Intelligent temperature control system for hot compress salt physiotherapy bag

Abstract

The invention relates to the technical field of local heating, in particular to an intelligent temperature control system for a hot compress salt physiotherapy bag, which comprises a multi-layer tissue modeling module for calculating the heat conduction rate of a multi-layer tissue according to the heat conduction characteristic of physiotherapy bag salt particles in combination with the heat conductivity of a skin layer and the thickness of a fat layer, integrating a heat flow input value and a heat attenuation path, and calculating the heat conduction rate of the multi-layer tissue; the accumulated heat flow decreases progressively and the surface heat conduction rate is increased. According to the invention, the heat conduction rate and path are accurately predicted by combining the heat conduction characteristic of the salt particles and the heat transfer rule of the multi-layer tissue, the heat concentration or dispersion phenomenon is avoided, the heat distribution uniformity is improved, the temperature difference is monitored in real time, the power distribution is dynamically adjusted, and the temperature control precision is improved and the energy consumption is optimized; heat loss is reduced, it is ensured that the temperature is stabilized in a suitable range, the problem of overheating or supercooling is avoided, the balance of the hot compress effect is integrally enhanced, the physical therapy requirements of multiple parts are met, energy waste is reduced, and meanwhile the safety and reliability of equipment operation are improved.

Description

Technical Field

[0001] The present invention relates to the field of local heating technology, and in particular to an intelligent temperature control system for a hot compress salt therapy bag. Background Art

[0002] The field of local heating technology mainly involves regulating the temperature of specific parts of the human body through heat transfer technology, and combining intelligent control means to achieve precise heating management, thereby promoting blood circulation, relieving muscle tension and pain, and assisting in the treatment of local inflammation and edema. The software system in this field usually provides core control support for physical therapy equipment, including real-time temperature monitoring, adjusting heating parameters, and protecting the safe operation of equipment. It is widely used in medical rehabilitation equipment, emphasizing intelligence, stability and safety.

[0003] Among them, the hot compress salt therapy bag intelligent temperature control system refers to the special intelligent control software developed for the hot compress salt therapy bag product, which realizes the precise temperature control management of the hot compress process through temperature sensors and control algorithms. The system can monitor the operating status of the equipment in real time, prevent overheating or overheating, and ensure that the salt bag always maintains a stable therapy temperature. Users can set the heating area, time and temperature through the system, which is suitable for multi-part hot compress therapy needs in medical and home care scenarios.

[0004] The existing technology lacks comprehensive application of the heat transfer characteristics of salt particles, which makes it difficult to accurately control the heat distribution, and some areas may be overheated or underheated. Temperature control parameters mainly rely on static settings and lack a dynamic adjustment mechanism. Local temperature differences cannot be corrected in time, and the thermal efficiency utilization rate is low. Failure to effectively combine the characteristics of human blood flow and ignoring the dynamic balance requirements during heat transfer may lead to uneven physical therapy effects. The ability to control heat loss is insufficient, power consumption is high, and there are hidden dangers in the stability and safety of equipment operation, which cannot fully meet the diverse physical therapy needs. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent temperature control system for a hot compress salt therapy bag.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A hot compress salt therapy bag intelligent temperature control system comprises: The multi-layer tissue modeling module calculates the thermal conductivity of multi-layer tissues based on the thermal conductivity of the salt particles in the physiotherapy bag, combined with the thermal conductivity of the skin layer and the thickness of the fat layer, integrates the heat flow input value and the thermal attenuation path, accumulates the heat flow decrease and the surface thermal conductivity rate, and generates a heat transfer dynamic parameter matrix; The heat flux distribution simulation module, based on the heat transfer dynamic parameter matrix, calls the boundary heat flux value and the distribution length of the heating element, calculates the heat flux gradient and heat difference in the partition area, analyzes the heat flux accumulation and temperature change trajectory in the partition, and generates the partition heat flux distribution data; The dynamic feedback control module uses a temperature sensor to monitor the regional temperature in real time based on the partitioned heat flow distribution data, compares the collected temperature value with the target temperature, adjusts the power distribution of the heating element, corrects the power distribution according to the local temperature difference, and generates regional power control data; The power optimization control module adjusts the local power output based on the regional power control data, combines the thermal efficiency distribution and the temperature response value, calculates the overall power consumption and heat loss, corrects the thermal efficiency of the heating area, dynamically optimizes the local heat source power distribution, and generates local power optimization parameters; The temperature balance adjustment module analyzes heat transfer based on the local power optimization parameters and the blood circulation effect, calculates the local temperature offset value, adjusts the regional heat source distribution, calls the cooling parameters to control the temperature difference range, optimizes the dynamic heat balance, and generates the regional temperature balance result.

[0007] The heat transfer dynamic parameter matrix specifically includes heat flux input value, heat attenuation path, heat flux decrease, and surface thermal conductivity rate. The partitioned heat flux distribution data specifically includes heat flux gradient, heat difference, heat flux accumulation, and temperature change trajectory. The regional power control data specifically includes temperature monitoring value, target temperature difference, power allocation, and local temperature difference correction value. The local power optimization parameters specifically include local power output value, overall power consumption, heat loss, and thermal efficiency correction value. The regional temperature balance results include local temperature offset value, heat source distribution, cooling parameters, and dynamic thermal equilibrium state.

[0008] As a further solution of the present invention, the step of obtaining the heat transfer dynamic parameter matrix is ​​specifically as follows: Based on the thermal conductivity of the salt particles in the physiotherapy bag and the thermal conductivity of the skin layer, the heat flux input value and the thermal conductivity rate of the skin surface are integrated and calculated to calculate the thermal attenuation path of the multi-layer tissue and generate the heat flux reduction parameter matrix. Matching the heat flow rate values ​​in the heat flow decrease parameter matrix with the fat layer thickness point by point, determining the heat distribution of differentiated layers in the heat flow path through trend analysis of the accumulated heat flow decrease values, and obtaining the dynamic distribution parameters of the heat attenuation path; The heat flux input value and the heat distribution parameter in the dynamic distribution parameter of the thermal attenuation path are calculated, with the thermal conductivity of the skin layer, the thickness of the fat layer and the heat flux reduction parameter matrix as the core, using the formula: ; The heat transfer dynamic parameter matrix is ​​calculated; in, represents the heat transfer dynamic parameter matrix, represents the heat flux input value in the heat flux reduction parameter matrix, represents the thermal conductivity of the skin layer, Represents the decreasing trend value of heat flow, Represents the thickness of the fat layer, represents the cumulative heat flow correction factor, Represents the cumulative calorific value, represents the dynamic heat transfer coefficient, Represents the number of data points processed.

[0009] As a further solution of the present invention, the step of acquiring the partitioned heat flow distribution data is specifically as follows: By combining the data of the heat transfer dynamic parameter matrix with the boundary heat flux value and the distribution length of the heating element, a preliminary calculation of the heat and heat flux rate is performed to generate preliminary heat flux gradient estimation data; Using the preliminary heat flow gradient estimation data, analyzing the heat distribution in the region, extracting key heat flow change characteristics through point-by-point comparison of heat accumulation and temperature change data, and calculating heat difference analysis parameters; Applying the heat difference analysis parameters, combined with the location information of the heating element and the boundary heat flow value, the heat flow distribution algorithm is used to refine the multi-region heat flow accumulation and temperature change trajectory, using the formula: ; Calculate and obtain the partition heat flow distribution data; in, Represents the partition heat flow distribution data, Represents the estimated heat flow gradient data within multiple partitions, represents the heat flux attenuation coefficient, Represents the length parameter of the corresponding partition, represents the total heating element distribution length, Represents the number of partitions.

[0010] As a further solution of the present invention, the step of acquiring the regional power control data is specifically as follows: According to the zoned heat flow distribution data, temperature sensors are configured in key heating areas to monitor the temperature values ​​in the areas in real time and generate real-time temperature monitoring data; Using the real-time temperature monitoring data, a comparative analysis is performed region by region with the preset target temperature, the temperature difference of each region is calculated, the temperature difference values ​​of multiple regions are obtained, and local temperature difference data is generated; Based on the local temperature difference data, the power output of the multi-zone heating element is evaluated and adjusted to ensure that the temperature of the multiple heating zones is close to the target setting, using the formula: ; Refine the power control strategy, update the power allocation of heating elements in real time, and generate regional power control data; in, represents the adjusted regional power output, Represents the original power setting, Represents the difference between the actual and target temperatures, represents the target temperature, Represents the total number of heated zones.

[0011] As a further solution of the present invention, the step of obtaining the local power optimization parameter is specifically as follows: Based on the regional power control data and combined with the thermal efficiency distribution monitoring results, the power and thermal response data of multiple heating areas are analyzed in real time to generate multi-area real-time power and thermal response comparison data; According to the real-time power and thermal response comparison data of the multiple regions, the heat loss and power consumption of each region are calculated, and by comparing the thermal efficiency model with the actual output data, the power consumption abnormality region is identified, and the power consumption and heat loss evaluation results are generated; Based on the power consumption and heat loss assessment results, the power output of the heating elements is reconfigured with reference to the heat loss and regional power requirements, using the formula: ; Optimize power distribution to heating elements, dynamically adjust to minimize heat loss, and generate local power optimization parameters; in, represents the optimized local power output, Represents the current power setting, represents the measured heat loss, represents the standard energy input, Represents the adjustment factor.

[0012] As a further solution of the present invention, the steps of obtaining the regional temperature balance result are specifically as follows: Based on the local power optimization parameters, the influence of blood circulation on heat transfer is analyzed, the local temperature offset value is calculated, and the local temperature offset analysis data is generated by fusing the hemodynamic model and the heat transfer data; According to the local temperature offset analysis data, the regional heat source distribution is adjusted to match the heat transfer characteristics of blood circulation, a heat source dynamic adjustment strategy is adopted, the adjusted heat source distribution is calculated, and the adjusted heat source distribution data is generated; The heat source distribution data is adjusted and combined with the cooling parameter control algorithm to optimize the dynamic heat balance, using the formula: ; Calculate the equilibrium temperature of multiple points in the region and generate regional temperature equilibrium results; in, Represents the regional temperature equilibrium result, Represents the adjusted multi-zone temperature, Represents the temperature offset value, represents the cooling regulation coefficient, Represents the weight factor of multiple regions, Represents the total number of heated zones.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by combining the thermal conductivity of salt particles with the heat transfer law of multi-layer tissues, the heat conduction rate and path are accurately predicted to avoid heat concentration or dispersion and improve the uniformity of heat distribution. The temperature difference is monitored in real time, and the power distribution is adjusted dynamically to achieve improved temperature control accuracy and energy consumption optimization. Combined with the local temperature difference and blood circulation, heat loss is reduced, ensuring that the temperature is stable in an appropriate range and avoiding overheating or overcooling. The overall balance of the hot compress effect is enhanced to meet the needs of multi-site physical therapy, reduce energy waste, and improve the safety and reliability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart of the steps for obtaining the heat transfer dynamic parameter matrix of the present invention; Figure 3 A flow chart of the steps for obtaining the zone heat flow distribution data of the present invention; Figure 4 A flow chart of the steps for obtaining regional power control data of the present invention; Figure 5 A flow chart of the steps for obtaining local power optimization parameters of the present invention; Figure 6 The figure is a flow chart of the steps for obtaining the regional temperature balance result of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1 , a hot compress salt therapy bag intelligent temperature control system includes: The multi-layer tissue modeling module calculates the thermal conductivity of multi-layer tissues based on the thermal conductivity of the salt particles in the physiotherapy bag, combined with the thermal conductivity of the skin layer and the thickness of the fat layer, integrates the heat flow input value and the thermal attenuation path, accumulates the heat flow decrease and the surface thermal conductivity rate, and generates a heat transfer dynamic parameter matrix; The heat flux distribution simulation module is based on the heat transfer dynamic parameter matrix, calls the boundary heat flux value and the distribution length of the heating element, calculates the heat flux gradient and heat difference in the partition area, analyzes the heat flux accumulation and temperature change trajectory in the partition, and generates the partition heat flux distribution data; The dynamic feedback control module uses temperature sensors to monitor the regional temperature in real time based on the partitioned heat flow distribution data, compares the collected temperature value with the target temperature, adjusts the power distribution of the heating element, corrects the power distribution according to the local temperature difference, and generates regional power control data; The power optimization control module adjusts the local power output based on the regional power regulation data, combines the thermal efficiency distribution and the temperature response value, calculates the overall power consumption and heat loss, corrects the thermal efficiency of the heating area, dynamically optimizes the local heat source power distribution, and generates the local power optimization parameters; The temperature balance adjustment module is based on local power optimization parameters, combines blood circulation to analyze heat transfer, calculates local temperature offset values, adjusts regional heat source distribution, calls cooling parameters to control the temperature difference range, optimizes dynamic thermal balance, and generates regional temperature balance results.

[0018] The dynamic parameter matrix of heat transfer specifically includes heat flux input value, heat attenuation path, heat flux decrease, and surface thermal conductivity rate. The partitioned heat flux distribution data specifically includes heat flux gradient, heat difference, heat flux accumulation, and temperature change trajectory. The regional power control data specifically includes temperature monitoring value, target temperature difference, power allocation, and local temperature difference correction value. The local power optimization parameters specifically include local power output value, overall power consumption, heat loss, and thermal efficiency correction value. The regional temperature balance results include local temperature offset value, heat source distribution, cooling parameters, and dynamic thermal equilibrium state.

[0019] See also Figure 2 , the specific steps for obtaining the heat transfer dynamic parameter matrix are: Based on the thermal conductivity of the salt particles in the physiotherapy bag and the thermal conductivity of the skin layer, the heat flux input value and the thermal conductivity rate of the skin surface are integrated and calculated to calculate the thermal attenuation path of the multi-layer tissue and generate the heat flux reduction parameter matrix. The model takes into account the different thermal conductivity properties of the skin layer and the fat layer, and how these properties affect the propagation of heat energy in these layers. By using these thermal conductivity characteristic parameters and accurately calculating the thermal conductivity and thermal impedance of each layer, it further simulates how the heat flow attenuates in these different layers, thereby predicting and adjusting the thermal effect of the therapy pack in actual use. This simulation is critical for designing more effective therapy packs. It can help optimize the structure of the heat pack so that it can provide therapeutic effects while maximizing safety and comfort.

[0020] The heat flow rate values ​​in the heat flow decrease parameter matrix are matched point by point with the fat layer thickness. The heat distribution of the differentiated layers in the heat flow path is determined through the trend analysis of the cumulative heat flow decrease values, and the dynamic distribution parameters of the heat attenuation path are obtained. After obtaining the heat flux reduction parameter matrix, these data are compared and matched with the fat layer thickness point by point, and the heat propagation and attenuation at different thicknesses are further analyzed. This process involves complex data processing, including statistical analysis of the heat flux rate values ​​and graphical processing of the thermal attenuation trend. Through these processing, we can specifically observe how the heat is distributed in the fat layer and how the heat changes at different depths. This is crucial for understanding the actual effectiveness and safety of the heat pack during use, and provides a more intuitive way to observe and adjust the design of the heat pack.

[0021] The heat flux input value and heat distribution parameters in the dynamic distribution parameters of the thermal attenuation path are calculated, with the thermal conductivity of the skin layer, the thickness of the fat layer and the heat flux reduction parameter matrix as the core, using the formula: ; The heat transfer dynamic parameter matrix is ​​calculated; in, represents the heat transfer dynamic parameter matrix, represents the heat flux input value in the heat flux reduction parameter matrix, represents the thermal conductivity of the skin layer, Represents the decreasing trend value of heat flow, Represents the thickness of the fat layer, represents the cumulative heat flow correction factor, Represents the cumulative calorific value, represents the dynamic heat transfer coefficient, Represents the number of data points processed.

[0022] formula: ; The benefit of the formula is that it not only integrates the heat conduction parameters of multiple levels, but also increases the nonlinearity of the calculation by adding logarithmic terms and square root terms, which helps to more accurately simulate the complex heat transfer phenomenon in multi-layered tissues.

[0023] Detailed explanation of the formula and the process of formula calculation and derivation: set up is the heat flux input value. The initial heat flux received by the skin layer is 1000W / m2. is the thermal conductivity of the skin layer, taking the constant value as 0.5W / (m·K), is the decreasing trend value of heat flux, assuming that it decays to 800W / m2 during the heat conduction process of the fat layer. is the thickness of the fat layer, taken as 0.01m, is the cumulative heat flow correction coefficient, set to 0.05, For the cumulative heat value, take 950W / m2, is the dynamic thermal conductivity, which is taken as 0.45 W / (m·K).

[0024] ; The results show that the dynamic parameter matrix of heat transfer of the therapy pack in this configuration is 1589.82, which means that the heat transfer efficiency is high and the heat can effectively penetrate the skin layer to the fat layer to achieve the expected treatment depth.

[0025] See also Figure 3 , the specific steps for obtaining the partition heat flow distribution data are: By combining the data of the heat transfer dynamic parameter matrix with the boundary heat flux value and the distribution length of the heating element, a preliminary calculation of the heat and heat flux rate is performed to generate preliminary heat flux gradient estimation data; Thermodynamic principles and measured data are used to calculate the heat flow rate at the corresponding position of each heating element in detail to ensure the accuracy of each calculation point. Combined with the actual distribution and boundary conditions of the heating elements, a preliminary heat flow gradient model is gradually constructed. This model will reflect the heat flow changes between different positions, thereby laying the foundation for subsequent heat flow dynamic analysis and generating preliminary heat flow gradient estimation data.

[0026] Using preliminary heat flow gradient estimation data, analyze the heat distribution in the area, extract key heat flow change characteristics through point-by-point comparison of heat accumulation and temperature change data, and calculate heat difference analysis parameters; Thermodynamic analysis methods and actual measurement data are used to compare the heat accumulation and temperature changes in each area, the relationship between heat flow and temperature is carefully analyzed, and the heat difference in each area is calculated. This process involves precise measurement of heat differences and data processing technology to ensure that the obtained heat difference parameters accurately reflect the actual situation, extract key heat flow change characteristics, and obtain heat difference analysis parameters.

[0027] Apply the heat difference analysis parameters, combine the location information of the heating element and the boundary heat flow value, and use the heat flow distribution algorithm to refine the multi-region heat flow accumulation and temperature change trajectory using the formula: ; Calculate and obtain the partition heat flow distribution data; in, Represents the partition heat flow distribution data, Represents the estimated heat flow gradient data within multiple partitions, represents the heat flux attenuation coefficient, Represents the length parameter of the corresponding partition, represents the total heating element distribution length, Represents the number of partitions.

[0028] formula: ; The benefit of the formula is that it can take into account the heat flux attenuation and length differences in different areas, and calculate the heat flux distribution in each area in the form of exponential decay, thereby providing a more detailed view of the dynamic distribution of heat, which is suitable for accurate analysis of heat flow in complex heating environments.

[0029] Detailed explanation of the formula and the process of formula calculation and derivation: Given a numerical example, assuming ,, 100, 200, 150 W / m2 respectively. They are 0.1, 0.2, 0.15 respectively. They are 10, 20, and 15 meters respectively. The calculation process is as follows: ; ; ; The results show that the average heat flux density is about 0.95 W / m2, indicating that the overall heat flux density of the system is low under given heating conditions and regional characteristics, which helps to optimize the layout and power distribution of heating elements.

[0030] See also Figure 4 , the specific steps for obtaining regional power control data are: According to the zone heat flow distribution data, configure temperature sensors in key heating areas to monitor the temperature values ​​in the areas in real time and generate real-time temperature monitoring data; The process includes deploying multiple sensors in each heating area to ensure the accuracy and real-time nature of the data, and transmitting the data to the central monitoring system in real time via wireless signals. The central monitoring system updates the temperature monitoring interface in real time based on the incoming temperature data, and provides immediate data support to the operator. The operator can adjust the heating parameters based on the real-time data to ensure uniform heating of each area, avoid local overheating or failure to reach the preset temperature. The accurate acquisition and processing of real-time temperature monitoring data provides a key basis for subsequent power adjustments.

[0031] Use real-time temperature monitoring data to compare and analyze each area with the preset target temperature, calculate the temperature difference in each area, obtain the temperature difference values ​​of multiple areas, and generate local temperature difference data; This process involves extracting the temperature value of each monitoring point from the real-time data, performing a difference operation with the preset target temperature, and obtaining the specific temperature difference of each heating area. These temperature difference data will be converted into local temperature difference data through a data processing algorithm to provide decision support for the adjustment of the power of the heating element, ensuring that the temperature adjustment of each area is carried out accurately, thereby achieving energy efficiency optimization and uniformity of product quality. The key to the accurate calculation of local temperature difference data lies in accurate real-time temperature data and an effective data processing process.

[0032] Based on the local temperature difference data, the power output of the multi-zone heating element is evaluated and adjusted to ensure that the temperature of the multi-heating zone is close to the target setting, using the formula: ; Refine the power control strategy, update the power allocation of heating elements in real time, and generate regional power control data; in, represents the adjusted regional power output, Represents the original power setting, Represents the difference between the actual and target temperatures, represents the target temperature, Represents the total number of heated zones.

[0033] formula: ; The benefit of the formula is that it allows the power of each heating zone to be dynamically adjusted to match the actual temperature demand. This approach improves production efficiency and product quality by adapting to the difference between the actual temperature and the target temperature, optimizing energy consumption and ensuring uniformity of the heating process.

[0034] Detailed explanation of the formula and the process of formula calculation and derivation: Assume there are three heating zones, and the original power settings are , , ; Target temperature The actual temperatures are , , . Temperature difference They are , , . Calculate the adjusted power for each region: ; ; ; Total adjusted power: ; The results show that by precisely adjusting the heating power in each zone, more accurate temperature control can be achieved, energy waste can be reduced, and heating efficiency and product quality consistency can be improved.

[0035] See also Figure 5 , the steps for obtaining local power optimization parameters are as follows: Based on the regional power control data and combined with the thermal efficiency distribution monitoring results, the power and thermal response data of multiple heating areas are analyzed in real time to generate multi-area real-time power and thermal response comparison data; Data is collected through sensors installed in key areas. These sensors upload temperature and power information at a fixed frequency. The data is sent to the central monitoring system via a wireless network. The monitoring system uses this data to generate real-time power and thermal response comparison data. This data helps identify areas with insufficient thermal efficiency, so that immediate adjustments can be made to optimize energy distribution and reduce energy consumption, forming a detailed power and thermal efficiency result.

[0036] Based on the real-time power and thermal response comparison data of multiple regions, the heat loss and power consumption of each region are calculated. By comparing the thermal efficiency model with the actual output data, the power consumption abnormality area is identified and the power consumption and heat loss evaluation results are generated. Using advanced data analysis technology, the thermal efficiency model is compared with the actual output data. This analysis helps identify areas of abnormal power consumption and generates power consumption and heat loss assessment results. This result not only points out which areas have excessive power consumption, but also recommends adjustment measures to ensure that the overall efficiency of the heating system is maximized. The purpose of the result is to provide the operations team with actionable optimization suggestions to improve the heating process and reduce energy waste.

[0037] Based on the power consumption and heat loss assessment results, the power output of the heating elements is reconfigured with reference to the heat loss and regional power requirements, using the formula: ; Optimize power distribution to heating elements, dynamically adjust to minimize heat loss, and generate local power optimization parameters; in, represents the optimized local power output, Represents the current power setting, represents the measured heat loss, represents the standard energy input, Represents the adjustment factor.

[0038] formula: ; The formula is beneficial in that it optimizes heating efficiency and energy consumption by adjusting the raw power setting to compensate for measured heat losses.

[0039] Detailed explanation of the formula and the process of formula calculation and derivation: Assuming the original power For 1000watts, heat loss 50 watts, standard energy input For 1000 watts, the adjustment factor is 1.05, and the calculation process is as follows: 1. Calculate the heat loss ratio: ; 2. Calculate the new power before adjustment: watts; 3. Calculate the optimized power: watts; The results show that through subtle adjustments, the energy utilization of the heating system can be improved, energy waste can be reduced, and unnecessary energy consumption can be reduced while ensuring that the heating elements maintain their performance.

[0040] See also Figure 6 , the specific steps for obtaining the regional temperature equilibrium results are: Based on the local power optimization parameters, the effect of blood circulation on heat transfer is analyzed, the local temperature offset value is calculated, and the local temperature offset analysis data is generated by fusing the hemodynamic model and heat transfer data; Through integrated heat transfer and hemodynamic models, we can track in detail how heat propagates in tissues and identify key heat transfer paths. Through real-time data monitoring and combined with changes in blood flow speed and direction, we can calculate the heat distribution in different areas. These data can be visualized through simulation software, and the specific temperature value of each part can be accurately calculated. The output of the heating element can be adjusted in real time to ensure that heat is evenly distributed in the tissue and avoid local overheating or failure to reach the treatment temperature. This process ensures the safety and efficiency of treatment and generates local temperature offset analysis data, which directly affects subsequent adjustment decisions.

[0041] According to the local temperature offset analysis data, the regional heat source distribution is adjusted to match the heat transfer characteristics of blood circulation, and a heat source dynamic adjustment strategy is adopted to calculate the adjusted heat source distribution and generate adjusted heat source distribution data; This process takes into account in detail the impact of blood circulation on heat transfer, especially in areas with faster blood flow. By adjusting the output power of the heating elements near the heat source, the accumulation of heat in these areas is reduced, while increasing the heating intensity in areas with slower blood flow to ensure temperature balance in the entire treatment area. A dynamic adjustment strategy is adopted to monitor temperature feedback in real time and adjust the heating parameters to match the actual temperature response. In addition, the heat source distribution map is updated in real time through software tools, providing real-time data support to the operator for more precise control, thereby optimizing the overall thermal effect and producing adjusted heat source distribution data.

[0042] By adjusting the heat source distribution data and combining the cooling parameter control algorithm, the dynamic heat balance is optimized using the formula: ; Calculate the equilibrium temperature of multiple points in the region and generate regional temperature equilibrium results; in, Represents the regional temperature equilibrium result, Represents the adjusted multi-zone temperature, Represents the temperature offset value, represents the cooling regulation coefficient, Represents the weight factor of multiple regions, Represents the total number of heated zones.

[0043] formula: ; The benefit of the formula is that it takes into account the local temperature offset value and the specific cooling adjustment parameters of each area. By assigning different weights to different areas, it achieves precise temperature control, thereby achieving temperature balance in the entire area.

[0044] Detailed explanation of the formula and the process of formula calculation and derivation: Set the specific values ​​of each parameter. Assume there are three areas and the temperature value The temperature offset values ​​are 38°C, 40°C, and 37°C respectively. The cooling adjustment coefficient is 2°C, 1°C, 3°C. The regional weights are 0.5, 0.2, and 0.3 respectively. They are 0.3, 0.4, and 0.3 respectively. Calculate the adjusted temperature of each area: ; ; ; These values ​​are then plugged into the formula to calculate the overall equilibrium temperature: ; The results show that through this adjustment strategy, the average temperature of the overall area reached 32.2°C, which effectively reduced local temperature deviations and improved thermal comfort and therapeutic effects.

[0045] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent temperature control system for hot compress salt therapy packs, characterized in that: The system comprises: The multi-layer tissue modeling module calculates the thermal conductivity of multi-layer tissues based on the thermal conductivity of the salt particles in the physiotherapy bag, combined with the thermal conductivity of the skin layer and the thickness of the fat layer, integrates the heat flow input value and the thermal attenuation path, accumulates the heat flow decrease and the surface thermal conductivity rate, and generates a heat transfer dynamic parameter matrix; The heat flux distribution simulation module, based on the heat transfer dynamic parameter matrix, calls the boundary heat flux value and the distribution length of the heating element, calculates the heat flux gradient and heat difference in the partition area, analyzes the heat flux accumulation and temperature change trajectory in the partition, and generates the partition heat flux distribution data; The dynamic feedback control module uses a temperature sensor to monitor the regional temperature in real time based on the partitioned heat flow distribution data, compares the collected temperature value with the target temperature, adjusts the power distribution of the heating element, corrects the power distribution according to the local temperature difference, and generates regional power control data; The power optimization control module adjusts the local power output based on the regional power control data, combines the thermal efficiency distribution and the temperature response value, calculates the overall power consumption and heat loss, corrects the thermal efficiency of the heating area, dynamically optimizes the local heat source power distribution, and generates local power optimization parameters; The temperature balance adjustment module analyzes heat transfer based on the local power optimization parameters and the blood circulation effect, calculates the local temperature offset value, adjusts the regional heat source distribution, calls the cooling parameters to control the temperature difference range, optimizes the dynamic heat balance, and generates the regional temperature balance result.

2. The intelligent temperature control system for hot compress salt therapy pack according to claim 1 is characterized in that: The heat transfer dynamic parameter matrix specifically includes heat flux input value, heat attenuation path, heat flux decrease, and surface thermal conductivity rate. The partitioned heat flux distribution data specifically includes heat flux gradient, heat difference, heat flux accumulation, and temperature change trajectory. The regional power control data specifically includes temperature monitoring value, target temperature difference, power allocation, and local temperature difference correction value. The local power optimization parameters specifically include local power output value, overall power consumption, heat loss, and thermal efficiency correction value. The regional temperature balance results include local temperature offset value, heat source distribution, cooling parameters, and dynamic thermal equilibrium state.

3. The intelligent temperature control system for hot compress salt therapy pack according to claim 2 is characterized in that: The steps for obtaining the heat transfer dynamic parameter matrix are specifically as follows: Based on the thermal conductivity of the salt particles in the physiotherapy bag and the thermal conductivity of the skin layer, the heat flux input value and the thermal conductivity rate of the skin surface are integrated and calculated to calculate the thermal attenuation path of the multi-layer tissue and generate the heat flux reduction parameter matrix. Matching the heat flow rate values ​​in the heat flow decrease parameter matrix with the fat layer thickness point by point, determining the heat distribution of differentiated layers in the heat flow path through trend analysis of the accumulated heat flow decrease values, and obtaining the dynamic distribution parameters of the heat attenuation path; The heat flux input value and the heat distribution parameter in the dynamic distribution parameter of the thermal attenuation path are calculated, with the thermal conductivity of the skin layer, the thickness of the fat layer and the heat flux reduction parameter matrix as the core, using the formula: ； The heat transfer dynamic parameter matrix is ​​calculated; in, represents the heat transfer dynamic parameter matrix, represents the heat flux input value in the heat flux reduction parameter matrix, represents the thermal conductivity of the skin layer, Represents the decreasing trend value of heat flow, Represents the thickness of the fat layer, represents the cumulative heat flow correction factor, Represents the cumulative calorific value, represents the dynamic heat transfer coefficient, Represents the number of data points processed.

4. The intelligent temperature control system for hot compress salt therapy pack according to claim 3 is characterized in that: The steps for obtaining the partition heat flow distribution data are specifically as follows: By combining the data of the heat transfer dynamic parameter matrix with the boundary heat flux value and the distribution length of the heating element, a preliminary calculation of the heat and heat flux rate is performed to generate preliminary heat flux gradient estimation data; Using the preliminary heat flow gradient estimation data, analyzing the heat distribution in the region, extracting key heat flow change characteristics through point-by-point comparison of heat accumulation and temperature change data, and calculating heat difference analysis parameters; Applying the heat difference analysis parameters, combined with the location information of the heating element and the boundary heat flow value, the heat flow distribution algorithm is used to refine the multi-region heat flow accumulation and temperature change trajectory, using the formula: ； Calculate and obtain the partition heat flow distribution data; in, Represents the partition heat flow distribution data, Represents the estimated heat flow gradient data within multiple partitions, represents the heat flux attenuation coefficient, Represents the length parameter of the corresponding partition, represents the total heating element distribution length, Represents the number of partitions.

5. The intelligent temperature control system for hot compress salt therapy pack according to claim 4 is characterized in that: The steps for obtaining the regional power control data are specifically as follows: According to the zoned heat flow distribution data, temperature sensors are configured in key heating areas to monitor the temperature values ​​in the areas in real time and generate real-time temperature monitoring data; Using the real-time temperature monitoring data, a comparative analysis is performed region by region with the preset target temperature, the temperature difference of each region is calculated, the temperature difference values ​​of multiple regions are obtained, and local temperature difference data is generated; Based on the local temperature difference data, the power output of the multi-zone heating element is evaluated and adjusted to ensure that the temperature of the multiple heating zones is close to the target setting, using the formula: ； Refine the power control strategy, update the power allocation of heating elements in real time, and generate regional power control data; in, represents the adjusted regional power output, Represents the original power setting, Represents the difference between the actual and target temperatures, represents the target temperature, Represents the total number of heated zones.

6. The intelligent temperature control system for hot compress salt therapy pack according to claim 5, characterized in that: The steps of obtaining the local power optimization parameters are specifically as follows: Based on the regional power control data and combined with the thermal efficiency distribution monitoring results, the power and thermal response data of multiple heating areas are analyzed in real time to generate multi-area real-time power and thermal response comparison data; According to the real-time power and thermal response comparison data of the multiple regions, the heat loss and power consumption of each region are calculated, and by comparing the thermal efficiency model with the actual output data, the power consumption abnormality region is identified, and the power consumption and heat loss evaluation results are generated; Based on the power consumption and heat loss assessment results, the power output of the heating elements is reconfigured with reference to the heat loss and regional power requirements, using the formula: ； Optimize power distribution to heating elements, dynamically adjust to minimize heat loss, and generate local power optimization parameters; in, represents the optimized local power output, Represents the current power setting, represents the measured heat loss, represents the standard energy input, Represents the adjustment factor.

7. The intelligent temperature control system for hot compress salt therapy pack according to claim 6, characterized in that: The steps for obtaining the regional temperature balance result are specifically as follows: Based on the local power optimization parameters, the influence of blood circulation on heat transfer is analyzed, the local temperature offset value is calculated, and the local temperature offset analysis data is generated by fusing the hemodynamic model and the heat transfer data; According to the local temperature offset analysis data, the regional heat source distribution is adjusted to match the heat transfer characteristics of blood circulation, a heat source dynamic adjustment strategy is adopted, the adjusted heat source distribution is calculated, and the adjusted heat source distribution data is generated; The heat source distribution data is adjusted and combined with the cooling parameter control algorithm to optimize the dynamic heat balance, using the formula: ； Calculate the equilibrium temperature of multiple points in the region and generate regional temperature equilibrium results; in, Represents the regional temperature equilibrium result, Represents the adjusted multi-zone temperature, Represents the temperature offset value, represents the cooling regulation coefficient, Represents the weight factor of multiple regions, Represents the total number of heated zones.

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