Temperature control system of a physiotherapy hot compress

Abstract

A temperature control method and system for a physiotherapy hot compress patch relates to the field of biometric identification. The method is applied to an MCU control module, and the method includes: obtaining basic information of a target patient, the basic information including multiple sub-basic information, including the hot compress site, age, gender, hot compress time, and disease information; based on the basic information, generating a first prediction curve of the skin temperature change at the hot compress site; controlling a heating module to perform a skin test on the hot compress site to obtain a skin temperature change curve; based on the skin temperature change curve, correcting the first prediction curve to generate a second prediction curve of the skin temperature change at the hot compress site; and controlling the heating module to perform hot compress heating based on the second prediction curve. Implementation of the technical solution provided by this application solves the problem of overly simple temperature control of the physiotherapy hot compress patch.

Description

Technical Field

[0001] The present application relates to the technical field of biometric identification, and in particular to a temperature control system of a physiotherapy hot compress patch. Background Art

[0002] As people's lifestyles change, the incidence of chronic pain conditions such as cervical spondylosis, frozen shoulder, and arthritis is gradually increasing. Traditionally, applying a hot towel to the painful area to increase blood circulation and relieve the discomfort caused by the pain. However, this method is inefficient and very cumbersome, so a therapeutic heat compress patch that simulates the effect of a hot towel has been designed.

[0003] Current therapeutic hot compress patches evenly distribute heating wires across the adhesive surface. An MCU control module then controls the output unit to release PWM electrical pulses, causing the heating wires to continuously heat up. The temperature of the heating wires is then controlled by adjusting the duty cycle of the PWM electrical pulses. However, current therapeutic hot compress patches suffer from a single temperature control mechanism. Even with pre-set adjustable temperature settings, the patches often fail to effectively deliver their therapeutic effects due to individual patient differences. Summary of the Invention

[0004] In view of the problem that the temperature control of current physiotherapy hot compress patches is too simple, the present application provides a temperature control method and system for physiotherapy hot compress patches.

[0005] In a first aspect, the present application provides a method for controlling the temperature of a therapeutic hot compress, the method comprising:

[0006] Obtaining basic information of the target patient, wherein the basic information includes multiple sub-basic information, including hot compress site, age, gender, hot compress time, and disease information;

[0007] generating a first prediction curve of skin temperature change at the hot compress site based on the basic information;

[0008] Controlling the heating module to perform a skin test on the hot compress area to obtain a skin temperature change curve;

[0009] Based on the skin temperature change curve, the first prediction curve is corrected to generate a second prediction curve of the skin temperature change at the hot compress site;

[0010] Based on the second prediction curve, the heating module is controlled to perform hot compress heating.

[0011] Optionally, querying a preset user information database for historical user information having the highest similarity to the basic information and its corresponding historical skin temperature change curve, wherein the basic information and the historical user information each have a corresponding similarity value in a plurality of the sub-basic information;

[0012] Based on the similarity values ​​of the plurality of sub-basic information, querying the preset user information database for a plurality of historical skin temperature change curves corresponding to the respective sub-basic information;

[0013] determining, based on the slope distribution of the plurality of historical skin temperature change curves corresponding to the plurality of sub-basic information, an influence coefficient of the plurality of sub-basic information on the historical skin temperature change curve corresponding to the historical user information;

[0014] Based on the influence coefficients of the plurality of sub-basic information, the historical skin temperature change curve corresponding to the historical user information is adjusted to obtain the first prediction curve.

[0015] Optionally, performing principal component analysis on the plurality of sub-basic information to determine a main influence coefficient corresponding to each time point in the historical skin temperature change curve corresponding to the historical user information;

[0016] Based on the main influence coefficient corresponding to each time point, the historical skin temperature change curve corresponding to the historical user information is adjusted to generate the first prediction curve.

[0017] Optionally, the controlling the heating module to perform a skin test on the hot compress area further includes:

[0018] Obtaining a time period corresponding to a fluctuation curve in the first prediction curve, and setting the time period corresponding to the fluctuation curve as a test time for the skin test;

[0019] Setting a plurality of monitoring points during the test time, wherein each monitoring point corresponds to a heating power;

[0020] During the test, when the monitoring point is reached, the heating module is controlled to adjust to the heating power corresponding to each monitoring point.

[0021] Optionally, a plurality of skin temperature test curves are obtained based on a plurality of monitoring points set during the test time;

[0022] Similarity calculation is performed between the plurality of skin temperature test curves and the first prediction curve, and the skin temperature test curve with the highest similarity is selected as the skin temperature change curve.

[0023] Optionally, extracting multiple key feature points of the skin temperature change curve and multiple key feature points of the first prediction curve, and comparing the multiple key feature points of the two one by one to obtain a difference sequence between the skin temperature change curve and the first prediction curve;

[0024] Based on the difference sequence, the default correction factor is adjusted to obtain a comprehensive correction factor;

[0025] The first prediction curve is adjusted using the comprehensive correction factor to obtain a second prediction curve.

[0026] Optionally, after controlling the heating module to perform hot compress heating based on the second prediction curve, the method further includes:

[0027] Obtain feedback from target patients;

[0028] The feedback information is matched with a power adjustment table to obtain an adjustment amplitude and an adjustment rate corresponding to the feedback information.

[0029] In a second aspect, the present application provides a temperature control system for a therapeutic hot compress patch, wherein the system is an MCU control module, and the MCU control module includes an acquisition module, a processing module, and a sending module, wherein:

[0030] The acquisition module is used to acquire basic information of the target patient, wherein the basic information includes a plurality of sub-basic information, and the plurality of sub-basic information includes hot compress site, age, gender, hot compress time and disease information;

[0031] The processing module is configured to generate a first prediction curve of skin temperature changes at the hot compress site based on the basic information; control the heating module to perform a skin test on the hot compress site to obtain a skin temperature change curve; and modify the first prediction curve based on the skin temperature change curve to generate a second prediction curve of skin temperature changes at the hot compress site;

[0032] The sending module is used to control the heating module to perform hot compress heating based on the second prediction curve.

[0033] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a method as described in any one of the first aspects.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0035] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0036] 1. To address the problem of the single control method of current physiotherapy hot compress patches, this application first matches a skin temperature change curve that is most similar to the patient's physical condition from a preset user information database based on the patient's basic information, so that when the MCU control module controls the heating module to heat the hot compress patch according to the temperature change curve, it can have a better physiotherapy effect on the patient; however, due to the individual differences in physical conditions of different patients, relying solely on the above temperature change curve may not be able to adapt to the patient's physical condition. Therefore, this application then conducts actual skin tests on the patient to obtain the patient's skin temperature changes, and then infers the patient's blood circulation based on the skin temperature changes. The principle is that when the patient's blood circulation is poor, the blood cannot effectively carry heat to the whole body, resulting in a higher rate of increase in the skin temperature of the hot compress area, and vice versa, the rate of increase is lower. Based on this principle, this application modifies the skin temperature change curve that is most similar to the patient's physical condition according to the results of the skin test, thereby obtaining a personalized temperature change curve that is closer to the patient's physical condition, thereby enabling subsequent heating of the hot compress patch to have a better physiotherapy effect.

[0037] 2. In order to improve the reference value of the first prediction curve of the skin temperature change at the hot compress site during the skin test, this application adjusts the historical skin temperature change curve that is most similar to the basic information of the target patient to make it fit the physical condition of the target patient as much as possible. Specifically, the influence of each factor in the basic information on the skin temperature is analyzed separately to obtain the influence coefficients of multiple factors. Then, in the process of the patient using the hot compress, the influence degree of each factor will also change with the increase of heating time. Therefore, by performing principal component analysis on each factor at different time points, the main influence coefficients at different time points are obtained, and then the temperature value at each time point in the historical skin temperature change curve is adjusted based on the main influence coefficients to obtain the first prediction curve, thereby improving the reference value of the first prediction curve. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a structural schematic diagram of a therapeutic hot compress patch provided in an embodiment of the present application.

[0039] Figure 2 This is a flow chart of a temperature control method for a therapeutic hot compress provided in an embodiment of the present application.

[0040] Figure 3 This is a structural schematic diagram of a temperature control system of a therapeutic hot compress patch provided in an embodiment of the present application.

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0042] Explanation of the reference numerals: 1. Acquisition module; 2. Processing module; 3. Sending module; 400. Electronic device; 401. Processor; 402. Communication bus; 403. User interface; 404. Network interface; 405. Memory. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0044] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0045] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0046] As a device that simulates the effect of hot towel compress, the structure of the physiotherapy hot compress patch is as follows: Figure 1 As shown, it includes an adhesive surface, a heating wire, a temperature detection circuit, an output unit and an MCU control module. In this structure, the heating wire is evenly arranged on the adhesive surface. The MCU control module controls the output unit to release PWM electric pulses to make the heating wire continuously generate heat. The temperature detection circuit detects the temperature of the heating wire and feeds the temperature of the heating wire back to the MCU control module. The MCU module can then adjust the duty cycle of the PWM electric pulse to achieve temperature control of the heating wire.

[0047] However, today's physiotherapy hot compress patches face the problem of overly single temperature control. Even if multiple adjustable temperature levels are pre-set, due to individual differences between patients, the physiotherapy effect of the hot compress patch cannot be effectively exerted in many cases. In addition, when adjusting the temperature of the physiotherapy hot compress patch, it is more inclined to subjective judgment, resulting in the hot compress effect not being optimal, especially for middle-aged and elderly patients. Due to their poor temperature perception, they are even more unable to accurately judge the optimal hot compress temperature.

[0048] In order to solve the above problems, the present application provides a temperature control method for a physiotherapy hot compress patch, which is applied to an MCU control module, such as Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0049] S101. Obtain basic information of the target patient, where the basic information includes multiple sub-basic information, including hot compress site, age, gender, hot compress time, and disease information.

[0050] In the above steps, the MCU control module can be understood as a control instrument, which is divided into two modes: automatic adjustment and active adjustment. Among them, automatic adjustment is that the user inputs basic information on the control instrument, and the control instrument calculates the adjustment temperature suitable for the physiotherapy hot compress patch based on the basic information. Active adjustment is that the user directly sets the temperature of the physiotherapy hot compress patch for adjustment. The user can pre-enter the basic information of the target patient, where the basic information includes but is not limited to the hot compress site, age, gender, hot compress time, number of hot compresses, and disease information. At this time, the MCU control module converts the basic information into a standard data table and distinguishes it by number. When automatic adjustment is required, the MCU control module calls the standard data table with the corresponding number to obtain the basic information of the target patient.

[0051] S102: Generate a first prediction curve of skin temperature change at the hot compress site based on the basic information.

[0052] In the above steps, the first prediction curve can be understood as the temperature trend curve of the skin temperature of the target patient's hot compress part during the hot compress process. Therefore, in order to make the first prediction curve more consistent with the skin temperature change of the target patient, the present application queries the historical user information with the highest similarity to the basic information and its corresponding historical skin temperature change curve from the preset user information library; since the basic information of the target patient is not exactly the same as the historical user information, it is necessary to make some adjustments to the historical skin temperature change curve to make it more consistent with the basic information of the target patient; at this time, the basic information of the target patient is split into multiple basic sub-information, and then each basic sub-information is matched with the preset user information library to obtain multiple historical skin temperature change curves corresponding to each basic sub-information. During the matching process, in order to ensure the output of the matching results, the matching range of each basic sub-information needs to be set in advance. The matching range should not be too large. The specific setting standard is determined according to the information distribution of each historical user information in the preset user information library. Then, the slope distribution of multiple historical skin temperature change curves corresponding to each basic sub-information is analyzed. The slope distribution can reflect the degree of influence of the basic sub-information on the skin temperature change. During the analysis process, the slope curves of the multiple historical skin temperature change curves corresponding to the basic sub-information are averaged and fused to obtain a fused slope curve; then, the fused slope curves of multiple basic sub-information are compared to obtain the influence coefficients of multiple basic sub-information on skin temperature change. Finally, the influence coefficients of multiple basic sub-information are used to adjust each temperature point in the historical skin temperature change curve corresponding to the historical user information to generate a first prediction curve.

[0053] In one possible embodiment, when the physiotherapy hot compress patch is applied to the hot compress area, the dominance of different basic sub-information on skin temperature will change as the hot compress time increases; for example, in the early stage of the hot compress, due to differences in body structure, the hot compress area will dominate, and the impact of age and gender differences on skin temperature will not be as obvious as the hot compress area. In the middle stage of the hot compress, due to the different skin metabolism and blood circulation in different age groups, the impact of age and gender differences on skin temperature is more obvious. Therefore, when using the influence coefficients of multiple basic sub-information to adjust each temperature point in the historical skin temperature change curve, the adjustment strategy for different hot compress stages needs to be changed. Specifically, first, principal component analysis is performed on multiple sub-basic information at different hot compress stages, which can be obtained by comparing the fusion slope curves corresponding to each basic sub-information. For example, if the fusion slope curve of the first basic sub-information is faster than the fusion slope curve of the second basic sub-information in the early stage of the hot compress, it means that the first basic sub-information is more dominant. In this way, the dominant basic sub-information (main influence coefficient) at each temperature point (time point) can be further obtained, and then the proportion of the main influence coefficient at each time point can be increased. Finally, the historical skin temperature change curve corresponding to the historical user information can be adjusted to obtain the first prediction curve, so that the first prediction curve is more in line with the physical condition of the target patient.

[0054] S103: Control the heating module to perform a skin test on the hot compress area to obtain a skin temperature change curve.

[0055] In the above steps, the first prediction curve is only a skin temperature change curve estimated for the target patient, which may be different from the actual situation. Therefore, it is necessary to perform skin testing on the hot compress area of ​​the target patient, combine the actual situation with the estimated data, and thus obtain the personalized hot compress needs of the target patient.

[0056] When conducting skin tests, in order to avoid blind testing, the present application selects the fluctuation curve in the first prediction curve as the reference data for the skin test, wherein the fluctuation curve specifically refers to the interval range with larger fluctuation amplitude and frequency in the predicted skin temperature change curve, indicating that the data within the interval range contains more noise; then, the time period of the fluctuation curve is set as the test time of the skin test, and the upper and lower limit heating powers corresponding to the fluctuation curve are set as the upper and lower limit heating powers of the skin test, and then, multiple monitoring points are evenly set within the test time, wherein one monitoring point corresponds to one heating power. During the test, when the monitoring point is reached, the heating module is controlled to adjust to the heating power corresponding to the monitoring point, and the actual skin temperature change data of the target patient is detected.

[0057] In order to find the skin temperature change curve that is most suitable for correcting the first prediction curve, the skin temperature test curves detected by multiple monitoring points are obtained, and then the similarity of the multiple skin temperature test curves and the non-fluctuation curve part in the first prediction curve is calculated, and the skin temperature test curve with the highest similarity is selected as the skin temperature change curve for subsequent correction. It should be explained that since the fluctuation curve is an inferred curve, it introduces many irrelevant factors. If the noise in the fluctuation curve is removed, the fluctuation curve will be a regular stable curve. However, in the actual process, noise removal is more complicated and the effect is not good. Therefore, the present application chooses to calculate the similarity of multiple skin temperature test curves and the non-fluctuation curve part in the first prediction curve, thereby indirectly performing noise reduction processing on the fluctuation curve part, and treating the most similar skin temperature test curve as the stable curve after the fluctuation curve is denoised, so that the skin temperature change curve used for the subsequent correction of the first prediction curve is more valuable.

[0058] S104: Based on the skin temperature change curve, the first prediction curve is corrected to generate a second prediction curve of the skin temperature change at the hot compress site.

[0059] In the above steps, although the skin temperature change curve can be regarded as the stable curve after denoising corresponding to the fluctuation curve, the skin temperature change curve is only a curve segment and cannot completely replace the fluctuation curve. Its adaptability in the entire first prediction curve needs to be considered, and then the fluctuation amplitude of the fluctuation curve is adjusted to adjust the first prediction curve to a reasonable fluctuation range. Based on this, when correcting the first prediction curve, the first prediction curve is adjusted based on the skin temperature change curve. Specifically, multiple key feature points of the skin temperature change curve and multiple key feature points of the first prediction curve are extracted respectively. The key feature points of the two correspond one to one. The key feature points include but are not limited to the starting point, peak point, temperature drop starting point, temperature rise starting point, temperature change slope stabilization point, and temperature change slope turning point. Then, the multiple key feature points of the two are compared one by one to obtain a difference sequence between the skin temperature change curve and the first prediction curve. In the difference sequence, each sequence value represents an attribute offset between the skin temperature change curve and the first prediction curve. Then, based on the difference sequence, the default correction factor is adjusted to obtain a comprehensive correction factor. Specifically, the following formula can be used:

[0060]

[0061] In the above formula, is the default correction factor, is the comprehensive correction factor, The weight coefficient set for the i-th sequence value in the difference sequence, is the i-th sequence value, is the mean of the difference series.

[0062] It should be noted that in order to make the impact of different sequence values ​​on the default correction factor within a relatively reasonable range, Each sequence value is normalized; then, based on the weight coefficient corresponding to each sequence value, the weighted sum of the difference sequence is obtained, and finally, the weighted sum is used to correct the default correction factor to ensure that the generated comprehensive correction factor can accurately reflect the difference between the skin temperature change curve and the first prediction curve, and reasonably adjust the prediction curve.

[0063] Finally, the comprehensive correction factor is multiplied by the fluctuation curve part of the first prediction curve, and the newly generated curve is smoothed to obtain the second prediction curve.

[0064] S105: Based on the second prediction curve, control the heating module to perform hot compress heating.

[0065] In the above steps, the second prediction curve is used as the basis for temperature adjustment of the therapeutic hot compress patch, so that the blood circulation of the target patient is better improved, thereby enhancing the therapeutic effect of the therapeutic hot compress patch.

[0066] In one possible implementation, the second prediction curve can be further combined with the feedback information of the target patient to improve the comfort of use for the target patient. The feedback information is the temperature of the active adjustment model input. At this time, the feedback information is matched with the power adjustment table to obtain the adjustment amplitude and adjustment rate corresponding to the feedback information, so as to meet the needs of more patients.

[0067] Reference Figure 3 The present application also provides a temperature control system for a physiotherapy hot compress patch, which is an MCU control module. The MCU control module includes an acquisition module 1, a processing module 2, and a sending module 3, wherein:

[0068] Acquisition module 1 is used to obtain basic information of the target patient, which includes multiple sub-basic information, including hot compress site, age, gender, hot compress time and disease information;

[0069] Processing module 2 is configured to generate a first prediction curve of skin temperature changes at the hot compress site based on the basic information; control the heating module to perform a skin test on the hot compress site to obtain a skin temperature change curve; and modify the first prediction curve based on the skin temperature change curve to generate a second prediction curve of skin temperature changes at the hot compress site;

[0070] The sending module 3 is used to control the heating module to perform hot compress heating based on the second prediction curve.

[0071] In one possible implementation, historical user information having the highest similarity to the basic information and its corresponding historical skin temperature change curve are retrieved from a preset user information database, wherein the basic information and the historical user information each have a corresponding similarity value in multiple sub-basic information;

[0072] Based on the similarity values ​​of the multiple sub-basic information, multiple historical skin temperature change curves corresponding to each sub-basic information are searched from the preset user information database;

[0073] Determining, based on the slope distribution of the plurality of historical skin temperature change curves corresponding to the plurality of sub-basic information, an influence coefficient of the plurality of sub-basic information on the historical skin temperature change curve corresponding to the historical user information;

[0074] Based on the influence coefficients of the multiple sub-basic information, the historical skin temperature change curve corresponding to the historical user information is adjusted to obtain a first prediction curve.

[0075] In a possible implementation, principal component analysis is performed on the plurality of sub-basic information to determine the main influence coefficient corresponding to each time point in the historical skin temperature change curve corresponding to the historical user information;

[0076] Based on the main influence coefficient corresponding to each time point, the historical skin temperature change curve corresponding to the historical user information is adjusted to generate a first prediction curve.

[0077] In a possible implementation, controlling the heating module to perform a skin test on the hot compress area specifically includes:

[0078] Obtaining a time period corresponding to a fluctuation curve in the first prediction curve, and setting the time period corresponding to the fluctuation curve as a test time for the skin test;

[0079] Multiple monitoring points are set during the test time, wherein each monitoring point corresponds to a heating power;

[0080] During the test, when the monitoring point is reached, the heating module is controlled to adjust to the heating power corresponding to each monitoring point.

[0081] In one possible implementation, multiple skin temperature test curves are obtained based on multiple monitoring points set during the test time;

[0082] Similarity calculation is performed between the plurality of skin temperature test curves and the first prediction curve, and the skin temperature test curve with the highest similarity is selected as the skin temperature change curve.

[0083] In one possible implementation, multiple key feature points of the skin temperature change curve and multiple key feature points of the first prediction curve are extracted, and the multiple key feature points of the two are compared one by one to obtain a difference sequence between the skin temperature change curve and the first prediction curve;

[0084] Based on the difference sequence, the default correction factor is adjusted to obtain the comprehensive correction factor;

[0085] The first prediction curve is adjusted using a comprehensive correction factor to obtain a second prediction curve.

[0086] In a possible implementation, after controlling the heating module to perform hot compress heating based on the second prediction curve, the method further includes:

[0087] Obtain feedback from target patients;

[0088] The feedback information is matched with the power adjustment table to obtain the adjustment amplitude and adjustment rate corresponding to the feedback information.

[0089] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0090] This application also discloses an electronic device. Figure 4 , Figure 4 The electronic device 400 may include: at least one processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0091] The communication bus 402 is used to implement the connection and communication between these components.

[0092] The user interface 403 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 403 may also include a standard wired interface and a wireless interface.

[0093] The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0094] Processor 401 may include one or more processing cores. Using various interfaces and circuits, processor 401 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in memory 405, as well as accesses data stored in memory 405, to perform various server functions and process data. Optionally, processor 401 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). Processor 401 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 401 and implemented as a separate chip.

[0095] Among them, the memory 405 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 405 includes a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 405 may also optionally be at least one storage device located away from the aforementioned processor 401. Refer to Figure 4 , as a computer storage medium, the memory 405 may include an operating system, a network communication module, a user interface module, and an application program for a temperature control method of a physiotherapy hot compress.

[0096] exist Figure 4In the electronic device 400 shown, the user interface 403 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 401 can be used to call an application program for storing a temperature control method of a physiotherapy hot compress in the memory 405. When executed by one or more processors 401, the electronic device 400 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0097] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0099] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0100] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0102] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0103] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A temperature control system for a physiotherapy hot compress, characterized in that: The system is an MCU control module, and the MCU control module includes an acquisition module (1), a processing module (2) and a sending module (3), wherein: The acquisition module (1) is used to acquire basic information of a target patient, wherein the basic information includes a plurality of sub-basic information, and the plurality of sub-basic information includes hot compress site, age, gender, hot compress time and disease information; The processing module (2) is configured to generate a first prediction curve of skin temperature changes at the hot compress site based on the basic information; control the heating module to perform a skin test on the hot compress site to obtain a skin temperature change curve; and correct the first prediction curve based on the skin temperature change curve to generate a second prediction curve of skin temperature changes at the hot compress site, wherein the correction of the first prediction curve based on the skin temperature change curve to generate the second prediction curve of skin temperature changes at the hot compress site specifically includes: extracting a plurality of key feature points of the skin temperature change curve and a plurality of key feature points of the first prediction curve, and performing a one-to-one comparison on the plurality of key feature points of the two to obtain a difference sequence between the skin temperature change curve and the first prediction curve; Based on the difference sequence, the default correction factor is adjusted to obtain a comprehensive correction factor; Using the comprehensive correction factor, adjusting the first prediction curve to obtain a second prediction curve; The sending module (3) is used to control the heating module to perform hot compress heating based on the second prediction curve.

2. The system according to claim 1, wherein: The first prediction curve of the skin temperature change at the hot compress site is generated based on the basic information, specifically: Querying the historical user information having the highest similarity to the basic information and the corresponding historical skin temperature change curve from a preset user information database, wherein the basic information and the historical user information each have a corresponding similarity value in a plurality of the sub-basic information; Based on the similarity values ​​of the plurality of sub-basic information, querying the preset user information database for a plurality of historical skin temperature change curves corresponding to the respective sub-basic information; determining, based on the slope distribution of the plurality of historical skin temperature change curves corresponding to the plurality of sub-basic information, an influence coefficient of the plurality of sub-basic information on the historical skin temperature change curve corresponding to the historical user information; Based on the influence coefficients of the plurality of sub-basic information, the historical skin temperature change curve corresponding to the historical user information is adjusted to obtain the first prediction curve.

3. The system according to claim 2, characterized in that The adjusting the historical skin temperature change curve corresponding to the historical user information based on the influence coefficients of the plurality of sub-basic information to obtain the first prediction curve specifically includes: Performing principal component analysis on the plurality of sub-basic information to determine the main influence coefficient corresponding to each time point in the historical skin temperature change curve corresponding to the historical user information; Based on the main influence coefficient corresponding to each time point, the historical skin temperature change curve corresponding to the historical user information is adjusted to generate the first prediction curve.

4. The system according to claim 1, wherein: The controlling the heating module to perform a skin test on the hot compress area specifically includes: Obtaining a time period corresponding to a fluctuation curve in the first prediction curve, and setting the time period corresponding to the fluctuation curve as a test time for the skin test; Setting a plurality of monitoring points during the test time, wherein each monitoring point corresponds to a heating power; During the test, when the monitoring point is reached, the heating module is controlled to adjust to the heating power corresponding to each monitoring point.

5. The system according to claim 4, characterized in that The obtaining of the skin temperature change curve is specifically as follows: Acquiring multiple skin temperature test curves based on the multiple monitoring points set during the test time; Similarity calculation is performed between the plurality of skin temperature test curves and the first prediction curve, and the skin temperature test curve with the highest similarity is selected as the skin temperature change curve.

6. The system according to claim 1, wherein: After controlling the heating module to perform hot compress heating based on the second prediction curve, the method further includes: Obtain feedback from target patients; The feedback information is matched with a power adjustment table to obtain an adjustment amplitude and an adjustment rate corresponding to the feedback information.

7. An electronic device, characterized in that: The electronic device (400) comprises a processor (401), a memory (405), a user interface (403) and a network interface (404), wherein the memory (405) is used to store instructions, the user interface (403) and the network interface (404) are used to communicate with other devices, and the processor (401) is used to execute the instructions stored in the memory (405) so that the electronic device (400) performs the following method: Obtaining basic information of the target patient, wherein the basic information includes multiple sub-basic information, including hot compress site, age, gender, hot compress time, and disease information; generating a first prediction curve of skin temperature change at the hot compress site based on the basic information; Controlling the heating module to perform a skin test on the hot compress area to obtain a skin temperature change curve; Based on the skin temperature change curve, the first prediction curve is corrected to generate a second prediction curve of the skin temperature change at the hot compress site, wherein the correction of the first prediction curve based on the skin temperature change curve to generate the second prediction curve of the skin temperature change at the hot compress site specifically includes: extracting a plurality of key feature points of the skin temperature change curve and a plurality of key feature points of the first prediction curve, and performing a one-to-one comparison on the plurality of key feature points of the two to obtain a difference sequence between the skin temperature change curve and the first prediction curve; Based on the difference sequence, the default correction factor is adjusted to obtain a comprehensive correction factor; Using the comprehensive correction factor, adjusting the first prediction curve to obtain a second prediction curve; Based on the second prediction curve, the heating module is controlled to perform hot compress heating.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the following method is performed: Obtaining basic information of the target patient, wherein the basic information includes multiple sub-basic information, including hot compress site, age, gender, hot compress time, and disease information; generating a first prediction curve of skin temperature change at the hot compress site based on the basic information; Controlling the heating module to perform a skin test on the hot compress area to obtain a skin temperature change curve; Based on the skin temperature change curve, the first prediction curve is corrected to generate a second prediction curve of the skin temperature change at the hot compress site, wherein the correction of the first prediction curve based on the skin temperature change curve to generate the second prediction curve of the skin temperature change at the hot compress site specifically includes: extracting a plurality of key feature points of the skin temperature change curve and a plurality of key feature points of the first prediction curve, and performing a one-to-one comparison on the plurality of key feature points of the two to obtain a difference sequence between the skin temperature change curve and the first prediction curve; Based on the difference sequence, the default correction factor is adjusted to obtain a comprehensive correction factor; Using the comprehensive correction factor, adjusting the first prediction curve to obtain a second prediction curve; Based on the second prediction curve, the heating module is controlled to perform hot compress heating.

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