A mattress classification design method based on human characteristics

Abstract

The application discloses a bed mattress typing design method based on human characteristics, and relates to the technical field of electric digital data processing.The bed mattress typing design method based on human characteristics comprises the following steps: bed mattress pressure collection conformity monitoring, bed mattress pressure data transmission efficiency monitoring, bed mattress pressure data response accuracy monitoring, and bed mattress softness and hardness correlation model output feedback verification.The bed mattress pressure collection conformity is evaluated to determine whether to perform dynamic sampling optimization, the bed mattress pressure data transmission efficiency is evaluated to determine whether to perform bed mattress pressure data transmission efficiency grading optimization, the bed mattress pressure data response accuracy is analyzed, and finally, the bed mattress softness and hardness typing is verified, so that the matching accuracy of bed mattress pressure data and the actual support area of human body is improved, and the problem of low matching accuracy of the support area in the process of adjusting the bed mattress typing caused by the delay of bed mattress pressure data collection in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, and in particular to a mattress classification design method based on human body characteristics. Background Technology

[0002] The mattress classification design method based on human characteristics first collects data on the user's height, weight, body shape, pressure distribution during sleep, body temperature changes, and sleeping posture habits through sensing devices such as pressure sensors, temperature sensors, humidity sensors deployed on the mattress surface, and body posture monitoring cameras placed at the bedside. This data is then used to calculate and classify the user's BMI (Body Mass Index). The collected mattress data is transmitted wirelessly to the mattress control center. The control center uses big data analysis combined with a correlation model established from historical mattress users to classify the appropriate support area types and firmness levels based on BMI classification, gender, sleeping posture preferences, and other characteristics. When the user uses the mattress, the control center, based on the analysis results, dynamically adjusts the support strength and height of each area in real time, using adjustable airbags and electric springs within the mattress, to achieve precise, personalized mattress support.

[0003] In the process of collecting mattress pressure data, existing technologies utilize numerous high-precision pressure sensors deployed within the mattress to collect real-time data on mattress pressure generated by the human body in contact with the mattress, transmitting this data to a data processing center. The data processing module employs specific algorithms to analyze the mattress pressure data and accurately identify areas of abnormal pressure. When an abnormal pressure is detected, the system automatically sends instructions to the mattress's air bladders, electrically adjustable springs, and other adjustment mechanisms. By inflating, deflating, or adjusting the firmness of the electric springs, the pressure is redistributed to achieve a more even distribution. Simultaneously, the system features remote communication capabilities, enabling real-time transmission of mattress pressure data and mattress adjustment status to the user terminal, supporting remote monitoring and early warning.

[0004] For example, Chinese invention patent CN117313179A discloses a mattress selection method, terminal, and readable storage medium based on user information. This method involves: acquiring user spinal feature data and user characteristic data covering gender, age, height, body type, sleeping posture, etc., and dynamically calculating the mattress firmness suitable for the user. Specifically, each time new feature data is acquired, the firmness is optimized based on the previously generated firmness. If decisive factors such as spinal damage are encountered, the final firmness is directly generated. Furthermore, it can combine the user's habitual sleeping position to generate regional mattress firmness combinations, and then match the corresponding mattress sub-components based on the determined firmness to complete mattress customization.

[0005] For example, Chinese invention patent CN112733038A discloses a method for recommending mattress firmness levels and pillow heights based on human body shape and posture. The method includes: scanning the human body contour using optical mapping equipment to generate a three-dimensional model, collecting human body size data, age, gender and other information, and then calculating the mattress firmness level suitable for the user according to a specific formula. It can also recommend a suitable pillow height based on this, and can also adjust the recommendation results based on customer feedback.

[0006] The above-mentioned technology has at least the following technical problems:

[0007] When bedridden patients experience discomfort due to pressure on local tissues and frequently turn over, the dynamic changes in sleeping posture caused by these frequent shifts in mattress pressure may not be captured in time by pressure sensors. This results in inaccurate dynamic mattress pressure data, which is severely mismatched with the actual pressure distribution. Consequently, during the transmission of dynamic mattress pressure data, the transmission protocol cannot accurately extract valid information, leading to a backlog in the mattress pressure data queue. This further exacerbates the data transmission delay, causing the mattress control center to adjust mattress firmness based on the monitored pressure data. Because the delayed mattress pressure data cannot reflect the current real-time body pressure distribution, the preset correction data called by the control center does not match the actual needs. Ultimately, this leads to deviations in airbag inflation or electric spring height adjustment, resulting in low accuracy of support area matching during mattress adjustment due to the delay in mattress pressure data acquisition. Summary of the Invention

[0008] To address the technical problem of low accuracy in support area matching during mattress shaping due to delays in mattress pressure data acquisition in existing technologies, this invention provides a mattress shaping design method based on human characteristics. The technical solution is as follows: During mattress pressure data acquisition, a mattress pressure acquisition qualification assessment is performed, and based on the assessment results, it is determined whether dynamic sampling optimization is needed. Dynamic sampling optimization refers to improving the accuracy of capturing dynamic pressure data during frequent turning over by adjusting the pressure data sampling period. After the mattress pressure acquisition qualification assessment is passed, a mattress pressure data transmission efficiency assessment is performed, and based on the assessment results, it is determined whether graded optimization measures for mattress pressure data transmission efficiency are needed. Graded optimization measures for mattress pressure data transmission efficiency refer to improving overall transmission efficiency through graded optimization of mattress pressure data transmission, and also to improving the accuracy of capturing dynamic pressure data during frequent turning over. The system optimizes the transmission efficiency to improve the efficiency and stability of mattress pressure data transmission. Once the mattress pressure data transmission efficiency assessment is satisfactory, an accuracy analysis of the mattress pressure data response is performed. Based on the accuracy deviation assessment results, it is determined whether mattress pressure data response accuracy optimization is necessary. Optimization involves correcting the mattress airbag inflation volume and the height of the mattress electric springs to improve the matching degree between the mattress pressure data and the pressure distribution on the human body contact surface, and the fit between the mattress pressure data and the natural shape of the human spine. Once the mattress pressure data response accuracy analysis is satisfactory, mattress firmness classification and mattress firmness correlation model output feedback verification are performed.

[0009] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0010] 1. By conducting a mattress pressure data acquisition qualification assessment and determining whether dynamic sampling optimization is needed based on the assessment results, it helps ensure the integrity and timeliness of dynamic pressure data in scenarios with frequent turning over, providing high-quality raw data support for subsequent data processing. Once the mattress pressure data acquisition qualification assessment is passed, a mattress pressure data transmission efficiency assessment is conducted, and the results determine whether graded optimization measures for mattress pressure data transmission efficiency are needed. This helps reduce delays and losses during pressure data transmission, ensuring that critical pressure data is transmitted efficiently and stably to the mattress control center. Once the mattress pressure data transmission efficiency assessment is passed, mattress pressure data response... Accuracy analysis and assessment of mattress pressure data response accuracy deviation results determine whether mattress pressure data response accuracy optimization is needed. This helps to make mattress support adjustment more closely match the actual pressure needs of the human body, improving support precision. Once the mattress pressure data response accuracy analysis is qualified, mattress firmness classification and mattress firmness correlation model output feedback verification are performed. This helps to continuously optimize the mattress firmness correlation model, improve the matching accuracy of mattress firmness classification with the characteristics of different groups of people, and improve the accuracy of support area matching during mattress classification adjustment. This effectively solves the technical problem of low support area matching accuracy during mattress classification adjustment due to mattress pressure data acquisition delay in existing technologies.

[0011] 2. By quantifying the proportion of mattress pressure parameters to standard mattress pressure parameters, a mattress pressure data matching proportion parameter is obtained. Based on the weighted fusion calculation method of the mattress pressure data matching proportion parameter and the mattress pressure feature weighting parameter, a mattress pressure data transmission efficiency evaluation index is obtained. This helps to comprehensively consider the synergistic impact of mattress pressure parameters on the quality of mattress pressure data transmission, and achieve accurate quantitative evaluation of transmission efficiency. Based on the mattress pressure data transmission efficiency evaluation index, it is possible to determine whether to take mattress pressure data transmission efficiency classification optimization measures. This helps to dynamically adjust the transmission strategy to reduce transmission delay and data deviation, thereby achieving efficient, stable transmission and accurate evaluation of mattress pressure data in dynamic scenarios.

[0012] 3. By quantifying the feedback deviation values ​​of the mattress electric springs and the mattress airbags, an evaluation index for the accuracy deviation of mattress pressure data response is obtained. This helps to accurately identify the distribution of response deviations of the mattress adjustment components to control commands, and to capture in real time the degree of deviation between the actual feedback of the electric spring height and the airbag inflation volume and the preset value. Based on the mutual influence mechanism of the feedback deviations of the mattress electric springs and airbags, the correlation effect between response deviation and mattress adjustment accuracy can be analyzed, which helps to accurately assess the reliability of mattress pressure data response. This achieves accurate feedback and reliability assurance of mattress adjustment response data in dynamic scenarios. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a flowchart summarizing a mattress classification design method based on human body characteristics provided in an embodiment of the present invention;

[0015] Figure 2 This is a flowchart of a mattress classification design method based on human body characteristics provided in an embodiment of the present invention;

[0016] Figure 3 This is a logic diagram for the graded optimization of mattress pressure data transmission efficiency in a mattress classification design method based on human body characteristics, provided by an embodiment of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] like Figure 1 The diagram shown is a general overview flowchart of a mattress classification design method based on human body characteristics provided by an embodiment of the present invention. Figure 1It is known that: A mattress pressure data collection qualification assessment is conducted, and a mattress pressure data collection qualification assessment score is obtained. It is then determined whether the monitored mattress pressure data collection qualification assessment score is lower than the preset mattress pressure data monitoring qualification threshold. If so, a mattress pressure data collection qualification prompt is sent; otherwise, dynamic sampling optimization is performed. Once the mattress pressure data collection qualification assessment is passed, a mattress pressure data transmission efficiency assessment is conducted, and a mattress pressure data transmission efficiency assessment index is obtained. It is then determined whether the mattress pressure data transmission efficiency assessment index is within the preset mattress pressure data transmission efficiency safety threshold range. If so, a mattress pressure data response accuracy analysis is performed; otherwise, graded optimization measures for mattress pressure data transmission efficiency are adopted. The graded optimization measures for mattress pressure data transmission efficiency represent the sequence... The process involves optimizing mattress pressure data transmission by grading and improving the efficiency of mattress pressure data response. Once the efficiency of mattress pressure data transmission is deemed satisfactory, the accuracy of mattress pressure data response is analyzed, and an evaluation index for mattress pressure data response accuracy deviation is obtained. It is then determined whether the evaluation index for mattress pressure data response accuracy deviation is within the preset mattress pressure data accuracy threshold range. If it is, mattress firmness is categorized; otherwise, mattress pressure data response accuracy is optimized. This optimization includes correcting the inflation volume of mattress airbags and the height of the mattress's electric springs. After the mattress pressure data response accuracy optimization is completed, the model outputting the correlation between population characteristics and mattress firmness is validated based on the obtained actual matching score of mattress firmness.

[0019] like Figure 2 The diagram shown is a flowchart of a mattress classification design method based on human body characteristics provided in an embodiment of the present invention. Figure 2 It can be known that:

[0020] First, mattress pressure data collection compliance monitoring: During the mattress pressure data collection process, a mattress pressure data collection compliance assessment is conducted, and based on the assessment results, it is determined whether dynamic sampling optimization is needed. Dynamic sampling optimization means adjusting the pressure data sampling period to improve the accuracy of capturing dynamic pressure data when frequently turning over. Through mattress pressure data collection compliance monitoring, it is helpful to promptly identify data loss or deviation caused by frequent turning over during the pressure collection process, ensuring that the collected dynamic pressure data can truly reflect the actual contact status between different people and the mattress.

[0021] Next, mattress pressure data transmission efficiency monitoring: After the mattress pressure acquisition and evaluation are deemed satisfactory, a mattress pressure data transmission efficiency assessment is conducted. Based on the assessment results, it is determined whether graded optimization measures for mattress pressure data transmission efficiency are necessary. These graded optimization measures aim to improve overall transmission efficiency through graded optimization of mattress pressure data transmission, and to improve the efficiency and stability of mattress pressure data transmission through optimization of mattress pressure data response transmission efficiency. Monitoring mattress pressure data transmission efficiency helps identify delays or data distortion issues during transmission, ensuring that critical pressure data is transmitted to the mattress control center preferentially and accurately, avoiding adjustment lags caused by transmission problems.

[0022] Secondly, mattress pressure data response accuracy monitoring: After the mattress pressure data transmission efficiency assessment is qualified, mattress pressure data response accuracy analysis is performed, and the mattress pressure data response accuracy deviation assessment results determine whether mattress pressure data response accuracy optimization is needed. Mattress pressure data response accuracy optimization means improving the matching degree between mattress pressure data and human body contact surface pressure distribution and the fit between mattress pressure data and the natural shape of the human spine by correcting the mattress airbag inflation volume and the mattress electric spring height. Through mattress pressure data response accuracy monitoring, it is helpful to correct the adjustment deviation of airbag inflation volume and spring height in a timely manner, ensuring that the support strength and height of each area of ​​the mattress can accurately adapt to the real-time changes in human body position.

[0023] Finally, the mattress firmness correlation model output feedback verification: After the mattress pressure data response accuracy analysis is qualified, the mattress firmness classification and mattress firmness correlation model output feedback verification are carried out; through the mattress firmness correlation model output feedback verification, it is helpful to ensure that the output mattress firmness classification can be highly matched with the body characteristics of different people, and improve the accuracy of support area matching during the mattress classification adjustment process.

[0024] It should be noted that before designing the mattress classification design method based on human characteristics provided in this application, a database storing various set data is established. The database includes, but is not limited to, standard mattress pressure data transmission time deviation value, standard key pressure data deviation value, standard mattress pressure acquisition qualification assessment score, standard mattress pressure acquisition integrity interference value, preset mattress pressure data transmission efficiency safety threshold range, preset mattress pressure data monitoring qualification threshold, etc., among which various preset values ​​are directly set by technical personnel.

[0025] In this embodiment, the mattress pressure data acquisition qualification monitoring, mattress pressure data transmission efficiency monitoring, mattress pressure data response accuracy monitoring, and mattress firmness correlation model output feedback verification work together and influence each other. Mattress pressure data acquisition qualification monitoring provides a high-quality raw data foundation for subsequent mattress pressure data transmission efficiency monitoring. If the acquired data is unqualified, dynamic sampling optimization is performed. The results of mattress pressure data transmission efficiency monitoring affect the accuracy of mattress pressure data response accuracy monitoring. Transmission delay or distortion will lead to deviations in response adjustment. Mattress pressure data response accuracy monitoring provides data support for the actual adjustment effect of the mattress firmness correlation model output feedback verification. Its deviation will affect the direction and amplitude of the mattress firmness correlation model calibration, forming a closed loop of dynamic adjustment. This ensures accurate capture and efficient transmission of dynamic pressure data, achieves precise matching of mattress support adjustment with human needs, and improves the adaptation accuracy of firmness classification through continuous feedback optimization. Overall, it improves the personalized support capability of the mattress for different groups of people and effectively solves the problem of low support area matching accuracy during mattress classification adjustment due to mattress pressure data acquisition delay in the prior art.

[0026] Furthermore, the specific process of mattress pressure data acquisition qualification assessment is as follows: The mattress pressure data acquisition qualification assessment score represents the number of times the pressure value change collected by the pressure sensor exceeds the preset mattress pressure data mutation threshold within a preset monitoring period. The preset mattress pressure data mutation threshold is represented by the average value of instantaneous pressure value changes over historical time periods. The mattress pressure data acquisition qualification assessment score reflects the degree of interference to the pressure sensor's response speed under frequent turning movements. It is determined whether the monitored mattress pressure data acquisition qualification assessment score is less than the preset mattress pressure data monitoring qualification threshold, which is represented by the average value of mattress pressure data acquisition qualification assessment scores over historical time periods. If so, a mattress pressure data acquisition qualification prompt is sent; otherwise, dynamic sampling optimization is performed. By conducting mattress pressure data acquisition qualification assessment, it is helpful to quantitatively evaluate the response sensitivity of the pressure sensor in frequent turning scenarios, promptly identify the sensor's lag in capturing pressure mutation signals, and ensure that the pressure data acquisition process can adapt to the dynamic positional changes of different groups of people.

[0027] In this embodiment, by monitoring the mattress pressure acquisition qualification assessment results, it is helpful to accurately measure the degree of interference in the response of the pressure sensor when frequently turning over. Combined with the preset mattress pressure data monitoring qualification threshold, dynamic sampling optimization or sending a mattress pressure acquisition qualification prompt can be flexibly triggered. This ensures that the pressure data acquisition can keenly capture the pressure changes under the dynamic body position changes of different groups of people, and avoids unnecessary waste of sampling resources. It lays a reliable data foundation for the subsequent transmission, response adjustment and classification optimization of mattress pressure data, and improves the adaptability of the entire mattress classification to dynamic scenarios.

[0028] Furthermore, the specific process of dynamic sampling optimization is as follows: The mattress pressure collection pass assessment score and the duration of mattress pressure data changes are input into the database. The corresponding mattress pressure data sampling period adjustment value is retrieved from the database. The database contains a mapping set reflecting the mapping relationship between different combinations of mattress pressure collection pass assessment scores and mattress pressure data change durations, and the corresponding mattress pressure data sampling period adjustment value. It is determined whether the mattress pressure data sampling period adjustment value exceeds a preset mattress pressure data sampling period adjustment threshold, which is set by preset personnel. If so, an abnormal sampling period adjustment alarm is sent; otherwise, the mattress pressure data sampling period adjustment value is marked as a qualified mattress pressure data sampling period adjustment value. The qualified mattress pressure... The adjustment range corresponding to the force data sampling period adjustment value gradually reduces the pressure data sampling period. Specifically, gradually reducing the pressure data sampling period by the adjustment range corresponding to the qualified mattress pressure data sampling period adjustment value helps to accurately adapt to the dynamic changes in mattress pressure, improving the timeliness and accuracy of pressure data sampling. After completing dynamic sampling optimization, mattress pressure data for adjacent preset monitoring time periods is collected, and the corresponding mattress pressure collection qualification assessment score is obtained. If the mattress pressure collection qualification assessment score is still not less than the preset mattress pressure data monitoring qualification threshold, a sampling period adjustment failure alarm is triggered. Otherwise, the collected mattress pressure data is transmitted to the mattress control center for mattress pressure data transmission efficiency evaluation. The preset monitoring time period refers to the preset time period corresponding to the dynamic sampling optimization process.

[0029] In this embodiment, by optimizing dynamic sampling and precisely adjusting the pressure data sampling period, it is ensured that the pressure data can capture pressure fluctuations under dynamic changes in body position of different groups in a timely and accurate manner, and provides high-quality raw data support for subsequent transmission. This enhances the adaptability of the mattress pressure data acquisition system to various dynamic scenarios and improves the stability and reliability of the system operation.

[0030] Furthermore, the specific process for evaluating the effectiveness of mattress pressure data transmission is as follows: First, the mattress pressure data matching ratio parameter is obtained by quantifying the ratio of mattress pressure parameters and standard mattress pressure parameters retrieved from the database. The standard mattress pressure parameter is obtained by averaging the mattress pressure parameters over a historical period. The ratio quantification means performing a ratio calculation.

[0031] Specifically, the expression for the mattress pressure data transmission time deviation rate is as follows: , This represents the mattress pressure data transmission time deviation rate during the Hth preset analysis pressure transmission time period. This represents the mattress pressure data transmission time deviation value during the Hth preset analysis pressure transmission time period. The standard mattress pressure data transmission time deviation value is represented by the average time it takes for mattress pressure data to be transmitted to the control center within a preset pressure transmission period, monitored by a high-precision timestamp sensor. The difference between this average and the standard mattress pressure data transmission time deviation value is used as the mattress pressure data transmission time deviation value. The units for both the mattress pressure data transmission time deviation value and the standard mattress pressure data transmission time deviation value are seconds.

[0032] Specifically, the expression for the key pressure data deviation rate is as follows: , This represents the deviation rate of key pressure data during the Hth preset analysis pressure transmission time period. This represents the key pressure data deviation value for the Hth preset analysis pressure transmission time period. The standard critical pressure data deviation value is the average pressure of a preset mattress area monitored by the pressure sensor within the preset analysis pressure transmission time period. Both the critical pressure data deviation value and the standard critical pressure data deviation value are in Pascals.

[0033] Specifically, the expression for the interference assessment rate of qualified mattress pressure collection is as follows: , This represents the qualified mattress pressure acquisition interference evaluation rate during the Hth preset analysis pressure transmission time period. This represents the qualified mattress pressure acquisition assessment score for the Hth preset analysis pressure transmission time period. The qualified mattress pressure collection assessment score represents the score of mattresses whose pressure data is less than the preset qualified mattress pressure data monitoring threshold. c represents a preset constant, which is set by preset personnel to avoid interference from qualified mattress pressure collection. The denominator of the assessment rate is meaningless.

[0034] Specifically, the expression for the mattress pressure acquisition integrity interference rate is as follows: , This represents the mattress pressure acquisition integrity interference rate during the Hth preset analysis pressure transmission time period. This represents the mattress pressure acquisition integrity interference value during the Hth preset analysis pressure transmission time period. The standard mattress pressure acquisition integrity interference value is represented by the ratio of the preset total number of mattress pressure data points to the preset analysis pressure transmission time period, which is used as the mattress pressure acquisition integrity interference value.

[0035] Secondly, the mattress pressure data matching ratio parameter and the mattress pressure characteristic weighting parameter are weighted and fused to obtain the mattress pressure data transmission efficiency evaluation index.

[0036] The mattress pressure data transmission efficiency evaluation index was obtained through the following methods:

[0037] ;

[0038] In the formula, The index represents the performance evaluation index of mattress pressure data transmission in the Hth preset analysis time period, where H=1,2,...,G, H represents the number of the preset analysis time period, G is the total number of preset analysis time periods, w1 represents the weight of mattress pressure data transmission delay, w2 represents the weight of key pressure data deviation value, w3 represents the weight of qualified mattress pressure collection evaluation score, and w4 represents the weight of mattress pressure collection integrity interference value.

[0039] The mattress pressure parameters include mattress pressure data transmission time deviation, key pressure data deviation, qualified mattress pressure acquisition assessment score, and mattress pressure acquisition integrity interference value. Standard mattress pressure parameters include standard mattress pressure data transmission time deviation, standard key pressure data deviation, standard mattress pressure acquisition assessment score, and standard mattress pressure acquisition integrity interference value. The standard mattress pressure data transmission time deviation is represented by the average of historical mattress pressure data transmission time deviations, the standard key pressure data deviation is represented by the average of historical key pressure data deviations, the standard mattress pressure acquisition assessment score is represented by the average of qualified mattress pressure acquisition assessment scores, and the standard mattress pressure acquisition integrity interference value is represented by the average of historical mattress pressure acquisition integrity interference values. The mattress pressure data matching ratio parameters include mattress pressure data transmission time deviation rate, key pressure data deviation rate, qualified mattress pressure acquisition interference assessment rate, and mattress pressure acquisition integrity interference rate. The preset analysis time period represents the preset time period for evaluating the effectiveness of mattress pressure data transmission. The mattress pressure data transmission effectiveness evaluation index reflects the degree of influence of mattress pressure parameters on the qualification of mattress pressure data transmission.

[0040] Determine whether the mattress pressure data transmission efficiency evaluation index is within the preset mattress pressure data transmission efficiency safety threshold range. The preset mattress pressure data transmission efficiency safety threshold range is set in advance by preset personnel and includes both ends of the range. If it is, then perform mattress pressure data response accuracy analysis; otherwise, take mattress pressure data transmission efficiency grading optimization measures.

[0041] It should be added that the mattress pressure characteristic weighting parameters include the weight of mattress pressure data transmission delay, the weight of key pressure data deviation, the weight of qualified mattress pressure acquisition assessment score, and the weight of mattress pressure acquisition integrity interference value, which are used to reflect the degree of influence of each mattress pressure parameter on the mattress pressure data transmission efficiency.

[0042] In this embodiment of the application, there is a mapping group obtained from the database. The mapping group contains a mapping set and is preset by a professional technician. The mapping relationship in the mapping set can be in the form of one-to-one correspondence or many-to-one.

[0043] Specifically, a correspondence needs to be established between mattress pressure parameters and mattress pressure feature weighting parameters, and the weighting percentage should be represented by a numerical range of 0-1. When the real-time collected mattress pressure parameters are input, the corresponding mattress pressure feature weighting parameters can be obtained from the mapping group, thereby accurately quantifying the impact of mattress pressure parameters on the efficiency of mattress pressure data transmission, and effectively improving the accuracy and adaptability of transmission efficiency assessment.

[0044] In this embodiment, there is a close correlation between the mattress pressure data matching ratio parameters, and their synergistic effect is crucial for evaluating the effectiveness of mattress pressure data transmission. The mattress pressure data matching ratio parameter is calculated by comparing the mattress pressure parameters with standard mattress pressure parameters retrieved from the database. Specifically, it compares the mattress pressure data transmission time deviation value with the standard mattress pressure data transmission time deviation value, the key pressure data deviation value with the standard key pressure data deviation value, the standard qualified mattress pressure collection qualification assessment score with the sum of the qualified mattress pressure collection qualification assessment score and a preset constant, and the mattress pressure collection integrity interference value with the standard mattress pressure collection integrity interference value. This transforms the mattress pressure parameters into quantifiable proportional indicators. Specifically, a larger value for the mattress pressure data matching ratio parameter indicates a greater impact on the qualification of mattress pressure data transmission, leading to greater fluctuations in the qualification of mattress pressure data transmission. The coupling relationship between these parameters causes transmission deviations in different dimensions to influence each other. Through weighted fusion quantification, standardized mapping of multi-dimensional data is achieved, effectively avoiding the one-sidedness of single-parameter evaluation.

[0045] By analyzing the correlation between mattress pressure data and matching parameters, we can accurately capture the comprehensive impact of multiple parameters on the qualification of mattress pressure data collection. The mattress pressure data transmission time deviation rate and the critical pressure data deviation rate are generally positively correlated. A larger mattress pressure data transmission time deviation rate indicates more severe data transmission delays, leading to information loss of critical pressure data due to insufficient timeliness during transmission, thus increasing the critical pressure data deviation rate. Conversely, a lower qualified mattress pressure data collection score corresponds to a higher qualified mattress pressure data collection interference evaluation rate, indicating decreased data quality. Invalid data transmission consumes bandwidth, directly affecting the real-time performance of pressure data transmission, which may further increase the mattress pressure data transmission time deviation rate. When the mattress pressure data transmission time deviation rate increases, the real-time performance of pressure collection and the number of actual detected mattress pressure data points are affected, reducing data quality and indirectly increasing the qualified mattress pressure data collection interference evaluation rate and the mattress pressure data collection integrity interference rate, resulting in a reduction in the effective evaluation of qualified mattress pressure data. Correlation analysis between parameters helps quantify the qualification of mattress pressure data collection and improve the comprehensiveness of mattress pressure data quality assessment.

[0046] like Figure 3 The diagram shown is a logic diagram for the hierarchical optimization of mattress pressure data transmission efficiency in a mattress classification design method based on human body characteristics, provided by an embodiment of the present invention. Figure 3 It can be seen that the mattress pressure data transmission efficiency grading optimization measures involve sequentially performing mattress pressure data transmission efficiency grading optimization and mattress pressure data response transmission efficiency optimization. Specifically, the specific process of mattress pressure data transmission efficiency grading optimization is as follows: It is determined whether the mattress pressure data transmission efficiency evaluation index is not greater than the preset mattress pressure data transmission efficiency safety threshold and is greater than the maximum value of the preset mattress pressure data transmission efficiency safety threshold range. The preset mattress pressure data transmission efficiency safety threshold is represented by the average value of the mattress pressure data transmission efficiency evaluation index over a historical time period. If so, the corresponding mattress pressure data is marked as secondary mattress pressure transmission data, and mattress pressure data response transmission efficiency optimization is performed. Conversely, mattress pressure data greater than the preset mattress pressure data transmission efficiency safety threshold is marked as primary mattress pressure transmission data, and mattress pressure data response transmission efficiency optimization is performed. The mattress pressure data transmission efficiency safety threshold is not within the preset mattress pressure data transmission efficiency safety threshold range, and the mattress pressure data transmission efficiency safety threshold is greater than the maximum value of the preset mattress pressure data transmission efficiency safety threshold range. Furthermore, both primary and secondary mattress pressure transmission data are data that are not within the preset mattress pressure data transmission efficiency safety threshold range and are greater than the maximum value of the preset mattress pressure data transmission efficiency safety threshold range.

[0047] Specifically, the optimization process for mattress pressure data response transmission efficiency is as follows: When primary mattress pressure transmission data is detected, network flow optimization is performed, and after network flow optimization, the effectiveness of mattress pressure data transmission is verified. Network flow optimization involves dynamic planning of transmission paths and bandwidth resource allocation based on the minimum cost maximum flow algorithm. Network flow optimization prioritizes the real-time transmission of critical pressure data, reduces transmission latency, and improves the transmission stability of high-priority data. When secondary mattress pressure transmission data is detected, adaptive redundancy adjustment transmission optimization is performed, and after adaptive redundancy adjustment transmission optimization, the effectiveness of mattress pressure data transmission is verified. Adaptive redundancy adjustment transmission optimization is based on the fountain code coding algorithm, dynamically adjusting the data redundancy coding ratio according to the real-time network congestion status. Adaptive redundancy adjustment transmission optimization reduces redundancy to reduce data transmission volume when the network is smooth, and increases redundancy to reduce data transmission volume when the network fluctuates. To ensure data integrity, this approach saves transmission resources while improving the anti-interference capability and transmission reliability of low-priority data. It also verifies the effectiveness of mattress pressure data transmission, meaning that a performance verification index for mattress pressure data transmission is obtained in the next adjacent preset pressure transmission time period. If the performance verification index is not within the preset qualified mattress pressure data transmission verification safety threshold, a graded adjustment anomaly alarm is sent; otherwise, mattress pressure data accuracy analysis is performed. The mattress pressure data transmission performance verification index is represented by the difference between the performance evaluation index obtained at the end of the next adjacent preset pressure transmission time period and the performance evaluation index obtained at the end of the preset pressure transmission time period. This effectiveness verification helps to promptly identify fluctuations in mattress pressure data transmission performance after optimization measures are implemented, providing a basis for the dynamic adjustment of the mattress pressure data transmission performance grading standard and forming a closed-loop mechanism for continuous improvement.

[0048] In this embodiment, through graded optimization of mattress pressure data transmission, optimization of mattress pressure data response transmission efficiency, and verification of mattress pressure data transmission efficiency, a precise adaptation optimization strategy can be implemented for mattress pressure data with different transmission qualities. This strategy ensures real-time transmission delay of mattress pressure data during rolling over by optimizing network flow, avoids wasting pressure data transmission resources during stable positions by adjusting adaptive redundancy, and ensures that the mattress pressure data transmission efficiency evaluation index is always controlled within the preset mattress pressure data transmission efficiency safety threshold. This directly improves the integrity and accuracy of mattress pressure data from collection to transmission, providing reliable data for the subsequent dynamic adjustment of mattress electric spring height and airbag inflation, ultimately achieving a synergistic improvement in pressure data transmission efficiency and mattress adaptation adjustment accuracy.

[0049] Furthermore, the specific process of mattress pressure data response accuracy analysis is as follows: Obtain mattress pressure data response accuracy deviation evaluation indicators; these indicators include the mattress electric spring feedback deviation value and the mattress airbag feedback deviation value; the mattress electric spring feedback deviation value is represented by the difference between the average height of the mattress electric springs after receiving instructions from the mattress control center within a preset mattress response time period and the preset mattress spring height. The preset mattress spring height is represented by the average height of the electric springs over a historical time period, and the preset mattress response time period represents the preset time period corresponding to the mattress pressure data response accuracy analysis process; the mattress airbag feedback deviation value is obtained by... The difference between the average inflation value of the mattress airbags after the electric springs receive instructions from the mattress control center and the preset inflation value is represented by the time period. The preset inflation value is represented by the average inflation value of airbags over historical time periods. The mattress pressure data response accuracy deviation evaluation index is used to reflect the degree of deviation between the mattress and the instructions from the mattress control center. It is determined whether the mattress pressure data response accuracy deviation evaluation index is within the preset mattress pressure data accuracy threshold range. The preset mattress pressure data accuracy threshold range is set in advance by preset personnel and includes both ends of the range. If it is, the mattress firmness is classified; otherwise, the mattress pressure data response accuracy is optimized.

[0050] In this embodiment, by analyzing the accuracy of mattress pressure data response, the actual feedback of the height of the electric springs and the inflation volume of the mattress airbags can be accurately compared with preset standards, quantifying the degree of deviation between the two. This allows for a direct assessment of whether the mattress's response to the control center's commands meets expectations. This provides reliable response data support for mattress firmness classification, ensuring that the classification results are suitable for the body characteristics of different groups of people. It also allows for the timely detection of situations where response deviations exceed the standard, pointing the way for subsequent optimization of mattress pressure data response accuracy and improving the precision of mattress adjustments based on pressure data.

[0051] Furthermore, the optimization of mattress pressure data response accuracy includes correction of mattress airbag inflation volume and correction of mattress electric spring height. Specifically, the process of correcting mattress airbag inflation volume is as follows: The actual monitored mattress pressure value, mattress airbag feedback deviation value, and actual mattress airbag inflation volume data are input into the database. The corresponding preset airbag inflation volume correction value is retrieved from the database. The actual mattress airbag inflation volume is adjusted step by step according to the range corresponding to the preset airbag inflation volume correction value. The database contains a mapping set to reflect the mapping relationship between different combinations of the actual monitored mattress pressure value, mattress airbag feedback deviation value, and actual mattress airbag inflation volume and the corresponding preset airbag inflation volume correction value. Adjusting the actual mattress airbag inflation volume step by step according to the range corresponding to the preset airbag inflation volume correction value helps to accurately compensate for the deviation of airbag inflation volume, so that the airbag inflation volume can better adapt to the pressure distribution of the human body contact area and avoid local pressure that is too high or too low due to improper inflation volume.

[0052] Specifically, the process for correcting the height of the electric mattress springs is as follows: The actual monitored mattress pressure value, the electric mattress spring feedback deviation value, and the actual electric mattress spring height data are input into the database. The corresponding preset electric spring height correction value is retrieved from the database. The actual mattress spring height is adjusted step-by-step according to the magnitude corresponding to the preset electric spring height correction value. The database contains a mapping set that reflects the mapping relationship between different combinations of the actual monitored mattress pressure value, the electric mattress spring feedback deviation value, and the actual mattress spring height, and the corresponding preset electric spring height correction value. Adjusting the actual mattress spring height step-by-step according to the magnitude corresponding to the preset electric spring height correction value helps to accurately correct spring height deviations.

[0053] In this embodiment, by correcting the airbag inflation volume and the height of the electric springs, the deviation between the airbag inflation volume and the preset standard can be specifically addressed. This makes the actual state of the airbag inflation volume and the spring height more closely match the pressure distribution requirements of the mattress contact area and better adapt to the force pattern of the mattress support parts. This not only effectively reduces the deviation of the mattress pressure data response and improves the accuracy of the mattress response to the control center commands, but also makes the support strength and height distribution of the mattress more in line with the body characteristics of different people, further enhancing the comfort of the human body in contact with the mattress and the stability of spinal support.

[0054] Furthermore, the specific process of mattress firmness classification is as follows: Input qualified mattress pressure data and BMI data into a preset population characteristic-mattress firmness correlation model, such as a multiple linear regression model. Input the peak pressure distribution and pressure coverage area percentage from the qualified mattress pressure data, and the weight, height, and BMI values ​​from the BMI data. Output the corresponding mattress firmness range, thus outputting the mattress firmness classification range. The mattress firmness classification range includes a first mattress firmness range, a second mattress range, and a third mattress range. The first mattress firmness range can be set to 3 ≤ Hs ≤ 6, and the second mattress firmness range can be set to 4 ≤ Hs. The third mattress firmness range can be set to 3≤Hs≤8, where Hs represents mattress firmness in Shore A hardness. BMI data includes weight, height, and BMI value. Qualified mattress pressure data represents the mattress pressure data corresponding to the accuracy deviation evaluation index of mattress pressure data response within the preset mattress pressure data accuracy threshold range. Mattress firmness classification is used to combine the body characteristics of different BMI groups with the human body pressure distribution characteristics reflected by qualified mattress pressure data to accurately classify mattress firmness into ranges suitable for various body characteristics, ensuring that the firmness support requirements of different weight groups are met, and achieving precise matching between mattress firmness and individual body conditions.

[0055] Specifically, the process for verifying the output feedback of the mattress firmness correlation model is as follows: Obtain the actual mattress firmness matching score; the actual mattress firmness matching score includes a first mattress firmness deviation value, a second mattress firmness deviation value, and a third mattress firmness deviation value; the first mattress firmness deviation value is represented by the difference between the maximum mattress firmness value within the first mattress firmness range and the preset maximum first mattress firmness value, where the preset maximum first mattress firmness value is represented by the average of the maximum mattress firmness values ​​corresponding to the first mattress firmness range over a historical time period; the second mattress firmness deviation value is represented by the difference between the maximum mattress firmness value within the second mattress firmness range and the preset maximum second mattress firmness value, where the preset maximum second mattress firmness value is represented by the average of the maximum mattress firmness values ​​corresponding to the second mattress firmness range over a historical time period; the third mattress firmness deviation value is represented by the difference between the maximum mattress firmness value within the third mattress firmness range and the preset maximum third mattress firmness value, where the preset maximum third mattress firmness value is represented by the average of the maximum mattress firmness values ​​corresponding to the third mattress firmness range over a historical time period.

[0056] In this embodiment, the mattress firmness classification and the output feedback verification of the mattress firmness correlation model are used to not only accurately match the mattress to the needs of people with different BMIs by clearly defining the firmness range, but also to verify the accuracy of the correlation model output results by analyzing the deviation value of the actual matching score. This timely detection of deviations in the classification process provides a basis for model optimization, which not only achieves precise adaptation of mattress firmness to individual body conditions, but also improves the accuracy of support area matching during the mattress classification adjustment process.

[0057] In summary, this application embodiment, by conducting a mattress pressure acquisition qualification assessment and determining whether dynamic sampling optimization is needed based on the assessment results, helps ensure the integrity and timeliness of dynamic pressure data in scenarios with frequent turning over, providing high-quality raw data support for subsequent data processing. Once the mattress pressure acquisition qualification assessment is passed, a mattress pressure data transmission efficiency assessment is conducted, and the results determine whether graded optimization measures for mattress pressure data transmission efficiency are needed. This helps reduce delays and losses during pressure data transmission, ensuring that critical pressure data is transmitted efficiently and stably to the mattress control center. Once the mattress pressure data transmission efficiency assessment is passed, the mattress pressure data... Analyzing the response accuracy and evaluating the deviation of mattress pressure data response accuracy to determine whether mattress pressure data response accuracy optimization is needed helps to make mattress support adjustments more closely match the actual pressure needs of the human body, improving the accuracy of support. Once the mattress pressure data response accuracy analysis is qualified, mattress firmness classification and mattress firmness correlation model output feedback verification are performed. This helps to continuously optimize the mattress firmness correlation model, improve the matching accuracy of mattress firmness classification with the characteristics of different groups of people, and improve the accuracy of support area matching during mattress classification adjustment. This effectively solves the technical problem of low support area matching accuracy during mattress classification adjustment caused by the delay in mattress pressure data acquisition in existing technologies.

[0058] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0059] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0060] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0061] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0062] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A human feature-based mattress typing design method, characterized in that, The method comprises: During the mattress pressure data acquisition process, mattress pressure acquisition qualification evaluation is performed, and it is judged whether dynamic sampling optimization is needed according to the mattress pressure acquisition qualification evaluation result, wherein the dynamic sampling optimization means that the pressure data sampling period is adjusted to improve the capture accuracy of dynamic pressure data during frequent turning over; When the mattress pressure data transmission efficiency evaluation is qualified, mattress pressure data response accuracy analysis is performed, and it is judged whether mattress pressure data response accuracy optimization is needed according to the mattress pressure data response accuracy deviation evaluation result, wherein the mattress pressure data response accuracy optimization means that the mattress air bag inflation amount is corrected and the mattress electric spring height is corrected to improve the matching degree of the mattress pressure data and the human body contact surface pressure distribution and the fitting degree of the mattress pressure data and the human body spine natural form; When the mattress pressure data response accuracy analysis is qualified, mattress softness and hardness typing and mattress softness and hardness correlation model output feedback verification are performed; The specific process of the mattress pressure acquisition qualification evaluation is as follows: The mattress pressure acquisition qualification evaluation score represents the number of times that the pressure value change amplitude collected by the pressure sensor exceeds the preset mattress pressure data mutation threshold in the preset monitoring time period; The mattress pressure acquisition qualification evaluation score is used to reflect the interference degree of the response speed of the pressure sensor under frequent turning over action; It is judged whether the monitored mattress pressure acquisition qualification evaluation score is less than the preset mattress pressure data monitoring qualified threshold, if yes, a mattress pressure acquisition qualification monitoring prompt is sent, otherwise, dynamic sampling optimization is performed; The specific process of the dynamic sampling optimization is as follows: The mattress pressure acquisition qualification evaluation score and the mattress pressure data change duration are input into the database, and the corresponding mattress pressure data sampling period adjustment value is retrieved in the database; It is judged whether the mattress pressure data sampling period adjustment value exceeds the preset mattress pressure data sampling period adjustment threshold, if yes, a sampling period adjustment abnormal alarm is sent, otherwise, the mattress pressure data sampling period adjustment value is marked as a qualified mattress pressure data sampling period adjustment value; The mattress pressure data sampling period is gradually reduced by the adjustment amplitude corresponding to the qualified mattress pressure data sampling period adjustment value; After completing the dynamic sampling optimization, the mattress pressure data of the adjacent preset monitoring time period is collected, and the corresponding mattress pressure acquisition qualification evaluation score is obtained, if the mattress pressure acquisition qualification evaluation score is still not less than the preset mattress pressure data monitoring qualified threshold, a sampling period adjustment failure alarm is triggered, otherwise, the collected mattress pressure data is transmitted to the mattress control center, and mattress pressure data transmission efficiency evaluation is performed; ​ The mattress pressure data transmission performance grading optimization measure represents sequentially performing mattress pressure data transmission grading optimization and mattress pressure data response transmission performance optimization. The specific process of the mattress pressure data transmission grading optimization is as follows: If the mattress pressure data transmission performance evaluation index is not greater than the preset mattress pressure data transmission performance safety threshold and is greater than the maximum value of the preset mattress pressure data transmission performance safety threshold range, the corresponding mattress pressure data is marked as second-level mattress pressure transmission data, and mattress pressure data response transmission performance optimization is performed. Otherwise, the mattress pressure data greater than the preset mattress pressure data transmission performance safety threshold is marked as first-level mattress pressure transmission data, and mattress pressure data response transmission performance optimization is performed. The specific process of the mattress pressure data response transmission performance optimization is as follows: When monitoring the first-level mattress pressure transmission data, network flow optimization is performed, and after the network flow optimization is completed, mattress pressure data transmission effectiveness verification is performed. When monitoring the second-level mattress pressure transmission data, adaptive redundancy adjustment transmission optimization is performed, and after the adaptive redundancy adjustment transmission optimization is completed, mattress pressure data transmission effectiveness verification is performed. The mattress pressure data response accuracy optimization includes mattress air bag inflation amount correction and mattress electric spring height correction. The specific process of the mattress air bag inflation amount correction is as follows. The actual monitored mattress pressure value, mattress air bag feedback deviation value, and actual mattress air bag inflation amount data are input into the database, the corresponding preset air bag inflation amount correction value is retrieved in the database, and the actual mattress air bag inflation amount is adjusted step by step according to the corresponding amplitude of the preset air bag inflation amount correction value. The specific process of the mattress electric spring height correction is as follows. The actual monitored mattress pressure value, mattress electric spring feedback deviation value, and actual mattress electric spring height data are input into the database, the corresponding preset electric spring height correction value is retrieved in the database, and the actual mattress electric spring height is adjusted step by step according to the corresponding amplitude of the preset electric spring height correction value. The specific process of the mattress softness and hardness classification is as follows: The qualified mattress pressure data and BMI data are input into the preset population characteristics and mattress softness and hardness association model, and the mattress softness and hardness classification range is output.

2. The body feature-based mattress typing design method of claim 1, wherein, The specific process of the mattress pressure data transmission performance evaluation is as follows: The mattress pressure data matching proportion parameter is obtained by quantifying the proportion of the mattress pressure parameter and the standard mattress pressure parameter retrieved from the database. The mattress pressure data transmission performance evaluation index is obtained by weighting and fusing the mattress pressure data matching proportion parameter and the mattress pressure feature weighting parameter. The mattress pressure parameter includes mattress pressure data transmission time deviation value, key pressure data deviation value, qualified mattress pressure collection qualification evaluation score, and mattress pressure collection integrity interference value. The mattress pressure data matching proportion parameter includes mattress pressure data transmission time deviation rate, key pressure data deviation rate, qualified mattress pressure collection interference evaluation rate, and mattress pressure collection integrity interference rate. The qualified mattress pressure collection qualification evaluation score represents the mattress pressure collection qualification evaluation score less than the preset mattress pressure data monitoring qualification threshold. The mattress pressure data transmission performance evaluation index is used to reflect the influence degree of the mattress pressure parameter on the mattress pressure data transmission eligibility; It is judged whether the mattress pressure data transmission performance evaluation index is within the preset mattress pressure data transmission performance safety threshold range. If yes, mattress pressure data response accuracy analysis is performed. Otherwise, mattress pressure data transmission performance grading optimization measures are taken.

3. The method of claim 1, wherein the method further comprises: The network flow optimization representation performs transmission path dynamic planning and bandwidth resource allocation, which is used to preferentially guarantee the real-time transmission of key pressure data, reduce transmission delay, and improve the transmission stability of high-priority data; The adaptive redundancy adjustment transmission optimization representation dynamically adjusts the data redundancy coding ratio according to the real-time congestion state of the network, which is used to reduce the redundancy to reduce the data transmission amount when the network is smooth, and at the same time save transmission resources, improve the anti-interference ability and transmission reliability of low-priority data; The mattress pressure data transmission effectiveness verification represents that the mattress pressure data transmission effectiveness verification index is obtained at the next adjacent preset pressure transmission time period. If the mattress pressure data transmission effectiveness verification index is not within the preset qualified mattress pressure data transmission verification safety threshold range, a grading adjustment abnormal alarm is sent. Otherwise, mattress pressure data accuracy analysis is performed. The difference between the mattress pressure data transmission effectiveness evaluation index obtained at the end of the next adjacent preset pressure transmission time period and the mattress pressure data transmission effectiveness evaluation index obtained at the end of the preset pressure transmission time period represents the mattress pressure data transmission effectiveness verification index.

4. The method of claim 2, wherein the method further comprises: The specific process of the mattress pressure data response accuracy analysis is as follows: An mattress pressure data response accuracy deviation evaluation index is obtained. The mattress pressure data response accuracy deviation evaluation index includes a mattress electric spring feedback deviation value and a mattress air bag feedback deviation value. The mattress electric spring feedback deviation value is represented by the difference between the average value of the mattress electric spring height feedback after the mattress electric spring receives the instruction from the mattress control center in the preset mattress response time period and the preset mattress spring height. The mattress air bag feedback deviation value is represented by the difference between the average value of the mattress air bag inflation amount feedback after the mattress electric spring receives the instruction from the mattress control center in the preset mattress response time period and the preset air bag inflation amount. The mattress pressure data response accuracy deviation evaluation index is used to reflect the deviation degree of the mattress feedback to the mattress control center instruction. It is judged whether the mattress pressure data response accuracy deviation evaluation index is within the preset mattress pressure data accuracy threshold range. If yes, mattress softness classification is performed. Otherwise, mattress pressure data response accuracy optimization is taken.

5. The body feature-based mattress typing design method of claim 4, wherein, The mattress softness classification range includes a first mattress softness range, a second mattress softness range, and a third mattress softness range. The qualified mattress pressure data represents the mattress pressure data corresponding to the mattress pressure data response accuracy deviation evaluation index within the preset mattress pressure data accuracy threshold range. The mattress hardness classification is used for accurately classifying mattress hardness into ranges suitable for different human body feature groups, and ensures that the hardness support requirements of different weight groups are met to realize accurate matching of mattress hardness and individual body conditions.

6. The method of claim 4, wherein the method further comprises: The mattress hardness classification further comprises performing mattress hardness correlation model output feedback verification. The specific process of the mattress hardness correlation model output feedback verification is as follows: An actual matching score of mattress hardness is obtained. The actual matching score of mattress hardness comprises a first mattress hardness deviation value, a second mattress hardness deviation value and a third mattress hardness deviation value. The first mattress hardness deviation value is represented by a difference between a maximum mattress hardness in a first mattress hardness range and a preset first mattress hardness maximum value. The second mattress hardness deviation value is represented by a difference between a maximum mattress hardness in a second mattress hardness range and a preset second mattress hardness maximum value. The third mattress hardness deviation value is represented by a difference between a maximum mattress hardness in a third mattress hardness range and a preset third mattress hardness maximum value. It is judged whether the actual matching score of mattress hardness is within a corresponding preset mattress hardness classification range, if yes, the hardness classification data and the mattress pressure data are stored into a database, otherwise, a model output mattress hardness classification range data invalidation prompt is sent.

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