Disinfection cabinet temperature control method and device and disinfection cabinet

Abstract

The application discloses a sterilization cabinet temperature control method and device and a sterilization cabinet. The method comprises the following steps: in the running process of the sterilization cabinet, the first detection temperatures of M temperature sensors in the sterilization cabinet are acquired, the M temperature sensors correspond to M target positions arranged on the inner tank wall of the sterilization cabinet, M is an integer greater than 1; the first detection temperatures of the M temperature sensors are input into a temperature optimization model, and the sterilization temperature of the target cavity position in the sterilization cabinet is obtained through the temperature optimization model, wherein the model coefficients of the temperature optimization model comprise M weight coefficients corresponding to the M temperature sensors, and the M weight coefficients and the M target positions are determined by testing N loading states of the sterilization cabinet. The application can solve the technical problem that the real sterilization temperature in the cavity of the sterilization cabinet is greatly different from the standard value.

Description

Technical Field

[0001] This invention belongs to the field of kitchen appliance technology, and particularly relates to a method, device and disinfection cabinet for temperature control. Background Technology

[0002] With the upgrading of consumption, users demand a higher quality and healthier lifestyle, making disinfection cabinets increasingly popular. Temperature sensors, as a crucial component of disinfection cabinets, play a role in transmitting temperature data to indirectly control the disinfection process.

[0003] In related technologies, disinfection cabinets often use thermostats or single sensors to control the temperature inside the cabinet cavity. However, the actual loading conditions inside disinfection cabinets vary greatly among different users. For example, the actual loading quantity differs from the standard loading in the laboratory, leading to significant inaccuracies in temperature control under non-standard loading conditions. This results in a large difference between the actual disinfection temperature inside the cabinet cavity and the standard value (e.g., the temperature value required by national standards or the design value). In cases of excessive temperature differences (e.g., a difference of 15 degrees Celsius), the actual disinfection temperature inside the cabinet cavity may have reached the standard value, but the program will determine that it has not yet reached its set standard value, and the disinfection cabinet will continue heating, resulting in wasted energy from the 15-degree Celsius temperature difference. Summary of the Invention

[0004] The present invention provides a method, device and disinfection cabinet for temperature control, which is used to solve the technical problem that the actual disinfection temperature reached in the cavity of the disinfection cabinet differs greatly from the standard value.

[0005] In a first aspect of the present invention, a method for controlling the temperature of a disinfection cabinet is provided, comprising: during the operation of the disinfection cabinet, acquiring first detected temperatures from M temperature sensors inside the disinfection cabinet, wherein the M temperature sensors correspond to M target positions disposed on the inner wall of the disinfection cabinet, and M is an integer greater than 1; inputting the first detected temperatures from the M temperature sensors into a temperature optimization model, and determining the current disinfection temperature of the target cavity position inside the disinfection cabinet through the temperature optimization model, wherein the model coefficients of the temperature optimization model include M weight coefficients corresponding to the M temperature sensors, and the M weight coefficients and the M target positions are determined by testing N loading states of the disinfection cabinet, where N is an integer greater than 1; and controlling the disinfection cabinet based on the current disinfection temperature.

[0006] In conjunction with the first aspect, in some embodiments, the temperature optimization model is determined based on the following steps of pre-testing the disinfection cabinet: with the M temperature sensors corresponding to the M target locations on the inner wall of the disinfection cabinet, the disinfection cabinet is tested under N loading states to obtain the second detection temperature of each of the M temperature sensors under the N loading states; for each temperature sensor, a weighting coefficient is determined based on the standard disinfection temperature at the target cavity location within the disinfection cabinet and the second detection temperatures of the M temperature sensors under the N loading states; based on the M weighting coefficients corresponding to the M temperature sensors, a temperature optimization model is generated for determining the disinfection temperature at the target cavity location within the disinfection cabinet during the operation of the disinfection cabinet.

[0007] In conjunction with the first aspect, in some embodiments, the method further includes: when the M temperature sensors are arranged at M first candidate placement positions on the inner wall of the disinfection cabinet, testing the N loading states of the disinfection cabinet respectively to obtain the third detection temperature of each of the M temperature sensors in the N loading states; for each loading state, determining the weighting coefficient range of each of the M temperature sensors in the loading state based on the third detection temperature of the M temperature sensors in the loading state and the standard disinfection temperature of the target cavity position; for each temperature sensor, determining the target position for placing the temperature sensor based on the weighting coefficient range of the temperature sensor in the N loading states and the first candidate placement position of the temperature sensor.

[0008] In conjunction with the first aspect, some embodiments further include: constructing a simulation model of the disinfection cabinet, the simulation model being used to simulate the disinfection cabinet and the simulated placement of M temperature sensors on the inner wall of the disinfection cabinet; simulating the heating process of the disinfection cabinet and the temperature detection process of the M temperature sensors based on the simulation model, and obtaining M first candidate placement positions corresponding to the M temperature sensors.

[0009] In conjunction with the first aspect, in some embodiments, determining the target location for deploying the temperature sensor for each of the temperature sensors, based on the weighting coefficient range of the temperature sensor in the N loading states and the first candidate deployment location of the temperature sensor, includes: for each of the temperature sensors, if the weighting coefficient range of the temperature sensor in the N loading states overlaps, the first candidate deployment location of the temperature sensor is taken as the target location of the temperature sensor; for each of the temperature sensors, if the weighting coefficient range of the temperature sensor in the N loading states does not overlap, a second candidate deployment location of the temperature sensor is determined based on the first candidate deployment location of the temperature sensor, and it is determined whether the second candidate deployment location of the temperature sensor is taken as the target location for deploying the temperature sensor.

[0010] In conjunction with the first aspect, in some embodiments, determining the weighting coefficient of each temperature sensor based on the standard disinfection temperature at the target cavity location within the disinfection cabinet and the second detection temperatures of the M temperature sensors in the N loading states includes: determining the weighting coefficient range of each temperature sensor in the N loading states based on the standard disinfection temperature at the target cavity location within the disinfection cabinet and the second detection temperatures of the M temperature sensors in the N loading states; determining a numerical overlap interval based on the weighting coefficient range of the temperature sensor in the N loading states; and obtaining the weighting coefficient of the temperature sensor from the numerical overlap interval based on the measured temperature difference data of the disinfection cabinet in the N loading states.

[0011] In conjunction with the first aspect, in some embodiments, determining the second candidate placement position of the temperature sensor based on the first candidate placement position of the temperature sensor includes: for each loading state, determining the calculated disinfection temperature of the target cavity position in the loading state based on the third detection temperature of the M temperature sensors in that loading state, and determining the measured temperature difference range in that loading state based on the standard disinfection temperature of the target cavity position and the calculated disinfection temperature in that loading state; and correcting the first candidate placement position of the temperature sensor based on at least the N measured temperature difference ranges corresponding to the N loading states to obtain the second candidate placement position of the temperature sensor.

[0012] In conjunction with the first aspect, in some embodiments, the step of correcting the first candidate placement position of the temperature sensor based on at least N measured temperature difference ranges corresponding to the N loading states to obtain the second candidate placement position of the temperature sensor includes: correcting the first candidate placement position of the temperature sensor based on the simulated temperature difference and the N measured temperature difference ranges corresponding to the N loading states, wherein the simulated temperature difference is obtained by simulation based on the simulation model of the disinfection cabinet.

[0013] In conjunction with the first aspect, in some embodiments, generating a temperature optimization model based on the M weighting coefficients corresponding to the M temperature sensors for determining the disinfection temperature at the target cavity location within the disinfection cabinet during its operation includes: determining a temperature difference correction coefficient based on measured temperature difference data of the disinfection cabinet in the N loading states; and generating the temperature optimization model based on the M weighting coefficients and the temperature difference correction coefficient.

[0014] In a second aspect of the present invention, a temperature control device for a disinfection cabinet is provided, comprising: a temperature acquisition unit, configured to acquire, during operation of the disinfection cabinet, a first detected temperature of M temperature sensors inside the disinfection cabinet, wherein the M temperature sensors correspond to M target positions disposed on the inner wall of the disinfection cabinet, and M is an integer greater than 1; a temperature optimization unit, configured to input the first detected temperatures of the M temperature sensors into a temperature optimization model, and determine the current disinfection temperature of the target cavity position inside the disinfection cabinet through the temperature optimization model, wherein the model coefficients of the temperature optimization model include M weight coefficients corresponding to the M temperature sensors, and the M weight coefficients and the M target positions are determined by testing N loading states of the disinfection cabinet, where N is an integer greater than 1; and a control execution unit, configured to control the disinfection cabinet based on the current disinfection temperature.

[0015] In a third aspect of the present invention, a disinfection cabinet is provided, comprising: a cabinet body; M temperature sensors, corresponding to M target positions disposed on the inner wall of the cabinet body, where M is an integer greater than 1; one or more processors and one or more memories, wherein the one or more memories store at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to implement the method described in any embodiment of the first aspect.

[0016] According to one or more technical solutions provided in the embodiments of the present invention, at least the following technical effects or advantages are achieved:

[0017] In this embodiment of the invention, during the operation of the disinfection cabinet, the first detected temperatures of M temperature sensors inside the disinfection cabinet are obtained. The M temperature sensors correspond to M target positions located on the inner wall of the disinfection cabinet, where M is an integer greater than 1. The first detected temperatures of the M temperature sensors are input into a temperature optimization model. The temperature optimization model determines the current disinfection temperature of the target cavity position inside the disinfection cabinet, and the operation of the disinfection cabinet is controlled by the current disinfection temperature. The model coefficients of the temperature optimization model include M weight coefficients corresponding to the M temperature sensors. The M weight coefficients and the M target positions are determined by testing N loading states of the disinfection cabinet. The above technical solution achieves accurate temperature sensing inside the disinfection cabinet by arranging multiple temperature sensors on the inner wall of the cabinet. Since the weighting coefficients and placement of each temperature sensor are determined by testing N loading states of the disinfection cabinet, the placement of the temperature sensors can better match the actual loading situation of the user's dishes. The disinfection temperature at the target cavity location inside the disinfection cabinet, determined by the temperature optimization model, can also be closer to the actual disinfection temperature achieved under the actual loading situation of the user's dishes. In this way, the difference between the actual disinfection temperature inside the disinfection cabinet cavity and the standard disinfection temperature required by the design can be reduced, thus reducing unnecessary energy consumption of the disinfection cabinet. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the temperature control method for a disinfection cabinet in an embodiment of the present invention;

[0020] Figure 2 This is a flowchart illustrating the process of testing and determining the temperature optimization model in an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of the temperature control device for the disinfection cabinet in an embodiment of the present invention;

[0022] Figure 4 This is a schematic diagram of the structure of the disinfection cabinet in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0024] This invention provides a method for controlling the temperature of a disinfection cabinet, which can be applied to disinfection cabinets that disinfect by heating. Figure 1 This is a flowchart of the temperature control method for a disinfection cabinet in an embodiment of the present invention, such as... Figure 1 As shown, the temperature control method of the disinfection cabinet includes the following steps S101 to S103.

[0025] S101. Obtain the first detected temperature of M temperature sensors inside the disinfection cabinet. The M temperature sensors correspond to M target positions arranged on the inner wall of the disinfection cabinet, where M is an integer greater than 1.

[0026] It should be understood that factors such as the shape and size of the inner liner of the disinfection cabinet, the heating power used for disinfection, the loading conditions inside the disinfection cabinet, and the number of temperature sensors will affect the placement of the sensors. Two, three, or even more temperature sensors can be placed inside the disinfection cabinet. Therefore, the M target locations are determined by testing the disinfection cabinet in N loading states. In some embodiments, after determining the M first candidate placement locations on the inner wall of the disinfection cabinet, temperature sensors are placed at each of the M first candidate placement locations. With the M temperature sensors corresponding to the M first candidate placement locations on the inner wall of the disinfection cabinet, the N loading states of the disinfection cabinet are tested one by one to determine whether the M first candidate placement locations are suitable. For each temperature sensor, if the first candidate placement location corresponding to that temperature sensor is suitable, then that first candidate placement location is used as the target location for placing that temperature sensor; if the first candidate placement location corresponding to that temperature sensor is unsuitable, then the target location for placing that temperature sensor needs to be re-determined.

[0027] It is understandable that the M first candidate placement positions corresponding to the M temperature sensors can be determined through simulation. In some embodiments, determining the M first candidate placement positions on the inner wall of the disinfection cabinet through simulation may include: constructing a simulation model of the disinfection cabinet, wherein the simulation model is used to simulate the disinfection cabinet and the M temperature sensors simulated to be placed on the inner wall of the disinfection cabinet; simulating the heating process of the disinfection cabinet and the temperature detection process of the M temperature sensors based on the simulation model to obtain the M first candidate placement positions corresponding to the M temperature sensors. Determining the M first candidate placement positions on the inner wall of the disinfection cabinet corresponding to the M temperature sensors through the above simulation can improve the accuracy of the M first candidate placement positions, eliminating the need for repeated simulations and tests to correct the placement positions of the temperature sensors, and enabling the rapid determination of suitable target placement positions of the temperature sensors. Therefore, it can improve the efficiency of determining the M target positions. In other embodiments, M first candidate placement positions can also be randomly selected on the inner wall of the disinfection cabinet, and the placement positions of the temperature sensors can be corrected through repeated pattern simulations and tests, which can also determine suitable target placement positions of the temperature sensors.

[0028] In some embodiments, to determine whether M first candidate placement positions are suitable as target positions for placing temperature sensors, the following steps may be taken: Given that M temperature sensors are placed at the M first candidate placement positions on the inner wall of the disinfection cabinet, test N loading states of the disinfection cabinet to obtain the third detection temperature of each of the M temperature sensors in the N loading states; for each loading state of the disinfection cabinet, determine the weighting coefficient range of each of the M temperature sensors in that loading state based on the third detection temperature of the M temperature sensors in that loading state and the standard disinfection temperature at the target cavity location; for each of the M temperature sensors, determine the target position on the inner wall of the disinfection cabinet for placing the temperature sensor based on the weighting coefficient range of the temperature sensor in the N loading states and the first candidate placement position of the temperature sensor.

[0029] By deploying M temperature sensors at M candidate locations on the inner wall of the disinfection cabinet, and testing N loading states of the cabinet, the weighting coefficient range for each temperature sensor on the inner wall of the disinfection cabinet can be obtained for each of the N loading states. It should be noted that the weighting coefficient range for each temperature sensor in the N loading states specifically includes N weighting coefficient ranges for each of the N loading states.

[0030] In some implementations, each temperature sensor receives a weighted coefficient range for each loading state. Each weighted coefficient range is determined based on a preset temperature difference threshold, the standard disinfection temperature of the target cavity location, and the third detection temperature of the M temperature sensors in that loading state. In this embodiment, a smaller preset temperature difference threshold ensures a smaller difference between the actual disinfection temperature reached at the target cavity location and the standard disinfection temperature when the disinfection cabinet is actually used by the user. In specific implementations, the preset temperature difference threshold can be a positive value of 5°C or even smaller. Specifically, for each loading state of the disinfection cabinet, based on the inequality that the absolute value of the temperature difference between the standard disinfection temperature of the target cavity location and the calculated disinfection temperature of the target cavity location in that loading state is no greater than the preset temperature difference threshold, the weighted coefficient range for each of the M temperature sensors in that loading state can be determined. The calculated disinfection temperature of the target cavity location in that loading state can be expressed as a weighted sum of the third detection temperatures of the M temperature sensors in that loading state, used to characterize the actual disinfection temperature reached at the target cavity location.

[0031] In some implementations, the calculated disinfection temperature at the target cavity location within the disinfection cabinet is expressed as a weighted sum of the third detection temperatures from M temperature sensors. The corresponding temperature optimization model can be represented as follows: Where T′ represents the disinfection temperature at the target cavity location inside the disinfection cabinet, K i T represents the weighting coefficient of the i-th temperature sensor. i Let represent the detected temperature of the i-th temperature sensor, where i ranges from 1 to M, and M is the number of temperature sensors installed inside the disinfection cabinet. Based on this, during the testing of the disinfection cabinet, for each loading state of the cabinet, based on the third detected temperature of the M temperature sensors in that loading state and the standard disinfection temperature at the target cavity location, determine the weighting coefficient range for each of the M temperature sensors in that loading state. This can include: obtaining the third detected temperature of the M temperature sensors in that loading state, and determining the weighting coefficient range for each of the M temperature sensors in that loading state based on the following formula:

[0032]

[0033] Since the standard disinfection temperature T, the preset temperature difference threshold ΔT′, and the third detection temperature Ti of each of the M temperature sensors in this loading state are all known values, it is equivalent to solving a multivariate linear inequality to determine the range of values ​​that the weight coefficients of the M temperature sensors can take in this loading state, which means that the range of the weight coefficients of each of the M temperature sensors in this loading state can be obtained.

[0034] In some implementations, for each of the M temperature sensors, determining the target location for deploying the temperature sensor based on the weighting coefficient range of the temperature sensor in N loading states and the first candidate deployment location of the temperature sensor may include: determining whether the first candidate deployment location of the temperature sensor is the target location of the temperature sensor based on the N weighting coefficient ranges that correspond one-to-one with the N loading states; if not, then it is necessary to redetermine the target location for deploying the temperature sensor.

[0035] Specifically, for each temperature sensor, determining whether its first candidate placement location is the target location can include: for each temperature sensor, if the ranges of N weighting coefficients corresponding to the N loading states of the temperature sensor overlap, then the first candidate placement location is taken as the target location; if the ranges of N weighting coefficients corresponding to the N loading states do not overlap, it indicates that the first candidate placement location is unsuitable as the target location, and a second candidate placement location needs to be determined based on the first candidate placement location, and then it is determined whether the second candidate placement location is taken as the target location. This allows for accurate determination of suitable placement locations for the temperature sensors, which is more conducive to more precise temperature sensing within the disinfection cabinet cavity.

[0036] Taking the determination of the target location for deploying one of the temperature sensors by testing three loading states of a disinfection cabinet as an example, the test can yield three weighting coefficient ranges for that temperature sensor:

[0037] If the temperature sensor is located at point A on the inner wall of the disinfection cabinet, the three weighting coefficient ranges obtained by testing the three loading states of the disinfection cabinet are as follows: the weighting coefficient range for the unloaded state is (0.25~0.3), the weighting coefficient range for the half-loaded state is (0.28~0.35), and the weighting coefficient range for the fully loaded state is (0.4~0.5). It can be seen that the three weighting coefficient ranges do not overlap, that is, they do not intersect. This indicates that the first candidate placement location of the temperature sensor is not suitable, and a new target location for placing the temperature sensor needs to be selected.

[0038] If the temperature sensor is located at point B on the inner wall of the disinfection cabinet, the three weighting coefficient ranges obtained by testing the three loading states of the disinfection cabinet are as follows: the weighting coefficient range for the unloaded state is (0.45~0.5), the weighting coefficient range for the half-loaded state is (0.42~0.52), and the weighting coefficient range for the fully loaded state is (0.45~0.55). It can be seen that there is a numerical overlap (0.45~0.5) among the three weighting coefficient ranges, which means there is an intersection. This indicates that the first candidate placement position of the temperature sensor is suitable and can be used as the target placement position for the temperature sensor.

[0039] In some implementations, for each of the M temperature sensors, determining a second candidate placement position for the temperature sensor based on a first candidate placement position may include: for each loading state, determining the calculated sterilization temperature of the target cavity location in that loading state based on the third detection temperature of the M temperature sensors in that loading state, and determining the measured temperature difference range of that loading state based on the standard sterilization temperature of the target cavity location and the calculated sterilization temperature of the target cavity location in that loading state; correcting the first candidate placement position of the temperature sensor based on at least N measured temperature difference ranges corresponding to N loading states to obtain the second candidate placement position of the temperature sensor, making the second candidate placement position more accurate and more suitable for placing the temperature sensor than the first candidate placement position.

[0040] In some implementations, if the first candidate placement location of the temperature sensor is not suitable, the first candidate placement location of the temperature sensor can be corrected by simulation to obtain a second candidate placement location of the temperature sensor. Specifically, this includes correcting the first candidate placement location based on the simulated temperature difference and N measured temperature difference ranges corresponding to N loading states. The simulated temperature difference is obtained by simulation based on the simulation model of the disinfection cabinet.

[0041] In some implementations, the simulated temperature difference is obtained by simulating a simulation model of the disinfection cabinet. This can include simulating the heating process of the disinfection cabinet under N loading states and the temperature detection process of M temperature sensors based on the simulation model, obtaining N simulated temperature differences corresponding one-to-one with the N loading states of the disinfection cabinet. The simulated temperature difference is the absolute value of the temperature difference between the standard disinfection temperature at the target cavity location and the simulated disinfection temperature at the target cavity location obtained from the simulation. The measured temperature difference range corresponding to each loading state is the range of values ​​for the absolute value of the temperature difference between the standard disinfection temperature at the target cavity location and the calculated disinfection temperature at the target cavity location in that loading state, calculated based on the third detection temperature of the M temperature sensors in that loading state.

[0042] It should be noted that after obtaining the second candidate placement position of the temperature sensor according to any of the above embodiments, the method for determining whether the second candidate placement position of the temperature sensor is the target position of the temperature sensor is the same as or similar to the method for determining whether the first candidate placement position of the temperature sensor is the target position of the temperature sensor, and will not be repeated here. If the second candidate placement position is not suitable, it is necessary to re-perform the simulation to continue to correct the position used to place the temperature sensor until the target position for placing the temperature sensor is found.

[0043] It is understandable that the N loading states of a disinfection cabinet can include: empty state, half-loaded state, full-loaded state, and various non-standard loading states. Among them, non-standard loading states refer to the uneven placement of items inside the disinfection cabinet, such as: more on one side and less on the other, or having items on one side and none on the other, etc.

[0044] Understandably, with M temperature sensors deployed at M candidate locations, the loading state of the disinfection cabinet is adjusted according to the possible loading scenarios used by the user. Each of the N loading states of the disinfection cabinet is then tested, and the third detection temperature from the M temperature sensors can be obtained for each loading state. It should be noted that testing each of the N loading states of the disinfection cabinet yields N sets of third detection temperatures corresponding to those N states. Each set of third detection temperatures includes the third detection temperatures from the M temperature sensors under the same loading state.

[0045] Understandably, the target cavity location can be the center point inside the disinfection cabinet cavity, or it can be any other point near the center point. The standard disinfection temperature for the target cavity location can refer to the temperature at the center point inside the disinfection cabinet cavity as required by national standards or other standards.

[0046] S102. Input the first detected temperatures of the M temperature sensors into the temperature optimization model, and determine the current disinfection temperature of the target cavity position in the disinfection cabinet through the temperature optimization model. The model coefficients of the temperature optimization model include M weight coefficients corresponding to the M temperature sensors. The M weight coefficients and the M target positions are determined by testing the N loading states of the disinfection cabinet, where N is an integer greater than 1.

[0047] The temperature optimization model is used to estimate the current disinfection temperature reached at the target cavity location inside the disinfection cabinet based on the first detected temperatures from M temperature sensors installed on the inner wall of the disinfection cabinet. In some embodiments, the temperature optimization model can be expressed as a weighted sum of the detected temperatures from the M temperature sensors and the corresponding M weighting coefficients. In some embodiments, the resulting temperature optimization model can refer to the following:

[0048]

[0049] Among them, T ′ This represents the calculated disinfection temperature at the target cavity location within the disinfection cabinet, which is also the estimated disinfection temperature at that location. Ki represents the weighting coefficient of the i-th temperature sensor, Ti represents the detection temperature of the i-th temperature sensor, and ΔT is the temperature difference correction coefficient. i ranges from 1 to M, where M is the number of temperature sensors deployed on the inner wall of the disinfection cabinet. By setting the temperature difference correction coefficient, the difference between the disinfection temperature estimated by the temperature optimization model at the target cavity location and the actual disinfection temperature reached at that location can be further reduced during the operation of the disinfection cabinet.

[0050] For ease of understanding, the following example uses only two temperature sensors, T1 and T2, and the resulting temperature optimization model can be represented as follows:

[0051] T′=αT1+βT2+△T

[0052] Where T′ represents the calculated disinfection temperature at the target cavity location inside the disinfection cabinet, T1 and T2 represent the detected temperatures of two temperature sensors installed on the inner wall of the disinfection cabinet, and α and β represent the weighting coefficients of the two temperature sensors.

[0053] In other implementations, the temperature optimization model may not require setting a temperature difference correction coefficient, and the resulting temperature optimization model can be referenced as follows:

[0054]

[0055] Among them, T ′ The calculated disinfection temperature is indicated by the target cavity location inside the disinfection cabinet. Ki represents the weighting coefficient of the i-th temperature sensor, and Ti represents the detection temperature of the i-th temperature sensor. i ranges from 1 to M, where M is the number of temperature sensors installed on the inner wall of the disinfection cabinet.

[0056] It should be understood that the shape and size of the inner liner of the disinfection cabinet, the heating power used for disinfection, and the number of temperature sensors will affect the weighting coefficient of each temperature sensor. Therefore, in some implementations, the model coefficients of the temperature optimization model (that is, the M weighting coefficients corresponding to the M temperature sensors) need to be determined by testing the disinfection cabinet in N loading states. Figure 2 This is a flowchart for testing and determining the temperature optimization model in an embodiment of the present invention, such as... Figure 2 As shown, in this embodiment of the invention, the temperature optimization model can be determined based on the N loading states of the disinfection cabinet tested in advance in steps S201 to S203.

[0057] S201. With M temperature sensors installed at M target locations on the inner wall of the disinfection cabinet, the N loading states of the disinfection cabinet are tested respectively to obtain the second detection temperature of each of the M temperature sensors in the N loading states.

[0058] S202. For each temperature sensor, determine the weighting coefficient of the temperature sensor based on the standard disinfection temperature at the target cavity position inside the disinfection cabinet and the second detection temperature of M temperature sensors in N loading states.

[0059] In some implementations, for each temperature sensor, based on the standard disinfection temperature at the target cavity location inside the disinfection cabinet and the second detection temperatures of M temperature sensors in N loading states, the weighting coefficient range of the temperature sensor in the N loading states is determined. Based on the weighting coefficient range of the temperature sensor in the N loading states, a numerical overlap interval is determined. The weighting coefficient of the temperature sensor is obtained from the numerical overlap interval based on the measured temperature difference data of the disinfection cabinet in the N loading states. The measured temperature difference data includes the measured temperature difference in each loading state, which is the temperature difference between the calculated disinfection temperature and the standard disinfection temperature calculated by the temperature optimization model based on the second detection temperatures of M temperature sensors in that loading state.

[0060] In some implementations, determining the target locations for deploying M temperature sensors and the corresponding M weighting coefficients is done within the same testing process, eliminating the need for separate testing of the disinfection cabinet. That is, for each temperature sensor, if there is numerical overlap in the weighting coefficient ranges of the N loading states, the first candidate deployment location of that temperature sensor is taken as its target location, and the numerical overlap interval is determined based on the weighting coefficient ranges of the N loading states.

[0061] S203. Based on the M weight coefficients corresponding to the M temperature sensors, generate a temperature optimization model for determining the disinfection temperature at the target cavity location inside the disinfection cabinet during its operation.

[0062] In some implementations, the temperature optimization model is formed based on M weighting coefficients and a temperature difference correction coefficient. Determining the temperature difference correction coefficient may include: determining the temperature difference correction coefficient based on measured temperature difference data of the disinfection cabinet in N loading states; and forming a temperature optimization model based on the M weighting coefficients and the temperature difference correction coefficient. Since the temperature difference coefficient is determined based on the measured temperature difference coefficient, the accuracy of the temperature difference correction coefficient is improved, which is beneficial for reducing and calculating the difference between the disinfection temperature and the standard disinfection temperature. Specifically, the measured temperature difference data of the disinfection cabinet in N loading states is obtained by: for each loading state, inputting the second detection temperature of the M temperature sensors in that loading state into the temperature optimization model without a temperature difference correction coefficient, as follows:

[0063]

[0064] The calculated disinfection temperature can be obtained, and the temperature difference between this calculated disinfection temperature and the standard disinfection temperature can be determined. A temperature difference correction factor is then determined based on the N temperature difference values ​​corresponding to the N loading states. For example, the average temperature difference of the N temperature difference values ​​corresponding to the N loading states can be used as the temperature difference correction factor; alternatively, a temperature difference value within the middle range can be selected as the temperature difference correction factor.

[0065] It should be noted that the first detection temperature, the second detection temperature, and the third detection temperature refer to the detection temperatures of M temperature sensors. The names are only used to distinguish the three scenarios: actual user use of the disinfection cabinet, testing to determine the location of the temperature sensors, and determining the weighting coefficients.

[0066] S103: Disinfection cabinet controlled based on current disinfection temperature.

[0067] In some implementations, if the current disinfection temperature at the target cavity location determined by the temperature optimization model reaches the standard disinfection temperature, the disinfection cabinet is controlled to stop heating or the heating power of the disinfection cabinet is reduced to regulate the temperature of the disinfection cabinet cavity so that the temperature difference between the actual disinfection temperature reached at the target cavity location and the standard disinfection temperature does not exceed a preset temperature difference threshold.

[0068] Based on the same inventive concept, this invention also provides a temperature control device for a disinfection cabinet. Figure 3 This is a schematic diagram of the temperature control device for the disinfection cabinet in an embodiment of the present invention, as shown below. Figure 3As shown, the temperature control device for the disinfection cabinet includes: a temperature acquisition unit 301, used to acquire the first detected temperature of M temperature sensors inside the disinfection cabinet during operation, wherein the M temperature sensors correspond to M target positions arranged on the inner wall of the disinfection cabinet, and M is an integer greater than 1; a temperature optimization unit 302, used to input the first detected temperature of the M temperature sensors into a temperature optimization model, and determine the current disinfection temperature of the target cavity position inside the disinfection cabinet through the temperature optimization model, wherein the model coefficients of the temperature optimization model include M weight coefficients corresponding to the M temperature sensors, and the M weight coefficients and M target positions are determined by testing N loading states of the disinfection cabinet, where N is an integer greater than 1; and a control execution unit, used to control the disinfection cabinet based on the current disinfection temperature.

[0069] In some embodiments, the temperature control device for the disinfection cabinet further includes a model generation unit, wherein the model generation unit includes: an acquisition subunit, used to test N loading states of the disinfection cabinet when M temperature sensors are arranged at M target positions on the inner wall of the disinfection cabinet, and obtain the second detection temperature of each of the M temperature sensors in the N loading states; a determination subunit, used to determine the weight coefficient of each temperature sensor based on the standard disinfection temperature at the target cavity position in the disinfection cabinet and the second detection temperatures of the M temperature sensors in the N loading states; and a generation subunit, used to generate a temperature optimization model for determining the disinfection temperature at the target cavity position in the disinfection cabinet during the operation of the disinfection cabinet based on the M weight coefficients corresponding to the M temperature sensors.

[0070] In some embodiments, the temperature control device for the disinfection cabinet further includes: a test execution unit, used for

[0071] With M temperature sensors installed at M first candidate placement positions on the inner wall of the disinfection cabinet, N loading states of the disinfection cabinet are tested respectively to obtain the third detection temperature of each of the M temperature sensors in the N loading states; a range determination unit is used to determine the weight coefficient range of each of the M temperature sensors in each loading state based on the third detection temperature of the M temperature sensors in that loading state and the standard disinfection temperature of the target cavity position; a position determination unit is used to determine the target position for installing each temperature sensor based on the weight coefficient range of the temperature sensor in the N loading states and the first candidate placement position of the temperature sensor.

[0072] In some embodiments, the temperature control device for the disinfection cabinet further includes: a model building unit for building a simulation model of the disinfection cabinet, the simulation model being used to simulate the disinfection cabinet and the simulated arrangement of M temperature sensors on the inner wall of the disinfection cabinet; and a simulation execution unit for simulating the heating process of the disinfection cabinet and the temperature detection process of the M temperature sensors based on the simulation model, and obtaining M first candidate placement positions corresponding to the M temperature sensors.

[0073] In some implementations, the position determination unit may be used to: for each temperature sensor, if the weight coefficients of the temperature sensor overlap in the range of N loading states, take the first candidate placement position of the temperature sensor as the target position of the temperature sensor; for each temperature sensor, if the weight coefficients of the temperature sensor do not overlap in the range of N loading states, determine the second candidate placement position of the temperature sensor based on the first candidate placement position of the temperature sensor, and determine whether the second candidate placement position of the temperature sensor is taken as the target position for placing the temperature sensor.

[0074] In some implementations, the determining subunit may be used to: for each of the temperature sensors, based on the standard disinfection temperature at the target cavity position inside the disinfection cabinet and the second detection temperature of the M temperature sensors in the N loading states, determine the weighting coefficient range of the temperature sensor in the N loading states, determine the numerical overlap interval based on the weighting coefficient range of the temperature sensor in the N loading states, and obtain the weighting coefficient of the temperature sensor from the numerical overlap interval based on the measured temperature difference data of the disinfection cabinet in the N loading states.

[0075] In some implementations, the position determination unit may be used to: for each loading state, determine the calculated disinfection temperature of the target cavity position in that loading state based on the third detection temperature of M temperature sensors in that loading state, and determine the measured temperature difference range of that loading state based on the standard disinfection temperature of the target cavity position and the calculated disinfection temperature in that loading state; and correct the first candidate placement position of the temperature sensor based on at least N measured temperature difference ranges corresponding to N loading states to obtain the second candidate placement position of the temperature sensor.

[0076] In some implementations, the location determination unit can be used to: correct the first candidate placement location based on the simulated temperature difference and N measured temperature difference ranges corresponding to N loading states, wherein the simulated temperature difference is obtained by simulation based on the simulation model of the disinfection cabinet.

[0077] In some implementations, the generation subunit can be used to: determine the temperature difference correction coefficient based on the measured temperature difference data of the disinfection cabinet in N loading states; and generate a temperature optimization model based on M weighting coefficients and the temperature difference correction coefficient.

[0078] The device embodiments described above can be used to execute the disinfection cabinet temperature control method in the above embodiments of the present invention. For details not disclosed in the device embodiments of the present invention, please refer to the disinfection cabinet temperature control method described in the embodiments of the present invention.

[0079] Thirdly, based on the same inventive concept, this invention provides a disinfection cabinet, which includes: a cabinet body; and M temperature sensors, corresponding to M target positions disposed on the inner wall of the cabinet body, where M is an integer greater than 1, such as... Figure 4 As shown, the disinfection cabinet also includes one or more processors 402 and one or more memories 404, wherein the one or more memories 404 store at least one piece of program code, which is loaded and executed by the one or more processors 402 to implement the above-mentioned temperature control method for the disinfection cabinet.

[0080] Among them, Figure 4 In this document, a bus architecture (represented by bus 400) is used. Bus 400 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 402 and memory represented by memory 404. Bus 400 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 405 provides an interface between bus 400 and receiver 401 and transmitter 403. Receiver 401 and transmitter 403 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 402 is responsible for managing bus 400 and general processing, while memory 404 can be used to store data used by processor 402 during operation.

[0081] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this invention and the appended claims. For example, due to the nature of software, the functions described above can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units can be integrated into a single processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0083] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0085] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for controlling the temperature of a disinfection cabinet, characterized in that, include: During the operation of the disinfection cabinet, the first detected temperature of M temperature sensors inside the disinfection cabinet is obtained. The M temperature sensors correspond to M target positions arranged on the inner wall of the disinfection cabinet, where M is an integer greater than 1. The first detected temperatures of the M temperature sensors are input into a temperature optimization model. The temperature optimization model determines the current disinfection temperature at the target cavity location within the disinfection cabinet. The model coefficients of the temperature optimization model include M weighting coefficients corresponding to the M temperature sensors. Both the M weighting coefficients and the M target locations are determined by testing N loading states of the disinfection cabinet, where N is an integer greater than 1. With the M temperature sensors positioned at the M first candidate locations on the inner wall of the disinfection cabinet, the N loading states of the disinfection cabinet are tested to obtain the third detected temperature of each of the M temperature sensors in each of the N loading states. For each loading state, based on the third detected temperature of the M temperature sensors in that loading state and the standard disinfection temperature at the target cavity location, the weighting coefficient range of each of the M temperature sensors in that loading state is determined. For each temperature sensor, the target location for its placement is determined based on the weighting coefficient range of that temperature sensor in the N loading states and the first candidate placement location of that temperature sensor. The disinfection cabinet is controlled based on the current disinfection temperature.

2. The method according to claim 1, characterized in that, The temperature optimization model was determined based on prior testing of the disinfection cabinet using the following steps: With the M temperature sensors installed at M target locations on the inner wall of the disinfection cabinet, the N loading states of the disinfection cabinet are tested respectively to obtain the second detection temperature of each of the M temperature sensors in the N loading states. For each temperature sensor, a weighting coefficient is determined based on the standard disinfection temperature at the target cavity location inside the disinfection cabinet and the second detection temperature of the M temperature sensors in the N loading states. Based on the M weighting coefficients corresponding to the M temperature sensors, a temperature optimization model is generated to determine the disinfection temperature at the target cavity location inside the disinfection cabinet during its operation.

3. The method according to claim 2, characterized in that, Also includes: A simulation model of the disinfection cabinet is constructed, which is used to simulate the disinfection cabinet and simulate the arrangement of M temperature sensors on the inner wall of the disinfection cabinet. Based on the simulation model, the heating process of the disinfection cabinet and the temperature detection process of the M temperature sensors are simulated to obtain the M first candidate placement positions corresponding to the M temperature sensors.

4. The method according to claim 2, characterized in that, For each of the temperature sensors, determining the target location for deploying the temperature sensor based on the weighting coefficient range of the temperature sensor in the N loading states and the first candidate deployment location of the temperature sensor includes: For each of the temperature sensors, if the weighting coefficients of the temperature sensor overlap in the range of the N loading states, the first candidate placement position of the temperature sensor shall be taken as the target position of the temperature sensor. For each temperature sensor, if the weighting coefficients of the temperature sensor do not overlap in the range of the N loading states, the second candidate placement position of the temperature sensor is determined based on the first candidate placement position of the temperature sensor, and it is determined whether the second candidate placement position of the temperature sensor is used as the target position for placing the temperature sensor.

5. The method according to claim 4, characterized in that, For each of the temperature sensors, based on the standard disinfection temperature at the target cavity location within the disinfection cabinet and the second detection temperatures of the M temperature sensors in the N loading states, a weighting coefficient for that temperature sensor is determined, including: For each temperature sensor, based on the standard disinfection temperature at the target cavity position inside the disinfection cabinet and the second detection temperature of the M temperature sensors in the N loading states, the weighting coefficient range of the temperature sensor in the N loading states is determined. Based on the weighting coefficient range of the temperature sensor in the N loading states, a numerical overlap interval is determined. Based on the measured temperature difference data of the disinfection cabinet in the N loading states, the weighting coefficient of the temperature sensor is obtained from the numerical overlap interval.

6. The method according to claim 4, characterized in that, Determining the second candidate placement location of the temperature sensor based on the first candidate placement location includes: For each loading state, the calculated disinfection temperature of the target cavity position in the loading state is determined based on the third detection temperature of the M temperature sensors in that loading state, and the measured temperature difference range in that loading state is determined based on the standard disinfection temperature of the target cavity position and the calculated disinfection temperature in that loading state. The first candidate placement position of the temperature sensor is corrected based on at least N measured temperature difference ranges corresponding to the N loading states to obtain the second candidate placement position of the temperature sensor.

7. The method according to claim 6, characterized in that, The step of correcting the first candidate placement position of the temperature sensor based on at least N measured temperature difference ranges corresponding to the N loading states to obtain the second candidate placement position of the temperature sensor includes: Based on the simulated temperature difference and the N measured temperature difference ranges corresponding to the N loading states, the first candidate placement position of the temperature sensor is corrected, wherein the simulated temperature difference is obtained by simulation based on the simulation model of the disinfection cabinet.

8. The method according to claim 1, characterized in that, The step of generating a temperature optimization model based on the M weighting coefficients corresponding to the M temperature sensors to determine the disinfection temperature at the target cavity location within the disinfection cabinet during its operation includes: Based on the measured temperature difference data of the disinfection cabinet under the N loading states, determine the temperature difference correction coefficient; The temperature optimization model is generated based on the M weighting coefficients and the temperature difference correction coefficient.

9. A temperature control device for a disinfection cabinet, characterized in that, include: The temperature acquisition unit is used to acquire the first detected temperature of M temperature sensors inside the disinfection cabinet during the operation of the disinfection cabinet. The M temperature sensors are corresponding to M target positions arranged on the inner wall of the disinfection cabinet, where M is an integer greater than 1. A temperature optimization unit is used to input the first detected temperatures of the M temperature sensors into a temperature optimization model, and to determine the current disinfection temperature at the target cavity location inside the disinfection cabinet through the temperature optimization model. The model coefficients of the temperature optimization model include M weighting coefficients corresponding to the M temperature sensors. Both the M weighting coefficients and the M target locations are determined by testing N loading states of the disinfection cabinet, where N is an integer greater than 1. When the M temperature sensors are deployed at the M first candidate placement locations on the inner wall of the disinfection cabinet, the N loading states of the disinfection cabinet are tested respectively to obtain the third detected temperature of each of the M temperature sensors in the N loading states. For each loading state, based on the third detected temperature of the M temperature sensors in that loading state and the standard disinfection temperature at the target cavity location, the weighting coefficient range of each of the M temperature sensors in that loading state is determined. For each temperature sensor, based on the weighting coefficient range of that temperature sensor in the N loading states and the first candidate placement location of that temperature sensor, a target location for deploying that temperature sensor is determined. A control execution unit is used to control the disinfection cabinet based on the current disinfection temperature.

10. A disinfection cabinet, characterized in that, include: Cabinet; M temperature sensors are installed at M target locations on the inner wall of the cabinet, where M is an integer greater than 1; One or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, the at least one piece of program code being loaded and executed by the one or more processors to implement the method according to any one of claims 1-8.

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