking training according to the modal dimension sorting or the dimension selected by human-computer interaction; Step S107: Calculate and evaluate the thinking characteristic balance index B by combining the training process and result data of multidimensional thinking training. The thinking characteristic balance index B is calculated using the formula B=F(K×(wi)). Bi is calculated, where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; wi is the weight of the i-th thinking training dimension; and Bi is a single-dimensional thinking characteristic index calculated based on the user's score on the standardized evaluation scale of the corresponding dimension. The calculation process is then performed to determine whether the thinking characteristic balance index meets the standard. If the result is yes, the process proceeds to step S108; if the result is no, the process returns to step S106. Step S108: Output the training report and store it in the preset database to end the training.

[0010] In a preferred embodiment, step S102 includes: A human-computer interaction loop that responds to individual psychological distress and needs; Based on the human-computer interaction results and in conjunction with the preset database, user needs are identified. If the needs are ambiguous, the process returns to step S101 to supplement and input basic individual information. If the needs are clear, user characteristic data is generated. If the user has a balanced personality, the process proceeds to step S108.

[0011] In a preferred embodiment, step S106 includes: in each single-dimensional thinking training, the input and output of human-computer interaction information are presented in the form of one or more combinations of text, graphics, animation, and virtual reality.

[0012] In a preferred embodiment, after completing each single-dimensional thinking training, the thinking characteristic index of that dimension is evaluated to see if it meets the standard. If it does not meet the standard, the user's choice of whether to continue training in this dimension is obtained; if it meets the standard, it is determined whether the thinking training of all dimensions is completed. If the user chooses to continue training in this dimension, then repeat the cognitive thinking training for that dimension; otherwise, generate a current training progress report and exit training. If it is determined that the thinking training plan for all dimensions has been completed, proceed to step S107; otherwise, obtain the user's choice of whether to continue training. If the user chooses to continue training, then cognitive thinking training for the unfinished dimensions will proceed; otherwise, a current training progress report will be generated, and training will be terminated.

[0013] In a preferred embodiment, in step S107, the normalization function F is: F(x) = 0.1 + 0.9 (x - X min ) / (X max - X min ), where X max and X min These are the maximum and minimum expected values ​​of the comprehensive index of thinking characteristics, which are determined in advance based on historical data statistics; the single-dimensional thinking characteristic index Bi is the standard score T-score of the user on the standardized psychological assessment scale corresponding to the i-th dimension.

[0014] In a preferred embodiment, step S107 further includes determining whether the thinking characteristic balance index is quasi-compliant; if B≥0.95, it is compliant; if 0.6≤B<0.95, it is quasi-compliant; if B<0.6, it is not compliant; if the result is quasi-compliant, then coping strategy training is performed.

[0015] In a preferred embodiment, step S101 includes: The evaluation module is started in response to the user's startup command on the network terminal device or stand-alone device. User identity ID generation and identification based on user communication or biological identity information; Acquire multimodal individual cognitive characteristic data, physiological state, and environmental relationship data from user input, including text, audio, and video.

[0016] A second aspect of the present invention provides a digital training device for cognitive thinking, comprising: The information receiving module is used to receive basic personal information and environmental status information input by the user and store them in a preset database; The needs clarification module is used to conduct needs clarification and grading assessments of users. Based on the user's feedback data on the preset standardized psychological scale, the total score of the scale is calculated and compared with the preset judgment threshold to identify the user's needs classification. After the needs are confirmed, user characteristic data is generated and stored in the preset database. The feature classification module is used to perform a classification and grading analysis of thinking features based on the user feature data to determine the type of subsequent training: if the thinking features are poor, the goal setting module is activated; if the adaptation strategy is poor, the coping strategy training is executed; if the thinking is balanced, the report output module is activated. The target setting module is used to perform scenario data matching, environment data matching, individual characteristic matching and modal data matching in the preset database according to the user feature data, and then combine the target value and reward mode to perform training dimension plan matching and training indicator target setting. The plan generation module is used to generate a multidimensional and multimodal training plan based on the matched training dimension plan and the set training indicator target; wherein, the multidimensional and multimodal training plan includes a mind training dimension and a modal dimension ranking; The single-dimensional training module is used to perform single-dimensional cognitive thinking training according to the modal dimensions or the dimensions selected by human-computer interaction. The balanced assessment module is used to calculate and assess the balance index of thinking characteristics by combining the training process and result data of multidimensional thinking training. The balance index of thinking characteristics B is calculated using the formula B=F(K×(wi)). Bi is calculated, where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; wi is the weight of the i-th thinking training dimension; and Bi is a single-dimensional thinking feature index calculated based on the user's score on the standardized evaluation scale of the corresponding dimension. The system then determines whether the thinking feature balance index meets the standard. If the result is yes, the report output module is started; if the result is no, the system returns to start the single-dimensional training module. The report output module is used to output a training report and store it in the preset database to end the training.

[0017] A third aspect of the present invention provides a terminal, the terminal including a memory, a processor, and a computer program stored in the memory, wherein the computer program, when executed by the processor, implements the various steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0018] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0019] The fifth aspect of the present invention provides a computer program product, including a computer program or instructions, which, when processed and executed, implement the various steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0020] The cognitive thinking digital training method, device, terminal, and storage medium provided by this invention objectify and automate the needs clarification process by defining needs classification rules based on the total score of a standardized psychological scale (such as SCL-90) and a fixed threshold (such as 160 points). By disclosing the specific calculation formula for the thinking characteristic balance index B, including the linear transformation form of the normalization function F and the calculation method of the T-score of the one-dimensional thinking characteristic index Bi derived from the standardized scale, the evaluation of training effectiveness has a clear and calculable standard, thus constituting a complete, clear, and implementable technical solution. Therefore, this invention has at least the following beneficial effects: (1) Significantly improve the personalization and effectiveness of training: Through multi-dimensional data matching and dynamic plan generation, the training content is ensured to be highly relevant to the user's current psychological state, cognitive shortcomings and environment, thereby greatly improving the pertinence and effectiveness of the intervention.

[0021] (2) Realize an intelligent and adaptive training process: The closed-loop evaluation mechanism enables the system to automatically adjust the training path based on the user's real-time training feedback, realizing the leap from "static course" to "intelligent coach", enhancing user experience and training efficiency.

[0022] (3) Ensure the systematic and scientific nature of the intervention: The whole process simulates the structured thinking of professional psychological intervention, integrating scattered psychological training techniques into a complete system with data input, planned generation, effect evaluation, and feedback adjustment, thus ensuring the scientific nature and replicability of the intervention method; (4) Deeper intervention: The training intervention process is aimed at changing the underlying thinking logic, not just the surface symptoms, so as to achieve the fundamental goal of deep psychological intervention and better intervention effect. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 The overall flowchart of psychological intervention provided by this invention; Figure 2 A flowchart of the cognitive thinking digital training method provided by the present invention; Figure 3 This is a framework diagram of the cognitive thinking digital training device provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described in this specification are merely for explaining the invention and are not intended to limit the invention.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] Example 1 In embodiments of this invention, a digital cognitive thinking training method is provided. Based on theories, skills, and methods of psychological science, it constructs a computer intelligent system method, optimizes personality psychological structure, and establishes a logical intelligent thinking training method system, which is realized through multimodal computer information interaction. This establishes a core foundation for the humanized and intelligent realization of various forms of psychological intervention (human-computer intelligent dialogue, virtual reality (VR), companion robots, mobile network terminals, artificial assistance, etc.), simulating the systematic and structural nature of expert intervention while possessing the scalability and privacy of digital technology. Ultimately, it achieves efficient, low-cost, and in-depth cognitive thinking self-help training, enabling more than 90% of people with psychological distress to potentially receive private and professional psychological maintenance self-help or auxiliary services, thus safeguarding public mental health.

[0029] Through intervention and treatment of psychological dysfunction, and with the assistance of a cognitive thinking training system, individuals experiencing distress can externalize their thinking, become aware of and correct underlying logical biases, and develop effective coping strategies to resolve their disordered states. Furthermore, through bidirectional reshaping training of central thinking functions via "cognitive thinking training," the "thinking 'viruses'" that cause various emotional pathologies are deeply resolved—namely, the elimination of step-like extreme thinking judgment patterns—thereby strengthening the rationality and stability of the individual's psychological structure.

[0030] It should be noted that the training system starts primarily through an interactive response between a network terminal application and an online server. System storage and dynamic data storage are mainly handled by the online server's associated storage, with some runtime data temporarily stored on the client side. The system can also be implemented as a single, independent computer function.

[0031] Please see Figure 1 and Figure 2 The cognitive thinking digital training method includes the following steps S101-S108.

[0032] Step S101: Receive the user's basic individual information and environmental status information, and store them in a preset database.

[0033] The process involves the end user requesting system activation and inputting individual cognitive characteristic data, physiological state, and environmental relationship data via multimodal input of text, audio, and video. This includes information on individual physical health, family history, emotions, mood, economic status, and social status. Specifically, step S101 includes: responding to the user's activation command on a network terminal device or standalone device and activating the evaluation module; generating and recognizing the user's identity ID based on the user's communication or biological identity information; and acquiring the user's input multimodal individual cognitive characteristic data, physiological state, and environmental relationship data, including text, audio, and video.

[0034] Step S102: Conduct a needs clarification and grading assessment for the user. Based on the user's feedback data on a preset standardized psychological scale, calculate the total score of the scale and compare it with a preset judgment threshold to identify the user's needs type. After the needs are confirmed, user characteristic data is generated and stored in a preset database. That is, the user's needs type is identified through human-computer intelligent interaction, linked with the basic database and the dynamic database. Specifically, step S102 includes: responding to the individual's psychological distress and demands through a human-computer interaction loop; identifying the user's needs based on the human-computer interaction results and the preset database; if the needs are ambiguous, return to step S101, supplement the individual's basic information, and then reassess; if the needs are clear, generate user characteristic data; if the personality is balanced, proceed to step S108.

[0035] In one specific implementation, the "needs clarification and grading assessment" in step S102 can be achieved by presenting the user with an online questionnaire of the Symptom Checklist-90 (SCL-90). After receiving the user's answers, the system calculates the total score of the scale. The "judgment threshold" is set to 160 points (based on the scale's conventional clinical cutoff value). "Identifying user needs classification" specifically means: if the total score is ≥160 points, it is judged as "vague needs," indicating that the user's psychological distress symptoms are obvious, and it is necessary to return to step S101 to supplement more detailed basic information and possibly suggest seeking artificial intervention; if the total score is <160 points, it is judged as "clear needs," and the system stores each factor score (such as somatization, obsessive-compulsive symptoms, interpersonal sensitivity, etc.) as part of the "user characteristic data" in the database. If the user's historical data shows that their various factor scores have been within the healthy norm range for a long time (such as T scores all below 60), it can be judged as "balanced personality," and directly proceed to step S108 to output a health report.

[0036] Step S103: Perform cognitive characteristic classification and grading analysis based on user characteristic data to determine the subsequent training type: if the cognitive characteristics are poor, proceed to step S104; if the adaptation strategy is poor, execute coping strategy training; if the cognitive characteristics are balanced, proceed to step S108. Therefore, the process first integrates multi-dimensional user data and clarifies needs, laying the foundation for accurate matching in the future.

[0037] It should be noted that the focus of this invention is on "cognitive thinking training", while the specific steps and procedures for "coping strategy training" are not limited and can be referred to existing technologies.

[0038] Step S104: Based on user feature data, perform scenario data matching, environmental data matching, individual feature matching, and modal data matching in the preset database (including the basic database and the dynamic database). Then, combine the target value and reward mode to perform training dimension plan matching and training indicator target setting.

[0039] It's important to note that this is crucial for achieving personalization and scientific rigor. The system doesn't randomly or empirically select training content; instead, it uses user characteristic data and collaboratively matches it against a scenario library, environmental model, individual characteristic profile, and interaction modality library. It also introduces a behavior-shaping mechanism of "target value and reward pattern" to generate a preliminary training framework that aligns with both the user's psychological state and the principles of learning science. One specific implementation of the "target value and reward pattern" is as follows: the system sets a target T-score for each training dimension (e.g., lowering the target for the "egocentric dimension" from an initial 70 points to 60 points). The reward pattern uses virtual points and progress visualization. For example, if a user's retest subscale score decreases by 1 point after a training session, they receive 100 points, advancing the corresponding percentage on the progress bar. These points can be used to unlock new training scenarios or redeem virtual badges, thus positively incentivizing the user.

[0040] Step S105: Generate a multidimensional multimodal training plan based on the matched training dimension plan and the set training indicator target; wherein, the multidimensional multimodal training plan includes the thinking training dimension and the modality dimension ranking.

[0041] The multidimensional, multimodal training program integrates the logic of mainstream intervention methods in the field of psychology, combining personality structure theory and clinical practice. It divides thinking into multiple dimensions, with core dimensions including: egocentrism, perfectionism, rational emotion, emotional intelligence, and coping strategies (D1, D2, D3, D4, D5). Immersive thinking training is provided targeting these dimensions, including decentralization training, de-extreme training, rational emotion training, emotional intelligence scenario training, and (self-maintenance of distress) coping strategy training. This achieves truly "systematic, structured, and in-depth" professional intelligent psychological intervention, improving individual psychological characteristic indicators and helping those with psychological distress to heal themselves.

[0042] Step S106: Perform single-dimensional cognitive thinking training by sorting according to modal dimensions or selecting dimensions through human-computer interaction.

[0043] In each single-dimensional thinking training session, human-computer interaction information is presented in one or more combinations of text, graphics, animation, and virtual reality.

[0044] Furthermore, after completing each single-dimensional cognitive thinking training, the thinking characteristic index of that dimension is evaluated to see if it meets the standard. If it does not meet the standard, the user's choice of whether to continue training in this dimension is obtained. If it meets the standard, it is determined whether the thinking training of all dimensions is completed. If it is determined that the user chooses to continue training in this dimension, the thinking training of this dimension is repeated. Otherwise, a current training progress report is generated and training is exited. If it is determined that the thinking training plan of all dimensions has been completed, step S107 is entered. Otherwise, the user's choice of whether to continue training is obtained. If it is determined that the user chooses to continue training, the thinking training of the unfinished dimensions is carried out. Otherwise, a current training progress report is generated and training is exited.

[0045] Among them, the assessment of the thinking characteristic index of a single dimension can be achieved by norm comparison based on standardized scales. For example, the test subject's score on a standardized questionnaire (such as a cognitive assessment scale) is compared and transformed with a large-scale population sample (norm) to obtain percentiles or standard scores.

[0046] Combining steps S105 and S106, based on the matching results, a specific and orderly training plan is dynamically "generated" (S105) and flexibly executed (S106), breaking the limitations of preset content and realizing dynamic adaptability of training.

[0047] Step S107: Calculate and evaluate the thinking characteristic balance index by combining the training process and result data of multidimensional thinking training. The thinking characteristic balance index B is calculated using the formula B=F(K×(wi)). Bi is calculated, where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; wi is the weight of the i-th thinking training dimension; Bi is a single-dimensional thinking characteristic index calculated based on the user's score on the standardized evaluation scale of the corresponding dimension; and it is determined whether the thinking characteristic balance index meets the standard. If the result is yes, then proceed to step S108; if the result is no, then return to step S106.

[0048] Therefore, a multi-dimensional "thinking characteristic balance index" is introduced as a comprehensive quantitative indicator to calculate and evaluate the training effect. This index serves as the basis for judging whether the target has been met and as a decision signal for process jumps (returning to S106 or ending). This constitutes an intelligent feedback loop, enabling the system to "observe the effect and adjust the strategy" like an expert.

[0049] In step S107, the normalization function F is: F(x) = 0.1 + 0.9 (x - X min ) / (X max -X min ), where X max and X minThese are the maximum and minimum expected values ​​of the comprehensive index of thinking characteristics, which are determined in advance based on historical data statistics; the single-dimensional thinking characteristic index Bi is the standard score T-score of the user on the standardized psychological assessment scale corresponding to the i-th dimension.

[0050] In one specific embodiment, the calculation method for the "Thinking Characteristics Balance Index B" is as follows: (1) Calculation of the single-dimensional thinking characteristic index Bi: For each thinking training dimension Di (e.g., D1: egocentric dimension), a corresponding standardized assessment tool is preset, such as a specific subscale of the Cognitive Tendency Scale. Users need to complete the corresponding assessment before and after training in this dimension. Bi is the user's standard score T on this subscale (mean 50, standard deviation 10). The T score is calculated based on a large sample norm, ensuring the comparability between scores of different dimensions.

[0051] (2) Determination of weight wi: The weight wi can be pre-set according to the importance of the dimension in the overall psychological structure (such as by the expert Delphi method), or it can be dynamically adjusted according to the degree of deviation of the dimension in the user's initial feature data (the greater the deviation, the higher the initial weight). For example, in the initial assessment, if the user's T score for dimension D1 is 70 (significantly high), then a higher weight such as 0.3 can be assigned to w1, and the remaining weights can be evenly distributed among the other dimensions.

[0052] 3. Comprehensive Calculation and Normalization: First, calculate the weighted comprehensive value Sum = Σ(wi Bi). Then, the normalization function F is applied. In one embodiment, historical data statistics indicate that the distribution range of the user group weighted composite value Sum is approximately [200, 500] (corresponding to the hypothetical scenario of extreme poor performance to overall equilibrium in each dimension Bi), then X is set. min = 200, X max =500.

[0053] (3) Combine Sum and X min X max Substituting into the formula B = 0.1 + 0.9 (Sum - 200) / (500 - 200). The correction coefficient K is usually set to 1.0 for subsequent algorithm tuning. The calculated B value falls within the interval [0.1, 1.0].

[0054] Furthermore, in step S107, this method also includes determining whether the thinking characteristic balance index is quasi-compliant; wherein, if B≥0.95, it is compliant; if 0.6≤B<0.95, it is quasi-compliant; if B<0.6, it is not compliant; if the result is quasi-compliant, it indicates that the user's cognitive thinking training can no longer solve the problem, and there may also be poor environmental coping strategies. At this time, coping strategy training is performed.

[0055] Step S108: Output the training report and store it in a preset database (such as a dynamic database) to end the training.

[0056] In summary, the cognitive thinking digital training method, device, terminal, and storage medium provided by this invention have the following beneficial effects: (1) Significantly improve the personalization and effectiveness of training: Through multi-dimensional data matching and dynamic plan generation, the training content is ensured to be highly relevant to the user's current psychological state, cognitive shortcomings and environment, thereby greatly improving the pertinence and effectiveness of the intervention.

[0057] (2) Realize an intelligent and adaptive training process: The closed-loop evaluation mechanism enables the system to automatically adjust the training path based on the user's real-time training feedback, realizing the leap from "static course" to "intelligent coach", enhancing user experience and training efficiency.

[0058] (3) Ensure the systematic and scientific nature of the intervention: The whole process simulates the structured thinking of professional psychological intervention, integrating scattered psychological training techniques into a complete system with data input, planned generation, effect evaluation, and feedback adjustment, thus ensuring the scientific nature and replicability of the intervention method; (4) Deeper intervention: The training intervention process is aimed at changing the underlying thinking logic, not just the surface symptoms, so as to achieve the fundamental goal of deep psychological intervention and better intervention effect.

[0059] Example 2 This invention provides a digital cognitive thinking training device 100, which simulates the systematic and structural nature of expert intervention while possessing the scalability and privacy of digital technology, ultimately achieving efficient, low-cost, and in-depth self-directed cognitive thinking training. It should be noted that the implementation principle and specific implementation method of the digital cognitive thinking training device 100 can refer to the aforementioned digital cognitive thinking training method, and therefore will not be repeated below.

[0060] like Figure 3 As shown, the cognitive thinking digital training device 100 includes: The information receiving module 10 is used to receive basic individual information and environmental status information input by the user and store them in a preset database; The needs clarification module 20 is used to conduct needs clarification and grading assessments on users. Based on the user's feedback data on the preset standardized psychological scale, it calculates the total score of the scale and compares it with the preset judgment threshold to identify the user's needs classification. After the needs are confirmed, user characteristic data is generated and stored in the preset database. The feature classification module 30 is used to perform a classification and grading analysis of thinking features based on the user feature data to determine the type of subsequent training: if the thinking features are poor, the target setting module 40 is activated; if the adaptation strategy is poor, the coping strategy training is executed; if the thinking is balanced, the report output module 80 is activated. The target setting module 40 is used to perform scenario data matching, environment data matching, individual characteristic matching and modal data matching respectively in the preset database based on the user feature data, and then combine the target value and reward mode to perform training dimension plan matching and training indicator target setting. The plan generation module 50 is used to generate a multidimensional multimodal training plan based on the matched training dimension plan and the set training indicator target; wherein, the multidimensional multimodal training plan includes a mind training dimension and a modal dimension ranking; The single-dimensional training module 60 is used to perform single-dimensional cognitive thinking training according to the modal dimensions sorted or the dimensions selected by human-computer interaction. The balanced assessment module 70 is used to calculate and assess the balance index of thinking characteristics by combining the training process and result data of multidimensional thinking training. The balance index of thinking characteristics B is calculated using the formula B=F(K×(wi)). Bi is calculated, where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; wi is the weight of the i-th thinking training dimension; and Bi is a single-dimensional thinking feature index calculated based on the user's score on the standardized evaluation scale of the corresponding dimension. The system then determines whether the thinking feature balance index meets the standard. If the result is yes, the report output module is started; if the result is no, the system returns to start the single-dimensional training module 70. The report output module 80 is used to output a training report and store it in a preset database to end the training.

[0061] Example 3 The present invention provides a terminal, the terminal including a memory, a processor and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the various steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0062] Example 4 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0063] Example 5 The fifth aspect of the present invention provides a computer program product, including a computer program or instructions, which, when processed and executed, implement the various steps of the cognitive thinking digital training method as described in any of the above embodiments.

[0064] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0065] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0066] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0067] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0069] The present invention is not limited to the description in the specification and embodiments, and thus other advantages and modifications can be readily realized by those skilled in the art. Therefore, the present invention is not limited to the specific details, representative devices and illustrated examples shown and described herein without departing from the spirit and scope of the general concept as defined by the claims and their equivalents.

Claims

1. A cognitive thinking digital training method, characterized in that, Includes the following steps: Step S101: Receive the user's basic individual information and environmental status information, and store them in a preset database; Step S102: Conduct a needs clarification and grading assessment for users. Based on the user's feedback data on the preset standardized psychological scale, calculate the total score of the scale and compare it with the preset judgment threshold to identify the user's needs classification. After the needs are confirmed, user characteristic data is generated and stored in the preset database. Step S103: Perform a cognitive characteristic classification and grading analysis based on the user characteristic data to determine the type of subsequent training: if the cognitive characteristics are poor, proceed to step S104; if the adaptation strategy is poor, execute coping strategy training; if the cognitive characteristics are balanced, proceed to step S108. Step S104: Based on the user feature data, perform scenario data matching, environment data matching, individual feature matching, and modal data matching in the preset database respectively, and then combine the target value and reward mode to perform training dimension plan matching and training indicator target setting; Step S105: Generate a multidimensional multimodal training plan based on the matched training dimension plan and the set training indicator targets; wherein, the multidimensional multimodal training plan includes a thinking training dimension and a modal dimension ranking; the thinking training dimension includes five dimensions: egocentrism, perfectionism, rational emotion, emotional intelligence, and coping strategies; Step S106: Perform single-dimensional cognitive thinking training according to the modal dimension sorting or the dimension selected by human-computer interaction; Step S107: Calculate and evaluate the thinking characteristic balance index by combining the training process and result data of multidimensional thinking training. The thinking characteristic balance index B is calculated using the formula B=F(K×(w)). i B i The calculation is performed, where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; w i B is the weight of the i-th thinking training dimension. i The normalization function F is: F(x) = 0.1 + 0.9, which is a one-dimensional thinking characteristic index calculated based on the user's score on the standardized assessment scale of the corresponding dimension. (x - X min ) / (X max - X min ), where X max and X min These are the maximum and minimum expected values ​​of the comprehensive index of thinking characteristics, which are determined in advance based on historical data. Determine whether the thinking characteristic balance index meets the standard. If the result is yes, proceed to step S108. If the result is negative, return to step S106. Specifically, it is determined whether the thinking characteristic balance index is nearly met; if B≥0.95, it meets the standard; if 0.6≤B<0.95, it is nearly met; if B<0.6, it does not meet the standard; if the result is nearly met, then coping strategy training is performed. Step S108: Output the training report and store it in the preset database to end the training.

2. The cognitive thinking digital training method according to claim 1, characterized in that, Step S102 includes: A human-computer interaction loop that responds to individual psychological distress and needs; Based on the human-computer interaction results and in conjunction with the preset database, user needs are identified. If the needs are ambiguous, the process returns to step S101 to supplement and input basic individual information. If the needs are clear, user characteristic data is generated. If the user has a balanced personality, the process proceeds to step S108.

3. The cognitive thinking digital training method according to claim 1, characterized in that, Step S106 includes: in each single-dimensional thinking training, the input and output of human-computer interaction information are presented in the form of one or more combinations of text, graphics, animation, and virtual reality.

4. The cognitive thinking digital training method according to claim 3, characterized in that, After completing each single-dimensional thinking training, assess whether the thinking characteristic index of that dimension meets the standard. If it does not meet the standard, obtain the user's choice of whether to continue training in this dimension; if it meets the standard, determine whether the thinking training of all dimensions is completed. If the user chooses to continue training in this dimension, then repeat the cognitive thinking training for that dimension; otherwise, generate a current training progress report and exit training. If it is determined that the cognitive thinking training plan for all dimensions has been completed, proceed to step S107; otherwise, obtain the user's choice of whether to continue training. If the user chooses to continue training, then cognitive thinking training for the unfinished dimensions will proceed; otherwise, a current training progress report will be generated, and training will be terminated.

5. The cognitive thinking digital training method according to claim 1, characterized in that, Step S101 includes: The evaluation module is started in response to the user's startup command on the network terminal device or stand-alone device. User identity ID generation and identification based on user communication or biological identity information; Acquire multimodal individual cognitive characteristic data, physiological state, and environmental relationship data from user input, including text, audio, and video.

6. A digital training device for cognitive thinking, characterized in that, include: The information receiving module is used to receive basic personal information and environmental status information input by the user and store them in a preset database; The needs clarification module is used to conduct needs clarification and grading assessments of users. Based on the user's feedback data on the preset standardized psychological scale, the total score of the scale is calculated and compared with the preset judgment threshold to identify the user's needs classification. After the needs are confirmed, user characteristic data is generated and stored in the preset database. The feature classification module is used to perform cognitive feature classification and grading analysis based on the user feature data to determine the subsequent training type: if the cognitive features are poor, the target setting module is activated. If the adaptation strategy is ineffective, then coping strategy training will be implemented; If the thought process is balanced, then the report output module will be activated; The target setting module is used to perform scenario data matching, environment data matching, individual characteristic matching and modal data matching in the preset database according to the user feature data, and then combine the target value and reward mode to perform training dimension plan matching and training indicator target setting. The plan generation module is used to generate a multidimensional and multimodal training plan based on the matched training dimension plan and the set training indicator target; wherein, the multidimensional and multimodal training plan includes a mind training dimension and a modal dimension ranking; the mind training dimension includes five dimensions: egocentrism, perfectionism, rational emotion, emotional intelligence, and coping strategies; The single-dimensional training module is used to perform single-dimensional cognitive thinking training according to the modal dimensions or the dimensions selected by human-computer interaction. The balanced assessment module is used to calculate and assess the balance index of thinking characteristics by combining the training process and result data of multidimensional thinking training. The balance index of thinking characteristics B is calculated using the formula B=F(K×(wi)). Bi is calculated as follows: F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a correction coefficient; wi is the weight of the i-th thinking training dimension; and Bi is a single-dimensional thinking characteristic index calculated based on the user's score on the standardized evaluation scale for the corresponding dimension. The normalization function F is: F(x) = 0.1 + 0.9 (x - Xmin) / (Xmax - Xmin), where Xmax and Xmin are the maximum and minimum expected values ​​of the comprehensive index of thinking characteristics, which are determined in advance based on historical data. The system then determines whether the thinking characteristic balance index meets the standard. If yes, the report output module is activated; if no, the system returns to the single-dimensional training module. The balance assessment module also determines whether the thinking characteristic balance index is nearly met. If B ≥ 0.95, it meets the standard; if 0.6 ≤ B < 0.95, it is nearly met; if B < 0.6, it is not met. If the result is nearly met, coping strategy training is executed. The report output module is used to output a training report and store it in the preset database to end the training.

7. A terminal, characterized in that, The terminal includes a memory, a processor, and a computer program stored in the memory, which, when executed by the processor, implements the steps of the cognitive thinking digital training method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the cognitive thinking digital training method as described in any one of claims 1-5.

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