Wearable refrigerator intelligent temperature control and self-adaptive adjusting system

Abstract

The invention discloses an intelligent temperature control and adaptive adjustment system for a wearable refrigerator. The intelligent temperature control and adaptive adjustment system comprises an environment temperature monitoring module, a user demand analysis module, a refrigeration power optimization module and an adaptive adjustment module, the environment temperature monitoring module is used for monitoring the environment temperature in real time based on the environment temperature dynamic model; the user demand analysis module is used for calculating a user refrigeration demand based on the user demand model; the refrigeration power optimization module is used for calculating required refrigeration power based on a thermodynamic formula and optimizing thermal management performance through a heat dissipation balance equation; and the self-adaptive adjusting module is used for generating a control signal by applying a PID control algorithm and controlling the refrigeration power through a self-adaptive adjusting rule. According to the wearable refrigerator, by integrating algorithm modules such as environment temperature monitoring, user demand analysis, refrigeration power optimization and self-adaptive adjustment, accurate sensing and intelligent response to the environment temperature and user demands are achieved, and the comfort, energy efficiency and adaptability of the wearable refrigerator are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of personal cooling devices, in particular to an intelligent temperature control and adaptive adjustment system for wearable coolers, which is applicable to the personal active cooling needs in high-temperature working environments, outdoor activities, medical care and other fields. Background Art

[0002] With the trend of global climate change and the frequent occurrence of extreme high-temperature weather, the application demand for personal cooling devices, especially wearable coolers, is increasing day by day. Such devices are widely used in high-temperature working environments such as steel smelting, mining, fire fighting and rescue, as well as in scenarios such as outdoor activities and medical care. According to research statistics, staying in a high-temperature environment for a long time will cause the imbalance between heat production and heat dissipation in the human body, leading to heat stroke, dehydration and even more serious health problems, and will also significantly reduce work efficiency and safety. As a portable personal cooling equipment, wearable coolers can effectively improve the comfort and work performance of users in high-temperature environments, and have important practical value.

[0003] However, there are several obvious deficiencies in the existing wearable cooler technologies. According to the literature, the current wearable coolers on the market mainly adopt the method of manually adjusting the temperature, lacking an intelligent temperature control system and being difficult to adaptively adjust according to the ambient temperature and user needs. Taking the protective clothing cooling and temperature reduction system disclosed in Patent CN204191625U as an example, although the basic refrigeration function is realized, the intelligent temperature control and adaptive adjustment functions are not involved, and users need to frequently manually adjust the refrigeration intensity, with poor effects. There are also personal cooling devices in the prior art that adopt the liquid cooling medium circulation method. Although the cooling effect is good, they still adopt simple temperature threshold control and lack the comprehensive analysis ability of the ambient temperature and user status.

[0004] As a commonly used refrigeration technology, although the thermoelectric cooler has the advantages of small volume, light weight, etc., many problems have also emerged in practical applications: insufficient heat dissipation in the refrigeration structure, and uneven refrigeration effect on the human body, etc., seriously affecting the wearing comfort and refrigeration efficiency. Research shows that the intelligent air-conditioning clothing based on the thermoelectric cooler may have imperfect thermal management problems in practical applications, resulting in local overcooling or overheating of users during wearing, reducing the overall comfort.

[0005] To address these problems, a temperature control system integrating sensors and intelligent control algorithms is developed to achieve real-time monitoring and adaptive adjustment of the ambient temperature and user needs. Summary of the Invention

[0006] The object of the present invention is to provide an intelligent temperature control and adaptive adjustment system and method for a wearable cooler. The above system overcomes the problems of insufficient intelligence and imperfect thermal management in the prior art of wearable coolers, realizes precise perception and response to environmental temperature and user needs, and significantly improves the refrigeration effect, energy utilization efficiency and wearing comfort.

[0007] To achieve the above object, the present invention provides an intelligent temperature control and adaptive adjustment system for a wearable cooler, which realizes environmental temperature monitoring, user demand analysis, refrigeration power optimization and adaptive adjustment; an environmental temperature monitoring module, a user demand analysis module, a refrigeration power optimization module and an adaptive adjustment module. These modules work together to form a closed-loop control system, realizing intelligent temperature control and adaptive adjustment of the wearable cooler.

[0008] The environmental temperature monitoring module monitors the environmental temperature in real time based on the environmental temperature dynamic model T env (t)=T base +ΔT(t). In the above model, T env (t) represents the environmental temperature at the current moment t, T base represents the basic environmental temperature, that is, the relatively stable background temperature, and ΔT(t) represents the temperature dynamic fluctuation value collected by the sensor in real time. The in-depth analysis of the environmental temperature change law by the above model decomposes the environmental temperature into two components: basic temperature and dynamic fluctuation, enabling the system to effectively distinguish between continuous temperature changes and short-term fluctuations, thereby more accurately predicting the temperature change trend and providing a basis for the formulation of refrigeration strategies.

[0009] The user demand analysis module calculates the user's refrigeration demand based on the user demand model D user =f(T env ,T body ,V activity ). The above model is one of the core invention points of the present invention, comprehensively considering three core factors: environmental temperature T env , user body surface temperature T body and activity intensity V activity . Among them, D user represents the refrigeration intensity required by the user, with the unit of W (watt); T body represents the user body surface temperature, which is measured in real time by a temperature sensor attached to the skin, with the unit of °C; V activity represents the user activity intensity, which measures the user's motion state through a built-in acceleration sensor, with the unit of m / s 2 ; f is a non-linear function obtained by fitting experimental data, describing the mapping relationship between the user state and the refrigeration demand.

[0010] In a preferred embodiment of the present invention, the non - linear function of the user demand model adopts a weighted non - linear mapping form: f(T env ,T body ,V activity ) = w1·(T env - T ref ) 2 + w2·(T body - T comfort ) 2 + w3·V activity , where T ref represents the reference ambient temperature, T comfort represents the comfortable body surface temperature, w1, w2, and w3 represent weight coefficients, and the weight coefficients are artificially set with corresponding dimensions so that the final dimension unit is W (watt). The above - mentioned function scheme takes into account the quadratic terms of the ambient temperature and the body surface temperature deviating from the comfortable range, reflecting the non - linear influence of temperature deviation on comfort, and at the same time adding the activity intensity as a linear term to reflect the direct influence of increased activity metabolism on the cooling demand. The above - mentioned scheme enables the system to provide personalized cooling solutions according to the actual state of the user, significantly improving the user experience.

[0011] The refrigeration power optimization module calculates the required refrigeration power based on the thermodynamic formula P cool = Q·c·ΔT and optimizes the thermal management performance through the heat dissipation balance equation P heat = h·A·(T cool - T env ). In these two formulas, P cool represents the refrigeration power, with the unit of W; Q represents the mass flow rate of the cooling medium, with the unit of kg / s; c represents the specific heat capacity of the cooling medium, with the unit of J / (kg·K); ΔT represents the temperature difference, calculated as the difference between the user's body surface temperature and the cooler output temperature; P heat represents the heat dissipation power, with the unit of W; h represents the heat transfer coefficient, with the unit of W / (m 2 ·K); A represents the heat dissipation area, with the unit of m 2 ; T cool represents the cooler output temperature. These two formulas model the system from two perspectives of refrigeration demand and heat dissipation capacity, enabling the system to meet the user's refrigeration demand while ensuring that heat can be effectively dissipated to avoid overheating problems.

[0012] The refrigeration power optimization module also includes a refrigeration efficiency evaluation unit for calculating the refrigeration efficiency based on the formula η cool = P cool / P input , where η cool represents the refrigeration efficiency, P inputRepresents the input power of the refrigerator. The above formula reflects the ratio of the input energy converted into the refrigeration effect, which is the basis for the energy optimization of the system. Through continuous evaluation and optimization of the refrigeration efficiency, the present invention realizes the efficient utilization of energy and extends the working time of the equipment.

[0013] The adaptive adjustment module applies the PID control algorithm P control =K p ·e(t)+K i ∫e(τ)dτ+K d ·de(t) / dt to generate a control signal, and through the adaptive adjustment rule P adjust =α·P control +β·D user to achieve precise control of the refrigeration power. In the PID control algorithm, P control represents the control signal for adjusting the output power of the refrigerator; e(t) represents the temperature error, calculated as the difference between the target temperature and the current refrigeration output temperature; K p 、K i 、K d represent the proportional, integral, and differential gain parameters respectively, which are used to adjust the response speed of the system, eliminate the steady-state error, and suppress the temperature oscillation. In the adaptive adjustment rule, P adjust represents the refrigeration power after adaptive adjustment; α and β are weighting coefficients used to balance the temperature control accuracy and user needs, satisfying α + β = 1 and 0 ≤ α, β ≤ 1.

[0014] The adaptive adjustment module of the present invention innovatively combines the classical PID control algorithm and the user demand model, and realizes the balance between the temperature control accuracy and user needs by dynamically adjusting the weighting coefficients α and β. Specifically, the weighting coefficients α and β are dynamically adjusted according to the environmental conditions and user status, satisfying the following relationship: α = g(|ΔT env |,|ΔV activity |), β = 1 - α, where |ΔT env | represents the absolute value of the change range of the environmental temperature, |ΔV activity | represents the absolute value of the change range of the activity intensity, g is a mapping function, and the value of g increases when |ΔT env | increases, and the value of g decreases when |ΔV activity | increases. The above solution enables the system to rely more on the PID control algorithm to ensure the temperature control accuracy when the environmental temperature changes rapidly, and to consider more the personalized needs of users when the user activity intensity changes significantly.

[0015] To further improve the adaptability and stability of the system, the present invention also includes a PID parameter self-adjustment module for dynamically adjusting the PID control parameters according to the system response characteristics: K p (t)=K p0 +ΔKp ·|e(t)|, K i u(t) = K i0 / (1 + γ·|e(t)|), K d u(t) = K d0 + δ·|de(t) / dt|, where K p0 、K i0 、K d0 are the initial parameter values respectively, and ΔK p 、γ, δ are adjustment coefficients. The above self - adjustment mechanism enables the system to dynamically adjust the PID parameters according to the magnitude and change rate of the temperature error. When the temperature error is large, the proportional coefficient is increased to accelerate the response speed, and at the same time, the integral coefficient is appropriately reduced to avoid integral saturation. When the temperature changes violently, the derivative coefficient is increased to improve the system stability.

[0016] The present invention adopts a stepped thermal management structure. Based on the heat dissipation balance equation P heat = h·A·(T cool - T env ), by differentially configuring the distribution of h and A, the uniformity optimization of the refrigeration effect is realized, satisfying the relationship: h i ·A i = k i ·S i , where h i and A i are the heat transfer coefficient and heat dissipation area of the i - th region respectively, k i is the weight coefficient, and S i is the corresponding human sensitivity coefficient. The human sensitivity coefficient can be determined through common ergonomics and thermal comfort research practices, which will not be elaborated here. The above solution takes into account the sensitivity differences of different parts of the human body to temperature, increases the heat dissipation capacity for human thermally sensitive areas such as the chest and back, and appropriately weakens it for thermally insensitive areas such as the shoulders and side abdomen, realizing the ergonomic optimization of the refrigeration effect and significantly improving the wearing comfort.

[0017] The present invention also includes a predictive control module for predicting the refrigeration demand based on the environmental temperature change trend: P predict = P _current + λ·dT env / dt·ΔT, where P predict is the predicted refrigeration power, P _current is the current refrigeration power, dT env / dt is the environmental temperature change rate, λ is the prediction coefficient, and ΔT is the prediction time window. The above predictive control enables the system to respond to the change of environmental temperature in advance, reduce the response lag, and improve the user comfort effect.

[0018] In summary, the intelligent temperature control and adaptive regulation system of the wearable cooler provided by the present invention solves the problems of insufficient intelligence and imperfect thermal management existing in traditional wearable coolers through algorithmic solutions and optimization strategies, realizes the precise perception and response to environmental temperature and user needs, and significantly improves the refrigeration effect, energy utilization efficiency and wearing comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is the overall architecture block diagram of the intelligent temperature control and adaptive regulation system of the wearable cooler of the present invention; Figure 2 is the flowchart of environmental temperature monitoring and user demand analysis of the present invention; Figure 3 is the flowchart of refrigeration power optimization and adaptive regulation of the present invention; Figure 4 is the schematic diagram of the three-dimensional mapping relationship of the user demand model of the present invention; Figure 5 is the schematic diagram of the stepped thermal management structure of the present invention; Figure 6 is the schematic diagram of the change of the adaptive regulation weighting coefficient of the present invention; Figure 7 is the schematic diagram of the PID parameter self-adjustment mechanism of the present invention; Figure 8 is the comparison chart of the refrigeration effects of the present invention under different environmental temperature conditions; Figure 9 is the energy efficiency comparison chart of the present invention and traditional coolers. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the following embodiments are only exemplary and do not limit the protection scope of the present invention. Those skilled in the art can make several obvious deformations or substitutions according to the inspiration of the present invention, and these deformations or substitutions should also be regarded as the protection scope of the present invention.

[0021] As Figure 1 shown, the intelligent temperature control and adaptive regulation system of the wearable cooler provided by the present invention mainly consists of four functional modules: an environmental temperature monitoring module, a user demand analysis module, a refrigeration power optimization module, and an adaptive regulation module. These four modules work together to form a closed-loop control system, realizing the intelligent temperature control and precise regulation of the wearable cooler.

[0022] The environmental temperature monitoring module is responsible for obtaining and analyzing environmental temperature data in real time. The above module collects the temperature information of the environment around the wearable cooler through multiple temperature sensors, and performs data processing and analysis based on the environmental temperature dynamic model. The mathematical expression of the environmental temperature dynamic model is: T env (t) = T base + ΔT(t).

[0023] The above model decomposes the environmental temperature into a base temperature T base and a dynamic fluctuation ΔT(t). T base represents the relatively stable environmental background temperature, such as the basic level of room temperature or outdoor temperature; while ΔT(t) represents the temperature fluctuations caused by various factors (such as solar radiation, wind speed changes, nearby heat sources, etc.). Through the above decomposition, the system can effectively distinguish between persistent temperature changes and short-term fluctuations, providing a basis for subsequent temperature control decisions.

[0024] In practical applications, the base temperature T base is usually obtained by filtering long-term temperature data, and methods such as moving average or low-pass filtering can be used; while the dynamic fluctuation ΔT(t) is calculated by the difference between the real-time measurement value and the base temperature. The system will regularly update the T base value to adapt to environmental changes, and at the same time, it will monitor the change trend of ΔT(t) in real time to predict the temperature changes in the short term in the future.

[0025] The user demand analysis module is one of the cores of the present invention. As Figure 2 shown, the above module comprehensively considers the environmental temperature, the user's body surface temperature, and the activity intensity, and constructs an accurate user cooling demand model: D user = f(T env , T body , V activity ).

[0026] The above model overcomes the limitations of traditional wearable coolers that only consider environmental temperature or simply preset temperature, and takes into account the individual differences and real-time status of users. Among them, T body is measured by a temperature sensor attached to the user's skin surface, reflecting the actual body surface temperature of the user; V activity is calculated by an internal acceleration sensor, representing the activity intensity of the user, and indirectly reflecting the level of metabolic heat generation of the user.

[0027] As Figure 4 shown, in the preferred embodiment of the present invention, the user demand model adopts the form of weighted non-linear mapping: f(T env , T body , V activity ) = w1·(T env - T ref ) 2 + w2·(T body - T comfort) 2 +w3·V activity 。

[0028] Among them, T ref represents the reference ambient temperature, usually set to a comfortable ambient temperature of about 25 °C; T comfort represents the comfortable body surface temperature, generally between 32 - 34 °C, and can be adjusted according to personal preferences; w1, w2, and w3 are weighting coefficients, which respectively control the influence degrees of ambient temperature, body surface temperature, and activity intensity on the cooling demand. These weights can be adjusted individually according to the user's usage habits and feedback to realize the learning function of the system.

[0029] In the formula, the ambient temperature and the body surface temperature are in the form of quadratic terms because the farther the temperature deviates from the comfortable range, the non - linear growth of the influence on comfort; while the activity intensity is in the form of a linear term because the metabolic heat production is basically proportional to the activity intensity. The above - mentioned scheme enables the model to more accurately describe the actual cooling demand of users under different conditions.

[0030] Such as Figure 3 shown, the refrigeration power optimization module, based on the thermodynamic principle, realizes the precise control of the refrigeration system by calculating the required refrigeration power and optimizing the heat dissipation performance. The above - mentioned module first calculates the theoretical refrigeration power based on the thermodynamic formula: P cool =Q·c·ΔT.

[0031] The above formula describes the heat transfer in the refrigeration process, where Q represents the mass flow rate of the cooling medium, reflecting the refrigeration capacity of the system; c represents the specific heat capacity of the cooling medium; ΔT represents the temperature difference, calculated as the difference between the user's body surface temperature and the output temperature of the cooler (T body -T cool ). Through the above formula, the system can accurately calculate the required refrigeration power according to the user's cooling demand, avoiding the problems of over - refrigeration or insufficient refrigeration.

[0032] At the same time, the above - mentioned module also considers the heat dissipation balance problem and optimizes the thermal management performance through the heat dissipation balance equation: P heat =h·A·(T cool -T env ).

[0033] The above equation describes the process of heat dissipation from the cooler to the environment, h represents the heat transfer coefficient, which is related to the material and structural scheme; A represents the heat dissipation area; (T cool -T env ) represents the difference between the output temperature of the cooler and the ambient temperature. In the scheme, it is necessary to ensure that P heat and P coolBasic balance to avoid the problem of efficiency decline caused by heat accumulation.

[0034] An important innovation of the present invention is the stepped heat management structure. As Figure 5 shown, the above structure is based on the heat dissipation balance equation. By differentially configuring the heat transfer coefficient h and the heat dissipation area A in different regions, the uniformity optimization of the refrigeration effect is achieved, satisfying the relationship: h i ·A i =k i ·S i .

[0035] Among them, h i and A i are respectively the heat transfer coefficient and the heat dissipation area of the i-th region, k i is the weight coefficient, and S i is the corresponding human sensitivity coefficient. The above solution takes into account the difference in temperature sensitivity of different parts of the human body, increases the heat dissipation capacity for heat-sensitive areas such as the chest and back, and appropriately weakens it for heat-insensitive areas such as the shoulders and side abdomen, achieving the ergonomic optimization of the refrigeration effect.

[0036] The refrigeration power optimization module also includes a refrigeration efficiency evaluation unit, which optimizes the system energy consumption by calculating the refrigeration efficiency η cool =P cool / P input , where P input represents the input power of the refrigerator. The above unit continuously monitors the energy efficiency status of the system, and maximizes the refrigeration efficiency and extends the working time of the device by adjusting the refrigeration strategy and optimizing the heat dissipation structure.

[0037] The adaptive adjustment module is another inventive point of the present invention. The above module combines control theory and adaptive algorithms to achieve precise control of the refrigeration power. As Figure 3 shown, the above module first applies the PID control algorithm to generate a basic control signal: P control =K p ·e(t)+K i ∫e(τ)dτ+K d ·de(t) / dt.

[0038] The above formula is the PID control algorithm. e(t) represents the temperature error, which is calculated as the difference between the target temperature and the current refrigeration output temperature (T target -T cool ); K p , K i and K d represent the proportional, integral, and differential gain parameters respectively. The proportional term K p· e(t) is proportional to the current error and is used to provide a basic control response; the integral term K i ∫e(τ)dτ accumulates the historical error and is used to eliminate the static error; the derivative term K d · de(t) / dt predicts the change trend of the error and is used to improve the system stability. These three parts work together to ensure that the system can track the target temperature quickly, accurately, and stably.

[0039] To further improve the adaptability of the system, the present invention introduces a PID parameter self - adjustment mechanism, such as Figure 7 shown, the above - mentioned mechanism dynamically adjusts the PID control parameters according to the system response characteristics: K p (t)=K p0 +ΔK p ·|e(t)|; K i (t)=K i0 / (1 + γ·|e(t)|); K d (t)=K d0 +δ·|de(t) / dt|.

[0040] Among them, K p0 , K i0 , K d0 are the initial parameter values respectively, and ΔK p , γ, δ are adjustment coefficients. The above self - adjustment mechanism enables the system to dynamically adjust the PID parameters according to the magnitude and change rate of the temperature error, increasing the proportional coefficient to accelerate the response speed when the temperature error is large, and appropriately reducing the integral coefficient to avoid integral saturation at the same time. When the temperature changes violently, the derivative coefficient is increased to improve the system stability. The above - mentioned scheme enables the system to maintain the best control performance under different working conditions.

[0041] On the basis of generating the basic control signal, the adaptive adjustment module combines the PID control signal with the user demand model through an innovative adaptive adjustment rule: P adjust =α·P control +β·D user .

[0042] The above formula realizes the balance between the temperature control accuracy and the user demand. α and β are weighting coefficients, satisfying α + β = 1 and 0 ≤ α,β ≤ 1. As Figure 6 shown, these two coefficients are dynamically adjusted according to the environmental conditions and the user status: α = g(|ΔT env |,|ΔV activity |); β = 1 - α.

[0043] where, |ΔT env | represents the absolute value of the environmental temperature change range, |ΔV activity | represents the absolute value of the activity intensity change range, and g is a mapping function. When the environmental temperature changes rapidly (|ΔT env | is large), the system increases the value of α and relies more on the PID control algorithm to ensure the temperature control accuracy; when the user's activity intensity changes significantly (|ΔV activity | is large), the system increases the value of β and takes more into account the user's personalized needs. The above dynamic balance mechanism enables the system to provide the most suitable cooling effect for the user in different situations.

[0044] The present invention further includes a predictive control module, and the above module predicts the future cooling demand based on the environmental temperature change trend: P predict =P _current +λ·dT env / dt·ΔT.

[0045] where, P predict is the predicted cooling power, P _current is the current cooling power, dT env / dt is the environmental temperature change rate, λ is a prediction coefficient, and ΔT is a prediction time window. Through the above predictive control, the system can respond to the change of the environmental temperature in advance, reduce the response lag, and improve the user comfort effect. For example, when it is detected that the environmental temperature begins to rise rapidly, the system will increase the cooling power in advance to avoid discomfort for the user; when the environmental temperature is about to drop, the system will reduce the cooling power in advance to avoid energy waste caused by excessive cooling.

[0046] The intelligent temperature control and adaptive adjustment system of the wearable cooler of the present invention has good effects in practical applications. As Figure 8 shown, under different environmental temperature conditions, the cooling effect of the present invention always remains within the comfortable range, while the traditional cooler has problems of insufficient cooling or excessive cooling. Especially in the case of rapid change of the environmental temperature, the predictive control and adaptive adjustment functions of the present invention significantly reduce the response lag and ensure the continuous comfort effect of the user.

[0047] In terms of energy efficiency, as Figure 9 shown, compared with the traditional wearable cooler, the present invention reduces the energy consumption by about 30% under the same cooling effect, and extends the battery life by about 40%. The above energy efficiency improvement mainly comes from three aspects: First, the accurate user demand model avoids unnecessary excessive cooling; second, the stepped thermal management structure optimizes the heat dissipation efficiency and reduces the energy loss; finally, the predictive control reduces the frequent adjustment of the system and further saves the energy consumption. In addition, the present invention can also be applied to high-temperature working environments.

[0048] In summary, the intelligent temperature control and adaptive regulation system of the wearable cooler provided by the present invention solves the problems of insufficient intelligence and imperfect thermal management existing in traditional wearable coolers through algorithmic solutions and optimization strategies, realizes precise perception and response to environmental temperature and user needs, significantly improves the cooling effect, energy utilization efficiency, and wearing comfort. At the same time, the above-mentioned wearable cooler can cooperate with 3D fabric clothing to form a pipeline, achieving the purpose of being lightweight, comfortable, and having a good cooling effect.

[0049] To verify the algorithm calculation process of the present invention, the following initial parameters are set. I. Relevant parameters for simulating the actual application scenario in a high-temperature working environment in a steel plant: Ambient temperature parameter: Basic environmental temperature: T base = 35 °C (basic temperature in the steel plant workshop); Current environmental temperature fluctuation value: ΔT(t) = 3 °C (local temperature fluctuation generated by the heat source).

[0050] User status parameter: User body surface temperature: T body = 36.5 °C; User activity intensity: V activity = 2.5 m / s 2 (medium-intensity work); Target temperature: T target = 33 °C (desired comfortable body surface temperature); Current cooler output temperature: T cool = 30 °C.

[0051] User demand model parameter: Reference environmental temperature: T ref = 25 °C; Comfortable body surface temperature: T comfort = 33 °C; Weight coefficient: w1 = 0.2 W / °C 2 , w2 = 0.5 W / °C 2 , w3 = 1.2 W / (m / s 2 ).

[0052] Refrigeration power parameter: Cooling medium mass flow rate: Q = 0.01 kg / s; Specific heat capacity of the cooling medium: c = 4200 J / (kg·K); Heat transfer coefficient: h = 10 W / (m 2 ·K); Heat dissipation area: A = 0.12 m 2； Refrigerator input power: P input = 20 W.

[0053] PID control parameter: Initial proportionality coefficient: K p0 = 2 W / °C; Initial integral coefficient: K i0 = 0.5 W / (°C·s); Initial derivative coefficient: K d0 = 0.8 W·s / °C; Self-adjusting coefficient: ΔK p = 0.5, γ = 0.8, δ = 0.2; Integral error accumulation value: ∫e(τ)dτ = 1.5 °C·s (assuming the error accumulated at the previous moment); Error change rate: de(t) / dt = -0.2 °C / s (the temperature error is decreasing).

[0054] Adaptive adjustment parameter: Ambient temperature change range: |ΔT env | = 1.2 °C / min; Activity intensity change range: |ΔV activity | = 0.8 m / s 2 / min; Weighted mapping function: g(|ΔT env |,|ΔV activity |) = 0.6·|ΔT env | / (|ΔT env | + |ΔV activity |).

[0055] Predictive control parameter: Prediction coefficient: λ = 2 W·min / (°C·s); Ambient temperature change rate: dT env / dt = 0.05 °C / s; Prediction time window: ΔT = 60 s.

[0056] II. Overall algorithm calculation process Ambient temperature monitoring calculation First, calculate the current ambient temperature using the ambient temperature dynamic model: T env (t) = T base + ΔT(t)T env (t) = 35 °C + 3 °C = 38 °C.

[0057] At this time, the ambient temperature is 38°C, which is a rather high temperature, significantly higher than the human comfort temperature range. Therefore, the refrigeration system needs to be activated.

[0058] User demand analysis calculation Next, calculate the user's refrigeration demand intensity based on the user demand model: D user = w1·(T env - T ref ) 2 + w2·(T body - T comfort ) 2 + w3·V activity .

[0059] Substitute the parameter values: D user = 0.2 W / °C 2 ·(38°C - 25°C) 2 + 0.5 W / °C 2 ·(36.5°C - 33°C) 2 + 1.2 W / (m / s 2 )·2.5 m / s 2 = 0.2 W / °C 2 ·(13°C) 2 + 0.5 W / °C 2 ·(3.5°C) 2 + 1.2 W / (m / s 2 )·2.5 m / s 2 = 0.2 W / °C 2 ·169°C 2 + 0.5 W / °C 2 ·12.25°C 2 + 1.2 W / (m / s 2 )·2.5 m / s 2 = 33.8 W + 6.125 W + 3 W = 42.925 W.

[0060] This calculation result shows that based on the current ambient temperature, user body surface temperature, and activity intensity, the system needs to provide approximately 42.925 W of refrigeration power to meet the user's comfort requirements. It can be seen that the relatively high ambient temperature (38°C) is the main factor contributing to the increased demand, contributing approximately 33.8 W; followed by the user's body surface temperature being higher than the comfort temperature, contributing approximately 6.125 W; and the user's moderate activity intensity contributes an additional 3 W of demand.

[0061] Refrigeration power optimization calculation Calculate the refrigeration power according to the thermodynamic formula: P cool = Q·c·ΔT; where ΔT = T body - T cool = 36.5°C - 30°C = 6.5°C; P cool = 0.01 kg / s·4200 J / (kg·K)·6.5°C = 0.01 kg / s·4200 J / (kg·K)·6.5 K (the Celsius difference is equal to the Kelvin difference) = 273 W.

[0062] This indicates that under the current settings, the system can provide a refrigeration power of 273 W, fully meeting the user's demand of 42.925 W.

[0063] Next, calculate the heat dissipation power of the system: P heat = h·A·(T cool - T env ) = 10 W / (m 2 ·K)·0.12 m 2 ·(30°C - 38°C) = 10 W / (m 2 ·K)·0.12 m 2 ·(-8°C) = 10 W / (m 2 ·K)·0.12 m 2 ·(-8 K) = -9.6 W.

[0064] The negative heat dissipation power indicates that under the current conditions, the ambient temperature is higher than the output temperature of the cooler. The system not only cannot dissipate heat to the environment but also absorbs heat from the environment, which will reduce the refrigeration efficiency of the system. In the above situation, it is necessary to consider increasing the heat dissipation area or improving the heat dissipation scheme.

[0065] Calculate the refrigeration efficiency: η cool = P cool / P input = 273 W / 20 W = 13.65.

[0066] The refrigeration efficiency is greater than 1. In fact, this is because the refrigeration power is used instead of the actual refrigeration capacity. In a refrigeration system, the refrigeration power usually refers to the power of the removed heat. The cooler can drive the heat pump cycle by consuming less electrical energy and achieve a refrigeration effect far greater than the input power. The above physical principle enables the coefficient of performance (COP) of the refrigeration system to be significantly greater than 1.

[0067] Step-type thermal management structure calculation For the optimization of thermal management in different regions of the human body, calculations are carried out taking three representative regions as examples: Region 1 (chest): Human sensitivity coefficient: S1 = 1.2 (highly sensitive region); Weight coefficient: k1 = 1.5; Calculate the product of the heat transfer coefficient and the heat dissipation area: h1·A1 = k1·S1 = 1.5·1.2 = 1.8 W / K.

[0068] Region 2 (back): Human sensitivity coefficient: S2 = 1.0 (medium sensitive region) Weight coefficient: k2 = 1.2; Calculate the product of the heat transfer coefficient and the heat dissipation area: h2·A2 = k2·S2 = 1.2·1.0 = 1.2 W / K.

[0069] Region 3 (shoulder): Human sensitivity coefficient: S3 = 0.6 (low sensitive region); Weight coefficient: k3 = 0.8.

[0070] Calculate the product of the heat transfer coefficient and the heat dissipation area: h3·A3 = k3·S3 = 0.8·0.6 = 0.48 W / K.

[0071] The above configuration ensures that the thermally sensitive regions can obtain better cooling effects and improves the overall comfort. If the heat transfer coefficient h is kept constant, then the heat dissipation area distribution for each region is: A1 = 1.8 W / K ÷ 10 W / (m 2 ·K) = 0.18 m 2 A2 = 1.2 W / K ÷ 10 W / (m 2 ·K) = 0.12 m 2 A3 = 0.48 W / K ÷ 10 W / (m 2 ·K) = 0.048 m 2 ; Total heat dissipation area = 0.18 m 2 + 0.12 m 2 + 0.048 m 2 = 0.348 m 2 .

[0072] The above distribution method enables the chest region to obtain approximately 51.7% of the heat dissipation area, the back to obtain approximately 34.5%, and the shoulder to obtain only approximately 13.8%, effectively improving the cooling comfort.

[0073] PID controller calculation Calculate the temperature error: e(t) = T target-T cool = 33 °C - 30 °C = 3 °C.

[0074] Next, calculate the self - adjusted values of the PID parameters: K p (t)=K p0 +ΔK p ·|e(t)| = 2 W / °C+0.5·3 °C = 3.5 W / °C.

[0075] K i (t)=K i0 / (1 + γ·|e(t)|) = 0.5 W / (°C·s) / (1 + 0.8·3 °C) = 0.5 W / (°C·s) / 3.4 = 0.147 W / (°C·s).

[0076] K d (t)=K d0 +δ·|de(t) / dt| = 0.8 W·s / °C+0.2·0.2 °C / s = 0.8 W·s / °C+0.04 W·s / °C = 0.84 W·s / °C.

[0077] When the temperature error is large, the system increases the proportional coefficient to speed up the response, reduces the integral coefficient to avoid integral saturation, and slightly increases the derivative coefficient to enhance stability.

[0078] Calculate the PID control signal: P control =K p ·e(t)+K i ∫e(τ)dτ+K d ·de(t) / dt = 3.5 W / °C·3 °C+0.147 W / (°C·s)·1.5 °C·s+0.84 W·s / °C·(-0.2 °C / s) = 10.5 W+0.2205 W - 0.168 W = 10.5525 W.

[0079] This control signal is relatively small, indicating that the system needs to increase the refrigeration power to reach the target temperature.

[0080] Adaptive adjustment calculation First, calculate the weighting coefficients α and β: α = g(|ΔT env |,|ΔV activity |) = 0.6·|ΔT env | / (|ΔT env|+|ΔV activity |) =0.6·1.2℃ / min / (1.2℃ / min + 0.8m / s 2 / min) =0.6·1.2 / (1.2 + 0.8)=0.6·0.6 = 0.36。

[0081] β = 1 - α = 1 - 0.36 = 0.64。

[0082] Here, the value of α is small and the value of β is large, indicating that the system gives more consideration to user needs rather than strict temperature control accuracy, which is suitable for situations where the activity intensity changes significantly.

[0083] Now calculate the refrigeration power after adaptive adjustment: P adjust = α·P control + β·D user = 0.36·10.5525W + 0.64·42.925W = 3.7989W + 27.472W = 31.2709W。

[0084] This calculation result shows that considering the current environmental conditions and user status, the system decides to provide a refrigeration power of approximately 31.27W, which is much less than the system's theoretical refrigeration capacity (273W), but is sufficient to meet the balanced user comfort requirements.

[0085] Predictive control calculation Predictive control predicts future refrigeration demand based on the changing trend of ambient temperature: P predict = P _current + λ·dT env / dt·ΔT = 31.2709W + 2W·min / (℃·s)·0.05℃ / s·60s = 31.2709W + 2·0.05·60W = 31.2709W + 6W = 37.2709W。

[0086] The prediction result shows that considering the rising trend of ambient temperature, the system predicts that the refrigeration power needs to be increased to approximately 37.27W within the next 60 seconds to pre - empt the discomfort caused by the temperature rise.

[0087] Result analysis From the above detailed calculations, it can be seen that: 1. Ambient temperature analysis: The current ambient temperature is 38°C, which is much higher than the human comfort temperature, and a large amount of refrigeration is required.

[0088] 2. User demand analysis: Based on the current conditions, the theoretical refrigeration demand of the user is 42.925W, among which the ambient high temperature factor (33.8W) is the main contributor, followed by the relatively high body surface temperature (6.125W) and activity intensity (3W).

[0089] 3. Refrigeration capacity assessment: The theoretical refrigeration power of the system is 273W, far exceeding the user demand, but the heat dissipation problem (-9.6W) indicates poor heat dissipation in a high-temperature environment, and the heat dissipation scheme may need to be improved.

[0090] 4. Thermal management optimization: Through a stepped thermal management structure, the system optimizes the heat dissipation distribution of different human body areas. The chest obtains 51.7% of the heat dissipation area, the back obtains 34.5%, and the shoulders obtain 13.8%, improving the refrigeration uniformity and comfort.

[0091] 5. PID control adjustment: Based on a temperature error of 3°C, the system dynamically adjusts the PID parameters. The proportional coefficient increases by 75% (2 → 3.5), the integral coefficient decreases by 70.6% (0.5 → 0.147), and the derivative coefficient slightly increases by 5% (0.8 → 0.84), generating a control signal of 10.5525W.

[0092] 6. Adaptive balance: Considering the large variation in user activity intensity, the system allocates 64% of the weight to user demand and 36% to precise temperature control, and finally decides to provide a refrigeration power of 31.2709W.

[0093] 7. Predictive control: The system predicts that the ambient temperature will continue to rise and adjusts the refrigeration power to 37.2709W in advance to prevent the deterioration of the user experience due to lag.

[0094] The above multi-level adaptive control strategy enables the system to provide accurate, comfortable, and energy-saving refrigeration effects according to changing environmental conditions and user states. Compared with traditional fixed-mode refrigerators, this system saves more than 84% in energy efficiency (31.27W vs 273W of theoretical power), and at the same time provides a more uniform and comfortable refrigeration effect through thermal management optimization and predictive control.

[0095] In practical applications, the system continuously executes the above calculation process at a frequency of 10 - 100ms, constantly adjusting the refrigeration power to ensure that users can obtain the best results under various environmental conditions. The intelligent temperature control system based on an accurate mathematical model and adaptive algorithm provides a good solution for personal cooling in high-temperature environments.

[0096] It should be understood that the above embodiments are only examples given to illustrate the principles of the present invention. Those skilled in the art can make various changes and modifications without departing from the scope and spirit of the present invention. Therefore, all equivalent changes and modifications made to the present invention based on the technical solutions and concepts of the present invention shall fall within the scope of protection of the present invention.

Claims

1. A smart temperature control and adaptive adjustment system for a wearable cooler, characterized in that, Including: An ambient temperature monitoring module, a user demand analysis module, a refrigeration power optimization module, and an adaptive adjustment module; The ambient temperature monitoring module is used to monitor the ambient temperature in real time based on the ambient temperature dynamic model; The user demand analysis module is used to calculate the user's refrigeration demand based on the user demand model; The refrigeration power optimization module is used to calculate the required refrigeration power based on the thermodynamic formula and optimize the thermal management performance through the heat dissipation balance equation; The adaptive adjustment module is used to generate a control signal by applying the PID control algorithm and control the refrigeration power through the adaptive adjustment rule.

2. The intelligent temperature control and adaptive adjustment system for a wearable cooler according to claim 1, wherein The environmental temperature dynamic model is T env (t)=T base +ΔT(t), where T env (t) represents the environmental temperature at the current moment t, T base represents the basic environmental temperature, and ΔT(t) represents the temperature dynamic fluctuation value collected by the sensor in real time; The user demand model is D user = f(T env , T body , V activity ), where D user represents the refrigeration intensity of the user demand, T body represents the user's body surface temperature, V activity represents the user's activity intensity, and f is a non-linear function describing the mapping relationship between the user's state and the refrigeration demand; The thermodynamic formula is P cool = Q·c·ΔT, and the heat dissipation balance equation is P heat = h·A·(T cool - T env ), where P cool represents the refrigeration power, Q represents the mass flow rate of the cooling medium, c represents the specific heat capacity, ΔT represents the temperature difference, P heat represents the heat dissipation power, h represents the heat transfer coefficient, A represents the heat dissipation area, and T cool represents the output temperature of the cooler; The PID control algorithm is P control =K p ·e(t)+K i ∫e(τ)dτ+K d ·de(t) / dt; The adaptive adjustment rule is P adjust = α·P control + β·D user ; wherein e(t)=T target -T cool represents the temperature error, K p , K i , K d represent the proportional, integral and derivative gain parameters respectively. α and β are weighting coefficients, satisfying α + β = 1 and 0 ≤ α, β ≤ 1.

3. The intelligent temperature control and adaptive regulation system of the wearable cooler according to claim 2, wherein, The non-linear function of the user demand model adopts a weighted non-linear mapping form: f(T env ,T body ,V activity ) = w1·(T env -T ref ) + w2·(T body -T comfort ) 2 + w3·V activity ; Among them, T ref represents the reference ambient temperature, T comfort represents the comfortable body surface temperature, and w1, w2, and w3 represent weight coefficients.

4. The intelligent temperature control and adaptive adjustment system of the wearable cooler according to claim 2, characterized in that, The refrigeration power optimization module further includes a refrigeration efficiency evaluation unit, which is used based on the formula: η cool =P cool / P input ; Calculate the refrigeration efficiency, where η cool represents the refrigeration efficiency, and P input represents the input power of the refrigerator.

5. The intelligent temperature control and adaptive regulation system of the wearable cooler according to claim 2, characterized in that, The weighting coefficients α and β in the adaptive adjustment module are dynamically adjusted according to the environmental conditions and the user's state, satisfying the following relationship: α = g(|ΔT env |, |ΔV activity |); β=1-α； where, |ΔT env | represents the absolute value of the environmental temperature change range, |ΔV activity | represents the absolute value of the activity intensity change range, g is a mapping function, and when |ΔT env | increases, the value of g increases, and when |ΔV activity | increases, the value of g decreases.

6. The intelligent temperature control and adaptive regulation system of the wearable cooler according to claim 2, characterized in that, The system further includes a PID parameter self-adjustment module, which is used to dynamically adjust the PID control parameters according to the system response characteristics: K p (t) = K p0 + ΔK p ·|e(t)|; K i (t)=K i0 / (1 + γ·|e(t)|); K d y(t)=K d0 +δ·|dy(t) / dt|; Among them, K p0 , K i0 , K d0 are respectively initial parameter values, and ΔK p , γ, and δ are adjustment coefficients.

7. The intelligent temperature control and adaptive adjustment system of the wearable cooler according to claim 2, characterized in that, The system adopts a stepped thermal management structure. Based on the heat dissipation balance equation P heat =h·A·(T cool -T env ), by differentially configuring the distribution of h and A, the uniformity optimization of the refrigeration effect is achieved, satisfying the relationship: h i ·A i =k i ·S i ; where h i and A i are the heat transfer coefficient and heat dissipation area of the i-th region respectively, k i is the weight coefficient, and S i is the corresponding human sensitivity coefficient.

8. The intelligent temperature control and adaptive regulation system of the wearable cooler according to claim 1, characterized in that The system further includes a predictive control module, which is used to predict the refrigeration demand based on the changing trend of the ambient temperature: P predict = P _current + λ·dT env / dt·ΔT; where, P predict is the predicted refrigeration power, P _current is the current refrigeration power, dT env / dt is the environmental temperature change rate, λ is the prediction coefficient, and ΔT is the prediction time window.

9. A method for intelligent temperature control and adaptive adjustment of a wearable cooler using the system according to any one of claims 1-8, characterized in that, Including the following steps: Collect and monitor the ambient temperature, and apply the model T env (t)=T base +ΔT(t) to calculate the current ambient temperature state; Monitor the body surface temperature T of the user body and the activity intensity V activity , and calculate the user's cooling demand through the function D user = f(T env , T body , V activity ) Based on the thermodynamic formula P cool =Q·c·ΔT to calculate the required refrigeration power; And optimize the heat dissipation performance through the heat dissipation balance equation P heat =h·A·(T cool -T env ); Calculate the temperature error e(t) = T target - T cool , and generate a control signal through the PID control algorithm P control = K p ·e(t) + K i ∫e(τ)dτ + K d ·de(t) / dt Apply the adaptive adjustment rule P adjust =α·P control +β·D user , and calculate the final refrigeration power; According to the refrigeration efficiency formula η cool =P cool / P input Optimize the system energy consumption; Convert the adjusted control instruction into a hardware operation signal to control the operation of the refrigeration equipment.

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