Cognitive impairment intervention method, device, medium and product based on multi-modal data

Abstract

The application relates to the technical field of industrial control, in particular to a cognitive impairment intervention method and device based on multi-modal data, a medium and a product. The method evaluates multi-dimensional cognitive behavior data of a user, obtains a preliminary cognitive evaluation result, matches the cognitive state of the user based on the preliminary cognitive evaluation result, determines an intervention scheme of the user, respectively performs sensory stimulation and aromatherapy intervention on the user based on audio-visual decoupling training plans and aromatherapy auxiliary intervention formulas in the intervention scheme, and acquires feedback data of the user in response to the intervention scheme, so that intervention parameters of the intervention scheme are adjusted, and the efficiency and effectiveness of cognitive impairment identification intervention are improved.

Description

Technical Field

[0001] This application relates to the field of health management technology, and in particular to a method, device, medium and product for cognitive impairment intervention based on multimodal data. Background Technology

[0002] With the increasing aging of the global population, cognitive impairment in the elderly, represented by mild cognitive impairment and Alzheimer's disease, has become a serious social and health challenge. Early identification and effective intervention for cognitive impairment are of great significance for delaying cognitive decline and improving the quality of life for the elderly. Therefore, developing efficient and precise cognitive health management technology solutions is an important research direction in the current medical and health field.

[0003] In traditional techniques, screening and assessment for cognitive impairment in the elderly primarily rely on clinical scales such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MCA). However, these traditional methods have limitations in practical application, including complex and time-consuming assessment processes that heavily depend on physicians' professional experience and subjective judgment. This makes it difficult to achieve large-scale, standardized, and routine screening in grassroots communities, causing many patients in the early stages to miss the optimal window for non-pharmacological intervention. Furthermore, in terms of intervention methods, traditional techniques often utilize digital interventions such as various cognitive training games, which often lack rigorous neuroscientific mechanism design. The conclusions drawn from the assessment phase are severely disconnected from the subsequent intervention plan, failing to provide personalized and dynamic adjustments based on the patient's real-time cognitive status and training feedback. In addition, traditional approaches often overlook the emotional fluctuations that elderly trainees may experience during intervention, such as anxiety and resistance, leading to poor user compliance and difficulty in maintaining the intervention's effectiveness.

[0004] Therefore, there is an urgent need for a cognitive impairment intervention method based on multimodal data to improve the efficiency and effectiveness of cognitive impairment identification and intervention. Summary of the Invention

[0005] This invention provides a method, device, medium, and product for cognitive impairment intervention based on multimodal data, in order to improve the efficiency and effectiveness of cognitive impairment identification and intervention.

[0006] In a first aspect, this application provides a cognitive impairment intervention method based on multimodal data, the method comprising: The user's multidimensional cognitive behavior data is evaluated to obtain preliminary cognitive assessment results; the multidimensional cognitive behavior data represents the corresponding user's speech fluency, semantic relevance, and executive function data. Based on the preliminary cognitive assessment results, the user's cognitive state is matched with interventions to determine the user's intervention plan; Based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention program, sensory stimulation and aromatherapy intervention were respectively applied to the user. Obtain user feedback data in response to the intervention plan, and adjust the intervention parameters of the intervention plan based on the feedback data.

[0007] Optionally, the evaluation of the user's multidimensional cognitive behavioral data to obtain preliminary cognitive evaluation results includes: Acquire multidimensional cognitive behavior data generated by the user during human-computer interaction tasks; the human-computer interaction tasks include at least tasks involving interaction through the user's voice expression, memory retrieval, attention testing, and reaction speed. Based on a pre-set large model algorithm, pattern recognition and semantic understanding are performed on the multidimensional cognitive behavior data to obtain the preliminary cognitive assessment results. The preliminary cognitive assessment results include at least the risk stratification level and emotional sleep index of the corresponding user. The risk stratification level represents the risk assessment results of the corresponding user's cognitive function decline, mild cognitive impairment, or early brain function degeneration.

[0008] Optionally, the step of matching the user's cognitive state with interventions based on the preliminary cognitive assessment results to determine the user's intervention plan includes: Based on the risk stratification level, a corresponding audiovisual decoupling training plan is matched from the preset training plan library; Based on the aforementioned mood and sleep indicators, a corresponding target essential oil formula is matched from a preset essential oil molecule space; the essential oil molecule space includes at least relaxing, awakening, or calming essential oil formulas. Based on a pre-defined physiological pharmacokinetic model, the diffusion concentration and blending amount of the target essential oil formulation are calculated to determine the aromatherapy adjuvant intervention formulation.

[0009] Optionally, the sensory stimulation and aromatherapy intervention for the user based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention program include: Based on the audiovisual decoupling training plan, including the audiovisual channel-based task, the audiovisual alternation task, and the audiovisual recombination task, combined with physical stimulation targets, the user's damaged brain regions are activated. Based on the aforementioned aromatherapy-assisted intervention formula, the atomizing device is controlled to release the corresponding essential oil combination; Based on the user's individual physiological parameters and a preset physiological pharmacokinetics model, the operating parameters of the nebulizer are adjusted to control the concentration of essential oil molecules inhaled by the user within a preset effective treatment window.

[0010] Optionally, adjusting the operating parameters of the nebulizer based on the user's individual physiological parameters and a preset physiological pharmacokinetic model includes: Based on the individual physiological parameters, the respiratory absorption rate coefficient of the user is determined; Based on the respiratory absorption rate coefficient, the output parameters of the nebulizer, and the preset metabolic elimination rate coefficient, combined with the kinetic differential equation, the concentration of essential oil molecules in the user's body is predicted. Based on the comparison between the essential oil molecule concentration and the effective treatment window, the output parameters of the atomizing device are adjusted.

[0011] Optionally, the feedback data includes at least real-time interaction data and periodic retest data. The real-time interaction data represents the user's task accuracy, reaction time, and audiovisual focus data in response to the audiovisual decoupling training plan. The periodic retest data represents the cognitive assessment results of the user after completing the intervention plan for a preset period.

[0012] Optionally, adjusting the intervention parameters of the intervention plan based on the feedback data includes: Based on the comparison results of the periodic retest data and baseline data, the difficulty level of the audiovisual decoupling training plan for the next period is adjusted; and / or, Based on the user status identified by the real-time interactive data, the aromatherapy parameters of the aromatherapy-assisted intervention formula are adjusted. The aromatherapy parameters include at least one of the formula composition, intervention duration, and atomization intensity.

[0013] In a second aspect, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the cognitive impairment intervention methods based on multimodal data described in the first aspect above.

[0014] Thirdly, this application provides a computer storage medium storing computer program instructions, which are executed by a processor using any of the cognitive impairment intervention methods based on multimodal data described in the first aspect above.

[0015] Fourthly, an embodiment of this application provides a computer program product including computer program instructions, which, when executed by a processor, implement any one of the cognitive impairment intervention methods based on multimodal data described in the first aspect above.

[0016] The beneficial effects of this invention are as follows: This application provides a method, device, medium, and product for cognitive impairment intervention based on multimodal data. The method evaluates the user's multidimensional cognitive behavior data to obtain a preliminary cognitive assessment result. Based on the preliminary cognitive assessment result, the method matches the user's cognitive state with an intervention plan to determine the user's intervention plan. Based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention plan, the method provides sensory stimulation and aromatherapy intervention to the user, and obtains the user's feedback data on the intervention plan. This allows for the adjustment of the intervention parameters of the intervention plan to improve the efficiency and effectiveness of cognitive impairment identification and intervention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a cognitive impairment intervention method based on multimodal data, provided for an embodiment of this application; Figure 2 A flowchart of the full-link closed-loop management of a cognitive impairment closed-loop intervention system provided in this application embodiment; Figure 3 A cover and functional navigation diagram of a cognitive impairment intervention system provided in this application embodiment; Figure 4 A schematic diagram of the interface of a large-scale intelligent cognitive assessment system provided in this application embodiment; Figure 5 An interactive interface for an audiovisual decoupling training system provided in this application embodiment; Figure 6 A schematic diagram of intelligent formulation results and component analysis provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0020] The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The term "multiple" in this application can mean at least two, for example, two, three, or more, and this application does not impose limitations.

[0021] The term "and / or" in the embodiments of this application is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0022] It is understood that the following specific embodiments of this application involve user cognition-related data. When the various embodiments of this application are applied to specific products or technologies, relevant licenses or consents are required, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, relevant volunteers can be recruited and agreements can be signed to authorize their data, thereby enabling the use of their data; or, implementation can be carried out within an authorized organization, using data from internal members to implement the following implementation methods for data management; or, the relevant data used in the specific implementation may be simulated data, such as simulated data generated in a virtual scene.

[0023] The design concept of the embodiments of this application is briefly introduced below: With the increasing aging of the global population, cognitive impairment in the elderly, represented by mild cognitive impairment (MCI) and Alzheimer's disease, has become a serious social and health challenge. Early identification and effective intervention for cognitive impairment are of great significance for delaying cognitive decline and improving the quality of life of the elderly. Therefore, developing efficient and precise cognitive health management technology solutions is an important research direction in the current medical and health field.

[0024] In traditional techniques, screening and assessment for cognitive impairment in the elderly primarily rely on clinical scales such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). However, these traditional methods have limitations in practical application, including complex and time-consuming assessment processes, and a high dependence on physicians' professional experience and subjective judgment. This makes it difficult to achieve large-scale, standardized, and routine screening in grassroots communities, causing many patients in the early stages to miss the optimal window for non-pharmacological intervention. Furthermore, in terms of intervention methods, traditional techniques often use digital interventions such as various cognitive training games, which often lack rigorous neuroscientific mechanism design. The conclusions drawn from the assessment are severely disconnected from the subsequent intervention plan, failing to provide personalized and dynamic adjustments based on the patient's real-time cognitive status and training feedback. In addition, traditional approaches often overlook the emotional fluctuations that elderly trainees may experience during intervention, such as anxiety and resistance, leading to poor user compliance and difficulty in maintaining the intervention's effectiveness.

[0025] In view of the above problems, embodiments of this application provide a method, device, medium, and product for cognitive impairment intervention based on multimodal data. The method evaluates the user's multidimensional cognitive behavior data to obtain a preliminary cognitive assessment result, and performs intervention matching on the user's cognitive state based on the preliminary cognitive assessment result to determine the user's intervention plan. Based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention plan, sensory stimulation and aromatherapy intervention are performed on the user, respectively, and feedback data of the user's response to the intervention plan is obtained, thereby adjusting the intervention parameters of the intervention plan to improve the efficiency and effectiveness of cognitive impairment identification and intervention.

[0026] Furthermore, this application's embodiments utilize a large-scale model to capture multi-dimensional behavioral information, improving the sensitivity and accuracy of early screening for cognitive impairment. The assessment data is directly transformed into intervention plans, resolving the disconnect between assessment and training. Audiovisual decoupling training is employed to target and activate brain regions, while a physiological pharmacokinetic model is simultaneously introduced to precisely control aromatherapy concentrations. This enhances brain neuroplasticity while optimizing trainees' emotional stability and physical and mental compliance. Simultaneously, the receptor action axiom is innovatively introduced for dynamic regulation of aromatherapy. Combined with cognitive accuracy and interactive performance, the system adaptively adjusts training difficulty and formulation strategies, effectively slowing cognitive decline while ensuring the scientific rigor and safety of the intervention process. Thus, this application addresses the inefficiency and subjectivity of traditional assessment methods, overcoming the bottleneck of disconnect between assessment and training and the inability to dynamically adapt in existing intervention methods. By introducing aromatherapy intervention, it considers the user's physical and mental state, thereby improving the overall scientific rigor, accuracy, and user compliance of cognitive impairment management. This provides a feasible technical path for achieving efficient and sustainable closed-loop management of brain health.

[0027] Please refer to Figure 1 The following is a flowchart illustrating a cognitive impairment intervention method based on multimodal data, provided in an embodiment of this application. The specific implementation process of this method is as follows: Step 101: Evaluate the user's multidimensional cognitive behavior data to obtain preliminary cognitive assessment results.

[0028] In this embodiment, the user is guided to complete a series of preset human-computer interaction tasks. Multi-dimensional cognitive behavior data generated during this process is collected by the device. This data covers various behavioral characteristics that reflect the user's cognitive function level, including but not limited to the user's speech fluency, semantic relevance, and executive function data. Next, the collected raw multi-dimensional cognitive behavior data is input into a specially trained large-scale model algorithm for processing. This large-scale model automatically outputs the corresponding preliminary cognitive assessment results for the user through comprehensive pattern recognition and deep analysis of this data. This replaces the traditional subjective assessment method that relies on manual labor and professional scales, achieving a rapid, objective, and standardized assessment of cognitive state.

[0029] In one possible implementation, this application embodiment will acquire multidimensional cognitive behavior data generated by the user when performing human-computer interaction tasks, and perform pattern recognition and semantic understanding on the multidimensional cognitive behavior data based on a preset large model algorithm to obtain preliminary cognitive assessment results.

[0030] Specifically, in this application embodiment, the human-computer interaction task includes at least the task of interacting through the user's voice expression, memory retrieval, attention test and reaction speed. The preliminary cognitive assessment results include at least the risk stratification level and emotional sleep index of the corresponding user. The risk stratification level represents the risk assessment result of the corresponding user's cognitive decline, mild cognitive impairment or early brain function degeneration.

[0031] Step 102: Based on the preliminary cognitive assessment results, the user's cognitive state is matched with interventions to determine the user's intervention plan.

[0032] In this embodiment of the application, the preliminary cognitive assessment results will be analyzed, and based on the cognitive function level and emotional state indicators included therein, the user's cognitive state will be risk-stratified and specific needs will be judged. Based on the results of the risk stratification and needs judgment, intelligent matching will be performed to determine a personalized and integrated intervention plan for the specific user.

[0033] Specifically, in this application embodiment, the intervention plan includes the user's audiovisual decoupling training plan and aromatherapy-assisted intervention formula. This application will match audiovisual decoupling training plans of corresponding difficulty and type according to the cognitive function level from a preset audiovisual training plan library and a preset essential oil molecule database. At the same time, it will match aromatherapy-assisted intervention formulas with corresponding effects according to emotional state indicators. Finally, the matched training plan and aromatherapy formula will be combined to determine the intervention plan.

[0034] In one possible implementation, the embodiments of this application will match the corresponding audiovisual decoupling training plan from a preset training plan library based on risk stratification level, and match the corresponding target essential oil formula from a preset essential oil molecule space based on emotion and sleep index, such as relaxing, awakening or calming essential oil formulas, and calculate the diffusion concentration and blending amount of the target essential oil formula based on a preset physiological pharmacokinetic model to determine the aromatherapy auxiliary intervention formula.

[0035] Step 103: Based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention program, sensory stimulation and aromatherapy intervention are performed on the user respectively.

[0036] In this embodiment of the application, after determining the user's personalized intervention plan, a parallel and collaborative audiovisual decoupling training plan and aromatherapy-assisted intervention formula will be executed to provide sensory stimulation and aromatherapy intervention to the user.

[0037] In one possible implementation, embodiments of this application will, based on the audiovisual decoupling training program's audiovisual channel-based tasks, audiovisual alternation tasks, and audiovisual recombination tasks, combined with physical stimulation targets, activate the user's damaged brain regions, and, based on aromatherapy-assisted intervention formulas, control the nebulizer to release corresponding essential oil combinations; and, based on the user's individual physiological parameters and a preset physiological pharmacokinetics model, adjust the nebulizer's operating parameters to control the concentration of essential oil molecules inhaled by the user within a preset effective treatment window.

[0038] Specifically, the audiovisual decoupling training program controls audiovisual output devices to present users with specially designed sensory stimulation tasks. The core feature of these tasks is the temporal or logical decoupling, delaying, or recombination of auditory and visual information, requiring users to respond. This aims to specifically stimulate and strengthen specific neural networks in the brain responsible for cross-modal information integration, enhancing cross-modal information processing capabilities. The aromatherapy-assisted intervention formula controls the release of corresponding essential oil combinations by a smart nebulizer. Simultaneously, based on the user's individual physiological parameters and a pre-set physiological pharmacokinetic model, the program dynamically adjusts the nebulizer's operating parameters to precisely control the theoretical concentration of inhaled essential oil molecules in the user's body, maintaining it within a preset effective therapeutic concentration window. This ensures the stable and controllable biological effects of the aromatherapy intervention.

[0039] In one possible implementation, this application embodiment will determine the user's respiratory absorption rate coefficient based on the user's individual physiological parameters, and predict the concentration of essential oil molecules in the user's body based on the respiratory absorption rate coefficient, the output parameters of the nebulizer, and the preset metabolic elimination rate coefficient, combined with the kinetic differential equation. Based on the comparison results of the essential oil molecule concentration and the effective treatment window, the output parameters of the nebulizer will be adjusted.

[0040] Specifically, this application can use kinetic formulas to precisely quantify the intervention dose and calibrate the absorption coefficient by combining physiological parameters such as the elderly person's weight and vital capacity. By solving differential equations in real time, the on-time and interval of the atomizing device are dynamically adjusted to avoid olfactory fatigue caused by excessively high essential oil molecule concentrations or ineffective intervention due to excessively low concentrations. Thus, through the aforementioned kinetic feedback control method, this application ensures the scientific validity and controllability of the non-drug intervention process.

[0041] Step 104: Obtain user feedback data on the intervention plan.

[0042] In this embodiment, user feedback data on the intervention plan will be obtained, namely, a multi-dimensional dataset of the user's immediate intervention effect and long-term trend, which will be used to adjust the user's personalized intervention plan in the future, so as to realize an adaptive closed loop of evaluation, intervention, feedback and optimization.

[0043] In one possible implementation, the feedback data in this application embodiment includes at least real-time interactive data and periodic retest data. The real-time interactive data characterizes the user's task accuracy, reaction time, and audiovisual focus data in response to the audiovisual decoupling training plan. The periodic retest data characterizes the cognitive assessment results of the user after completing the intervention plan for a preset period.

[0044] Specifically, real-time interactive data refers to the immediate performance indicators such as user operation accuracy and reaction time continuously collected during the execution of the audiovisual decoupling training plan. Periodic retest data refers to the new evaluation results obtained by re-evaluating the user's cognition, similar to step 101, after completing a preset period of intervention.

[0045] Step 105: Adjust the intervention parameters of the intervention plan based on feedback data.

[0046] In this embodiment of the application, the intervention plan will be dynamically optimized and adjusted based on feedback data. This adjustment process will vary depending on the data type and the objective.

[0047] In one possible implementation, embodiments of this application will adjust the difficulty level of the audiovisual decoupling training plan for the next period based on the comparison results of periodic retest data and baseline data; and / or, based on the user status identified by real-time interactive data, adjust the aromatherapy parameters of the aromatherapy-assisted intervention formula, the aromatherapy parameters including at least one of the formula composition, intervention duration and atomization intensity.

[0048] Specifically, this application will adjust the difficulty level of the audiovisual decoupling training plan in subsequent periods based on the long-term cognitive ability change trend reflected in the periodic retest data. For example, the task complexity will be increased when the user's performance continues to exceed expectations. Regarding the instantaneous physical and mental state reflected in the real-time interaction data, dynamic adjustments will be made to the aromatherapy-assisted intervention parameters. For example, when a preset negative state is identified through real-time interaction data, the response state of the olfactory receptors will be assessed based on the receptor action model and the current essential oil molecule concentration. If it is determined that the receptor's sensitivity to the current odor is approaching saturation, the aromatherapy parameters will be automatically adjusted, including but not limited to switching to another essential oil formula with different chemical spatial characteristics, adjusting the atomization intensity, or the intervention duration, to regain the expected physiological and psychological regulatory effects.

[0049] Specifically, this application will automatically increase the complexity level of the audiovisual training task when the retest accuracy rate is continuously higher than a preset threshold, such as by shortening the stimulus duration or adding interference items. When user fatigue or anxiety levels are detected to be excessive through real-time interactive data, the steady-state occupancy of olfactory receptors and signaling pathway targets is calculated based on the receptor action axiom. As shown below:

[0050] in, Where C is the total number of receptors, C is the real-time concentration calculated in step 103 above, and Kd is the molecular dissociation constant.

[0051] Thus, this application will dynamically adjust the intervention duration and scent intensity of aromatherapy intervention based on the changing trend of receptor occupancy number n. If it is determined that receptor occupancy is approaching saturation and causing a decrease in sensitivity, a formula switching procedure will be triggered to maintain the intervention efficacy of the closed-loop system by replacing essential oil molecules with different chemical spatial characteristics.

[0052] In one possible implementation, refer to Figure 2 The diagram illustrates a full-link closed-loop management flowchart of a cognitive impairment closed-loop intervention system provided in this application. This application collects raw user data through multimodal data acquisition and performs cognitive state assessment based on a preset large-scale model algorithm, generating a preliminary cognitive report including risk stratification and emotional sleep indicators. Next, intervention plan matching is performed. Based on the report, a personalized intervention plan including an audiovisual decoupling training plan and aromatherapy-assisted intervention formula is intelligently generated, driving the simultaneous implementation of audiovisual cognitive training and olfactory-assisted intervention. During execution, real-time interactive data monitors the user's training performance and physiological feedback. After completing a preset cycle, periodic retesting and effect feedback are used for comparative analysis. Finally, the closed-loop plan is dynamically optimized based on feedback data. The optimized parameters and plan are fed back to the initial assessment and matching model through a continuous learning mechanism, thereby forming an intelligent cognitive intervention closed-loop system capable of self-iteration and continuous, accurate adaptation to the user's state.

[0053] In one possible implementation, according to the same inventive concept, this application also provides a cognitive impairment intervention system based on multimodal data, the system comprising: The intelligent assessment unit is equipped with a large cognitive assessment model, which enables user information input, human-computer interaction via natural voice and text, and completes preliminary assessment of cognitive status and risk stratification.

[0054] The multimodal execution unit, serving as the management and execution module for intervention tasks, includes an audiovisual training console and an intelligent aromatherapy formulation module. It supports parameterized settings based on evaluation results and implements synergistic interventions involving physical senses and odor chemistry.

[0055] The data tracking unit, which includes a cloud-based management platform, is responsible for recording the elderly’s interaction data during the intervention process, in order to establish a full life-cycle health record and achieve long-term continuous tracking.

[0056] The closed-loop optimization unit integrates dynamic feedback logic, receives data from periodic retests and real-time interactive data, and optimizes intervention plans and operating parameters in real time based on internal evaluation indicators and dynamic formulas.

[0057] For details, please refer to Figure 3 The image shown is a cover and functional navigation diagram of a cognitive impairment intervention system provided in an embodiment of this application. Figure 3 This page demonstrates one implementation of the embodiments of this application on a user interaction terminal, intuitively showcasing the core functional architecture of this application that integrates intelligent assessment and multimodal intervention. The page lists three core entry points: First, disease cognition assessment, corresponding to the backend multi-dimensional data collection and cognitive state assessment steps based on large-scale model algorithms, aiming to provide users with scientific cognitive function testing through intelligent analysis. Next is audiovisual decoupling training, corresponding to the sensory stimulation module in the user execution phase, helping users conduct cognitive training through dual-channel information processing tasks. Finally, there is cognitive-assisted aromatherapy, mapping to the olfactory-assisted intervention formula in the system, providing users with non-drug emotional and cognitive support. In this way, the overall UI layout transforms the complex backend algorithms and closed-loop management logic into a lightweight health service that is conveniently accessible to users.

[0058] In one possible implementation, embodiments of this application will specifically describe a method and system for intervening in cognitive impairment: First, this application will conduct a preliminary assessment of the user's cognitive state using an intelligent assessment unit. (Reference) Figure 4 The diagram shown is a schematic representation of the interface of a large-scale intelligent cognitive assessment system provided in an embodiment of this application. Users can access the system through methods such as... Figure 4 The large-scale intelligent cognitive assessment system shown participates in a variety of pre-designed human-computer interaction tasks. This allows the system to collect multi-dimensional cognitive behavioral data from users, including elderly individuals, including speech fluency, semantic relevance, and executive function data. Utilizing large-scale model algorithms, it captures multi-dimensional feature indicators through pattern recognition and semantic understanding to identify potential cognitive decline, mild cognitive impairment (MCI), or early brain function degeneration risks. Finally, it generates a preliminary cognitive assessment report. Based on the assessment report generated in step S1, the system stratifies the elderly individual's cognitive status according to risk. Next, the intelligent assessment unit, based on the stratification results, issues instructions to the multimodal execution unit to automatically match individualized audiovisual decoupling training plans and corresponding aromatherapy-assisted intervention formulas.

[0059] Furthermore, this application and the obtained technology implement collaborative intervention through multimodal execution units. On the one hand, refer to Figure 5 The image shown is an interactive interface of an audiovisual decoupling training system provided in an embodiment of this application. This application will call the audiovisual decoupling training system to enter the interface shown above. Figure 5The interactive interface shown is as follows. It employs auditory and visual channels, alternating or recombining tasks, to perform sensory stimulation tasks based on the separation and precise recombination of audiovisual signals. Through physical stimulation, it targets and activates damaged brain regions, enhancing the brain's perceptual integration ability and neuroplasticity.

[0060] On the other hand, this application will simultaneously activate an aromatherapy-assisted intervention system, which will read the mood and sleep indicators in the assessment report and extract them from the preset essential oil molecule space. The system intelligently matches relaxing, awakening, or calming essential oil blends. (Reference) Figure 6 The diagram shown illustrates a smart formulation result and component analysis provided in this application embodiment. The top of the prescription clearly indicates that the plan is primarily for patients with mild cognitive impairment / Alzheimer's disease. Regarding the specific intervention plan, the interface details the applicable dosage and usage, such as the recommended procedure of inhaling twice daily for three minutes each time, and explicitly recommends compound ethyl terpene phenolate fragrance as the core formula. The auxiliary formulation suggestions below further expand to include other intervention options such as rosemary essential oil. Thus, this application can concretize the abstract aromatherapy auxiliary intervention formula into a medical order that doctors or nurses can directly execute. Through standardized dosage control and scientific essential oil ratios, it ensures that olfactory stimulation can accurately target the user's limbic system, thereby achieving cognitive improvement and mood regulation effects in synergy with audiovisual decoupling training.

[0061] Furthermore, this application will utilize kinetic formulas to precisely quantify the intervention dose. The absorption coefficient will be calibrated in conjunction with physiological parameters of the elderly. By solving differential equations in real time, the dynamic concentration of essential oil molecules in the body after inhalation can be controlled. Its filtering formula (dynamic differential equation) is:

[0062] in, This represents the real-time output of the atomizing device. This is the metabolic elimination rate coefficient in the body.

[0063] Thus, this application can adjust the output power and interval of the atomizing device in real time according to the equation to ensure that the penetration concentration of essential oil molecules at the blood-brain barrier (BBB) ​​is within the preset effective treatment window, avoiding olfactory fatigue caused by excessive concentration or ineffective intervention caused by excessively low concentration.

[0064] Furthermore, during the intervention process, this application will monitor real-time interactive data through a data tracking unit, specifically recording the elderly person's task accuracy, reaction time, and audiovisual focus data in real time to construct a dynamic feedback sequence and capture the user's fatigue or anxiety levels. Additionally, the assessment module will be periodically invoked to retest cognitive function, and the retest results will be compared and analyzed with the baseline data established by the intelligent assessment unit during the initial cognitive assessment to generate an effect feedback report, which will then be transmitted to the closed-loop optimization unit. Subsequently, the closed-loop optimization unit will automatically adjust the audiovisual training difficulty level and aromatherapy formula parameters based on the feedback report, achieving full-link closed-loop management.

[0065] Please see Figure 7 As shown, based on the same technical concept, this application also provides a computer device 70. In one embodiment, the computer device can be a device specifically designed for cognitive impairment intervention. Figure 7 As shown, it includes a memory 701, a communication module 703, and one or more processors 702.

[0066] The memory 701 is used to store computer programs executed by the processor 702. The memory 701 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0067] Memory 701 may be volatile memory, such as random-access memory (RAM); memory 701 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 701 may be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 701 may be a combination of the above-described memories.

[0068] The processor 702 may include one or more central processing units (CPUs) or digital processing units, etc. The processor 702 is used to implement the aforementioned cognitive impairment intervention method when it invokes a computer program stored in the memory 701.

[0069] The communication module 703 is used to communicate with other terminals and systems such as MCP and external tools.

[0070] This application embodiment does not limit the specific connection medium between the memory 701, communication module 703, and processor 702 described above. This application embodiment... Figure 7 The memory 701 and the processor 702 are connected via a bus 704, and the bus 704 is in Figure 7 The diagram uses thick lines to describe the connections between other components; these are for illustrative purposes only and should not be considered limiting. The 704 bus can be divided into address bus, data bus, control bus, etc. For ease of description, Figure 7 It is described using only a thick line, but does not indicate that there is only one bus or one type of bus.

[0071] The memory 701 stores a computer storage medium, which stores computer-executable instructions. The computer-executable instructions are used to implement the cognitive impairment intervention method of the embodiments of this application, and the processor 702 is used to execute the cognitive impairment intervention method of the above embodiments.

[0072] Based on the same inventive concept, embodiments of this application also provide a storage medium storing a computer program that, when run on a computer, causes the computer to perform the steps in the cognitive impairment intervention methods according to various exemplary embodiments of this application described above.

[0073] In some possible implementations, various aspects of the cognitive impairment intervention method provided in this application can also be implemented in the form of a computer program product, which includes a computer program that, when run on a computer device, causes the computer device to perform the steps in the cognitive impairment intervention method according to various exemplary embodiments of this application described above. For example, the computer device can perform the steps of the various embodiments.

[0074] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0075] The program product of the embodiments of this application may employ a portable compact disc read-only memory (CD-ROM) and include a computer program, and may run on a computer device. However, the program product of this application is not limited thereto. In this application, the readable storage medium may be any tangible medium that contains or stores a program, and the computer program included therein may be used by or in conjunction with a command execution system, apparatus, or device.

[0076] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a readable computer program. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with a command execution system, apparatus, or device.

[0077] Computer programs contained on readable media may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0078] Computer programs for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++ and Python, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0079] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0080] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0081] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0083] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A cognitive impairment intervention method based on multimodal data, characterized in that, The method includes: The user's multidimensional cognitive behavior data is evaluated to obtain preliminary cognitive assessment results; the multidimensional cognitive behavior data represents the corresponding user's speech fluency, semantic relevance, and executive function data. Based on the preliminary cognitive assessment results, the user's cognitive state is matched with interventions to determine the user's intervention plan; Based on the audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention program, sensory stimulation and aromatherapy intervention were respectively applied to the user. Obtain user feedback data in response to the intervention plan, and adjust the intervention parameters of the intervention plan based on the feedback data.

2. The method as described in claim 1, characterized in that, The evaluation of users' multidimensional cognitive behavioral data to obtain preliminary cognitive assessment results includes: Acquire multidimensional cognitive behavior data generated by the user during human-computer interaction tasks; the human-computer interaction tasks include at least tasks involving interaction through the user's voice expression, memory retrieval, attention testing, and reaction speed. Based on a pre-set large model algorithm, pattern recognition and semantic understanding are performed on the multidimensional cognitive behavior data to obtain the preliminary cognitive assessment results. The preliminary cognitive assessment results include at least the risk stratification level and emotional sleep index of the corresponding user. The risk stratification level represents the risk assessment results of the corresponding user's cognitive function decline, mild cognitive impairment, or early brain function degeneration.

3. The method as described in claim 2, characterized in that, The step of matching the user's cognitive state with interventions based on the preliminary cognitive assessment results to determine the user's intervention plan includes: Based on the risk stratification level, a corresponding audiovisual decoupling training plan is matched from the preset training plan library; Based on the aforementioned mood and sleep indicators, a corresponding target essential oil formula is matched from a preset essential oil molecule space; the essential oil molecule space includes at least relaxing, awakening, or calming essential oil formulas. Based on a pre-defined physiological pharmacokinetic model, the diffusion concentration and blending amount of the target essential oil formulation are calculated to determine the aromatherapy adjuvant intervention formulation.

4. The method as described in claim 1, characterized in that, The audiovisual decoupling training plan and aromatherapy-assisted intervention formula in the intervention program respectively provide sensory stimulation and aromatherapy intervention to the user, including: Based on the audiovisual decoupling training plan, including the audiovisual channel-based task, the audiovisual alternation task, and the audiovisual recombination task, combined with physical stimulation targets, the user's damaged brain regions are activated. Based on the aforementioned aromatherapy-assisted intervention formula, the atomizing device is controlled to release the corresponding essential oil combination; Based on the user's individual physiological parameters and a preset physiological pharmacokinetics model, the operating parameters of the nebulizer are adjusted to control the concentration of essential oil molecules inhaled by the user within a preset effective treatment window.

5. The method as described in claim 4, characterized in that, The adjustment of the operating parameters of the nebulizer based on the user's individual physiological parameters and a preset physiological pharmacokinetic model includes: Based on the individual physiological parameters, the respiratory absorption rate coefficient of the user is determined; Based on the respiratory absorption rate coefficient, the output parameters of the nebulizer, and the preset metabolic elimination rate coefficient, combined with the kinetic differential equation, the concentration of essential oil molecules in the user's body is predicted. Based on the comparison between the essential oil molecule concentration and the effective treatment window, the output parameters of the atomizing device are adjusted.

6. The method as described in claim 1, characterized in that, The feedback data includes at least real-time interaction data and periodic retest data. The real-time interaction data represents the user's task accuracy, reaction time, and audiovisual focus data in response to the audiovisual decoupling training plan. The periodic retest data represents the cognitive assessment results of the user after completing the intervention plan for a preset period.

7. The method as described in claim 6, characterized in that, The adjustment of intervention parameters based on the feedback data includes: Based on the comparison results of the periodic retest data and baseline data, adjust the difficulty level of the audiovisual decoupling training plan for the next period; and / or, Based on the user status identified by the real-time interactive data, the aromatherapy parameters of the aromatherapy-assisted intervention formula are adjusted. The aromatherapy parameters include at least one of the formula composition, intervention duration, and atomization intensity.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

9. A computer storage medium storing computer program instructions thereon, characterized in that, When executed by a processor, the computer program instructions implement the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising computer program instructions, characterized in that, When executed by a processor, the computer program instructions implement the steps of the method according to any one of claims 1 to 7.

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