User emotion adjustment method and device, equipment and storage medium

Abstract

The invention relates to a user emotion adjustment method and device, equipment and a storage medium. The method comprises the following steps: monitoring first electrocardiogram data of a target user; inputting the first electrocardiogram data of the target user into the autonomic nerve model, and outputting a first emotion evaluation result of the target user; inputting the obtained basic information of the target user and the first emotion evaluation result into a stress steady-state model, and outputting an emotion adjustment scheme of the target user, the emotion adjustment scheme of the target user comprising a target music type and a target playing duration; sending the emotion adjustment scheme of the target user to the target user; monitoring second electrocardiogram data of the target user; inputting the second electrocardiogram data of the target user into the autonomic nerve model, and outputting a second emotion evaluation result of the target user; and generating an emotion adjustment report of the target user in response to the fact that the second emotion evaluation result of the target user is normal. By adopting the method, the user emotion can be automatically monitored and adjusted.

Description

Technical Field

[0001] The present application relates to the field of medical rehabilitation technology, and in particular to a user emotion adjustment method, device, equipment and storage medium. Background Art

[0002] Stress is a systemic nonspecific response of the body to changes in the internal and external environment and social and psychological stimulation. Psychological stress can cause excitement of the sympathetic-adrenal medullary system, leading to autonomic dysfunction, affecting neuroendocrine and inflammatory reactions. Moreover, people are often unable to regulate their psychological stress in a timely manner, resulting in certain harm to themselves or the people around them.

[0003] Research has shown that emotions are closely related to autonomic nervous activity, and music can affect the autonomic nervous system through sensory resonance, thereby regulating people's emotions and moods. Existing judgments on psychological trauma or stress memory are based on subjective judgments from questionnaires or conversations, lacking objective evaluation. At the same time, the regulation of stress emotions is generally based on the selection of music based on people's subjective judgment, which lacks intelligence and is time-consuming and laborious.

[0004] Therefore, there is an urgent need to propose a user emotion adjustment method, device, equipment and storage medium that can automatically monitor and adjust the user's emotions. Summary of the invention

[0005] Based on this, it is necessary to provide a user emotion adjustment method, device, equipment and storage medium that can automatically monitor and adjust user emotions in response to the above technical problems.

[0006] In a first aspect, a method for adjusting user emotions is provided, the method comprising:

[0007] Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user;

[0008] Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user;

[0009] Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time;

[0010] Sending the target user's emotion adjustment plan to the target user;

[0011] Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user;

[0012] Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user;

[0013] In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

[0014] In one embodiment, the training method of the autonomic neural model includes:

[0015] Acquire electrocardiogram data of a historical user, wherein the electrocardiogram data of the historical user includes first electrocardiogram data and second electrocardiogram data of the historical user;

[0016] Inputting the electrocardiogram data of the historical user into the autonomic nerve model, and outputting the emotion evaluation result of the historical user;

[0017] Acquire the emotion tag of the historical user, and verify the emotion evaluation result of the historical user according to the emotion tag of the historical user to generate an emotion verification result;

[0018] In response to the emotion verification result being consistent, determining that the training of the autonomic nervous system model is completed;

[0019] In response to the emotion verification result being inconsistent, regenerating the emotion evaluation result of the historical user until the emotion verification result is consistent.

[0020] In one embodiment, the method for generating the stress homeostasis model comprises:

[0021] Obtain the historical user's first electrocardiogram data, basic information, emotion adjustment plan, and second emotion evaluation results;

[0022] In response to the second emotion evaluation result of the historical user being normal, establishing a mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user;

[0023] The stress steady-state model is generated according to the mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user.

[0024] In one embodiment, before inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model, the method further includes:

[0025] Establishing a music library, and classifying the music in the music library according to music attributes to generate music classification results, wherein the music attributes include rhythm, melody, harmony, pitch and volume, and the music classification results include music type labels;

[0026] Acquire the emotion adjustment plan of the historical user, and analyze the emotion adjustment plan of the historical user to obtain the target music type and target playing time of the historical user;

[0027] Acquire the first electrocardiogram data and basic information of the historical user, and use the first electrocardiogram data and basic information of the historical user as the historical user input data of the stress steady-state model;

[0028] Establishing a mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user, and the target playing time of the historical user;

[0029] The stress steady-state model is generated according to the mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user and the target playing time of the historical user.

[0030] In one embodiment, inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model and outputting the emotion adjustment plan of the target user includes:

[0031] According to the basic information of the target user and the first emotion evaluation result, a first machine learning algorithm is used to obtain the target music type of the target user, wherein the first machine learning algorithm includes a decision tree algorithm, a random forest parsing algorithm, and a logistic regression algorithm;

[0032] According to the basic information of the target user and the first emotion evaluation result, a second machine learning algorithm is used to obtain the target playback time of the target user, wherein the second machine learning algorithm includes a linear regression algorithm and a random forest parsing algorithm;

[0033] An emotion adjustment plan for the target user is generated according to the target music type and target playing time of the target user.

[0034] In one embodiment, the training method of the stress homeostasis model includes:

[0035] Acquire the emotion adjustment scheme and the second emotion evaluation result of the historical user, and use the second emotion evaluation result of the historical user as feedback data of the stress homeostasis model;

[0036] In response to the second emotion evaluation result of the historical user being normal, determining that the training of the stress homeostasis model is completed;

[0037] In response to the second emotion evaluation result of the historical user being abnormal, the basic information and the first emotion evaluation result of the historical user are obtained, and the emotion adjustment plan of the historical user is regenerated according to the basic information and the first emotion evaluation result of the historical user until the second emotion evaluation result of the historical user is normal.

[0038] In one embodiment, the method further comprises:

[0039] Sort the music type tags according to the target user's preference, and generate the music type priority of the target user;

[0040] The basic information of the target user, the first emotion assessment result and the music type priority are input into the stress homeostasis model, and the emotion adjustment plan of the target user is output.

[0041] In a second aspect, a user emotion adjustment device is provided, the device comprising:

[0042] A first monitoring module, wherein the first monitoring module is used to monitor first electrocardiogram data of a target user;

[0043] A first data processing module, the first data processing module is used to process the first ECG data of the target user to obtain the first ECG time domain parameter and the first ECG frequency domain parameter of the target user;

[0044] A first input-output module, the first input-output module is used to input the first ECG time-domain parameter and the first ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the first emotion evaluation result of the target user;

[0045] An acquisition module, the acquisition module is used to acquire basic information of the target user;

[0046] A second input-output module, the second input-output module is used to input the basic information of the target user and the first emotion assessment result into the stress homeostasis model, and output the emotion adjustment plan of the target user, wherein the emotion adjustment plan of the target user includes a target music type and a target playing time;

[0047] A transmission module, the transmission module is used to send the emotion adjustment plan of the target user to the target user;

[0048] A second monitoring module, the second monitoring module is used to monitor the second electrocardiogram data of the target user;

[0049] A second data processing module, the second data processing module is used to process the second ECG data of the target user to obtain the second ECG time domain parameter and the second ECG frequency domain parameter of the target user;

[0050] A third input-output module, the third input-output module is used to input the second ECG time-domain parameter and the second ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the second emotion evaluation result of the target user;

[0051] A generating module, wherein the generating module is used to generate an emotion adjustment report for the target user according to the first emotion evaluation result and the second emotion evaluation result of the target user in response to the second emotion evaluation result of the target user being normal.

[0052] In a third aspect, an electronic device is provided, comprising one or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions, which, when read and executed by the one or more processors, execute the steps of the method described in any one of the first aspects above.

[0053] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the first aspects are performed.

[0054] In a fifth aspect, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method described in any one of the above-mentioned first aspects are implemented.

[0055] The above-mentioned user emotion adjustment method, device, equipment and storage medium monitor the user's emotions before and after emotion adjustment according to the emotion adjustment plan through the autonomic nervous system model, and generate an emotion adjustment plan through the stress homeostasis model to adjust the user's emotions, thereby improving the efficiency of user emotion monitoring and adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A schematic diagram of a flow chart of a method for adjusting user emotions in one embodiment;

[0057] Figure 2 is a structural block diagram of a user emotion adjustment device in one embodiment;

[0058] Figure 3 FIG. 4 is a diagram showing the internal structure of an electronic device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] Embodiment 1

[0061] In one embodiment, Figure 1 As shown, a method for adjusting user emotions is provided, the method comprising:

[0062] Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user;

[0063] Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user;

[0064] Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time;

[0065] Sending the target user's emotion adjustment plan to the target user;

[0066] Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user;

[0067] Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user;

[0068] In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

[0069] Specifically, the user's basic information includes the user's health data, age and gender, etc. After the emotion adjustment plan is sent to the user, the user's electrocardiogram data is monitored at preset intervals to observe the user's emotional changes before and after the adjustment. The user's emotions before and after the emotion adjustment according to the emotion adjustment plan are monitored through the autonomic nervous system model, and the emotion adjustment plan is generated through the stress homeostasis model to adjust the user's emotions, thereby improving the efficiency of user emotion monitoring and adjustment.

[0070] In one embodiment, the training method of the autonomic neural model includes:

[0071] Acquire electrocardiogram data of a historical user, wherein the electrocardiogram data of the historical user includes first electrocardiogram data and second electrocardiogram data of the historical user;

[0072] Inputting the electrocardiogram data of the historical user into the autonomic nerve model, and outputting the emotion evaluation result of the historical user;

[0073] Acquire the emotion tag of the historical user, and verify the emotion evaluation result of the historical user according to the emotion tag of the historical user to generate an emotion verification result;

[0074] In response to the emotion verification result being consistent, determining that the training of the autonomic nervous system model is completed;

[0075] In response to the emotion verification result being inconsistent, regenerating the emotion evaluation result of the historical user until the emotion verification result is consistent.

[0076] Specifically, the emotion assessment results are verified according to the emotion labels to improve the accuracy of the emotion assessment results generated by the autonomic neural model.

[0077] Specifically, ECG time domain parameters include: standard deviation of sinus beat RR interval (SDNN), average value of RR interval standard deviation (SDNN index), root mean square value of difference between adjacent NN intervals (RMSSD), percentage of number of adjacent heartbeat intervals with difference greater than 50ms in total heartbeat intervals (pNN50). ECG frequency domain parameters include: low frequency power (LF), high frequency power (HF), LF / HF. SDNN is an important indicator for evaluating the degree of heart rate variability. A large SDNN value indicates a high degree of complexity of the heart rate change signal, whereas a low SDNN value indicates a relatively simple heart rate change signal. SDNNIndex is a time domain indicator that reflects sympathetic nerve activity. RMSSD is a time domain indicator that reflects the root mean square value of difference between adjacent NN intervals, reflecting parasympathetic nerve activity. LF / HF reflects the balance state of sympathetic and parasympathetic nerves and sympathetic nerve regulation. pNN50 reflects the parasympathetic nerve regulation function. The stress homeostasis model combines SDNN, pNN50, LF / HF and continuous dynamic graph indicators of instantaneous heart rate and RR interval.

[0078]

[0079] SDNNIndex: Calculate the average of all standard deviations based on the standard deviation of the NN interval every 5 minutes.

[0080] pNN5: NN50 / N, the percentage of heart beats in which the difference between adjacent NN intervals exceeds 50ms to the total number of heart beats in the NN interval.

[0081] LF / HF: refers to the ratio of LF to HF, HF is the energy from 0.15hz to 0.4hz, and LF is the energy from 0.04hz to 0.15hz.

[0082] In one embodiment, the method for generating the stress homeostasis model includes:

[0083] Obtain the historical user's first electrocardiogram data, basic information, emotion adjustment plan, and second emotion evaluation results;

[0084] In response to the second emotion evaluation result of the historical user being normal, establishing a mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user;

[0085] The stress steady-state model is generated according to the mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user.

[0086] In one embodiment, before inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model, the method further includes:

[0087] Establishing a music library, and classifying the music in the music library according to music attributes to generate music classification results, wherein the music attributes include rhythm, melody, harmony, pitch and volume, and the music classification results include music type labels;

[0088] Acquire the emotion adjustment plan of the historical user, and analyze the emotion adjustment plan of the historical user to obtain the target music type and target playing time of the historical user;

[0089] Acquire the first electrocardiogram data and basic information of the historical user, and use the first electrocardiogram data and basic information of the historical user as the historical user input data of the stress steady-state model;

[0090] Establishing a mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user, and the target playing time of the historical user;

[0091] The stress steady-state model is generated according to the mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user and the target playing time of the historical user.

[0092] In one embodiment, inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model and outputting the emotion adjustment plan of the target user includes:

[0093] According to the basic information of the target user and the first emotion evaluation result, a first machine learning algorithm is used to obtain the target music type of the target user, wherein the first machine learning algorithm includes a decision tree algorithm, a random forest parsing algorithm, and a logistic regression algorithm;

[0094] According to the basic information of the target user and the first emotion evaluation result, a second machine learning algorithm is used to obtain the target playback time of the target user, wherein the second machine learning algorithm includes a linear regression algorithm and a random forest parsing algorithm;

[0095] An emotion adjustment plan for the target user is generated according to the target music type and target playing time of the target user.

[0096] In one embodiment, the training method of the stress homeostasis model includes:

[0097] Acquire the emotion adjustment scheme and the second emotion evaluation result of the historical user, and use the second emotion evaluation result of the historical user as feedback data of the stress homeostasis model;

[0098] In response to the second emotion evaluation result of the historical user being normal, determining that the training of the stress homeostasis model is completed;

[0099] In response to the second emotion evaluation result of the historical user being abnormal, the basic information and the first emotion evaluation result of the historical user are obtained, and the emotion adjustment plan of the historical user is regenerated according to the basic information and the first emotion evaluation result of the historical user until the second emotion evaluation result of the historical user is normal.

[0100] Specifically, the second emotion evaluation result generated by the autonomic nervous system model is used as feedback data of the stress homeostasis model, so that the emotion adjustment scheme generated by the stress homeostasis model is more effective.

[0101] In one embodiment, the method further comprises:

[0102] Sort the music type tags according to the target user's preference, and generate the music type priority of the target user;

[0103] The basic information of the target user, the first emotion assessment result and the music type priority are input into the stress homeostasis model, and the emotion adjustment plan of the target user is output.

[0104] Specifically, the mood adjustment scheme can better meet personalized needs by taking into account the music types preferred by users.

[0105] It should be understood that although Figure 1The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0106] Embodiment 2

[0107] In one embodiment, Figure 2 As shown, a user emotion adjustment device is provided, and the device includes:

[0108] A first monitoring module, wherein the first monitoring module is used to monitor first electrocardiogram data of a target user;

[0109] A first data processing module, the first data processing module is used to process the first ECG data of the target user to obtain the first ECG time domain parameter and the first ECG frequency domain parameter of the target user;

[0110] A first input-output module, the first input-output module is used to input the first ECG time-domain parameter and the first ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the first emotion evaluation result of the target user;

[0111] An acquisition module, the acquisition module is used to acquire basic information of the target user;

[0112] A second input-output module, the second input-output module is used to input the basic information of the target user and the first emotion assessment result into the stress homeostasis model, and output the emotion adjustment plan of the target user, wherein the emotion adjustment plan of the target user includes a target music type and a target playing time;

[0113] A transmission module, the transmission module is used to send the emotion adjustment plan of the target user to the target user;

[0114] A second monitoring module, the second monitoring module is used to monitor the second electrocardiogram data of the target user;

[0115] A second data processing module, the second data processing module is used to process the second ECG data of the target user to obtain the second ECG time domain parameter and the second ECG frequency domain parameter of the target user;

[0116] A third input-output module, the third input-output module is used to input the second ECG time-domain parameter and the second ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the second emotion evaluation result of the target user;

[0117] A generating module, wherein the generating module is used to generate an emotion adjustment report for the target user according to the first emotion evaluation result and the second emotion evaluation result of the target user in response to the second emotion evaluation result of the target user being normal.

[0118] In one embodiment, the device further comprises a training module, wherein the training module is used to train the autonomic neural model, comprising:

[0119] Acquire electrocardiogram data of a historical user, wherein the electrocardiogram data of the historical user includes first electrocardiogram data and second electrocardiogram data of the historical user;

[0120] Inputting the electrocardiogram data of the historical user into the autonomic nerve model, and outputting the emotion evaluation result of the historical user;

[0121] Acquire the emotion tag of the historical user, and verify the emotion evaluation result of the historical user according to the emotion tag of the historical user to generate an emotion verification result;

[0122] In response to the emotion verification result being consistent, determining that the training of the autonomic nervous system model is completed;

[0123] In response to the emotion verification result being inconsistent, regenerating the emotion evaluation result of the historical user until the emotion verification result is consistent.

[0124] In one embodiment, the generating module is further used to generate a stress homeostasis model, including:

[0125] Obtain the historical user's first electrocardiogram data, basic information, emotion adjustment plan, and second emotion evaluation results;

[0126] In response to the second emotion evaluation result of the historical user being normal, establishing a mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user;

[0127] The stress steady-state model is generated according to the mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user.

[0128] In one embodiment, before the second input-output module inputs the basic information of the target user and the first emotion assessment result into the stress homeostasis model, the device further includes:

[0129] A building module, wherein the building module is used to build a music library;

[0130] A classification module, the classification module is used to classify the music in the music library according to music attributes and generate music classification results, wherein the music attributes include rhythm, melody, harmony, pitch and volume, and the music classification results include music type labels;

[0131] The acquisition module is also used to acquire emotion adjustment plans of historical users;

[0132] An analysis module, the analysis module is used to analyze the emotion adjustment plan of the historical user to obtain the target music type and target playing time of the historical user;

[0133] The acquisition module is also used to acquire the first electrocardiogram data and basic information of the historical user, and use the first electrocardiogram data and basic information of the historical user as the historical user input data of the stress steady-state model;

[0134] The establishment module is also used to establish a mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user, and the target playing time of the historical user;

[0135] The generation module is also used to generate the stress steady-state model according to the mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user and the target playback time of the historical user.

[0136] In one embodiment, the second input-output module inputs the basic information of the target user and the first emotion assessment result into the stress homeostasis model, and outputs the emotion adjustment plan of the target user, including:

[0137] According to the basic information of the target user and the first emotion evaluation result, a first machine learning algorithm is used to obtain the target music type of the target user, wherein the first machine learning algorithm includes a decision tree algorithm, a random forest parsing algorithm, and a logistic regression algorithm;

[0138] According to the basic information of the target user and the first emotion evaluation result, a second machine learning algorithm is used to obtain the target playback time of the target user, wherein the second machine learning algorithm includes a linear regression algorithm and a random forest parsing algorithm;

[0139] An emotion adjustment plan for the target user is generated according to the target music type and target playing time of the target user.

[0140] In one embodiment, the generating module is further used to generate a stress homeostasis model, including:

[0141] Acquire the emotion adjustment scheme and the second emotion evaluation result of the historical user, and use the second emotion evaluation result of the historical user as feedback data of the stress homeostasis model;

[0142] In response to the second emotion evaluation result of the historical user being normal, determining that the training of the stress homeostasis model is completed;

[0143] In response to the second emotion evaluation result of the historical user being abnormal, the basic information and the first emotion evaluation result of the historical user are obtained, and the emotion adjustment plan of the historical user is regenerated according to the basic information and the first emotion evaluation result of the historical user until the second emotion evaluation result of the historical user is normal.

[0144] In one embodiment, the apparatus further comprises:

[0145] A sorting module, the sorting module is used to sort the music type tags according to the target user's preference, and generate the music type priority of the target user;

[0146] The second input-output module is also used to input the basic information of the target user, the first emotion assessment result and the music type priority into the stress homeostasis model, and output the emotion adjustment plan of the target user.

[0147] For the specific definition of the user emotion adjustment device, please refer to the definition of the user emotion adjustment method above, which will not be repeated here. Each module in the above-mentioned user emotion adjustment device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0148] Embodiment 3

[0149] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0150] Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user;

[0151] Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user;

[0152] Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time;

[0153] Sending the target user's emotion adjustment plan to the target user;

[0154] Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user;

[0155] Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user;

[0156] In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

[0157] When the program instructions are read and executed by the one or more processors, the program instructions may also perform operations corresponding to the steps in the above method embodiments, and the above description may be referred to, which will not be repeated here. Figure 3 , which exemplarily shows the architecture of the electronic device, which may specifically include a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, and a memory 320. The processor 310, the video display adapter 311, the disk drive 312, the input / output interface 313, the network interface 314, and the memory 320 may be communicatively connected via a communication bus 330.

[0158] Among them, the processor 310 can be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits, etc., to execute relevant programs to implement the technical solution provided in this application.

[0159] The memory 320 can be implemented in the form of a read-only memory (ROM), a random access memory (RAM), a static storage device, a dynamic storage device, etc. The memory 320 can store an operating system 321 for controlling the operation of the electronic device 300, and a basic input and output system (BIOS) 322 for controlling the low-level operation of the electronic device 300. In addition, a web browser 323, a data storage management 324, and an icon font processing system 325, etc. can also be stored. The above-mentioned icon font processing system 325 can be an application program that specifically implements the operations of the aforementioned steps in the embodiment of the present application. In short, when the technical solution provided in the present application is implemented by software or firmware, the relevant program code is stored in the memory 320 and is called and executed by the processor 310.

[0160] The input / output interface 313 is used to connect the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0161] The network interface 314 is used to connect to a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0162] The bus 330 comprises a pathway for transmitting information between the various components of the device (eg, the processor 310, the video display adapter 311, the disk drive 312, the input / output interface 313, the network interface 314, and the memory 320).

[0163] In addition, the electronic device 300 can also obtain information on specific collection conditions from the virtual resource object collection condition information database 341 for use in condition judgment, etc.

[0164] It should be noted that, although the electronic device 300 only shows a processor 310, a video display adapter 311, a disk drive 312, an input / output interface 313, a network interface 314, a memory 320, a bus 330, etc., in the specific implementation process, the electronic device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include components necessary for implementing the solution of the present application, and does not necessarily include all the components shown in the figure.

[0165] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling an electronic device (which can be a personal computer, a cloud server, or a network device, etc.) to execute the methods described in the various embodiments of the present application or certain parts of the embodiments.

[0166] Embodiment 4

[0167] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0168] Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user;

[0169] Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user;

[0170] Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time;

[0171] Sending the target user's emotion adjustment plan to the target user;

[0172] Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user;

[0173] Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user;

[0174] In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

[0175] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0176] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0177] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

[0178] Embodiment 5

[0179] In one embodiment, a computer program product is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0180] Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user;

[0181] Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user;

[0182] Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time;

[0183] Sending the target user's emotion adjustment plan to the target user;

[0184] Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user;

[0185] Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user;

[0186] In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

[0187] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a computer program product. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0188] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0189] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for adjusting user emotions, characterized in that: The method comprises: Monitoring first ECG data of a target user, and performing data processing on the first ECG data of the target user to obtain first ECG time domain parameters and first ECG frequency domain parameters of the target user; Inputting the first ECG time domain parameter and the first ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the first emotion evaluation result of the target user; Acquire basic information of the target user, input the basic information of the target user and the first emotion assessment result into a stress homeostasis model, and output an emotion adjustment plan for the target user, wherein the emotion adjustment plan for the target user includes a target music type and a target playing time; Sending the target user's emotion adjustment plan to the target user; Monitoring the second ECG data of the target user, and performing data processing on the second ECG data of the target user to obtain the second ECG time domain parameters and second ECG frequency domain parameters of the target user; Inputting the second ECG time domain parameter and the second ECG frequency domain parameter of the target user into the autonomic nerve model, and outputting the second emotion evaluation result of the target user; In response to the second emotion assessment result of the target user being normal, an emotion adjustment report of the target user is generated according to the first emotion assessment result and the second emotion assessment result of the target user.

2. The method according to claim 1, characterized in that: The training method of the autonomic neural model includes: Acquire electrocardiogram data of a historical user, wherein the electrocardiogram data of the historical user includes first electrocardiogram data and second electrocardiogram data of the historical user; Inputting the electrocardiogram data of the historical user into the autonomic nerve model, and outputting the emotion evaluation result of the historical user; Acquire the emotion tag of the historical user, and verify the emotion evaluation result of the historical user according to the emotion tag of the historical user to generate an emotion verification result; In response to the emotion verification result being consistent, determining that the training of the autonomic nervous system model is completed; In response to the emotion verification result being inconsistent, regenerating the emotion evaluation result of the historical user until the emotion verification result is consistent.

3. The method according to claim 1, characterized in that: The method for generating the stress homeostasis model comprises: Obtain the historical user's first electrocardiogram data, basic information, emotion adjustment plan, and second emotion evaluation results; In response to the second emotion evaluation result of the historical user being normal, establishing a mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user; The stress steady-state model is generated according to the mapping relationship between the first electrocardiogram data and basic information of the historical user and the emotion adjustment scheme of the historical user.

4. The method according to claim 1, characterized in that: Before inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model, the method further includes: Establishing a music library, and classifying the music in the music library according to music attributes to generate music classification results, wherein the music attributes include rhythm, melody, harmony, pitch and volume, and the music classification results include music type labels; Acquire the emotion adjustment plan of the historical user, and analyze the emotion adjustment plan of the historical user to obtain the target music type and target playing time of the historical user; Acquire the first electrocardiogram data and basic information of the historical user, and use the first electrocardiogram data and basic information of the historical user as the historical user input data of the stress steady-state model; Establishing a mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user, and the target playing time of the historical user; The stress steady-state model is generated according to the mapping relationship between the historical user input data of the stress steady-state model, the target music type of the historical user and the target playing time of the historical user.

5. The method according to claim 1, characterized in that: The step of inputting the basic information of the target user and the first emotion assessment result into the stress homeostasis model and outputting the emotion adjustment plan of the target user comprises: According to the basic information of the target user and the first emotion evaluation result, a first machine learning algorithm is used to obtain the target music type of the target user, wherein the first machine learning algorithm includes a decision tree algorithm, a random forest parsing algorithm, and a logistic regression algorithm; According to the basic information of the target user and the first emotion evaluation result, a second machine learning algorithm is used to obtain the target playback time of the target user, wherein the second machine learning algorithm includes a linear regression algorithm and a random forest parsing algorithm; An emotion adjustment plan for the target user is generated according to the target music type and target playing time of the target user.

6. The method according to claim 1, characterized in that: The training method of the stress homeostasis model comprises: Acquire the emotion adjustment scheme and the second emotion evaluation result of the historical user, and use the second emotion evaluation result of the historical user as feedback data of the stress homeostasis model; In response to the second emotion evaluation result of the historical user being normal, determining that the training of the stress homeostasis model is completed; In response to the second emotion evaluation result of the historical user being abnormal, the basic information and the first emotion evaluation result of the historical user are obtained, and the emotion adjustment plan of the historical user is regenerated according to the basic information and the first emotion evaluation result of the historical user until the second emotion evaluation result of the historical user is normal.

7. The method according to claim 4, characterized in that: The method further comprises: Sort the music type tags according to the target user's preference, and generate the music type priority of the target user; The basic information of the target user, the first emotion assessment result and the music type priority are input into the stress homeostasis model, and the emotion adjustment plan of the target user is output.

8. A user emotion adjustment device, characterized in that: The device comprises: A first monitoring module, wherein the first monitoring module is used to monitor first electrocardiogram data of a target user; A first data processing module, the first data processing module is used to process the first ECG data of the target user to obtain the first ECG time domain parameter and the first ECG frequency domain parameter of the target user; A first input-output module, the first input-output module is used to input the first ECG time-domain parameter and the first ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the first emotion evaluation result of the target user; An acquisition module, the acquisition module is used to acquire basic information of the target user; A second input-output module, the second input-output module is used to input the basic information of the target user and the first emotion assessment result into the stress homeostasis model, and output the emotion adjustment plan of the target user, wherein the emotion adjustment plan of the target user includes a target music type and a target playing time; A transmission module, the transmission module is used to send the emotion adjustment plan of the target user to the target user; A second monitoring module, the second monitoring module is used to monitor the second electrocardiogram data of the target user; A second data processing module, the second data processing module is used to process the second ECG data of the target user to obtain the second ECG time domain parameter and the second ECG frequency domain parameter of the target user; A third input-output module, the third input-output module is used to input the second ECG time-domain parameter and the second ECG frequency-domain parameter of the target user into the autonomic nerve model, and output the second emotion evaluation result of the target user; A generating module, wherein the generating module is used to generate an emotion adjustment report for the target user according to the first emotion evaluation result and the second emotion evaluation result of the target user in response to the second emotion evaluation result of the target user being normal.

9. An electronic 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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