Method and device for monitoring refrigerant loss and computer readable storage medium

Abstract

The invention relates to the technical field of air conditioner control, and discloses a method for monitoring refrigerant deficiency, which comprises the following steps: acquiring current operation data of an air conditioner system; inputting the current operation data of the air conditioning system into a first target model used for monitoring refrigerant loss so as to obtain a refrigerant loss proportion output by the first target model; according to the refrigerant missing proportion, a refrigerant missing prompt scheme is determined; and the air conditioning system is controlled to push information according to the refrigerant loss prompting scheme. According to the scheme, the refrigerant missing proportion of the air conditioning system can be accurately obtained by inputting the current operation data of the air conditioning system into the first target model; a personalized refrigerant loss prompting scheme is determined in combination with the refrigerant loss proportion; and furthermore, the air conditioning system is controlled to push information according to the refrigerant loss prompting scheme, so that a user can obtain a prompt about refrigerant loss in real time. The invention further discloses a device for monitoring refrigerant loss and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of air-conditioning control technology, for example, to a method, device and computer-readable storage medium for monitoring the loss of refrigerant. Background Art

[0002] Currently, multi-split air conditioning systems are widely used in small and medium-sized public buildings. Commonly known as "one-to-many," multi-split air conditioning systems involve one outdoor unit connected to two or more indoor units via piping, using a primary refrigerant air conditioning system with air cooling on the outdoor side and direct evaporative heat exchange on the indoor side.

[0003] Currently, refrigerant plays a key role in transferring heat in multi-split air conditioning systems. During operation, these systems may face the problem of refrigerant shortage, which can directly affect the cooling or heating performance of the system and may even cause system failure. Related art discloses a method for determining the amount of refrigerant. Its specific implementation scheme is as follows: obtaining the actual noise value of the air conditioner's outdoor unit; comparing the actual noise value with a preset standard noise value; and determining that the refrigerant is missing if the actual noise value is lower than the standard noise value. After obtaining the actual noise value of the air conditioner's outdoor unit, whether the refrigerant is missing is determined based on the relative size of the actual noise value and the standard noise value.

[0004] During the implementation of the embodiments of the present disclosure, it was found that at least the following problems exist in the related art:

[0005] Although the relevant technology can monitor the refrigerant shortage of the air-conditioning system, the refrigerant shortage monitoring by this method is not accurate and can easily lead to misjudgment of refrigerant shortage, making it difficult for users to accurately know the refrigerant shortage of the air-conditioning system.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical elements or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] The embodiments of the present disclosure provide a method, an apparatus, and a computer-readable storage medium for monitoring refrigerant loss, which can more accurately monitor the refrigerant loss of an air-conditioning system.

[0009] In some embodiments, the method for monitoring refrigerant loss includes: obtaining current operating data of the air-conditioning system; inputting the current operating data of the air-conditioning system into a first target model for monitoring refrigerant loss to obtain a refrigerant loss ratio output by the first target model; determining a refrigerant loss prompt scheme based on the refrigerant loss ratio; and controlling the air-conditioning system to push information according to the refrigerant loss prompt scheme.

[0010] In some embodiments, the device for monitoring refrigerant loss includes: an acquisition module configured to obtain the current operating data of the air-conditioning system; an input module configured to input the current operating data of the air-conditioning system into a first target model for monitoring refrigerant loss to obtain the refrigerant loss ratio output by the first target model; a determination module configured to determine a refrigerant loss prompt scheme based on the refrigerant loss ratio; and a control module configured to control the air-conditioning system to push information according to the refrigerant loss prompt scheme.

[0011] In some embodiments, the apparatus for monitoring the lack of refrigerant includes: a processor and a memory storing program instructions, and the processor is configured to execute the aforementioned method for monitoring the lack of refrigerant when running the program instructions.

[0012] In some embodiments, the computer-readable storage medium stores program instructions, which, when executed, enable the computer to execute the aforementioned method for monitoring the loss of refrigerant.

[0013] The method, device and computer-readable storage medium for monitoring refrigerant loss provided by the embodiments of the present disclosure can achieve the following technical effects: the refrigerant loss ratio of the air-conditioning system can be accurately obtained by inputting the current operating data of the air-conditioning system into the first target model; thereby, a personalized refrigerant loss prompt scheme can be determined in combination with the refrigerant loss ratio; and then, by controlling the air-conditioning system to push information according to the refrigerant loss prompt scheme, the user can obtain prompts about refrigerant loss in real time. This real-time nature helps users respond in a timely manner, avoids performance degradation or failure of the air-conditioning system caused by refrigerant loss, and improves the stability and reliability of the air-conditioning system. At the same time, users do not need to frequently check the operating status of the air-conditioning system, nor do they need to worry about potential problems caused by refrigerant loss, reducing the increase in energy consumption caused by the degradation of the air-conditioning system performance, thereby achieving energy-saving effects.

[0014] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings. These exemplary descriptions and drawings do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation. In addition,

[0016] Figure 1 is a schematic diagram of a method for monitoring refrigerant loss provided by an embodiment of the present disclosure;

[0017] Figure 2 is a schematic diagram of a method for determining a first target model provided by an embodiment of the present disclosure;

[0018] Figure 3 This is a schematic diagram of a method for determining a refrigerant shortage prompt solution provided by an embodiment of the present disclosure;

[0019] Figure 4 is a schematic diagram of another method for monitoring the loss of refrigerant provided by an embodiment of the present disclosure;

[0020] Figure 5 is a schematic diagram of a method for determining a first target control scheme provided by an embodiment of the present disclosure;

[0021] Figure 6 is a schematic diagram of a device for monitoring the loss of refrigerant provided by an embodiment of the present disclosure;

[0022] Figure 7 Schematic diagram of another device for monitoring the loss of refrigerant provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] In order to be able to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The accompanying drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0024] In the description and claims of the embodiments of the present disclosure, as well as in the accompanying drawings, the terms "first," "second," and the like are used to distinguish similar items and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate to describe the embodiments of the present disclosure herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions.

[0025] Unless otherwise stated, the term "plurality" means two or more.

[0026] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0027] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0028] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0029] Figure 1 This is a schematic diagram of a method for monitoring the loss of refrigerant provided by an embodiment of the present disclosure; Figure 1 As shown, the embodiment of the present disclosure provides a method for monitoring the loss of refrigerant, comprising:

[0030] S11, the air conditioning system obtains current operating data.

[0031] S12 , the air conditioning system inputs current operating data into a first target model for monitoring refrigerant shortage, to obtain a refrigerant shortage ratio output by the first target model.

[0032] S13, the air conditioning system determines a refrigerant shortage prompt plan according to the refrigerant shortage ratio.

[0033] S14, the air conditioning system controls the system to push information according to the refrigerant shortage prompt plan.

[0034] In this solution, the air conditioning system is a system for regulating indoor ambient temperature, humidity, and air quality. Its working principle is to achieve environmental parameter regulation through the circulation of refrigerant. Here, the air conditioning system includes an outdoor unit and one or more indoor units. In its specific implementation process, the refrigerant can be compressed into a high-temperature, high-pressure gas in the compressor, and dissipate heat through the condenser to become a high-pressure liquid. It is then reduced in pressure by the expansion valve to become a low-temperature, low-pressure liquid. Finally, it enters the evaporator to absorb indoor heat and become a low-temperature, low-pressure gas. The cycle is repeated to achieve ambient temperature regulation. Specifically, the current operating data of the air conditioning system includes: the number of operating indoor units of the air conditioning system and the current measurement point data of the air conditioning system. Among them, the current measurement point data of the air conditioning system includes the current outdoor measurement point data of the air conditioning system and the current indoor measurement point data of the air conditioning system.

[0035] Optionally, the current outdoor side measurement point data of the air-conditioning system includes the outdoor unit exhaust temperature, the outdoor unit intake temperature, the defrost sensor temperature, the outdoor ambient temperature, the outdoor unit total current, the compressor speed, the speed of each fan of the outdoor unit, the outdoor unit electronic expansion valve opening, the outdoor unit coil temperature, the outdoor unit auxiliary valve opening, the outdoor unit heat exchanger inlet temperature, the outdoor unit heat exchanger outlet temperature, the outdoor unit oil temperature, the outdoor unit low pressure pressure, the outdoor unit high pressure pressure and the outdoor unit pressure ratio.

[0036] Optionally, the current indoor side measurement point data of the air conditioning system includes the user's set temperature, the evaporator inlet temperature, the evaporator outlet temperature, the indoor fan speed, the opening of the indoor electronic expansion valve and the target superheat.

[0037] With this solution, the operating data of the air-conditioning system can be obtained in real time, so as to serve as an important basis for judging the operating status of the air-conditioning system.

[0038] Furthermore, the air conditioning system can determine a first target model for monitoring refrigerant loss. As an example, the air conditioning system determines the first target model for monitoring refrigerant loss by: obtaining sample data for model training. The sample data consists of refrigerant loss ratio data for the air conditioning system obtained by testing the air conditioning system under different operating conditions and measurement point data during the operation of the air conditioning system; and determining the first target model for monitoring refrigerant loss based on the sample data. This solution can accurately determine the first target model for monitoring refrigerant loss.

[0039] In this way, the air conditioning system can input the current operating data into the first target model for monitoring refrigerant loss to obtain the refrigerant loss ratio output by the first target model. Here, the refrigerant loss ratio can be 10%, 20%, 30%, 40% or 50%. With this solution, the refrigerant loss ratio can be accurately calculated by inputting the operating data into the first target model for monitoring refrigerant loss. The model-based diagnostic method in this case is more accurate and efficient than traditional manual inspection, which helps to reduce misjudgments and missed judgments.

[0040] Furthermore, the air conditioning system can determine a refrigerant shortage notification scheme based on the refrigerant shortage ratio. This includes: determining refrigerant shortage information based on the refrigerant shortage ratio; and selecting a notification scheme that matches the refrigerant shortage information as the refrigerant shortage notification scheme. The refrigerant shortage information includes the amount of refrigerant shortage and the level of refrigerant shortage. This allows for a personalized refrigerant shortage notification scheme based on the refrigerant shortage ratio, more accurately guiding users or maintenance personnel to take appropriate measures. Information can then be pushed according to the refrigerant shortage notification scheme. This ensures that relevant information is delivered to relevant personnel in a timely and accurate manner. This intelligent push method not only improves the efficiency of information transmission but also reduces the need for manual intervention, enhancing the overall intelligence level of the system. Furthermore, the air conditioning system controls information push according to the refrigerant shortage notification scheme.

[0041] The method for monitoring refrigerant loss provided by the embodiment of the present disclosure can accurately obtain the refrigerant loss ratio of the air-conditioning system by inputting the current operating data of the air-conditioning system into the first target model; thereby, a personalized refrigerant loss prompt scheme can be determined in combination with the refrigerant loss ratio; and then, by controlling the air-conditioning system to push information according to the refrigerant loss prompt scheme, the user can obtain prompts about refrigerant loss in real time. This real-time nature helps users respond in a timely manner, avoids performance degradation or failure of the air-conditioning system caused by refrigerant loss, and improves the stability and reliability of the air-conditioning system. At the same time, users do not need to frequently check the operating status of the air-conditioning system, nor do they need to worry about potential problems caused by refrigerant loss, which to a certain extent reduces the increase in energy consumption caused by the degradation of the air-conditioning system performance, thereby achieving energy-saving effects.

[0042] Figure 2 is a schematic diagram of a method for determining a first target model provided by an embodiment of the present disclosure; Figure 2 As shown, optionally, the first target model for monitoring the loss of refrigerant is determined in the following manner:

[0043] S21, the air-conditioning system obtains sample data for model training, where the sample data is the refrigerant loss ratio data of the air-conditioning system and the measurement point data when the air-conditioning system is running, obtained by testing the air-conditioning system under different working conditions.

[0044] S22: The air conditioning system determines a first target model for monitoring the lack of refrigerant based on the sample data.

[0045] In this solution, the air-conditioning system can obtain sample data for model training. The sample data is the refrigerant loss ratio data of the air-conditioning system and the measurement point data when the air-conditioning system is in operation, which are obtained by testing the air-conditioning system under different working conditions. It should be emphasized that the testing of the air-conditioning system under different working conditions and the collection of corresponding operating data and sample data in this case need to be carried out in an enthalpy difference chamber. Here, different working conditions include different indoor and outdoor temperatures and / or different numbers of indoor units in operation. Taking an air-conditioning system with one outdoor unit and four indoor units as an example, the number of different indoor units in operation can be one, two, three or four.

[0046] Optionally, different indoor and outdoor temperatures include but are not limited to:

[0047] Outdoor dry-bulb humidity 35°C / outdoor wet-bulb humidity 24°C, indoor dry-bulb humidity 27°C / indoor wet-bulb humidity 19°C;

[0048] Outdoor dry-bulb humidity 43°C / outdoor wet-bulb humidity 26°C, indoor dry-bulb humidity 32°C / indoor wet-bulb humidity 23°C;

[0049] Outdoor dry-bulb humidity 29°C / outdoor wet-bulb humidity 19°C, indoor dry-bulb humidity 27°C / indoor wet-bulb humidity 19°C;

[0050] Outdoor dry-bulb humidity 7°C / outdoor wet-bulb humidity 6°C, indoor dry-bulb humidity 20°C / indoor wet-bulb humidity 15°C;

[0051] Outdoor dry-bulb humidity 2°C / outdoor wet-bulb humidity 1°C, indoor dry-bulb humidity 20°C / indoor wet-bulb humidity 15°C;

[0052] Outdoor dry-bulb humidity is -7℃ / outdoor wet-bulb humidity is -8℃, indoor dry-bulb humidity is 20℃ / indoor wet-bulb humidity is 15℃.

[0053] This solution effectively acquired a large amount of sample data by testing the air conditioning system under various operating conditions in an enthalpy difference chamber. This sample data not only covered the refrigerant loss rate of the air conditioning system but also included data from measurement points during operation. This diverse data source ensured the richness and accuracy of the sample data, providing a solid foundation for subsequent model training.

[0054] Furthermore, the air conditioning system determines a first target model for monitoring refrigerant shortage based on the sample data. The air conditioning system can obtain the first target model in a variety of ways. The first target model can be constructed in a variety of ways. In one example, the air conditioning system can use statistical regression methods to construct a statistical regression-based model to monitor refrigerant shortages. In another example, the air conditioning system can use a machine learning algorithm to construct a machine learning model to monitor refrigerant shortages. In another example, the air conditioning system can construct a neural network model to monitor refrigerant shortages. In one optimized solution, the air conditioning system can use a statistical regression model for preliminary predictions, and then use a machine learning model for further optimization and adjustment to accurately monitor the specific refrigerant shortages. Using this solution, the sample data described above is used to determine the first target model for monitoring refrigerant shortages. This model is specifically designed for refrigerant shortages and is therefore highly targeted and accurate.

[0055] Optionally, in S22, the air conditioning system determines a first target model for monitoring refrigerant loss based on the sample data, including:

[0056] The air-conditioning system constructs a BP neural network model for monitoring the lack of refrigerant based on sample data.

[0057] The air conditioning system trains the BP neural network model to use the trained model as the first target model for monitoring the lack of refrigerant.

[0058] In this solution, the BP neural network model is called the back propagation neural network model, which is a multi-layer feedforward network trained according to the error back propagation algorithm.

[0059] Specifically, the air conditioning system uses sample data to build a BP (back propagation) neural network model, which can use the measurement point data during the operation of the air conditioning system as input and the refrigerant loss ratio data of the air conditioning system as output.

[0060] Next, after the BP neural network model for monitoring refrigerant shortages is constructed, it is trained. During training, the mean square error (MSE) can be used as the loss function. By minimizing the MSE, the model can more accurately predict the refrigerant shortage rate. Furthermore, during the actual training process, a backpropagation algorithm is employed to update the network's weights and thresholds. Specifically, the algorithm first calculates the error between the network's actual and expected outputs. The error is then backpropagated from the output layer to the input layer, layer by layer, while adjusting the weights and thresholds of each layer based on the error. This process is repeated iteratively until convergence conditions are met. For example, the convergence condition is a loss of less than 0.05. That is, when the model's MSE loss is less than 0.05, the model is considered converged and the training process is complete. At this point, the trained BP neural network model can be used as the primary target model for monitoring refrigerant shortages. This approach enables the construction of a precise BP neural network-based refrigerant shortage monitoring model that accurately predicts the refrigerant shortage rate based on measurement point data during air conditioning system operation, providing powerful support for air conditioning system maintenance and management.

[0061] In an optimized approach, after model training is complete, the model can be evaluated to verify its performance in real-world applications. This can be achieved by using an independent test dataset for model validation. If the model's performance does not meet requirements, optimizations can be made to the model, such as increasing the number of network layers or adjusting the learning rate, to improve its prediction accuracy.

[0062] Optionally, if the air conditioning system includes one outdoor unit and multiple indoor units, the measurement point data during operation of the air conditioning system includes:

[0063] The outdoor measuring point data of the air-conditioning system and the indoor measuring point data of the air-conditioning system; the outdoor measuring point data of the air-conditioning system include the outdoor unit exhaust temperature, the outdoor unit suction temperature, the defrost sensor temperature, the outdoor ambient temperature, the total current of the outdoor unit, the compressor speed, the speed of each fan of the outdoor unit, the opening of the outdoor unit electronic expansion valve, the outdoor unit coil temperature, the opening of the outdoor unit auxiliary valve, the outdoor unit heat exchanger inlet temperature, the outdoor unit heat exchanger outlet temperature, the outdoor unit oil temperature, the outdoor unit low pressure, the outdoor unit high pressure and the outdoor unit pressure ratio; the indoor measuring point data of the air-conditioning system include the user's set temperature, the evaporator inlet temperature, the evaporator outlet temperature, the indoor fan speed, the opening of the indoor electronic expansion valve and the target superheat.

[0064] In this solution, the air conditioning system's outdoor and indoor measurement point data are collected. The outdoor measurement point data includes the outdoor unit's exhaust temperature, outdoor unit's intake temperature, defrost sensor temperature, outdoor ambient temperature, outdoor unit total current, compressor speed, outdoor unit fan speed, outdoor unit electronic expansion valve opening, outdoor unit coil temperature, outdoor unit auxiliary valve opening, outdoor unit heat exchanger inlet temperature, outdoor unit heat exchanger outlet temperature, outdoor unit oil temperature, outdoor unit low pressure, outdoor unit high pressure, and outdoor unit pressure ratio.

[0065] Optionally, the indoor measurement point data of the air conditioning system includes the user's set temperature, the evaporator inlet temperature, the evaporator outlet temperature, the indoor fan speed, the opening of the indoor electronic expansion valve, and the target superheat. The target superheat is the difference between the evaporator inlet temperature and the evaporator outlet temperature.

[0066] It should be noted that the indoor measurement point data of the air conditioning system needs to be acquired in conjunction with the number of operating indoor units. For example, if the air conditioning system includes four indoor units, but only the first and third indoor units are operating, the indoor measurement point data acquired by the air conditioning system includes: the user's set temperature, the inlet temperature of the first indoor unit's evaporator, the inlet temperature of the third indoor unit's evaporator, the outlet temperature of the first evaporator, the outlet temperature of the third evaporator, the speed of the first indoor fan, the speed of the third indoor fan, the opening degree of the first indoor electronic expansion valve, the opening degree of the third indoor electronic expansion valve, the first target superheat, and the third target superheat.

[0067] This solution provides a detailed listing of measurement data from both the outdoor and indoor sides of the air conditioning system, covering multiple key parameters during system operation. By collecting this rich data, we can gain a more comprehensive understanding of the air conditioning system's operating status, providing a solid foundation for subsequent model training. Furthermore, by considering multiple indoor units, the model training process fully learns the interactions between different indoor units and their synergy with the outdoor unit. This enables the model to more accurately consider the operating status of the entire air conditioning system when monitoring for refrigerant loss. In other words, by incorporating data from both outdoor and indoor measurement points into model training, the model can gain a more comprehensive understanding of the overall performance of the air conditioning system, thereby improving the accuracy of refrigerant loss detection.

[0068] In an optimized solution, the indoor measurement point data of the air conditioning system also includes: the outlet temperature of the indoor unit, the inlet temperature of the indoor unit, and the internal ambient temperature of the indoor unit. Similarly, the indoor measurement point data also needs to be obtained in combination with the number of operating indoor units in the air conditioning system.

[0069] This solution, taking into account the fact that an air conditioning system consists of one outdoor unit and multiple indoor units, utilizes rich and accurate measurement point data to make the constructed BP neural network model more precise and effective in detecting refrigerant loss. This not only improves the maintenance efficiency and operational stability of the air conditioning system, but also helps optimize energy utilization and promote the achievement of energy conservation and emission reduction goals.

[0070] Figure 3 This is a schematic diagram of a method for determining a refrigerant shortage prompt solution provided by an embodiment of the present disclosure; Figure 3 As shown, optionally, in S13, the air conditioning system determines a refrigerant shortage prompting scheme according to the refrigerant shortage ratio, including:

[0071] S31: The air conditioning system determines refrigerant shortage information according to the refrigerant shortage ratio.

[0072] In step S32, the air conditioning system uses a prompt scheme that matches the refrigerant shortage information as a refrigerant shortage prompt scheme.

[0073] In this solution, the air conditioning system can determine refrigerant shortage information based on the refrigerant shortage ratio. This refrigerant shortage information includes the amount of refrigerant shortage and the level of refrigerant shortage. Specifically, the air conditioning system determines the amount of refrigerant shortage based on the refrigerant shortage ratio by obtaining the total amount of refrigerant charged and then multiplying the total amount of refrigerant charged by the refrigerant shortage ratio as the amount of refrigerant shortage. This solution enables accurate determination of the amount of refrigerant shortage.

[0074] Optionally, the air conditioning system determines a refrigerant loss level according to a refrigerant loss ratio, including:

[0075] If the refrigerant loss ratio is below a first threshold, the refrigerant loss level is determined to be level one. If the refrigerant loss ratio is above the first threshold but below a second threshold, the refrigerant loss level is determined to be level two. If the refrigerant loss ratio is above the second threshold, the refrigerant loss level is determined to be level three. The first threshold is 20%, and the second threshold is 40%. This solution enables precise determination of the refrigerant loss level.

[0076] Furthermore, the air conditioning system may use a prompt scheme that matches the refrigerant shortage information as a refrigerant shortage prompt scheme. Specifically, the air conditioning system may use a prompt scheme that matches the refrigerant shortage level as a refrigerant shortage prompt scheme.

[0077] As an example, if the refrigerant shortage level is the first level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant shortage information and control the air conditioning system's light display module to display yellow to advise the user to recharge the refrigerant; if the refrigerant shortage level is the second level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant shortage information and control the air conditioning system's light display module to display orange to remind the user that refrigerant recharging is required; if the refrigerant shortage level is the third level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant shortage information and control the air conditioning system's light display module to display red to warn the user that refrigerant recharging is necessary. This scheme, by accurately determining the refrigerant shortage information and matching the corresponding prompt scheme, effectively improves the timeliness and accuracy of air conditioning system maintenance, reduces maintenance costs, and enhances the user experience. This is of great significance for ensuring the stable operation of the air conditioning system and improving user satisfaction.

[0078] Figure 4 is another schematic diagram of a method for monitoring the lack of refrigerant provided by an embodiment of the present disclosure; Figure 4 As shown, optionally, another method for monitoring the loss of refrigerant provided by an embodiment of the present disclosure includes:

[0079] S41 , the air conditioning system obtains its current operating data.

[0080] S42 : The air conditioning system inputs current operating data into a first target model for monitoring refrigerant shortage, to obtain a refrigerant shortage ratio output by the first target model.

[0081] In step S43 , the air conditioning system obtains the set temperature of the indoor unit, the return air temperature, and the coil temperature of the indoor unit.

[0082] S44: The air conditioning system determines a first target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature, and the coil temperature of the indoor unit.

[0083] S45 , the air-conditioning system is controlled to execute a first target control scheme to perform energy-saving control on the air-conditioning system when the refrigerant in the air-conditioning system is missing.

[0084] In this solution, after obtaining the refrigerant loss ratio output by the first target model, the air conditioning system can obtain the indoor unit's set temperature, return air temperature, and the indoor unit's coil temperature. Based on these, the air conditioning system determines the first target control solution for the air conditioning system. Specifically, the air conditioning system determines the first target control solution based on the indoor unit's set temperature, return air temperature, and indoor unit's coil temperature. This includes: the air conditioning system calculating a first temperature difference between the indoor unit's set temperature and the return air temperature; calculating a second temperature difference between the indoor unit's coil temperature and the return air temperature; and determining the first target control solution based on the first and second temperature differences. This solution allows for precise determination of the first target control solution. Furthermore, the air conditioning system can control itself to execute the first target control solution to achieve energy-saving control of the air conditioning system when refrigerant loss occurs.

[0085] This solution can effectively improve the energy efficiency and user experience of the air-conditioning system by accurately obtaining the operating parameters of the indoor unit, formulating a personalized first-target control plan, and realizing energy-saving control of the air-conditioning system, while also making a positive contribution to energy conservation and emission reduction.

[0086] Figure 5 This is a schematic diagram of a method for determining a first target control scheme provided by an embodiment of the present disclosure; Figure 5 As shown, optionally, in S44, the air conditioning system determines a first target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature, and the coil temperature of the indoor unit, including:

[0087] S51: The air conditioning system calculates a first temperature difference between a set temperature of an indoor unit and a return air temperature.

[0088] S52: The air conditioning system calculates a second temperature difference between the coil temperature of the indoor unit and the return air temperature.

[0089] S53: The air-conditioning system determines a first target control scheme of the air-conditioning system according to the first temperature difference value and the second temperature difference value.

[0090] In this solution, the air conditioning system can calculate the difference between the indoor unit's set temperature and the return air temperature and use this difference as the first temperature difference. The air conditioning system can also calculate the difference between the indoor unit's coil temperature and the return air temperature and use this difference as the second temperature difference. This solution allows for precise determination of both the first and second temperature differences.

[0091] Furthermore, the air conditioning system may determine a first target control scheme for the air conditioning system based on the first temperature difference value and the second temperature difference value. Specifically, the air conditioning system determines the first target control scheme for the air conditioning system based on the first temperature difference value and the second temperature difference value, including:

[0092] If the first temperature difference is greater than or equal to the second temperature threshold and the second temperature difference is greater than or equal to the fourth temperature threshold, the first target control scheme for the air conditioning system is determined to be to control the air conditioning system to execute the fastest cooling strategy. The fastest cooling strategy controls the compressor to operate at the highest frequency, outputting maximum cooling capacity, while matching the valve opening and fan speed based on the current operating status to maximize the cooling effect.

[0093] If the first temperature threshold is less than or equal to the first temperature difference and less than or equal to the second temperature threshold, and the second temperature difference is greater than or equal to the fourth temperature threshold, then a first target control scheme for the air conditioning system is determined to be controlling the air conditioning system to execute a fastest cooling strategy. When the air conditioning system is controlled to execute the fastest cooling strategy, adjustments to the compressor, valves, and fans are more gentle than in the first scenario.

[0094] If the first temperature threshold ≤ the first temperature difference ≤ the second temperature threshold, and the third temperature threshold ≤ the second temperature difference ≤ the fourth temperature threshold, then the first target control scheme for the air conditioning system is determined to be controlling the air conditioning system to execute an energy-saving operation mode. The energy-saving operation mode involves controlling the compressor frequency to appropriately reduce energy consumption while also adjusting the valve opening and fan speed to maintain indoor comfort. This reduces the temperature difference between the indoor coil temperature and the return air temperature, reducing irreversible losses in the system and thus achieving energy savings.

[0095] If the first temperature difference is less than or equal to the first temperature threshold and the second temperature difference is less than or equal to the third temperature threshold, the air conditioning system's first target control strategy is to use a modified PID control strategy for precise temperature control. PID control is a commonly used closed-loop control method that continuously adjusts control parameters to keep the air conditioning system output close to the setpoint. In this strategy, the PID control algorithm fine-tunes the compressor, valves, and fan based on the current first and second temperature differences to achieve stable indoor temperature control.

[0096] As an example, the first temperature threshold is 1°C, the second temperature threshold is 3°C, the third temperature threshold is 5°C, and the fourth temperature threshold is 10°C.

[0097] This solution combines the first and second temperature differentials to select the fastest cooling strategy, energy-saving operation strategy, and a modified PID control strategy. These strategies automatically adjust the air conditioning system's operating state based on changes in indoor environmental parameters to meet the needs of different scenarios. This strategy achieves efficient, energy-saving, and precise control of the air conditioning system, improving the user experience.

[0098] The present disclosure provides a method for monitoring refrigerant overcharge, comprising:

[0099] The air-conditioning system inputs current operating data of the air-conditioning system into a second target model for monitoring refrigerant overcharge to obtain a refrigerant overcharge ratio output by the second target model.

[0100] The air conditioning system determines the refrigerant overcharge prompt plan based on the refrigerant overcharge ratio.

[0101] The air conditioning system controls it to push information according to the refrigerant overcharge reminder plan.

[0102] In this solution, the air conditioning system can determine a second target model for monitoring refrigerant overcharge. As an example, the air conditioning system determines the second target model for monitoring refrigerant overcharge, including: the air conditioning system obtains sample data for model training, the sample data being refrigerant overcharge ratio data of the air conditioning system obtained by testing the air conditioning system under different operating conditions and measurement point data during operation of the air conditioning system; and the air conditioning system determines the second target model for monitoring refrigerant overcharge based on the sample data.

[0103] In this solution, the air conditioning system can input its current operating data into a second target model used to monitor refrigerant overcharge, thereby obtaining the refrigerant overcharge ratio output by the second target model. The refrigerant overcharge ratio can be 0%, 10%, 20%, or 30%. This solution uses the output of the second target model to accurately determine whether the air conditioning system is overcharged and the refrigerant overcharge ratio. This intelligent monitoring method can promptly detect refrigerant overcharge issues, avoiding safety hazards caused by untimely or missed manual inspections.

[0104] Furthermore, the air conditioning system can determine a refrigerant overcharge prompt scheme based on the refrigerant overcharge ratio. Specifically, the air conditioning system determines the refrigerant overcharge prompt scheme based on the refrigerant overcharge ratio, including: the air conditioning system determines refrigerant overcharge information based on the refrigerant overcharge ratio; the air conditioning system uses the prompt scheme that matches the refrigerant overcharge information as the refrigerant overcharge prompt scheme. Specifically, the refrigerant overcharge information includes the refrigerant overcharge amount and the refrigerant overcharge level. In this way, corresponding refrigerant overcharge prompt schemes can be formulated based on different refrigerant overcharge ratios, achieving different levels of warnings and prompts, thereby improving the pertinence and effectiveness of the warnings. Furthermore, the air conditioning system can control itself to push information according to the refrigerant overcharge prompt scheme.

[0105] By adopting the method for monitoring refrigerant overcharge provided by the embodiment of the present disclosure, by inputting the operating data of the air-conditioning system into the second target model, the refrigerant filling status can be monitored in real time and accurately; thereby, according to the refrigerant overcharge ratio output by the second target model, a personalized refrigerant overcharge prompt scheme can be further determined; and then, by controlling the air-conditioning system to push information according to the refrigerant overcharge prompt scheme, a warning can be issued to the user in a timely manner. This scheme can not only issue a warning in a timely manner when refrigerant overcharge occurs, but also predict the changing trend of the refrigerant filling amount through long-term monitoring and analysis of operating data. For overcharge problems that have occurred, timely processing and maintenance can also be carried out according to the prompt scheme to avoid the problem from escalating. It effectively improves the operating efficiency and stability of the system, reduces maintenance costs, and brings a better user experience to users.

[0106] Optionally, the second target model for monitoring refrigerant overcharge is determined in the following manner:

[0107] The air-conditioning system obtains sample data for model training. The sample data is the refrigerant overcharge ratio data of the air-conditioning system and the measurement point data when the air-conditioning system is running, which is obtained by testing the air-conditioning system under different working conditions.

[0108] The air-conditioning system determines a second target model for monitoring refrigerant overcharge based on the sample data.

[0109] In this solution, the air conditioning system determines a second target model for monitoring refrigerant overcharge based on sample data. The air conditioning system can obtain the second target model in a variety of ways. The second target model can be constructed in a variety of ways. In one example, the air conditioning system can use statistical regression methods to build a statistical regression-based model to monitor refrigerant overcharge. In another example, the air conditioning system can use machine learning algorithms to build a machine learning model to monitor refrigerant overcharge. In another example, the air conditioning system can build a neural network model to monitor refrigerant overcharge. In an optimized solution, the air conditioning system can use a statistical regression model for preliminary predictions, and then use a machine learning model for further optimization and adjustment to accurately monitor the specific refrigerant overcharge situation. In this solution, through training with a large amount of sample data, the second target model can learn the refrigerant overcharge characteristics of the air conditioning system under various operating conditions. This learning not only ensures high accuracy, enabling accurate judgment of refrigerant overcharge ratios in various situations, but also improves the model's generalization ability, enabling it to cope with new operating conditions that may arise in the future.

[0110] Optionally, the air conditioning system determines a second target model for monitoring refrigerant overcharge based on the sample data, including:

[0111] The air-conditioning system constructs a BP neural network model for monitoring refrigerant overcharge based on sample data.

[0112] The air conditioning system trains the BP neural network model to use the trained model as a second target model for monitoring refrigerant overcharge.

[0113] Specifically, the air conditioning system uses sample data to build a BP (back propagation) neural network model, which can use the measurement point data during the operation of the air conditioning system as input and the refrigerant overcharge ratio data of the air conditioning system as output.

[0114] Next, after the BP neural network model for monitoring refrigerant overcharge is constructed, it is trained. During training, the mean square error (MSE) is used as the loss function. By minimizing the MSE, the model can more accurately predict the refrigerant overcharge ratio. Furthermore, during the actual training process, a backpropagation algorithm is employed to update the network's weights and thresholds. Specifically, the algorithm first calculates the error between the network's actual and expected outputs. The error is then backpropagated from the output layer to the input layer, layer by layer, while adjusting the weights and thresholds of each layer based on the error. This process is repeated iteratively until convergence conditions are met. For example, the convergence condition is a loss of less than 0.05. That is, when the model's MSE loss is less than 0.05, the model is considered converged and the training process is complete. At this point, the trained BP neural network model can be used as the second target model for monitoring refrigerant overcharge. This approach enables the construction of a precise BP neural network-based refrigerant overcharge monitoring model that accurately predicts the refrigerant overcharge ratio based on measurement point data during air conditioning system operation, providing powerful support for air conditioning system maintenance and management.

[0115] Optionally, the air conditioning system determines a refrigerant overcharge prompt scheme based on the refrigerant overcharge ratio, including:

[0116] The air conditioning system determines the refrigerant overcharge information based on the refrigerant overcharge ratio.

[0117] The air conditioning system uses the prompt scheme that matches the refrigerant overcharge information as the refrigerant overcharge prompt scheme.

[0118] In this solution, the air conditioning system can determine refrigerant overcharge information based on the refrigerant overcharge ratio. Refrigerant overcharge information includes the refrigerant overcharge amount and the refrigerant overcharge level. Specifically, the air conditioning system determines the refrigerant overcharge amount based on the refrigerant overcharge ratio by: obtaining the total refrigerant charge; and multiplying the total refrigerant charge by the refrigerant overcharge ratio as the refrigerant overcharge amount. This solution enables accurate determination of the refrigerant overcharge amount.

[0119] Optionally, the air conditioning system determines a refrigerant overcharge level according to a refrigerant overcharge ratio, including:

[0120] If the refrigerant overcharge ratio is lower than a third threshold, the refrigerant overcharge level is determined to be level one; if the refrigerant overcharge ratio is higher than the third threshold but lower than the first threshold, the refrigerant overcharge level is determined to be level two; and if the refrigerant overcharge ratio is higher than the first threshold, the refrigerant overcharge level is determined to be level three. The first threshold is 20%, and the third threshold is 10%. This solution enables precise determination of the refrigerant overcharge level.

[0121] Furthermore, the air conditioning system uses a prompt scheme that matches the refrigerant overcharge information as a refrigerant overcharge prompt scheme. Specifically, the air conditioning system uses a prompt scheme that matches the refrigerant overcharge level as a refrigerant overcharge prompt scheme.

[0122] As an example, if the refrigerant overcharge level is the first level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant overcharge information while controlling the air conditioning system's light display module to display yellow to advise the user to perform system maintenance; if the refrigerant overcharge level is the second level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant overcharge information while controlling the air conditioning system's light display module to display orange to remind the user that system maintenance is required; if the refrigerant overcharge level is the third level, the corresponding prompt scheme is to control the air conditioning system to display the refrigerant overcharge information while controlling the air conditioning system's light display module to display red to warn the user that system maintenance is necessary. This scheme can achieve effective monitoring and early warning of refrigerant overcharge in the air conditioning system by accurately describing the refrigerant overcharge status and personalizing the prompt scheme, providing strong support for the safe operation and maintenance of the system.

[0123] Optionally, after obtaining the refrigerant overcharge ratio output by the second target model, the method further includes:

[0124] The air conditioning system determines a second target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature and the coil temperature of the indoor unit.

[0125] The air-conditioning system controls the air-conditioning system to execute a second target control scheme to perform energy-saving control on the air-conditioning system when the refrigerant in the air-conditioning system is overcharged.

[0126] This solution, by acquiring key parameters such as the indoor unit's set temperature, return air temperature, and coil temperature, can assess the air conditioning system's operating status in real time. Based on these parameters, a secondary control strategy is determined, enabling more precise energy-saving control. When the air conditioning system is overcharged with refrigerant, the system can adjust operating parameters or modes to effectively reduce unnecessary energy consumption and improve the system's energy efficiency.

[0127] Optionally, the air conditioning system determines a second target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature, and the coil temperature of the indoor unit, including:

[0128] The air conditioning system calculates a first temperature difference between a set temperature of the indoor unit and a return air temperature.

[0129] The air conditioning system calculates a second temperature difference between the coil temperature of the indoor unit and the return air temperature.

[0130] The air-conditioning system determines a second target control scheme of the air-conditioning system according to the first temperature difference value and the second temperature difference value.

[0131] In this solution, the air conditioning system can determine a second target control solution for the air conditioning system based on the first temperature difference value and the second temperature difference value. Specifically, the air conditioning system determines the second target control solution for the air conditioning system based on the first temperature difference value and the second temperature difference value, including:

[0132] If the first temperature difference is ≥ the second temperature threshold and the second temperature difference is ≥ the fourth temperature threshold, the air conditioning system's second target control scheme is determined to be to control the air conditioning system to implement the fastest cooling strategy. The fastest cooling strategy controls the compressor to its highest frequency, outputting maximum cooling capacity while matching the valve opening and fan speed based on the current operating state to maximize cooling efficiency. Furthermore, to reduce power consumption during the initial phase, the system appropriately reduces the compressor speed and increases the valve opening, while maintaining the fan speed constant, thereby reducing the air conditioning system's power consumption.

[0133] If the first temperature threshold is less than or equal to the first temperature difference and less than or equal to the second temperature threshold, and the second temperature difference is greater than or equal to the fourth temperature threshold, then a second target control scheme for the air conditioning system is determined to be controlling the air conditioning system to execute a fastest cooling strategy. When the air conditioning system is controlled to execute the fastest cooling strategy, adjustments to the compressor, valves, and fans are more gentle than in the first scenario.

[0134] If the first temperature threshold ≤ the first temperature difference ≤ the second temperature threshold, and the third temperature threshold ≤ the second temperature difference ≤ the fourth temperature threshold, then the second target control scheme for the air conditioning system is determined to be controlling the air conditioning system to execute an energy-saving operation mode. The energy-saving operation mode involves controlling the compressor frequency to appropriately reduce energy consumption while also adjusting the valve opening and fan speed to maintain indoor comfort. This reduces the temperature difference between the indoor coil temperature and the return air temperature, reducing irreversible losses in the system and thus achieving energy savings.

[0135] If the first temperature difference is less than or equal to the first temperature threshold and the second temperature difference is less than or equal to the third temperature threshold, the second target control scheme for the air conditioning system is determined to be precise temperature control using a modified PID control strategy. PID control is a commonly used closed-loop control method that continuously adjusts control parameters to keep the air conditioning system output close to the setpoint. In this strategy, the PID control algorithm fine-tunes the compressor, valves, and fan based on the current first and second temperature differences to achieve stable control of the indoor temperature.

[0136] As an example, the first temperature threshold is 1°C, the second temperature threshold is 3°C, the third temperature threshold is 5°C, and the fourth temperature threshold is 10°C. This solution can combine the first and second temperature differences to select the fastest cooling strategy, energy-saving operation strategy, and modified PID control strategy. These strategies automatically adjust the operating status of the air conditioning system based on changes in indoor environmental parameters to meet the needs of different scenarios. Through the implementation of this strategy, efficient, energy-saving, and precise control of the air conditioning system can be achieved, improving the user experience.

[0137] The present disclosure provides a method for monitoring heat exchanger fouling and blockage, comprising:

[0138] The air conditioning system inputs current operating data of the air conditioning system into a third target model for monitoring heat exchanger fouling and blockage to obtain a heat exchanger fouling and blockage ratio output by the third target model.

[0139] The air conditioning system determines the heat exchanger blockage warning plan based on the heat exchanger blockage ratio.

[0140] Air conditioning system control The air conditioning system pushes information according to the heat exchanger dirty and blocked prompt plan.

[0141] Optionally, the air conditioning system can determine a third target model for monitoring heat exchanger contamination. As an example, the air conditioning system determines this third target model by obtaining sample data for model training. The sample data consists of heat exchanger contamination percentage data and measurement point data obtained from testing the air conditioning system under different operating conditions. Based on the sample data, the air conditioning system determines the third target model for monitoring heat exchanger contamination. This approach allows for accurate determination of the third target model for monitoring heat exchanger contamination.

[0142] In this way, the air conditioning system can input current operating data into a third target model used to monitor heat exchanger contamination and blockage, generating a heat exchanger contamination ratio as output by the third target model. Here, the heat exchanger contamination ratio can be 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100%. This approach, by inputting operating data into the third target model, outputs the heat exchanger contamination ratio, allowing for an accurate assessment of the extent of heat exchanger contamination. This quantitative analysis not only provides information on the severity of the contamination problem but also provides data support for subsequent maintenance decisions.

[0143] Furthermore, the air conditioning system can determine a heat exchanger blockage alert based on the heat exchanger blockage ratio. This includes determining a heat exchanger blockage level based on the heat exchanger blockage ratio and selecting the alert that matches the blockage level as the heat exchanger blockage alert. This allows for personalized alerts based on the heat exchanger blockage ratio, providing tailored solutions for varying degrees of blockage, ensuring that the alerts are tailored to the specific problem and improving their relevance and effectiveness. Furthermore, the air conditioning system controls the push notifications based on the heat exchanger blockage alert.

[0144] The method for monitoring heat exchanger blockage provided by the disclosed embodiments can monitor the status of the heat exchanger in real time by obtaining the current operating data of the air conditioning system. The current operating data of the air conditioning system is then input into a third target model for monitoring heat exchanger blockage. The model's algorithms and logic can be used to conduct in-depth analysis and processing of the data to determine the heat exchanger blockage ratio. Based on the heat exchanger blockage ratio, a more personalized heat exchanger blockage alert scheme can be determined. The air conditioning system can then be controlled to push information according to the heat exchanger blockage alert scheme. This automated push notification process can immediately convey blockage alerts to users, improving their user experience and satisfaction. This further enhances the air conditioning system's ability to monitor and address heat exchanger blockage issues, providing users with higher-quality and more convenient services.

[0145] Optionally, the third target model for monitoring heat exchanger fouling and blockage is determined in the following manner:

[0146] The air-conditioning system obtains sample data for model training. The sample data is the heat exchanger dirty and blocked ratio data of the air-conditioning system obtained by testing the air-conditioning system under different working conditions and the measurement point data when the air-conditioning system is running.

[0147] The air-conditioning system determines a third target model for monitoring heat exchanger fouling and blockage based on sample data.

[0148] In this solution, the air conditioning system determines a third target model for monitoring heat exchanger blockage based on sample data. The air conditioning system can obtain the third target model in a variety of ways. The third target model can be constructed in a variety of ways. In one example, the air conditioning system can use statistical regression methods to construct a statistical regression-based model to monitor heat exchanger blockage. In another example, the air conditioning system can use machine learning algorithms to construct a machine learning model to monitor heat exchanger blockage. In another example, the air conditioning system can construct a neural network model to monitor heat exchanger blockage. In an optimized solution, the air conditioning system can use a statistical regression model for preliminary predictions, and then use a machine learning model for further optimization and adjustment to accurately monitor the specific blockage of the heat exchanger.

[0149] This approach, by acquiring sample data for model training, enables the construction of a third objective model for monitoring heat exchanger fouling and blockage based on actual test data. This data-based model construction approach ensures that the model more closely reflects actual operating conditions, thereby improving its accuracy and reliability. Furthermore, because the sample data encompasses test data from diverse operating conditions, the constructed third objective model exhibits greater adaptability to operating conditions. This means that regardless of the air conditioning system's operating state, the model can effectively monitor heat exchanger fouling and blockage, providing strong support for the system's stable operation.

[0150] Optionally, the air conditioning system determines a third target model for monitoring heat exchanger fouling and blockage based on the sample data, including:

[0151] Based on sample data, a convolutional neural network model is constructed to monitor heat exchanger blockage in the air-conditioning system.

[0152] The air conditioning system trains a convolutional neural network model to use the trained model as the third target model for monitoring heat exchanger fouling and blockage.

[0153] In this solution, a convolutional neural network (CNN) was selected as the model architecture. This CNN model consists of 10 convolutional layers and 3 fully connected layers. This design aims to capture deep features in the air conditioning system's operating measurement data and effectively map them to the heat exchanger's dirty and blocked percentage data.

[0154] Optionally, we use ReLU (Rectified Linear Unit) as the activation function after each convolutional layer and fully connected layer. The introduction of the ReLU function helps the model learn nonlinear features, accelerates the training process, and alleviates the gradient vanishing problem.

[0155] Optionally, the input of the model is determined to be the measurement point data during the operation of the air-conditioning system to reflect the operating status of the air-conditioning system; the output of the model is determined to be the dirty and blocked ratio data of the heat exchanger of the air-conditioning system to serve as the target value that the model needs to predict.

[0156] Furthermore, after building a convolutional neural network model for monitoring heat exchanger fouling in an air conditioning system, the model can be trained based on sample data. For example, the convolutional neural network model training phase may include preparing training data, initializing model parameters, performing forward and backward propagation, selecting an optimizer, validating and tuning the model, and saving the model.

[0157] Optionally, preparing training data includes utilizing sample data collected in advance and dividing it into a training set and a validation set. The training set is used to update the model's weights, while the validation set is used to monitor the model's performance during training to prevent overfitting. Initializing model parameters includes randomly initializing the model's weights and biases before training begins. These parameters will be iteratively updated through the backpropagation algorithm during subsequent training. Forward propagation and backpropagation include, in each iteration, the model first receiving a batch of training data as input, calculating the output value through the convolutional layer and the fully connected layer, and then calculating the loss function between the output value and the true value, and updating the model parameters through the backpropagation algorithm to reduce the loss. Optimizer selection includes using an appropriate optimizer to guide the model parameter update process. The optimizer intelligently adjusts the learning rate based on the gradient of the loss function, accelerating model convergence. Validation and tuning involves regularly evaluating the model using a validation set during training to monitor performance changes. If overfitting occurs, adjustments can be made using strategies such as regularization and early stopping. Hyperparameters such as the learning rate are also adjusted based on validation results to optimize model performance. Saving the model involves saving the trained model as a third target model when satisfactory performance is achieved on the validation set. This model will be used in subsequent practical applications to monitor the real-time fouling and blockage rates of heat exchangers in air conditioning systems.

[0158] This solution enables the precise construction and training of a convolutional neural network model for monitoring heat exchanger fouling and blockage. This model demonstrates high accuracy and generalization capabilities, enabling effective application in real-world scenarios and providing strong support for the stable operation of air conditioning systems.

[0159] Optionally, the air conditioning system determines a heat exchanger blockage prompt scheme based on the heat exchanger blockage ratio, including:

[0160] The air conditioning system determines the heat exchanger blockage level based on the heat exchanger blockage ratio.

[0161] The air conditioning system uses the reminder plan that matches the heat exchanger blockage level as the heat exchanger blockage reminder plan.

[0162] In this solution, the air conditioning system can determine the heat exchanger blockage level based on the heat exchanger blockage ratio. Specifically, the air conditioning system determines the heat exchanger blockage level based on the heat exchanger blockage ratio, including:

[0163] Based on a pre-stored correspondence, the heat exchanger's blockage level corresponding to the heat exchanger's blockage ratio is used as the heat exchanger's blockage level. For example, if the heat exchanger's blockage ratio is 10%, the corresponding blockage level is level 1; if the heat exchanger's blockage ratio is 20%, the corresponding blockage level is level 2; if the heat exchanger's blockage ratio is 30%, the corresponding blockage level is level 3, and so on. This solution allows for precise determination of the heat exchanger's blockage level.

[0164] Furthermore, the air conditioning system may use a prompt scheme that matches the heat exchanger dirtiness and blockage level as a heat exchanger dirtiness and blockage prompt scheme.

[0165] As an example, if the heat exchanger blockage level is the first, second or third level, the corresponding prompt scheme is to control the air-conditioning system to display the heat exchanger blockage level while controlling the air-conditioning system's light display module to display yellow to suggest the user to clean the heat exchanger; if the heat exchanger blockage level is the fourth, fifth or sixth level, the corresponding prompt scheme is to control the air-conditioning system to display the heat exchanger blockage level while controlling the air-conditioning system's light display module to display orange to prompt the user that the heat exchanger needs to be cleaned; if the heat exchanger blockage level is the seventh, eighth, ninth or tenth level, the corresponding prompt scheme is to control the air-conditioning system to display the heat exchanger blockage level while controlling the air-conditioning system's light display module to display red to warn the user that the heat exchanger must be cleaned.

[0166] This solution effectively monitors and addresses heat exchanger blockage issues in air conditioning systems by accurately assessing the extent of heat exchanger blockage and providing personalized alerts. This not only improves maintenance efficiency and optimizes system performance, but also enhances the user experience and provides strong support for the stable operation and efficient performance of air conditioning systems.

[0167] Optionally, after obtaining the heat exchanger fouling and blocking ratio output by the third target model, the method further includes:

[0168] The air conditioning system determines a third target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature and the coil temperature of the indoor unit.

[0169] The air conditioning system controls the air conditioning system to execute a third target control scheme to perform energy-saving control on the air conditioning system when the heat exchanger of the air conditioning system is dirty or blocked.

[0170] This solution can effectively improve the energy efficiency and user experience of the air-conditioning system by accurately obtaining the indoor unit operating parameters, formulating personalized third-target control plans, and realizing energy-saving control of the air-conditioning system, while also making a positive contribution to energy conservation and emission reduction.

[0171] Optionally, the air conditioning system determines a third target control scheme of the air conditioning system according to the set temperature of the indoor unit, the return air temperature, and the coil temperature of the indoor unit, including:

[0172] The air conditioning system calculates a first temperature difference between a set temperature of the indoor unit and a return air temperature.

[0173] The air conditioning system calculates a second temperature difference between the coil temperature of the indoor unit and the return air temperature.

[0174] The air-conditioning system determines a third target control scheme of the air-conditioning system according to the first temperature difference value and the second temperature difference value.

[0175] In this solution, the air conditioning system can determine a third target control solution for the air conditioning system based on the first temperature difference value and the second temperature difference value. Specifically, the air conditioning system determines the third target control solution for the air conditioning system based on the first temperature difference value and the second temperature difference value, including:

[0176] If the first temperature difference is greater than or equal to the second temperature threshold and the second temperature difference is greater than or equal to the fourth temperature threshold, the third target control scheme for the air conditioning system is determined to be the fastest cooling strategy. The fastest cooling strategy controls the compressor to run at its highest frequency, delivering maximum cooling capacity. The valve opening and fan speed are adjusted based on the current operating state to maximize cooling efficiency.

[0177] If the first temperature threshold is less than or equal to the first temperature difference and less than or equal to the second temperature threshold, and the second temperature difference is greater than or equal to the fourth temperature threshold, then the third target control scheme for the air conditioning system is determined to be controlling the air conditioning system to execute the fastest cooling strategy. When controlling the air conditioning system to execute the fastest cooling strategy, adjustments to the compressor, valves, and fans are more gentle than in the first scenario.

[0178] If the first temperature threshold ≤ the first temperature difference ≤ the second temperature threshold, and the third temperature threshold ≤ the second temperature difference ≤ the fourth temperature threshold, then the third target control scheme for the air conditioning system is to control the air conditioning system to execute an energy-saving operation mode. The energy-saving operation mode involves controlling the compressor frequency to appropriately reduce energy consumption while also adjusting the valve opening and fan speed to maintain indoor comfort. This reduces the temperature difference between the indoor coil temperature and the return air temperature, reducing irreversible losses in the system and thus achieving energy savings.

[0179] If the first temperature difference is less than or equal to the first temperature threshold and the second temperature difference is less than or equal to the third temperature threshold, the third target control scheme for the air conditioning system is determined to be precise temperature control using a modified PID control strategy. PID control is a commonly used closed-loop control method that continuously adjusts control parameters to keep the air conditioning system output close to the setpoint. In this strategy, the PID control algorithm fine-tunes the compressor, valves, and fan based on the current first and second temperature differences to achieve stable indoor temperature control.

[0180] As an example, the first temperature threshold is 1°C, the second temperature threshold is 3°C, the third temperature threshold is 5°C, and the fourth temperature threshold is 10°C.

[0181] This solution combines the first and second temperature differentials to select the fastest cooling strategy, energy-saving operation strategy, and a modified PID control strategy. These strategies automatically adjust the air conditioning system's operating state based on changes in indoor environmental parameters to meet the needs of different scenarios. This strategy achieves efficient, energy-saving, and precise control of the air conditioning system, improving the user experience.

[0182] Figure 6 is a schematic diagram of a device for monitoring the lack of refrigerant provided by an embodiment of the present disclosure; Figure 6 As shown, an embodiment of the present disclosure provides an apparatus 200 for monitoring refrigerant loss, comprising an acquisition module 61, an input module 62, a determination module 63, and a control module 64. The acquisition module 61 is configured to obtain current operating data of the air-conditioning system; the input module 62 is configured to input the current operating data of the air-conditioning system into a first target model for monitoring refrigerant loss to obtain a refrigerant loss ratio output by the first target model; the determination module 63 is configured to determine a refrigerant loss prompt scheme based on the refrigerant loss ratio; and the control module 64 is configured to control the air-conditioning system to push information according to the refrigerant loss prompt scheme.

[0183] The apparatus 200 for monitoring refrigerant loss provided in an embodiment of the present disclosure obtains the current operating data of the air conditioning system; inputs the current operating data into a first target model for monitoring refrigerant loss to obtain a refrigerant loss ratio output by the first target model; determines a refrigerant loss notification scheme based on the refrigerant loss ratio; and controls the air conditioning system to push information according to the refrigerant loss notification scheme. This scheme accurately determines the refrigerant loss ratio of the air conditioning system by inputting the current operating data into the first target model; then, based on the refrigerant loss ratio, determines a personalized refrigerant loss notification scheme; and controls the air conditioning system to push information according to the refrigerant loss notification scheme, allowing users to receive real-time notifications and related information about refrigerant loss. This real-time nature facilitates timely user response, avoids performance degradation or failure of the air conditioning system caused by refrigerant loss, and improves the stability and reliability of the air conditioning system. Furthermore, users do not need to frequently check the operating status of the air conditioning system or worry about potential problems caused by refrigerant loss, thus reducing energy consumption due to performance degradation and achieving energy savings.

[0184] Figure 7 is another schematic diagram of a device for monitoring the lack of refrigerant provided by an embodiment of the present disclosure; Figure 7 As shown, an embodiment of the present disclosure provides a device 200 for monitoring the lack of refrigerant, including a processor 201 and a memory 202. Optionally, the device 200 may also include a communication interface 203 and a bus 204. The processor 201, the communication interface 203, and the memory 202 may communicate with each other through the bus 204. The communication interface 203 may be used for information transmission. The processor 201 may call the logic instructions in the memory 202 to execute the method for monitoring the lack of refrigerant of the above embodiment.

[0185] In addition, the logic instructions in the memory 202 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0186] Memory 202, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the methods in the embodiments of the present disclosure. Processor 201 executes the program instructions / modules stored in memory 202 to execute functional applications and data processing, thereby implementing the method for detecting refrigerant loss in the above-mentioned embodiments.

[0187] The memory 202 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data generated based on the use of the terminal device. Furthermore, the memory 202 may include a high-speed random access memory and a non-volatile memory.

[0188] The embodiment of the present disclosure provides a multi-split air conditioner, comprising: a multi-split air conditioner body, and the above-mentioned device 200 for monitoring the lack of refrigerant. The device 200 for monitoring the lack of refrigerant is installed on the multi-split air conditioner body. The installation relationship described here is not limited to placement inside the multi-split air conditioner body, but also includes installation connections with other components of the multi-split air conditioner, including but not limited to physical connections, electrical connections or signal transmission connections, etc. Those skilled in the art will understand that the device 200 for monitoring the lack of refrigerant can be adapted to a feasible multi-split air conditioner body, thereby realizing other feasible embodiments.

[0189] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for monitoring the loss of refrigerant.

[0190] The technical solutions of the embodiments of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present disclosure. The aforementioned storage medium may be a non-transitory storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0191] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0192] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0193] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical functional division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, and can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. In addition, the functional units in the embodiments of the present disclosure may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0194] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for monitoring the loss of refrigerant, characterized in that: include: Obtain the current operating data of the air conditioning system; Inputting current operating data of the air-conditioning system into a first target model for monitoring refrigerant shortage to obtain a refrigerant shortage ratio output by the first target model; Determine a refrigerant shortage prompt plan based on the refrigerant shortage ratio; The air conditioning system is controlled to push information according to the refrigerant shortage prompt scheme.

2. The method according to claim 1, characterized in that The first target model for monitoring the loss of refrigerant is determined by: Obtaining sample data for model training, wherein the sample data is refrigerant loss ratio data of the air-conditioning system obtained by testing the air-conditioning system under different working conditions and measurement point data when the air-conditioning system is in operation; A first target model for monitoring the lack of refrigerant is determined based on the sample data.

3. The method according to claim 2, characterized in that Determining a first target model for monitoring refrigerant loss based on the sample data includes: Constructing a BP neural network model for monitoring refrigerant shortage based on the sample data; Training the BP neural network model to use the trained model as a first target model for monitoring refrigerant shortage; The input of the BP neural network model is the measurement point data during the operation of the air-conditioning system, and the output of the BP neural network model is the refrigerant loss ratio data of the air-conditioning system.

4. The method according to claim 2, characterized in that If the air conditioning system includes one outdoor unit and multiple indoor units, the measurement point data during the operation of the air conditioning system includes: Air conditioning system outdoor side measurement point data and air conditioning system indoor side measurement point data; The outdoor measurement point data of the air-conditioning system include outdoor unit exhaust temperature, outdoor unit suction temperature, defrost sensor temperature, outdoor ambient temperature, outdoor unit total current, compressor speed, outdoor unit fan speed, outdoor unit electronic expansion valve opening, outdoor unit coil temperature, outdoor unit auxiliary valve opening, outdoor unit heat exchanger inlet temperature, outdoor unit heat exchanger outlet temperature, outdoor unit oil temperature, outdoor unit low pressure, outdoor unit high pressure and outdoor unit pressure ratio; The indoor measuring point data of the air-conditioning system includes the user's set temperature, the evaporator inlet temperature, the evaporator outlet temperature, the indoor fan speed, the opening of the indoor electronic expansion valve and the target superheat.

5. The method according to claim 1, wherein According to the refrigerant shortage ratio, a refrigerant shortage prompting plan is determined, including: Determining refrigerant shortage information according to the refrigerant shortage ratio; Using the prompt scheme that matches the refrigerant shortage information as the refrigerant shortage prompt scheme; The refrigerant shortage information includes the refrigerant shortage amount and the refrigerant shortage level.

6. The method according to claim 1, characterized in that After obtaining the refrigerant shortage ratio output by the first target model, the method further includes: Get the set temperature, return air temperature and coil temperature of the indoor unit; determining a first target control scheme for the air conditioning system according to a set temperature of the indoor unit, a return air temperature, and a coil temperature of the indoor unit; The air-conditioning system is controlled to execute the first target control scheme to perform energy-saving control on the air-conditioning system when the refrigerant in the air-conditioning system is missing.

7. The method according to claim 6, characterized in that Determining a first target control scheme for the air conditioning system according to a set temperature of the indoor unit, a return air temperature, and a coil temperature of the indoor unit includes: Calculating a first temperature difference between the set temperature of the indoor unit and the return air temperature; Calculate the second temperature difference between the indoor unit coil temperature and the return air temperature; A first target control scheme for the air-conditioning system is determined according to the first temperature difference value and the second temperature difference value.

8. A device for monitoring the loss of refrigerant, characterized in that: include: an acquisition module configured to obtain current operating data of the air-conditioning system; an input module configured to input current operating data of the air-conditioning system into a first target model for monitoring refrigerant shortage, so as to obtain a refrigerant shortage ratio output by the first target model; a determination module configured to determine a refrigerant shortage prompting scheme according to the refrigerant shortage ratio; The control module is configured to control the air-conditioning system to push information according to the refrigerant shortage prompt scheme.

9. A device for monitoring the loss of refrigerant, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for monitoring the loss of refrigerant according to any one of claims 1 to 7 when running the program instructions.

10. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed, the computer is configured to execute the method for monitoring the loss of refrigerant according to any one of claims 1 to 7.

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