A big data-based mattress individualized manufacturing optimization method, system and medium

Abstract

The application discloses a mattress personalized manufacturing optimization method and system based on big data and a medium, wherein the method comprises the following steps: collecting sensing data information of a preset area of a mattress, obtaining sleep quality monitoring data through a preset algorithm, comparing the sleep quality monitoring data with a preset sleep quality threshold, obtaining sleep quality rating information and sleep quality level data, obtaining mattress process category information, optimizing the mattress process category information according to the sleep quality level data and sleep health information of a user to obtain first optimization result information, and matching corresponding mattress material selection information according to the first optimization result information and obtaining optimized mattress information after manufacturing optimization.

Description

Technical Field

[0001] This invention relates to the fields of big data and mattress manufacturing technology, and more specifically, to a method, system, and medium for personalized mattress manufacturing optimization based on big data. Background Technology

[0002] With social development and technological advancements, modern people's living standards are constantly improving. At the same time, people's body shapes have also changed significantly, with each generation becoming more robust than the last. This places higher demands on mattress manufacturing processes, and the materials used in mattress production are constantly being updated. Therefore, the selection of materials and manufacturing processes for a mattress must seek greater breakthroughs based on technological advancements and changes in the human body. This requires that sufficiently comprehensive and accurate information be obtained in the early stages of mattress manufacturing in order to specifically improve manufacturing processes and precisely select mattress materials.

[0003] To address the above problems, a design solution is urgently needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and medium for personalized mattress manufacturing optimization based on big data. By matching user needs with mattresses, the manufacturing process and material selection technology are optimized and improved to better meet the different needs of different types of users. The effectiveness of mattress manufacturing optimization and material selection is verified through simulation, thereby optimizing the mattress manufacturing method.

[0005] The first aspect of this invention provides a method for optimizing personalized mattress manufacturing based on big data, comprising the following steps:

[0006] Collect sensor data from a preset area on the mattress;

[0007] Sleep quality monitoring data is calculated using a preset algorithm based on the sensor data. The sleep quality monitoring data is then compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data.

[0008] Obtain mattress manufacturing information and categorize it to obtain mattress manufacturing category information;

[0009] The mattress manufacturing process category information is optimized based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information;

[0010] Based on the first optimization result information, match the corresponding mattress material selection information and obtain the corresponding optimized mattress information after manufacturing optimization.

[0011] In this solution, the collection of sensor data information from a preset area of ​​the mattress includes:

[0012] Obtain the user's human body attribute information, including height, age, weight, and gender.

[0013] Based on the human body attribute feature information, the corresponding first preset area of ​​the mattress is obtained, and the corresponding parts of the user are matched and adjusted through the first preset area of ​​the mattress, and the matching result information after the matching adjustment is collected.

[0014] First calibration matching information is obtained by performing a first calibration based on the matching result information;

[0015] The first calibration matching information is compared with a preset first matching threshold. If the threshold comparison meets the preset requirements, the first calibration matching information is marked as qualified.

[0016] If the first calibration matching information is qualified, then the sensor data information of the second preset area of ​​the mattress is collected, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

[0017] In this solution, the process of calculating sleep quality monitoring data based on the sensed data information using a preset algorithm, comparing the sleep quality monitoring data with a preset sleep quality threshold to obtain sleep quality rating information, and acquiring the corresponding sleep quality level data includes:

[0018] Based on the user's bedtime and bedtime information, nighttime wake-up time information, and sleep status monitoring information, sleep quality monitoring data is calculated using a preset algorithm;

[0019] The sleep quality monitoring data is compared with a preset sleep quality threshold to obtain sleep quality rating information.

[0020] Based on the sleep quality rating information, extract the corresponding sleep quality level data, generate a message from the sleep quality level data, and send it to the terminal.

[0021] In this solution, obtaining mattress manufacturing information and classifying it to obtain mattress manufacturing category information includes:

[0022] Establish a smart mattress manufacturing information database;

[0023] Mattress manufacturing information, including mattress type, elasticity, and firmness, is obtained by querying the mattress intelligent manufacturing information database.

[0024] The mattresses are categorized and organized according to the mattress manufacturing process information to obtain mattress manufacturing category information. The mattress manufacturing category information is then combined with the corresponding mattress information and stored in the mattress intelligent manufacturing information database.

[0025] In this solution, the step of optimizing the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information includes:

[0026] Collect the user's sleep health information within a preset time period, including light sleep ratio, sleep dysphagia information, and insomnia duration information;

[0027] The sleep health information and sleep quality level data are processed using a preset sleep health quality assessment model to obtain the user's sleep instability coefficient.

[0028] The sleep instability coefficient and the mattress manufacturing process information are input into a preset mattress adaptation optimization model for optimization processing to obtain the first optimization result information.

[0029] In this solution, the step of matching the corresponding mattress material selection information based on the first optimization result information and obtaining the corresponding optimized mattress information after manufacturing optimization includes:

[0030] Extract mattress process optimization information based on the first optimization result information;

[0031] The mattress process optimization information is processed through a preset mattress intelligent manufacturing material selection model to obtain the corresponding mattress material selection information.

[0032] Based on the mattress material selection information and the mattress manufacturing process category information, a virtual manufacturing process model is used to simulate the manufacturing process and obtain a virtual mattress.

[0033] Collect virtual data of the product indicators of the virtual mattress, and perform quality verification on the virtual data of the product indicators to obtain the verification results;

[0034] The reasonableness of the mattress material selection information is determined based on the verification results.

[0035] If the rationality assessment is passed, the manufacturing information of the virtual mattress will be used as the optimized mattress information.

[0036] This plan also includes:

[0037] Obtain user fatigue information at different time periods;

[0038] The firmness of the mattress's sensing and adjustment area is adjusted based on the fatigue level information, and the adjustment result is obtained.

[0039] The adjustment result is compared with the preset intensity value to obtain an intensity threshold comparison result. If the comparison result meets the preset requirements, the adjustment result is marked as qualified.

[0040] A second aspect of the present invention also provides a mattress personalized manufacturing optimization system based on big data, comprising a memory and a processor. The memory includes a mattress personalized manufacturing optimization method program based on big data. When the processor executes the mattress personalized manufacturing optimization method program based on big data, it performs the following steps:

[0041] Collect sensor data from a preset area on the mattress;

[0042] Sleep quality monitoring data is calculated using a preset algorithm based on the sensor data. The sleep quality monitoring data is then compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data.

[0043] Obtain mattress manufacturing information and categorize it to obtain mattress manufacturing category information;

[0044] The mattress manufacturing process category information is optimized based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information;

[0045] Match the corresponding mattress material selection information based on the first optimization result information;

[0046] Based on the first optimization result information and the mattress material selection information, the corresponding optimized mattress information after manufacturing optimization is obtained.

[0047] In this solution, the collection of sensor data information from a preset area of ​​the mattress includes:

[0048] Obtain the user's human body attribute information, including height, age, weight, and gender.

[0049] Based on the human body attribute feature information, the corresponding first preset area of ​​the mattress is obtained, and the corresponding parts of the user are matched and adjusted through the first preset area of ​​the mattress, and the matching result information after the matching adjustment is collected.

[0050] First calibration matching information is obtained by performing a first calibration based on the matching result information;

[0051] The first calibration matching information is compared with a preset first matching threshold. If the threshold comparison meets the preset requirements, the first calibration matching information is marked as qualified.

[0052] If the first calibration matching information is qualified, then the sensor data information of the second preset area of ​​the mattress is collected, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

[0053] A third aspect of the present invention provides a computer-readable storage medium comprising a big data-based mattress personalized manufacturing optimization method program, wherein when the big data-based mattress personalized manufacturing optimization method program is executed by a processor, it implements the steps of the big data-based mattress personalized manufacturing optimization method as described in any of the preceding claims.

[0054] This invention discloses a method, system, and medium for personalized mattress manufacturing optimization based on big data. It collects sensor data from a preset area of ​​the mattress, uses a preset algorithm to obtain sleep quality monitoring data, compares this data with a preset sleep quality threshold to obtain sleep quality rating and level data, acquires mattress manufacturing category information, and optimizes the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain a first optimization result. Then, it matches the corresponding mattress material selection information based on the first optimization result to obtain the corresponding optimized mattress information. This optimizes and improves the manufacturing process and material selection technology to better meet the different needs of different user groups. The effectiveness of the mattress manufacturing optimization and material selection is verified through simulation, thus optimizing the mattress manufacturing method. Attached Figure Description

[0055] Figure 1 A flowchart of the present invention is shown, illustrating a mattress personalized manufacturing optimization method based on big data.

[0056] Figure 2 The present invention illustrates a flowchart of the process for collecting sensor data information of a preset area of ​​a mattress in a mattress-based personalized manufacturing optimization method based on big data.

[0057] Figure 3 This invention illustrates a flowchart of obtaining sleep quality level data in a mattress personalization manufacturing optimization method based on big data according to the present invention.

[0058] Figure 4 The diagram illustrates a structural block diagram of the mattress personalized manufacturing optimization system based on big data according to the present invention. Detailed Implementation

[0059] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0060] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0061] Figure 1 A flowchart of a mattress personalization manufacturing optimization method based on big data is shown in this application.

[0062] like Figure 1 As shown, this application discloses a method for optimizing personalized mattress manufacturing based on big data, including the following steps:

[0063] S101, collect sensor data information of the preset area of ​​the mattress;

[0064] S102, sleep quality monitoring data is calculated based on the sensing data information using a preset algorithm, and the sleep quality monitoring data is compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data.

[0065] S103, Obtain mattress manufacturing information and classify it to obtain mattress manufacturing category information;

[0066] S104, Based on the sleep quality level data and the user's sleep health information, optimize the mattress manufacturing category information to obtain the first optimization result information;

[0067] S105, Match the corresponding mattress material selection information according to the first optimization result information, and obtain the corresponding optimized mattress information after manufacturing optimization.

[0068] It should be noted that the process involves dividing the mattress into preset areas corresponding to different parts of the user's body, and using sensors installed on the mattress to sense and collect data from these areas. The preset areas corresponding to different body parts are set according to the user's key sensory points. Sleep quality monitoring data is then calculated and compared with preset sleep quality thresholds to obtain corresponding sleep quality ratings and levels. Mattress manufacturing information is then acquired and categorized to obtain mattress manufacturing category information. Based on the sleep quality level data and the collected user sleep health information, the mattress manufacturing information is optimized to obtain the first optimization result. Finally, based on the... The optimization results are processed through a mattress intelligent manufacturing material selection model to obtain corresponding mattress material selection information, such as raw material type and firmness. Then, based on the mattress material selection information and mattress process category information, a virtual mattress is obtained through a virtual manufacturing processing model. Finally, the virtual data of the virtual mattress's product indicators are quality verified, and the verification results are obtained. If the verification is successful, it indicates that the optimized manufacturing is effective. The processing and manufacturing information of the virtual mattress is then used as the optimized mattress information. Through this personalized mattress manufacturing optimization method, the manufacturing process and material selection technology can be optimized and improved to better meet the different needs of different types of users, and the effectiveness of the mattress manufacturing optimization is verified through use.

[0069] Figure 2 This paper presents a flowchart illustrating the process of collecting sensor data from a preset area of ​​a mattress, as part of a big data-based mattress personalization manufacturing optimization method.

[0070] like Figure 2 As shown in the embodiment of the present invention, the collection of sensor data information of a preset area of ​​the mattress includes:

[0071] S201, Obtain the user's human body attribute characteristics information, including height information, age information, weight information and gender information;

[0072] S202, obtain the first preset area of ​​the corresponding mattress according to the human body attribute feature information, and adjust the corresponding parts of the user through the first preset area of ​​the mattress, and collect the matching result information after the matching adjustment.

[0073] S203, perform a first calibration based on the matching result information to obtain first calibration matching information;

[0074] S204, compare the first calibration matching information with a preset first matching threshold. If the threshold comparison meets the preset requirements, mark the first calibration matching information as qualified.

[0075] S205, if the first calibration matching information is qualified, then collect the sensing data information of the second preset area of ​​the mattress, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

[0076] It should be noted that, for different users, the body characteristics of the user's group are identified based on height, age, weight, and gender information. The first preset area of ​​the mattress used for sensing is matched with various important parts of the human body. The first preset area is a sensing area preset according to the body distribution characteristics of users such as adolescents, the elderly, and obese individuals. The preset sensors in the sensing area obtain the matching result information after the mattress and user are matched and adjusted, that is, to know the fit between the mattress and the human body. Then, the matching result information is calibrated to obtain the first calibration matching information. The first calibration matching information is compared with the preset first matching threshold information. If the threshold comparison meets the requirements, the first calibration matching is marked as qualified, indicating that the fit between the user and the mattress meets the requirements. Then, the sensing data information of the second preset area can be collected. Through the second preset area, information such as the user's time of getting into and out of bed, time of getting up at night, and sleep status monitoring can be obtained.

[0077] Figure 3 This paper presents a flowchart illustrating the acquisition of sleep quality level data in a mattress personalization manufacturing optimization method based on big data, as described in this application.

[0078] like Figure 3 As shown in the embodiment of the present invention, the step of calculating sleep quality monitoring data based on the sensed data information using a preset algorithm, comparing the sleep quality monitoring data with a preset sleep quality threshold to obtain sleep quality rating information, and acquiring the corresponding sleep quality level data includes:

[0079] S301, based on the user's bedtime and bed-out time information, nighttime wake-up time information, and sleep status monitoring information, sleep quality monitoring data is calculated using a preset algorithm;

[0080] S302, compare the sleep quality monitoring data with a preset sleep quality threshold to obtain sleep quality rating information;

[0081] S303, extract the corresponding sleep quality level data based on the sleep quality rating information, and generate a message from the sleep quality level data and send it to the terminal.

[0082] It should be noted that sleep quality monitoring data is calculated using a preset algorithm based on the user's bedtime and nighttime bedtime information and sleep status monitoring information. This data is then compared with preset sleep quality thresholds to obtain sleep quality rating information. In other words, the corresponding sleep quality rating information is obtained based on the range of sleep quality monitoring data falling within the preset sleep quality threshold. In this case, the preset sleep quality thresholds are (90, 100], (70, 90], and [0, 70]. For example, if user A's sleep quality threshold is 85, the resulting sleep quality rating information is the corresponding second level and is displayed as "Good 85". The corresponding sleep quality level data is extracted based on the sleep quality rating information, and a message is generated and sent to the terminal.

[0083] The formula for calculating the sleep quality monitoring data is as follows:

[0084] ;

[0085] in, For sleep quality monitoring data, , , These include information on bedtime and bed-out time, nighttime wake-up time, and sleep status monitoring information. To preset the monitoring correction coefficient, , , These are the characteristic coefficients.

[0086] According to an embodiment of the present invention, obtaining mattress manufacturing information and classifying and organizing it to obtain mattress manufacturing category information includes:

[0087] Establish a smart mattress manufacturing information database;

[0088] Mattress manufacturing information, including mattress type, elasticity, and firmness, is obtained by querying the mattress intelligent manufacturing information database.

[0089] The mattresses are categorized and organized according to the mattress manufacturing process information to obtain mattress manufacturing category information. The mattress manufacturing category information is then combined with the corresponding mattress information and stored in the mattress intelligent manufacturing information database.

[0090] It should be noted that by establishing a smart mattress manufacturing information database, mattresses are categorized and summarized according to specific processes, materials, and value. This database allows users to query mattress manufacturing information, including mattress type, elasticity, and firmness. Categorizing mattresses according to their manufacturing process information yields mattress manufacturing category information, which enriches and describes the mattress manufacturing process and its types. This category information, combined with the corresponding mattress information, is stored in the smart mattress manufacturing information database to facilitate categorized queries of mattress manufacturing information.

[0091] According to an embodiment of the present invention, the step of optimizing the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain first optimization result information includes:

[0092] Collect the user's sleep health information within a preset time period, including light sleep ratio, sleep dysphagia information, and insomnia duration information;

[0093] The sleep health information and sleep quality level data are processed using a preset sleep health quality assessment model to obtain the user's sleep instability coefficient.

[0094] The sleep instability coefficient and the mattress manufacturing process information are input into a preset mattress adaptation optimization model for optimization processing to obtain the first optimization result information.

[0095] It should be noted that after obtaining the user's sleep quality information, it is combined with the user's sleep health information to optimize the mattress manufacturing process information. This optimization is achieved by collecting the user's sleep health information over a certain preset time period, including the proportion of light sleep and full sleep, information on abnormal restlessness during sleep instability, and information on insomnia duration. This information is then processed with sleep quality level data to obtain an instability coefficient reflecting the user's sleep instability. The sleep instability coefficient and mattress manufacturing process information are then input into a preset, trained mattress adaptation optimization model for further processing. This model provides information on the user's poor mattress instability and the need for improvement, adaptation, and adjustment of the mattress manufacturing process. The first optimization result obtained through this model can further improve the mattress manufacturing process to compensate for the user's insufficient sleep and provide information on fit and directional design. The mattress adaptation optimization model is an information processing model obtained from the mattress intelligent manufacturing information database. It is trained by processing the sleep instability coefficient, mattress manufacturing process information, and corresponding optimization result information from a large amount of sample data in the database to obtain the corresponding result information.

[0096] The formula for calculating the sleep instability coefficient is as follows:

[0097] ;

[0098] in, The sleep instability coefficient, , , These include information on the percentage of light sleep, sleep erratic behavior, and duration of insomnia. This is data on sleep quality levels. , , These are the characteristic coefficients.

[0099] According to an embodiment of the present invention, the step of matching the corresponding mattress material selection information based on the first optimization result information and obtaining the corresponding optimized mattress information after manufacturing optimization includes:

[0100] Extract mattress process optimization information based on the first optimization result information;

[0101] The mattress process optimization information is processed through a preset mattress intelligent manufacturing material selection model to obtain the corresponding mattress material selection information.

[0102] Based on the mattress material selection information and the mattress manufacturing process category information, a virtual manufacturing process model is used to simulate the manufacturing process and obtain a virtual mattress.

[0103] Collect virtual data of the product indicators of the virtual mattress, and perform quality verification on the virtual data of the product indicators to obtain the verification results;

[0104] The reasonableness of the mattress material selection information is determined based on the verification results.

[0105] If the rationality assessment is passed, the manufacturing information of the virtual mattress will be used as the optimized mattress information.

[0106] It should be noted that, based on the first optimization result information, mattress process optimization information is extracted, including relevant information on the optimization and adjustment of process parameters such as mattress function type, elasticity distribution, and firmness adaptation. This optimized process information is input into a preset intelligent mattress manufacturing material selection model for processing to obtain corresponding mattress material selection information. Based on the mattress material selection information and mattress process category information, a virtual manufacturing process model is used to simulate processing to obtain a virtual mattress. The virtual manufacturing process model is a virtual model of virtual products and related virtual parameters obtained by virtual processing based on pre-optimized manufacturing information parameters. The virtual mattress obtained through virtual manufacturing processing of the model collects virtual data of its product indicators, including data on material selection, structural design, elastic module layout, and processing procedures. These virtual data of product indicators are quality verified and verification results are obtained. Finally, the rationality of the mattress material selection information is judged based on the verification results. If the rationality judgment is passed, it means that the mattress process design optimization parameters of the virtual manufactured mattress are reasonable. Then, the processing and manufacturing information is used as the optimized mattress information, thereby realizing the optimization and improvement of manufacturing process and material selection technology to meet the matching needs of users and mattresses, and realizing manufacturing optimization technology for personalized user requirements.

[0107] According to an embodiment of the present invention, it further includes:

[0108] Obtain user fatigue information at different time periods;

[0109] The firmness of the mattress's sensing and adjustment area is adjusted based on the fatigue level information, and the adjustment result is obtained.

[0110] The adjustment result is compared with the preset intensity value to obtain an intensity threshold comparison result. If the comparison result meets the preset requirements, the adjustment result is marked as qualified.

[0111] It should be noted that, firstly, the user's fatigue information at different time periods is obtained to determine the user's fatigue level at different times. Then, based on the fatigue level, the firmness of the mattress sensing and adjustment area corresponding to the human body is adjusted, and the adjustment result is obtained. The adjustment result is compared with a preset intensity value to a threshold intensity. The preset intensity value can be automatically adjusted or specified by the user, and the adjustment degree is represented by 0-10. If the comparison result meets the threshold comparison requirements, the adjustment result is marked as qualified.

[0112] It is worth mentioning that the sleep monitoring information includes:

[0113] Heart rate information is obtained based on the user's sleep monitoring data;

[0114] The heart rate information is compared with a preset heart rate range to obtain a second threshold comparison result.

[0115] If the comparison result of the second threshold does not meet the threshold requirement, an alarm will be triggered.

[0116] It should be noted that the sleep monitoring information also includes heart rate monitoring. The real-time heart rate information is compared with a preset heart rate range. Considering that the person is in a sleep state, the preset heart rate range can be 55-80 beats / minute. If the heart rate is 100, it does not meet the preset heart rate range and will trigger an alarm to remind the user or others.

[0117] It is worth mentioning that the sensed data information also includes:

[0118] Vibration information is obtained based on the sensor data;

[0119] The vibration information is compared with a preset vibration threshold to obtain a third threshold comparison result.

[0120] If the comparison result of the third threshold does not meet the requirements, it will be marked and displayed to the user.

[0121] It should be noted that the sensed data information also includes vibration information, mainly vibration information caused by the human body itself, including amplitude, frequency and force. The vibration information is compared with a preset vibration threshold, which can be 36%. If the detected vibration information is 42%, it does not meet the requirements and the specific information is reflected in the sleep quality rating report to remind the user to correct their sleeping posture.

[0122] Figure 4 The diagram shows a structural block diagram of a mattress personalized manufacturing optimization system based on big data according to the present invention.

[0123] like Figure 4As shown, this invention discloses a mattress personalized manufacturing optimization system 4 based on big data, including a memory 401 and a processor 402. The memory includes a mattress personalized manufacturing optimization method program based on big data. When the processor executes the mattress personalized manufacturing optimization method program based on big data, it performs the following steps:

[0124] Collect sensor data from a preset area on the mattress;

[0125] Sleep quality monitoring data is calculated using a preset algorithm based on the sensor data. The sleep quality monitoring data is then compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data.

[0126] Obtain mattress manufacturing information and categorize it to obtain mattress manufacturing category information;

[0127] The mattress manufacturing process category information is optimized based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information;

[0128] Based on the first optimization result information, match the corresponding mattress material selection information and obtain the corresponding optimized mattress information after manufacturing optimization.

[0129] It should be noted that the process involves dividing the mattress into preset areas corresponding to different parts of the user's body, and using sensors installed on the mattress to sense and collect data from these areas. The preset areas corresponding to different body parts are set according to the user's key sensory points. Sleep quality monitoring data is then calculated and compared with preset sleep quality thresholds to obtain corresponding sleep quality ratings and levels. Mattress manufacturing information is then acquired and categorized to obtain mattress manufacturing category information. Based on the sleep quality level data and the collected user sleep health information, the mattress manufacturing information is optimized to obtain the first optimization result. Finally, based on the... The optimization results are processed through a mattress intelligent manufacturing material selection model to obtain corresponding mattress material selection information, such as raw material type and firmness. Then, based on the mattress material selection information and mattress process category information, a virtual mattress is obtained through a virtual manufacturing processing model. Finally, the virtual data of the virtual mattress's product indicators are quality verified, and the verification results are obtained. If the verification is successful, it indicates that the optimized manufacturing is effective. The processing and manufacturing information of the virtual mattress is then used as the optimized mattress information. Through this personalized mattress manufacturing optimization method, the manufacturing process and material selection technology can be optimized and improved to better meet the different needs of different types of users, and the effectiveness of the mattress manufacturing optimization is verified through use.

[0130] According to an embodiment of the present invention, the collection of sensor data information of a preset area of ​​the mattress includes:

[0131] Obtain the user's human body attribute information, including height, age, weight, and gender.

[0132] Based on the human body attribute feature information, the corresponding first preset area of ​​the mattress is obtained, and the corresponding parts of the user are matched and adjusted through the first preset area of ​​the mattress, and the matching result information after the matching adjustment is collected.

[0133] First calibration matching information is obtained by performing a first calibration based on the matching result information;

[0134] The first calibration matching information is compared with a preset first matching threshold. If the threshold comparison meets the preset requirements, the first calibration matching information is marked as qualified.

[0135] If the first calibration matching information is qualified, then the sensor data information of the second preset area of ​​the mattress is collected, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

[0136] It should be noted that, for different users, the body characteristics of the user's group are identified based on height, age, weight, and gender information. The first preset area of ​​the mattress used for sensing is matched with various important parts of the human body. The first preset area is a sensing area preset according to the body distribution characteristics of users such as adolescents, the elderly, and obese individuals. The preset sensors in the sensing area obtain the matching result information after the mattress and user are matched and adjusted, that is, to know the fit between the mattress and the human body. Then, the matching result information is calibrated to obtain the first calibration matching information. The first calibration matching information is compared with the preset first matching threshold information. If the threshold comparison meets the requirements, the first calibration matching is marked as qualified, indicating that the fit between the user and the mattress meets the requirements. Then, the sensing data information of the second preset area can be collected. Through the second preset area, information such as the user's time of getting into and out of bed, time of getting up at night, and sleep status monitoring can be obtained.

[0137] According to an embodiment of the present invention, the step of calculating sleep quality monitoring data based on the sensed data information using a preset algorithm, comparing the sleep quality monitoring data with a preset sleep quality threshold to obtain sleep quality rating information, and acquiring the corresponding sleep quality level data includes:

[0138] Based on the user's bedtime and bedtime information, nighttime wake-up time information, and sleep status monitoring information, sleep quality monitoring data is calculated using a preset algorithm;

[0139] The sleep quality monitoring data is compared with a preset sleep quality threshold to obtain sleep quality rating information.

[0140] Based on the sleep quality rating information, extract the corresponding sleep quality level data, generate a message from the sleep quality level data, and send it to the terminal.

[0141] It should be noted that sleep quality monitoring data is calculated using a preset algorithm based on the user's bedtime and nighttime bedtime information and sleep status monitoring information. This data is then compared with preset sleep quality thresholds to obtain sleep quality rating information. In other words, the corresponding sleep quality rating information is obtained based on the range of sleep quality monitoring data falling within the preset sleep quality threshold. In this case, the preset sleep quality thresholds are (90, 100], (70, 90], and [0, 70]. For example, if user A's sleep quality threshold is 85, the resulting sleep quality rating information is the corresponding second level and is displayed as "Good 85". The corresponding sleep quality level data is extracted based on the sleep quality rating information, and a message is generated and sent to the terminal.

[0142] The formula for calculating the sleep quality monitoring data is as follows:

[0143] ;

[0144] in, For sleep quality monitoring data, , , These include information on bedtime and bed-out time, nighttime wake-up time, and sleep status monitoring information. To preset the monitoring correction coefficient, , , These are the characteristic coefficients.

[0145] According to an embodiment of the present invention, obtaining mattress manufacturing information and classifying and organizing it to obtain mattress manufacturing category information includes:

[0146] Establish a smart mattress manufacturing information database;

[0147] Mattress manufacturing information, including mattress type, elasticity, and firmness, is obtained by querying the mattress intelligent manufacturing information database.

[0148] The mattresses are categorized and organized according to the mattress manufacturing process information to obtain mattress manufacturing category information. The mattress manufacturing category information is then combined with the corresponding mattress information and stored in the mattress intelligent manufacturing information database.

[0149] It should be noted that by establishing a smart mattress manufacturing information database, mattresses are categorized and summarized according to specific processes, materials, and value. This database allows users to query mattress manufacturing information, including mattress type, elasticity, and firmness. Categorizing mattresses according to their manufacturing process information yields mattress manufacturing category information, which enriches and describes the mattress manufacturing process and its types. This category information, combined with the corresponding mattress information, is stored in the smart mattress manufacturing information database to facilitate categorized queries of mattress manufacturing information.

[0150] According to an embodiment of the present invention, the step of optimizing the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain first optimization result information includes:

[0151] Collect the user's sleep health information within a preset time period, including light sleep ratio, sleep dysphagia information, and insomnia duration information;

[0152] The sleep health information and sleep quality level data are processed using a preset sleep health quality assessment model to obtain the user's sleep instability coefficient.

[0153] The sleep instability coefficient and the mattress manufacturing process information are input into a preset mattress adaptation optimization model for optimization processing to obtain the first optimization result information.

[0154] It should be noted that after obtaining the user's sleep quality information, it is combined with the user's sleep health information to optimize the mattress manufacturing process information. This optimization is achieved by collecting the user's sleep health information over a certain preset time period, including the proportion of light sleep and full sleep, information on abnormal restlessness during sleep instability, and information on insomnia duration. This information is then processed with sleep quality level data to obtain an instability coefficient reflecting the user's sleep instability. The sleep instability coefficient and mattress manufacturing process information are then input into a preset, trained mattress adaptation optimization model for further processing. This model provides information on the user's poor mattress instability and the need for improvement, adaptation, and adjustment of the mattress manufacturing process. The first optimization result obtained through this model can further improve the mattress manufacturing process to compensate for the user's insufficient sleep and provide information on fit and directional design. The mattress adaptation optimization model is an information processing model obtained from the mattress intelligent manufacturing information database. It is trained by processing the sleep instability coefficient, mattress manufacturing process information, and corresponding optimization result information from a large amount of sample data in the database to obtain the corresponding result information.

[0155] The formula for calculating the sleep instability coefficient is as follows:

[0156] ;

[0157] in, The sleep instability coefficient, , , These include information on the percentage of light sleep, sleep erratic behavior, and duration of insomnia. This is data on sleep quality levels. , , These are the characteristic coefficients.

[0158] According to an embodiment of the present invention, the step of matching the corresponding mattress material selection information based on the first optimization result information and obtaining the corresponding optimized mattress information after manufacturing optimization includes:

[0159] Extract mattress process optimization information based on the first optimization result information;

[0160] The mattress process optimization information is processed through a preset mattress intelligent manufacturing material selection model to obtain the corresponding mattress material selection information.

[0161] Based on the mattress material selection information and the mattress manufacturing process category information, a virtual manufacturing process model is used to simulate the manufacturing process and obtain a virtual mattress.

[0162] Collect virtual data of the product indicators of the virtual mattress, and perform quality verification on the virtual data of the product indicators to obtain the verification results;

[0163] The reasonableness of the mattress material selection information is determined based on the verification results.

[0164] If the rationality assessment is passed, the manufacturing information of the virtual mattress will be used as the optimized mattress information.

[0165] It should be noted that, based on the first optimization result information, mattress process optimization information is extracted, including relevant information on the optimization and adjustment of process parameters such as mattress function type, elasticity distribution, and firmness adaptation. This optimized process information is input into a preset intelligent mattress manufacturing material selection model for processing to obtain corresponding mattress material selection information. Based on the mattress material selection information and mattress process category information, a virtual manufacturing process model is used to simulate processing to obtain a virtual mattress. The virtual manufacturing process model is a virtual model of virtual products and related virtual parameters obtained by virtual processing based on pre-optimized manufacturing information parameters. The virtual mattress obtained through virtual manufacturing processing of the model collects virtual data of its product indicators, including data on material selection, structural design, elastic module layout, and processing procedures. These virtual data of product indicators are quality verified and verification results are obtained. Finally, the rationality of the mattress material selection information is judged based on the verification results. If the rationality judgment is passed, it means that the mattress process design optimization parameters of the virtual manufactured mattress are reasonable. Then, the processing and manufacturing information is used as the optimized mattress information, thereby realizing the optimization and improvement of manufacturing process and material selection technology to meet the matching needs of users and mattresses, and realizing manufacturing optimization technology for personalized user requirements.

[0166] According to an embodiment of the present invention, it further includes:

[0167] Obtain user fatigue information at different time periods;

[0168] The firmness of the mattress's sensing and adjustment area is adjusted based on the fatigue level information, and the adjustment result is obtained.

[0169] The adjustment result is compared with the preset intensity value to obtain an intensity threshold comparison result. If the comparison result meets the preset requirements, the adjustment result is marked as qualified.

[0170] It should be noted that, firstly, the user's fatigue information at different time periods is obtained to determine the user's fatigue level at different times. Then, based on the fatigue level, the firmness of the mattress sensing and adjustment area corresponding to the human body is adjusted, and the adjustment result is obtained. The adjustment result is compared with a preset intensity value to a threshold intensity. The preset intensity value can be automatically adjusted or specified by the user, and the adjustment degree is represented by 0-10. If the comparison result meets the threshold comparison requirements, the adjustment result is marked as qualified.

[0171] It is worth mentioning that the sleep monitoring information includes:

[0172] Heart rate information is obtained based on the user's sleep monitoring data;

[0173] The heart rate information is compared with a preset heart rate range to obtain a second threshold comparison result.

[0174] If the comparison result of the second threshold does not meet the threshold requirement, an alarm will be triggered.

[0175] It should be noted that the sleep monitoring information also includes heart rate monitoring. The real-time heart rate information is compared with a preset heart rate range. Considering that the person is in a sleep state, the preset heart rate range can be 55-80 beats / minute. If the heart rate is 100, it does not meet the preset heart rate range and will trigger an alarm to remind the user or others.

[0176] It is worth mentioning that the sensed data information also includes:

[0177] Vibration information is obtained based on the sensor data;

[0178] The vibration information is compared with a preset vibration threshold to obtain a third threshold comparison result.

[0179] If the comparison result of the third threshold does not meet the requirements, it will be marked and displayed to the user.

[0180] It should be noted that the sensed data information also includes vibration information, mainly vibration information caused by the human body itself, including amplitude, frequency and force. The vibration information is compared with a preset vibration threshold, which can be 36%. If the detected vibration information is 42%, it does not meet the requirements and the specific information is reflected in the sleep quality rating report to remind the user to correct their sleeping posture.

[0181] A third aspect of the present invention provides a computer-readable storage medium comprising a big data-based mattress personalized manufacturing optimization method program, wherein when the big data-based mattress personalized manufacturing optimization method program is executed by a processor, it implements the steps of the big data-based mattress personalized manufacturing optimization method as described in any of the preceding claims.

[0182] This invention discloses a method, system, and medium for personalized mattress manufacturing optimization based on big data. It collects sensor data from a preset area of ​​the mattress, uses a preset algorithm to obtain sleep quality monitoring data, compares this data with a preset sleep quality threshold to obtain sleep quality rating and level data, acquires mattress manufacturing category information, and optimizes the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain a first optimization result. Then, it matches the corresponding mattress material selection information based on the first optimization result to obtain the corresponding optimized mattress information. This optimizes and improves the manufacturing process and material selection technology to better meet the different needs of different user groups. The effectiveness of the mattress manufacturing optimization and material selection is verified through simulation, thus optimizing the mattress manufacturing method.

[0183] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0184] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0185] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0186] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0187] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, 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 methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for optimizing personalized mattress manufacturing based on big data, characterized in that, Includes the following steps: Collect sensor data from a preset area on the mattress; Sleep quality monitoring data is calculated using a preset algorithm based on the sensor data. The sleep quality monitoring data is then compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data. Obtain mattress manufacturing information and categorize it to obtain mattress manufacturing category information; The mattress manufacturing process category information is optimized based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information; Based on the first optimization result information, match the corresponding mattress material selection information and obtain the corresponding optimized mattress information after manufacturing optimization; The step of optimizing the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information includes: collecting the user's sleep health information within a preset time period, including light sleep ratio information, sleep dysphagia information, and insomnia duration information; The sleep health information and sleep quality level data are processed using a preset sleep health quality assessment model to obtain the user's sleep instability coefficient. The sleep instability coefficient and the mattress manufacturing process information are input into a preset mattress adaptation optimization model for optimization processing to obtain the first optimization result information.

2. The mattress personalized manufacturing optimization method based on big data according to claim 1, characterized in that, The collection of sensor data information from the preset area of ​​the mattress includes: acquiring the user's human body attribute characteristics, including height, age, weight, and gender information; Based on the human body attribute feature information, the corresponding first preset area of ​​the mattress is obtained, and the corresponding parts of the user are matched and adjusted through the first preset area of ​​the mattress, and the matching result information after the matching adjustment is collected. First calibration matching information is obtained by performing a first calibration based on the matching result information; The first calibration matching information is compared with a preset first matching threshold. If the threshold comparison meets the preset requirements, the first calibration matching information is marked as qualified. If the first calibration matching information is qualified, then the sensor data information of the second preset area of ​​the mattress is collected, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

3. The mattress personalized manufacturing optimization method based on big data according to claim 1, characterized in that, The process of calculating sleep quality monitoring data based on the sensed data information using a preset algorithm, comparing the sleep quality monitoring data with a preset sleep quality threshold to obtain sleep quality rating information, and acquiring corresponding sleep quality level data includes: calculating sleep quality monitoring data based on the user's bedtime and bed-out time information, nighttime wake-up time information, and sleep status monitoring information using a preset algorithm; The sleep quality monitoring data is compared with a preset sleep quality threshold to obtain sleep quality rating information. Based on the sleep quality rating information, extract the corresponding sleep quality level data, generate a message from the sleep quality level data, and send it to the terminal.

4. The mattress personalized manufacturing optimization method based on big data according to claim 3, characterized in that, The process of acquiring mattress manufacturing information and classifying and organizing it to obtain mattress manufacturing category information includes: establishing a mattress intelligent manufacturing information database; Mattress manufacturing information, including mattress type, elasticity, and firmness, is obtained by querying the mattress intelligent manufacturing information database. The mattresses are categorized and organized according to the mattress manufacturing process information to obtain mattress manufacturing category information. The mattress manufacturing category information is then combined with the corresponding mattress information and stored in the mattress intelligent manufacturing information database.

5. The mattress personalized manufacturing optimization method based on big data according to claim 1, characterized in that, The step of matching the corresponding mattress material selection information based on the first optimization result information and obtaining the corresponding optimized mattress information after manufacturing optimization includes: extracting mattress process optimization information based on the first optimization result information; The mattress process optimization information is processed through a preset mattress intelligent manufacturing material selection model to obtain the corresponding mattress material selection information. Based on the mattress material selection information and the mattress manufacturing process category information, a virtual manufacturing process model is used to simulate the manufacturing process and obtain a virtual mattress. Collect virtual data of the product indicators of the virtual mattress, and perform quality verification on the virtual data of the product indicators to obtain the verification results; The reasonableness of the mattress material selection information is determined based on the verification results. If the rationality assessment is passed, the manufacturing information of the virtual mattress will be used as the optimized mattress information.

6. The mattress personalized manufacturing optimization method based on big data according to claim 1, characterized in that, Also includes: Obtain user fatigue information at different time periods; The firmness of the mattress's sensing and adjustment area is adjusted based on the fatigue level information, and the adjustment result is obtained. The adjustment result is compared with the preset intensity value to obtain an intensity threshold comparison result. If the comparison result meets the preset requirements, the adjustment result is marked as qualified.

7. A mattress personalized manufacturing optimization system based on big data, characterized in that, The system includes a memory and a processor. The memory contains a program for a mattress personalization manufacturing optimization method based on big data. When the processor executes the program, the mattress personalization manufacturing optimization method based on big data performs the following steps: Collect sensor data from a preset area on the mattress; Sleep quality monitoring data is calculated using a preset algorithm based on the sensor data. The sleep quality monitoring data is then compared with a preset sleep quality threshold to obtain sleep quality rating information and acquire the corresponding sleep quality level data. Obtain mattress manufacturing information and categorize it to obtain mattress manufacturing category information; The mattress manufacturing process category information is optimized based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information; Based on the first optimization result information, match the corresponding mattress material selection information and obtain the corresponding optimized mattress information after manufacturing optimization; The step of optimizing the mattress manufacturing category information based on the sleep quality level data and the user's sleep health information to obtain the first optimization result information includes: collecting the user's sleep health information within a preset time period, including light sleep ratio information, sleep dysphagia information, and insomnia duration information; The sleep health information and sleep quality level data are processed using a preset sleep health quality assessment model to obtain the user's sleep instability coefficient. The sleep instability coefficient and the mattress manufacturing process information are input into a preset mattress adaptation optimization model for optimization processing to obtain the first optimization result information.

8. The mattress personalized manufacturing optimization system based on big data according to claim 7, characterized in that, The collection of sensor data information from the preset area of ​​the mattress includes: acquiring the user's human body attribute characteristics, including height, age, weight, and gender information; Based on the human body attribute feature information, the corresponding first preset area of ​​the mattress is obtained, and the corresponding parts of the user are matched and adjusted through the first preset area of ​​the mattress, and the matching result information after the matching adjustment is collected. First calibration matching information is obtained by performing a first calibration based on the matching result information; The first calibration matching information is compared with a preset first matching threshold. If the threshold comparison meets the preset requirements, the first calibration matching information is marked as qualified. If the first calibration matching information is qualified, then the sensor data information of the second preset area of ​​the mattress is collected, including the time information of getting into and out of bed, the time information of getting up at night, and the sleep status monitoring information.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a big data-based mattress personalization manufacturing optimization method program, which, when executed by a processor, implements the steps of the big data-based mattress personalization manufacturing optimization method as described in any one of claims 1 to 6.

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