Safety test method for hot compress salt physiotherapy bag

Abstract

The invention discloses a hot compress salt physiotherapy bag safety test method, and relates to the technical field of safety test, and the method comprises the following steps: preparing a plurality of physiotherapy bags containing particle salts in different states, detecting the state of the particle salts in each physiotherapy bag, and obtaining the state of each physiotherapy bag; setting a physical therapy bag state label, and associating the physical therapy bag state label with the physical therapy bag corresponding to the physical therapy bag state; according to the safety test method for the hot compress salt physiotherapy bag, a prediction model is established by acquiring historical temperature change data of a built-in temperature sensor in the working process of the physiotherapy bag and the state of particle salt in the corresponding physiotherapy bag according to the principle that the particle salt is different in heat conduction efficiency and temperature change rate in different states; according to the obtained temperature change data, the state of the particle salt in the physiotherapy bag is predicted, the abnormity of the particle salt state is timely detected, and the problems of uneven heating, unstable temperature control and low precision of the physiotherapy bag are prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of safety testing, and in particular to a safety testing method for a hot compress salt therapy bag. Background Art

[0002] The hot compress salt therapy pack heats the salt pack to a suitable temperature through electric heating. Hot compress on the human body promotes local blood circulation and tissue metabolism, helps dissipate edema, inhibits inflammation, and relieves pain caused by muscle tension and other therapeutic effects. In order to ensure the safety of the hot compress salt therapy pack during use, it is particularly necessary to ensure the stability of the hot compress salt therapy pack's electric heating components and temperature control.

[0003] However, for hot compress salt therapy packs using granular salt, during long-term heating and use, the humidity inside the hot compress salt therapy pack may increase due to temperature fluctuations, high air humidity, etc., and even cause the granular salt to agglomerate, thereby affecting the thermal conduction efficiency of the granular salt, thereby affecting the uniformity of heating and the accuracy of temperature control of the hot compress salt therapy pack. For example, a general therapy pack is generally provided with at least one temperature sensor at the center of the heating part to monitor the temperature of the granular salt so that the temperature control circuit can control the temperature of the therapy pack. However, the humidity of the granular salt at the edge of the therapy pack is too high, resulting in reduced thermal conduction efficiency, and the temperature of the granular salt at the edge will rise slower than that of the granular salt in the center due to heat consumption due to evaporation of moisture. The temperature sensor monitors the temperature of the granular salt in the center, which reduces the temperature control accuracy. As a result, when the temperature sensor detects that the temperature of the granular salt in the center has reached the set temperature and stops heating, the temperature of the granular salt at the edge may not have reached the set temperature, and the temperature of the granular salt at the edge may even drop faster due to evaporation of moisture, thereby affecting the hot compress effect of the therapy pack. Summary of the invention

[0004] The purpose of the present invention is to provide a hot compress salt therapy bag safety testing method to solve the above-mentioned deficiencies in the prior art.

[0005] In order to achieve the above object, the present invention provides the following technical solution: a hot compress salt therapy bag safety testing method, comprising the following steps: S1. Prepare multiple physiotherapy bags containing granular salts in different states, detect the state of the granular salts in each physiotherapy bag, and obtain the state of the physiotherapy bag, wherein the state of the physiotherapy bag includes the humidity distribution of the granular salts in the physiotherapy bag and the agglomeration rate of the granular salts; wherein the internal space of the physiotherapy bag can be divided into multiple layers from the inside to the outside, and the humidity of the granular salts in each layer can be detected respectively to obtain the humidity distribution of the granular salts in the physiotherapy bag; and the agglomeration rate of the granular salts can be obtained by calculating the percentage of the volume of the agglomerated granular salts to the total volume of the granular salts; S2. Setting a therapy bag status label, and associating the therapy bag status label with the therapy bag corresponding to the therapy bag status, wherein the therapy bag status label can be set according to whether the humidity of each layer exceeds the set humidity threshold. For example, the therapy bag is divided into two layers, the inner layer is the first layer, and the outer layer is the second layer. If the humidity of the outer layer of the therapy bag exceeds the set humidity threshold, and the humidity of the inner layer does not exceed, then the outer layer abnormal label can be set to be associated with the therapy bag; S3. Start heating each therapy bag, and at the same time, obtain the internal temperature of the therapy bag through the built-in temperature sensor of the therapy bag, until the therapy bag automatically stops heating, and obtain the first temperature data; generally, the therapy bag is provided with at least one temperature sensor at the center of the heating part, which is used to monitor the temperature of the granular salt, so that the temperature control circuit can stop heating when the granular salt is heated to a set first temperature threshold, such as 50 degrees Celsius, and automatically heat when the granular salt temperature is lower than a set second temperature threshold, such as 45 degrees Celsius; obtaining the first temperature data can obtain the temperature change rate of the granular salt near the temperature sensor at each moment during the process of heating to the first temperature threshold, so as to judge the thermal conductivity efficiency of the granular salt negative of the temperature sensor and the granular salt as a whole through the temperature change rate, and judge whether the state of the granular salt is abnormal through the thermal conductivity efficiency of the granular salt, such as whether the humidity is too high, agglomeration occurs, etc.

[0006] S4. Associating the first temperature data with the physiotherapy package status label of the corresponding physiotherapy package, and training a first machine learning model based on the first temperature data and its associated physiotherapy package status label to obtain a first abnormality prediction model for outputting a predicted first physiotherapy package status label based on the input first temperature data; thereby determining whether the physiotherapy package status is abnormal based on the temperature change characteristics of the granular salt near the physiotherapy package temperature sensor when the temperature is raised.

[0007] S5, obtaining temperature change data of a built-in temperature sensor of the physiotherapy bag during a period from when the physiotherapy bag automatically stops heating to when it automatically heats again, to obtain second temperature data; S6. Associate the second temperature data with the physiotherapy bag status label of the corresponding physiotherapy bag, and train the second machine learning model based on the second temperature data and its associated physiotherapy bag status label to obtain a second abnormality prediction model for outputting a predicted second physiotherapy bag status label based on the input second temperature data; thereby, it is possible to determine whether the state of the physiotherapy bag is abnormal based on the temperature change characteristics of the granular salt near the physiotherapy bag temperature sensor during cooling, and the judgment can be combined with the temperature change characteristics during heating to further improve the accuracy of the judgment.

[0008] S7, selecting a physiotherapy bag to be tested, obtaining first temperature data of a built-in temperature sensor of the physiotherapy bag to be tested, inputting the first temperature data into a first abnormality prediction model, and obtaining a first physiotherapy bag status label; S8. Obtain second temperature data of the built-in temperature sensor of the physiotherapy package to be tested, input the second temperature data into a second abnormality prediction model, and obtain a second physiotherapy package status label.

[0009] Furthermore, the method further includes dividing the first temperature data and the second temperature data into multiple parts, including the following steps: Get the ambient temperature when the therapy bag is heated; A plurality of ambient temperature intervals are set, and the first temperature data and the second temperature data are respectively divided into a plurality of first temperature data subsets and second temperature data subsets according to the ambient temperature when the corresponding physiotherapy package is working.

[0010] Furthermore, the S4 further comprises the following steps: Preprocessing each first temperature data subset, wherein the specific steps of the preprocessing can be adjusted according to the selected first machine learning model. For example, if the first machine learning model is a long short-term memory network, the preprocessing can be data cleaning, normalization or standardization, serialization, etc. The present invention does not limit the specific machine learning model, and a specific model can be selected according to actual conditions, such as a random forest model, a neural network model, a gradient boosting tree model, etc.; Based on each first temperature data subset, a first machine learning model is trained to obtain a corresponding first abnormality prediction sub-model, wherein the first abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input first temperature data; Setting a first output probability threshold so that the probability that the first abnormality prediction sub-model finally outputs a therapy kit status label is greater than the therapy kit status label of the first output probability threshold; The first abnormality prediction sub-model is integrated to obtain the first abnormality prediction model, which is used to input the first temperature data into the corresponding first abnormality prediction sub-model based on the ambient temperature of the physiotherapy bag when it is heated, corresponding to the input first temperature data.

[0011] Furthermore, the S6 further comprises the following steps: Preprocessing each second temperature data subset, wherein the specific steps of the preprocessing can be adjusted according to the selected second machine learning model; Based on each second temperature data subset, a second machine learning model is trained to obtain a corresponding second abnormality prediction sub-model, wherein the second abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input second temperature data; Setting a second output probability threshold so that the probability that the second abnormality prediction sub-model finally outputs a therapy kit status label is greater than the therapy kit status label of the second output probability threshold; The second abnormality prediction sub-model is integrated to obtain the second abnormality prediction model, which is used to input the second temperature data into the corresponding second abnormality prediction sub-model based on the ambient temperature when the physiotherapy bag is heated corresponding to the input second temperature data.

[0012] Furthermore, the method further comprises the following steps: Obtain a first physiotherapy kit status label output by the first abnormality prediction model and a second physiotherapy kit status label output by the second abnormality prediction model; Select the physiotherapy package status output included in both the first physiotherapy package status label and the second physiotherapy package status label.

[0013] Furthermore, the method further comprises the following steps: Classifying the status labels of the physiotherapy packages, wherein the classification includes normal status labels and abnormal status labels; Determine whether the outputted physiotherapy kit status label is an abnormal status label, wherein the physiotherapy kit status label includes a first physiotherapy kit status label and a second physiotherapy kit status label; If so, an abnormal reminder is issued.

[0014] Furthermore, the method further comprises the following steps: S9, set the initial n=1, set n=n+1, and determine whether n is greater than the set number of times. If not, return to S7.

[0015] Furthermore, the S9 further includes the following steps: Obtain all first physiotherapy package status labels and second physiotherapy package status labels, and arrange the first physiotherapy package status labels and the second physiotherapy package status labels in the order of output time; Based on the arrangement order of the first physiotherapy kit status label and the second physiotherapy kit status label, judging whether the physiotherapy kit status is approaching the physiotherapy kit status corresponding to the normal status label, or whether it reaches the physiotherapy kit status corresponding to the normal status label; If not, an abnormal reminder is given.

[0016] Compared with the prior art, the present invention provides a hot compress salt therapy bag safety testing method, which obtains the historical temperature change data of the built-in temperature sensor during the operation of the therapy bag and the state of the granular salt in the corresponding therapy bag, and establishes a prediction model based on the principle that the thermal conductivity efficiency of the granular salt in different states is different and the temperature change rate is different. The method can predict the state of the granular salt in the therapy bag based on the acquired temperature change data, detect abnormalities in the state of the granular salt in time, and prevent problems such as uneven heating of the therapy bag, unstable temperature control, and low precision.

[0017] Compared with the prior art, the safety testing method for a hot compress salt therapy pack provided by the present invention further improves the accuracy of model prediction and judgment by separately analyzing the temperature change characteristics of the granular salt in the therapy pack when heated and cooled, and combining them for judgment.

[0018] Compared with the prior art, the present invention provides a hot compress salt therapy bag safety testing method, which determines whether the state of the granular salt in the therapy bag is normal or tends to be normal by repeatedly predicting the heating and cooling process of the therapy bag, and can determine whether the therapy bag requires manual intervention and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0020] Figure 1 An overall step diagram of the method provided by an embodiment of the present invention; Figure 2 An abnormal reminder step diagram provided by an embodiment of the present invention; Figure 3 Another step diagram of abnormal reminder provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0022] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes, and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined. In addition, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0024] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0025] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0026] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0027] See also Figure 1-Figure 3 , a hot compress salt therapy bag safety testing method, comprising the following steps: S1. Prepare multiple physiotherapy bags containing granular salts in different states, detect the state of the granular salts in each physiotherapy bag, and obtain the state of the physiotherapy bag, which includes the humidity distribution of the granular salts in the physiotherapy bag and the agglomeration rate of the granular salts; the internal space of the physiotherapy bag can be divided into multiple layers from the inside to the outside, and the humidity of the granular salts in each layer can be detected respectively to obtain the humidity distribution of the granular salts in the physiotherapy bag; the agglomeration rate of the granular salts can be obtained by calculating the percentage of the volume of the agglomerated granular salts to the total volume of the granular salts; S2. Setting a therapy bag status label, and associating the therapy bag status label with the therapy bag corresponding to the therapy bag status, wherein the therapy bag status label can be set according to whether the humidity of each layer exceeds the set humidity threshold. For example, the therapy bag is divided into two layers, the inner layer is the first layer, and the outer layer is the second layer. If the humidity of the outer layer of the therapy bag exceeds the set humidity threshold, and the humidity of the inner layer does not exceed, then the outer layer abnormal label can be set to be associated with the therapy bag; S3. Start heating each therapy bag, and at the same time, obtain the internal temperature of the therapy bag through the built-in temperature sensor of the therapy bag, until the therapy bag automatically stops heating, and obtain the first temperature data; generally, the therapy bag is provided with at least one temperature sensor at the center of the heating part, which is used to monitor the temperature of the granular salt, so that the temperature control circuit can stop heating when the granular salt is heated to a set first temperature threshold, such as 50 degrees Celsius, and automatically heat when the granular salt temperature is lower than a set second temperature threshold, such as 45 degrees Celsius; obtaining the first temperature data can obtain the temperature change rate of the granular salt near the temperature sensor at each moment during the process of heating to the first temperature threshold, so as to judge the thermal conductivity efficiency of the granular salt negative of the temperature sensor and the granular salt as a whole through the temperature change rate, and judge whether the state of the granular salt is abnormal through the thermal conductivity efficiency of the granular salt, such as whether the humidity is too high, agglomeration occurs, etc.

[0028] S4. Associating the first temperature data with the physiotherapy package status label of the corresponding physiotherapy package, and training a first machine learning model based on the first temperature data and its associated physiotherapy package status label to obtain a first abnormality prediction model for outputting a predicted first physiotherapy package status label based on the input first temperature data; thereby determining whether the physiotherapy package status is abnormal based on the temperature change characteristics of the granular salt near the physiotherapy package temperature sensor when the temperature is raised.

[0029] S5, obtaining temperature change data of a built-in temperature sensor of the physiotherapy bag during a period from when the physiotherapy bag automatically stops heating to when it automatically heats again, to obtain second temperature data; S6. Associate the second temperature data with the physiotherapy bag status label of the corresponding physiotherapy bag, and train the second machine learning model based on the second temperature data and its associated physiotherapy bag status label to obtain a second abnormality prediction model for outputting a predicted second physiotherapy bag status label based on the input second temperature data; thereby, it is possible to determine whether the state of the physiotherapy bag is abnormal based on the temperature change characteristics of the granular salt near the physiotherapy bag temperature sensor during cooling, and the judgment can be combined with the temperature change characteristics during heating to further improve the accuracy of the judgment.

[0030] S7, selecting a physiotherapy bag to be tested, obtaining first temperature data of a built-in temperature sensor of the physiotherapy bag to be tested, inputting the first temperature data into a first abnormality prediction model, and obtaining a first physiotherapy bag status label; S8, obtaining the second temperature data of the built-in temperature sensor of the physiotherapy bag to be tested, inputting the second temperature data into the second abnormal prediction model, and obtaining the second physiotherapy bag status label. Further, obtaining the first physiotherapy bag status label output by the first abnormal prediction model and the second physiotherapy bag status label output by the second abnormal prediction model; and selecting the physiotherapy bag status output included in both the first physiotherapy bag status label and the second physiotherapy bag status label.

[0031] S9, set the initial n=1, set n=n+1, and determine whether n is greater than the set number of times. If not, return to S7.

[0032] Furthermore, S9 also includes the following steps: Obtain all first physiotherapy package status labels and second physiotherapy package status labels, and arrange the first physiotherapy package status labels and the second physiotherapy package status labels in the order of output time; Based on the arrangement order of the first physiotherapy kit status label and the second physiotherapy kit status label, judging whether the physiotherapy kit status is approaching the physiotherapy kit status corresponding to the normal status label, or whether it reaches the physiotherapy kit status corresponding to the normal status label; If not, an abnormal reminder is given.

[0033] In one embodiment, the method further includes dividing the first temperature data and the second temperature data into multiple parts, including the following steps: Get the ambient temperature when the therapy bag is heated; A plurality of ambient temperature intervals are set, and the first temperature data and the second temperature data are respectively divided into a plurality of first temperature data subsets and second temperature data subsets according to the ambient temperature when the corresponding physiotherapy package is working.

[0034] S4 also includes the following steps: Preprocessing each first temperature data subset, wherein the specific steps of the preprocessing can be adjusted according to the selected first machine learning model. For example, if the first machine learning model is a long short-term memory network, the preprocessing can be data cleaning, normalization or standardization, serialization, etc. The present invention does not limit the specific machine learning model, and a specific model can be selected according to actual conditions, such as a random forest model, a neural network model, a gradient boosting tree model, etc.; Based on each first temperature data subset, a first machine learning model is trained to obtain a corresponding first abnormality prediction sub-model, wherein the first abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input first temperature data; Setting a first output probability threshold so that the probability of the first abnormal prediction sub-model finally outputting a therapy kit status label is greater than the therapy kit status label of the first output probability threshold; The first abnormality prediction sub-model is integrated to obtain the first abnormality prediction model, which is used to input the first temperature data into the corresponding first abnormality prediction sub-model based on the ambient temperature of the physiotherapy bag when it is heated, corresponding to the input first temperature data.

[0035] S6 further includes the following steps: Preprocessing each second temperature data subset, wherein the specific steps of the preprocessing can be adjusted according to the selected second machine learning model; Based on each second temperature data subset, a second machine learning model is trained to obtain a corresponding second abnormality prediction sub-model, where the second abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input second temperature data; A second output probability threshold is set so that the probability that the second abnormal prediction sub-model finally outputs a therapy bag status label is greater than the therapy bag status label of the second output probability threshold; The second abnormality prediction sub-model is integrated to obtain the second abnormality prediction model, which is used to input the second temperature data into the corresponding second abnormality prediction sub-model based on the ambient temperature when the physiotherapy bag is heated corresponding to the input second temperature data.

[0036] In one embodiment, the method further comprises the following steps: Classify the status labels of the physiotherapy bags, including normal status labels and abnormal status labels; Determine whether the outputted physiotherapy kit status label is an abnormal status label, where the physiotherapy kit status label includes a first physiotherapy kit status label and a second physiotherapy kit status label; If so, an abnormal reminder is issued.

[0037] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A hot compress salt therapy pack safety testing method, characterized in that: The following steps are involved: S1. preparing a plurality of physiotherapy bags containing granular salts in different states, detecting the state of the granular salts in each physiotherapy bag, and obtaining the state of the physiotherapy bag; S2. Setting a therapy bag status label, and associating the therapy bag status label with a therapy bag corresponding to the therapy bag status; S3, instructing each physiotherapy bag to start heating, and at the same time obtaining the internal temperature of the physiotherapy bag through the built-in temperature sensor of the physiotherapy bag, until the physiotherapy bag automatically stops heating, thereby obtaining first temperature data; S4, associating the first temperature data with a physiotherapy bag status label of the corresponding physiotherapy bag, and training a first machine learning model based on the first temperature data and the physiotherapy bag status label associated therewith to obtain a first abnormality prediction model for outputting a predicted first physiotherapy bag status label according to the input first temperature data; S5, obtaining temperature change data of a built-in temperature sensor of the physiotherapy bag during a period from when the physiotherapy bag automatically stops heating to when it automatically heats again, to obtain second temperature data; S6, associating the second temperature data with the physiotherapy bag status label of the corresponding physiotherapy bag, and training a second machine learning model based on the second temperature data and the physiotherapy bag status label associated therewith to obtain a second abnormality prediction model for outputting a predicted second physiotherapy bag status label according to the input second temperature data; S7, selecting a physiotherapy bag to be tested, obtaining first temperature data of a built-in temperature sensor of the physiotherapy bag to be tested, inputting the first temperature data into a first abnormality prediction model, and obtaining a first physiotherapy bag status label; S8. Obtain second temperature data of the built-in temperature sensor of the physiotherapy package to be tested, input the second temperature data into a second abnormality prediction model, and obtain a second physiotherapy package status label.

2. A hot compress salt therapy pack safety testing method according to claim 1, characterized in that: The method further includes dividing the first temperature data and the second temperature data into a plurality of parts, including the following steps: Get the ambient temperature when the therapy bag is heated; A plurality of ambient temperature intervals are set, and the first temperature data and the second temperature data are respectively divided into a plurality of first temperature data subsets and second temperature data subsets according to the ambient temperature when the corresponding physiotherapy package is working.

3. A hot compress salt therapy pack safety testing method according to claim 2, characterized in that: The S4 further comprises the following steps: Based on each first temperature data subset, a first machine learning model is trained to obtain a corresponding first abnormality prediction sub-model, wherein the first abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input first temperature data; Setting a first output probability threshold so that the probability that the first abnormality prediction sub-model finally outputs a therapy kit status label is greater than the therapy kit status label of the first output probability threshold; The first abnormality prediction sub-model is integrated to obtain the first abnormality prediction model, which is used to input the first temperature data into the corresponding first abnormality prediction sub-model based on the ambient temperature of the physiotherapy bag when it is heated, corresponding to the input first temperature data.

4. A hot compress salt therapy pack safety testing method according to claim 2, characterized in that: The S6 further comprises the following steps: Based on each second temperature data subset, a second machine learning model is trained to obtain a corresponding second abnormality prediction sub-model, wherein the second abnormality prediction sub-model is used to output the probability of each physical therapy bag status label based on the input second temperature data; Setting a second output probability threshold so that the probability that the second abnormality prediction sub-model finally outputs a therapy kit status label is greater than the therapy kit status label of the second output probability threshold; The second abnormality prediction sub-model is integrated to obtain the second abnormality prediction model, which is used to input the second temperature data into the corresponding second abnormality prediction sub-model based on the ambient temperature when the physiotherapy bag is heated corresponding to the input second temperature data.

5. A hot compress salt therapy pack safety testing method according to claim 1, characterized in that: The method further comprises the following steps: Obtain a first physiotherapy kit status label output by the first abnormality prediction model and a second physiotherapy kit status label output by the second abnormality prediction model; Select the physiotherapy package status output included in both the first physiotherapy package status label and the second physiotherapy package status label.

6. A hot compress salt therapy pack safety testing method according to claim 1, characterized in that: The method further comprises the following steps: Classifying the status labels of the physiotherapy packages, wherein the classification includes normal status labels and abnormal status labels; Determine whether the outputted physiotherapy kit status label is an abnormal status label, wherein the physiotherapy kit status label includes a first physiotherapy kit status label and a second physiotherapy kit status label; If so, an abnormal reminder is issued.

7. A hot compress salt therapy pack safety testing method according to claim 6, characterized in that: The method further comprises the following steps: S9, set the initial n=1, set n=n+1, and determine whether n is greater than the set number of times. If not, return to S7.

8. A hot compress salt therapy pack safety testing method according to claim 7, characterized in that: The S9 further comprises the following steps: Obtain all first physiotherapy package status labels and second physiotherapy package status labels, and arrange the first physiotherapy package status labels and the second physiotherapy package status labels in the order of output time; Based on the arrangement order of the first physiotherapy kit status label and the second physiotherapy kit status label, judging whether the physiotherapy kit status is approaching the physiotherapy kit status corresponding to the normal status label, or whether it reaches the physiotherapy kit status corresponding to the normal status label; If not, an abnormal reminder is given.

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