Dynamic adjustment method based on depression metrorphism treatment scheme

Abstract

The invention provides a dynamic adjustment method based on a depression mind-correcting treatment scheme, and relates to the technical field of medical care information, and the method comprises the steps: collecting multi-dimensional information of a patient, and collecting life behavior risk data, psychological scale data and cognitive function evaluation data; comprehensively evaluating the depression level of the patient; determining the priority of a treatment therapy module according to the multi-dimensional information of the patient; generating an initial treatment scheme; calculating a daily task comprehensive weight value, and dynamically adjusting the training content of the next day; and evaluating the state of the patient in stages, recalculating the priority of each therapy module, and optimizing the therapeutic scheme. Through multi-dimensional data fusion evaluation and intelligent algorithm dynamic adjustment, a complete closed-loop feedback optimization system is constructed, precision and individuation of a depression treatment scheme are achieved, the treatment effect and patient compliance are effectively improved, the recurrence risk is reduced, and a more scientific and effective treatment scheme is provided for depression patients.

Description

Technical Field

[0001] The present invention relates to the field of medical care information technology, and in particular to a dynamic adjustment method based on a mindfulness treatment program for depression. Background Art

[0002] Depression is a common psychological disorder, affecting about 300 million people worldwide. Depression not only seriously affects the quality of life of patients, but also increases the risk of suicide, placing a heavy burden on society and families. At present, the treatments for depression mainly include drug therapy, psychotherapy, and comprehensive treatment. Among them, non-drug interventions such as mindfulness-based cognitive therapy (MBCT), cognitive behavioral therapy (CBT), and exercise therapy (ET) are increasingly receiving clinical attention due to their high safety and lack of drug dependence.

[0003] However, traditional depression treatment has the following limitations and technical problems: First, the evaluation method is single. Traditional depression evaluation mainly relies on patient self-assessment scales (such as PHQ-9, HAMD, etc.), lacking the support of objective physiological data (such as heart rate variability, cortisol levels) and behavioral data (exercise, sleep, social activities), resulting in large subjective deviations in the evaluation results, making it difficult to fully reflect the patient's true condition.

[0004] Secondly, the treatment plan is static. Existing treatment technologies (such as CBT applications) mostly use fixed intervention modules, and do not dynamically adjust the intensity and content of treatment according to the patient's real-time status, which makes it impossible to achieve personalized treatment and limits the efficacy. For example, the Chinese patent application publication number CN116936036A discloses "a method and system for generating mindfulness treatment task training plans for insomnia". Although it considers dynamically adjusting the training items according to the patient's education level, sleep efficiency and age, its treatment plan adjustment mechanism is still relatively simple, mainly based on a single indicator (sleep efficiency) judgment, lacks comprehensive analysis of multidimensional data, and has not established a therapy module priority allocation mechanism, and cannot provide differentiated treatment strategies for patients with different symptom severity.

[0005] Third, multimodal data is fragmented. In existing technologies, there is insufficient data interaction between physiological monitoring devices (such as smart watches) and treatment systems, and it is impossible to achieve real-time linkage between physiological indicators and psychological interventions. Due to the lack of fusion analysis capabilities for multi-source heterogeneous data, it is difficult to reveal the complex relationship between depressive symptoms and physical conditions and behavioral patterns, which in turn affects the accuracy of treatment plans.

[0006] Fourth, patient compliance is low. The lack of a compliance prediction model based on behavioral data makes it impossible to accurately identify patients' treatment participation and compliance with doctor's orders, which makes it easy for patients to drop out of the intervention plan. At the same time, existing technologies mostly use simple task difficulty adjustment strategies, without considering individual differences such as patient preferences and optimal training time windows, and cannot specifically improve patient participation.

[0007] Fifth, there is a lack of intelligent feedback loops. Most existing methods are open-loop systems that cannot adjust treatment strategies in real time based on intervention effects, and it is difficult to establish a treatment mechanism for continuous improvement. The formulation and adjustment of treatment plans mainly rely on the subjective judgment of professionals, and there is a lack of standardized, data-driven intelligent decision support systems.

[0008] With the development of technologies such as the Internet of Things and artificial intelligence, wearable devices and smartphones can collect multi-dimensional health data of the human body in real time, providing a data basis for intelligent and personalized treatment of depression. However, how to effectively integrate these multi-dimensional data, establish a dynamic adjustment mechanism for generating treatment plans, and improve the personalization and effectiveness of depression treatment are still technical problems that need to be solved urgently. Summary of the invention

[0009] The purpose of the present invention is to provide a dynamic adjustment method based on the mindfulness treatment plan for depression. By integrating multi-source data such as physiological, psychological, cognitive, and behavioral data, a comprehensive evaluation model is established, and an intelligent algorithm is used to dynamically generate and adjust personalized treatment plans to improve the accuracy and effectiveness of depression treatment.

[0010] To achieve the above object, the present invention is implemented through the following technical solutions: A dynamic adjustment method based on a mindfulness-based treatment program for depression, comprising the following steps: S1: Collection of multi-dimensional information of patients, including data on life behavior risks, psychological scales and cognitive function assessment; S2: Comprehensively assess the patient's depression level based on the patient's multidimensional information; S3: Determine the priority of treatment therapy modules based on the patient's multidimensional information and depression level; S4: Generate the initial treatment plan for the patient based on the priority of the therapy module; S5: Calculate the comprehensive weight of daily tasks and dynamically adjust the training content for the next day; S6: Periodically evaluate the patient's condition, recalculate the priority of each therapy module, and optimize the treatment plan.

[0011] Further: In step S2, the method for comprehensively evaluating the depression level of the patient includes: Standardize risk indicators that are strongly associated with depression; Multiple linear regression analysis was performed to establish a model for determining depression levels; The patient's depression level is determined according to the calculation results of the depression level determination model.

[0012] Furthermore: the risk indicators strongly related to depression include: anxiety value, attention bias, memory function, processing speed, executive function, insomnia severity value, interpersonal problem scale value, daily step count, daily screen time, daily sedentary time, and physical illness.

[0013] Further: In step S3, the method for determining the priority of the therapy module according to the multi-dimensional information of the patient includes: S31: Calculate the initial comprehensive weight value of each therapy; S32: Correction of the comprehensive weight of each therapy; S33: judging the priority of each therapy module according to the corrected comprehensive weight value; The therapies include: mindfulness-based cognitive therapy, cognitive behavioral therapy, exercise therapy, cognitive behavioral therapy for insomnia, interpersonal psychotherapy and cognitive training.

[0014] Further: In step S32, the formula for correcting the comprehensive weight value of each therapy is:

[0015] in, I j For therapy j The comprehensive weight value of α is the basic weight of the composite index, β is the time decay factor weight, i is the user feature weight, U t is the weighted value of patient characteristics, t is the current time point, c and t 0 is a parameter that controls the decay speed.

[0016] Further: U t The correction rules for patient characteristic weights include: Age weight correction: When the patient is older than 60 years old, the interpersonal psychotherapy weighted index is +0.1; Occupational weight correction: When the patient is an Internet worker, the exercise therapy weighted index is +0.1; Body mass index (BMI) correction: When the patient's BMI level is overweight or obese, the exercise therapy weighted index is +0.1; Weight adjustment for physical diseases: According to the type of disease the patient suffers from, the weight of the corresponding treatment is increased accordingly.

[0017] Further: In step S5, the method for calculating the comprehensive weight value of the daily task includes: Calculation progress item:

[0018] in, P current is the current task progress, P target To target progress; Calculation task evaluation items:

[0019] in,

[0020] D Rate the difficulty. L Rate the likeability, E Score effectiveness; Calculate the time factor:

[0021] in W n 、W h They are the weights of the same day and the same time period in history. C n 、C h They are the average completion rate of the same day and the same period of history; Comprehensive weight value = progress item × evaluation item × time coefficient.

[0022] Further: according to the calculated comprehensive weight value of the daily task, the method of dynamically adjusting the training content of the next day includes: Adjustment of the task types for the next day: sort by the ratio of the task's comprehensive weight value, and push tasks with high ratio as must-do tasks; The number of tasks for the next day has been adjusted:

[0023] in, T Count is the number of tasks for the next day; N Base is the benchmark value of the number of tasks, W t is the comprehensive weight value of the task; Next day's mission difficulty adjustment:

[0024] in Dnew The difficulty of the next day's task; D current is the current task difficulty, P a For actual completion, P t For target progress, f(D) is the ideal difficulty function; Reset the training time period for the next day: Dynamically adjust the weight of the optimal training time period based on past training completion.

[0025] Further: In step S6, the period of periodic evaluation of the patient's status is once every two weeks, and the formula for periodically recalculating the priority of each therapy is:

[0026] in, α =0.4, β =0.3, c =0.2, d =0.1, W t is the last comprehensive weight value, F r is the user feedback coefficient, T r is the time decay factor, P r The degree of achievement of stage goals.

[0027] Further: the user feedback coefficient Fr The calculation formula is:

[0028] in, ΔMetric is the indicator improvement rate, w i is the weight of the indicator, Compliance Bonus It is a compliance reward calculated based on the patient's completion of treatment tasks.

[0029] Further: The calculation rule of the compliance reward is: If the average task completion rate in the first two weeks is greater than 90%, the reward is 0.5; If the average task completion rate in the first two weeks is between 60% and 90%, the reward is 0.3; If the average task completion rate in the first two weeks is less than 60%, the reward is 0; When the suicide factor is triggered, that is, the score of item 9 in PHQ-9 is 2 points or above, the system will force manual intervention.

[0030] Furthermore: the therapeutic therapy modules include mindfulness cognitive therapy, cognitive behavioral therapy, exercise therapy, insomnia cognitive behavioral therapy, interpersonal psychotherapy and cognitive training. The training cycle of the treatment plan is nine weeks, seven days a week, and the daily training content includes theoretical learning, required tasks and optional tasks.

[0031] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention uses a multidimensional data fusion evaluation model to comprehensively consider multiple factors such as the patient's physiological data, psychological state, cognitive function and behavioral habits, and can more comprehensively and objectively evaluate the patient's depression state. Compared with the traditional evaluation method that only relies on a single scale, this method can reduce subjective bias and provide more accurate depression level evaluation results, laying the foundation for subsequent personalized treatment.

[0032] Second, the present invention adopts a therapy priority calculation mechanism based on weight coefficients and a complex task weight calculation model, which can automatically adjust the content, difficulty and time arrangement of the treatment plan according to the patient's real-time feedback and status changes. This dynamic optimization mechanism breaks through the limitations of the static nature of traditional treatment plans and can better adapt to the individual differences of patients and status changes during the treatment process.

[0033] 3. Through the calculation of the comprehensive weight value of daily tasks and the intelligent adjustment of the training content of the next day, this method can provide patients with training tasks of moderate difficulty and reasonable time, and optimize according to the patient's preferences and the best training time window, effectively improving the patient's compliance. At the same time, through periodic evaluation and user feedback coefficient calculation, continuous improvement of treatment effect can be achieved.

[0034] Fourth, the present invention builds a complete closed-loop feedback system through a dual optimization mechanism of short-term (daily adjustment) and long-term (two-week re-evaluation). This reinforcement learning-like mechanism can continuously learn from patient feedback and optimize treatment strategies to achieve continuous improvement in treatment effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a flowchart diagram of a method for dynamically adjusting a mindfulness-based treatment plan for depression in one embodiment of the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0038] The present invention provides a dynamic adjustment method based on a mindfulness-based treatment plan for depression, which realizes precision and personalization of depression treatment through multi-dimensional data collection, comprehensive evaluation, priority determination, plan generation, dynamic adjustment and phased optimization. The technical solution of the present invention is described in detail below in conjunction with specific embodiments.

[0039] In one embodiment of the present invention, the dynamic adjustment method based on the mindfulness treatment plan for depression includes six main steps: multi-dimensional information collection of patients, comprehensive assessment of depression level, priority determination of treatment therapy modules, generation of initial treatment plan, dynamic adjustment of daily tasks and phased optimization. Specifically, the following steps are included: S1: Collect multi-dimensional information of patients.

[0040] Specifically, the collected information includes three types of data: life behavior risk data, psychological scale data and cognitive function assessment data. Life behavior risk data include the patient's name, gender, date of birth, average daily steps, average daily sedentary time, night sleep duration, daily cumulative screen usage time, physical disease information, eating habits, training content form preference (audio and video), training time preference (morning, noon, evening), etc. Psychological scale data include depression scale score (PHQ-9), anxiety scale score (HAMA), insomnia scale score (ISI), interpersonal problem scale (IIP-SC), five-factor mindfulness scale (FFMQ), rumination scale (RSS), adjustment disorder scale (ADNM), etc. Cognitive function assessment data are obtained through four cognitive tasks: dot detection task, spatial recognition task, symbol-digit conversion and STROOP task, which respectively assess the patient's attention bias, memory, processing speed and executive function.

[0041] S2: Based on the collected multidimensional information, comprehensively assess the patient's depression level.

[0042] Specifically, the evaluation process is divided into two steps: first, the risk indicators that are strongly related to depression are standardized to make them comparable; then, multiple linear regression analysis is performed to establish a hierarchical model for determining depression levels.

[0043] The standardized indicators include: anxiety value, attention bias, memory function, processing speed, executive function, insomnia severity value, interpersonal problem scale value, daily steps, daily screen time, daily sedentary time, and physical illness. Taking sedentary time as an example, daily sedentary time ≤ 2 hours is the best case (normalized value is 1); daily sedentary time ≥ 6 hours is the worst case (normalized value is 0); for the calculation of the normalized value of sedentary time between 2 and 6 hours, the following formula is used for calculation:

[0044] in, T jz is the normalized sedentary time, t Actual sitting time.

[0045] Screen time and step counts were normalized similarly.

[0046] Multiple linear regression analysis is to collect risk index data and depression scale PHQ-9 scores of a large number of patients, take each risk index as the independent variable, depression score as the dependent variable, and use multiple linear regression method to establish a regression model: S phq = a + β 1 ×Anxiety+ β 2 ×Attention Bias+ β 3 ×Memory function+...+ β 12 ×Physical illness.

[0047] According to the predicted PHQ-9 score, the depression level is divided into three levels: low risk (predicted PHQ-9 ≤ 9 points); medium risk (predicted PHQ-9 10-14 points); high risk (predicted PHQ-9 ≥ 15 points).

[0048] S3: Determine the priority of treatment therapy modules based on the patient's multidimensional information.

[0049] Specifically, the therapy modules in this embodiment include: mindfulness cognitive therapy, cognitive behavioral therapy, exercise therapy, insomnia cognitive behavioral therapy, interpersonal psychotherapy and cognitive training. The method for determining the priority includes three sub-steps: calculating the initial comprehensive weight value of each therapy, correcting the comprehensive weight value of each therapy, and judging the priority according to the corrected comprehensive weight value.

[0050] The initial comprehensive weight of each therapy is calculated by weighted summing the key indicators of each therapy module. For example, the key indicators and weights of mindfulness-based cognitive therapy are: mindfulness level (0.3), relapse history (0.25), anxiety (0.2), attention bias (0.15), and physical illness (0.1). The comprehensive weight is:

[0051] in w j is the weight, x j' is the standardized index value. Other therapies such as cognitive behavioral therapy and interpersonal psychotherapy also use similar methods to calculate their initial comprehensive weight values.

[0052] The formula for correcting the comprehensive weight value of each therapy is:

[0053] in, I j For therapy j The comprehensive weight value of α is the basic weight of the comprehensive index (default 0.6), β is the time decay factor weight (initial 0.3, decreasing exponentially as treatment progresses), i is the user feature weight (default 0.1), U t is the weighted value of patient characteristics, t is the current time point, c and t 0 The parameters that control the decay speed (such as c =0.1, t 0 = 14 days).

[0054] Weighted values ​​of patient characteristics U t The correction rules include: age weight correction (when the patient is over 60 years old, the interpersonal psychotherapy weighted index is +0.1); occupation weight correction (when the patient is in the Internet industry, etc., the exercise therapy weighted index is +0.1); body mass index correction (when the patient's BMI level is overweight or obese, the exercise therapy weighted index is +0.1); physical disease weight correction (adjust the corresponding therapy weight according to the patient's specific disease type, such as the exercise therapy index for patients with metabolic diseases +0.05, the MBCT index for patients with chronic pain +0.05, etc.).

[0055] According to the corrected comprehensive weight value, the therapies are sorted in descending order, and the top 3 therapies are recommended as mandatory training tasks, and the remaining therapies are pushed as optional training tasks. In addition, the system also sets two special rules: mandatory rules (when the patient's comprehensive depression assessment level is moderate or above, MBCT is mandatory as the first priority) and safety rules (when the suicide factor is triggered, that is, the score of item 9 in PHQ-9 is 2 points or more, the system mandatory recommends manual intervention).

[0056] S4: Generate the initial treatment plan for the patient based on the priority of the therapy module.

[0057] Specifically, the system presets a nine-week training task plan, 7 days a week, at least 45 minutes a day. According to the treatment priorities determined above, the corresponding training content is matched to the patient. The initial treatment plan includes theoretical learning, required tasks and optional tasks, and each task has detailed training content, time and form instructions.

[0058] S5: Calculate the comprehensive weight of daily tasks and dynamically adjust the training content for the next day.

[0059] Specifically, the method for calculating the comprehensive weight value of the daily task includes calculating the progress item, the evaluation item and the time coefficient.

[0060] The progress item calculation formula is:

[0061] in, P current is the current task progress, P target The target progress.

[0062] The evaluation item calculation formula is:

[0063] in,

[0064] D Rate the difficulty (1-5), L Rate the likeability (1-5), E Score effectiveness (1-5).

[0065] The time coefficient calculation formula is:

[0066] in W n 、W h They are the weights of the same day and the same time period in history. C n、C h They are the average completion rate for the same day and historical time period respectively.

[0067] Finally, the comprehensive weight value = progress item × evaluation item × time coefficient.

[0068] Based on the calculated comprehensive weight value of daily tasks, the system dynamically adjusts the next day's training content in four aspects: task type adjustment, task quantity adjustment, task difficulty adjustment and training time period reset.

[0069] Task type adjustment is to sort in descending order by calculating the proportion of each task. Tasks with a high proportion are pushed as must-do tasks. The calculation formula for the proportion of each task is: .

[0070] The formula for adjusting the number of tasks is:

[0071] in, T Count is the number of tasks for the next day; N Base is the benchmark value of the number of tasks, W t It is the comprehensive weight value of the task. The specific rules are: when Wt≥0.8, add 1-2 similar tasks; when 0.5≤Wt<0.8, maintain the current number of similar tasks; when Wt<0.5, reduce 1 similar task.

[0072] The task difficulty is adjusted using the formula:

[0073] in D new The difficulty of the next day's task; D current is the current task difficulty, P a For actual completion, P t is the target progress (expected to be 100%), f(D) is the ideal difficulty function. The training time period reset is to calculate the best training time period by analyzing the patient's training completion, effect score and preference score in each time period in the past week, and adjust the weight distribution accordingly.

[0074] S6: Periodically evaluate the patient's condition, recalculate the priority of each therapy module, and optimize the treatment plan.

[0075] Specifically, the periodic evaluation cycle is once every two weeks, starting from the third week. The formula for recalculating the priority of each therapy in stages is:

[0076] in, α =0.4, β =0.3, c =0.2, d =0.1, W t is the last comprehensive weight value, F r is the user feedback coefficient, T r is the time decay factor, P r The degree of achievement of stage goals.

[0077] The calculation formula of user feedback coefficient Fr is:

[0078] in, ΔMetric is the indicator improvement rate, w i is the weight of the indicator, Compliance Bonus It is a compliance reward, which is calculated based on the patient's completion of treatment tasks. The calculation rules for compliance rewards are as follows: if the average task completion rate in the first two weeks is greater than 90%, the reward is 0.5; if the average task completion rate in the first two weeks is between 60% and 90%, the reward is 0.3; if the average task completion rate in the first two weeks is less than 60%, the reward is 0.

[0079] Time decay factor T r Using Sigmoid function control: .

[0080] Stage goal achievement P r The triggering conditions are: stage goal achieved, PHQ-9 score decreased by ≥20%, HAMA score decreased by ≥15%; if the goal is achieved, Pr=1, otherwise it is 0.

[0081] Based on the recalculated treatment priorities based on the periodic evaluation results, the system will adjust the content and difficulty of subsequent treatment plans to achieve continuous optimization of treatment effects. This dual adjustment mechanism (daily task adjustment and two-week periodic optimization) constitutes a complete closed-loop feedback system that can continuously learn from patient feedback and optimize treatment strategies.

[0082] In practical applications, this method can also be customized according to the special needs of patients. For example, for patients with severe physical illness, the weight of the corresponding therapy can be increased; for patients at risk of suicide, the safety intervention mechanism can be forcibly activated; for patients with severe cognitive impairment, the cognitive difficulty requirements of the training task can be reduced.

[0083] Through the introduction of the above specific implementation methods, it can be seen that the dynamic adjustment method based on the mindfulness treatment plan for depression provided by the present invention realizes the precision and personalization of depression treatment through multi-dimensional data fusion and dynamic adjustment of intelligent algorithms, which can effectively improve the treatment effect, reduce the risk of recurrence, and provide patients with depression with a more scientific and effective treatment plan.

[0084] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent transformation or modification made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic adjustment method based on a mindfulness-based treatment plan for depression, characterized in that: The following steps are involved: S1: Collection of multi-dimensional information of patients, including data on life behavior risks, psychological scales and cognitive function assessment; S2: Comprehensively assess the patient's depression level based on the patient's multidimensional information; S3: Determine the priority of treatment therapy modules based on the patient's multidimensional information and depression level; S4: Generate the initial treatment plan for the patient based on the priority of the therapy module; S5: Calculate the comprehensive weight of daily tasks and dynamically adjust the training content for the next day; S6: Periodically evaluate the patient's condition, recalculate the priority of each therapy module, and optimize the treatment plan.

2. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 1, characterized in that: In step S2, the method for comprehensively evaluating the depression level of the patient includes: Standardize risk indicators that are strongly associated with depression; Multiple linear regression analysis was performed to establish a model for determining depression levels; The patient's depression level is determined according to the calculation results of the depression level determination model.

3. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 2, characterized in that: The risk indicators strongly associated with depression include: anxiety value, attention bias, memory function, processing speed, executive function, insomnia severity value, interpersonal problem scale value, daily steps, daily screen time, daily sedentary time, and physical illness.

4. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 1, characterized in that: In step S3, the method for determining the priority of the therapy module according to the multi-dimensional information of the patient includes: S31: Calculate the initial comprehensive weight value of each therapy; S32: Correction of the comprehensive weight of each therapy; S33: judging the priority of each therapy module according to the corrected comprehensive weight value; The therapies include: mindfulness-based cognitive therapy, cognitive behavioral therapy, exercise therapy, cognitive behavioral therapy for insomnia, interpersonal psychotherapy and cognitive training.

5. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 4, characterized in that: In step S32, the formula for correcting the comprehensive weight value of each therapy is: in, I j For therapy j The comprehensive weight value of α is the basic weight of the composite index, β is the time decay factor weight, θ is the user feature weight, U t is the weighted value of patient characteristics, t is the current time point, γ and t 0 is the parameter that controls the decay speed.

6. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 5, characterized in that: Said U t The correction rules for patient characteristic weights include: Age weight correction: When the patient is older than 60 years old, the interpersonal psychotherapy weighted index is +0.1; Occupational weight correction: When the patient is an Internet worker, the exercise therapy weighted index is +0.1; Body mass index (BMI) correction: When the patient's BMI level is overweight or obese, the exercise therapy weighted index is +0.1; Weight adjustment for physical diseases: According to the type of disease the patient suffers from, the weight of the corresponding treatment is increased accordingly.

7. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 1, characterized in that: In step S5, the method for calculating the comprehensive weight value of the daily task includes: Calculation progress item: in, P current The current task progress; P target To target progress; Calculation task evaluation items: in, D Rate the difficulty. L Rate the likeability, E Score effectiveness; Calculate the time factor: in W n 、W h They are the weights of the same day and the same time period in history. C n 、C h They are the average completion rate of the same day and the same period of history; Comprehensive weight value = progress item × evaluation item × time coefficient.

8. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 7, characterized in that: According to the calculated comprehensive weight value of daily tasks, the method of dynamically adjusting the training content of the next day includes: Adjustment of the task types for the next day: sort by the ratio of the task's comprehensive weight value, and push tasks with high ratio as must-do tasks; The number of tasks for the next day has been adjusted: in, T Count is the number of tasks for the next day; N Base is the benchmark value of the number of tasks, W t is the comprehensive weight value of the task; Next day's mission difficulty adjustment: in D new The difficulty of the next day's task; D current is the current task difficulty, P a For actual completion, P t For target progress, f (D) is the ideal difficulty function; Reset the training time period for the next day: Dynamically adjust the weight of the optimal training time period based on past training completion.

9. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 1, characterized in that: In step S6, the period of periodic evaluation of the patient's status is once every two weeks, and the formula for periodically recalculating the priority of each therapy is: in, α =0.4, β =0.3, γ =0.2, δ =0.1, W t is the last comprehensive weight value, F r is the user feedback coefficient, T r is the time decay factor, P r The degree of achievement of stage goals.

10. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 9, characterized in that: The user feedback factor Fr The calculation formula is: in, ΔMetric is the indicator improvement rate, w i is the weight of the indicator, Compliance Bonus It is a compliance reward calculated based on the patient's completion of treatment tasks.

11. A method for dynamic adjustment of a mindfulness-based treatment plan for depression according to claim 10, characterized in that: The calculation rules for the compliance reward are: If the average task completion rate in the first two weeks is greater than 90%, the reward is 0.5; If the average task completion rate in the first two weeks is between 60% and 90%, the reward is 0.3; If the average task completion rate in the first two weeks is less than 60%, the reward is 0; When the suicide factor is triggered, that is, the score of item 9 in PHQ-9 is 2 points or above, the system will force manual intervention.

12. A method for dynamically adjusting a mindfulness-based treatment plan for depression according to any one of claims 1 to 11, characterized in that: The treatment modules include mindfulness-based cognitive therapy, cognitive behavioral therapy, exercise therapy, cognitive behavioral therapy for insomnia, interpersonal psychotherapy and cognitive training. The training cycle of the treatment program is nine weeks, seven days a week, and the daily training content includes theoretical learning, required tasks and optional tasks.

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