Individual state analysis method and system under multi-dimensional data fusion

Abstract

This disclosure provides a method and system for individual state analysis based on multidimensional data fusion, relating to the field of data processing technology. The method includes: obtaining a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters, wherein the multiple physiological parameters include first-class physiological parameters and second-class physiological parameters; acquiring form-filled data from a target user; obtaining a first rate of change and a second rate of change; obtaining a first sub-evaluation result and a second sub-evaluation result; performing a weighted calculation on the first sub-evaluation result and the second sub-evaluation result to obtain a first evaluation result; inputting the form-filled data into a form-filled data model to obtain a second evaluation result; and combining the first evaluation result to obtain a mental state evaluation result. This solves the technical problem in the prior art where the lack of multi-faceted data collection for mental state evaluation leads to insufficient efficiency and accuracy in evaluating the user's mental state.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to a method and system for individual state analysis under multidimensional data fusion. Background Technology

[0002] With the development of society, more and more people are experiencing mental health problems due to learning, life, and work. Therefore, it is necessary to establish a mental health assessment method to evaluate the mental state of users, thereby playing a role in the prevention of mental illness or assisting in the treatment of patients.

[0003] Currently, existing technologies suffer from insufficient efficiency and accuracy in assessing users' mental state due to the lack of comprehensive data collection for mental state evaluation. Summary of the Invention

[0004] This disclosure provides a method and system for individual state analysis based on multidimensional data fusion, which solves the technical problem in the prior art that the lack of data collection from multiple sources for mental state assessment leads to insufficient efficiency and accuracy in assessing users' mental state.

[0005] According to a first aspect of this disclosure, a method for individual state analysis based on multidimensional data fusion is provided, comprising: acquiring multiple physiological parameters of a target user through a data acquisition device in a first time window and a second time window to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters, wherein the multiple physiological parameters include a type I physiological parameter and a type II physiological parameter; acquiring form-filled data of the target user; calculating the rate of change of the type I and type II physiological parameters between the first and second time windows based on the first and second sets of physiological parameters to obtain a first rate of change and a second rate of change; inputting the multiple physiological parameters in the average set of physiological parameters into a first and second assessment unit within a mental state assessment model to obtain a first sub-assessment result and a second sub-assessment result; performing a weighted calculation on the first and second sub-assessment results based on the magnitude of the first and second rates of change to obtain a first assessment result; inputting the form-filled data into a form-filled data model to obtain a second assessment result; and combining the first assessment result to obtain a mental state assessment result.

[0006] According to a second aspect of this disclosure, an individual state analysis system based on multidimensional data fusion is provided, comprising: a physiological parameter acquisition module, which is used to acquire multiple physiological parameters of a target user through an acquisition device in a first time window and a second time window to obtain a first physiological parameter set, a second physiological parameter set, and an average physiological parameter set, wherein the multiple physiological parameters include a type I physiological parameter and a type II physiological parameter; a user form completion data acquisition module, which is used to acquire form completion data of the target user; and a rate of change calculation module, which is used to calculate the rate of change of the type I physiological parameter and the type II physiological parameter between the first time window and the second time window based on the first physiological parameter set and the second physiological parameter set. The system includes: a rate of change, obtaining a first rate of change and a second rate of change; a mental state assessment module, which inputs multiple physiological parameters from the average physiological parameter set into a first assessment unit and a second assessment unit within the mental state assessment model to obtain a first sub-assessment result and a second sub-assessment result; a first assessment result acquisition module, which performs a weighted calculation on the first sub-assessment result and the second sub-assessment result based on the magnitude of the first rate of change and the second rate of change to obtain a first assessment result; and a mental state assessment result acquisition module, which inputs the completed data into a completed data model to obtain a second assessment result, and combines the first assessment result to obtain a mental state assessment result.

[0007] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the first aspect.

[0008] According to the individual state analysis method under multidimensional data fusion adopted in this disclosure, multiple physiological parameters of the target user are collected through a data acquisition device in a first time window and a second time window to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters. The multiple physiological parameters include a type I physiological parameter and a type II physiological parameter. The target user's completed form data is acquired. Based on the first and second sets of physiological parameters, the rate of change of the type I and type II physiological parameters between the first and second time windows is calculated to obtain a first rate of change and a second rate of change. The multiple physiological parameters in the average set of physiological parameters are input into the first and second assessment units of the mental state assessment model to obtain a first sub-assessment result and a second sub-assessment result. The first and second sub-assessment results are weighted according to the magnitude of the first and second rates of change to obtain a first assessment result. The completed form data is input into the completed form data model to obtain a second assessment result. Combined with the first assessment result, a mental state assessment result is obtained. This disclosure first assesses the user's mental state by collecting physiological parameters to obtain a first assessment result, and then obtains a second assessment result by collecting data filled in by the user. The user's mental state is then assessed by combining the first and second assessment results, thereby achieving the technical effect of ensuring the accuracy of the mental state assessment while ensuring the efficiency of the assessment.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating the individual state analysis method under multidimensional data fusion provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of the process for obtaining the first sub-evaluation result and the second sub-evaluation result in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of the process for obtaining the second evaluation result in an embodiment of the present invention.

[0014] Figure 4This is a schematic diagram of the structure of an individual state analysis system based on multidimensional data fusion provided in an embodiment of the present invention.

[0015] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0016] Figure labeling: Physiological parameter acquisition module 11, user form data acquisition module 12, rate of change calculation module 13, mental state assessment module 14, first assessment result acquisition module 15, mental state assessment result acquisition module 16, electronic device 800, processor 801, memory 802, bus 803. Detailed Implementation

[0017] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0018] In order to solve the technical problem that the existing technology lacks the ability to assess mental state from multiple sources, resulting in insufficient efficiency and accuracy in assessing users' mental state, the inventors of this disclosure have creatively developed a method and system for individual state analysis based on multidimensional data fusion.

[0019] Example 1, Figure 1 A diagram illustrating the individual state analysis method under multidimensional data fusion provided in the embodiments of this application is shown below. Figure 1 As shown, the method includes:

[0020] Step S100: Using the acquisition device, collect multiple physiological parameters of the target user in the first time window and the second time window to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters, wherein the multiple physiological parameters include a type I physiological parameter and a type II physiological parameter.

[0021] In this embodiment, step S100 includes:

[0022] Step S110: During the first time window, collect multiple physiological parameters of the target user to obtain the first set of physiological parameters, wherein the first type of physiological parameter is an electrocardiogram (ECG) signal parameter and the second type of physiological parameter is an electroencephalogram (EEG) signal parameter; Step S120: During the second time window, collect multiple physiological parameters of the target user to obtain the second set of physiological parameters; Step S130: Calculate the average set of physiological parameters based on the first set of physiological parameters and the second set of physiological parameters.

[0023] Specifically, the aforementioned data acquisition device is used to collect users' physiological parameters, such as an electrocardiogram (ECG) acquisition device or an electroencephalogram (EEG) monitoring device. The target user refers to any user who needs to undergo a mental state assessment. Through the data acquisition device, the first time window and the second time window are two different time periods used for collecting physiological data. Multiple physiological parameters of the target user are collected in the first time window and the second time window, respectively. These multiple physiological parameters include Class I physiological parameters and Class II physiological parameters. Class I physiological parameters are ECG signal parameters, and Class II physiological parameters are EEG signal parameters.

[0024] Specifically, electrocardiogram (ECG) and electroencephalogram (EEG) signals of the target user are collected at two different time points, for example, 10 seconds apart, to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters. The first set of physiological parameters includes ECG and EEG signal parameters collected in the first time window, and the second set of physiological parameters includes ECG and EEG signal parameters collected in the second time window. The average values ​​of the ECG and EEG signal parameters collected in the first and second time windows are calculated respectively, and the average values ​​of the ECG and EEG signal parameters constitute the average set of physiological parameters. By obtaining the first set of physiological parameters, the second set of physiological parameters, and the average set of physiological parameters, the basic data for subsequent mental state is provided.

[0025] Step S200: Obtain the form-filled data of the target user.

[0026] Specifically, users are provided with a scale for assessing their mental state. The scale includes questions that examine users' memory, attention, recall abilities, etc. Users fill out the questions on the scale, and the data obtained from the target users' completed questionnaires is what we call acquiring the content they filled out on the scale.

[0027] Step S300: Based on the first set of physiological parameters and the second set of physiological parameters, calculate the rate of change of a type of physiological parameter and a type of physiological parameter between the first time window and the second time window to obtain the first rate of change and the second rate of change.

[0028] In this embodiment, step S300 includes:

[0029] Step S310: Calculate the difference between a class of physiological parameters based on the first set of physiological parameters and the second set of physiological parameters, and calculate the first rate of change; Step S320: Calculate the difference between two classes of physiological parameters based on the first set of physiological parameters and the second set of physiological parameters, and calculate the second rate of change.

[0030] Specifically, based on the first set of physiological parameters and the second set of physiological parameters, the rate of change of the first and second types of physiological parameters between the first and second time windows is calculated. That is, the target user's electrocardiogram (ECG) signal parameters and electroencephalogram (EEG) signal parameters are collected in the first and second time windows, respectively, resulting in two sets of ECG and EEG signal parameters. By subtracting the two sets of ECG and EEG signal parameters, the rate of change of the ECG and EEG signal parameters can be obtained. For example, compared to the first time window, how much the frequency parameter of the ECG or EEG signal collected in the second time window has increased or decreased, thus obtaining the first rate of change and the second rate of change. The first rate of change is the rate of change of the ECG signal parameters, and the second rate of change is the rate of change of the EEG signal parameters.

[0031] Specifically, firstly, based on the first and second sets of physiological parameters, the difference between two types of physiological parameters is calculated. This is done by subtracting the ECG signal parameters from the first set from the ECG signal parameters in the second set. The rate of change of the ECG signal parameters is then calculated based on this difference, resulting in the first rate of change. This rate of change describes the speed of change of the ECG signal parameters; the larger the difference, the larger the rate of change. Next, based on the first and second sets of physiological parameters, the difference between two types of physiological parameters is calculated. This is done by subtracting the EEG signal parameters from the first set from the second set. The rate of change of the EEG signal parameters is then calculated based on this difference, resulting in the second rate of change. By calculating the rates of change for both ECG and EEG signal parameters, data support is provided for subsequent mental state assessments.

[0032] Step S400: Input multiple physiological parameters from the average physiological parameter set into the first and second assessment units of the mental state assessment model to obtain the first sub-assessment result and the second sub-assessment result.

[0033] Among them, such as Figure 2 As shown, step S400 in this embodiment includes:

[0034] Step S410: Construct the first assessment unit; Step S420: Construct the second assessment unit, and combine it with the first assessment unit to obtain the mental state assessment model; Step S430: Input the first type of physiological parameter and the second type of physiological parameter in the set of average physiological parameters into the first assessment unit and the second assessment unit respectively to obtain the first sub-assessment result and the second sub-assessment result.

[0035] Specifically, the mental state assessment model is a functional model used to analyze multiple physiological parameters within the average set of physiological parameters and output assessment results. The mental state assessment model includes a first assessment unit and a second assessment unit. The first assessment unit and the second assessment unit output a first sub-assessment result and a second sub-assessment result, which are used as the output results of the mental state assessment model.

[0036] Specifically, the system first needs to construct a first assessment unit and a second assessment unit. The first assessment unit is an intermediate layer used to analyze the user's mental state based on one type of physiological parameter, namely electrocardiogram (ECG) signal parameters, and outputs a first sub-assessment result, which is the result of assessing the user's mental state based on ECG signal parameters. The second assessment unit is an intermediate layer used to analyze the user's mental state based on two types of physiological parameters, namely electroencephalogram (EEG) signal parameters, and outputs a second sub-assessment result, which is the result of assessing the user's mental state based on EEG signal parameters. The first and second assessment units together constitute a mental state assessment model, achieving the effect of accurately assessing the user's mental state.

[0037] Specifically, it is necessary to train the first evaluation unit, acquire multiple sample-level physiological parameters, and multiple sample-level first sub-evaluation results, which means acquiring multiple electrocardiogram (ECG) signal parameters and the corresponding sample-level first sub-evaluation results. The sample-level physiological parameters and the multiple sample-level first sub-evaluation results are corresponding. Data labeling is performed on the multiple sample-level physiological parameters and the multiple sample-level first sub-evaluation results, meaning each sample physiological parameter corresponds to one sample-level first sub-evaluation result. Inputting sample physiological parameters and outputting sample-level first sub-evaluation results, labeling these data yields the first constructed dataset. The first constructed dataset is used to train and construct the first evaluation unit. Further, based on the BP neural network model in machine learning, the first evaluation unit is constructed. The first constructed dataset can be divided into two parts, one part serving as the first evaluation... The training dataset for each evaluation unit serves as a validation dataset for the first evaluation unit. The first evaluation unit, which includes multiple simple units simulating human brain neurons, is trained using this dataset. Physiological parameters from each sample in the training dataset are input into the first evaluation unit. The output of the first evaluation unit is adjusted using the first sub-evaluation results corresponding to these physiological parameters. Training for the current group ends when the output matches the corresponding first sub-evaluation result. Training is completed when all data in the training dataset is used. To ensure the accuracy of the first evaluation unit, its output is tested using the validation dataset. If the accuracy meets the expected requirements, the first evaluation unit is considered complete; otherwise, retraining is required. After training, the first evaluation unit performs complex nonlinear logical operations based on the input parameters and outputs the predicted parameters. The input parameters for the first evaluation unit are a class of physiological parameters, and the output data is the first sub-evaluation result. The construction method for the second evaluation unit is the same as that for the first evaluation unit. Together, the first and second evaluation units constitute a mental state assessment model, achieving accurate mental state assessment and evaluation of the user's mental state.

[0038] Step S500: Based on the magnitudes of the first rate of change and the second rate of change, perform a weighted calculation on the first sub-evaluation result and the second sub-evaluation result to obtain the first evaluation result.

[0039] In this embodiment, step S500 includes:

[0040] Step S510: Based on the magnitudes of the first rate of change and the second rate of change, perform weight allocation to obtain a weight allocation result, wherein the larger the rate of change, the smaller the weight coefficient; Step S520: Based on the weight allocation result, perform weighted calculation on the first sub-evaluation result and the second sub-evaluation result to obtain the first evaluation result.

[0041] Specifically, the first sub-evaluation result and the second sub-evaluation result are weighted and calculated according to the magnitude of the first rate of change and the second rate of change to obtain the first evaluation result, which is the comprehensive evaluation result derived from the first sub-evaluation result and the second sub-evaluation result.

[0042] Specifically, the first sub-assessment result and the second sub-assessment result are assessment results obtained based on Class I and Class II physiological parameters, respectively. Now, it is necessary to perform a weighted calculation on the first and second sub-assessment results to obtain the first assessment result. It is necessary to determine the weight of the first and second sub-assessment results. The weight is allocated according to the magnitude of the first and second rates of change to obtain the weight allocation result. Among them, the larger the rate of change, the worse the stability of the corresponding electrocardiogram or electroencephalogram data, the lower the reliability of the data used for mental state assessment, and the smaller the weight coefficient. That is to say, the first rate of change corresponds to the first sub-assessment result, and the second rate of change corresponds to the second sub-assessment result. By comparing the first and second rates of change, the larger the rate of change, the smaller the weight coefficient of the corresponding sub-assessment result. The weight coefficient represents the importance of the first and second sub-assessment results when performing weighted calculation. Based on the weight coefficients of the first and second sub-assessment results, the first sub-assessment result is obtained by weighted calculation, thus achieving the technical effect of assessing mental state based on the user's physiological parameters.

[0043] Step S600: Input the completed data into the completed data model to obtain the second assessment result, and combine it with the first assessment result to obtain the mental state assessment result.

[0044] The data filled in the form is input into the data filling model to obtain a second evaluation result, such as... Figure 3 As shown, step S600 in this embodiment includes:

[0045] Step S610: Obtain multiple sample form-filled data; Step S620: Obtain multiple sample second evaluation results; Step S630: Construct a mapping relationship between the multiple sample form-filled data and the multiple sample second evaluation results to obtain the form-filled data model; Step S640: Input the form-filled data into the mapping relationship within the form-filled data model for traversal, and use the corresponding sample second evaluation result as the second evaluation result.

[0046] Specifically, the form-filling data model is a functional model used to analyze form-filling data and obtain the user's mental state assessment results based on the form-filling data. The input data of the form-filling data model is skip list data, and the output result is the second assessment result. Then, the first assessment result and the second assessment result are combined to comprehensively assess the target user's mental state and obtain the mental state assessment result. The mental state assessment result is a comprehensive assessment result of the target user's mental state, such as the stability of the user's mental state.

[0047] Specifically, the completed data is input into the completed data model to obtain the second assessment result. This process requires constructing the completed data model. First, multiple sample completed data are obtained. These multiple sample completed data can be data filled out by multiple users on a mental state scale. Each sample completed data corresponds to a sample second assessment result. That is, there is a corresponding relationship, or mapping relationship, between the multiple sample completed data and the multiple sample second assessment results. Based on the correspondence between the multiple sample completed data and the multiple sample second assessment results, the completed data model can be obtained according to the mapping relationship. In other words, the completed data model contains multiple sets of corresponding sample completed data and sample second assessment results, thus completing the construction of the completed data model. Further, the completed data is input into the completed data model. The completed data is traversed through the mapping relationship within the completed data model to obtain the sample second assessment result corresponding to the completed data. The corresponding sample second assessment result is used as the second assessment result, which facilitates the subsequent assessment of the user's mental state based on the first assessment result and the second assessment result, thereby improving the efficiency and accuracy of mental state assessment.

[0048] In this embodiment, after obtaining the second assessment result and combining it with the first assessment result to obtain the mental state assessment result, the following step S640 is also included:

[0049] Step S650: Read the form completion time information of each table unit in the form completion data; Step S660: Collect the preset time range of the form completion data and compare it with the form completion time information of each table unit, configure the orientation weight of the form completion data based on the time deviation, and generate the orientation weight; Step S670: Use the orientation weight as the fusion weight of the second evaluation result and fuse it with the first evaluation result to generate the mental state evaluation result.

[0050] Specifically, in the individual status assessment process, the accuracy of the completed form data affects the assessment results because some users may fill out the form quickly and carelessly. This method employs a weighted configuration technique based on the form completion time to identify and correct potential instances of rapid or careless filling. First, the form completion time information for each table cell needs to be read. This time information indicates the time spent by the user on each table cell, reflecting their level of engagement. Typically, an unusually short completion time may indicate insufficient thought or random selection by the user. The completion time information for each table cell is then compared to a preset table-like time range. This preset range is usually set as a reasonable time interval based on past data analysis or industry standards. If the completion time for a particular table cell deviates from this preset range, especially if it is significantly shorter than expected, it usually means that the user may not have filled out that section carefully. By comparing these deviations, potential non-standard filling behaviors can be identified, and the form data can then be weighted accordingly. Specifically, when the filling time of each table cell is less than the preset table-like time range, a weight is assigned based on the degree of deviation, generating a fixed weight.

[0051] The placement weight will be used as the fusion weight of the second assessment result and will be fused together with the first assessment result to generate the final mental state assessment result. The fusion process is a weighted process. In this way, the data that is filled out in a short time and may indicate random selection will be appropriately diluted, thereby avoiding the deviation of the assessment result by these invalid data. It can effectively avoid the interference of erroneous data caused by users filling out the form quickly and randomly, and improve the accuracy and reliability of the assessment.

[0052] Based on the above analysis, this disclosure provides a method for individual state analysis under multi-dimensional data fusion. In this embodiment, the user's mental state is first assessed by collecting the user's physiological parameters to obtain a first assessment result. Then, the user's completed form data is collected to obtain a second assessment result. The user's mental state is assessed by combining the first and second assessment results, thereby achieving the technical effect of ensuring the accuracy of the mental state assessment while ensuring the efficiency of the assessment.

[0053] Example 2, based on the same inventive concept as the individual state analysis method under multidimensional data fusion in the foregoing examples, such as... Figure 4 As shown, this application also provides an individual state analysis system based on multidimensional data fusion, the system comprising:

[0054] The system includes a physiological parameter acquisition module 11, which collects multiple physiological parameters of a target user through an acquisition device within a first time window and a second time window to obtain a first physiological parameter set, a second physiological parameter set, and an average physiological parameter set. The multiple physiological parameters include Class I physiological parameters and Class II physiological parameters. A user form completion data acquisition module 12 is used to acquire form completion data from the target user. A rate of change calculation module 13 is used to calculate the rate of change of Class I and Class II physiological parameters between the first and second time windows based on the first and second physiological parameter sets, obtaining a first rate of change and a second rate of change. The mental state assessment module 14 is used to input multiple physiological parameters from the average physiological parameter set into the first assessment unit and the second assessment unit of the mental state assessment model to obtain a first sub-assessment result and a second sub-assessment result; the first assessment result acquisition module 15 is used to perform weighted calculation on the first sub-assessment result and the second sub-assessment result according to the magnitude of the first rate of change and the second rate of change to obtain a first assessment result; the mental state assessment result acquisition module 16 is used to input the form-filled data into the form-filled data model to obtain a second assessment result, and combine the first assessment result to obtain a mental state assessment result.

[0055] Furthermore, the system also includes:

[0056] A first physiological parameter set acquisition module is used to collect multiple physiological parameters of the target user within a first time window to obtain a first physiological parameter set, wherein the first type of physiological parameter is an electrocardiogram (ECG) signal parameter and the second type of physiological parameter is an electroencephalogram (EEG) signal parameter; a second physiological parameter set acquisition module is used to collect multiple physiological parameters of the target user within a second time window to obtain a second physiological parameter set; and an average physiological parameter set acquisition module is used to calculate an average physiological parameter set based on the first physiological parameter set and the second physiological parameter set.

[0057] Furthermore, the system also includes:

[0058] A first rate of change calculation module is used to calculate the difference between a class of physiological parameters based on the first set of physiological parameters and the second set of physiological parameters, and to calculate the first rate of change; a second rate of change calculation module is used to calculate the difference between two classes of physiological parameters based on the first set of physiological parameters and the second set of physiological parameters, and to calculate the second rate of change.

[0059] Furthermore, the system also includes:

[0060] A first assessment unit construction module is used to construct the first assessment unit; a second assessment unit construction module is used to construct the second assessment unit, and in combination with the first assessment unit, the mental state assessment model is obtained; an assessment result acquisition module is used to input one type of physiological parameter and two types of physiological parameters from the set of average physiological parameters into the first assessment unit and the second assessment unit respectively, and obtain the first sub-assessment result and the second sub-assessment result.

[0061] Furthermore, the system also includes:

[0062] The weight allocation module is used to allocate weights according to the magnitudes of the first rate of change and the second rate of change to obtain a weight allocation result, wherein the larger the rate of change, the smaller the weight coefficient; the weight calculation module is used to perform weighted calculation on the first sub-evaluation result and the second sub-evaluation result according to the weight allocation result to obtain the first evaluation result.

[0063] Furthermore, the system also includes:

[0064] The system includes a duration reading module for reading the duration information of each table cell in the form-filling data; a positional configuration module for collecting a preset duration range of the form-filling data for each table type and comparing it with the duration information of each table cell, configuring the positional weight of the form-filling data based on the duration deviation, and generating a positional weight; and a fusion module for using the positional weight as the fusion weight of the second evaluation result and fusing it with the first evaluation result to generate the mental state evaluation result.

[0065] Furthermore, the system also includes:

[0066] The system includes: a sample form completion data acquisition module for acquiring multiple sample form completion data; a sample evaluation result acquisition module for acquiring multiple sample second evaluation results; a form completion data model acquisition module for constructing a mapping relationship between the multiple sample form completion data and the multiple sample second evaluation results to obtain the form completion data model; and a form completion data traversal module for traversing the mapping relationship within the form completion data model by inputting the form completion data into the form completion data model, and using the corresponding sample second evaluation result as the second evaluation result.

[0067] The specific example of the individual state analysis method under multidimensional data fusion in Embodiment 1 described above is also applicable to the individual state analysis system under multidimensional data fusion in this embodiment. Through the foregoing detailed description of the individual state analysis method under multidimensional data fusion, those skilled in the art can clearly understand the individual state analysis system under multidimensional data fusion in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0068] Example 3, Figure 5 This is a schematic diagram based on the third embodiment of the present disclosure, as shown below. Figure 5 As shown, the electronic device 800 in this disclosure may include a processor 801 and a memory 802.

[0069] Memory 802 is used to store programs. Memory 802 may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; memory may also include non-volatile memory, such as flash memory. Memory 802 is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc. The computer programs and computer instructions can be partitioned and stored in one or more memories 802. Furthermore, the computer programs, computer instructions, data, etc., can be accessed by processor 801.

[0070] The aforementioned computer programs and instructions can be stored in one or more partitions of memory 802. Furthermore, the aforementioned computer programs and instructions can be invoked by processor 801.

[0071] The processor 801 is configured to execute the computer program stored in the memory 802 to implement the various steps in the methods described in the above embodiments.

[0072] For details, please refer to the relevant descriptions in the preceding method embodiments.

[0073] The processor 801 and the memory 802 can be independent structures or integrated structures. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 can be coupled together via bus 803.

[0074] The electronic device in this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principle are the same, and will not be repeated here.

[0075] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0076] According to embodiments of this disclosure, this disclosure also provides a computer program product comprising: a computer program stored in a readable storage medium, at least one processor of an electronic device being able to read the computer program from the readable storage medium, and the at least one processor executing the computer program causing the electronic device to perform the scheme provided in any of the above embodiments.

[0077] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders.

[0078] This document does not impose any restrictions as long as the desired results of the disclosed technical solution can be achieved.

[0079] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for individual state analysis based on multidimensional data fusion, characterized in that, The method includes: The acquisition device collects multiple physiological parameters of the target user in the first and second time windows to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters. The multiple physiological parameters include a type I physiological parameter and a type II physiological parameter. Obtain the form-filled data from the target user; Based on the first set of physiological parameters and the second set of physiological parameters, calculate the rate of change of a type I physiological parameter and a type II physiological parameter between the first time window and the second time window to obtain the first rate of change and the second rate of change. Multiple physiological parameters from the set of average physiological parameters are input into the first and second assessment units of the mental state assessment model to obtain the first and second sub-assessment results. Based on the magnitudes of the first rate of change and the second rate of change, the first sub-evaluation result and the second sub-evaluation result are weighted and calculated to obtain the first evaluation result; The completed data is input into the completed data model to obtain a second assessment result. Combined with the first assessment result, a mental state assessment result is obtained. Within the first and second time windows, various physiological parameters of the target user are collected to obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters, including: During the first time window, multiple physiological parameters of the target user are collected to obtain the first set of physiological parameters, wherein the first type of physiological parameter is electrocardiogram signal parameter and the second type of physiological parameter is electroencephalogram signal parameter; During the second time window, multiple physiological parameters of the target user are collected to obtain the second set of physiological parameters; The average set of physiological parameters is calculated based on the first set of physiological parameters and the second set of physiological parameters. Specifically, multiple physiological parameters from the average physiological parameter set are input into the first and second assessment units within the mental state assessment model to obtain the first and second sub-assessment results, including: Construct the first evaluation unit; Construct the second assessment unit and combine it with the first assessment unit to obtain the mental state assessment model; The first type of physiological parameter and the second type of physiological parameter in the set of average physiological parameters are respectively input into the first evaluation unit and the second evaluation unit to obtain the first sub-evaluation result and the second sub-evaluation result; Specifically, the completed data is input into the completed data model to obtain a second assessment result, and combined with the first assessment result to obtain a mental state assessment result, including: Read the form filling time information for each table cell in the form filling data; The preset time range of the form data is collected and compared with the form filling time information of each form unit. The set weight of the form data is configured based on the time deviation, and a set weight is generated. The placement weight is used as the fusion weight of the second evaluation result, and fused with the first evaluation result to generate the mental state evaluation result; Specifically, based on the magnitudes of the first rate of change and the second rate of change, the first sub-evaluation result and the second sub-evaluation result are weighted and calculated to obtain the first evaluation result, including: Based on the magnitudes of the first rate of change and the second rate of change, weights are allocated to obtain the weight allocation result, wherein the larger the rate of change, the smaller the weight coefficient; The first sub-evaluation result and the second sub-evaluation result are weighted and calculated according to the weight allocation result to obtain the first evaluation result.

2. The method according to claim 1, characterized in that, Based on the first set of physiological parameters and the second set of physiological parameters, calculate the rate of change of Class I and Class II physiological parameters between the first time window and the second time window, including: Based on the first set of physiological parameters and the second set of physiological parameters, the difference of a class of physiological parameters is calculated, and the first rate of change is calculated. Based on the first set of physiological parameters and the second set of physiological parameters, the difference between the two types of physiological parameters is calculated, and the second rate of change is calculated.

3. The method according to claim 1, characterized in that, The filled-in data is input into the filled-in data model to obtain a second evaluation result, including: Obtain data from multiple sample forms; Obtain the second evaluation results for multiple samples; Construct a mapping relationship between the multiple sample completed data and the multiple sample second evaluation results to obtain the completed data model; The filled-in data is input into the mapping relationship within the filled-in data model for traversal, and the corresponding sample second evaluation result is used as the second evaluation result.

4. An individual state analysis system based on multidimensional data fusion, characterized in that, The system is used to implement the steps of the method according to any one of claims 1 to 3, and the system comprises: A physiological parameter acquisition module is used to acquire multiple physiological parameters of a target user through an acquisition device in a first time window and a second time window, and obtain a first set of physiological parameters, a second set of physiological parameters, and an average set of physiological parameters, wherein the multiple physiological parameters include a type I physiological parameter and a type II physiological parameter; User form data acquisition module, the user form data acquisition module is used to acquire the form data of the target user; The rate of change calculation module is used to calculate the rate of change of a type of physiological parameter and a type of physiological parameter between the first time window and the second time window based on the first set of physiological parameters and the second set of physiological parameters, so as to obtain a first rate of change and a second rate of change. A mental state assessment module is used to input multiple physiological parameters from the set of average physiological parameters into the first assessment unit and the second assessment unit of the mental state assessment model to obtain the first sub-assessment result and the second sub-assessment result. The first evaluation result acquisition module is used to perform weighted calculation on the first sub-evaluation result and the second sub-evaluation result according to the magnitude of the first rate of change and the second rate of change to obtain the first evaluation result; The mental state assessment result acquisition module is used to input the completed data into the completed data model to obtain a second assessment result, and combine it with the first assessment result to obtain a mental state assessment result.

5. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method according to any one of claims 1-3.

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