Carbon behavior quantification method and system based on psychological intervention

Abstract

The invention discloses a carbon behavior quantification method and system based on psychological intervention, and belongs to the technical field of carbon behavior quantification of psychological intervention, and the method comprises the steps: collecting user genes, living habits, psychological states and daily carbon behavior data in real time through household detection equipment and an AI system, and building a dynamic personal database; an AI algorithm is utilized to analyze user data, a personalized psychological health scheme is generated, disease risks and carbon behavior relevance are predicted, potential disease problems are warned in advance, a user psychological health feature portrait is constructed by utilizing deep learning and a knowledge graph technology, and the disease risks are predicted; identifying potential health risks by associating gene defects, living habits and carbon behavior tracks; a time sequence prediction model is combined to realize early warning of disease tendency; clinical psychology standards and low-carbon behavior guide are integrated, a personalized scheme of psychotherapy healing and carbon emission reduction is generated, the scientificity and adaptability of an intervention strategy are improved, the health risk of a user is reduced, and sustainable behaviors are guided.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon behavior quantification based on psychological intervention, and specifically refers to a carbon behavior quantification method and system based on psychological intervention. Background Art

[0002] Although traditional mental health management tools can assess mood swings, they are not linked to carbon footprint data and cannot reveal the driving role of psychological factors in low-carbon decision-making;

[0003] However, existing carbon behavior quantification systems for psychological interventions still have certain flaws. Data collection is biased or missing, potentially leading to ineffective health risk assessments and intervention strategies. The universality of Traditional Chinese Medicine's Five-tone Theory and the effects of sound therapy have not been fully verified, and individual differences may weaken the effectiveness of behavioral guidance. Multi-terminal collaboration in the Internet of Things (IoT) requires addressing device compatibility and real-time performance challenges. Federated learning and edge computing can increase system complexity and hardware costs. Therefore, a carbon behavior quantification method and system based on psychological interventions is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a carbon behavior quantification method and system based on psychological intervention to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a carbon behavior quantification method based on psychological intervention, comprising the following steps:

[0006] S1. Use home testing equipment and AI systems to collect user genetics, living habits, psychological state, and daily carbon behavior data in real time to establish a dynamic personal database;

[0007] S2. Utilize AI algorithms to analyze user data, generate personalized mental health plans, and predict disease risks and carbon behavior correlations, providing early warning of potential disease issues.

[0008] S3. Quantify the correlation between user psychological fluctuations and carbon footprint behavior, and build a dynamic model of psychological intervention driving low-carbon behavior;

[0009] S4. Based on the theory of the five tones entering the five internal organs, AI matches the musical scales of the jiao, zhi, gong, shang, and yu tubes to adjust the user's emotional state and indirectly guide low-carbon behavior decisions.

[0010] S5. Combining interactive entertainment with the multifunctional experience of health music therapy, it provides real-time feedback on users' psychological improvement and carbon emission reduction results, and reinforces positive behavior through a virtual reward mechanism.

[0011] S6, mobile phones, computers and home testing equipment are linked to continuously track the effects of psychological interventions and changes in carbon behavior, dynamically optimizing health plans and carbon neutrality goals;

[0012] S7. Integrate psychological intervention data with carbon behavior quantification results, generate visual reports, provide closed-loop improvement strategies, and coordinate the dual goals of mental health and carbon emission reduction.

[0013] Among them, the S1, through the collaboration of home detection equipment and AI system, obtains multi-dimensional data of users in real time; deploys smart sensors and wearable devices to continuously monitor users' daily carbon behavior indicators, and at the same time combines genetic testing tools to collect biological information; psychological state data is obtained by analyzing users' voice, facial expressions and physiological signals through embedded AI algorithms; all data is encrypted and transmitted to the cloud, and a cross-dimensional personal database is constructed through dynamic data cleaning and fusion technology; the system adopts a real-time update mechanism and uses blockchain technology to ensure privacy and security.

[0014] Among them, the S2, based on a dynamic database, the AI ​​system adopts deep learning and knowledge graph technology to establish a user's mental health feature portrait; the algorithm identifies the potential health risks of genetic defects and lifestyle habits through association rule mining, and builds a probability prediction model based on the carbon behavior trajectory; the system conducts spatiotemporal correlation analysis on the psychological stress index and carbon emission intensity, and uses time series prediction to provide early warning of disease tendencies; the personalized program generation module integrates clinical psychology standards and low-carbon behavior guidelines, and dynamically optimizes intervention strategies through reinforcement learning, so that the program has dual adaptability of psychological healing and carbon emission reduction, and outputs customized suggestions.

[0015] Among them, the S3 quantifies the influence weight of psychological state on carbon behavior by constructing a causal reasoning model; uses a structural equation model to analyze the statistical correlation between emotional fluctuations and energy waste and excessive consumption behaviors, and introduces a Bayesian network to dynamically correct the correlation parameters; the system divides the psychological intervention threshold, and when the user's psychological indicators deviate from the safe range, the low-carbon behavior guidance strategy is automatically triggered; the model integrates environmental psychology theory, maps carbon footprint data into a visual psychological load index, and adjusts the intervention intensity in real time through a feedback loop to accurately match intervention measures with user behavior patterns.

[0016] Among them, the S4, based on the theory of five tones entering five internal organs, systematically constructs a mapping relationship library between music and internal organ functions; AI analyzes the user's physical test data and emotional state, matches the five-tone combination of Jiao, Zhi, Gong, Shang, and Yu, and generates personalized healing soundtracks; the music frequency is optimized through acoustic simulation, combined with brain wave feedback technology, to stimulate the parasympathetic nerves in a targeted manner to reduce anxiety levels; synchronously associated with low-carbon scenes, the sound therapy effect is verified through real-time physiological indicator monitoring, and the pitch, rhythm and playback time are dynamically adjusted.

[0017] Among them, the S5, by integrating natural language processing and emotional computing technology, designs a virtual assistant to provide conversational entertainment services, identifies user psychological needs through semantic analysis, and embeds a carbon neutrality knowledge quiz game; the health music therapy module automatically switches the music style according to the user's emotional changes, and at the same time converts the carbon emission reduction results into a visual progress bar; the virtual reward mechanism adopts a token economic model, and users can accumulate points by completing low-carbon tasks, and redeem them for personalized music therapy courses or discounts on environmentally friendly products; the system establishes a social sharing function to encourage users to join low-carbon communities, and strengthen behavioral stickiness through group incentives and ranking competitions.

[0018] Among them, the S6 uses the Internet of Things protocol to interconnect data between mobile phones, computers and home detection equipment to build a distributed monitoring network; the mobile phone APP receives the psychological indicators and carbon behavior data of the detection equipment in real time, and the computer analysis platform uses edge computing technology to pre-process the local data and upload it to the cloud; through federated learning, multi-device model collaborative training is carried out to improve the accuracy of early warning; the dynamic optimization module automatically adjusts the exercise intensity, diet structure and carbon neutrality goals in the health plan every week according to the monitoring results, and pushes device linkage prompts at the same time.

[0019] Among them, S7 adopts the life cycle assessment method to quantify the total carbon emission reduction during the user psychological intervention period and compares it with the baseline scenario; the evaluation model integrates the psychological improvement index and the carbon behavior conversion rate, and the implementation formula is:

[0020] ΔC=k·P α ·B β

[0021] In the formula, ΔC represents the net carbon emission reduction during the psychological intervention cycle, P represents the psychological improvement index, and B represents the carbon behavior conversion rate.

[0022] Among them, the S7 generates multi-dimensional radar charts through data fusion; the visual report automatically marks key improvement areas and recommends targeted strategies; the closed-loop improvement module introduces an adaptive optimization algorithm to predict future dual-target collaborative paths based on historical data, and ultimately outputs a comprehensive rating system covering personal health and family carbon neutrality.

[0023] Among them, the carbon behavior quantification system based on psychological intervention includes a multimodal data collection module: integrating home detection equipment, wearable sensors and genetic testing tools to collect physiological, psychological and carbon behavior data in real time, and building a personal database through blockchain encryption and dynamic cleaning technology;

[0024] AI health modeling module: Based on deep learning and knowledge graphs, it analyzes the relationship between genes, habits, and carbon behavior, builds a disease risk prediction model, and generates personalized health plans that combine psychological healing and carbon emission reduction;

[0025] Psychological Behavior Association Module: Utilizes structural equation models and Bayesian networks to quantify the causal impact of mood swings on energy waste, dynamically set intervention thresholds, and trigger low-carbon behavior guidance strategies;

[0026] Five-tone healing control module: Based on the five-tone theory, it generates personalized healing soundtracks through acoustic simulation and EEG feedback technology. It also optimizes musical parameters in combination with low-carbon scenarios to regulate emotions and behavioral decisions.

[0027] Interactive Incentive Feedback Module: Integrates emotional computing with token economic models, designs virtual assistant dialogues, carbon neutrality games, and visualizes emission reduction progress, and strengthens user stickiness through point redemption and community incentives;

[0028] Cross-device collaborative optimization module: Based on the Internet of Things protocol, it enables multi-terminal data intercommunication, uses edge computing and federated learning to optimize local processing and cloud collaboration, and dynamically adjusts health plans and carbon neutrality goals;

[0029] Carbon neutrality assessment closed-loop module: uses life cycle assessment and adaptive algorithms to quantify the synergistic effects of psychological intervention and carbon emission reduction, generate multi-dimensional radar charts and improvement strategies, and output a comprehensive rating system.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. This invention uses deep learning and knowledge graph technology to construct a user's mental health profile and predict disease risks. It accurately identifies potential health risks by correlating genetic defects, lifestyle habits, and carbon behavior trajectories. It also combines time series prediction models to achieve early warning of disease tendencies. It integrates clinical psychology standards and low-carbon behavior guidelines to generate personalized solutions that combine psychological healing and carbon emission reduction, improving the scientific nature and adaptability of intervention strategies, effectively reducing user health risks, and guiding sustainable behavior.

[0032] 2. This invention generates personalized healing soundtracks based on the Five Tones theory of Traditional Chinese Medicine. Its beneficial effects include: optimizing the frequency of the music through acoustic simulation, combining brainwave feedback technology to reduce anxiety levels and enhance mood regulation; playing music in conjunction with low-carbon scenarios to subtly guide energy-saving decisions; real-time physiological indicator monitoring to verify the effect of sound therapy, dynamically adjusting the tone and playback duration, and achieving non-invasive low-carbon behavioral intervention;

[0033] 3. This invention uses the Internet of Things protocol to achieve multi-terminal collaborative monitoring and optimization. The distributed monitoring network ensures real-time data intercommunication and improves early warning accuracy. Edge computing and federated learning technologies optimize local data processing efficiency while balancing privacy protection and cloud collaboration. It dynamically adjusts health plans and carbon neutrality goals, achieves efficient resource utilization through device linkage prompts, and forms a closed-loop optimization mechanism.

[0034] 4. This invention integrates home testing equipment, wearable sensors, and genetic testing tools to collect multi-dimensional user data in real time, build a dynamic personal database, and achieve comprehensive coverage of physiological, psychological, and behavioral data, providing a high-precision foundation for subsequent analysis. It ensures data security and timeliness through blockchain encryption and dynamic cleaning technology, enhances the global understanding of user health and carbon footprint, and lays the foundation for cross-dimensional correlation analysis, significantly enhancing the system's data-driven capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The operating process of the carbon behavior quantification method based on psychological intervention of the present invention Figure 1 ;

[0036] Figure 2 The operating process of the carbon behavior quantification method based on psychological intervention of the present invention Figure 2 ;

[0037] Figure 3 This is the operating process of the carbon behavior quantification system based on psychological intervention of the present invention Figure 3 ;

[0038] Figure 4 This is a structural flow chart of the carbon behavior quantification system based on psychological intervention of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention 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 making creative efforts are within the scope of protection of the present invention.

[0040] Example

[0041] See also Figures 1-4 As shown, the present invention provides a technical solution: comprising the following steps:

[0042] S1. Use home testing equipment and AI systems to collect user genetics, living habits, psychological state, and daily carbon behavior data in real time to establish a dynamic personal database;

[0043] S2. Utilize AI algorithms to analyze user data, generate personalized mental health plans, and predict disease risks and carbon behavior correlations, providing early warning of potential disease issues.

[0044] S3. Quantify the correlation between user psychological fluctuations and carbon footprint behavior, and build a dynamic model of psychological intervention driving low-carbon behavior;

[0045] S4. Based on the theory of the five tones entering the five internal organs, AI matches the musical scales of the jiao, zhi, gong, shang, and yu tubes to adjust the user's emotional state and indirectly guide low-carbon behavior decisions.

[0046] S5. Combining interactive entertainment with the multifunctional experience of health music therapy, it provides real-time feedback on users' psychological improvement and carbon emission reduction results, and reinforces positive behavior through a virtual reward mechanism.

[0047] S6, mobile phones, computers and home testing equipment are linked to continuously track the effects of psychological interventions and changes in carbon behavior, dynamically optimizing health plans and carbon neutrality goals;

[0048] S7. Integrate psychological intervention data with carbon behavior quantification results, generate visual reports, provide closed-loop improvement strategies, and coordinate the dual goals of mental health and carbon emission reduction.

[0049] Among them, the S1, through the collaboration of home detection equipment and AI system, obtains multi-dimensional data of users in real time; deploys smart sensors and wearable devices to continuously monitor users' daily carbon behavior indicators, and at the same time combines genetic testing tools to collect biological information; psychological state data is obtained by analyzing users' voice, facial expressions and physiological signals through embedded AI algorithms; all data is encrypted and transmitted to the cloud, and a cross-dimensional personal database is constructed through dynamic data cleaning and fusion technology; the system adopts a real-time update mechanism and uses blockchain technology to ensure privacy and security.

[0050] Among them, the S2, based on a dynamic database, the AI ​​system adopts deep learning and knowledge graph technology to establish a user's mental health feature portrait; the algorithm identifies the potential health risks of genetic defects and lifestyle habits through association rule mining, and builds a probability prediction model based on the carbon behavior trajectory; the system conducts spatiotemporal correlation analysis on the psychological stress index and carbon emission intensity, and uses time series prediction to provide early warning of disease tendencies; the personalized program generation module integrates clinical psychology standards and low-carbon behavior guidelines, and dynamically optimizes intervention strategies through reinforcement learning, so that the program has dual adaptability of psychological healing and carbon emission reduction, and outputs customized suggestions.

[0051] Among them, the S3 quantifies the influence weight of psychological state on carbon behavior by constructing a causal reasoning model; uses a structural equation model to analyze the statistical correlation between emotional fluctuations and energy waste and excessive consumption behaviors, and introduces a Bayesian network to dynamically correct the correlation parameters; the system divides the psychological intervention threshold, and when the user's psychological indicators deviate from the safe range, the low-carbon behavior guidance strategy is automatically triggered; the model integrates environmental psychology theory, maps carbon footprint data into a visual psychological load index, and adjusts the intervention intensity in real time through a feedback loop to accurately match intervention measures with user behavior patterns.

[0052] Among them, the S4, based on the theory of five tones entering five internal organs, systematically constructs a mapping relationship library between music and internal organ functions; AI analyzes the user's physical test data and emotional state, matches the five-tone combination of Jiao, Zhi, Gong, Shang, and Yu, and generates personalized healing soundtracks; the music frequency is optimized through acoustic simulation, combined with brain wave feedback technology, to stimulate the parasympathetic nerves in a targeted manner to reduce anxiety levels; synchronously associated with low-carbon scenes, the sound therapy effect is verified through real-time physiological indicator monitoring, and the pitch, rhythm and playback time are dynamically adjusted.

[0053] Among them, the S5, by integrating natural language processing and emotional computing technology, designs a virtual assistant to provide conversational entertainment services, identifies user psychological needs through semantic analysis, and embeds a carbon neutrality knowledge quiz game; the health music therapy module automatically switches the music style according to the user's emotional changes, and at the same time converts the carbon emission reduction results into a visual progress bar; the virtual reward mechanism adopts a token economic model, and users can accumulate points by completing low-carbon tasks, and redeem them for personalized music therapy courses or discounts on environmentally friendly products; the system establishes a social sharing function to encourage users to join low-carbon communities, and strengthen behavioral stickiness through group incentives and ranking competitions.

[0054] Among them, the S6 uses the Internet of Things protocol to interconnect data between mobile phones, computers and home detection equipment to build a distributed monitoring network; the mobile phone APP receives the psychological indicators and carbon behavior data of the detection equipment in real time, and the computer analysis platform uses edge computing technology to pre-process the local data and upload it to the cloud; through federated learning, multi-device model collaborative training is carried out to improve the accuracy of early warning; the dynamic optimization module automatically adjusts the exercise intensity, diet structure and carbon neutrality goals in the health plan every week according to the monitoring results, and pushes device linkage prompts at the same time.

[0055] Among them, S7 adopts the life cycle assessment method to quantify the total carbon emission reduction during the user psychological intervention period and compares it with the baseline scenario; the evaluation model integrates the psychological improvement index and the carbon behavior conversion rate, and the implementation formula is:

[0056] ΔC=k·P α ·B β

[0057] In the formula, ΔC represents the net carbon emission reduction during the psychological intervention cycle, P represents the psychological improvement index, and B represents the carbon behavior conversion rate.

[0058] Among them, the S7 generates multi-dimensional radar charts through data fusion; the visual report automatically marks key improvement areas and recommends targeted strategies; the closed-loop improvement module introduces an adaptive optimization algorithm to predict future dual-target collaborative paths based on historical data, and ultimately outputs a comprehensive rating system covering personal health and family carbon neutrality.

[0059] Among them, the carbon behavior quantification system based on psychological intervention includes a multimodal data collection module: integrating home detection equipment, wearable sensors and genetic testing tools to collect physiological, psychological and carbon behavior data in real time, and building a personal database through blockchain encryption and dynamic cleaning technology;

[0060] AI health modeling module: Based on deep learning and knowledge graphs, it analyzes the relationship between genes, habits, and carbon behavior, builds a disease risk prediction model, and generates personalized health plans that combine psychological healing and carbon emission reduction;

[0061] Psychological Behavior Association Module: Utilizes structural equation models and Bayesian networks to quantify the causal impact of mood swings on energy waste, dynamically set intervention thresholds, and trigger low-carbon behavior guidance strategies;

[0062] Five-tone healing control module: Based on the five-tone theory, it generates personalized healing soundtracks through acoustic simulation and EEG feedback technology. It also optimizes musical parameters in combination with low-carbon scenarios to regulate emotions and behavioral decisions.

[0063] Interactive Incentive Feedback Module: Integrates emotional computing with token economic models, designs virtual assistant dialogues, carbon neutrality games, and visualizes emission reduction progress, and strengthens user stickiness through point redemption and community incentives;

[0064] Cross-device collaborative optimization module: Based on the Internet of Things protocol, it enables multi-terminal data intercommunication, uses edge computing and federated learning to optimize local processing and cloud collaboration, and dynamically adjusts health plans and carbon neutrality goals;

[0065] Carbon neutrality assessment closed-loop module: uses life cycle assessment and adaptive algorithms to quantify the synergistic effects of psychological intervention and carbon emission reduction, generate multi-dimensional radar charts and improvement strategies, and output a comprehensive rating system.

[0066] Working principle: Through home detection equipment and wearable sensors, users' genes, living habits, psychological state and daily carbon behavior data are collected in real time, and a multi-dimensional personal database is constructed after blockchain encryption and dynamic cleaning; AI algorithms analyze data based on deep learning and knowledge graphs, identify the correlation between health risks and carbon behavior, and generate personalized psychological intervention plans and low-carbon behavior recommendations; quantify the impact of mood fluctuations on energy consumption through causal reasoning models, dynamically set intervention thresholds and trigger sound therapy control modules - match personalized music based on the five-tone theory of traditional Chinese medicine, combine EEG feedback technology to reduce anxiety levels, and simultaneously associate high-carbon scenarios to guide energy-saving decisions; virtual assistants use natural language processing and token incentive mechanisms to provide real-time feedback on psychological improvement and carbon emission reduction results, enhancing user participation; mobile phones, computers and detection equipment are linked to form a distributed monitoring network, and federated learning is used to optimize model accuracy and dynamically adjust health plans and carbon neutrality goals; finally, psychological intervention and carbon behavior data are integrated to generate visual reports and closed-loop improvement strategies to achieve coordinated optimization of the two goals of mental health and carbon emission reduction.

[0067] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0068] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A carbon behavior quantification method based on psychological intervention, characterized by: The following steps are involved: S1. Use home testing equipment and AI systems to collect user genetics, living habits, psychological state, and daily carbon behavior data in real time to establish a dynamic personal database; S2. Utilize AI algorithms to analyze user data, generate personalized mental health plans, and predict disease risks and carbon behavior correlations, providing early warning of potential disease issues. S3. Quantify the correlation between user psychological fluctuations and carbon footprint behavior, and build a dynamic model of psychological intervention driving low-carbon behavior; S4. Based on the theory of the five tones entering the five internal organs, AI is used to match the musical scales of the Jiao, Zheng, Gong, Shang, and Yu instruments to adjust the user's emotional state and indirectly guide low-carbon behavior decisions. S5. Combining interactive entertainment with the multifunctional experience of health music therapy, it provides real-time feedback on users' psychological improvement and carbon emission reduction results, and reinforces positive behavior through a virtual reward mechanism. S6, mobile phones, computers and home testing equipment are linked to continuously track the effects of psychological interventions and changes in carbon behavior, dynamically optimizing health plans and carbon neutrality goals; S7. Integrate psychological intervention data with carbon behavior quantification results, generate visual reports, provide closed-loop improvement strategies, and coordinate the dual goals of mental health and carbon emission reduction.

2. The carbon behavior quantification method based on psychological intervention according to claim 1, characterized in that: The S1, through the collaboration between home detection equipment and AI system, obtains multi-dimensional data of users in real time; deploys smart sensors and wearable devices to continuously monitor users' daily carbon behavior indicators, and at the same time combines genetic testing tools to collect biological information; psychological state data is obtained by analyzing users' voice, facial expressions and physiological signals through embedded AI algorithms; all data is encrypted and transmitted to the cloud, and a cross-dimensional personal database is constructed through dynamic data cleaning and fusion technology; the system adopts a real-time update mechanism and uses blockchain technology to ensure privacy and security.

3. The carbon behavior quantification method based on psychological intervention according to claim 1, characterized in that: The S2, based on a dynamic database, uses an AI system that uses deep learning and knowledge graph technology to establish a user's mental health profile. The algorithm uses association rule mining to identify potential health risks from genetic defects and lifestyle habits, and builds a probability prediction model based on carbon behavior trajectories. The system conducts spatiotemporal correlation analysis between the psychological stress index and carbon emission intensity, and uses time series prediction to provide early warning of disease tendencies. The personalized program generation module integrates clinical psychology standards and low-carbon behavior guidelines, dynamically optimizes intervention strategies through reinforcement learning, and develops programs that have both psychological healing and carbon emission reduction adaptability, outputting customized recommendations.

4. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: The S3 quantifies the influence of psychological state on carbon behavior by constructing a causal reasoning model; uses a structural equation model to analyze the statistical correlation between emotional fluctuations and energy waste and excessive consumption behaviors, and introduces a Bayesian network to dynamically correct the correlation parameters; the system divides psychological intervention thresholds, and automatically triggers low-carbon behavior guidance strategies when the user's psychological indicators deviate from the safe range; the model integrates environmental psychology theory, maps carbon footprint data into a visual psychological load index, and adjusts the intervention intensity in real time through a feedback loop to accurately match intervention measures with user behavior patterns.

5. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: The S4, based on the theory of the five tones entering the five internal organs, systematically constructs a mapping library between musical rhythms and organ functions. AI analyzes the user's physical fitness data and emotional state to match the five tones of Jiao, Zhi, Gong, Shang, and Yu to generate a personalized healing soundtrack. The musical frequency is optimized through acoustic simulation and combined with brainwave feedback technology to stimulate the parasympathetic nervous system in a targeted manner to reduce anxiety levels. Synchronously associated with low-carbon scenarios, the sound therapy effect is verified through real-time physiological indicator monitoring, and the pitch, rhythm and playback time are dynamically adjusted.

6. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: The S5 integrates natural language processing and emotional computing technologies to design a virtual assistant that provides conversational entertainment services, identifies user psychological needs through semantic analysis, and embeds a carbon neutrality knowledge quiz game; the health music therapy module automatically switches music styles according to user mood changes, and at the same time converts carbon emission reduction results into a visual progress bar; the virtual reward mechanism adopts a token economic model, and users can accumulate points by completing low-carbon tasks, which can be redeemed for personalized music therapy courses or discounts on environmentally friendly products; the system establishes a social sharing function to encourage users to join low-carbon communities, and strengthen behavioral stickiness through group incentives and ranking competitions.

7. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: The S6 uses the Internet of Things protocol to interconnect data between mobile phones, computers and home detection equipment to build a distributed monitoring network; the mobile phone APP receives the psychological indicators and carbon behavior data of the detection equipment in real time, and the computer analysis platform uses edge computing technology to pre-process the local data and upload it to the cloud; multi-device model collaborative training is carried out through federated learning; the dynamic optimization module automatically adjusts the exercise intensity, diet structure and carbon neutrality goals in the health plan every week according to the monitoring results, and pushes device linkage prompts at the same time.

8. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: S7 adopts the life cycle assessment method to quantify the total carbon emission reduction during the user psychological intervention period and compares it with the baseline scenario. The evaluation model integrates the psychological improvement index and the carbon behavior conversion rate, and the implementation formula is: ΔC=k·P α ·B β In the formula, ΔC represents the net carbon emission reduction during the psychological intervention cycle, P represents the psychological improvement index, and B represents the carbon behavior conversion rate.

9. The carbon behavior quantification method based on psychological intervention according to claim 1 is characterized by: The S7 generates multi-dimensional radar charts through data fusion; the visual report automatically marks key improvement areas and recommends targeted strategies; the closed-loop improvement module introduces an adaptive optimization algorithm to predict future dual-target collaborative paths based on historical data, and ultimately outputs a comprehensive rating system covering personal health and family carbon neutrality.

10. A carbon behavior quantification system based on psychological intervention, characterized by: It includes a multimodal data collection module: integrating home testing equipment, wearable sensors and genetic testing tools to collect physiological, psychological and carbon behavior data in real time, and building a personal database through blockchain encryption and dynamic cleaning technology; AI health modeling module: Based on deep learning and knowledge graphs, it analyzes the relationship between genes, habits, and carbon behavior, builds a disease risk prediction model, and generates personalized health plans that combine psychological healing and carbon emission reduction; Psychological Behavior Association Module: Utilizes structural equation models and Bayesian networks to quantify the causal impact of mood swings on energy waste, dynamically set intervention thresholds, and trigger low-carbon behavior guidance strategies; Five-tone healing control module: Based on the five-tone theory, it generates personalized healing soundtracks through acoustic simulation and EEG feedback technology. It also optimizes musical parameters in combination with low-carbon scenarios to regulate emotions and behavioral decisions. Interactive Incentive Feedback Module: Integrates emotional computing with token economic models, designs virtual assistant dialogues, carbon neutrality games, and visualizes emission reduction progress, and strengthens user stickiness through point redemption and community incentives; Cross-device collaborative optimization module: Based on the Internet of Things protocol, it enables multi-terminal data intercommunication, uses edge computing and federated learning to optimize local processing and cloud collaboration, and dynamically adjusts health plans and carbon neutrality goals; Carbon neutrality assessment closed-loop module: uses life cycle assessment and adaptive algorithms to quantify the synergistic effects of psychological intervention and carbon emission reduction, generate multi-dimensional radar charts and improvement strategies, and output a comprehensive rating system.

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