CN110132815B - Prediction system for comfort of sleep product - Google Patents
Prediction system for comfort of sleep product Download PDFInfo
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- CN110132815B CN110132815B CN201910279020.XA CN201910279020A CN110132815B CN 110132815 B CN110132815 B CN 110132815B CN 201910279020 A CN201910279020 A CN 201910279020A CN 110132815 B CN110132815 B CN 110132815B
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C31/00—Details or accessories for chairs, beds, or the like, not provided for in other groups of this subclass, e.g. upholstery fasteners, mattress protectors, stretching devices for mattress nets
- A47C31/12—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons
- A47C31/123—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons for beds or mattresses
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/08—Investigating permeability, pore-volume, or surface area of porous materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/36—Textiles
Abstract
The invention provides a prediction system for comfort of a sleep product, which comprises a sleep product heat preservation database, a model establishing module, a model verifying module, a parameterization research module, a prediction module and a recommending module. The system establishment comprises the following steps: s1: establishing a sleep human body comfort prediction model; s2: determining boundary conditions and substituting the boundary conditions into the established model, and modifying output data until the error between the experimental data and the output data is within 7%; s3: carrying out parametric study; s4: obtaining a graph relation among the sleep product parameters, the human body factors and the environmental factors based on S3 to obtain a prediction system of the comfort of the sleep product; s5: based on the prediction system of the comfort of the sleep product obtained in the S4, the sleep product used in a specific environment is recommended, and the comfort of the human body in sleeping is ensured. The prediction system for the comfort of the sleep product can more scientifically and accurately predict the comfort of the sleep product.
Description
Technical Field
The invention relates to the technical field of smart home, in particular to a system for predicting comfort of a sleep product.
Background
With the improvement of the social and economic level and the consumption level, people always want to pursue higher-level needs on the basis of meeting the aesthetic property of sleep products. Because the propaganda and popularization of the Internet to the comfort and health of the sleep product and the improvement of the education level and the cognitive level of people, the attention of people to the physiological condition and the mental condition of the people is higher and higher, and the interest of people to the comfort of the sleep product is gradually changed from the interest of the beauty of the sleep product. In addition, the social pressure of high-speed operation makes most people tired, and it is especially necessary to improve the comfort of sleep products so as to improve the sleep quality of people.
When a consumer selects a sleep product, the comfort of the sleep product is estimated mainly according to the type of the fabric, the thickness of the quilt cover, the type and content of the filling material and the like, and the aim of improving the comfort of the sleep product is achieved by adjusting the thickness of the fabric and the content of the filling material to prevent the heat of a human body from being radiated outwards.
The prior art mainly has the following disadvantages: the comfort of the product is estimated according to the thickness of the sleep product, the types and the content of the fillers, more uncertain factors exist, and the comfort temperature range of the sleep product is difficult to accurately judge. First, consumers often place themselves in relatively comfortable store environments when they purchase products, not necessarily in actual use environments, and it is difficult to make accurate predictions. Secondly, the consumer is in a wakeful state when judging, and is in a sleeping state when actually using, and the difference of the human body physiological state is large, so that the comfort of the product cannot be accurately estimated. Finally, the actual sleeping environment is also dynamically changed, and the comfort degree of the product estimated by consumers is difficult.
Therefore, a more scientific and accurate system for predicting the comfort of the sleep product is needed, which predicts the relationship between the human body and the sleep product and the environment in the sleep state, makes an intelligent recommendation, and improves the comfort of the sleep product.
Disclosure of Invention
The technical problem to be solved is as follows: the invention provides a prediction system for the comfort of a sleep product, which can more scientifically and accurately predict the comfort of the sleep product.
The technical scheme is as follows: a prediction system for comfort of a sleep product comprises a sleep product heat preservation database, a model establishing module, a model verifying module, a parameterization research module, a prediction module and a recommendation module;
the sleep product heat preservation quantity database is a sleep product database which is constructed according to the fabric type, the fabric thickness, the filler type, the filler weight, the human body posture and the like of the sleep product and is recommended under a specific environment;
the model building module builds a sleep human body comfort prediction model about a sleep human body, a sleep product and a local environment according to the human body heat transfer model;
the model verification module obtains the graph relation among the human body factors, the sleep product factors and the environment factors by continuously correcting the difference value between the experimental data and the output data;
the parameterized research module can carry out parameterized research on various human body factors, sleep product factors and environmental factors based on the verified sleep product comfort prediction model to obtain a sleep product comfort prediction system;
the prediction module predicts the comfort of the human body sleep product when the environmental factors change according to the obtained sleep human body comfort prediction model of the final 'sleep human body-sleep product-local environment';
the recommending module can obtain a dynamic map of time-core temperature-body surface temperature through the predicting module of the established predicting system, so that the skin temperature and core body temperature change trends of the testee after different types of sleep products are used under different environmental temperature conditions can be evaluated, and a consumer is guided to select a proper sleep product under a specific environmental condition based on the sleep product heat preservation quantity database.
Further, the establishment of the prediction system comprises the following steps:
s1: establishing a sleep human body comfort prediction model of a 'sleep human body-sleep product-local environment' according to the human body physiological heat transfer characteristics;
s2: performing an experiment, determining boundary conditions, substituting the boundary conditions into the established model, comparing result data obtained by the experiment with output data of the model, and modifying the result data and the output data continuously repeatedly until the error between the experiment data and the output data is within 7%;
s3: carrying out parametric research on various environmental factors, sleep product factors and human body factors based on the verified sleep product comfort prediction model;
s4: obtaining a graph relation among the sleep product parameters, the human body factors and the environmental factors based on S3 to obtain a prediction system of the comfort of the sleep product;
s5: the prediction system for the comfort of the sleep product obtained based on the S4 can recommend the sleep product used in a specific environment based on the sleep product heat-preservation quantity database according to the temperature, humidity, radiation temperature, wind speed and other parameters of the weather forecast, so as to ensure the comfort of the sleep of the human body.
Further, the model for predicting the comfort of the sleeping human body-sleeping product-local environment in S1 is a multi-node physiological heat transfer model established according to the heat transfer related theory, and the human body is subdivided into 32 segments, each segment is divided into four layers from inside to outside, a blood center is added, and 129 nodes are provided, and the four layers in each segment are a body core layer, a muscle layer, a fat layer and a skin layer.
Further, the physiological heat transfer model in S1 considers the periodicity of the thermal regulation of the sleeping human body, i.e. the rapid eye movement period and the non-rapid eye movement period, and has different physiological parameters such as metabolic capacity, sweating rate, blood flow, and cold-fibrillation heating value.
Further, the local environment in S1 is the thermal resistance and the wet resistance of the sleeping human body in the 32 local sections and the surrounding environmental parameters, including the air temperature, the relative humidity, the radiation temperature, the wind speed, and the like.
Further, the experimental data in S2 are the core body temperature and the 12-point average skin temperature measured in the real human experiment, and the output data are the core body temperature and the average skin temperature predicted by the model.
Further, the sleeping products and environmental factors in S3 include local thermal and moisture resistances of bedding and a mattress during sleeping, local thermal and moisture resistances of a garment worn, an ambient temperature, a humidity, a radiation temperature, and a wind speed, and the human body factors include changes in posture of the human body, i.e., lying down, lying on the side, and rolling.
Further, the sleep product heat preservation amount database in S5 is constructed according to the fabric type, the fabric thickness, the filler type, and the filler weight of the sleep product, and needs to be obtained by accumulating a large amount of experimental data.
Has the advantages that:
1. the prediction system of the invention divides the human body into 32 sections, each section is divided into four layers from inside to outside, a blood center is added, 129 nodes are totally added, the human body is highly subdivided, a more accurate and scientific physiological model is established, and the prediction system has more accurate and scientific theoretical support.
2. The prediction system of the invention fully considers the physiological characteristics of the human body, divides the sleep state of the human body into two different metabolic states of a rapid eye movement period and a non-rapid eye movement period, and considers the local thermal resistance and the moisture resistance of different parts of the human body and the local thermal resistance of the human body under different postures, so that the basic data is more accurate and more conforms to the actual state of the human body.
3. The prediction system further considers various factors influencing the comfort of the sleep product, namely the sleep product, environmental factors and human factors, so that the research is more detailed and comprehensive. Therefore, the sleeping bag helps consumers to select comfortable sleeping products, ensures the sleeping quality, and guides manufacturers to know the relation between the warmth retention property and the using temperature of the sleeping products, so that the corresponding products are designed according to the using environment of the consumers.
Drawings
Fig. 1 is a flow diagram of a sleep product comfort prediction system of the present invention.
Fig. 2 is a schematic structural diagram of a comfort prediction system of a sleep product of the present invention.
Detailed Description
As shown in fig. 1 and 2, a system for predicting comfort of a sleep product comprises a sleep product heat preservation database, a model establishing module, a model verifying module, a parameterization research module, a predicting module and a recommending module; the sleep product heat preservation quantity database is used for obtaining the skin temperature and core body temperature change trends of sleep human bodies of different sleep products after the sleep products are used under different environmental temperature conditions according to the fabric types, the fabric thicknesses, the filler types, the filler weights and the human body postures of the sleep products, so that the optimal sleep products under specific environments are recommended for consumers and manufacturers are instructed to design the optimal products according to the use environments of the consumers; the model establishing module establishes a sleep human body comfort prediction model related to 'sleep human body-sleep product-local environment' according to the human body heat transfer model; the model verification module obtains the graph relation among the human body factors, the sleep product factors and the environment factors by continuously correcting the difference value between the experimental data and the output data; the parameterized research module is used for carrying out parameterized research on various human body factors, sleep product factors and environmental factors based on the verified sleep product comfort prediction model to obtain a sleep product comfort prediction system; the prediction module predicts the comfort of the human body sleep product when the environmental factors change according to the obtained sleep human body comfort prediction model of the final 'sleep human body-sleep product-local environment'; the recommendation module obtains a dynamic map of 'time-core temperature-body surface temperature' through the prediction module of the prediction system established above, so that the skin temperature and core body temperature change trends of the testee after different types of sleep products are used under different environmental temperature conditions are evaluated, and a consumer is guided to select a proper sleep product under a specific environmental condition based on the sleep product heat preservation quantity database.
The establishment of the prediction system of the embodiment comprises the following steps:
s1: establishing a comfort prediction model of 'sleeping human body-sleeping product-local environment' according to a human body physiological heat transfer model; the model building module comprises a human body physiological heat transfer model, namely a multi-node heat transfer model of 'sleeping human body-sleeping product-local environment' built according to a heat transfer related theory, and a sleeping human body comfort prediction model is built; wherein, the human body is subdivided into 32 sections, each section is divided into four layers from inside to outside, namely a body nucleus layer, a muscle layer, a fat layer and a skin layer, a blood center is added, and 129 nodes are provided in total; meanwhile, the sleep human body comfort prediction model also considers the periodicity of sleep human body thermal regulation, namely a rapid eye movement period and a non-rapid eye movement period. In the two different thermoregulation periods, the sleeping human body is in two different metabolic states and has different physiological parameters such as metabolic capacity, sweating rate, blood flow, cold tremor heating value and the like. On the basis, 32 sections of the sleeping human body correspond to 32 local environments, and the local environments comprise the thermal resistance and the wet resistance of the 32 sections and the temperature, the humidity, the radiation temperature, the wind speed and the like of the surrounding environment;
s2: carrying out 9 groups of experiments, determining boundary conditions, bringing the boundary conditions into the established model, and modifying result data obtained by comparing the experiments with output data of the model, wherein the results are continuously repeated until the error between the experiment data and the output data is within 7%; the result data obtained by the experiment and the output data of the model are input into the model verification module, and the experimental data are obtained by a plurality of real-person experiments under the specific experimental conditions, wherein the experimental conditions comprise human body factors, namely metabolic state, sleep product factors, namely thermal resistance and wet resistance of a mattress and a bedding, and environmental factors, namely thermal resistance, wet resistance of each section, temperature, humidity, wind speed and the like of the surrounding environment, and the experimental data comprise average skin temperature measured by a core body temperature and 12-point method; the output data is the core body temperature and the average skin temperature which are obtained by model prediction under the same parameters with the experimental parameters, the experimental data and the output data are repeatedly compared and modified until the error between the two is within an acceptable range, and finally the graph relation among the human body factors, the sleep product factors and the environmental factors is obtained;
s3: carrying out parametric research on various environmental factors, sleep product factors and human body factors based on the verified sleep product comfort prediction model; the parameterization research module is used for further researching the influence of environmental factors, sleeping product factors and human factors on the comfort of the sleeping product, wherein the environmental factors comprise ambient temperature, humidity, radiation temperature, wind speed and the like, the sleeping product factors comprise local thermal resistance and wet resistance of bedding and a mattress and local thermal resistance and wet resistance of clothes during sleeping, and the human factors comprise the change of human posture, namely lying on the back, lying on the side and curling;
s4: obtaining a graph relation among the sleep product parameters, the human body factors and the environmental factors based on S3 to obtain a prediction system of the comfort of the sleep product; the comfort of the human sleep product is predicted when the environmental factors change by the sleep human comfort prediction model established by the invention;
s5: based on the prediction system of the comfort of the sleep product obtained in the S4, the sleep product used in a specific environment can be recommended based on the sleep product heat preservation amount database according to the temperature, humidity, radiation temperature and wind speed parameters of weather forecast, so that the comfort of the human body in sleeping is ensured; according to the environmental temperature parameters reported by weather forecast at different time points, the dynamic map of 'time-core temperature-body surface temperature' is obtained through the prediction module of the established prediction system, so that the skin temperature and core body temperature change trends of the testee after different types of sleep products are used under different environmental temperature conditions are evaluated, and a consumer is guided to select a proper sleep product under a specific environmental condition based on the sleep product heat preservation quantity database, namely, the sleep product recommendation is completed.
Specifically, in S5, the sleep product heat preservation amount database is constructed according to the fabric type, the fabric thickness, the filler type, the filler weight, the human body posture, and the like of the sleep product, and needs to be obtained through a large amount of experimental data analysis. In this embodiment, the wood bedThe upper part is laid with a bed sheet and a down quilt, which adopts a cotton quilt cover and a down quilt core, wherein the square gram weight of the down quilt core is 300g/m21.5 m.times.2 m in size and 900g in weight. According to the sleep product heat preservation quantity database obtained through experimental data analysis, the thermal resistance of the down quilt when a human body lies down can be obtained to be about 4.4clo, and the change trends of the skin temperature and the core temperature of the sleeping human body can be known based on a comfort prediction model after the down quilt is used under different environmental temperature and humidity conditions, so that the sleep product can be recommended under specific environmental conditions, namely, the human body is in a comfortable sleep state when the down quilt is used under 9.5 ℃, 50% RH and no-wind environment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A system for predicting comfort of a sleep product, comprising: the system comprises a sleep product heat preservation database, a model establishing module, a model verifying module, a parameterization research module, a prediction module and a recommendation module;
the sleep product heat preservation database is used for constructing a sleep product database recommended under a specific environment according to the fabric type, the fabric thickness, the filler type, the filler weight and the human body posture of the sleep product;
the model building module is used for building a sleep product comfort prediction model related to a sleeping human body, a sleeping product and a local environment according to the human body heat transfer model, and outputting data which are core body temperature and average skin temperature obtained through model prediction;
the model verification module obtains a comfort prediction model of the sleep product finally through continuously correcting the difference value between the experimental data and the output data;
the parameterized research module can perform parameterized research on various human factors, sleep product factors and environmental factors based on the verified sleep product comfort prediction model to obtain the graph relation of the change of the core body temperature and the average skin temperature along with the time when the human factors, the sleep product factors and the environmental factors change;
the prediction module predicts the comfort of the sleep product when the environmental factors change according to the obtained sleep product comfort prediction model of 'sleep human body-sleep product-local environment';
the recommendation module can obtain a dynamic map of 'time-core body temperature-average skin temperature' through the prediction module of the established prediction system, so that the skin temperature and core body temperature change trends of subjects after different types of sleep products are used under different environmental temperature conditions can be evaluated, and consumers are guided to select proper sleep products under specific environmental conditions based on the sleep product heat preservation quantity database;
the establishment of the prediction system of the comfort of the sleep product comprises the following steps:
s1: establishing a sleep product comfort prediction model of 'sleeping human body-sleep product-local environment' according to the physiological heat transfer characteristics of the human body;
s2: performing an experiment to obtain a core body temperature and an average skin temperature of 12 points, determining a boundary condition and substituting the boundary condition into the established model, comparing result data obtained by the experiment with output data of the model to modify, and repeating continuously until the error between the experiment data and the output data is within 7 percent to obtain a prediction model of the comfort of the sleep product;
s3: carrying out parametric study on various environmental factors, sleep product factors and human factors based on the verified sleep product comfort prediction model, and obtaining the graph relation of the core body temperature and the average skin temperature along with the change of the sleep product factors, the human factors and the environmental factors along with the change of time;
s4: based on the prediction model of the comfort of the sleep product obtained in the S2, the sleep product used in a specific environment can be recommended based on the sleep product heat preservation amount database according to the temperature, humidity, radiation temperature and wind speed parameters of weather forecast, so that the comfort of the human body in sleeping is ensured.
2. The system for predicting comfort of a sleep product of claim 1, wherein: the sleep human body-sleep product-local environment comfort prediction model in the S1 is a multi-node physiological heat transfer model established according to a heat transfer related theory, a human body is subdivided into 32 sections, each section is divided into four layers from inside to outside, a blood center is additionally arranged, 129 nodes are totally arranged, and four layers in each section are a body core layer, a muscle layer, a fat layer and a skin layer.
3. The system of claim 2, wherein the system is configured to predict comfort of the sleep product: the physiological heat transfer model in the S1 considers the periodicity of the sleep human body thermal regulation, namely the rapid eye movement period and the non-rapid eye movement period, and has different physiological parameters of metabolic capacity, sweating rate, blood flow and cold and fibrillation heating values.
4. The system for predicting comfort of a sleep product of claim 1, wherein: the local environment in S1 is the thermal resistance and the wet resistance of the sleeping human body in 32 sections and the surrounding environmental parameters, including the air temperature, the relative humidity, the radiation temperature and the wind speed.
5. The system for predicting comfort of a sleep product of claim 1, wherein: the sleeping products and environmental factors in the S3 include local thermal and moisture resistance of bedding and a mattress during sleeping, local thermal and moisture resistance of clothing, ambient temperature, humidity, radiation temperature and wind speed, and the human factors include changes of human postures, namely lying down, lying on side and curling.
6. The system for predicting comfort of a sleep product of claim 1, wherein: the sleep product heat preservation amount database in the S4 is constructed according to the fabric type, the fabric thickness, the filler type and the filler weight of the sleep product and needs to be obtained through a large amount of experimental data accumulation.
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