Temperature detection control method for skin thermal therapy itching relieving equipment and medium

Abstract

The invention discloses a temperature detection control method for skin thermal therapy itching relieving equipment and a medium, and relates to the technical field of intelligent control, and the method comprises the following steps: activating a control interaction channel of the skin thermal therapy itching relieving equipment; constructing an equipment application scene; performing multi-stage temperature control collaborative decision on the skin thermal therapy itching relieving equipment, and establishing an equipment temperature control collaborative strategy space; based on the equipment temperature control multi-layer optimization network, performing multi-layer joint optimization on the equipment temperature control collaborative strategy space to obtain an equipment temperature control strategy optimization result; based on the target user, the skin thermal therapy itching relieving equipment is controlled according to the equipment temperature control strategy optimization result, and control monitoring data is obtained; and performing optimization control on the skin thermal therapy itching relieving equipment based on the control monitoring data. The technical problems of low temperature control precision, poor adaptability and difficulty in dynamic optimization when temperature detection control is performed on the skin thermal therapy itching relieving equipment in the prior art are solved, and the technical effect of improving user experience is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to a temperature detection and control method and a medium for skin thermal therapy and antipruritic equipment. Background Art

[0002] Skin thermal therapy and antipruritic devices are increasingly used in skin care and health management. Such devices usually use heating elements to perform thermal therapy on the skin to relieve itching, promote blood circulation or assist in treatment. However, the existing technology generally has problems such as low temperature control accuracy, poor adaptability, and difficulty in dynamic optimization when performing temperature detection and control on skin thermal therapy and antipruritic devices. Traditional devices lack the ability to dynamically adapt to individual user characteristics, environmental changes, and thermal therapy tasks. The control strategy is single and static, and it is difficult to cope with complex and changeable usage scenarios, resulting in unsatisfactory equipment operation results and affecting user experience. Summary of the invention

[0003] The present application provides a temperature detection and control method and medium for skin thermal therapy and antipruritic equipment, which is used to solve the technical problems of low temperature control accuracy, poor adaptability and difficulty in dynamic optimization in the prior art when performing temperature detection and control on skin thermal therapy and antipruritic equipment.

[0004] In view of the above problems, the present application provides a temperature detection and control method and medium for a skin thermal therapy and itching relief device.

[0005] In a first aspect of the present application, a temperature detection and control method for a skin thermal therapy and antipruritic device is provided, the method comprising: Activate the control interaction channel of the skin thermotherapy and antipruritic device, wherein the skin thermotherapy and antipruritic device includes a power supply unit, a control unit and a heating element, and the control interaction channel includes a data interaction layer, multiple usage scenario factors and multiple control stage factors, wherein the multiple usage scenario factors include user characteristics, user environment and thermotherapy tasks, and the multiple control stage factors include an initial heating stage, a constant temperature control stage and a cooling end stage; based on the data interaction layer, perform thermotherapy request feature interaction on the target user according to the multiple usage scenario factors to construct a device application scenario; activate a multi-order temperature control decision-making computing power constraint network, perform multi-order temperature control collaborative decision-making on the skin thermotherapy and antipruritic device in combination with the device application scenario and the multiple control stage factors, and establish a device temperature control collaborative strategy space; based on the device temperature control multi-layer optimization network, perform multi-level joint optimization on the device temperature control collaborative strategy space to obtain a device temperature control strategy optimization result; based on the target user, control the skin thermotherapy and antipruritic device according to the device temperature control strategy optimization result to obtain control monitoring data; optimize the skin thermotherapy and antipruritic device based on the control monitoring data.

[0006] The second aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the temperature detection and control method for a skin thermal therapy and antipruritic device provided in the present application.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The present application activates the control interaction channel of the skin thermotherapy and antipruritic device, wherein the skin thermotherapy and antipruritic device includes a power supply unit, a control unit and a heating element, and the control interaction channel includes a data interaction layer, multiple usage scenario factors and multiple control stage factors, wherein the multiple usage scenario factors include user characteristics, user environment and thermotherapy tasks, and the multiple control stage factors include an initial heating stage, a constant temperature control stage and a cooling end stage; based on the data interaction layer, according to the multiple usage scenario factors, the target user is subjected to thermotherapy request feature interaction to construct a device application scenario; the multi-order temperature control decision-making computing power constraint network is activated, and the skin thermotherapy and antipruritic device is subjected to multi-order temperature control collaborative decision-making in combination with the device application scenario and the multiple control stage factors, and a device temperature control collaborative strategy space is established; based on the device temperature control multi-layer optimization network, the device temperature control collaborative strategy space is subjected to multi-level joint optimization to obtain the device temperature control strategy optimization result; based on the target user, the skin thermotherapy and antipruritic device is controlled according to the device temperature control strategy optimization result to obtain control monitoring data; the skin thermotherapy and antipruritic device is optimized and controlled based on the control monitoring data. The present invention solves the technical problems in the prior art of low temperature control accuracy, poor adaptability and difficulty in dynamic optimization when performing temperature detection and control on skin thermal therapy and antipruritic equipment. By activating control interaction channels, building equipment application scenarios, and combining multi-order temperature control decision-making computing power constraint networks and equipment temperature control multi-layer optimization networks, precise temperature control collaborative decision-making and optimization control of skin thermal therapy and antipruritic equipment are achieved, ensuring that the temperature control process is efficient, safe and stable, and achieving the technical effect of improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of a temperature detection and control method for a skin thermal therapy and antipruritic device provided in an embodiment of the present application; Figure 2 A flow chart of establishing a device temperature control collaborative strategy space in a temperature detection and control method for a skin thermal therapy and antipruritic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0010] The present application provides a temperature detection and control method and medium for skin thermal therapy and antipruritic equipment, aiming to solve the technical problems of low temperature control accuracy, poor adaptability and difficulty in dynamic optimization in the prior art when performing temperature detection and control on skin thermal therapy and antipruritic equipment. By activating control interaction channels, building equipment application scenarios, combining multi-order temperature control decision-making computing power constraint networks and equipment temperature control multi-layer optimization networks, precise temperature control collaborative decision-making and optimized control of skin thermal therapy and antipruritic equipment are achieved, ensuring that the temperature control process is efficient, safe and stable, thereby achieving the technical effect of improving user experience.

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0012] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, medium, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0013] Embodiment 1, as Figure 1 As shown, the present application provides a temperature detection and control method for a skin thermal therapy and antipruritic device, the method comprising: Step S100: Activate the control interaction channel of the skin thermal therapy and antipruritic device, wherein the skin thermal therapy and antipruritic device includes a power supply unit, a control unit and a heating element, the control interaction channel includes a data interaction layer, multiple usage scenario factors and multiple control stage factors, the multiple usage scenario factors include user characteristics, user environment and thermal therapy tasks, and the multiple control stage factors include an initial heating stage, a constant temperature control stage and a cooling end stage.

[0014] In an embodiment of the present application, the control interaction channel of the skin thermal therapy and antipruritic device is first activated. The control interaction channel is used to connect the power supply unit, control unit and heating element of the device to achieve interaction and matching between user needs and device operations.

[0015] The skin thermal therapy and antipruritic device consists of a power supply unit, a control unit and a heating element, and the control interaction channel is its core logical framework, which includes a data interaction layer, multiple usage scenario factors and multiple control stage factors.

[0016] The multi-use scenario factors include user characteristics (such as skin sensitivity), user environment (such as indoor or outdoor ambient temperature), and thermal therapy tasks (such as relieving itching or fatigue). The multi-control stage factors are divided into the initial heating stage (rapid heating), constant temperature control stage (temperature remains stable), and cooling end stage (smooth cooling ends).

[0017] Step S200: Based on the data interaction layer and according to the multiple usage scenario factors, the target user is subjected to a heat therapy request feature interaction to construct a device application scenario.

[0018] In the embodiment of the present application, based on the data interaction layer, the skin thermotherapy and antipruritic device collects the user's skin surface temperature through an infrared temperature sensor. This non-contact temperature detection method can quickly obtain temperature information and avoid direct pressure on the skin. At the same time, a capacitive touch sensor is used to detect the contact state between the device and the skin to confirm that the device has properly fitted the skin. The collected skin temperature and contact state data are transmitted to the data interaction layer of the device as the key input of the user's characteristics.

[0019] Next, the device uses temperature and humidity sensors to monitor the temperature and humidity of the user's environment in real time, and combines positioning modules such as GPS to determine whether the user is indoors or outdoors. Environmental information is parsed through the device's internal rule base. For example, under high humidity conditions, the device is marked as a "high heat dissipation environment."

[0020] The user then selects a specific thermal therapy task, such as rapid itching relief or muscle fatigue relief, through the device's physical buttons or the APP interface. Based on the user's selection, the device extracts the corresponding operating parameters from the internal task parameter database, such as heating rate, target temperature range, and constant temperature time. These task parameters are associated with user characteristics and environmental information at the data interaction layer to form a complete interaction result.

[0021] Finally, the device builds a specific device application scenario by integrating user characteristics, environmental conditions and task requirements. For example, for a user with sensitive skin who chooses a quick anti-itching task and is in a high-humidity outdoor environment, an application scenario of "low-power rapid heating to 40°C and short-term constant temperature" is generated.

[0022] Through the above process, the construction of the device application scenario is completed.

[0023] Step S300: Activate the multi-stage temperature control decision computing power constraint network, combine the device application scenario and the multivariate control stage factor to make a multi-stage temperature control collaborative decision on the skin thermal therapy and antipruritic device, and establish a device temperature control collaborative strategy space.

[0024] In the embodiment of the present application, the multi-stage temperature control decision computing power constraint network is activated, and the multivariate control stage factors are first analyzed to calculate the decision constraint computing power of the initial heating stage, the constant temperature control stage, and the cooling end stage. Based on the initial stage decision constraint computing power, combined with the equipment application scenario, the equipment makes the control decision of the initial heating stage and builds the initial stage temperature control decision space; similarly, based on the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power, the equipment completes the control decisions of the constant temperature stage and the cooling stage in turn, and builds the constant temperature stage temperature control decision space and the cooling stage temperature control decision space respectively.

[0025] Subsequently, the decision constraint computing power of the initial stage, constant temperature stage and cooling stage is used to calculate the overall temperature control collaborative decision computing power. Finally, based on this collaborative decision computing power, the device makes random decision combinations for the temperature control decision space of the three stages, generates the temperature control collaborative strategy of the entire process of the device, and thus establishes the device temperature control collaborative strategy space.

[0026] Further, such as Figure 2 As shown, in the method provided by the embodiment of the application, a multi-stage temperature control decision computing power constraint network is activated, and a multi-stage temperature control collaborative decision is made on the skin thermal therapy antipruritic device in combination with the device application scenario and the multivariate control stage factor, and a device temperature control collaborative strategy space is established, which also includes: Based on the multi-order temperature control decision computing power constraint network, the decision constraint computing power of the multivariate control stage factors is analyzed to obtain the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power; based on the initial stage decision constraint computing power, the skin thermal therapy and antipruritic device is subjected to the initial heating stage control decision according to the device application scenario, and the initial stage temperature control decision space is constructed; based on the constant temperature stage decision constraint computing power, the skin thermal therapy and antipruritic device is subjected to the constant temperature stage control decision according to the device application scenario, and the constant temperature stage temperature control decision space is constructed; based on the cooling stage decision constraint computing power, the skin thermal therapy and antipruritic device is subjected to the cooling stage control decision according to the device application scenario, and the cooling stage temperature control decision space is constructed; based on the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power, the temperature control collaborative decision computing power is calculated; based on the temperature control collaborative decision computing power, the initial stage temperature control decision space, the constant temperature stage temperature control decision space and the cooling stage temperature control decision space are randomly combined to generate the device temperature control collaborative strategy space.

[0027] In an embodiment of the present application, based on the multi-stage temperature control decision-making computing power constraint network, by analyzing the multivariate control stage factors, the decision constraint computing power calculations of the initial stage, constant temperature stage and cooling stage are completed, and the temperature control decision space of each stage is gradually constructed, and finally the equipment temperature control collaborative strategy space is generated.

[0028] Specifically, the multi-order temperature control decision power constraint network is first used to analyze the initial heating stage, constant temperature control stage and cooling end stage, and the decision constraint computing power of the initial stage, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power are obtained respectively. For example, the decision constraint computing power of the initial stage is 30, the constant temperature stage is 50, and the cooling stage is 20, which means that a corresponding number of temperature control decisions can be generated in each stage. Then, based on the decision constraint computing power of the initial stage, combined with the device application scenario (such as the user's skin sensitivity, ambient temperature, etc.), the control decision of the skin thermal therapy antipruritic device in the initial heating stage is made. The specific process includes retrieving the historical decision records of the equipment, extracting the initial stage temperature control decisions that meet the current application scenarios, and forming an initial stage temperature control decision sample set; then cleaning the sample set, removing outliers and redundant data, and generating a structured initial stage temperature control decision sample domain; then identifying the characteristic parameters of the temperature control interval (such as heating rate, target temperature range, etc.) based on the sample domain, and constructing the initial stage temperature control constraint domain; finally, generating a corresponding number of initial heating strategies within the constraint domain according to the initial stage decision constraint computing power, and completing the initial stage temperature control decision space.

[0029] In the same way, based on the decision-constrained computing power of the constant temperature stage and the cooling stage, combined with the equipment application scenario, the control decisions of the constant temperature stage and the cooling stage are completed in sequence, and the temperature control decision space of the constant temperature stage and the temperature control decision space of the cooling stage are built respectively. In the constant temperature stage, the decisions generated by the equipment cover different combinations of target temperature and constant temperature time; in the cooling stage, the decisions generated cover a variety of strategies from rapid cooling to gradual cooling.

[0030] After completing the construction of the temperature control decision space for each stage, integrate the decision constraint computing power of the initial stage, constant temperature stage, and cooling stage to calculate the overall temperature control collaborative decision computing power. When calculating, first find the sum of the decision constraint computing power of the three stages; secondly, divide the maximum value of the three stages by the minimum value to get the computing power ratio; then multiply the computing power ratio by the total computing power; finally, round up the result to get the temperature control collaborative decision computing power. For example, if the initial stage is 30, the constant temperature stage is 50, and the cooling stage is 20, the total is 100, the ratio of the maximum value to the minimum value is 50÷20=2.5, and the result is 2.5×100=250. After rounding, the collaborative decision computing power is 250.

[0031] According to the calculated temperature control collaborative decision-making power, a decision is randomly selected from the temperature control decision space of the initial stage, constant temperature stage, and cooling stage, and combined to form a complete equipment temperature control collaborative strategy. Through multiple random combinations, until the number of generated strategies reaches the temperature control collaborative decision-making power, such as 250. Finally, the generation of the equipment temperature control collaborative strategy space is completed.

[0032] Furthermore, in the method provided in the embodiment of the application, the decision constraint computing power of the multivariate control stage factor is analyzed based on the multi-stage temperature control decision constraint computing power constraint network to obtain the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power, and further includes: The target user scores the importance of the multivariate control stage factors to obtain a multi-order factor importance score, wherein the multi-order factor importance score includes an initial stage importance score, a constant temperature stage importance score, and a cooling stage importance score; loads a multi-order factor importance score sample set and a multi-order temperature control decision constraint computing power sample set; uses the multi-order factor importance score sample set as input data and the multi-order temperature control decision constraint computing power sample set as output data to train a BP neural network and build the multi-order temperature control decision computing power constraint network; inputs the multi-order factor importance score into the multi-order temperature control decision computing power constraint network to obtain a multi-order temperature control decision constraint computing power, wherein the multi-order temperature control decision constraint computing power includes the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power, and the cooling stage decision constraint computing power.

[0033] In the embodiment of the present application, the target user first scores the multivariate control stage factor of the device. The user scores the importance of the initial heating stage, the constant temperature stage, and the cooling stage according to their own needs through the interactive interface of the device, such as physical buttons or APP. For example, in the scenario of rapid itching relief, the user may pay more attention to the initial heating stage, which is scored as 8 points, while the constant temperature stage and the cooling stage are scored as 5 points and 3 points respectively. These scores reflect the user's priority of needs for each stage, and the user's scores are recorded as the initial stage importance score, the constant temperature stage importance score, and the cooling stage importance score.

[0034] Then load the order factor importance score sample set and the multi-order temperature control decision constraint computing power sample set from the historical database. The importance score sample set contains the historical user's rating records, describing the user's preference for each stage of demand in different scenarios; the decision constraint computing power sample set records the computing power data actually allocated to the device under each rating condition, which is used to characterize the number of temperature control decisions required by the device at each stage to meet user needs. For example, the computing power allocation corresponding to a certain rating combination (9 points in the initial stage, 6 points in the constant temperature stage, and 2 points in the cooling stage) may be 40 in the initial stage, 30 in the constant temperature stage, and 10 in the cooling stage.

[0035] Then, a BP neural network is used to establish a mapping model between multi-order factor importance scores and multi-order temperature control decision constraint computing power, namely, a multi-order temperature control decision computing power constraint network. The network input layer receives the user rating vector (such as [8,5,3]), and the output layer generates the computing power allocation value for each stage. During the training process, the multi-order factor importance score sample set is used as input, and the decision constraint computing power sample set is used as output. The network learns the mapping relationship between the score and the computing power allocation by adjusting the weights and biases, and finally generates a multi-order temperature control decision computing power constraint network.

[0036] Finally, the target user's current importance score (e.g. 8 points in the initial stage, 5 points in the constant temperature stage, and 3 points in the cooling stage) is input into the trained multi-order temperature control decision computing power constraint network to generate multi-order temperature control decision constraint computing power. For example, the corresponding computing power value of the output may be 35 in the initial stage, 25 in the constant temperature stage, and 15 in the cooling stage, which respectively represent the number of temperature control decisions that the device needs to generate in each stage. These computing power allocation values ​​directly represent the control complexity of each stage when the device meets user needs.

[0037] Through the above process, based on user importance scores, combined with historical sample data and the BP neural network model, multi-stage temperature control decision constraint computing power was successfully generated, including the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power.

[0038] Furthermore, in the method provided in the embodiment of the application, based on the initial stage decision constraint computing power, the skin thermal therapy antipruritic device is controlled to make an initial heating stage control decision according to the device application scenario, and the initial stage temperature control decision space is constructed, which also includes: According to the device application scenario, the control decision records of the initial heating stage of the skin thermal therapy and antipruritic device are retrieved to obtain the initial stage temperature control decision sample set; discrete data cleaning is performed based on the initial stage temperature control decision sample set to obtain the initial stage temperature control decision sample domain; temperature control interval features are identified based on the initial stage temperature control decision sample domain to establish the initial stage temperature control constraint domain; based on the initial stage decision constraint computing power, the initial stage temperature control decision is performed on the device application scenario according to the initial stage temperature control constraint domain to build the initial stage temperature control decision space.

[0039] In an embodiment of the present application, firstly, according to the current application scenario, such as user skin characteristics, environmental conditions and task requirements, historical decision records are retrieved, and records related to the current scenario are extracted to form a sample set of temperature control decisions in the initial stage. Specifically, it is implemented through a rule matching method based on conditional filtering, for example, filtering out records that meet the conditions of "sensitive skin", "ambient temperature 30°C" and "rapid heating requirements". The sample set contains control parameters and effect data in historical scenarios, such as a heating rate of 2°C / second, a target temperature of 40°C, and a heating time of 10 seconds.

[0040] Then, the data extracted from the sample set is cleaned for discrete data. During the cleaning process, abnormal data is eliminated by setting thresholds and rules, such as removing records where the heating rate exceeds the safety upper limit (such as more than 5°C / second) and invalid data where the target temperature is lower than the normal range (such as less than 35°C). At the same time, duplicate records are merged and classified to ensure the uniqueness and standardization of the data. After the cleaning is completed, the initial stage temperature control decision sample domain is obtained, that is, a sample data set that has been standardized and noise removed.

[0041] Then, the temperature control interval feature is identified based on the key parameters of the sample domain. This process extracts the range of the heating rate, target temperature and heating time by analyzing the data distribution range. For example, according to the sample domain, the heating rate may be distributed between 1℃ / second and 3℃ / second, the target temperature is limited to 38℃ to 45℃, and the heating time is 5 seconds to 15 seconds. Through these analysis results, the temperature control constraint domain in the initial stage is constructed to clarify the safety range and effective parameters of all decisions in the initial heating stage.

[0042] Finally, the initial stage decision constraint computing power (such as 30) generates a corresponding number of heating strategies within the temperature control constraint domain. This step is completed through the parameter combination method, such as combining the various interval values ​​of the heating rate, target temperature and heating time to generate 30 different control strategies. These strategies may include "heating rate 1.5℃ / second, target temperature 40℃, heating time 8 seconds" and "heating rate 2℃ / second, target temperature 42℃, heating time 10 seconds", etc., covering a variety of combinations from rapid heating to slow heating. Finally, all generated strategies are integrated to obtain the initial stage temperature control decision space.

[0043] Step S400: Based on the equipment temperature control multi-layer optimization network, a multi-level joint optimization is performed on the equipment temperature control collaborative strategy space to obtain an equipment temperature control strategy optimization result.

[0044] In an embodiment of the present application, the multi-layer optimization network for device temperature control includes a device temperature control prediction network, a temperature control expectation constraint optimization network, and a temperature control fitness optimization network. The temperature control characteristics in the strategy space are analyzed and screened through the device temperature control prediction network and the temperature control expectation constraint optimization network, and strategies that do not meet the constraints such as user experience, temperature control safety, and equipment loss are eliminated to generate a temperature control collaborative strategy optimization space that meets the conditions. Next, the temperature control fitness optimization network is used to perform fitness scoring and maximize optimization on the strategy space, and the user experience weight, temperature control safety weight, and equipment loss weight are comprehensively considered to select the strategy with the highest fitness, and finally generate the device temperature control strategy optimization result.

[0045] Furthermore, in the method provided in the embodiment of the application, based on the multi-layer optimization network of the device temperature control, the multi-level joint optimization of the device temperature control collaborative strategy space is performed to obtain the device temperature control strategy optimization result, and further includes: The device temperature control multi-layer optimization network includes a device temperature control prediction network, a temperature control expectation constraint optimization network and a temperature control fitness optimization network; based on the device temperature control prediction network and the temperature control expectation constraint optimization network, the device temperature control collaborative strategy space is optimized and analyzed to obtain the temperature control collaborative strategy optimization space; based on the temperature control fitness optimization network, the temperature control collaborative strategy optimization space is optimized to maximize the temperature control fitness to generate the device temperature control strategy optimization result, wherein the temperature control fitness optimization network includes a temperature control fitness weight condition, and the temperature control fitness weight condition includes a user experience weight, a temperature control safety weight and a device loss weight.

[0046] In an embodiment of the present application, the device temperature control multi-layer optimization network includes a device temperature control prediction network, a temperature control expectation constraint optimization network, and a temperature control fitness optimization network.

[0047] First, the device temperature control prediction network is used to analyze each strategy in the temperature control collaborative strategy space one by one. The prediction network simulates its operation process based on the core parameters of the strategy, such as heating rate, target temperature, constant temperature time, etc., and evaluates key features such as the time of the heating stage, the stability of the temperature control stage, and the rate of the cooling stage. For example, for the strategy "heating rate 2℃ / second, target temperature 40℃, constant temperature time 3 minutes", the prediction network calculates the user experience prediction coefficient, temperature control safety prediction coefficient, and equipment loss prediction coefficient.

[0048] Then, the prediction results are screened through the temperature control expectation constraint optimization network, the strategies that do not meet the multi-dimensional constraints are eliminated, and the temperature control collaborative strategy optimization space is generated.

[0049] The temperature control fitness optimization network is then used to perform fitness analysis on the selected strategies and calculate the temperature control fitness of each strategy. The fitness score is calculated based on the temperature control fitness weight conditions, which include user experience weight, temperature control safety weight, and equipment loss weight, which are pre-set by technical experts. The corresponding prediction coefficients in each strategy are weighted according to these pre-set weights, and the strategy with the highest prediction coefficient is selected as the optimal strategy, which is output as the device temperature control strategy optimization result.

[0050] Through the above steps, the multi-layer optimization network of equipment temperature control realizes multi-level joint optimization from strategy analysis to fitness scoring, and finally generates the temperature control strategy that best suits the application scenario and generates the optimization result of the equipment temperature control strategy.

[0051] Furthermore, in the method provided in the embodiment of the application, based on the device temperature control prediction network and the temperature control expectation constraint optimization network, the device temperature control collaborative strategy space is optimized and analyzed to obtain the temperature control collaborative strategy optimization space, and further includes: According to the device temperature control collaborative strategy space, extract the first device temperature control collaborative strategy; based on the device application scenario, predict the temperature control characteristics of the first device temperature control collaborative strategy according to the device temperature control prediction network to obtain the first device temperature control prediction result; input the first device temperature control prediction result into the temperature control expectation constraint optimization network to determine whether the first device temperature control prediction result satisfies the multi-dimensional temperature control expectation constraints in the temperature control expectation constraint optimization network, wherein the multi-dimensional temperature control expectation constraints include user experience constraints, temperature control safety constraints and device loss constraints; if the first device temperature control prediction result satisfies the multi-dimensional temperature control expectation constraints, add the first device temperature control collaborative strategy to the temperature control collaborative strategy optimization space; continue to perform optimization analysis on the device temperature control collaborative strategy space according to the device temperature control prediction network and the temperature control expectation constraint optimization network to generate the temperature control collaborative strategy optimization space.

[0052] In the embodiment of the present application, the device temperature control coordination strategy space is first randomly extracted to obtain a first device temperature control coordination strategy.

[0053] Next, based on the device application scenario, the temperature control characteristics of the first device temperature control collaborative strategy are predicted according to the device temperature control prediction network. The temperature control collaborative strategy of the first device is calculated and processed accordingly by each sub-network in the device temperature control prediction network to obtain the user experience prediction coefficient, temperature control safety prediction coefficient and device loss prediction coefficient, and these prediction coefficients are integrated to obtain the temperature control prediction result of the first device.

[0054] Then, the temperature control prediction result of the first device is input into the temperature control expectation constraint optimization network, and the temperature control prediction result of the first device is judged according to the multi-dimensional temperature control expectation constraint set in the temperature control expectation constraint optimization network to see whether it meets the multi-dimensional temperature control expectation constraint. The multi-dimensional temperature control expectation constraint includes user experience constraint, temperature control safety constraint and equipment loss constraint. These user experience constraints, temperature control safety constraints and equipment loss constraints are all pre-set coefficients. The user experience prediction coefficient, temperature control safety prediction coefficient and equipment loss prediction coefficient are respectively compared with the pre-set coefficients in the multi-dimensional temperature control expectation constraint. If the user experience prediction coefficient and the temperature control safety prediction coefficient are both higher than the pre-set coefficients, and the equipment loss prediction coefficient is lower than the coefficient corresponding to the equipment loss constraint, it is considered that the temperature control prediction result of the first device meets the multi-dimensional temperature control expectation constraint, and the temperature control collaborative strategy of the first device is added to the temperature control collaborative strategy optimization space.

[0055] Then, according to the equipment temperature control prediction network and the temperature control expectation constraint optimization network, the equipment temperature control collaborative strategy space is continued to be optimized and analyzed, that is, random strategy selection is performed from the equipment temperature control collaborative strategy space to repeat the above steps until the traversal of the temperature control collaborative strategy space is completed to generate the temperature control collaborative strategy optimization space.

[0056] Furthermore, in the method provided in the embodiment of the application, based on the device application scenario, the temperature control characteristics of the first device temperature control collaborative strategy are predicted according to the device temperature control prediction network to obtain the first device temperature control prediction result, and further includes: The device temperature control prediction network includes a user experience prediction subnetwork, a temperature control safety prediction subnetwork and an equipment loss prediction subnetwork; the basic parameters of the skin thermal therapy and antipruritic device are collected to obtain device basic data; the device application scenario, the first device temperature control coordination strategy and the device basic data are input into the user experience prediction subnetwork to obtain a first user experience prediction coefficient; the device application scenario, the first device temperature control coordination strategy and the device basic data are input into the temperature control safety prediction subnetwork to obtain a first temperature control safety prediction coefficient; the device application scenario, the first device temperature control coordination strategy and the device basic data are input into the device loss prediction subnetwork to obtain a first device loss prediction coefficient; the first user experience prediction coefficient, the first temperature control safety prediction coefficient and the first device loss prediction coefficient are added to the first device temperature control prediction result.

[0057] In an embodiment of the present application, the device temperature control prediction network includes a user experience prediction subnetwork, a temperature control safety prediction subnetwork, and a device loss prediction subnetwork.

[0058] In order to obtain the first device temperature control prediction result, the basic parameters of the skin thermal therapy and antipruritic device are first collected to obtain the basic data of the device, including the power of the heating element, the sensitivity of the temperature sensor, the current operating status of the device (such as power output and temperature change rate), and environmental conditions (such as room temperature and humidity).

[0059] In the user experience prediction subnetwork, the model is trained using a supervised learning method, and the training data includes input and labeling coefficients. The input data is the historical temperature control strategy parameters (such as heating rate, target temperature, constant temperature time, etc.) and the corresponding basic equipment data. The labeling coefficient is the user experience coefficient, which is used to reflect the user's comfort rating of the temperature control strategy. These ratings are stored in the historical database through questionnaires. In the training phase, by minimizing the error (such as mean square error) between the predicted output and the user experience coefficient, the network weights are gradually optimized to establish a mapping relationship between the strategy parameters and user comfort. In the prediction phase, the subnetwork analyzes the impact of characteristics such as heating rate and temperature control fluctuation on user comfort based on the input device application scenario and temperature control strategy, outputs the first user experience prediction coefficient, and quantifies the performance of the strategy in terms of user experience.

[0060] Next, the device application scenario (such as the user's skin type, current task requirements, environmental conditions, etc.), the first device temperature control collaborative strategy (including heating rate, target temperature, constant temperature time, etc.) and the collected device basic data are input into the user experience prediction subnetwork. The user experience prediction subnetwork is designed based on a convolutional neural network (CNN), which predicts the performance of the strategy in terms of user experience by extracting feature patterns from the input data. In the training phase, the model uses historical strategy parameters (such as heating rate and target temperature) and device basic data as input, and user feedback scores as annotation labels for supervised learning. By minimizing the error (such as mean square error) between the predicted value and the user experience score, the model establishes a mapping relationship between strategy parameters and user comfort. In the prediction phase, the subnetwork analyzes the impact of temperature control accuracy, heating rate and temperature fluctuation on user comfort based on the input strategy parameters and device basic data, outputs the first user experience prediction coefficient, and quantifies the performance of the strategy in terms of user experience.

[0061] Subsequently, the same input data is passed to the temperature control safety prediction subnetwork. Based on the support vector machine, this subnetwork combines historical policy parameters with the device's safety event data (such as overheating, hardware failure) to learn the impact of temperature control policies on device safety. In the training phase, the model uses historical temperature control policy parameters and basic device data as input, and safety event records as labels. It optimizes network weights through the classification cross entropy loss function to learn the safety risk characteristics of the policy. In the prediction phase, the temperature control safety prediction subnetwork evaluates whether the policy's heating rate is too fast or whether the target temperature exceeds the safety range based on the input data, and outputs the first temperature control safety prediction coefficient, which is used to quantify the performance of the current policy in terms of equipment operation safety.

[0062] Next, the data is input into the equipment loss prediction subnetwork. This subnetwork is based on a multi-layer perceptron (MLP) and is used to analyze the long-term impact of temperature control strategies on equipment hardware, such as power consumption and hardware life. In the training phase, the model uses historical temperature control strategy parameters and equipment operation data (such as cumulative heating time and operating power) as input, and the equipment's energy consumption records and life test data as labels. Through regression analysis methods, the error between the predicted value and the labeled value is minimized, and a mapping between strategy parameters and equipment loss is established. In the prediction phase, the subnetwork analyzes the impact of the input strategy on the equipment's energy efficiency and hardware life, outputs the first equipment loss prediction coefficient, and quantifies the energy efficiency level and hardware loss degree of the current strategy.

[0063] Finally, the first user experience prediction coefficient, the first temperature control safety prediction coefficient and the first equipment loss prediction coefficient are integrated to generate the first equipment temperature control prediction result.

[0064] Step S500: Based on the target user, the skin thermal therapy and antipruritic device is controlled according to the optimization result of the device temperature control strategy to obtain control monitoring data.

[0065] In the embodiment of the present application, the control unit of the skin thermotherapy antipruritic device loads the device temperature control strategy optimization result into the heating element and the temperature control system, and drives the device to accurately execute the instructions in the strategy. During the execution process, the operating state of the skin thermotherapy antipruritic device is monitored in real time, and data including the current temperature, power consumption and device operating status are collected. These monitoring data are integrated to obtain control monitoring data.

[0066] Step S600: Optimizing the control of the skin thermal therapy and antipruritic device based on the control monitoring data.

[0067] In the embodiment of the present application, the control monitoring data is first analyzed to identify the deviations in the operation process, such as insufficient heating rate or fluctuations in the constant temperature stage that exceed the range. Then, an adaptive adjustment algorithm is used to dynamically adjust key parameters, such as heating rate, target temperature and constant temperature time, to generate an optimized operation strategy. The new control strategy is then loaded into the control system of the device, driving the heating element and the temperature control module to work according to the optimized parameters to ensure accurate and stable operation. Finally, the operation is completed under the optimized strategy to achieve optimized control.

[0068] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects: The present application activates the control interaction channel of the skin thermotherapy and antipruritic device, wherein the skin thermotherapy and antipruritic device includes a power supply unit, a control unit and a heating element, and the control interaction channel includes a data interaction layer, multiple usage scenario factors and multiple control stage factors, wherein the multiple usage scenario factors include user characteristics, user environment and thermotherapy tasks, and the multiple control stage factors include an initial heating stage, a constant temperature control stage and a cooling end stage; based on the data interaction layer, according to the multiple usage scenario factors, the target user is subjected to thermotherapy request feature interaction to construct a device application scenario; the multi-order temperature control decision-making computing power constraint network is activated, and the skin thermotherapy and antipruritic device is subjected to multi-order temperature control collaborative decision-making in combination with the device application scenario and the multiple control stage factors, and a device temperature control collaborative strategy space is established; based on the device temperature control multi-layer optimization network, the device temperature control collaborative strategy space is subjected to multi-level joint optimization to obtain the device temperature control strategy optimization result; based on the target user, the skin thermotherapy and antipruritic device is controlled according to the device temperature control strategy optimization result to obtain control monitoring data; the skin thermotherapy and antipruritic device is optimized and controlled based on the control monitoring data. The present invention solves the technical problems in the prior art of low temperature control accuracy, poor adaptability and difficulty in dynamic optimization when performing temperature detection and control on skin thermal therapy and antipruritic equipment. By activating control interaction channels, building equipment application scenarios, and combining multi-order temperature control decision-making computing power constraint networks and equipment temperature control multi-layer optimization networks, precise temperature control collaborative decision-making and optimization control of skin thermal therapy and antipruritic equipment are achieved, ensuring that the temperature control process is efficient, safe and stable, and achieving the technical effect of improving user experience.

[0069] Embodiment 2, based on the same inventive concept as the temperature detection and control method for skin thermal therapy and antipruritic equipment in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the steps of any one of the methods described in the aforementioned embodiment 1 when executed.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0072] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A temperature detection and control method for a skin thermal therapy and antipruritic device, characterized in that: The method comprises: Activate a control interaction channel of a skin thermotherapy and antipruritic device, wherein the skin thermotherapy and antipruritic device includes a power supply unit, a control unit, and a heating element, and the control interaction channel includes a data interaction layer, a multi-use scenario factor, and a multi-control stage factor, wherein the multi-use scenario factor includes user characteristics, the user's environment, and the thermotherapy task, and the multi-control stage factor includes an initial heating stage, a constant temperature control stage, and a cooling and finishing stage; Based on the data interaction layer, according to the multiple usage scenario factors, the target user is interacted with the characteristics of the hyperthermia request to build a device application scenario; Activate the multi-stage temperature control decision computing power constraint network, combine the device application scenario and the multi-control stage factor to make a multi-stage temperature control collaborative decision for the skin thermal therapy antipruritic device, and establish a device temperature control collaborative strategy space; Based on the equipment temperature control multi-layer optimization network, a multi-level joint optimization is performed on the equipment temperature control collaborative strategy space to obtain the equipment temperature control strategy optimization result; Based on the target user, the skin thermal therapy and antipruritic device is controlled according to the optimization result of the device temperature control strategy to obtain control monitoring data; The skin thermal therapy and antipruritic device is optimized and controlled based on the control monitoring data.

2. The method according to claim 1, characterized in that Activate the multi-stage temperature control decision computing power constraint network, combine the device application scenario and the multi-control stage factor to make a multi-stage temperature control collaborative decision for the skin thermal therapy antipruritic device, and establish a device temperature control collaborative strategy space, including: Based on the multi-stage temperature control decision computing power constraint network, the decision constraint computing power of the multivariate control stage factors is analyzed to obtain the decision constraint computing power of the initial stage, the decision constraint computing power of the constant temperature stage and the decision constraint computing power of the cooling stage; Based on the initial stage decision constraint computing power, the initial temperature rise stage control decision is made for the skin thermal therapy antipruritic device according to the device application scenario, and the initial stage temperature control decision space is established; Based on the constant temperature stage decision constraint computing power, a constant temperature stage control decision is made for the skin thermal therapy antipruritic device according to the device application scenario, and a constant temperature stage temperature control decision space is built; Based on the cooling stage decision constraint computing power, a cooling stage control decision is made for the skin thermal therapy and antipruritic device according to the device application scenario, and a cooling stage temperature control decision space is established; Calculating the temperature control collaborative decision computing power based on the decision constraint computing power in the initial stage, the decision constraint computing power in the constant temperature stage, and the decision constraint computing power in the cooling stage; Based on the temperature control collaborative decision computing power, the initial stage temperature control decision space, the constant temperature stage temperature control decision space and the cooling stage temperature control decision space are randomly combined to generate the equipment temperature control collaborative strategy space.

3. The method according to claim 2, characterized in that Based on the multi-stage temperature control decision computing power constraint network, the decision constraint computing power of the multivariate control stage factors is analyzed to obtain the decision constraint computing power of the initial stage, the decision constraint computing power of the constant temperature stage and the decision constraint computing power of the cooling stage, including: The target user performs importance scoring on the multivariate control stage factors to obtain multi-order factor importance scores, wherein the multi-order factor importance scores include initial stage importance scores, constant temperature stage importance scores, and cooling stage importance scores; Load the multi-order factor importance score sample set and the multi-order temperature control decision constraint computing power sample set; Taking the multi-order factor importance score sample set as input data and the multi-order temperature control decision constraint computing power sample set as output data, the BP neural network is trained to build the multi-order temperature control decision computing power constraint network; The multi-order factor importance scores are input into the multi-order temperature control decision computing power constraint network to obtain the multi-order temperature control decision constraint computing power, wherein the multi-order temperature control decision constraint computing power includes the initial stage decision constraint computing power, the constant temperature stage decision constraint computing power and the cooling stage decision constraint computing power.

4. The method according to claim 2, characterized in that Based on the initial stage decision constraint computing power, the initial temperature rise stage control decision is made for the skin thermal therapy antipruritic device according to the device application scenario, and the initial stage temperature control decision space is built, including: According to the application scenario of the device, the control decision record of the skin thermal therapy antipruritic device in the initial heating stage is retrieved to obtain a sample set of temperature control decisions in the initial stage; Perform discrete data cleaning according to the temperature control decision sample set in the initial stage to obtain the temperature control decision sample domain in the initial stage; Perform temperature control interval feature recognition based on the temperature control decision sample domain in the initial stage to establish a temperature control constraint domain in the initial stage; Based on the initial stage decision constraint computing power, an initial stage temperature control decision is made for the device application scenario according to the initial stage temperature control constraint domain, and the initial stage temperature control decision space is constructed.

5. The method according to claim 1, characterized in that Based on the multi-layer optimization network of equipment temperature control, a multi-level joint optimization is performed on the equipment temperature control collaborative strategy space to obtain the equipment temperature control strategy optimization results, including: The equipment temperature control multi-layer optimization network includes an equipment temperature control prediction network, a temperature control expectation constraint optimization network and a temperature control fitness optimization network; Based on the device temperature control prediction network and the temperature control expectation constraint optimization network, the device temperature control collaborative strategy space is optimized and analyzed to obtain the temperature control collaborative strategy optimization space; Based on the temperature control fitness optimization network, the temperature control collaborative strategy optimization space is optimized for maximizing the temperature control fitness, and the device temperature control strategy optimization result is generated, wherein the temperature control fitness optimization network includes a temperature control fitness weight condition, and the temperature control fitness weight condition includes a user experience weight, a temperature control safety weight, and a device loss weight.

6. The method according to claim 5, characterized in that Based on the device temperature control prediction network and the temperature control expectation constraint optimization network, the device temperature control collaborative strategy space is optimized and analyzed to obtain the temperature control collaborative strategy optimization space, including: Extracting a first device temperature control coordination strategy according to the device temperature control coordination strategy space; Based on the device application scenario, predicting the temperature control characteristics of the first device temperature control collaborative strategy according to the device temperature control prediction network, to obtain a first device temperature control prediction result; Inputting the temperature control prediction result of the first device into the temperature control expectation constraint optimization network, and determining whether the temperature control prediction result of the first device satisfies the multi-dimensional temperature control expectation constraint in the temperature control expectation constraint optimization network, wherein the multi-dimensional temperature control expectation constraint includes a user experience constraint, a temperature control safety constraint, and an equipment loss constraint; If the temperature control prediction result of the first device meets the multi-dimensional temperature control expectation constraint, adding the temperature control collaborative strategy of the first device to the temperature control collaborative strategy optimization space; The device temperature control collaborative strategy space is continuously optimized and analyzed according to the device temperature control prediction network and the temperature control desired constraint optimization network to generate the temperature control collaborative strategy optimization space.

7. The method according to claim 6, characterized in that Based on the device application scenario, a temperature control feature prediction is performed on the first device temperature control collaborative strategy according to the device temperature control prediction network to obtain a first device temperature control prediction result, including: The device temperature control prediction network includes a user experience prediction subnetwork, a temperature control safety prediction subnetwork and a device loss prediction subnetwork; Collecting basic parameters of the skin thermal therapy and antipruritic device to obtain basic device data; Inputting the device application scenario, the first device temperature control collaborative strategy and the device basic data into the user experience prediction subnetwork to obtain a first user experience prediction coefficient; Inputting the device application scenario, the first device temperature control coordination strategy and the device basic data into the temperature control safety prediction subnetwork to obtain a first temperature control safety prediction coefficient; Inputting the device application scenario, the first device temperature control collaborative strategy and the device basic data into the device loss prediction subnetwork to obtain a first device loss prediction coefficient; The first user experience prediction coefficient, the first temperature control safety prediction coefficient, and the first equipment loss prediction coefficient are added to the first equipment temperature control prediction result.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the temperature detection and control method for a skin thermal therapy and antipruritic device as described in any one of claims 1 to 7 is implemented.

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