Temperature adjusting method and system for wide-temperature air conditioning suit

Abstract

The invention discloses a temperature adjusting method and system for wide-temperature air-conditioning clothes, relates to the technical field of air-conditioning clothes temperature control, and aims to solve the problems that existing air-conditioning clothes are low in comfort and often cause low-temperature frostbite, and the method comprises the following steps: S1, collecting real-time temperature data including internal and external temperature data of the air-conditioning clothes and real-time user temperature data, determining a preliminary comfort interval according to the real-time temperature data; s2, determining the maximum risk position in the air conditioning suit according to the initial comfort interval and the environment temperature; s3, carrying out targeted temperature regulation on the whole air conditioning suit and the maximum risk position to generate a preliminary regulation scheme; and S4, based on a low-temperature frostbite protection mechanism, the preliminary adjustment scheme is adjusted, and a final adjustment scheme is obtained. According to the method, the comfort level of the air conditioning suit can be remarkably improved, the low-temperature frostbite phenomenon can be prevented, and the user experience feeling is improved.

Description

A method and system for temperature regulation of wide-temperature air-conditioned clothing Technical Field

[0001] This invention relates to the field of temperature control technology for air-conditioned clothing, specifically to a method and system for temperature regulation of wide-temperature air-conditioned clothing. Background Technology

[0002] An air-conditioned garment incorporates a fan to circulate air within the clothing. This air then exchanges heat with a semiconductor temperature-controlled device mounted on the outside via a metal heat exchanger. This process regulates the air temperature within the garment. Semiconductor temperature control devices hold great potential for application in wearable and portable devices. However, untimely temperature regulation in air-conditioned garments can lead to reduced comfort and frequently results in frostbite. Currently, a common solution to frostbite is to lower the cooling temperature, limiting the cooling temperature of air-conditioned clothing to around 25°C. For example, Chinese patent CN113439895A discloses a contact semiconductor cooling air-conditioned garment, including a garment body with circular holes on the back and front, a cooling unit, and a control unit. The cooling unit is detachably fixed to the garment body through the circular holes. The cooling unit includes an outer shell, an inner shell, a fan, a semiconductor cooling chip, and a cooling shell. The outer shell is connected to the inner shell, and the fan and semiconductor cooling chip are located between the outer shell and the inner shell. The cooling shell is fixed to the inner shell near the inside of the garment body. The outer shell has an air outlet, and the inner shell has an air inlet. The control unit is connected to a wiring harness and is connected to the cooling unit through the wiring harness. However, it does not consider comfort or frostbite prevention. Summary of the Invention

[0003] This invention addresses the problems of low comfort and frequent frostbite associated with current air-conditioned clothing. It proposes a wide-temperature air-conditioned clothing temperature regulation method and system, which can significantly improve the comfort of air-conditioned clothing, prevent frostbite, and enhance the user experience.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: a method for temperature regulation of a wide-temperature air-conditioned garment, comprising the following steps: S1, collecting real-time temperature data, including temperature data inside and outside the air-conditioned garment and real-time user temperature data, and determining a preliminary comfort range based on the real-time temperature data; S2, determining the maximum risk location inside the air-conditioned garment based on the preliminary comfort range and the ambient temperature; S3, performing targeted temperature regulation on the air-conditioned garment as a whole and the maximum risk location to generate a preliminary regulation plan; S4, adjusting the preliminary regulation plan based on a low-temperature frostbite protection mechanism to obtain a final regulation plan.

[0005] In this invention, real-time temperature data is first collected and acquired. Then, a preliminary comfort range is determined based on the real-time temperature data. Subsequently, the location of maximum risk is determined by combining the ambient temperature with the degree of difference and the transmission rate. The temperature of the air-conditioned clothing is then adjusted accordingly to obtain a preliminary adjustment plan. Finally, the initial adjustment plan is fine-tuned based on the low-temperature frostbite protection mechanism to obtain the final temperature adjustment plan.

[0006] The present invention is further configured such that step S1 includes the following steps: S11, acquiring temperature data inside the air-conditioned clothing through a temperature detection device, and acquiring user temperature data through a wearable device; S12, acquiring real-time temperature data and recording its timestamp, and marking temperature data acquired at the same timestamp; S13, creating a comfort judgment model, calculating the model input parameters by weighting the temperature data, outputting the comfort coefficient, and determining the preliminary comfort range.

[0007] In this technical solution, temperature data inside and outside the air-conditioned clothing, as well as the user's temperature data, are obtained, and a comfort coefficient is obtained through a comfort judgment model to determine the initial comfort range, which facilitates the subsequent determination of the location of maximum risk.

[0008] The present invention is further configured such that step S13 includes the following steps: S131, acquiring historical temperature data, generating a dataset about model input parameters and comfort coefficients, training the data based on a recurrent neural network model, and obtaining a comfort judgment model; S132, assigning weight coefficients to each type of temperature data, obtaining model input parameters through a weight calculation formula, processing the data through the comfort judgment model, obtaining comfort coefficients, and determining a preliminary comfort range based on the PMV-PPD thermal comfort model.

[0009] In this technical solution, a comfort coefficient is obtained by creating a comfort judgment model, and the PMV-PPD thermal comfort model is combined to determine the preliminary comfort range that conforms to the individual.

[0010] The present invention is further configured such that step S2 includes the following steps: S21, obtaining the ambient temperature, calculating the degree of difference between the ambient temperature and the temperature range to which the preliminary comfort zone belongs, and determining the temperature to be adjusted by the semiconductor temperature control module inside the air-conditioned garment; S22, determining the location of the greatest risk and marking it as a key location based on the magnitude of the temperature to be adjusted and the heat transfer rate of each location inside the air-conditioned garment.

[0011] In this technical solution, the difference between the ambient temperature and the temperature range to which the preliminary comfort zone belongs is first calculated to determine the temperature to be adjusted, and finally the maximum risk location of the air-conditioned clothing is determined by combining the heat transfer rate.

[0012] The present invention is further configured such that: step S3 includes: after determining the normal position and the maximum risk position of the air-conditioned clothing within the corresponding time period, adjusting the temperature of the normal position of the air-conditioned clothing with energy saving as the main focus, and using a multi-objective optimization algorithm to dynamically adjust the temperature of the maximum risk position of the air-conditioned clothing, and elevating the adjustment priority of the maximum risk position to the optimal level, and finally obtaining a preliminary adjustment scheme.

[0013] In this technical solution, the preliminary adjustment scheme specifically includes temperature adjustment parameters and instructions, as well as the adjustment priority.

[0014] The present invention is further configured such that: the maximum risk position of the air-conditioned clothing is dynamically adjusted using a target optimization algorithm, including: setting multiple targets, setting target weights, and the targets including adjustment response time, risk disappearance index and safety factor index according to their importance; and outputting target temperature parameters and control order after iterative calculation.

[0015] In this technical solution, a multi-objective optimization algorithm is adopted, and the corresponding adjustment parameters and control sequence can be obtained through iterative calculation.

[0016] The present invention is further configured such that: the adjustment of the preliminary adjustment scheme includes: first, cooling down to a high temperature, the time to cool down to the high temperature state is recorded as T1; after detecting that the duration of the high temperature low temperature reaches a threshold, switching to a medium temperature, the time to cool down to the medium temperature state is recorded as T3; after detecting that the duration of the medium temperature high temperature reaches a threshold, switching to a low temperature, the time to cool down from the medium temperature state to the low temperature state is recorded as T5, the duration of the low temperature state is recorded as T6, and finally obtaining the adjustment of the target temperature parameter.

[0017] In this technical solution, the above-mentioned multi-zone, multi-time period temperature regulation is used to prevent low-temperature frostbite.

[0018] The present invention is further configured such that step S4 includes: adjusting the preliminary adjustment scheme according to individual user differences, and then obtaining the final adjustment scheme.

[0019] In this technical solution, individual users may have differences, but these differences are not significant and only minor adjustments are needed.

[0020] The present invention is further configured such that: the maximum risk location is specifically a set of adjustment areas including multiple air-conditioned clothing location areas, and the adjustment order within the set of adjustment areas is determined according to the size of the set of adjustment areas and the adjustment duration.

[0021] In this technical solution, there are usually multiple locations with the greatest risk, and these multiple locations need to be adjusted in the order described above.

[0022] A wide-temperature air-conditioned garment temperature regulation system, applicable to the aforementioned wide-temperature air-conditioned garment temperature regulation method, includes a semiconductor temperature control module disposed inside the air-conditioned garment. The semiconductor temperature control module is connected to a temperature detection device. Both the semiconductor temperature control module and the temperature detection device are connected to a control module. The temperature detection device is capable of detecting temperature data from the semiconductor temperature control module and the interior of the air-conditioned garment.

[0023] The system of this technical solution includes a semiconductor control module, a temperature detection device, and a control module. The control module is connected to both the semiconductor control module and the temperature detection device, and the temperature detection device is connected to the semiconductor control module.

[0024] The present invention can bring the following beneficial effects: the wide-temperature air-conditioned clothing temperature regulation method of the present invention can achieve the effect of rapid cooling in high-temperature environments, so that users can quickly feel the coolness and increase comfort, while avoiding the occurrence of low-temperature frostbite. Attached Figure Description

[0025] Figure 1 is a flowchart of a method for adjusting the temperature of a wide-temperature air-conditioned garment according to the present invention.

[0026] Figure 2 is a flowchart of a wide-temperature air-conditioned clothing temperature regulation method according to the present invention, specifically for temperature regulation.

[0027] Figure 3 is a temperature curve diagram of the initial adjustment scheme of the temperature adjustment method of the wide-temperature air-conditioned clothing of the present invention. Detailed Implementation

[0028] Example 1: In order to solve the problem of low comfort and frequent frostbite caused by current air-conditioned clothing, this example proposes a temperature regulation method for wide-temperature air-conditioned clothing. Referring to Figures 1 and 2, it mainly includes the following steps.

[0029] Step S1: First, collect real-time temperature data, including temperature data inside and outside the air-conditioned clothing, as well as real-time user temperature data. Determine the initial comfort range based on the aforementioned real-time temperature data.

[0030] Step S2: Based on the preliminary comfort zone determined in the above steps, and combined with the ambient temperature, the location of the greatest risk inside the air-conditioned clothing can be finally identified.

[0031] Step S3: Based on step S2, targeted temperature adjustments are made to the overall air-conditioned clothing and the areas with the greatest risk to obtain a preliminary adjustment plan.

[0032] Step S4: Based on the low-temperature frostbite protection mechanism, the preliminary adjustment scheme generated above is adjusted to obtain the final adjustment scheme.

[0033] Step S1 above is mainly implemented through the following sub-steps.

[0034] Step S11: Obtain the temperature data inside the air-conditioned garment. Specifically, this data is collected by a temperature detection device. In this embodiment, the temperature detection device is a temperature sensor. The temperature detection device is connected to a semiconductor temperature control module. The user's temperature data can be obtained through a wearable device. The wearable device is a medically approved device, and before obtaining the user's temperature data, it needs to obtain the user's individual permission and consent, which complies with legal regulations and ethical standards.

[0035] In addition, the temperature data outside the air-conditioned clothing can be obtained through fixed temperature detection devices or through mobile acquisition devices, without any limitation.

[0036] Step S12: When obtaining the real-time temperature data mentioned above, it is necessary to record the timestamp of obtaining the data, mark the temperature data collected under the same timestamp conditions, and store them in the same directory.

[0037] Step S13: Establish a comfort judgment model, calculate the weights of the temperature data mentioned above, and finally obtain the model input parameters that meet the input of the model. Finally, the model can output the comfort coefficient and determine the corresponding preliminary comfort range.

[0038] In this technical solution, temperature data inside and outside the air-conditioned clothing, as well as the user's temperature data, are obtained, and a comfort coefficient is obtained through a comfort judgment model to determine the initial comfort range, which facilitates the subsequent determination of the location of maximum risk.

[0039] The comfort determination model described above is implemented in more detail through the following process.

[0040] Step S131: Acquire and store the corresponding historical temperature data. The type of historical temperature data is the same as the real-time temperature data acquired in step S1. Obtain a dataset containing model input parameters and comfort coefficients. Based on the recurrent neural network framework, train the dataset and validate it to obtain the final comfort judgment model.

[0041] Historical temperature data collection includes user temperature data from different individuals (including different genders and ages), and covers temperature data under different external environmental temperature conditions. The comfort coefficient is determined based on user feedback on comfort level. In this embodiment, the comfort coefficient is a parameter between 0 and 1. The dataset includes a training set, a test set, and a validation set. Through training, testing, and validation, the final comfort judgment model is obtained.

[0042] In addition, the comfort judgment model can continuously learn deep and adjust the model parameters according to the set period.

[0043] Step S132: After step S131, the trained comfort judgment model is used to assign weight coefficients to each type of temperature data. The model input parameters are obtained through the weight calculation formula, processed by the comfort judgment model, and the comfort coefficient is obtained. The preliminary comfort range is determined based on the PMV-PPD thermal comfort model.

[0044] In this embodiment, a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient are assigned to the temperature data inside and outside the air-conditioned clothing and the real-time user temperature data, respectively. The first weighting coefficient, the second weighting coefficient, and the third weighting coefficient can be flexibly set according to the actual situation; specifically, the ratio of the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient is 2 / 1 / 1.

[0045] After obtaining the model input parameters, the model input parameters are input into the comfort judgment model, and the comfort coefficient is generated after processing by the comfort judgment model.

[0046] In this embodiment, based on the PMV-PPD thermal comfort model, we can conclude that: 1. When the ambient temperature is between 18-24℃ and the relative humidity is between 40%-70%, the human body feels most comfortable; 2. When the temperature is between 24-30℃ and the humidity is less than 60%, the human body feels hot but not stuffy; 3. When the temperature is above 30℃ and the humidity is above 70%, the human body feels stuffy and hot; 4. When the temperature is above 36℃ and the humidity is above 80%, the human body feels unbearably stuffy and hot, and the sweating mechanism is blocked, making it very easy to suffer from heatstroke.

[0047] Based on the thermal comfort model, several comfort zones are generated, and the specific comfort zone is determined according to the specific value of the comfort coefficient. In this embodiment, the aforementioned comfort zones include the most comfortable zone of 18-25℃, the hot zone of 24-30℃, the muggy zone of 30℃, and the heatstroke zone of above 36℃.

[0048] In this technical solution, a comfort coefficient is obtained by creating a comfort judgment model, and the PMV-PPD thermal comfort model is combined to determine the preliminary comfort range that conforms to the individual.

[0049] Step S2 above is implemented through the following sub-steps.

[0050] Step S21: Obtain the corresponding ambient temperature, and calculate the degree of difference between the ambient temperature and the temperature range to which the initial comfort zone belongs. Specifically, the ambient temperature is subtracted from the maximum and minimum values ​​of the temperature range to obtain the first parameter and the second parameter. The first parameter, the second parameter, and the difference between the first parameter and the second parameter are used as numerical representations of the degree of difference.

[0051] Step S22: Based on the temperature to be adjusted and the heat transfer rate of each location inside the air-conditioned garment, determine the location of the greatest risk and mark it accordingly.

[0052] The location of maximum risk is specifically a set of adjustment areas that includes multiple air-conditioned clothing locations. The adjustment order within the set of adjustment areas is determined according to the size of the set and the adjustment duration.

[0053] In this technical solution, there are usually multiple locations with the greatest risk, and these multiple locations need to be adjusted in the order described above.

[0054] In this embodiment, the heat transfer rate at various locations inside the air-conditioned garment needs to be comprehensively evaluated in conjunction with the path distance to the semiconductor temperature control module. The transfer time is determined by the quotient of the path distance to the semiconductor temperature control module and the heat transfer rate. This transfer time is divided by the value of the temperature adjustment to obtain a third parameter. The magnitude of the third parameter is used to determine the final maximum risk location. Generally, one or more of the third parameters with the largest values ​​will be used as the maximum risk location.

[0055] In this technical solution, the difference between the ambient temperature and the temperature range to which the preliminary comfort zone belongs is first calculated to determine the temperature to be adjusted, and finally the maximum risk location of the air-conditioned clothing is determined by combining the heat transfer rate.

[0056] For step S3 above, referring to Figure 2, it can be implemented in the following way.

[0057] After determining the normal and maximum risk positions of the air-conditioned clothing within a fixed time period, the temperature of the normal positions of the air-conditioned clothing is adjusted with energy saving as the main focus, while the maximum risk positions of the air-conditioned clothing are dynamically adjusted using a multi-objective optimization algorithm, and the adjustment priority of the maximum risk positions is given the highest priority, thus obtaining a preliminary adjustment plan.

[0058] The main process of using a target optimization algorithm to dynamically adjust the temperature of the most risky location in air-conditioned clothing includes the following parts.

[0059] First, multiple objectives are set, and then corresponding objective weights are set. The objectives, ranked by importance, include adjustment response time, risk disappearance indicators, and safety factor indicators. After iterative calculation, the target temperature parameters and control sequence are finally output.

[0060] In this embodiment, the adjustment response time is the duration of this temperature adjustment, the risk disappearance index is the time it takes for the position of maximum risk to become a normal position during this temperature adjustment, and the safety factor index is the indicator of the overall safety inside the air-conditioned clothing during this temperature adjustment.

[0061] In this technical solution, the preliminary adjustment scheme specifically includes temperature adjustment parameters and instructions, as well as the adjustment priority.

[0062] In this technical solution, a multi-objective optimization algorithm is adopted, and the corresponding adjustment parameters and control sequence can be obtained through iterative calculation.

[0063] The process of adjusting the initial adjustment plan, as shown in Figure 3, includes the following:

[0064] First, the temperature drops to high setting: the time from power-on to high setting for the semiconductor temperature control module is recorded as T1; the duration of the semiconductor temperature control module in high setting is recorded as T2; after the control module detects that the duration of high-temperature setting reaches the threshold, it switches to medium setting, and the time from high setting to medium setting is recorded as T3; the duration of semiconductor temperature control module in medium setting is recorded as T4; after the control module detects that the duration of medium-temperature setting reaches the threshold, it switches to low setting, and the time from medium setting to low setting is recorded as T5; the duration of semiconductor temperature control module in low setting is recorded as T6.

[0065] In this embodiment, the preset temperature is set to high temperature (Temp1) with a temperature sensor trigger threshold of Temp1-X℃; medium temperature (Temp2) with a temperature sensor trigger threshold of Temp2-X℃; and low temperature (Temp3) with a temperature sensor trigger threshold of Temp3-X℃. The test condition is an outdoor high temperature of 35℃, where X is a numerical temperature greater than 0.

[0066] In this technical solution, the above-mentioned multi-zone, multi-time period temperature regulation is used to prevent low-temperature frostbite.

[0067] Step S4 above includes the following: adjusting the preliminary adjustment plan based on individual user differences, and then obtaining the final adjustment plan.

[0068] In this technical solution, individual users may have differences, but these differences are not significant and only minor adjustments are needed.

[0069] Example 2 This example proposes a method for temperature regulation of wide-temperature air-conditioned clothing, which mainly includes the following steps.

[0070] Step S1: First, collect real-time temperature data, including temperature data inside and outside the air-conditioned clothing, as well as real-time user temperature data. Determine the initial comfort range based on the aforementioned real-time temperature data.

[0071] Step S2: Based on the preliminary comfort zone determined in the above steps, and combined with the ambient temperature, the location of the greatest risk inside the air-conditioned clothing can be finally identified.

[0072] Step S3: Based on step S2, targeted temperature adjustments are made to the overall air-conditioned clothing and the areas with the greatest risk to obtain a preliminary adjustment plan.

[0073] Step S4: Based on the low-temperature frostbite protection mechanism, the preliminary adjustment scheme generated above is adjusted to obtain the final adjustment scheme.

[0074] Step S1 above is mainly implemented through the following sub-steps.

[0075] Step S11: Obtain the temperature data inside the air-conditioned garment. Specifically, this data is collected by a temperature detection device. In this embodiment, the temperature detection device is a temperature sensor. The temperature detection device is connected to a semiconductor temperature control module. The user's temperature data can be obtained through a wearable device. The wearable device is a medically approved device, and before obtaining the user's temperature data, it needs to obtain the user's individual permission and consent, which complies with legal regulations and ethical standards.

[0076] In addition, the temperature data outside the air-conditioned clothing can be obtained through fixed temperature detection devices or through mobile acquisition devices, without any limitation.

[0077] Step S12: When obtaining the real-time temperature data mentioned above, it is necessary to record the timestamp of obtaining the data, mark the temperature data collected under the same timestamp conditions, and store them in the same directory.

[0078] Step S13: Establish a comfort judgment model, calculate the weights of the temperature data mentioned above, and finally obtain the model input parameters that meet the input of the model. Finally, the model can output the comfort coefficient and determine the corresponding preliminary comfort range.

[0079] In this technical solution, temperature data inside and outside the air-conditioned clothing, as well as the user's temperature data, are obtained, and a comfort coefficient is obtained through a comfort judgment model to determine the initial comfort range, which facilitates the subsequent determination of the location of maximum risk.

[0080] The comfort determination model described above is implemented in more detail through the following process.

[0081] Step S131: Acquire and store the corresponding historical temperature data. The type of historical temperature data is the same as the real-time temperature data acquired in step S1. Obtain a dataset containing model input parameters and comfort coefficients. Based on the recurrent neural network framework, train the dataset and validate it to obtain the final comfort judgment model.

[0082] In this embodiment, the comfort coefficient is a parameter between 0 and 1; the dataset includes a training set, a test set, and a validation set. Through training, testing, and validation, the final comfort judgment model is obtained.

[0083] In addition, the comfort judgment model can continuously learn deep and adjust the model parameters according to the set period.

[0084] Step S132: After step S131, the trained comfort judgment model is used to assign weight coefficients to each type of temperature data. The model input parameters are obtained through the weight calculation formula, processed by the comfort judgment model, and the comfort coefficient is obtained. The preliminary comfort range is determined based on the PMV-PPD thermal comfort model.

[0085] In this embodiment, a first weighting coefficient, a second weighting coefficient, and a third weighting coefficient are assigned to the temperature data inside and outside the air-conditioned clothing and the real-time user temperature data, respectively. The first weighting coefficient, the second weighting coefficient, and the third weighting coefficient can be flexibly set according to the actual situation; specifically, the ratio of the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient is 2 / 1 / 1.

[0086] After obtaining the model input parameters, the model input parameters are input into the comfort judgment model, and the comfort coefficient is generated after processing by the comfort judgment model.

[0087] In this embodiment, based on the PMV-PPD thermal comfort model, we can conclude that: 1. When the ambient temperature is between 18-24℃ and the relative humidity is between 40%-70%, the human body feels most comfortable; 2. When the temperature is between 24-30℃ and the humidity is less than 60%, the human body feels hot but not stuffy; 3. When the temperature is above 30℃ and the humidity is above 70%, the human body feels stuffy and hot; 4. When the temperature is above 36℃ and the humidity is above 80%, the human body feels unbearably stuffy and hot, and the sweating mechanism is blocked, making it very easy to suffer from heatstroke.

[0088] Based on the thermal comfort model, several comfort zones are generated, and the specific comfort zone is determined according to the specific value of the comfort coefficient. In this embodiment, the aforementioned comfort zones include the most comfortable zone of 18-25℃, the hot zone of 24-30℃, the muggy zone of 30℃, and the heatstroke zone of above 36℃.

[0089] In this technical solution, a comfort coefficient is obtained by creating a comfort judgment model, and the PMV-PPD thermal comfort model is combined to determine the preliminary comfort range that conforms to the individual.

[0090] Step S2 above is implemented through the following sub-steps.

[0091] Step S21: Obtain the corresponding ambient temperature, and calculate the degree of difference between the ambient temperature and the temperature range to which the initial comfort zone belongs. Specifically, the ambient temperature is subtracted from the maximum and minimum values ​​of the temperature range to obtain the first parameter and the second parameter. The first parameter, the second parameter, and the difference between the first parameter and the second parameter are used as numerical representations of the degree of difference.

[0092] Step S22: Based on the temperature to be adjusted and the heat transfer rate of each location inside the air-conditioned garment, determine the location of the greatest risk and mark it accordingly.

[0093] The location of maximum risk is specifically a set of adjustment areas that includes multiple air-conditioned clothing locations. The adjustment order within the set of adjustment areas is determined according to the size of the set and the adjustment duration.

[0094] In this technical solution, there are usually multiple locations with the greatest risk, and these multiple locations need to be adjusted in the order described above.

[0095] In this embodiment, the heat transfer rate at various locations inside the air-conditioned garment needs to be comprehensively evaluated in conjunction with the path distance to the semiconductor temperature control module. The transfer time is determined by the quotient of the path distance to the semiconductor temperature control module and the heat transfer rate. This transfer time is divided by the value of the temperature adjustment to obtain a third parameter. The magnitude of the third parameter is used to determine the final maximum risk location. Generally, one or more of the third parameters with the largest values ​​will be used as the maximum risk location.

[0096] In this technical solution, the difference between the ambient temperature and the temperature range to which the preliminary comfort zone belongs is first calculated to determine the temperature to be adjusted, and finally the maximum risk location of the air-conditioned clothing is determined by combining the heat transfer rate.

[0097] For step S3 above, referring to Figure 2, it can be implemented in the following way.

[0098] After determining the normal and maximum risk positions of the air-conditioned clothing within a fixed time period, the temperature of the normal positions of the air-conditioned clothing is adjusted with energy saving as the main focus, while the maximum risk positions of the air-conditioned clothing are dynamically adjusted using a multi-objective optimization algorithm, and the adjustment priority of the maximum risk positions is given the highest priority, thus obtaining a preliminary adjustment plan.

[0099] The main process of using a target optimization algorithm to dynamically adjust the temperature of the most risky location in air-conditioned clothing includes the following parts.

[0100] First, multiple objectives are set, and then corresponding objective weights are set. The objectives, ranked by importance, include adjustment response time, risk disappearance indicators, and safety factor indicators. After iterative calculation, the target temperature parameters and control sequence are finally output.

[0101] In this embodiment, the adjustment response time is the duration of this temperature adjustment, the risk disappearance index is the time it takes for the position of maximum risk to become a normal position during this temperature adjustment, and the safety factor index is the indicator of the overall safety inside the air-conditioned clothing during this temperature adjustment.

[0102] In this technical solution, the preliminary adjustment scheme specifically includes temperature adjustment parameters and instructions, as well as the adjustment priority.

[0103] In this technical solution, a multi-objective optimization algorithm is adopted, and the corresponding adjustment parameters and control sequence can be obtained through iterative calculation.

[0104] The process of adjusting the initial adjustment plan, as shown in Figure 3, includes the following:

[0105] First, the temperature drops to high setting: the time from power-on to high setting for the semiconductor temperature control module is recorded as T1; the duration of the semiconductor temperature control module in high setting is recorded as T2; after the control module detects that the duration of high-temperature setting reaches the threshold, it switches to medium setting, and the time from high setting to medium setting is recorded as T3; the duration of semiconductor temperature control module in medium setting is recorded as T4; after the control module detects that the duration of medium-temperature setting reaches the threshold, it switches to low setting, and the time from medium setting to low setting is recorded as T5; the duration of semiconductor temperature control module in low setting is recorded as T6.

[0106] In this technical solution, the above-mentioned multi-zone, multi-time period temperature regulation is used to prevent low-temperature frostbite.

[0107] Step S4 above includes the following: adjusting the preliminary adjustment plan based on individual user differences, and then obtaining the final adjustment plan.

[0108] Based on Example 1, this example also proposes a wide-temperature air-conditioned clothing temperature regulation system, including a semiconductor temperature control module installed inside the air-conditioned clothing. The semiconductor temperature control module is connected to a temperature detection device, and both the semiconductor temperature control module and the temperature detection device are connected to a control module. The temperature detection device can detect the temperature data of the semiconductor temperature control module and the inside of the air-conditioned clothing.

[0109] The control module includes a control board with a built-in MCU. The control board is electrically connected to the semiconductor temperature control module and the temperature detection device. The MCU detects the temperature measured by the temperature detection device in real time, thereby adjusting the on / off state of the semiconductor temperature control module and the temperature regulation.

[0110] In this embodiment, the temperature sensor of the temperature detection device is an NTC thermistor, which is a negative temperature sensor resistor whose resistance decreases as the temperature increases.

Claims

1. A method for temperature regulation of a wide-temperature air-conditioned garment, characterized in that, Includes the following steps: S1. Collect real-time temperature data, including temperature data inside and outside the air-conditioned clothing and real-time user temperature data, and determine the preliminary comfort range based on the real-time temperature data; S2. Based on the preliminary comfort range and the ambient temperature, determine the maximum risk location inside the air-conditioned clothing; S3. Perform targeted temperature adjustments on the air-conditioned clothing as a whole and the maximum risk location to generate a preliminary adjustment plan. S4, based on the low-temperature frostbite protection mechanism, adjusts the preliminary adjustment scheme to obtain the final adjustment scheme.

2. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 1, characterized in that, Step S1 includes the following steps: S11, acquiring temperature data inside the air-conditioned clothing through a temperature detection device and acquiring user temperature data through a wearable device; S12, acquiring real-time temperature data and recording its timestamp, and marking temperature data acquired at the same timestamp; S13, creating a comfort judgment model, calculating the model input parameters by weighting the temperature data, outputting the comfort coefficient, and determining the preliminary comfort range.

3. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 2, characterized in that, Step S13 includes the following steps: S131, acquiring historical temperature data, generating a dataset of model input parameters and comfort coefficients, training the data based on a recurrent neural network model, and obtaining a comfort judgment model; S132, assigning weight coefficients to each type of temperature data, obtaining model input parameters through a weight calculation formula, processing the data through the comfort judgment model to obtain the comfort coefficient, and determining a preliminary comfort range based on the PMV-PPD thermal comfort model.

4. A method for temperature regulation of a wide-temperature air-conditioned garment according to claim 1, 2, or 3, characterized in that, Step S2 includes the following steps: S21, acquiring the ambient temperature, calculating the degree of difference between the ambient temperature and the temperature range to which the preliminary comfort zone belongs, and determining the temperature to be adjusted by the semiconductor temperature control module inside the air-conditioned clothing; S22, determining the location of the greatest risk and marking it as a key location based on the magnitude of the temperature to be adjusted and the heat transfer rate of each location inside the air-conditioned clothing.

5. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 4, characterized in that, Step S3 includes: after determining the normal position and the maximum risk position of the air-conditioned clothing in the corresponding time period, adjusting the temperature of the normal position of the air-conditioned clothing with energy saving as the main goal, and using a multi-objective optimization algorithm to dynamically adjust the temperature of the maximum risk position of the air-conditioned clothing, and giving the adjustment priority of the maximum risk position the optimal one, and finally obtaining a preliminary adjustment plan.

6. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 5, characterized in that, The algorithm for dynamic temperature adjustment of the location with the greatest risk in the air-conditioned clothing is a target optimization algorithm. This algorithm includes setting multiple targets and assigning target weights. The targets are ranked by importance and include adjustment response time, risk disappearance index, and safety factor index. After iterative calculation, the target temperature parameters and control order are output.

7. A method for temperature regulation of a wide-temperature air-conditioned garment according to claim 1, 2, or 6, characterized in that, The adjustment of the preliminary adjustment scheme includes: first, cooling down to a high setting, and the time to cool down to the high setting is recorded as T1; after detecting that the duration of the low temperature at the high setting has reached a threshold, switching to a medium setting, and the time to cool down to the medium setting is recorded as T3; after detecting that the duration of the high temperature at the medium setting has reached a threshold, switching to a low setting, and the time to cool down from the medium setting to the low setting is recorded as T5, and the duration of the low setting is recorded as T6, thus obtaining the adjustment of the target temperature parameter.

8. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 7, characterized in that, Step S4 includes: adjusting the preliminary adjustment plan based on individual user differences, and then obtaining the final adjustment plan.

9. The method for temperature regulation of a wide-temperature air-conditioned garment according to claim 4, characterized in that, The maximum risk location is specifically a set of adjustment areas including multiple air-conditioned clothing location areas. The adjustment order within the set of adjustment areas is determined according to the size of the set of adjustment areas and the adjustment duration.

10. A temperature regulation system for a wide-temperature air-conditioned garment, applicable to the temperature regulation method for a wide-temperature air-conditioned garment as described in any one of claims 1-9, characterized in that, The device includes a semiconductor temperature control module installed inside the air-conditioned garment. The semiconductor temperature control module is connected to a temperature detection device. Both the semiconductor temperature control module and the temperature detection device are connected to a control module. The temperature detection device can detect the temperature data of the semiconductor temperature control module and the inside of the air-conditioned garment.

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