Male massager intelligent adjustment model establishment method based on biological feedback monitoring

Abstract

The invention discloses a male massager intelligent adjustment model establishment method based on biological feedback monitoring, and relates to the technical field of male massager adjustment, and the male massager intelligent adjustment model establishment method comprises the following steps: installing a multi-mode sensor in a massager; monitoring the biological feedback of the user in real time to obtain feedback data; extracting feedback features of different feedback data; extracting feedback features of the testers as test features, and recording adjustment expectations of the testers on the adjustment parameters; analyzing adjustment relevance between different feedback features and adjustment parameters based on the test features and adjustment expectation; adjusting parameters of the massager are intelligently adjusted based on the feedback characteristics and the adjusting relevance; the method and the device are used for solving the problem that the use experience of a user is reduced due to the fact that the massage parameter adjustment is not intelligent enough and the user needs to set the massager frequently in the existing male massager adjustment technology.

Description

Technical Field

[0001] The present invention relates to the technical field of male massager adjustment, and in particular to a method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring. Background Art

[0002] Male massager adjustment technology refers to a technical system that dynamically adjusts, programs, or provides intelligent feedback to male massager operating parameters (such as massage frequency, intensity, rhythm, and mode) in order to achieve better personalized experience, safety, comfort, and effect control.

[0003] Existing male massager adjustment technology usually uses a fixed parameter combination to form different preset modes, which are then selected by the user, or the user adjusts the gear of each massage parameter by himself. It lacks intelligence and requires the user to frequently set the massager. On the one hand, the operation steps are relatively cumbersome, and on the other hand, it will affect the user's immersive experience, resulting in a certain reduction in the comfort level of the massager. The existing male massager adjustment technology also has the problem that the adjustment of massage parameters is not intelligent enough and requires the user to frequently set the massager, which reduces the user experience. Summary of the Invention

[0004] The present invention aims to at least partially address one of the technical problems in the prior art. A multimodal sensor is installed in a massager, and then the multimodal sensor is used to monitor a user's biofeedback in real time to obtain feedback data. An intelligent adjustment model for adjustment parameters is constructed, and features are extracted from the feedback data to obtain feedback features of different feedback data. A first number of testers are selected to test the massager, and feedback features of the testers are extracted as test features. The testers' adjustment expectations for the adjustment parameters are recorded. The test features and adjustment expectations are then entered into the intelligent adjustment model for adjustment parameters, and values ​​are assigned to the adjustment expectations. Correlation validity is then analyzed based on the assigned test features and adjustment expectations. The test features and adjustment expectations are then grouped based on the correlation validity to obtain correlation groups, and analysis is performed based on the correlation groups to obtain adjustment correlations. Finally, biofeedback is monitored every first adjustment period, and feedback features are extracted. The adjustment parameters of the massager are intelligently adjusted based on the feedback features and adjustment correlations. This solves the problem that existing male massager adjustment technology still suffers from insufficiently intelligent adjustment of massage parameters and requires the user to frequently set the massager, thereby reducing the user experience.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring, comprising the following steps:

[0006] Installing a multimodal sensor in the massager;

[0007] The user's biofeedback is monitored in real time through multimodal sensors to obtain feedback data;

[0008] Construct an intelligent adjustment model for adjustment parameters, perform feature extraction on feedback data, and extract feedback features of different feedback data;

[0009] A first number of testers are selected to test the massager, and feedback features of the testers are extracted as test features, and adjustment expectations of the testers on adjustment parameters are recorded;

[0010] Entering the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and analyzing the adjustment correlation between different feedback characteristics and adjustment parameters based on the test characteristics and adjustment expectations;

[0011] The biofeedback is monitored once every first adjustment cycle and feedback features are extracted, and the adjustment parameters of the massager are intelligently adjusted based on the feedback features and the adjustment correlation.

[0012] Furthermore, installing the multimodal sensor in the massager includes the following sub-steps:

[0013] The multimodal sensor includes a PPG photoelectric volume pulse wave sensor, an electrode patch sensor, a surface electromyography sensor, and a thermal sensor;

[0014] The PPG photoplethysmography sensor and the electrode patch sensor are arranged at the gripping part of the massager to monitor the palm of the user;

[0015] The surface electromyography sensor and the thermal sensor are both arranged inside the massager to monitor the contact area.

[0016] Furthermore, real-time monitoring of the user's biofeedback by a multimodal sensor to obtain feedback data includes the following sub-steps:

[0017] The PPG photoplethysmography sensor, electrode patch sensor, surface electromyography sensor, and thermal sensor are used to monitor the user's heart rate, galvanic skin response, electromyography of the contact area, and skin temperature of the contact area, respectively;

[0018] The heart rate, galvanic skin response, electromyography and skin temperature are feedback data.

[0019] Furthermore, an intelligent adjustment model for adjustment parameters is constructed to extract features from the feedback data. Extracting feedback features of different feedback data includes the following sub-steps:

[0020] Constructing an intelligent adjustment model for adjustment parameters, and inputting feedback data into the intelligent adjustment model for adjustment parameters, wherein the intelligent adjustment model for adjustment parameters is equipped with an HRV algorithm, a GSR change rate algorithm, an EMG activation amplitude algorithm, and a temperature change trend algorithm;

[0021] Analyze the heart rate through the HRV algorithm to obtain the HRV index;

[0022] The skin electrical response is analyzed using the GSR change rate algorithm to obtain the GRS change rate;

[0023] The EMG activation amplitude is obtained by analyzing the electromyography using the EMG activation amplitude algorithm;

[0024] The skin temperature is analyzed by the temperature change trend algorithm to obtain the temperature change slope;

[0025] The HRV index, GRS change rate, EMG activation amplitude and temperature change slope are collectively referred to as feedback features.

[0026] Furthermore, selecting a first number of testers to test the massager, extracting the testers' feedback features as test features, and recording the testers' adjustment expectations for adjustment parameters includes the following sub-steps:

[0027] A first number of testers are randomly selected to test the massager;

[0028] During the test, the massager massages the tester using a preset massage plan for the duration of the first test;

[0029] Record the feedback characteristics of the testers monitored for the last time before the end of the test, and name them as test characteristics;

[0030] The adjustment parameters include massage amplitude, massage frequency, massage rhythm and cycle period;

[0031] After the test, record the tester's adjustment expectations for the adjustment parameters, including the expected amplitude, expected frequency, expected rhythm and expected period.

[0032] Furthermore, the test characteristics and adjustment expectations are entered into the adjustment parameter intelligent adjustment model, and the adjustment correlation between different feedback characteristics and adjustment parameters is analyzed based on the test characteristics and adjustment expectations, including the following sub-steps:

[0033] Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and assign values ​​to the adjustment expectations;

[0034] Analyze the association validity based on the assigned test characteristics and adjustment expectations;

[0035] The test characteristics and adjustment expectations are grouped based on the association validity to obtain the association grouping, and the adjustment correlation is obtained based on the analysis of the association grouping.

[0036] Furthermore, entering the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model and assigning values ​​to the adjustment expectations includes the following sub-steps:

[0037] Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model;

[0038] Assign a value to the desired amplitude. If the tester wishes to increase the massage amplitude by N1 steps, the desired amplitude is assigned to +N1. If the tester wishes to decrease the massage amplitude by N1 steps, the desired amplitude is assigned to -N1. If the tester wishes to maintain the current massage amplitude, the desired amplitude is assigned to 0.

[0039] Assign a value to the desired frequency. If the tester wishes to increase the massage frequency by N2, the desired frequency is assigned to +N2. If the tester wishes to decrease the massage frequency by N2, the desired frequency is assigned to -N2. If the tester wishes to maintain the existing massage frequency, the desired frequency is assigned to 0.

[0040] Assign a value to the desired rhythm. If the tester wishes to increase the massage rhythm by N3 steps, the desired rhythm is assigned to +N3. If the tester wishes to decrease the massage rhythm by N3 steps, the desired rhythm is assigned to -N3. If the tester wishes to maintain the current massage rhythm, the desired rhythm is assigned to 0.

[0041] Assign a value to the expected cycle, where if the tester wants to increase the gear of N4 massage cycles, the expected cycle is assigned to +N4; if the tester wants to reduce the gear of N4 massage cycles, the expected cycle is assigned to -N4; if the tester wants to maintain the gear of the existing massage cycle, the expected cycle is assigned to 0.

[0042] Furthermore, analyzing the association validity based on the assigned test features and adjustment expectations includes the following sub-steps:

[0043] A two-dimensional coordinate system was constructed with adjustment expectation as the Y-axis and HRV index, GRS change rate, EMG activation amplitude, and temperature change slope as the X-axis. These were named heart rate correlation diagram, electrical response correlation diagram, electromyography correlation diagram, and temperature correlation diagram, respectively. Adjustment expectation was entered into the corresponding two-dimensional coordinate system according to the test characteristics.

[0044] The coordinate points corresponding to the expected amplitude, expected frequency, expected rhythm and expected period are named amplitude coordinates, frequency coordinates, rhythm coordinates and period coordinates respectively, and polynomial regression is performed on the amplitude coordinates, frequency coordinates, rhythm coordinates and period coordinates in the heart rate correlation graph, electrical response correlation graph, electromyography correlation graph and temperature correlation graph respectively to obtain amplitude correlation curves, frequency correlation curves, rhythm correlation curves and period correlation curves, which are collectively referred to as correlation curves;

[0045] Get the fitting metrics of the amplitude correlation curve, frequency correlation curve, rhythm correlation curve, and period correlation curve, i.e. R 2 , marked as R1, R2, R3 and R4 respectively;

[0046] Obtain the relevant threshold. In the heart rate correlation graph, if R1 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the HRV index. If R2 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the GRS change rate. If R3 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the EMG activation amplitude. If R4 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the temperature change slope.

[0047] In the frequency correlation diagram, if R1 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the HRV index; if R2 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the GRS change rate; if R3 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the temperature change slope;

[0048] In the rhythm correlation diagram, if R1 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the HRV index; if R2 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the GRS change rate; if R3 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the temperature change slope;

[0049] In the cycle correlation diagram, if R1 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the HRV index; if R2 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the GRS change rate; if R3 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the temperature change slope.

[0050] Furthermore, the test features and adjustment expectations are grouped based on the correlation validity to obtain the correlation groups, and the adjustment correlation is obtained by analyzing the correlation groups, which includes the following sub-steps:

[0051] The test features that are effectively correlated with the massage amplitude are counted and summarized into the same correlation group, named amplitude group; the test features that are effectively correlated with the massage frequency are counted and summarized into the same correlation group, named frequency group; the test features that are effectively correlated with the massage rhythm are counted and summarized into the same correlation group, named rhythm group; the test features that are effectively correlated with the cycle period are counted and summarized into the same correlation group, named period group;

[0052] Any associated group contains at most two types of test features. If there are more than two types, the two types of test features with the largest fitting measure are retained;

[0053] When analyzing any associated group, it is marked as a group to be analyzed. If there is only one type of test feature in the group to be analyzed, the function of the association curve corresponding to the group to be analyzed is the adjusted correlation;

[0054] If there are two types of test features in the group to be analyzed, the test features are marked as the first feature and the second feature respectively, and the corresponding adjustment expectations are marked as the expectation to be analyzed;

[0055] A three-dimensional coordinate system is established with the first feature as the X-axis, the second feature as the Y-axis, and the expectation to be analyzed as the Z-axis, and is named the adjustment association coordinate system. The test features in the group to be analyzed are entered into the adjustment association coordinate system according to the corresponding adjustment expectations. The adjustment association coordinate system is the adjustment correlation.

[0056] Furthermore, monitoring the biofeedback once every first adjustment period and extracting feedback features, and intelligently adjusting the adjustment parameters of the massager based on the feedback features and the adjustment correlation includes the following sub-steps:

[0057] The biofeedback is monitored once every first adjustment cycle and feedback features are extracted and named as real-time features. When any adjustment parameter is adjusted and analyzed, it is named as a target parameter, and the real-time features that are effectively associated with the target parameter are marked as target features.

[0058] If the target feature has only one type of real-time feature, the target feature is substituted into the adjustment correlation corresponding to the target parameter to obtain the target adjustment value of the target parameter;

[0059] If there are two types of real-time features in the target feature, a top view of the adjustment correlation corresponding to the target parameter is obtained, that is, only the X-axis and Y-axis of the adjustment correlation coordinate system are considered, and the Z-axis is ignored to obtain a two-dimensional correlation diagram;

[0060] Name the coordinate point in the two-dimensional association diagram as the original point, enter the target feature into the two-dimensional association diagram to obtain the target point, find the original point closest to the target point in the two-dimensional association diagram, mark it as the adjustment point, obtain the value of the adjustment point on the Z axis of the adjustment association coordinate system, and you can get the target adjustment value. Adjust the target parameter, and the adjusted value is the target adjustment value.

[0061] Beneficial effects of the present invention: The present invention installs a multimodal sensor in the massager, and then uses the multimodal sensor to monitor the user's biofeedback in real time to obtain feedback data, construct an intelligent adjustment model for adjustment parameters, perform feature extraction on the feedback data, and extract feedback features of different feedback data. At the same time, a first number of testers are selected to test the massager, and the testers' feedback features are extracted as test features. The testers' adjustment expectations for the adjustment parameters are recorded, and the test features and adjustment expectations are entered into the intelligent adjustment model for adjustment parameters, and values ​​are assigned to the adjustment expectations. The correlation validity is analyzed based on the assigned test features and adjustment expectations. The advantage is that the correlation between different massage parameters and feedback data is tested based on the feedback data during user use, and effective feedback data that affects the massage parameters required by the user is found, providing an effective data basis for subsequent analysis, thereby improving the validity and rationality of the intelligent regulation of the massager.

[0062] The present invention groups test features and adjustment expectations based on association validity to obtain association groups, and analyzes the association groups to obtain adjustment correlation. Finally, the biofeedback is monitored once every first adjustment cycle and the feedback features are extracted. The adjustment parameters of the massager are intelligently adjusted based on the feedback features and the adjustment correlation. The advantage is that the adjustment correlation between the test features and the adjustment expectations is analyzed, and the user's independent and precise adjustment of different massage parameters under different feedback features is calculated to automatically adapt to the user's current massage needs, thereby improving the accuracy and effectiveness of the intelligent control of the massager. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 is a flow chart of the steps of the method of the present invention;

[0064] Figure 2 is a heart rate association diagram of the present invention;

[0065] Figure 3 is a schematic diagram of the correlation curve of the present invention;

[0066] Figure 4 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0067] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0068] Example 1, please refer to Figure 1 As shown, the present application provides a method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring, comprising the following steps:

[0069] Step S1: Install a multimodal sensor in the massager. Step S1 includes the following sub-steps:

[0070] Step S101, the multimodal sensor includes a PPG photoplethysmography sensor, an electrode patch sensor, a surface electromyography sensor, and a thermal sensor;

[0071] Step S102: A PPG photoplethysmography sensor and an electrode patch sensor are placed on the grip of the massager to monitor the user's palm.

[0072] Step S103: a surface electromyography sensor and a thermal sensor are both installed inside the massager to monitor the contact area;

[0073] In the specific implementation, the PPG photoelectric volumetric pulse wave sensor, electrode patch sensor, surface electromyography sensor and thermal sensor are all existing sensors. A gripping area is provided on the surface of the massager to restrict the user's gripping method and facilitate the collection of feedback data.

[0074] Step S2, real-time monitoring of the user's biofeedback through a multimodal sensor to obtain feedback data; Step S2 includes the following sub-steps:

[0075] Step S201: The PPG photoplethysmography sensor, the electrode patch sensor, the surface electromyography sensor, and the thermal sensor are used to monitor the user's heart rate, galvanic skin response, electromyography of the contact area, and skin temperature of the contact area, respectively.

[0076] Step S202: Heart rate, galvanic skin response, electromyography, and skin temperature are the feedback data;

[0077] In a specific implementation, the sampling frequencies of the PPG photoplethysmography sensor, electrode patch sensor, surface electromyography sensor, and thermal sensor are kept consistent, and sampling is performed at the same time.

[0078] Step S3: construct an intelligent adjustment model for adjustment parameters, perform feature extraction on the feedback data, and extract feedback features of different feedback data; Step S3 includes the following sub-steps:

[0079] Step S301: construct an intelligent adjustment model for adjustment parameters, input feedback data into the intelligent adjustment model for adjustment parameters, and the intelligent adjustment model for adjustment parameters is equipped with an HRV algorithm, a GSR change rate algorithm, an EMG activation amplitude algorithm, and a temperature change trend algorithm;

[0080] Step S302, analyzing the heart rate using an HRV algorithm to obtain an HRV index;

[0081] Step S303, analyzing the skin electrical response using a GSR change rate algorithm to obtain a GRS change rate;

[0082] Step S304, analyzing the electromyography using an EMG activation amplitude algorithm to obtain the EMG activation amplitude;

[0083] Step S305, analyzing the skin temperature using a temperature change trend algorithm to obtain a temperature change slope;

[0084] Step S306 , the HRV index, GRS change rate, EMG activation amplitude, and temperature change slope are collectively referred to as feedback features;

[0085] In the specific implementation, the HRV algorithm, GSR change rate algorithm, EMG activation amplitude algorithm and temperature change trend algorithm are all existing algorithms, and the calculation process is relatively simple. Various existing sensors usually have preset processing models and can directly output the feedback characteristics in this embodiment. This embodiment does not provide specific descriptions of them; for example, in the feedback characteristics obtained in a certain monitoring, the HRV index is 15.65ms, the GRS change rate is 0.076μS / s, the EMG activation amplitude is 0.0598mV, and the temperature change slope is -0.02.

[0086] Step S4, selecting a first number of testers to test the massager, extracting the testers' feedback features as test features, and recording the testers' adjustment expectations for the adjustment parameters; Step S4 includes the following sub-steps:

[0087] Step S401, selecting a first number of testers to test the massager;

[0088] Step S402: During the test, the massager massages the tester using a preset massage plan for a first test duration.

[0089] Step S403: Record the tester's feedback characteristics monitored last time before the test ends, and name them as test characteristics;

[0090] Step S404, adjusting parameters including massage amplitude, massage frequency, massage rhythm and cycle period;

[0091] Step S405: After the test is completed, the tester's adjustment expectations for the adjustment parameters are recorded, including the expected amplitude, expected frequency, expected rhythm, and expected period;

[0092] In the specific implementation, the tester can recruit volunteers and select company employees, and the test process adopts anonymous testing to fully protect the privacy of each tester. The first test number and the first test duration are set by the manufacturer. In this embodiment, the first test number is set to 400, and the first test duration is set to 1 minute. Each adjustment parameter of the massager has 5 gears. The preset massage plan in this embodiment is to adjust all adjustment parameters to the third gear. This is because when using the massager, the user usually does not directly increase from the lowest gear to the highest gear, or directly reduce from the highest gear to the lowest gear. The gear raising and lowering must follow a certain degree of soothingness, and because the gears are not uniform, the massager is not easy to operate. The massager is limited to two gears at a time in the three situations of changing, increasing and decreasing. Therefore, the third gear is used as the preset massage plan to accurately obtain the gear increase and decrease requirements of the massager. For example, in a certain test, the test characteristics of the tester monitored included an HRV index of 28.4ms, a GRS change rate of 0.076μS / s, an EMG activation amplitude of 0.0598mV, and a temperature change slope of 0.02. At this time, the tester expected the amplitude to increase by 2 massage amplitudes, the frequency to increase by 1 massage frequency, the rhythm to increase by 1 massage rhythm, and the period to increase by 1 cycle.

[0093] Step S5: Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and analyze the adjustment correlation between different feedback characteristics and adjustment parameters based on the test characteristics and adjustment expectations. Step S5 includes the following sub-steps:

[0094] Step S501: Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and assign values ​​to the adjustment expectations;

[0095] Step S501 includes the following sub-steps:

[0096] Step S5011: Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model;

[0097] Step S5012, assigning a value to the desired amplitude. If the tester wishes to increase the massage amplitude by N1 steps, the desired amplitude is assigned to +N1. If the tester wishes to decrease the massage amplitude by N1 steps, the desired amplitude is assigned to -N1. If the tester wishes to maintain the existing massage amplitude, the desired amplitude is assigned to 0.

[0098] Step S5013, assigning a value to the desired frequency. If the tester wishes to increase the massage frequency by N2, the desired frequency is assigned to +N2; if the tester wishes to decrease the massage frequency by N2, the desired frequency is assigned to -N2; if the tester wishes to maintain the existing massage frequency, the desired frequency is assigned to 0.

[0099] Step S5014, assigning a value to the desired rhythm. If the tester wishes to increase the massage rhythm by N3 steps, the desired rhythm is assigned to +N3. If the tester wishes to decrease the massage rhythm by N3 steps, the desired rhythm is assigned to -N3. If the tester wishes to maintain the current massage rhythm, the desired rhythm is assigned to 0.

[0100] Step S5015, assigning a value to the desired period. If the tester wishes to increase the gear position by N4 massage cycles, the desired period is assigned to +N4. If the tester wishes to decrease the gear position by N4 massage cycles, the desired period is assigned to -N4. If the tester wishes to maintain the current gear position, the desired period is assigned to 0.

[0101] In a specific implementation, since the processes and principles of assigning values ​​to the expected amplitude, assigning values ​​to the expected frequency, assigning values ​​to the expected rhythm, and assigning values ​​to the expected period are all the same, this embodiment only takes the assignment of the expected amplitude as an example for explanation. Taking the expected amplitude in step S4 as an example, the tester expects to increase the massage amplitude by 2 gears, so N1 is 2. At this time, the expected amplitude is assigned to +2, that is, positive 2. The plus sign only corresponds to the minus sign in negative 2, indicating a positive value, rather than an addition.

[0102] Step S502 , analyzing the correlation validity based on the assigned test features and adjustment expectations;

[0103] Step S502 includes the following sub-steps:

[0104] Step S5021: Construct a two-dimensional coordinate system with the adjustment expectation as the Y-axis and the HRV index, GRS change rate, EMG activation amplitude, and temperature change slope as the X-axis. These are named heart rate correlation graph, electrical response correlation graph, electromyography correlation graph, and temperature correlation graph, respectively. The adjustment expectation is entered into the corresponding two-dimensional coordinate system according to the test characteristics.

[0105] See also Figures 2 to 3As shown, in step S5022, the coordinate points corresponding to the expected amplitude, expected frequency, expected rhythm, and expected period are named as amplitude coordinates, frequency coordinates, rhythm coordinates, and period coordinates, respectively, and polynomial regression is performed on the amplitude coordinates, frequency coordinates, rhythm coordinates, and period coordinates in the heart rate correlation graph, the electrical response correlation graph, the electromyography correlation graph, and the temperature correlation graph, respectively, to obtain amplitude correlation curves, frequency correlation curves, rhythm correlation curves, and period correlation curves, collectively referred to as correlation curves;

[0106] In a specific implementation, since the value range of the adjustment expectation after assignment is the same and the meaning is the same, the adjustment expectation can be uniformly used as the Y-axis, and the HRV index, GRS change rate, EMG activation amplitude and temperature change slope are respectively used as the X-axis to construct the heart rate correlation diagram, electrical response correlation diagram, electromyography correlation diagram and temperature correlation diagram. Since the analysis process and analysis principle of the heart rate correlation diagram, electrical response correlation diagram, electromyography correlation diagram and temperature correlation diagram are exactly the same in the subsequent analysis process, this embodiment only takes the analysis of the heart rate correlation diagram as an example to explain the subsequent analysis process; Figure 2 This is the heart rate correlation diagram, where the coordinate points of different colors represent the amplitude coordinate, frequency coordinate, rhythm coordinate and period coordinate respectively. The correlation curve is obtained by regression as follows: Figure 3 As shown;

[0107] Step S5023: Obtain the fitting metrics of the amplitude correlation curve, frequency correlation curve, rhythm correlation curve, and period correlation curve, i.e., R 2 , marked as R1, R2, R3 and R4 respectively;

[0108] Step S5024: Obtain the correlation threshold. In the heart rate correlation graph, if R1 is greater than or equal to the correlation threshold, then the massage amplitude is marked as effectively correlated with the HRV index. If R2 is greater than or equal to the correlation threshold, then the massage amplitude is marked as effectively correlated with the GRS change rate. If R3 is greater than or equal to the correlation threshold, then the massage amplitude is marked as effectively correlated with the EMG activation amplitude. If R4 is greater than or equal to the correlation threshold, then the massage amplitude is marked as effectively correlated with the temperature change slope.

[0109] Step S5025: In the frequency correlation graph, if R1 is greater than or equal to the relevant threshold, it is marked that the massage frequency is effectively correlated with the HRV index; if R2 is greater than or equal to the relevant threshold, it is marked that the massage frequency is effectively correlated with the GRS change rate; if R3 is greater than or equal to the relevant threshold, it is marked that the massage frequency is effectively correlated with the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, it is marked that the massage frequency is effectively correlated with the temperature change slope;

[0110] Step S5026: In the rhythm correlation graph, if R1 is greater than or equal to the relevant threshold, it is marked that the massage rhythm is effectively correlated with the HRV index; if R2 is greater than or equal to the relevant threshold, it is marked that the massage rhythm is effectively correlated with the GRS change rate; if R3 is greater than or equal to the relevant threshold, it is marked that the massage rhythm is effectively correlated with the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, it is marked that the massage rhythm is effectively correlated with the temperature change slope;

[0111] Step S5027: In the cycle correlation graph, if R1 is greater than or equal to the relevant threshold, then the marked cycle period is effectively correlated with the HRV index; if R2 is greater than or equal to the relevant threshold, then the marked cycle period is effectively correlated with the GRS change rate; if R3 is greater than or equal to the relevant threshold, then the marked cycle period is effectively correlated with the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, then the marked cycle period is effectively correlated with the temperature change slope;

[0112] In the specific implementation, we get Figure 3 The R1, R2, R3 and R4 in the data are 0.939, 0.1304, 0.1778 and 0.0166 respectively, and the correlation threshold is 0.9. The correlation threshold is obtained from a large number of correlation experiments in different fields. By selecting two data with obvious correlation recognized in society for analysis, it can be obtained from the analysis results that in different fields, as long as the data are recognized to be correlated, their R 2 Usually, they are above 0.9, so the correlation threshold is set to 0.9. By comparison, only R1 is greater than the correlation threshold, so the massage amplitude is only effectively correlated with the HRV index. Similarly, the massage frequency is effectively correlated with the EMG activation amplitude and the temperature change slope, the massage rhythm is effectively correlated with the GRS change rate and the HRV index, and the cycle period is effectively correlated with the HRV index and the EMG activation amplitude.

[0113] Step S503: grouping the test features and adjustment expectations based on the correlation validity to obtain correlation groups, and performing analysis based on the correlation groups to obtain adjustment correlation;

[0114] Step S503 includes the following sub-steps:

[0115] Step S5031, counting the test features that are effectively associated with the massage amplitude and grouping them into the same associated group, named amplitude grouping; counting the test features that are effectively associated with the massage frequency and grouping them into the same associated group, named frequency grouping; counting the test features that are effectively associated with the massage rhythm and grouping them into the same associated group, named rhythm grouping; counting the test features that are effectively associated with the cycle period and grouping them into the same associated group, named period grouping;

[0116] Step S5032: any associated group contains at most two types of test features. If there are more than two types, the two types of test features with the largest fitting metrics are retained;

[0117] Step S5033: When analyzing any associated group, mark it as a group to be analyzed. If there is only one type of test feature in the group to be analyzed, the function of the association curve corresponding to the group to be analyzed is the adjusted association;

[0118] In the specific implementation, taking the amplitude grouping as an example, there is only the test feature of the HRV index class in the amplitude grouping, that is, there is only one type of test feature, and it is directly obtained Figure 3 The function of the corresponding correlation curve in the equation is Y1=0.0001×X1. 2 -0.1057×X1+4.4992, where Y1 is the adjustment expectation and X1 is the HRV index;

[0119] Step S5034: If there are two types of test features in the group to be analyzed, mark the test features as the first feature and the second feature respectively, and mark the corresponding adjustment expectations as the expectation to be analyzed;

[0120] Step S5035: Establish a three-dimensional coordinate system with the first feature as the X-axis, the second feature as the Y-axis, and the expected value to be analyzed as the Z-axis. This is named the adjustment association coordinate system. Enter the test features in the group to be analyzed into the adjustment association coordinate system according to the corresponding adjustment expectations. The adjustment association coordinate system is the adjustment association.

[0121] In the specific implementation, adjusting the associated coordinate system is actually to Figure 3 The Y-axis is replaced with another test feature, and the adjustment expectation is transferred to the Z-axis dimension. Since the three-dimensional coordinate system is difficult to display on a two-dimensional plane, it will not be specifically displayed in this embodiment.

[0122] Step S6, monitoring biofeedback once every first adjustment period and extracting feedback features, and intelligently adjusting adjustment parameters of the massager based on the feedback features and adjustment correlation; Step S6 includes the following sub-steps:

[0123] Step S601: monitor biofeedback once every first adjustment period and extract feedback features, which are named real-time features; when performing adjustment analysis on any adjustment parameter, name it as a target parameter, and mark the real-time features that are effectively associated with the target parameter as target features;

[0124] Step S602: If the target feature only has one type of real-time feature, the target feature is substituted into the adjustment correlation corresponding to the target parameter to obtain the target adjustment value of the target parameter;

[0125] Step S603: If there are two types of real-time features in the target feature, a top view of the adjustment correlation corresponding to the target parameter is obtained, that is, only the X-axis and Y-axis of the adjustment correlation coordinate system are considered, and the Z-axis is ignored, to obtain a two-dimensional correlation diagram;

[0126] Step S604: Name the coordinate point in the two-dimensional association graph as the original point, enter the target feature into the two-dimensional association graph to obtain the target point, find the original point closest to the target point in the two-dimensional association graph, mark it as the adjustment point, obtain the value of the adjustment point on the Z axis of the adjustment association coordinate system, and thus obtain the target adjustment value. Adjust the target parameter, and the adjusted value is the target adjustment value.

[0127] In specific implementation, the first adjustment period is set by the manufacturer. In this embodiment, the first adjustment period is set to 30s. For example, in a certain use, the user's HRV index is monitored to be 28.4ms, the GRS change rate is 0.076μS / s, the EMG activation amplitude is 0.0572mV, and the temperature change slope is 0.02. Substitute X1=28.4 into Y1=0.0001×X1 2 -0.1057×X1+4.4992, the target adjustment value of the massage amplitude is 2, and the calculation result is retained as an integer, so the massage amplitude gear of the massager is increased by 2; at this time, the HRV index is 28.4ms, and the EMG activation amplitude is 0.0572mV, which jointly affect the cycle period. In the two-dimensional correlation diagram of the cycle period, the X-axis is the HRV index and the Y-axis is the EMG activation amplitude. Therefore, the coordinates (28.4, 0.0572) are entered into the two-dimensional correlation diagram, and the coordinates α and (28.4, 0.0572) are found to be closest. Therefore, the value of the coordinate α on the Z-axis of the adjustment association coordinate system is read, and the target adjustment value is 1, so the gear of the cycle period is increased by 1.

[0128] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of the structure of an electronic device is provided. The electronic device may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions from the memory. When the computer-readable instructions are executed by the processor, the steps of a method for establishing an intelligent adjustment model for a male massager based on biofeedback monitoring are executed to implement the following functions: installing a multimodal sensor in the massager; monitoring the user's biofeedback in real time to obtain feedback data; extracting feedback features from different feedback data; extracting the tester's feedback features as test features and recording the tester's adjustment expectations for adjustment parameters; analyzing the adjustment correlations between different feedback features and adjustment parameters based on the test features and adjustment expectations; and intelligently adjusting the massager's adjustment parameters based on the feedback features and adjustment correlations.

[0129] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0130] Example 3. The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring provided by the above methods, the method including: installing a multimodal sensor in the massager; monitoring the user's biofeedback in real time to obtain feedback data; extracting feedback features of different feedback data; extracting the tester's feedback features as test features, and recording the tester's adjustment expectations for the adjustment parameters; analyzing the adjustment correlation between different feedback features and adjustment parameters based on the test features and adjustment expectations; and intelligently adjusting the adjustment parameters of the massager based on the feedback features and adjustment correlation.

[0131] Example 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring are executed to achieve the following functions: installing a multimodal sensor in the massager; monitoring the user's biofeedback in real time to obtain feedback data; extracting feedback features of different feedback data; extracting the tester's feedback features as test features, and recording the tester's adjustment expectations for the adjustment parameters; analyzing the adjustment correlation between different feedback features and adjustment parameters based on the test features and adjustment expectations; and intelligently adjusting the adjustment parameters of the massager based on the feedback features and adjustment correlation.

[0132] Through the description of the above embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the essence of the above technical solutions or the portion that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for enabling a computer device (such as a personal computer, server, or network device) to execute the methods described in various embodiments or certain portions of the embodiments.

[0133] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of systems, modules and units can be electrical, mechanical or other forms.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for establishing an intelligent adjustment model for a male massager based on biofeedback monitoring, characterized in that: The steps include: Installing a multimodal sensor in the massager; The user's biofeedback is monitored in real time through multimodal sensors to obtain feedback data; Construct an intelligent adjustment model for adjustment parameters, perform feature extraction on feedback data, and extract feedback features of different feedback data; A first number of testers are selected to test the massager, and feedback features of the testers are extracted as test features, and adjustment expectations of the testers on adjustment parameters are recorded; Entering the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and analyzing the adjustment correlation between different feedback characteristics and adjustment parameters based on the test characteristics and adjustment expectations; The biofeedback is monitored once every first adjustment cycle and feedback features are extracted, and the adjustment parameters of the massager are intelligently adjusted based on the feedback features and the adjustment correlation.

2. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 1, wherein: Installing a multimodal sensor in a massager includes the following sub-steps: The multimodal sensor includes a PPG photoelectric volume pulse wave sensor, an electrode patch sensor, a surface electromyography sensor, and a thermal sensor; The PPG photoplethysmography sensor and the electrode patch sensor are arranged at the gripping part of the massager to monitor the palm of the user; The surface electromyography sensor and the thermal sensor are both arranged inside the massager to monitor the contact area.

3. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 2, wherein: Real-time monitoring of the user's biofeedback through multimodal sensors to obtain feedback data includes the following sub-steps: The PPG photoplethysmography sensor, electrode patch sensor, surface electromyography sensor, and thermal sensor are used to monitor the user's heart rate, galvanic skin response, electromyography of the contact area, and skin temperature of the contact area, respectively; The heart rate, galvanic skin response, electromyography and skin temperature are feedback data.

4. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 3, wherein: Constructing an intelligent adjustment model for adjustment parameters and extracting features from feedback data to obtain feedback features of different feedback data includes the following sub-steps: Constructing an intelligent adjustment model for adjustment parameters, and inputting feedback data into the intelligent adjustment model for adjustment parameters, wherein the intelligent adjustment model for adjustment parameters is equipped with an HRV algorithm, a GSR change rate algorithm, an EMG activation amplitude algorithm, and a temperature change trend algorithm; Analyze the heart rate through the HRV algorithm to obtain the HRV index; The skin electrical response is analyzed using the GSR change rate algorithm to obtain the GRS change rate; The EMG activation amplitude is obtained by analyzing the electromyography using the EMG activation amplitude algorithm; The skin temperature is analyzed by the temperature change trend algorithm to obtain the temperature change slope; The HRV index, GRS change rate, EMG activation amplitude and temperature change slope are collectively referred to as feedback features.

5. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 4, characterized in that: Selecting a first number of testers to test the massager, extracting the testers' feedback features as test features, and recording the testers' adjustment expectations for adjustment parameters includes the following sub-steps: A first number of testers are randomly selected to test the massager; During the test, the massager massages the tester using a preset massage plan for the duration of the first test; Record the feedback characteristics of the testers monitored for the last time before the end of the test, and name them as test characteristics; The adjustment parameters include massage amplitude, massage frequency, massage rhythm and cycle period; After the test, record the tester's adjustment expectations for the adjustment parameters, including the expected amplitude, expected frequency, expected rhythm and expected period.

6. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 5, characterized in that: Entering the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and analyzing the adjustment correlation between different feedback characteristics and adjustment parameters based on the test characteristics and adjustment expectations includes the following sub-steps: Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model, and assign values ​​to the adjustment expectations; Analyze the association validity based on the assigned test characteristics and adjustment expectations; The test characteristics and adjustment expectations are grouped based on the association validity to obtain the association grouping, and the adjustment correlation is obtained based on the analysis of the association grouping.

7. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 6, characterized in that: Entering the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model and assigning values ​​to the adjustment expectations includes the following sub-steps: Enter the test characteristics and adjustment expectations into the adjustment parameter intelligent adjustment model; Assign a value to the desired amplitude. If the tester wishes to increase the massage amplitude by N1 steps, the desired amplitude is assigned to +N1. If the tester wishes to decrease the massage amplitude by N1 steps, the desired amplitude is assigned to -N1. If the tester wishes to maintain the current massage amplitude, the desired amplitude is assigned to 0. Assign a value to the desired frequency. If the tester wishes to increase the massage frequency by N2, the desired frequency is assigned to +N2. If the tester wishes to decrease the massage frequency by N2, the desired frequency is assigned to -N2. If the tester wishes to maintain the existing massage frequency, the desired frequency is assigned to 0. Assign a value to the desired rhythm. If the tester wishes to increase the massage rhythm by N3 steps, the desired rhythm is assigned to +N3. If the tester wishes to decrease the massage rhythm by N3 steps, the desired rhythm is assigned to -N3. If the tester wishes to maintain the current massage rhythm, the desired rhythm is assigned to 0. Assign a value to the expected cycle, where if the tester wants to increase the gear of N4 massage cycles, the expected cycle is assigned to +N4; if the tester wants to reduce the gear of N4 massage cycles, the expected cycle is assigned to -N4; if the tester wants to maintain the gear of the existing massage cycle, the expected cycle is assigned to 0.

8. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 7, characterized in that: Analyzing the association validity based on the assigned test features and adjusted expectations includes the following sub-steps: A two-dimensional coordinate system was constructed with adjustment expectation as the Y-axis and HRV index, GRS change rate, EMG activation amplitude, and temperature change slope as the X-axis. These were named heart rate correlation diagram, electrical response correlation diagram, electromyography correlation diagram, and temperature correlation diagram, respectively. Adjustment expectation was entered into the corresponding two-dimensional coordinate system according to the test characteristics. The coordinate points corresponding to the expected amplitude, expected frequency, expected rhythm and expected period are named amplitude coordinates, frequency coordinates, rhythm coordinates and period coordinates respectively, and polynomial regression is performed on the amplitude coordinates, frequency coordinates, rhythm coordinates and period coordinates in the heart rate correlation graph, electrical response correlation graph, electromyography correlation graph and temperature correlation graph respectively to obtain amplitude correlation curves, frequency correlation curves, rhythm correlation curves and period correlation curves, which are collectively referred to as correlation curves; Get the fitting metrics of the amplitude correlation curve, frequency correlation curve, rhythm correlation curve, and period correlation curve, i.e. R 2 , marked as R1, R2, R3 and R4 respectively; Obtain the relevant threshold. In the heart rate correlation graph, if R1 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the HRV index. If R2 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the GRS change rate. If R3 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the EMG activation amplitude. If R4 is greater than or equal to the relevant threshold, then the marked massage amplitude is effectively correlated with the temperature change slope. In the frequency correlation diagram, if R1 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the HRV index; if R2 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the GRS change rate; if R3 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, there is a valid correlation between the marked massage frequency and the temperature change slope; In the rhythm correlation diagram, if R1 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the HRV index; if R2 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the GRS change rate; if R3 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, the marked massage rhythm is effectively correlated with the temperature change slope; In the cycle correlation diagram, if R1 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the HRV index; if R2 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the GRS change rate; if R3 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the EMG activation amplitude; if R4 is greater than or equal to the relevant threshold, there is a valid correlation between the marked cycle period and the temperature change slope.

9. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 8, characterized in that: The test features and adjustment expectations are grouped based on the association validity to obtain the association groups, and the adjustment correlation is obtained by analysis based on the association groups. The sub-steps include: The test features that are effectively correlated with the massage amplitude are counted and summarized into the same correlation group, named amplitude group; the test features that are effectively correlated with the massage frequency are counted and summarized into the same correlation group, named frequency group; the test features that are effectively correlated with the massage rhythm are counted and summarized into the same correlation group, named rhythm group; the test features that are effectively correlated with the cycle period are counted and summarized into the same correlation group, named period group; Any associated group contains at most two types of test features. If there are more than two types, the two types of test features with the largest fitting measure are retained; When analyzing any associated group, it is marked as a group to be analyzed. If there is only one type of test feature in the group to be analyzed, the function of the association curve corresponding to the group to be analyzed is the adjusted correlation; If there are two types of test features in the group to be analyzed, the test features are marked as the first feature and the second feature respectively, and the corresponding adjustment expectations are marked as the expectation to be analyzed; A three-dimensional coordinate system is established with the first feature as the X-axis, the second feature as the Y-axis, and the expectation to be analyzed as the Z-axis, and is named the adjustment association coordinate system. The test features in the group to be analyzed are entered into the adjustment association coordinate system according to the corresponding adjustment expectations. The adjustment association coordinate system is the adjustment correlation.

10. The method for establishing an intelligent adjustment model of a male massager based on biofeedback monitoring according to claim 9, characterized in that: Monitoring biofeedback once every first adjustment cycle and extracting feedback features, and intelligently adjusting adjustment parameters of the massager based on the feedback features and adjustment correlations include the following sub-steps: The biofeedback is monitored once every first adjustment cycle and feedback features are extracted and named as real-time features. When any adjustment parameter is adjusted and analyzed, it is named as a target parameter, and the real-time features that are effectively associated with the target parameter are marked as target features. If the target feature has only one type of real-time feature, the target feature is substituted into the adjustment correlation corresponding to the target parameter to obtain the target adjustment value of the target parameter; If there are two types of real-time features in the target feature, a top view of the adjustment correlation corresponding to the target parameter is obtained, that is, only the X-axis and Y-axis of the adjustment correlation coordinate system are considered, and the Z-axis is ignored to obtain a two-dimensional correlation diagram; Name the coordinate point in the two-dimensional association diagram as the original point, enter the target feature into the two-dimensional association diagram to obtain the target point, find the original point closest to the target point in the two-dimensional association diagram, mark it as the adjustment point, obtain the value of the adjustment point on the Z axis of the adjustment association coordinate system, and you can get the target adjustment value. Adjust the target parameter, and the adjusted value is the target adjustment value.

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