Coding strategy digital training method and device, terminal and storage medium

Abstract

The invention discloses a coping strategy digital training method and device, a terminal and a storage medium. The method comprises the following steps: receiving individual basic information and environment state information input by a user; performing demand clarification grading evaluation on the user; performing thinking feature classification analysis according to the user feature data to judge a training type; setting individual coping strategy training parameters of each parameter component containing the multi-dimensional scene factor indexes; generating a coping strategy training plan; executing multi-mode man-machine interaction preset scene coping strategy loop training of the corresponding project; calculating and evaluating an adaptation index; and outputting a training report. The invention provides a self-service, systematized, structured and continuously optimized psychological adaptive ability training scheme, and the defects of the existing psychological service in the aspects of coverage, systematicness, self-adaptability and accessibility are effectively overcome.

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 coping strategies. 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 logic and the improvement of environmental coping abilities. Through training in underlying logic and developing coping strategies for intermediate-level environmental stimuli, individuals facing challenges can build stability and balance in their personality. This fundamentally alters and elevates the underlying logic of an individual's psychological structure, thereby resolving psychological obstacles.

[0004] Existing psychological intervention technologies mainly include manual psychological counseling and therapy, some electronic measurement systems, and biofeedback devices. While manual intervention has the advantage of personalization, it heavily relies on professionals, has limited service coverage, a long cycle, and is difficult to scale up. On the other hand, existing electronic systems are mostly limited to single-time or single-dimensional psychological measurement or superficial emotion regulation, lacking the ability to systematically, structurally, and adaptively train an individual's psychological structure. In particular, they cannot achieve closed-loop, continuous, multi-dimensional, and contextualized intelligent intervention from "thinking characteristic assessment" to "environmental coping strategy training" while protecting user privacy.

[0005] Therefore, current mainstream psychological service technologies suffer from the following shortcomings: First, they cannot achieve automated classification and grading based on individual characteristic data (including basic information, environmental status, and psychological needs), resulting in a lack of precise adaptation in intervention programs; second, they lack a systematic method to combine user-defined training goals with multidimensional situational factors (roles, states, psychological characteristics, etc.) to generate structured training plans; third, they are difficult to conduct cyclical training and dynamically evaluate the adaptation index through multimodal human-computer interaction during the training process, failing to form a continuous optimization closed loop of "training-evaluation-feedback-adjustment"; fourth, most existing computer-aided psychological tools are single-function and do not integrate the dual-path intervention logic of "thinking training" and "coping strategy training," failing to provide full-process support from cognitive adjustment to behavioral adaptation; fifth, existing psychological interventions usually only address the surface symptoms of mental illness patients without involving deep-level training interventions, resulting in "treating the symptoms but not the root cause" and poor training intervention effects.

[0006] Furthermore, existing technical solutions mostly remain at the conceptual level of functional modules, lacking disclosure of the following core aspects: 1) How to transform user-inputted multimodal raw data such as text, audio, and video into structured "user feature data" that can be used for subsequent logical judgments; 2) The specific rules, judgment thresholds, or algorithms upon which "demand classification" relies; 3) The specific calculation model constituting the "adaptation index," including the definition of each component, quantification methods, weight allocation, and the specific form of the normalization function. This insufficient disclosure also prevents those skilled in the art from implementing a stable and accurately evaluating automated response strategy training system based on the specifications.

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

[0008] The purpose of this invention is to provide a digital training method, device, terminal, and storage medium for coping strategies. It aims to solve the problem of how to automatically construct a training plan for coping strategies that includes multiple scenarios based on user-defined goals and training parameters generated by system analysis. It simulates the systematic and structured nature of expert intervention, while possessing the scalability and privacy of digital technology, thereby achieving personalized, structured, and continuously optimized improvement of psychological adaptability.

[0009] To achieve the above objectives, the first aspect of the present invention provides a digital training method for a coping strategy, 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. By analyzing the feedback data of users on the preset standardized psychological assessment scale, their quantitative scores are compared with preset thresholds to identify user 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, then perform cognitive thinking training; if the adaptive strategy is poor, then proceed to step S104; if the cognitive characteristics are balanced, then proceed to step S108. Step S104: Receive the individual coping strategy training objectives and adaptation index set by the user, and set individual coping strategy training parameters that include the parameter components of multi-dimensional situational factor indicators based on the user characteristic data analysis; the multi-dimensional situational factor indicators include role, state, and psychological characteristics. Step S105: Based on the individual coping strategy training parameters and the preset database, generate a coping strategy training plan, including four scenario items: "scenario data generation", "human data generation", "conflict intensity matching completion", and "conflict pattern generation". Step S106: According to the selected scenario dimension of human-computer interaction or the corresponding items in sequence, perform multimodal human-computer interaction preset scenario response strategy cyclic training; Step S107: Calculate and evaluate the adaptation index by combining the training process and result data of multiple scenario-based coping strategy training. The adaptation index S is calculated using the formula S = F( K × Σ(wi × Si) ), where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is the system correction coefficient; wi is the weight of the i-th scenario-based item; and Si is the single-item adaptation index obtained by quantifying the user's behavioral data during the training of the i-th scenario-based item. Determine whether the adaptation index meets the standard. If the result is yes, implement the preset reward mechanism and proceed to step S108. If the result is no, return to step S106 or perform cognitive thinking training. 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, after completing the coping strategy training for each scenario item, the individual adaptation index of that item 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 for this scenario item is obtained; if it meets the standard, a preset reward feedback mechanism is implemented and stored in a preset database. If the user chooses to continue training for this scenario, the training of the coping strategy for that scenario will be repeated; otherwise, a current training progress report will be generated and training will be terminated.

[0012] In a preferred embodiment, after completing the coping strategy training for each scenario item, it is determined whether the coping strategy training for all scenario items has been completed. If the result is yes, then proceed to step S107; otherwise, coping strategy training for the incomplete scenario items is performed.

[0013] In a preferred embodiment, in step S107, the normalization function F is a linear transformation function: F(x) = 0.1 + 0.9 (x - X min ) / (X max - X min ), where X max and X min The comprehensive index is defined by statistically setting its theoretical maximum and minimum values ​​based on historical training data. The individual adaptation index Si is calculated based on one or more data points from the user's task completion rate, reaction time, and emotional valence score in the corresponding scenario project.

[0014] In a preferred embodiment, step S107 further includes determining whether the adaptation index is quasi-compliant; wherein, if S≥0.95, the standard is met, and the process proceeds to step S108; if 0.6≤S<0.95, the standard is quasi-compliant, and the process returns to step S106; if S<0.6, the standard is not met, and cognitive thinking 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 apparatus for coping strategies, 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. By analyzing the feedback data of users on a preset standardized psychological assessment scale, the module compares their quantitative scores with preset thresholds to identify user needs types. 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, then cognitive thinking training is performed; if the adaptation strategy is poor, then the goal setting module is activated; if the thinking is balanced, then the report output module is activated. The goal setting module is used to receive the individual coping strategy training goals and adaptation index set by the user, and to set the individual coping strategy training parameters, which include the parameter components of multi-dimensional situational factor indicators, based on the analysis of the user characteristic data. The multi-dimensional situational factor indicators include role, state, and psychological characteristics. The plan generation module is used to generate a coping strategy training plan based on the individual coping strategy training parameters and the preset database, including four scenario items: "scenario data generation", "human data generation", "conflict intensity matching completion" and "conflict pattern generation". The single-item training module is used for cyclical training of multimodal human-computer interaction preset scenario response strategies according to the selected scenario dimension of human-computer interaction or by sequentially executing the corresponding items; The adaptation assessment module is used to calculate and evaluate the adaptation index by combining the training process and result data of coping strategy training for multiple scenario items. The adaptation index S is calculated using the formula S = F( K×Σ(wi×Si) ), where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is the system correction coefficient; wi is the weight of the i-th scenario item; and Si is the single-item adaptation index obtained by the user through behavioral data quantification in the training of the i-th scenario item. The module then determines whether the adaptation index meets the standard. If the result is yes, a preset reward mechanism is implemented and the report output module is activated. If the result is no, the module returns to the single-item training module or performs cognitive thinking training. 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 digital training method for coping strategies 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 digital training method for coping strategies 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 digital training method for coping strategies as described in any of the above embodiments.

[0020] This invention provides a digital training method, device, terminal, and storage medium for coping strategies. It receives and stores individual user information, performs needs clarification and classification analysis, and automatically constructs a multi-dimensional scenario training plan for users with poor adaptation strategies based on their self-set goals and system-generated parameters. The plan executes cyclical training through human-computer interaction and calculates the adaptation index in real time using training data. Based on the evaluation results, it determines whether to continue training, return to the previous training, or switch to another training module, ultimately outputting a training report. Furthermore, it defines an objective needs classification by specifying a total score threshold based on standardized tools such as the Symptom Checklist-90 (SCL-90); it clarifies the training plan generation process by specifically explaining the construction logic and data sources of four scenario items, including "scenario data generation"; and it discloses the calculation formula for the adaptation index S, including the quantification method of its component Si (such as a score based on task completion) and the determination principle of its weight wi, providing a clear, calculable, and reproducible standard for evaluating training effectiveness, thus constituting a complete and implementable technical solution. The entire training intervention process targets changing the underlying thinking logic, not just superficial symptom intervention, thereby achieving the fundamental goal of deep psychological intervention and resulting in better intervention effects. This method realizes a closed loop throughout the entire process from user feature identification, plan generation, training execution to effect evaluation. Under the premise of protecting user privacy, it provides a self-service, systematic, structured and continuously optimizable psychological adaptability training program, which effectively makes up for the shortcomings of existing psychological services in terms of coverage, systematicness, adaptability and accessibility. Attached Figure Description

[0021] 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.

[0022] Figure 1 The overall flowchart of psychological intervention provided by this invention; Figure 2A flowchart of the digital training method for the coping strategy provided by the present invention; Figure 3 A framework diagram of the digital training device for coping strategies provided by the present invention. Detailed Implementation

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Example 1 In embodiments of the present invention, a digital training method for coping strategies is provided to help individuals train their coping strategies under multi-dimensional environmental situational factors within a balanced logical framework, thereby enhancing their environmental adaptability. This method is based on theories, research findings, and methods of psychological science. Utilizing computer interaction technology, it structurally conducts coping strategy training. Through the installation of terminal applications (APPs) on mobile devices, personal computers, and other network terminal audiovisual interactive devices, users can conveniently and cost-effectively receive scientifically effective multimedia information interaction for psychological self-help or psychological services, overcoming the stigma associated with illness and resolving psychological distress.

[0027] This method summarizes mainstream intervention approaches in the field of psychology, combining structural theories of psychology with clinical practice. It examines individual adaptability across multiple dimensions of coping strategies, with core dimensions including family situations, school environments, social environments, and clinical settings. For each of these contextual dimensions, multimodal immersive self-help training in coping strategies is conducted. This achieves truly "systematic, structured, and in-depth" professional intelligent psychological intervention, improving individual psychological characteristics and assisting those experiencing psychological distress in self-healing.

[0028] 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.

[0029] Please see Figure 1 and Figure 2 The digital training method for coping strategies includes the following steps S101-S108.

[0030] Step S101: Receive the user's basic individual information and environmental status information, and store them in a preset database.

[0031] 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.

[0032] The specific implementation methods for "receiving basic individual information and environmental status information input by the user" include: (1) Text information: directly stored in the corresponding fields of the database as structured data.

[0033] (2) Audio information: It is converted into text through automatic speech recognition (ASR) technology, and then keywords (such as emotion words and stress event description words) are extracted and sentiment analysis is performed through natural language processing (NLP). The analysis results (such as emotion tags and keyword vectors) are stored as structured data.

[0034] (3) Video information (such as facial expressions): Facial emotions are recognized by pre-trained emotion computing models (such as convolutional neural networks trained on the FER2013 dataset), and the dominant emotion categories and confidence levels are stored as structured data.

[0035] All the processed structured data together constitute the "individual basic information and environmental status information" that can be used in subsequent steps.

[0036] Step S102: Conduct a needs clarification and grading assessment for users. By analyzing user feedback data on a pre-set standardized psychological assessment scale, the quantitative scores are compared with pre-set thresholds to identify user needs types. After needs are confirmed, user characteristic data is generated and stored in a pre-set database. That is, through human-computer intelligent interaction, in conjunction with a basic database and a dynamic database, user needs types are identified. Specifically, step S102 includes: responding to individual psychological distress and demands through a human-computer interaction loop; identifying user needs based on the human-computer interaction results and in conjunction with the pre-set database; if the needs are ambiguous, returning to step S101 to supplement and enter basic individual information, and then reassessing; if the needs are clear, user characteristic data is generated; if the personality is balanced, proceed to step S108.

[0037] Specifically, a concrete example of "conducting a clarification and grading assessment of user needs" is as follows: The system guides users to complete the Symptom Checklist-90 (SCL-90) online. It calculates the total score and scores for each factor. A threshold is set: a total score ≥ 160, or any factor score ≥ 3 (indicating "moderate" or higher level of distress).

[0038] The rules for "identifying user needs classification" are as follows: Vague needs: If the total score is ≥160, it indicates a high level of distress for the user, and their self-awareness may be unclear. They should return to S101 for supplementary information or seek a preliminary manual assessment. Clear needs: If the total score is <160 and there is a factor score ≥2 (indicating "mild" distress), it indicates a clear and manageable mild distress. The system integrates the factor scores and key structured information extracted from S101 (such as "main stressor in the past month: work reports") into "user characteristic data." Balanced personality: If the total score and all factor scores are consistently below the norm average (e.g., T-score <60), it is determined that no specific training is currently needed.

[0039] Step S103: Perform a classification and grading analysis of thinking characteristics based on user characteristic data to determine the type of subsequent training: if the thinking characteristics are poor, then perform cognitive thinking training; if the adaptation strategy is poor, then proceed to step S104; if the thinking is balanced, then proceed to step S108.

[0040] It should be noted that the focus of this invention is on "coping strategy training", while the specific steps and procedures for "cognitive thinking training" are not limited and can be referred to existing technologies.

[0041] Step S104: Receive the user-defined individual coping strategy training objectives and adaptation index, and based on user characteristic data analysis, set individual coping strategy training parameters that include various parameter components of multi-dimensional situational factor indicators. These multi-dimensional situational factor indicators include role, state, and psychological characteristics.

[0042] Step S105: Based on the individual coping strategy training parameters and combined with the preset database, generate a coping strategy training plan, including four scenario items: "scenario data generation", "human data generation", "conflict intensity matching completion" and "conflict pattern generation".

[0043] It's important to note that this is crucial for achieving personalization and scientific rigor. The system doesn't randomly or based on experience to select training content. Instead, it uses user characteristic data, simultaneously matching it with contextual data, humanistic data, conflict intensity, and conflict patterns. It also introduces a behavior-shaping mechanism of "target value and reward pattern," thereby generating a preliminary training framework that aligns with both the user's psychological state and the principles of learning science. This increases the effectiveness and adaptability of psychological intervention. Through the human-computer interactive training system and its methods, it enhances an individual's ability to cope with external stimuli or adapt to the environment, maintaining a balanced psychological state.

[0044] The generated coping strategy training plan includes four scenario projects, the specific content of which is shown in the following example: (1) Scenario data generation: Based on the "stressors" (such as "work report") in the user feature data, a specific virtual scenario (such as "giving a quarterly work report to leaders and colleagues in the meeting room") is generated by matching from the preset scenario library. (2) Humanistic data generation: Assign basic attributes to the virtual characters (roles) in the above scenarios, such as "Leader: serious and detail-oriented; Colleague A: competitive mentality"; (3) Conflict intensity matching completed: Based on the user's self-set "training goal" (such as "hoping to reduce anxiety during reporting") and the user's current anxiety level as assessed by the system, the initial conflict level of the scenario is set (such as language challenge difficulty, time pressure value). (4) Conflict mode generation: Define specific interaction modes that may trigger user discomfort in this scenario (such as "the leader suddenly asks for data details" or "a colleague raises questions").

[0045] Step S106: Perform cyclical training of multimodal human-computer interaction preset scenario coping strategies according to the selected scenario dimension or the corresponding items in sequence. In each individual coping strategy training, human-computer interaction information is presented through one or more combinations of text, graphics, animation, and virtual reality. Therefore, through bidirectional reshaping training of individual central nervous system functions, the "stimulus-response pattern" leading to various emotional pathologies is deeply resolved, strengthening the rationality and stability of the individual's psychological structure.

[0046] During the "Multimodal Human-Computer Interaction Preset Scenario Response Strategy Loop Training" exercise, the system presents scenarios in the form of images and text, dialogue options, virtual character interactions (such as selecting dialogue responses), and even short interactive animations. Users respond through clicking, dragging, text, or voice input.

[0047] For example, the quantification of the single-item fitness index Si: After training for a single scenario item is completed, the system automatically calculates Si (range 0-100) based on the following data: Task completion rate (weight 40%): Whether the preset coping steps were completed (such as "identifying one's own emotions" or "using at least one relaxation technique"); Response adaptability (weight 30%): The degree to which the user's response choice matches the system's preset "adaptive strategy" library; Simulated emotional feedback (weight 30%): After training, users give themselves a score (1-10) on their level of emotional control in simulated scenarios.

[0048] Si is obtained through weighted calculation and used for subsequent calculation of the comprehensive fitness index S.

[0049] Furthermore, after completing the coping strategy training for each scenario item, the individual adaptation index for that item 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 for this scenario item is obtained; if it meets the standard, a preset reward feedback mechanism is implemented and stored in a preset database; if it is determined that the user chooses to continue training for this scenario item, the coping strategy training for that scenario item is repeated; otherwise, a current training progress report is generated, and training is exited. Further, after completing the coping strategy training for each scenario item, it is determined whether the coping strategy training for all scenario items has been completed. If the result is yes, step S107 is entered; otherwise, coping strategy training for the incomplete scenario items is performed.

[0050] Step S107: Calculate and evaluate the adaptation index by combining the training process and result data of multiple scenario-based coping strategy training. The adaptation index S is calculated using the formula S = F( K × Σ(wi × Si) ), where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is the system correction coefficient; wi is the weight of the i-th scenario-based item; and Si is the single-item adaptation index obtained by the user through behavioral data quantification in the training of the i-th scenario-based item. Determine whether the adaptation index meets the standard. If the result is yes, implement the preset reward mechanism and proceed to step S108. If the result is no, return to step S106 or perform cognitive thinking training.

[0051] Therefore, a multidimensional "adaptation 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 transition (returning to S106 or ending, or executing cognitive thinking training). This constitutes an intelligent feedback loop, enabling the system to "observe the effect and adjust the strategy" like an expert.

[0052] Specifically, the normalization function F is a linear transformation function: F(x) = 0.1 + 0.9 (x - X min ) / (X max -X min ), where X max and X min The comprehensive index is defined by statistically setting its theoretical maximum and minimum values ​​based on historical training data. The individual adaptation index Si is calculated based on one or more data points from the user's task completion rate, reaction time, and emotional valence score in the corresponding scenario project.

[0053] Furthermore, this step also includes determining whether the adaptation index is nearly met; if S≥0.95, it meets the standard and proceeds to step S108; if 0.6≤S<0.95, it is nearly met and returns to step S106; if S<0.6, it does not meet the standard and cognitive thinking training is performed.

[0054] For example, in an exemplary embodiment, the calculation model for the fitness index S is specifically implemented as follows: Assume there are 4 scenario items (n=4), and their weights wi can all be set to 0.25, or dynamically allocated according to the user's initial weaknesses. Let the user's score Si for each item be: [S1=75, S2=80, S3=60, S4=90].

[0055] Calculate the weighted sum: Σ(wi×Si)=0.25 75+0.25 80+0.25 60+0.25 90 = 76.25. Normalization parameter setting: Based on a large amount of simulation data, the theoretical minimum value X is set. min = 20, Theoretical maximum value X max = 95. The initial value of the system correction coefficient K is set to 1.0. Substituting into the normalization function: S = 0.1 + 0.9 (76.25 - 20) / (95 - 20)≈0.1+0.9 (56.25 / 75)≈0.775. According to the rule (0.6≤S<0.95 is considered "quasi-acceptable"), the system determines it as "quasi-acceptable" and automatically returns to step S106 to select the weak items (such as the items corresponding to S3) for a new round of training.

[0056] Step S108: Output the training report and store it in the preset database to end the training.

[0057] In summary, the digital training method, device, terminal, and storage medium for coping strategies provided by this invention receive and store individual user information, perform needs clarification and classification analysis, and automatically construct multi-dimensional scenario training plans for users with poor adaptation strategies based on their self-set goals and parameters generated by system analysis. Cyclic training is executed through human-computer interaction, and the adaptation index is calculated in real time based on training data. The evaluation results determine whether to continue training, return to previous training, or switch to other training modules, ultimately outputting a training report. Furthermore, objective needs classification is performed by defining a total score threshold based on standardized tools such as the Symptom Checklist-90 (SCL-90); the construction logic and data sources of four scenario items, including "scenario data generation," are clearly explained, clarifying the training plan generation process; and the calculation formula for the adaptation index S, including the quantification method of its component Si (such as a score based on task completion) and the determination principle of its weight wi, provides a clear, calculable, and reproducible standard for evaluating training effectiveness, thus constituting a complete and implementable technical solution. The entire training and intervention process targets changing underlying thought processes, going beyond superficial symptom intervention to achieve a deeper, more fundamental psychological intervention, resulting in better intervention outcomes. This method achieves a closed-loop process from user characteristic identification, plan generation, training execution to effect evaluation. While protecting user privacy, it provides a self-service, systematic, structured, and continuously optimizable psychological adaptability training program, effectively addressing the shortcomings of existing psychological services in terms of coverage, systematicity, adaptability, and accessibility.

[0058] A second aspect of this invention provides a digital training device 100 for coping strategies, used to help individuals train coping strategies under multi-dimensional environmental situational factors within a balanced logical framework, thereby improving their environmental adaptability. It should be noted that the implementation principle and specific implementation method of the digital training device 100 for coping strategies can be referred to the aforementioned digital training method for coping strategies, and will not be repeated below.

[0059] like Figure 3 As shown, the digital training device 100 for response strategies 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. By analyzing the feedback data of users on the preset standardized psychological assessment scale, the quantitative scores are compared with preset thresholds to identify user needs types. 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, then cognitive thinking training is performed; if the adaptation strategy is poor, then the goal setting module 40 is activated; if the thinking is balanced, then the report output module 80 is activated. The goal setting module 40 is used to receive the individual coping strategy training goals and adaptation index set by the user, and to set individual coping strategy training parameters that include the parameter components of multi-dimensional situational factor indicators based on the analysis of the user characteristic data; the multi-dimensional situational factor indicators include role, state, and psychological characteristics. The plan generation module 50 is used to generate a coping strategy training plan based on the individual coping strategy training parameters and the preset database, including four scenario items: "scenario data generation", "human data generation", "conflict intensity matching completion" and "conflict pattern generation". The single-item training module 60 is used for cyclical training of multimodal human-computer interaction preset scenario response strategies according to the selected scenario dimension of human-computer interaction or the sequential execution of corresponding items; The adaptation assessment module 70 is used to calculate and evaluate the adaptation index by combining the training process and result data of coping strategy training for multiple scenario items. The adaptation index S is calculated using the formula S = F( K×Σ(wi×Si) ), where F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is the system correction coefficient; wi is the weight of the i-th scenario item; and Si is the single-item adaptation index obtained by the user through behavioral data quantification in the training of the i-th scenario item. The module 70 also determines whether the adaptation index meets the standard. If the result is yes, a preset reward mechanism is implemented and the report output module 80 is started. If the result is no, the module returns to the single-item training module or performs cognitive thinking training. The report output module 80 is used to output a training report and store it in the preset database to end the training.

[0060] 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 digital training method for coping strategies as described in any of the above embodiments.

[0061] Example 4 The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the digital training method for coping strategies as described in any of the above embodiments.

[0062] 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 digital training method for coping strategies as described in any of the above embodiments.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] In the embodiments provided by this invention, it should be understood that the disclosed systems, devices / terminal equipment, and methods can be implemented in other ways. For example, the system or device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of systems or units may be electrical, mechanical, or other forms.

[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 coping strategy digital training method, characterized in that, The method comprises the following steps: Step S101: receiving individual basic information and environmental state information input by a user and storing them in a preset database; Step S102: performing demand clarification classification evaluation on the user, comparing the quantized score of feedback data of the user on a preset standardized psychological evaluation scale with a preset threshold value to identify the demand type of the user, generating user feature data after demand confirmation, and storing the user feature data in the preset database; Step S103: performing thinking feature classification analysis according to the user feature data to determine the subsequent training type: if the thinking feature is poor, cognitive thinking training is performed; if the adaptive strategy is poor, step S104 is entered; if the thinking is balanced, step S108 is entered; Step S104: receiving an individual coping strategy training target and an adaptive index set by the user, and setting individual coping strategy training parameters of each parameter component containing a multi-dimensional scene factor index according to the user feature data analysis; the multi-dimensional scene factor index includes role, state, and psychological characteristics; Step S105: generating a coping strategy training plan according to the individual coping strategy training parameters, combining the preset database, including four scene items of "scene data generation", "humanistic data generation", "conflict intensity matching completion", and "conflict mode generation"; Step S106: performing multi-modal human-computer interaction preset scene coping strategy cyclic training according to the selected scene dimension of human-computer interaction or sequentially executing the corresponding items; Step S107: calculating and evaluating the adaptive index S by the formula S = F(K x ∑(wi x Si)), wherein F is a preset normalization function that maps the input value to the interval [0.1, 1.0]; K is a system correction coefficient; wi is the weight of the i-th scene item; Si is the single-item adaptive index of the user in the i-th scene item training quantified by behavior data; and judging whether the adaptive index meets the standard, if the result is yes, implementing a preset reward mechanism feedback and entering step S108, if the result is no, returning to step S106 or performing cognitive thinking training; Step S108: outputting a training report and storing it in the preset database to end the training.

2. The coping strategy digital training method according to claim 1, characterized in that, The step S102 comprises: Responding to the human-computer interaction cycle request of individual psychological distress and appeal; According to the human-computer interaction result and the preset database, the user demand is identified, if the demand is fuzzy, the step S101 is returned to supplement the input of individual basic information; if the demand is clear, the user feature data is generated; if the personality is balanced, the step S108 is entered.

3. The coping strategy digital training method according to claim 1, characterized in that, After completing the coping strategy training of each scene item, it is judged whether the single-item adaptive index of the item meets the standard, if not, the selection result of whether the user continues the training of the current scene item is obtained; if yes, a preset reward feedback mechanism is implemented and stored in the preset database. If it is judged that the user selects to continue the training of the current scenario project, the coping strategy training of the scenario project is repeated; otherwise, a current training progress report is generated and the training is exited.

4. The coping strategy digital training method according to claim 3, characterized in that, After the coping strategy training of each scenario project is completed, it is judged whether the coping strategy training of all scenario projects has been completed, and if the result is yes, the step S107 is entered; otherwise, the coping strategy training of the unfinished scenario project is performed.

5. The coping strategy digital training method according to claim 1, characterized in that, In the step S107, the normalization function F is a linear conversion function: F(x) = 0.1 + 0.9 (x - X min ) / (X max - X min ), wherein X max and X min are the maximum and minimum values of the comprehensive index set according to historical training data statistics; the single item adaptation index Si is calculated according to one or more of the data of the user's task completion degree, reaction time, and emotional valence score in the corresponding scenario item.

6. The coping strategy digital training method according to claim 5, wherein, In the step S107, it is further judged whether the adaptation index is quasi-standard; wherein, if S≥0.95, the standard is reached and the step S108 is entered; if 0.6≤S<0.95, the quasi-standard is reached and the step S106 is returned; if S<0.6, the standard is not reached, and the cognitive thinking training is performed.

7. The coping strategy digital training method according to claim 1, characterized in that, The step S101 comprises: in response to the starting instruction of the user on the network terminal device or the single machine running device, starting the evaluation module; based on the communication or biological identity information of the user, generating and identifying the user identity ID; obtaining the multi-modal individual thinking feature data, physiological state and environmental relationship data input by the user, including text, audio and video.

8. A coping strategy digital training device, characterized in that, It comprises: an information receiving module for receiving the individual basic information and environmental state information input by the user and storing them in a preset database; a demand clarification module for clarifying and grading the demand of the user, comparing the quantized score of the feedback data of the user on the preset standardized psychological assessment scale with the preset threshold value, identifying the demand type of the user, generating the user feature data after the demand is confirmed, and storing them in the preset database; a feature typing module for analyzing the thinking feature typing and grading according to the user feature data to determine the type of subsequent training: if the thinking feature is poor, the cognitive thinking training is performed; if the adaptation strategy is poor, the target setting module is started; if the thinking is balanced, the report output module is started; a target setting module for receiving the individual coping strategy training target and adaptation index set by the user independently, and setting the individual coping strategy training parameters of each parameter component including multi-dimensional scenario factor indexes according to the user feature data analysis; the multi-dimensional scenario factor indexes include roles, states and psychological characteristics; a plan generation module for generating a coping strategy training plan according to the individual coping strategy training parameters, combining the preset database, including four scenario projects of "scenario data generation", "humanistic data generation", "conflict intensity matching completion" and "conflict mode generation"; a single training module for performing multi-modal human-computer interaction preset scenario coping strategy cyclic training according to the selected scenario dimension of human-computer interaction or sequentially executing the corresponding projects. An adaptation evaluation module is configured to calculate and evaluate an adaptation index S in combination with a training process and result data of a plurality of scenario item coping strategy training, the adaptation index S is calculated by a formula S = F(Kx∑(wi×Si)), wherein F is a preset normalization function mapping an input value to an interval of [0.1, 1.0]; K is a system correction coefficient; wi is a weight of an i-th scenario item; Si is a single item adaptation index of a user in the i-th scenario item training quantified by behavior data; and whether the adaptation index meets a standard is judged, if the result is yes, a preset reward mechanism feedback is implemented and a report output module is started, if the result is no, the single item training module is returned or cognitive thinking training is executed; A report output module is configured to output a training report and store the training report into the preset database, and end the training.

9. A terminal, characterized by comprising: The terminal comprises a memory, a processor, and a computer program stored in the memory, and the computer program is executed by the processor to realize each step of the coping strategy digital training method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize each step of the coping strategy digital training method according to any one of claims 1-7.

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