Refrigeration equipment sterilization control method and device, electronic equipment and storage medium

Abstract

The invention provides a refrigeration equipment sterilization control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining pollution detection data corresponding to refrigeration equipment, the pollution detection data being data obtained by detecting condensate water generated in the operation process of the refrigeration equipment; calculating a pollution evaluation value based on the pollution detection data; determining a control parameter according to the pollution evaluation value; and performing sterilization treatment with corresponding intensity on the refrigeration space of the refrigeration equipment based on the control parameters. Therefore, the sterilization intensity can be adjusted in real time according to the pollution condition, the problem of insufficient sterilization or excessive sterilization caused by a fixed program in the prior art is avoided, the treatment problem of resistant microorganisms is effectively solved, and meanwhile, the energy utilization efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device control, and in particular to a refrigeration device sterilization control method and device, an electronic device, and a storage medium. BACKGROUND

[0002] In the food preservation process of refrigeration devices (such as refrigerators, freezers, etc.), food contamination is likely to occur due to microbial growth and pesticide residues. Traditional methods mainly rely on low temperature to inhibit bacterial reproduction, but cannot effectively kill existing pathogenic bacteria or degrade harmful chemicals. Therefore, developing efficient sterilization technology for refrigeration devices is of great significance to food safety.

[0003] Currently, two sterilization technologies are mainly used for refrigeration devices: the first is ozone sterilization technology, which generates ozone gas through high-voltage discharge principle and uses the strong oxidizing property of ozone to destroy the cell structure of microorganisms. The typical application method is to release a fixed concentration of ozone (usually 0.1-0.3 ppm) at fixed time intervals (such as every 8 hours) and circulate and diffuse in the refrigeration space for disinfection. The second is ultraviolet sterilization technology, which uses a UV-C ultraviolet lamp with a wavelength of 253.7 nm to achieve sterilization by destroying the DNA structure of microorganisms. The common implementation method is to install an ultraviolet lamp on the inner wall of the refrigeration chamber and start irradiation at regular times (such as 10 minutes each time), while cooperating with a fan to promote air flow, so that the ultraviolet light can cover more areas.

[0004] However, these existing sterilization methods have a fundamental flaw: they use fixed program control and cannot dynamically adjust the sterilization intensity according to the actual pollution level. Whether it is an ozone generator or an ultraviolet lamp, it works according to the preset time and dose, and cannot implement precise sterilization for different levels of pollution, nor can it cope with resistant microorganisms, resulting in either incomplete sterilization or excessive energy consumption and accelerated equipment aging. SUMMARY

[0005] The present application provides a refrigeration device sterilization control method, device, electronic device, and storage medium to solve the problem of fixed program control in the prior art, which cannot dynamically adjust the sterilization intensity according to the actual pollution level.

[0006] In a first aspect, the present application provides a refrigeration device sterilization control method, comprising:

[0007] obtaining pollution detection data corresponding to the refrigeration device, the pollution detection data being data obtained by detecting condensate water generated during operation of the refrigeration device;

[0008] calculating a pollution evaluation value based on the pollution detection data;

[0009] determining a control parameter according to the pollution evaluation value;

[0010] performing a sterilization process on the refrigeration space of the refrigeration equipment with a corresponding intensity based on the control parameter.

[0011] In one possible implementation, the contamination detection data includes pH detection data, conductivity detection data, and microorganism content detection data, and the calculating a contamination evaluation value based on the contamination detection data includes:

[0012] subtracting the pH detection data from a preset reference value to obtain a pH parameter;

[0013] using the conductivity detection data as a conductivity parameter;

[0014] using the microorganism content detection data to determine a microorganism content parameter, wherein, in a case where the microorganism content detection data does not exceed a preset upper limit value, the microorganism content detection data is determined as the microorganism content parameter, and in a case where the microorganism content detection data exceeds the preset upper limit value, the preset upper limit value is determined as the microorganism content parameter;

[0015] performing weighted summation calculation on the pH parameter, the conductivity parameter, and the microorganism content parameter to obtain the contamination evaluation value.

[0016] In one possible implementation, the method further includes:

[0017] monitoring an environmental humidity parameter of the refrigeration space in real time;

[0018] obtaining a preset humidity and weight mapping relationship, the mapping relationship being obtained by training historical operation data by a machine learning model;

[0019] querying the mapping relationship according to a current environmental humidity parameter, and dynamically adjusting a weight distribution ratio of the pH parameter, the conductivity parameter, and the microorganism content parameter.

[0020] In one possible implementation, the determining a control parameter according to the contamination evaluation value includes:

[0021] determining a contamination level according to the contamination evaluation value;

[0022] determining a target power and a target operation duration according to the contamination level;

[0023] configuring the target power and the target operation duration as the control parameter.

[0024] In one possible implementation, the method further includes:

[0025] After sterilization, the contamination detection data for the refrigeration equipment was reacquired.

[0026] The updated pollution assessment value is calculated based on the reacquired pollution detection data;

[0027] The updated contamination assessment values ​​were compared and analyzed with the contamination assessment values ​​before sterilization to obtain the comparative analysis results;

[0028] The control parameters for the next sterilization process will be dynamically adjusted based on the results of the comparative analysis.

[0029] In one possible implementation, the step of dynamically adjusting the control parameters for the next sterilization process based on the comparative analysis results includes:

[0030] If the comparative analysis results show that the rate of decrease in contamination assessment value after a preset number of sterilization treatments does not meet the expected standard, an emergency treatment mode is triggered, including: controlling the cold plasma generator to operate at emergency power for an emergency running time, and / or generating and sending equipment abnormality warning information.

[0031] In one possible implementation, performing sterilization treatment of appropriate intensity on the refrigeration space of the refrigeration equipment based on the control parameters includes:

[0032] According to the power setting value in the control parameters, the cold plasma is controlled to convert condensate into activation raw materials;

[0033] The processing time of the activated raw material under the action of cold plasma is controlled according to the duration setting value in the control parameters.

[0034] The sterilized material obtained after plasma activation treatment is transported to a cold storage space for sterilization.

[0035] Secondly, this application provides a sterilization control device for refrigeration equipment, comprising:

[0036] The acquisition module is used to acquire pollution detection data corresponding to the refrigeration equipment. The pollution detection data is the data obtained by detecting the condensate generated during the operation of the refrigeration equipment.

[0037] The calculation module is used to calculate the pollution assessment value based on the pollution detection data;

[0038] The determination module is used to determine control parameters based on the pollution assessment values;

[0039] The control module is used to perform sterilization treatment of corresponding intensity on the refrigeration space of the refrigeration equipment based on the control parameters.

[0040] In a possible implementation, the pollution detection data comprises pH detection data, conductivity detection data, and microbial content detection data, and the computing module is specifically configured to:

[0041] subtract the pH detection data from a preset reference value to obtain a pH parameter;

[0042] use the conductivity detection data as a conductivity parameter;

[0043] determine a microbial content parameter using the microbial content detection data, wherein, in a case where the microbial content detection data does not exceed a preset upper limit value, the microbial content detection data is determined as the microbial content parameter, and in a case where the microbial content detection data exceeds the preset upper limit value, the preset upper limit value is determined as the microbial content parameter;

[0044] perform weighted summation calculation on the pH parameter, the conductivity parameter, and the microbial content parameter to obtain the pollution evaluation value.

[0045] In a possible implementation, the device further comprises an adjusting module configured to:

[0046] monitor an environmental humidity parameter of the refrigeration space in real time;

[0047] obtain a preset humidity and weight mapping relationship, which is obtained by training historical operation data using a machine learning model;

[0048] query the mapping relationship according to a current environmental humidity parameter, and dynamically adjust a weight distribution ratio of the pH parameter, the conductivity parameter, and the microbial content parameter.

[0049] In a possible implementation, the generating module is specifically configured to:

[0050] determine a pollution level according to the pollution evaluation value;

[0051] determine a target power and a target operation duration according to the pollution level;

[0052] configure the target power and the target operation duration as the control parameter.

[0053] In a possible implementation, the device further comprises an adjusting module configured to:

[0054] after completing the sterilization process, reacquire pollution detection data corresponding to the refrigeration equipment;

[0055] calculate an updated pollution evaluation value based on the reacquired pollution detection data;

[0056] comparing the updated pollution evaluation value with the pollution evaluation value before the sterilization treatment, to obtain a comparison result;

[0057] adjusting a control parameter of next sterilization treatment according to the comparison result.

[0058] In one possible implementation, the adjusting module is further configured to:

[0059] In a case where the comparison result is that a pollution evaluation value after continuous sterilization treatments for a preset number of times does not reach an expected standard, triggering an emergency treatment mode, including: controlling the cold plasma generator to operate at an emergency power for an emergency operation duration, and / or, generating and sending device abnormality warning information.

[0060] In one possible implementation, the control module is specifically configured to:

[0061] controlling the cold plasma to convert the condensed water into activated raw material according to a power setting value in the control parameter;

[0062] controlling a processing time of the activated raw material under the action of the cold plasma according to a duration setting value in the control parameter;

[0063] delivering the sterilization material obtained after the plasma activation treatment to the refrigeration space for sterilization treatment.

[0064] In a third aspect, the present application provides a device, including: a processor and a memory, the processor is used to execute a refrigeration device sterilization control program stored in the memory, to realize the refrigeration device sterilization control method in any one of the first aspect.

[0065] In a fourth aspect, the present application provides a storage medium, the storage medium stores one or more programs, the one or more programs can be executed by one or more processors to realize the refrigeration device sterilization control method in any one of the first aspect.

[0066] The above technical solutions provided by the embodiments of the present application have the following advantages compared with the prior art: the method provided by the embodiments of the present application, by acquiring the pollution detection data of the condensed water and calculating the pollution evaluation value, comparing the evaluation value with the preset threshold interval to determine the pollution level, and then generating the corresponding control parameter according to the pollution level to control the sterilization treatment intensity, realizes the dynamic and accurate sterilization based on the actual pollution degree. Thus, the sterilization intensity can be adjusted in real time according to the pollution condition, which not only avoids the problems of insufficient sterilization or excessive sterilization caused by fixed programs in the prior art, but also effectively solves the processing problem of resistant microorganisms, and improves the energy utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0067] The accompanying drawings, which are incorporated herein and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the accompanying drawings required to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without any creative effort.

[0069] One or more embodiments are illustrated by way of example in the drawings that are not intended to be limiting of the present application, and the same or similar reference numerals designate similar or like elements in the various drawings. The drawings are not necessarily to scale, the emphasis instead being placed upon illustrating the principles of the embodiments.

[0070] Figure 1 An embodiment flow chart of a sterilization control method of a refrigeration equipment provided by the embodiments of the present application is provided.

[0071] Figure 2 An embodiment flow chart of another sterilization control method of a refrigeration equipment provided by the embodiments of the present application is provided.

[0072] Figure 3 An embodiment flow chart of still another sterilization control method of a refrigeration equipment provided by the embodiments of the present application is provided.

[0073] Figure 4 A work flow chart of a sterilization control method of a refrigeration equipment provided by the embodiments of the present application is provided.

[0074] Figure 5 An embodiment block diagram of a sterilization control device of a refrigeration equipment provided by the embodiments of the present application is provided.

[0075] Figure 6 A structural schematic diagram of an electronic device provided by the embodiments of the present application is provided. DETAILED DESCRIPTION

[0076] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort belong to the scope of protection of the present application.

[0077] The following disclosure provides a number of different embodiments or examples for implementing the different structures of the present application. In order to simplify the disclosure of the present application, the components and arrangements of specific examples are described below. Of course, they are merely examples and the purpose is not to limit the present application. In addition, reference numerals and / or letters can be repeated in different examples in the present application. Such repetition is for the purpose of simplification and clarity, and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0078] In order to solve the problem of fixed program control in the prior art, which cannot dynamically adjust the sterilization intensity according to the actual pollution degree, the present application provides a cold storage equipment sterilization control method, which can adjust the sterilization intensity in real time according to the pollution condition, avoids the problems of insufficient sterilization or excessive sterilization caused by fixed program in the prior art, effectively solves the problem of resistant microorganisms, and improves the energy utilization efficiency.

[0079] Figure 1 An embodiment flowchart of a cold storage equipment sterilization control method provided by an embodiment of the present application is shown in FIG. 1. In an embodiment, as shown in FIG. 1, the method comprises the following steps: Figure 1

[0080] Step 101, obtaining pollution detection data corresponding to the cold storage equipment, wherein the pollution detection data is data obtained by detecting condensate water generated during operation of the cold storage equipment.

[0081] The cold storage equipment is a food preservation device that maintains a low-temperature environment (usually 0-10°C) through refrigeration technology, such as a refrigerator, a freezer, etc., which is used to inhibit microbial growth and prolong the shelf life of food.

[0082] Condensate water refers to the moisture collected at the bottom of the evaporator of the refrigerator, which contains pollutants such as microbial metabolites and pesticide residues.

[0083] The detection data includes "pH detection data" (measured by a pH sensor to measure the pH level of the condensate water, reflecting the pH change caused by microbial activity), "conductivity detection data" (detected by a conductivity probe to indicate the inorganic pollutant content), and "microbial content detection data" (detected by an ATP bioluminescence method to detect adenosine triphosphate concentration, directly representing the number of microorganisms).

[0084] In an embodiment of the present application, a detection cycle can be started every preset time interval (such as 60 minutes), a certain amount (such as 1 mL) of condensate water is extracted by a peristaltic pump to a detection chamber, and three parameter detections are completed synchronously by multiple sensors (i.e. pH sensor, conductivity probe, ATP bioluminescence instrument) integrated in the detection chamber, providing accurate data basis for pollution assessment.

[0085] ​Step 102, calculating a pollution evaluation value based on the pollution detection data.

[0086] The pollution evaluation value is a comprehensive pollution index obtained by weighted calculation of the three detection data of the condensate water, including pH value, conductivity and microbial content.

[0087] In the embodiments of the present application, the detection parameters of different dimensions (pH value, conductivity, ATP concentration) are converted into a unified evaluation value through a specific algorithm, providing a quantitative basis for pollution level determination.

[0088] How to calculate the pollution evaluation value based on the pollution detection data will be explained in detail in the following embodiments, which will not be described here in detail.

[0089] Step 103, determining a control parameter according to the pollution evaluation value.

[0090] The control parameter refers to a set of instructions for adjusting the working state of the cold plasma generator, including power setting value (output power of the cold plasma generator, unit: watt) and running time (duration of a single sterilization operation, unit: minute).

[0091] In the embodiments of the present application, step 103 can include the following steps: determining a pollution level according to the pollution evaluation value; determining a target power and a target running time according to the pollution level; configuring the target power and the target running time as the control parameter.

[0092] In this scheme, the pollution evaluation value calculated in step 102 is compared with a preset threshold interval, and the corresponding pollution level is output according to the comparison result. For example, the pollution evaluation value PI<50% is low pollution, 50%≤PI<80% is moderate pollution, and PI≥80% is severe pollution. This scheme discretizes the continuous pollution evaluation value into a level identifier with a clear sterilization strategy direction by establishing a quantitative grading standard, providing a decision basis for subsequent differentiated sterilization control.

[0093] Then, three-level gradient control is implemented according to the pollution level: 1) for primary pollution (PI < 50%), the target power and target running time are respectively a first preset power (10 W) and a first running time (1 minute), and intermittent operation (1 minute per 2 hours) is adopted; 2) for intermediate pollution (50% ≤ PI < 80%), the target power and target running time are respectively a second preset power (50 W) and a second running time (5 minutes), and continuous operation mode (5 minutes per hour) is adopted; 3) for high-level pollution (PI ≥ 80%), the target power and target running time are respectively a third preset power (80 W) and a third running time (10 minutes), and high-intensity continuous operation (10 minutes) is adopted. The parameters strictly follow the progressive relationship of "third preset power > second preset power > first preset power" and "third running time > second running time > first running time". The scheme realizes precise matching of sterilization intensity and pollution level through power-time combination, avoids incomplete sterilization caused by low power processing of severe pollution, and prevents energy waste caused by high power processing of mild pollution.

[0094] Step 104, performing sterilization treatment of the refrigeration space of the refrigeration equipment with corresponding intensity based on the control parameters.

[0095] In the embodiment of the application, step 104 can include the following steps: controlling the cold plasma to convert the condensed water into activated raw materials according to the power setting value in the control parameters; controlling the processing time of the activated raw materials under the action of the cold plasma according to the time setting value in the control parameters; and delivering the sterilization material obtained after the plasma activation treatment to the refrigeration space for sterilization treatment.

[0096] The activated raw material refers to an intermediate material (such as 5-10 μm fine droplets formed by atomization) that can be activated by plasma after physical treatment of the condensed water; the sterilization material refers to a sterilization medium containing active components such as hydrogen peroxide, hydroxyl radicals and ozone generated after the activated raw material is subjected to the action of cold plasma.

[0097] In the scheme, the discharge intensity of the cold plasma generator is adjusted according to the power setting value (such as 50 W) to control the efficiency of the conversion of the condensed water into activated raw materials; the residence time of the activated raw materials under the action of the plasma is accurately controlled according to the time setting value (such as 5 minutes) to ensure sufficient reaction; then, the generated sterilization material is uniformly delivered to each area (such as the refrigeration chamber and the freezer of the refrigerator) of the refrigeration space through the gas circulation system (such as the circulating fan in the refrigerator). The scheme realizes the optimal balance between sterilization effect and energy consumption through the synergistic control of power-time, at the same time, avoids cross contamination through the integrated utilization of "detection-activation" of the condensed water, in addition, the dynamically generated sterilization material can kill different levels of microbial pollution.

[0098] In the application, the flow rate of the gas circulation system (0.5-2.0 m / s adjustable) can be monitored and adjusted in real time by the PID controller, specifically including: when it is detected that the concentration of active particles (referring to the content of hydrogen peroxide (H2O2), hydroxyl radical (·OH) and ozone (O3) and the like sterilization substances per unit volume) is insufficient, the fan speed is increased to enhance diffusion (up to 2.0 m / s); when it is detected that the concentration of active particles meets the standard, the flow rate is reduced to a maintenance flow rate (0.5-1.0 m / s); in addition, when resistant bacteria pollution is encountered, the pulse mode is automatically switched to. In this way, the effective coverage concentration of active particles in the refrigeration space can be ensured by closed-loop feedback control (such as PID algorithm).

[0099] The technical scheme provided by the embodiments of the present application can obtain pollution detection data of the condensed water and calculate a pollution evaluation value, compare the evaluation value with a preset threshold interval to determine a pollution level, and then generate corresponding control parameters according to the pollution level to control the sterilization treatment intensity, thereby achieving dynamic and accurate sterilization based on the actual pollution degree. Thus, the sterilization intensity can be adjusted in real time according to the pollution condition, avoiding the problems of insufficient sterilization or excessive sterilization caused by fixed programs in the prior art, effectively solving the problem of processing resistant microorganisms, and improving the energy utilization efficiency.

[0100] Figure 2 An embodiment flowchart of another sterilization control method for a refrigeration equipment provided by the embodiments of the present application is shown. Figure 2 The flowchart shown in Figure 1 On the basis of the flowchart shown in

[0101] Step 201, subtracting the pH detection data from a preset reference value to obtain a pH parameter.

[0102] The preset reference value is used to neutralize the dimension of the pH detection data (in the present application, a constant of 14 is set).

[0103] In the embodiments of the present application, the stronger the acidity (the lower the pH), the larger the calculation result by subtracting the measured pH value (for example, 14-5=9 when pH=5.0), which conforms to the positive correlation between the pollution degree and the acidity. Thus, the pH change characteristics caused by microbial metabolism are effectively highlighted.

[0104] Step 202, taking the conductivity detection data as a conductivity parameter.

[0105] The conductivity parameter directly uses the measured value of the conductivity probe (for example, 150 μS / cm), which reflects the inorganic ion concentration in the condensed water.

[0106] In the embodiments of the present application, the physical meaning of the original detection data is retained, errors introduced by secondary calculation are avoided, and the linear relationship between the conductivity index and the pollution degree is ensured.

[0107] Step 203, determining a microbial content parameter by using the microbial content detection data, wherein, in the case that the microbial content detection data does not exceed a preset upper limit value, the microbial content detection data is determined as the microbial content parameter, and in the case that the microbial content detection data exceeds the preset upper limit value, the preset upper limit value is determined as the microbial content parameter.

[0108] The microbial content parameter is represented by an ATP bioluminescence detection value (unit: RLU).

[0109] In the embodiment of the present application, when the detection value does not exceed the preset upper limit value (set as 100 RLU in the present application), the parameter value is taken as the actual detection value; when the detection value exceeds the preset upper limit value, the parameter value is fixed as the upper limit value (for example, 100 RLU is calculated when 150 RLU is detected). This limiting processing prevents excessive influence of extreme data on overall evaluation and maintains the balance of multiple parameters.

[0110] Step 204, performing weighted summation calculation on the pH parameter, the conductivity parameter and the microbial content parameter to obtain the pollution evaluation value.

[0111] In the embodiment of the present application, the three parameters are weighted and summed (for example: 0.4x9+0.3x150+0.3x50=63.6) according to a preset weight coefficient (for example, pH 40%, conductivity 30%, microbial content 30%). In this way, the overall condition of microbial activity, inorganic pollution and organic pollution can be comprehensively reflected through scientific allocation of weight proportion.

[0112] Figure 2 The illustrated process strengthens the indication of acidic environment on pollution through pH reference value conversion (14-pH); the accuracy of inorganic pollution index is ensured by directly referencing conductivity data; ATP concentration upper limit truncation (100 RLU) avoids interference of biological detection abnormal value. The finally generated pollution evaluation value (PI) realizes quantitative evaluation of pollution degree through the weighted model verified by experiments, and provides a reliable basis for classification sterilization.

[0113] In addition, in another embodiment of the present application, the method can further include the following steps: monitoring an environmental humidity parameter of the refrigeration space in real time; obtaining a preset humidity and weight mapping relationship, the mapping relationship being obtained by training historical operation data by a machine learning model; querying the mapping relationship according to the current environmental humidity parameter to dynamically adjust the weight distribution proportion of the pH parameter, the conductivity parameter and the microbial content parameter.

[0114] The environmental humidity parameter refers to the relative humidity value (e.g., 85% RH) of the refrigeration space collected in real time by a humidity sensor; and the humidity and weight mapping relationship refers to a feature correlation rule obtained by training historical operation data (containing humidity, pH value, conductivity, ATP concentration, and corresponding pollution evaluation value) by a machine learning model (e.g., a random forest algorithm).

[0115] When the scheme is implemented: the environmental humidity change is monitored in real time; a pre-trained model is called to dynamically adjust the weight distribution ratio (e.g., pH value: conductivity: microbial content, from 0.4:0.3:0.3 to 0.3:0.4:0.3); and the pollution evaluation value is recalculated according to the new weight. The scheme can solve the problem of interference of environmental humidity change on pollution evaluation by dynamically adjusting the weight associated with humidity (e.g., increasing the weight of conductivity under high humidity); and the machine learning model automatically optimizes the weight distribution, which is more suitable for complex environments than fixed weights.

[0116] Figure 3 An embodiment flowchart of another sterilization control method for a refrigeration device provided in the embodiments of the present application is provided. Figure 3 The flowchart shown in Figure 1 On the basis of the flowchart shown, the following steps are included:

[0117] Step 301: After completing the sterilization treatment, the pollution detection data corresponding to the refrigeration device is reacquired.

[0118] In the embodiments of the present application, after completing the sterilization treatment, the pH value, conductivity, and microbial content (ATP concentration) data of the condensate water are collected again. In this way, the real-time performance of the pollution evaluation can be ensured by periodic detection (e.g., every 60 minutes), and error accumulation caused by environmental changes or incomplete sterilization can be avoided.

[0119] Step 302: The updated pollution evaluation value is calculated based on the reacquired pollution detection data.

[0120] In the embodiments of the present application, based on the reacquired data, the updated pollution evaluation value is recalculated according to the calculation method of 204 (PI = 0.4 x (14-pH) + 0.3 x EC + 0.3 x ATP), reflecting the pollution condition after the current sterilization treatment. In this way, the dynamic update of the pollution evaluation can avoid misjudgment caused by relying on historical data.

[0121] Step 303: The updated pollution evaluation value is compared and analyzed with the pollution evaluation value before the sterilization treatment, to obtain a comparison and analysis result.

[0122] In the embodiments of the present application, the updated pollution assessment value (e.g., PI = 40 after sterilization) and the assessment value before sterilization (e.g., PI = 65 before sterilization) are calculated by difference (e.g., decrease by 25), and whether the decrease amplitude meets the expectation (e.g., the minimum decrease threshold of 5%) is analyzed. This scheme is used to evaluate the sterilization effect and identify resistant bacteria or abnormal pollution conditions.

[0123] Step 304: dynamically adjusting the control parameters of the next sterilization treatment according to the comparison and analysis result.

[0124] Dynamically adjusting the control parameters means optimizing the power and running time of the next sterilization according to the comparison and analysis result.

[0125] In the embodiments of the present application, the step 304 can include the following steps: in the case that the decrease rate of the pollution assessment value after continuous sterilization treatment for a preset number of times does not reach the expected standard, triggering an emergency treatment mode, including: controlling the cold plasma generator to run at an emergency power for an emergency running time, and / or generating and sending device abnormal warning information.

[0126] The emergency treatment mode refers to a strengthened control strategy triggered when the conventional sterilization program cannot effectively reduce pollution. The core parameters include emergency power (the emergency power is greater than a third preset power, e.g., the emergency power is set to 100 W, which is significantly higher than the upper limit of 80 W of the conventional running) and emergency running time (the emergency running time is greater than a third running time, e.g., the emergency running time is set to 15 minutes, which is longer than the conventional 10 minutes).

[0127] The specific implementation of this scheme includes: when the decrease rate of the pollution assessment value after continuous sterilization for 3 times is <5%, automatically switching to the emergency mode, i.e., controlling the cold plasma generator to run at an ultra-high power (100 W) and an ultra-long duration (15 minutes) to ensure the complete killing of resistant microorganisms; simultaneously generating a device abnormal warning (e.g., APP push alarm information) to prompt the user of possible device failure or stubborn pollution.

[0128] This scheme realizes three optimizations by intelligently identifying sterilization failure conditions: 1) breaking through the conventional power limit (80 W → 100 W) to solve the problem of resistant bacteria; 2) prolonging the action time (10 min → 15 min) to ensure complete sterilization; 3) abnormal alarm mechanism (e.g., real-time reminder on mobile terminal) to improve system reliability and avoid food safety risks caused by continuous pollution that is not detected.

[0129] Figure 3 The closed-loop feedback mechanism has three advantages: real-time: through periodic detection and calculation, it ensures that the pollution assessment always reflects the latest environmental conditions; adaptability: dynamically adjusting the strategy according to the sterilization effect, optimizing energy consumption and dealing with resistant bacteria; reliability: avoiding misjudgment through comparison and analysis to ensure accurate and effective pollution control.

[0130] In another embodiment of the present application, the method can further include the following steps: when the pollution level does not change as expected after continuous multiple sterilization processes, the following operations are performed: generating device maintenance prompt information, and / or transmitting current pollution detection data and historical processing records to a remote monitoring system.

[0131] The device maintenance prompt information refers to a system-generated maintenance request signal, which includes device self-check fault codes and recommended maintenance measures; the remote monitoring system refers to a cloud-deployed data management platform for storing and analyzing device operation data.

[0132] In the embodiment of the present application, when the pollution level does not decrease after three consecutive sterilizations, it is determined that the system is abnormal; maintenance prompts (such as "please clean the evaporator") are pushed through the display screen / APP; key data (pollution detection data, historical control parameters) are encrypted and transmitted to the cloud for remote diagnosis by technicians. This scheme can detect device performance degradation in advance through an intelligent early warning mechanism; improve user processing efficiency through maintenance guidelines (such as specific error codes corresponding to condensate drain pipe blockage); and provide a complete evidence chain for fault analysis through cloud data tracing (record the last 10 sterilization data), realize full-link management from local sterilization to remote operation and maintenance.

[0133] Figure 4 The workflow diagram of a sterilization control method for a refrigeration device provided in the embodiment of the present application is shown in FIG. 1. Figure 4

[0134] The technical solution of the present application realizes intelligent sterilization control through the following core components:

[0135] Condensate water collection and delivery system: The system is composed of a silica gel hose (preferably an inner diameter of 3 mm) and a micro peristaltic pump (preferably with a flow rate of 0.5 L / h and a power of 2 W). The silica gel hose is connected to the drain port at the bottom of the refrigerator evaporator, and the micro peristaltic pump is used to deliver the condensate water to the detection chamber.

[0136] ​Multi-parameter detection module: The detection chamber adopts a transparent polycarbonate material with a capacity of 10 mL. Inside: 1. pH sensor: Mettler Toledo InLab Micro Pro ISM series glass electrode, measurement range 0-14, accuracy ±0.1, response time <30 seconds, communicates with the main control board through the I2C interface. 2. Conductivity probe: Hanna Instruments HI76301 four-ring conductivity cell, range 0-1000 μS / cm, temperature compensation range 0-50℃, electrode constant K=1.0 / cm. 3. ATP bioluminescence instrument: integrated LuminUltra BioSnap detection module, based on luciferase reaction to detect ATP concentration, sensitivity 0.1 pmol / L, detection time 30 seconds.

[0137] Intelligent control center: high-performance embedded controller (such as STM32F407 microcontroller) is adopted, which is responsible for: sensor data acquisition and processing; pollution evaluation algorithm operation; system control instruction generation.

[0138] Cold plasma generation system, including: DBD (Dielectric Barrier Discharge, Dielectric Barrier Discharge) device and atomization module. The DBD device is composed of two parallel aluminum electrodes (spacing 2 mm) and a quartz dielectric layer, connected to a high-frequency alternating current power supply (5-30 kHz adjustable, maximum power 80 W). The atomization module uses a piezoelectric ceramic atomization sheet (diameter 20 mm) to convert condensed water into 5-10 μm particle size droplets, which are injected into the plasma reaction chamber at a rate of 0.2 L / min to generate active particles containing hydrogen peroxide, hydroxyl radicals, and ozone.

[0139] Each system realizes data interaction through a standard communication protocol to form a complete detection-treatment closed loop. When the system is working, the condensed water sample is first collected for multi-parameter detection, then the plasma generation parameters are automatically adjusted according to the detection results, and finally the generated active sterilization substances are uniformly distributed to the refrigerated space.

[0140] Figure 5 An embodiment block diagram of a sterilization control device for a refrigeration equipment is provided for the embodiments of the present application. As shown in Figure 5 , the device includes:

[0141] The acquisition module 51 is configured to acquire pollution detection data corresponding to the refrigeration equipment, wherein the pollution detection data is data obtained by detecting condensed water generated during operation of the refrigeration equipment.

[0142] The calculation module 52 is configured to calculate a pollution evaluation value based on the pollution detection data.

[0143] The determining module 53 is configured to determine a control parameter according to the pollution evaluation value.

[0144] The control module 54 is configured to perform a sterilization treatment on the refrigeration space of the refrigeration device with a corresponding intensity based on the control parameter.

[0145] In one possible implementation, the pollution detection data includes pH detection data, conductivity detection data, and microorganism content detection data, and the calculating module is specifically configured to:

[0146] Subtract the pH detection data from a preset reference value to obtain a pH parameter;

[0147] Take the conductivity detection data as a conductivity parameter;

[0148] Determine a microorganism content parameter by using the microorganism content detection data, wherein, in a case where the microorganism content detection data does not exceed a preset upper limit value, the microorganism content detection data is determined as the microorganism content parameter, and in a case where the microorganism content detection data exceeds the preset upper limit value, the preset upper limit value is determined as the microorganism content parameter;

[0149] Perform a weighted summation calculation on the pH parameter, the conductivity parameter, and the microorganism content parameter to obtain the pollution evaluation value.

[0150] In one possible implementation, the device further includes an adjusting module configured to:

[0151] Monitor an environmental humidity parameter of the refrigeration space in real time;

[0152] Obtain a preset humidity and weight mapping relationship, which is obtained by training historical operation data by using a machine learning model;

[0153] Query the mapping relationship according to a current environmental humidity parameter, and dynamically adjust a weight distribution ratio of the pH parameter, the conductivity parameter, and the microorganism content parameter.

[0154] In one possible implementation, the generating module is specifically configured to:

[0155] Determine a pollution level according to the pollution evaluation value;

[0156] Determine a target power and a target operation duration according to the pollution level;

[0157] Configure the target power and the target operation duration as the control parameter.

[0158] In one possible implementation, the device further includes an adjusting module configured to:

[0159] After the sterilization treatment is completed, reacquire the pollution detection data corresponding to the refrigeration equipment;

[0160] Calculate an updated pollution evaluation value based on the reacquired pollution detection data;

[0161] Compare and analyze the updated pollution evaluation value with the pollution evaluation value before the sterilization treatment to obtain a comparison and analysis result;

[0162] Dynamically adjust the control parameters of the next sterilization treatment according to the comparison and analysis result.

[0163] In one possible implementation, the adjusting module is further configured to:

[0164] In a case where the comparison and analysis result is that the pollution evaluation value after the sterilization treatment for a continuous preset number of times does not reach an expected standard, trigger an emergency treatment mode, including: controlling the cold plasma generator to operate at an emergency power for an emergency operation duration, and / or generating and sending device abnormal warning information.

[0165] In one possible implementation, the control module is specifically configured to:

[0166] Control the cold plasma to convert the condensed water into activated raw materials according to the power setting value in the control parameters;

[0167] Control the processing time of the activated raw materials under the action of the cold plasma according to the duration setting value in the control parameters;

[0168] Deliver the sterilization material obtained after the plasma activation treatment to the refrigeration space for sterilization treatment.

[0169] As shown in Figure 6 The embodiments of the present application provide a device, which includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112 and the memory 113 complete mutual communication through the communication bus 114,

[0170] The memory 113 is used to store a computer program;

[0171] In an embodiment of the present application, the processor 111 is used to execute the program stored in the memory 113, and the cold storage equipment sterilization control method provided by any one of the preceding method embodiments is implemented, including:

[0172] Acquire the pollution detection data corresponding to the refrigeration equipment, wherein the pollution detection data is obtained by detecting the condensed water generated during the operation of the refrigeration equipment;

[0173] Calculate a pollution evaluation value based on the pollution detection data;

[0174] determining a control parameter according to the pollution evaluation value;

[0175] performing a sterilization process of the refrigeration space of the refrigeration equipment with a corresponding intensity based on the control parameter.

[0176] The embodiments of the present application further provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the refrigeration equipment sterilization control method provided by any one of the preceding method embodiments.

[0177] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments.

[0178] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus a general hardware platform, and of course can also be implemented by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the various embodiments or some parts of the embodiments.

[0179] It should be understood that the terms used herein are for the purpose of describing particular example embodiments only and are not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises", "comprising", "includes", "including" and "has" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order in which they are described, unless specifically indicated as such. It is also to be understood that additional or alternative steps can be employed.

[0180] The foregoing detailed description of the application has been presented for purposes of illustration and description. Various modifications and changes can be made to these embodiments without departing from the spirit and scope of the application. It is intended that the scope of the application should not be limited by the particular representative embodiments described above.

Claims

1. A method of sterilization control of a refrigeration appliance, characterized in that, The method comprises: obtaining pollution detection data corresponding to the refrigeration equipment, the pollution detection data being data obtained by detecting condensate water generated during operation of the refrigeration equipment; calculating a pollution evaluation value based on the pollution detection data; determining a control parameter according to the pollution evaluation value; performing sterilization processing of the refrigeration space of the refrigeration equipment at a corresponding intensity based on the control parameter.

2. The method of claim 1, wherein, The pollution detection data includes pH detection data, conductivity detection data, and microbial content detection data, and the calculation of the pollution evaluation value based on the pollution detection data comprises: subtracting the pH detection data from a preset reference value to obtain a pH parameter; using the conductivity detection data as a conductivity parameter; determining a microbial content parameter using the microbial content detection data, wherein, in a case where the microbial content detection data does not exceed a preset upper limit value, the microbial content detection data is determined as the microbial content parameter, and in a case where the microbial content detection data exceeds the preset upper limit value, the preset upper limit value is determined as the microbial content parameter; performing weighted summation calculation on the pH parameter, the conductivity parameter, and the microbial content parameter to obtain the pollution evaluation value.

3. The method of claim 2, wherein, The method further comprises: monitoring the environmental humidity parameter of the refrigeration space in real time; obtaining a preset humidity and weight mapping relationship, the mapping relationship being obtained by training historical operation data using a machine learning model; querying the mapping relationship according to the current environmental humidity parameter to dynamically adjust the weight distribution ratio of the pH parameter, the conductivity parameter, and the microbial content parameter.

4. The method of claim 1, wherein, The determination of the control parameter according to the pollution evaluation value comprises: determining a pollution level according to the pollution evaluation value; determining a target power and a target operation time according to the pollution level; configuring the target power and the target operation time as the control parameter.

5. The method of claim 1, wherein, The method further comprises: after completing the sterilization processing, re-obtaining the pollution detection data corresponding to the refrigeration equipment; calculating an updated pollution evaluation value based on the re-obtained pollution detection data; comparing and analyzing the updated pollution evaluation value with the pollution evaluation value before the sterilization processing to obtain a comparison and analysis result; dynamically adjusting the control parameter of the next sterilization processing according to the comparison and analysis result.

6. The method of claim 5, wherein, The dynamic adjustment of the control parameter of the next sterilization processing according to the comparison and analysis result comprises: in a case where the comparison and analysis result is that the pollution evaluation value after the sterilization processing for a continuous preset number of times does not reach an expected standard, triggering an emergency processing mode, including: controlling the cold plasma generator to operate at an emergency power for an emergency operation time, and / or generating and sending device abnormal warning information.

7. The method of claim 1, wherein, The sterilization processing of the refrigeration space of the refrigeration equipment at a corresponding intensity based on the control parameter comprises: controlling the cold plasma to convert the condensate water into activated raw materials according to the power setting value in the control parameter; controlling the processing time of the activated raw materials under the action of the cold plasma according to the time setting value in the control parameter; delivering the sterilization material obtained after the plasma activation processing to the refrigeration space for sterilization processing.

8. A refrigeration appliance sterilization control device, characterized by, The device comprises: an acquisition module configured to acquire pollution detection data corresponding to the refrigeration equipment, the pollution detection data being data obtained by detecting condensate water generated during operation of the refrigeration equipment; a calculation module configured to calculate a pollution evaluation value based on the pollution detection data; a determination module configured to determine a control parameter based on the pollution evaluation value; a control module configured to perform sterilization processing of a refrigeration space of the refrigeration equipment at a corresponding intensity based on the control parameter.

9. An electronic device, comprising: comprise: a processor and a memory, the processor being configured to execute a refrigeration equipment sterilization control program stored in the memory to implement the refrigeration equipment sterilization control method of any one of claims 1-7.

10. A storage medium, characterized by The storage medium stores one or more programs, which can be executed by one or more processors to implement the refrigeration equipment sterilization control method of any one of claims 1-7.

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