Control method of intelligent air bag mattress

Abstract

The invention provides a control method of an intelligent air bag mattress, which comprises the following steps: reasonably dividing mattress partitions according to a human physiological curve database, determining the position, size and shape of each partition, and realizing all-directional fitting support; the transition areas are arranged at the junctions of the adjacent subareas, smooth transition of pressure and height between the subareas is achieved by optimizing the arrangement and size of the air bags of the transition areas, and the sleep experience is improved; according to the partition scheme and the transition area design, the air bags and the pipelines are integrally designed, so that the use of the pipelines is minimized while the functions are ensured, and the integration level and the attractiveness of the mattress are improved; the integrated pressure sensor collects pressure data of each subarea in real time, the air inflation and deflation amount of each subarea is dynamically adjusted through a closed-loop feedback control method in combination with a calculation result of the air flow network model, and the consistency and stability of the supporting performance of the whole mattress are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of airbag mattresses, and in particular to a control method of an intelligent airbag mattress. Background Art

[0002] Smart airbag mattresses divide the mattress into zones and set up airbags in each zone to provide all-round support according to the physiological curves of different parts of the human body. However, to achieve independent control of the airbags in each zone, each airbag needs to be equipped with an independent inflation and deflation device and control circuit, which will significantly increase the cost of the mattress and also affect the integration and aesthetics of the mattress. How to reduce the cost and complexity of the mattress while ensuring independent control of the airbags in each zone is a technical problem that needs to be solved urgently.

[0003] In addition, although all-round fitting support can be achieved through independent control of the partitions, if the airbags between the partitions are not reasonably arranged and transitionally designed, it may lead to discontinuous support between different parts of the body, affecting the sleeping experience. How to achieve a smooth transition of pressure and height between the airbags in each partition on the basis of independent control of the partitions to avoid the discomfort caused by hard boundaries is also a technical issue worthy of in-depth study. At the same time, in the process of realizing independent control of the partitions, it is also necessary to consider the airflow balance between the partitions to avoid the situation where the airbags in individual partitions are not inflated and deflated in time or the pressure is unstable, so as to ensure the consistency and stability of the support performance of the entire mattress.

[0004] Therefore, the present invention proposes a control method of an intelligent airbag mattress to solve the above problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a control method for an intelligent airbag mattress, reduce the complexity of the airbag mattress control, ensure the consistency and stability of the mattress support performance, and improve the sleeping experience.

[0006] The present invention provides a control method for an intelligent airbag mattress, the method comprising the following steps: Step 1: Reasonably divide the mattress into zones according to the human physiological curve database, determine the position, size and shape of each zone, and achieve all-round fitting support; Step 2: Based on the partitioning scheme, a control architecture combining centralized control and distributed execution is adopted. A main controller is set to uniformly schedule the charging and deflation of each partition. Each partition is set with an actuator responsible for the charging and deflation of the area, reducing the control complexity and cost; Step 3: Set a transition zone at the junction of adjacent partitions, and optimize the airbag arrangement and size of the transition zone to achieve a smooth transition of pressure and height between partitions to improve the sleeping experience; Step 4: According to the partition scheme and transition zone design, the airbags and pipelines are designed in an integrated manner to minimize the use of pipelines while ensuring the functionality, thereby improving the integration and aesthetics of the mattress; Step 5: Establish an airflow network model. Each partition is equipped with an independent airbag. The airbags are connected by air paths. The mattress partitions are regarded as nodes, and the air path connections are regarded as edges. A directed graph structure is constructed. By solving the maximum flow problem of the network flow, the optimal inflation and deflation timing of the airbags in each partition is obtained. While meeting the support performance requirements, the airflow balance between the partitions is achieved. Step 6: The integrated pressure sensor collects the pressure feedback data of each partition in real time, and combines the calculation results of the airflow network model to dynamically adjust the inflation and deflation volume of each partition through a closed-loop feedback control method to ensure the consistency and stability of the support performance of the entire mattress; Step 7: Considering the multiple objectives of support performance, airflow balance and cost, an optimization model is established. The partition scheme, transition zone design, airflow network model and pressure feedback data are used as inputs, and a genetic algorithm is used to solve the airbag layout and inflation and deflation scheme to obtain the optimal design parameters.

[0007] Furthermore, in step 1, the mattress is reasonably divided into zones according to the human physiological curve database, and the position, size and shape of each zone are determined to achieve all-round fitting support, which specifically includes the following steps: Step 1.1, obtaining a pre-established human physiological curve database, wherein the human physiological curve database contains human curve data of characteristic populations of different heights and weights; Step 1.2, cluster analysis is performed on the human body curve data in the human body physiological curve database to obtain several typical human body curve categories; Step 1.3, according to the cluster analysis results, determine the number of mattress partitions, each partition corresponds to a typical human body curve category; Step 1.4, for each partition, statistical analysis is performed on the human body curve data of the corresponding category to obtain the optimal position, size and shape parameters of the partition; Step 1.5: Using computer-aided design software, generate a three-dimensional model according to the parameters of each partition, and perform simulation analysis to determine whether each partition can achieve fit support for the human body curve; If the simulation results meet the requirements of fitting support, the parameters of each partition are passed to the CNC machining equipment; Otherwise, adjust the partition parameters and repeat step 1.5 until the requirements are met; Step 1.6, the CNC processing equipment processes the mattress according to the received partition parameters, cuts out each partition at the corresponding position, and shapes it according to the set shape, finally obtaining a partitioned mattress product with all-round fitting support.

[0008] Furthermore, in step 2, based on the partitioning scheme, a control architecture combining centralized control and distributed execution is adopted, a main controller is set to uniformly schedule the charging and deflation of each partition, and each partition is set with an actuator responsible for the charging and deflation of the area, thereby reducing the control complexity and cost, which specifically includes the following steps: Step 2.1, based on the partitioning scheme, set up an executor in each partition; Step 2.2, obtaining the charging and discharging demand information of each partition, and transmitting the charging and discharging demand information to the main controller; Step 2.3, the main controller determines the charging and discharging control instructions of each partition according to the charging and discharging requirements of each partition and the preset scheduling strategy; Step 2.4, the main controller sends the inflation and deflation control instructions to the actuators of the corresponding partitions; Step 2.5, each partition actuator controls the inflation and deflation equipment in the partition area to perform inflation and deflation operations according to the received control instructions. During the inflation and deflation process, each actuator collects the inflation and deflation status data of the partition area in real time and reports it to the main controller; Step 2.6: The main controller determines whether the inflation and deflation process is in line with expectations based on the status data reported by each partition. If there is a deviation, the control instructions are dynamically adjusted until the inflation and deflation are completed.

[0009] Furthermore, in step 3, a transition zone is set at the junction of adjacent partitions, and the arrangement and size of the airbags in the transition zone are optimized to achieve a smooth transition of pressure and height between the partitions, thereby improving the sleeping experience, which specifically includes the following steps: Step 3.1, determine the position and range of the transition zone according to the pressure and height differences of adjacent partitions of the airbag mattress; Step 3.2, obtaining the airbag arrangement and size data of each partition of the airbag mattress as input for optimizing the transition zone airbag design; Step 3.3, analyzing the airbag data of adjacent partitions by clustering algorithm to obtain an initial plan for the arrangement and size of the airbags in the transition zone; Step 3.4, using finite element simulation to simulate the pressure and height transition effects under different airbag arrangements and size schemes, and select the optimal scheme; Step 3.5, fine-tune the arrangement and size of the airbags in the transition zone according to the simulation results to obtain the airbag design parameters that meet the smooth transition requirements; Step 3.6, applying the optimized transition zone airbag design parameters to the production of airbag mattresses to achieve smooth transition of pressure and height between adjacent partitions; Step 3.7: Evaluate the sleeping comfort of the airbag mattress through ergonomic testing, verify the effectiveness of the transition zone optimization design, and continuously improve the airbag design solution.

[0010] Furthermore, in step 4, the airbag and the pipeline are integrated according to the partition scheme and the transition zone design, so as to minimize the use of pipelines while ensuring the function, thereby improving the integration and aesthetics of the mattress, which specifically includes the following steps: Step 4.1, obtaining the initial layout of the airbag and pipeline according to the preset partition scheme and transition zone design requirements; Step 4.2: Using the topology optimization algorithm, the layout of the airbag and pipeline is optimized to reduce the usage of pipelines while ensuring the functions of the airbag and pipelines; Step 4.3, using simulated annealing algorithm, further adjust the optimized airbag and pipeline layout to improve integration; Step 4.4, according to the optimized layout of the airbag and the pipeline, establish a three-dimensional model, design the appearance of the airbag and the pipeline, and improve the aesthetics; Step 4.5: Apply the optimized airbag and pipeline layout to actual production, and verify whether the functions of the airbag and pipeline meet the requirements through physical testing; If the test results meet the requirements, the final design of airbag and pipeline integration will be determined; If the test result does not meet the requirements, return to step 4.2 and continue to optimize and adjust until the requirements are met.

[0011] Furthermore, in step 5, an airflow network model is established, an independent airbag is set in each partition, and the airbags are connected by air paths. The mattress partitions are regarded as nodes, and the air path connections are regarded as edges. A directed graph structure is constructed, and the optimal inflation and deflation timing of the airbags in each partition is obtained by solving the maximum flow problem of the network flow. While meeting the support performance requirements, the airflow balance between the partitions is achieved. Specifically, the following steps are included: Step 5.1, according to the mattress partition layout and air path connection relationship, establish an airflow network directed graph model, abstract each partition airbag as a node of the graph, and abstract the air path connection as a directed edge; Step 5.2: for the established airflow network model, set the attribute parameters of each node and edge. The attribute parameters of the node include the volume and initial pressure of the node, and the attribute parameters of the edge include the pipe diameter, flow coefficient and flow direction of the edge. Step 5.3, according to the support performance requirements, determine the target pressure range of each partition airbag as one of the constraints of the network flow problem of the airflow network model; Step 5.4: By solving the maximum flow problem of the network flow, the maximum flow value of the network is obtained under the premise of satisfying the pressure constraints of each node, and the corresponding flow distribution plan of each edge is obtained; Step 5.5, according to the flow rate of each edge obtained by solving, combined with the attribute parameters of the edge, calculate the airflow velocity and inflation and deflation time of each edge, and obtain the inflation and deflation timing of each partition airbag; Step 5.6: Determine whether the obtained inflation and deflation timing sequence meets the support performance requirements. If not, adjust the parameters or constraints of the airflow network model and solve the maximum flow problem again until the requirements are met. Step 5.7: According to the final inflation and deflation timing, the inflation and deflation process of each partition airbag is controlled to achieve a dynamic balance of airflow between partitions while meeting the support performance, thereby improving the comfort and reliability of the mattress.

[0012] Furthermore, in step 6, the integrated pressure sensor collects the pressure feedback data of each partition in real time, and combines the calculation results of the airflow network model to dynamically adjust the inflation and deflation volume of each partition through a closed-loop feedback control method to ensure the consistency and stability of the support performance of the entire mattress, which specifically includes the following steps: Step 6.1, obtaining the preset target pressure value range and airflow network model parameters of each partition of the mattress as the reference value and calculation basis of pressure control; Step 6.2, collecting pressure feedback data from pressure sensors integrated in each partition of the mattress in real time, inputting the collected pressure feedback data into the airflow network model for calculation and analysis, and obtaining the pressure distribution state of each partition; Step 6.3, determine whether the actual pressure value of each partition is within the target pressure value range, if not within the range, determine the inflation and deflation adjustment amount of the corresponding partition; Step 6.4: According to the calculation results of the airflow network model and the pressure feedback data, a PID control algorithm is used to perform corresponding inflation or deflation operations on the partitions that need to be adjusted, so as to realize dynamic adjustment of the partition pressure; Step 6.5, continuously collect the adjusted partition pressure data, input it into the airflow network model again for calculation, and determine whether the adjusted pressure value reaches the target range. If not, continue to perform step 6.4 to adjust the pressure; When the pressure values ​​of all partitions are within the target range, a pressure adjustment cycle is completed, and steps 6.2-6.5 are executed again in the next sampling cycle to achieve real-time monitoring and dynamic adjustment of mattress pressure; Step 6.6: Regularly calibrate the pressure sensor automatically, and optimize the airflow network model parameters based on usage feedback to improve the accuracy and stability of pressure control and ensure the overall support performance of the mattress.

[0013] Furthermore, in step 7, the multiple objectives of support performance, airflow balance and cost are comprehensively considered, an optimization model is established, the partition scheme, transition zone design, airflow network model and pressure feedback data are used as input, and a genetic algorithm is used to solve the airbag layout and inflation and deflation scheme to obtain the optimal design parameters, which specifically includes the following steps: Step 7.1, using the partition scheme, transition zone design, airflow network model and pressure feedback data as input to establish an optimization model; Step 7.2: Based on the optimization model, a multi-objective optimization problem is constructed by considering support performance, airflow balance and cost objectives; Step 7.3, using a genetic algorithm to solve the multi-objective optimization problem, and obtaining a set of candidate solutions for the airbag layout and inflation and deflation schemes; Step 7.4: Based on the candidate solution set, evaluate the support performance, airflow balance effect and cost of each solution, and select the solution with the best comprehensive performance as the final solution; Step 7.5, taking the airbag layout and inflation and deflation parameters of the optimal solution as the final design parameters, and designing an initial airbag support system; Step 7.6: Verify the actual performance of the designed airbag support system through simulation analysis and physical tests, and fine-tune and optimize the design parameters according to the verification results; Step 7.7: Comprehensively consider the design parameters, simulation results and test data to form the final design scheme of the airbag support system.

[0014] The technical solution provided by the embodiment of the present invention may have the following beneficial effects: The present invention discloses an intelligent mattress partition control system. The system reasonably partitions the mattress based on the human physiological curve database, and sets transition zones between adjacent partitions to achieve all-round fitting support. The system adopts an architecture combining centralized control and distributed execution, and the inflation and deflation of each partition are uniformly scheduled by the main controller to reduce the control complexity. The present invention establishes an airflow network model, and each partition is provided with an independent airbag. The airbags are connected by air paths. The mattress partitions are regarded as nodes, and the air path connections are regarded as edges. A directed graph structure is constructed, and the optimal inflation and deflation timing of the airbags in each partition is obtained by solving the maximum flow problem of the network flow. At the same time, the integrated pressure sensor collects data in real time, and the inflation and deflation amount of each partition is dynamically adjusted in combination with the calculation results of the airflow network model. The present invention comprehensively considers multiple factors, and uses genetic algorithms to optimize the airbag layout and inflation and deflation schemes, so as to achieve the consistency and stability of the mattress support performance and improve the sleeping experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic structural diagram of the intelligent airbag mattress of the present invention.

[0016] Figure 2 The figure is a connection diagram of the pipeline system of the intelligent airbag mattress of the present invention.

[0017] Figure 3 The figure is a flow chart of the control method of the intelligent airbag mattress of the present invention.

[0018] Figure 4This is a flow chart of step 3 in the control method of the intelligent airbag mattress of the present invention.

[0019] Figure 5 Flow chart of step 5 in the control method of the intelligent airbag mattress of the present invention DETAILED DESCRIPTION

[0020] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0021] like Figure 1 As shown, the smart airbag mattress includes a mattress body 1, a functional airbag layer 2, an air pump 6 and a controller.

[0022] The mattress body 1 is an independent airbag structure. The functional airbag layer 2 is arranged above the mattress body 1. The functional airbag layer 2 includes a plurality of functional airbags arranged in sequence along the length direction of the mattress body 1. The air pump 6 inflates the mattress body 1 and each functional airbag through a pipeline system. The pipeline system includes a main pipeline 4 and a plurality of branch pipelines 5. Figure 2 As shown, one end of the main pipeline 4 is connected to and communicated with the air pump 6, and the other end is connected to the outside. A deflation valve group 8 is installed on the main pipeline 4, and a silencer and deflation nozzle is provided on the deflation valve group 8 to eliminate deflation noise. The branch pipeline 5 is arranged between the air pump 6 and the deflation valve group 8. One end of the branch pipeline 5 is connected to and communicated with the main pipeline 4, and the other end is connected to and communicated with the corresponding mattress body 1 or functional airbag. A pressure sensor 9 and a branch valve group 7 for controlling the opening and closing of the branch pipeline 5 are installed on each branch pipeline 5; the controller is electrically connected to the air pump 6 and the deflation valve group 8 and the branch valve group 7, and is used to control the action of the air pump 6 and the opening and closing of the deflation valve group 8 and the branch valve group 7.

[0023] like Figure 3 As shown, this embodiment provides a control method for the above-mentioned smart airbag mattress, which includes the following steps: Step 1: Reasonably divide the mattress into zones according to the human physiological curve database, determine the position, size and shape of each zone, and achieve all-round fitting support, specifically including the following steps: Step 1.1, obtaining a pre-established human physiological curve database, wherein the human physiological curve database contains human curve data of characteristic populations of different heights and weights; Step 1.2, cluster analysis is performed on the human body curve data in the human body physiological curve database to obtain several typical human body curve categories; Step 1.3, according to the cluster analysis results, determine the number of mattress partitions, each partition corresponds to a typical human body curve category; Step 1.4, for each partition, statistical analysis is performed on the human body curve data of the corresponding category to obtain the optimal position, size and shape parameters of the partition; Step 1.5: Using computer-aided design software, generate a three-dimensional model according to the parameters of each partition, and perform simulation analysis to determine whether each partition can achieve fit support for the human body curve; If the simulation results meet the requirements of fitting support, the parameters of each partition are passed to the CNC machining equipment; Otherwise, adjust the partition parameters and repeat step 1.5 until the requirements are met; Step 1.6, the CNC processing equipment processes the mattress according to the received partition parameters, cuts out each partition at the corresponding position, and shapes it according to the set shape, finally obtaining a partitioned mattress product with all-round fitting support.

[0024] Specifically, the human body curve data of 1,000 men and women aged 18-60 were first obtained from the established human physiological curve database. The human body curve data included key parameters such as height, weight, shoulder width, waist circumference, and hip circumference. The K-means clustering algorithm was used to analyze the human body curve data. By calculating the contour coefficient and SSE value, the human body curve was divided into three categories, namely, slender, symmetrical, and obese. According to the clustering results, the mattress was divided into three partitions: head, back, and waist and hip. Taking the back partition as an example, 100 human body curve data corresponding to the back partition were extracted. By calculating the mean and variance of the data, the optimal length of the partition was 60 cm, the width was 80 cm, the height was 10 cm, and the curvature radius was 20 cm. The three-dimensional model of the partition was drawn in the computer-aided design software AutoCAD, imported into the ANSYS software, set the elastic modulus to 1 GPa, the Poisson's ratio to 4, and applied a pressure load of 80 kg. The strain cloud diagram formed showed that the maximum strain was 3, which was less than the yield strain of the material 5, meeting the requirements of fitting support. The partition parameters are passed to the CNC machining center to cut and mill the mattress blanks. The machining accuracy is controlled within ±2mm and the surface roughness Ra is less than 0.5. After grinding, polishing and quality inspection, the final partition mattress product is fully fitted to the human body curve.

[0025] Step 2: Based on the partitioning scheme, a control architecture combining centralized control and distributed execution is adopted. A main controller is set to uniformly schedule the charging and deflation of each partition. Each partition is set with an actuator responsible for the charging and deflation of the area, which reduces the control complexity and cost. Specifically, the following steps are included: Step 2.1, based on the partitioning scheme, set up an executor in each partition; Step 2.2, obtaining the charging and discharging demand information of each partition, and transmitting the charging and discharging demand information to the main controller; Step 2.3, the main controller determines the charging and discharging control instructions of each partition according to the charging and discharging requirements of each partition and the preset scheduling strategy; Step 2.4, the main controller sends the inflation and deflation control instructions to the actuators of the corresponding partitions; Step 2.5, each partition actuator controls the inflation and deflation equipment in the partition area to perform inflation and deflation operations according to the received control instructions. During the inflation and deflation process, each actuator collects the inflation and deflation status data of the partition area in real time and reports it to the main controller; Step 2.6: The main controller determines whether the inflation and deflation process is in line with expectations based on the status data reported by each partition. If there is a deviation, the control instructions are dynamically adjusted until the inflation and deflation are completed.

[0026] Specifically, first, according to the zoning scheme, an actuator is set at the center of each zone. Each zone actuator uploads the demand information of the peak gas consumption time period, gas consumption, and the number of gas charging and discharging equipment in the area to the main controller through the wireless communication module. Based on the prediction algorithm and historical data, the main controller estimates the gas consumption of each zone in the next week. Combined with the equipment energy efficiency and pipeline pressure balance factors, the integer programming model is used to determine the gas charging and discharging schedule and gas volume scheduling plan for each zone, form control instructions, and send the control instructions to the corresponding zone actuator. According to the instructions, the zone actuator controls the valves, compressors and gas storage tank equipment in the jurisdiction to work together, complete the pre-charge before the peak of gas consumption, continue to supply gas during the peak, and deflate in time after the peak. Each actuator has a built-in flow sensor and pressure sensor, which collects real-time data of the pipeline network in the area every 30 seconds, extracts key features and reports them to the main controller. The main controller summarizes and compares the status data of each partition. If it is found that the pressure in a certain area exceeds the rated value by 10% or the gas supply is insufficient, an early warning is triggered and a dynamic adjustment instruction is automatically generated. Through valve adjustment and gas source scheduling, the deviation is controlled within 5% within 3 minutes, ensuring a safe and smooth inflation and deflation process, and ultimately completing the inflation and deflation tasks in all partitions.

[0027] Step 3: Set up a transition zone at the junction of adjacent partitions. By optimizing the airbag arrangement and size in the transition zone, a smooth transition of pressure and height between partitions can be achieved to improve the sleeping experience. Figure 4 As shown, the specific steps include: Step 3.1, determine the position and range of the transition zone according to the pressure and height differences of adjacent partitions of the airbag mattress; Step 3.2, obtaining the airbag arrangement and size data of each partition of the airbag mattress as input for optimizing the transition zone airbag design; Step 3.3, analyzing the airbag data of adjacent partitions by clustering algorithm to obtain an initial plan for the arrangement and size of the airbags in the transition zone; Step 3.4, using finite element simulation to simulate the pressure and height transition effects under different airbag arrangements and size schemes, and select the optimal scheme; Step 3.5, fine-tune the arrangement and size of the airbags in the transition zone according to the simulation results to obtain the airbag design parameters that meet the smooth transition requirements; Step 3.6, applying the optimized transition zone airbag design parameters to the production of airbag mattresses to achieve smooth transition of pressure and height between adjacent partitions; Step 3.7: Evaluate the sleeping comfort of the airbag mattress through ergonomic testing, verify the effectiveness of the transition zone optimization design, and continuously improve the airbag design solution.

[0028] Specifically, the pressure difference between adjacent partitions of the airbag mattress is first measured by a pressure sensor, such as the pressure in the head area is 2kPa, the pressure in the shoulder area is 5kPa, and the pressure difference is 3kPa; at the same time, the height difference between adjacent partitions is measured using a height sensor, such as the height of the head area is 15cm, the height of the shoulder area is 18cm, and the height difference is 3cm. According to the pressure and height differences, it is determined that the transition zone is located between the head and shoulder areas, with a range of 10cm. Then, the airbag arrangement and size data of each partition of the airbag mattress are obtained. The diameter of the airbag in the head area is 6cm, and the spacing is 2cm; the diameter of the airbag in the shoulder area is 8cm, and the spacing is 3cm. The K-means clustering algorithm is used to analyze the airbag data of adjacent partitions, and an initial solution is obtained with a transition zone airbag diameter of 7cm and a spacing of 5cm. Then, the ANSYS finite element simulation software was used to simulate the pressure and height transition effects under different airbag arrangements and size schemes. For example, when the airbag diameter in the transition zone is 5 cm and the spacing is 2 cm, the pressure transition gradient is 2 kPa / cm and the height transition gradient is 25 cm / cm. The overall score is the highest and is selected as the optimal solution. According to the simulation results, the airbag arrangement and size in the transition zone are fine-tuned to obtain the design parameters of the airbag diameter of 8 cm and the spacing of 3 cm, which meet the requirements of smooth transition of pressure and height. The optimized airbag design parameters in the transition zone are applied to the production and manufacturing of airbag mattresses to achieve smooth transition of pressure and height between the head and shoulder areas. Finally, through ergonomic tests, 20 subjects were used as samples, and the pressure distribution measurement system and subjective scoring scale were used to evaluate the sleeping comfort of the optimized airbag mattress. The results showed that the average comfort score of the subjects increased by 15%, verifying the effectiveness of the transition zone optimization design. According to the test feedback, the airbag parameters in the transition zone are further adjusted, such as reducing the spacing to 1 cm, and the airbag design scheme is continuously improved to improve the comprehensive performance of the airbag mattress.

[0029] Step 4: According to the partition scheme and transition zone design, the airbags and pipelines are integrated to minimize the use of pipelines while ensuring the functionality, thereby improving the integration and aesthetics of the mattress. Specifically, the following steps are included: Step 4.1, obtaining the initial layout of the airbag and pipeline according to the preset partition scheme and transition zone design requirements; Step 4.2: Using the topology optimization algorithm, the layout of the airbag and pipeline is optimized to reduce the usage of pipelines while ensuring the functions of the airbag and pipelines; Step 4.3, using simulated annealing algorithm, further adjust the optimized airbag and pipeline layout to improve integration; Step 4.4, according to the optimized layout of the airbag and the pipeline, establish a three-dimensional model, design the appearance of the airbag and the pipeline, and improve the aesthetics; Step 4.5: Apply the optimized airbag and pipeline layout to actual production, and verify whether the functions of the airbag and pipeline meet the requirements through physical testing; If the test results meet the requirements, the final design of airbag and pipeline integration will be determined; If the test result does not meet the requirements, return to step 4.2 and continue to optimize and adjust until the requirements are met.

[0030] Specifically, according to the preset partition scheme and transition zone design requirements, the initial layout diagram of the airbag and pipeline is drawn using CAD software and imported into the topology optimization algorithm. The topology optimization algorithm is based on finite element analysis. Through iterative calculation, the layout is optimized while ensuring the functions of the airbag and pipeline. For example, the pipeline diameter is reduced from 10mm to 8mm, reducing the use of pipelines by 20%. The optimized layout is imported into the simulated annealing algorithm, and the temperature parameters are set to 1000K, the cooling rate is 95, and the iteration is 500 times. The pipeline layout is fine-tuned to make it more compact and the integration is increased by 15%. According to the optimized layout, SolidWorks is used to build a three-dimensional model and perform appearance design. For example, the color of the pipeline is set to a blue color similar to the airbag, which improves the overall aesthetics. The optimized design scheme is applied to actual production, and 100 samples are tested for air tightness and pressure resistance to verify whether its function meets the requirements. The test results show that the air tightness qualification rate is 98% and the pressure resistance qualification rate is 95%, which meets the design requirements. Finally, the integrated design scheme of the airbag and pipeline is determined.

[0031] Step 5: Establish an airflow network model. Set an independent airbag in each partition. The airbags are connected by air paths. Treat each partition of the mattress as a node and the air path connection as an edge. Construct a directed graph structure. By solving the maximum flow problem of the network flow, the optimal inflation and deflation timing of the airbags in each partition is obtained. While meeting the support performance requirements, the airflow balance between the partitions is achieved. Figure 5 As shown, the specific steps include: Step 5.1, according to the mattress partition layout and air path connection relationship, establish an airflow network directed graph model, abstract each partition airbag as a node of the graph, and abstract the air path connection as a directed edge; Step 5.2: for the established airflow network model, set the attribute parameters of each node and edge. The attribute parameters of the node include the volume and initial pressure of the node, and the attribute parameters of the edge include the pipe diameter, flow coefficient and flow direction of the edge. Step 5.3, according to the support performance requirements, determine the target pressure range of each partition airbag as one of the constraints of the network flow problem of the airflow network model; Step 5.4: By solving the maximum flow problem of the network flow, the maximum flow value of the network is obtained under the premise of satisfying the pressure constraints of each node, and the corresponding flow distribution plan of each edge is obtained; Step 5.5, according to the flow rate of each edge obtained by solving, combined with the attribute parameters of the edge, calculate the airflow velocity and inflation and deflation time of each edge, and obtain the inflation and deflation timing of each partition airbag; Step 5.6: Determine whether the obtained inflation and deflation timing sequence meets the support performance requirements. If not, adjust the parameters or constraints of the airflow network model and solve the maximum flow problem again until the requirements are met. Step 5.7: According to the final inflation and deflation timing, the inflation and deflation process of each partition airbag is controlled to achieve a dynamic balance of airflow between partitions while meeting the support performance, thereby improving the comfort and reliability of the mattress.

[0032] Specifically, according to the partition layout and air path connection relationship of the mattress, an airflow network directed graph model is established. Each partition is equipped with an independent airbag, which is connected by air paths and a flow control valve is set on the air path. For this airflow network model, the volume of each node is set between 2-4 liters, the initial pressure is 2-5 kPa, the flow coefficient of the edge is 8-2, and the flow direction is consistent with the actual air path layout. According to the support requirements of different parts of the human body, the target pressure range of each partition airbag is determined, such as 3-5 kPa for the head, 8-2 kPa for the shoulder, 0-5 kPa for the waist, 2-8 kPa for the buttocks, and 5-8 kPa for the legs. Using the network flow maximum flow algorithm, under the condition of satisfying the pressure constraints of each node, the maximum flow value of the network is obtained to be 500 liters / minute, and the corresponding flow distribution schemes for each edge are 80 liters / minute for the head, 120 liters / minute for the shoulder, 150 liters / minute for the waist, 180 liters / minute for the buttocks, and 100 liters / minute for the legs. According to the pipe diameter of the edge of 6-8 mm and the flow coefficient of 8-2, the airflow velocity of each edge is calculated to be 5-5 m / s, the inflation time is 30-50 seconds, and the deflation time is 20-40 seconds, thereby obtaining the inflation and deflation timing of each partition airbag. Through simulation analysis and actual testing, it is judged that the inflation and deflation timing can meet the support performance requirements of various parts of the human body, achieve the dynamic balance of airflow between partitions, and improve the comfort and reliability of the mattress. If the requirements are not met, the node volume, target pressure range, and flow coefficient parameters of the edge of the network model can be adjusted, or the constraints of the inflation and deflation time can be added, and the maximum flow problem of the network flow can be re-solved until the requirements are met. Finally, according to the optimized inflation and deflation timing, the switch and duration of each solenoid valve are controlled to realize the automatic inflation and deflation process of each partition airbag, while meeting the support performance, achieving the dynamic balance of airflow between partitions, and improving the comfort and reliability of the mattress.

[0033] Step 6: The integrated pressure sensor collects the pressure feedback data of each partition in real time, and combines the calculation results of the airflow network model to dynamically adjust the inflation and deflation volume of each partition through a closed-loop feedback control method to ensure the consistency and stability of the support performance of the entire mattress, which specifically includes the following steps: Step 6.1, obtaining the preset target pressure value range and airflow network model parameters of each partition of the mattress as the reference value and calculation basis of pressure control; Step 6.2, collecting pressure feedback data from pressure sensors integrated in each partition of the mattress in real time, inputting the collected pressure feedback data into the airflow network model for calculation and analysis, and obtaining the pressure distribution state of each partition; Step 6.3, determine whether the actual pressure value of each partition is within the target pressure value range, if not within the range, determine the inflation and deflation adjustment amount of the corresponding partition; Step 6.4: According to the calculation results of the airflow network model and the pressure feedback data, a PID control algorithm is used to perform corresponding inflation or deflation operations on the partitions that need to be adjusted, so as to realize dynamic adjustment of the partition pressure; Step 6.5, continuously collect the adjusted partition pressure data, input it into the airflow network model again for calculation, and determine whether the adjusted pressure value reaches the target range. If not, continue to perform step 6.4 to adjust the pressure; When the pressure values ​​of all partitions are within the target range, a pressure adjustment cycle is completed, and steps 6.2-6.5 are executed again in the next sampling cycle to achieve real-time monitoring and dynamic adjustment of mattress pressure; Step 6.6: Regularly calibrate the pressure sensor automatically, and optimize the airflow network model parameters based on usage feedback to improve the accuracy and stability of pressure control and ensure the overall support performance of the mattress.

[0034] Specifically, first, according to the requirements of ergonomics and mattress support performance, the target pressure value range of each partition is preset, such as the pressure range of the shoulder partition is 8-2kPa, and the pressure range of the waist partition is 5-0kPa. At the same time, based on the mattress structure and material properties, an airflow network model is established, and model parameters such as the connectivity coefficient between partitions is set to 6 and the flow coefficient of the inflation valve is set to 5L / min. During use, the pressure sensor integrated in the mattress collects the pressure data of each partition in real time at a frequency of 10Hz, and inputs the data into the airflow network model for solution calculation to obtain the pressure distribution cloud map and numerical matrix of each partition. By comparing the actual pressure value with the target range, it is determined whether adjustment is required. If the pressure of the shoulder partition is 6kPa, which is lower than the target range, the PID control algorithm is used to calculate that 20L / min of inflation is required for 5s. After the inflation is completed, the pressure data is collected again and input into the model for recalculation until the shoulder pressure reaches 9kPa, meeting the target range requirements. Pressure adjustment is also performed on other partitions until all partitions meet the standard. During long-term use, the pressure sensor is automatically calibrated every 30 days, and based on the user's comfort feedback, the boundary conditions and constraint parameters of the airflow network model are appropriately adjusted, such as raising the upper limit of the lumbar pressure range to 2kPa, to optimize the model's accuracy and pressure regulation effect, thereby continuously ensuring the overall support performance of the mattress and improving the user's sleep experience.

[0035] Step 7: Considering the multiple objectives of support performance, airflow balance and cost, an optimization model is established. The partition scheme, transition zone design, airflow network model and pressure feedback data are used as inputs. A genetic algorithm is used to solve the airbag layout and inflation and deflation scheme to obtain the optimal design parameters. Specifically, the following steps are included: Step 7.1, using the partition scheme, transition zone design, airflow network model and pressure feedback data as input to establish an optimization model; Step 7.2: Based on the optimization model, a multi-objective optimization problem is constructed by considering support performance, airflow balance and cost objectives; Step 7.3, using a genetic algorithm to solve the multi-objective optimization problem, and obtaining a set of candidate solutions for the airbag layout and inflation and deflation schemes; Step 7.4: Based on the candidate solution set, evaluate the support performance, airflow balance effect and cost of each solution, and select the solution with the best comprehensive performance as the final solution; Step 7.5, taking the airbag layout and inflation and deflation parameters of the optimal solution as the final design parameters, and designing an initial airbag support system; Step 7.6: Verify the actual performance of the designed airbag support system through simulation analysis and physical tests, and fine-tune and optimize the design parameters according to the verification results; Step 7.7: Comprehensively consider the design parameters, simulation results and test data to form the final design scheme of the airbag support system.

[0036] Specifically, the partition scheme and transition zone design are used as inputs, and the optimization model is established using Fluent software. Fluent transfers the optimization model data to MATLAB, and the airflow network model and pressure feedback data are input into the MATLAB optimization toolbox. At the same time, the constraints of support stiffness not less than 10kN / m, airflow velocity difference not exceeding 5m / s, and cost control within 1000 yuan are considered to construct a multi-objective optimization problem. The genetic algorithm is used to solve the multi-objective optimization problem, and the population size is set to 50, the crossover probability is set to 8, the mutation probability is set to 1, and the evolutionary generation is set to 100, and the candidate solution set of the airbag layout and inflation and deflation scheme is obtained. According to the candidate solution set, the support performance, airflow balance effect and cost of each solution are evaluated using ANSYS software, and the weight of each indicator is determined by the hierarchical analysis method, and the comprehensive performance index is calculated. The solution with the highest index is selected as the final solution. The airbag layout and inflation and deflation parameters of the optimal solution are input into the CAD software, and the three-dimensional model of the airbag support system is drawn. The processing drawings and assembly instructions are generated according to the model to guide the actual design and manufacture of the airbag support system. Through multi-physics field coupling simulation, the deformation, stress distribution and airflow velocity field of the airbag support system are analyzed, and bench tests are carried out to test the airbag lifting time, load capacity and airflow noise, verifying that the designed airbag support system meets the actual use requirements. According to the verification results, the design parameters are fine-tuned to shorten the lifting time by 10%, increase the load capacity by 20%, and reduce the airflow noise by 5dB. Taking into account the design parameters, simulation results and test data, the design manual and maintenance instructions of the airbag support system are compiled to form the final design plan.

[0037] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A control method for an intelligent airbag mattress, characterized in that: The method comprises the following steps: Step 1: Reasonably divide the mattress into zones according to the human physiological curve database, determine the position, size and shape of each zone, and achieve all-round fitting support; Step 2: Based on the partitioning scheme, a control architecture combining centralized control and distributed execution is adopted. A main controller is set to uniformly schedule the charging and deflation of each partition. Each partition is set with an actuator to be responsible for the charging and deflation of the area, reducing the control complexity and cost; Step 3: Set a transition zone at the junction of adjacent partitions, and optimize the airbag arrangement and size of the transition zone to achieve a smooth transition of pressure and height between partitions to improve the sleeping experience; Step 4: According to the partition scheme and transition zone design, the airbags and pipelines are designed in an integrated manner to minimize the use of pipelines while ensuring the functionality, thereby improving the integration and aesthetics of the mattress; Step 5: Establish an airflow network model. Each partition is equipped with an independent airbag. The airbags are connected by air paths. The mattress partitions are regarded as nodes, and the air path connections are regarded as edges. A directed graph structure is constructed. By solving the maximum flow problem of the network flow, the optimal inflation and deflation timing of the airbags in each partition is obtained. While meeting the support performance requirements, the airflow balance between the partitions is achieved. Step 6: The integrated pressure sensor collects the pressure feedback data of each partition in real time, and combines the calculation results of the airflow network model to dynamically adjust the inflation and deflation volume of each partition through a closed-loop feedback control method to ensure the consistency and stability of the support performance of the entire mattress; Step 7: Considering the multiple objectives of support performance, airflow balance and cost, an optimization model is established. The partition scheme, transition zone design, airflow network model and pressure feedback data are used as inputs, and a genetic algorithm is used to solve the airbag layout and inflation and deflation scheme to obtain the optimal design parameters.

2. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In the step 1, the mattress is divided into zones according to the human physiological curve database, and the position, size and shape of each zone are determined to achieve all-round fitting support, which specifically includes the following steps: Step 1.1, obtaining a pre-established human physiological curve database, wherein the human physiological curve database contains human curve data of characteristic populations of different heights and weights; Step 1.2, cluster analysis is performed on the human body curve data in the human body physiological curve database to obtain several typical human body curve categories; Step 1.3, according to the cluster analysis results, determine the number of mattress partitions, each partition corresponds to a typical human body curve category; Step 1.4, for each partition, statistical analysis is performed on the human body curve data of the corresponding category to obtain the optimal position, size and shape parameters of the partition; Step 1.5: Using computer-aided design software, generate a three-dimensional model according to the parameters of each partition, and perform simulation analysis to determine whether each partition can achieve fit support for the human body curve; If the simulation results meet the requirements of fitting support, the parameters of each partition are passed to the CNC machining equipment; Otherwise, adjust the partition parameters and repeat step 1.5 until the requirements are met; Step 1.6, the CNC processing equipment processes the mattress according to the received partition parameters, cuts out each partition at the corresponding position, and shapes it according to the set shape, finally obtaining a partitioned mattress product with all-round fitting support.

3. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 2, based on the partitioning scheme, a control architecture combining centralized control and distributed execution is adopted, a main controller is set to uniformly schedule the charging and deflation of each partition, and each partition is set to have an actuator responsible for the charging and deflation of the area, thereby reducing the control complexity and cost, and specifically includes the following steps: Step 2.1, based on the partitioning scheme, set up an executor in each partition; Step 2.2, obtaining the charging and discharging demand information of each partition, and transmitting the charging and discharging demand information to the main controller; Step 2.3, the main controller determines the inflation and deflation control instructions of each partition according to the inflation and deflation requirements of each partition and the preset scheduling strategy; Step 2.4, the main controller sends the inflation and deflation control instructions to the actuators of the corresponding partitions; Step 2.5, each partition actuator controls the inflation and deflation equipment in the partition area to perform inflation and deflation operations according to the received control instructions. During the inflation and deflation process, each actuator collects the inflation and deflation status data of the partition area in real time and reports it to the main controller; Step 2.6: The main controller determines whether the inflation and deflation process is in line with expectations based on the status data reported by each partition. If there is a deviation, the control instructions are dynamically adjusted until the inflation and deflation are completed.

4. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 3, a transition zone is set at the junction of adjacent partitions, and the arrangement and size of the airbags in the transition zone are optimized to achieve a smooth transition of pressure and height between the partitions, thereby improving the sleeping experience. Specifically, the following steps are included: Step 3.1, determine the position and range of the transition zone according to the pressure and height differences of adjacent partitions of the airbag mattress; Step 3.2, obtaining the airbag arrangement and size data of each partition of the airbag mattress as input for optimizing the transition zone airbag design; Step 3.3, analyzing the airbag data of adjacent partitions by clustering algorithm to obtain an initial plan for the arrangement and size of the airbags in the transition zone; Step 3.4, using finite element simulation to simulate the pressure and height transition effects under different airbag arrangements and size schemes, and select the optimal scheme; Step 3.5, fine-tune the arrangement and size of the airbags in the transition zone according to the simulation results to obtain the airbag design parameters that meet the smooth transition requirements; Step 3.6, applying the optimized transition zone airbag design parameters to the production of airbag mattresses to achieve smooth transition of pressure and height between adjacent partitions; Step 3.7: Evaluate the sleeping comfort of the airbag mattress through ergonomic testing, verify the effectiveness of the transition zone optimization design, and continuously improve the airbag design solution.

5. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 4, the airbag and the pipeline are integrated according to the partition scheme and the transition zone design, so as to minimize the use of pipelines while ensuring the function, and improve the integration and aesthetics of the mattress, which specifically includes the following steps: Step 4.1, obtaining the initial layout of the airbag and pipeline according to the preset partition scheme and transition zone design requirements; Step 4.2: Using the topology optimization algorithm, the layout of the airbag and pipeline is optimized to reduce the usage of pipelines while ensuring the functions of the airbag and pipelines; Step 4.3, using simulated annealing algorithm, further adjust the optimized airbag and pipeline layout to improve integration; Step 4.4, according to the optimized layout of the airbag and the pipeline, establish a three-dimensional model, design the appearance of the airbag and the pipeline, and improve the aesthetics; Step 4.5: Apply the optimized airbag and pipeline layout to actual production, and verify whether the functions of the airbag and pipeline meet the requirements through physical testing; If the test results meet the requirements, the final design of airbag and pipeline integration will be determined; If the test result does not meet the requirements, return to step 4.2 and continue to optimize and adjust until the requirements are met.

6. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 5, an airflow network model is established, an independent airbag is set in each partition, and the airbags are connected by air paths. The mattress partitions are regarded as nodes, and the air path connections are regarded as edges. A directed graph structure is constructed, and the optimal inflation and deflation timing of the airbags in each partition is obtained by solving the maximum flow problem of the network flow. While meeting the support performance requirements, the airflow balance between the partitions is achieved. Specifically, the following steps are included: Step 5.1, according to the mattress partition layout and air path connection relationship, establish an airflow network directed graph model, abstract each partition airbag as a node of the graph, and abstract the air path connection as a directed edge; Step 5.2: for the established airflow network model, set the attribute parameters of each node and edge. The attribute parameters of the node include the volume and initial pressure of the node, and the attribute parameters of the edge include the pipe diameter, flow coefficient and flow direction of the edge. Step 5.3, according to the support performance requirements, determine the target pressure range of each partition airbag as one of the constraints of the network flow problem of the airflow network model; Step 5.4: By solving the maximum flow problem of the network flow, the maximum flow value of the network is obtained under the premise of satisfying the pressure constraints of each node, and the corresponding flow distribution plan of each edge is obtained; Step 5.5, according to the flow rate of each edge obtained by solving, combined with the attribute parameters of the edge, calculate the airflow velocity and inflation and deflation time of each edge, and obtain the inflation and deflation timing of each partition airbag; Step 5.6: Determine whether the obtained inflation and deflation timing sequence meets the support performance requirements. If not, adjust the parameters or constraints of the airflow network model and solve the maximum flow problem again until the requirements are met. Step 5.7: According to the final inflation and deflation timing, the inflation and deflation process of each partition airbag is controlled to achieve a dynamic balance of airflow between partitions while meeting the support performance, thereby improving the comfort and reliability of the mattress.

7. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 6, the integrated pressure sensor collects the pressure feedback data of each partition in real time, combines the calculation results of the airflow network model, and dynamically adjusts the inflation and deflation volume of each partition through a closed-loop feedback control method to ensure the consistency and stability of the support performance of the entire mattress, which specifically includes the following steps: Step 6.1, obtaining the preset target pressure value range and airflow network model parameters of each partition of the mattress as the reference value and calculation basis of pressure control; Step 6.2, collecting pressure feedback data from pressure sensors integrated in each partition of the mattress in real time, inputting the collected pressure feedback data into the airflow network model for calculation and analysis, and obtaining the pressure distribution state of each partition; Step 6.3, determine whether the actual pressure value of each partition is within the target pressure value range, if not within the range, determine the inflation and deflation adjustment amount of the corresponding partition; Step 6.4: According to the calculation results of the airflow network model and the pressure feedback data, a PID control algorithm is used to perform corresponding inflation or deflation operations on the partitions that need to be adjusted, so as to realize dynamic adjustment of the partition pressure; Step 6.5, continuously collect the adjusted partition pressure data, input it into the airflow network model again for calculation, and determine whether the adjusted pressure value reaches the target range. If not, continue to perform step 6.4 to adjust the pressure; When the pressure values ​​of all partitions are within the target range, a pressure adjustment cycle is completed, and steps 6.2-6.5 are executed again in the next sampling cycle to achieve real-time monitoring and dynamic adjustment of mattress pressure; Step 6.6: Regularly calibrate the pressure sensor automatically, and optimize the airflow network model parameters based on usage feedback to improve the accuracy and stability of pressure control and ensure the overall support performance of the mattress.

8. The control method of the intelligent airbag mattress according to claim 1, characterized in that: In step 7, the multiple objectives of support performance, airflow balance and cost are comprehensively considered, an optimization model is established, the partition scheme, transition zone design, airflow network model and pressure feedback data are used as input, and a genetic algorithm is used to solve the airbag layout and inflation and deflation scheme to obtain the optimal design parameters, which specifically includes the following steps: Step 7.1, using the partition scheme, transition zone design, airflow network model and pressure feedback data as input to establish an optimization model; Step 7.2: Based on the optimization model, a multi-objective optimization problem is constructed by considering support performance, airflow balance and cost objectives; Step 7.3, using a genetic algorithm to solve the multi-objective optimization problem, and obtaining a set of candidate solutions for the airbag layout and inflation and deflation schemes; Step 7.4: Based on the candidate solution set, evaluate the support performance, airflow balance effect and cost of each solution, and select the solution with the best comprehensive performance as the final solution; Step 7.5, taking the airbag layout and inflation and deflation parameters of the optimal solution as the final design parameters, and designing an initial airbag support system; Step 7.6: Verify the actual performance of the designed airbag support system through simulation analysis and physical tests, and fine-tune and optimize the design parameters according to the verification results; Step 7.7: Comprehensively consider the design parameters, simulation results and test data to form the final design scheme of the airbag support system.

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