An intelligent pre-cooling method and device based on an air conditioning system evaporative cooling technology

Abstract

The application relates to an intelligent precooling method and device based on an air conditioning system evaporative cooling technology, which comprises the following steps: acquiring operation parameters of an air conditioning system and outdoor environment data in real time and inputting the operation parameters and the outdoor environment data into a prediction model; determining initial evaporative cooling parameters based on refrigeration demand prediction information and environment change prediction information; acquiring indoor mode data and building characteristic data and inputting the indoor mode data, the building characteristic data and outdoor environment data into an optimization model; acquiring a current water temperature value of a condensing coil and determining operation evaporative cooling parameters and an operation water temperature value based on an adjustment strategy; adjusting the current water temperature value of the condensing coil to the operation water temperature value, configuring the operation evaporative cooling parameters in the air conditioning system, and monitoring and adjusting the condensing efficiency index of the condensing coil and the operation evaporative cooling parameters in real time. Through the above method, dynamic configuration and real-time optimization of evaporative cooling technology parameters are realized, and the problems of experience-dependent parameter setting and lack of flexibility in traditional technologies are effectively solved.

Description

Technical Field

[0001] The present application relates to the technical field of air-conditioning systems, and in particular to an intelligent pre-cooling method and device based on evaporative cooling technology of air-conditioning systems. Background Art

[0002] In modern architecture and industry, air conditioning systems are at the heart of temperature and humidity control. Their energy efficiency and stability are crucial for creating a comfortable environment and reducing energy consumption and emissions. Evaporative cooling technology, a key component of air conditioning systems, is highly sought after for its efficient and environmentally friendly cooling mechanism. However, the application of evaporative cooling technology in existing technologies faces numerous challenges:

[0003] First, the configuration of evaporative cooling technology parameters often relies on experience or fixed settings, lacking the flexibility to adapt to environmental changes and user needs. Because it lacks real-time capture and analysis of system operating parameters and outdoor environmental data, cooling demand forecasts are inaccurate, leading to improper parameter settings. This not only fails to fully utilize environmental resources, but can also lead to increased energy consumption or decreased comfort due to cooling imbalances.

[0004] Secondly, existing air conditioning systems ignore the impact of indoor occupant activity patterns when setting parameters. Key information such as user preferences and indoor activity status is not effectively integrated and analyzed, making it difficult to achieve personalized optimization of evaporative cooling technical parameters.

[0005] Furthermore, the existing technology's condensing coil water temperature control mechanism is not sound enough and lacks real-time monitoring and adjustment capabilities, making it difficult to accurately maintain the water temperature within the optimal range, impairing the condensing efficiency, and thus dragging down the cooling performance and energy efficiency ratio of the entire air-conditioning system. Summary of the Invention

[0006] In order to solve the above-mentioned defects, the present application provides an intelligent pre-cooling method and device based on evaporative cooling technology of an air-conditioning system.

[0007] The above-mentioned invention objective of this application is achieved through the following technical solutions:

[0008] An intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system comprises the following steps:

[0009] Obtain the operating parameters of the air conditioning system and outdoor environmental data in real time, and input them into the pre-trained prediction model to predict cooling demand and environmental changes;

[0010] Determining initial evaporative cooling parameters of the air conditioning system based on cooling demand forecast information and environmental change forecast information output by the forecast model;

[0011] Obtain indoor pattern data and building characteristic data, and input the indoor pattern data, building characteristic data and outdoor environment data into the pre-trained optimization model to formulate adjustment strategies;

[0012] Obtain the current water temperature of the condensing coil in the air conditioning system and determine the operating evaporative cooling parameters and operating water temperature based on the adjustment strategy output by the optimization model;

[0013] The current water temperature value of the condensing coil is adjusted to the operating water temperature value, the operating evaporative cooling parameters are configured in the air conditioning system, and the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters are monitored and adjusted in real time.

[0014] By adopting the above technical solution, the operating parameters of the air-conditioning system and the outdoor environmental data are obtained in real time and input into the pre-trained prediction model to accurately predict the cooling demand and the environmental changes. The initial evaporative cooling parameters of the air-conditioning system are determined by combining the cooling demand prediction information and the environmental change prediction information output by the prediction model. On this basis, the indoor pattern data and building characteristic data are further obtained, including user preferences, indoor activity conditions, building structure, space layout, etc., and input into the optimization model together with the outdoor environmental data to formulate a more refined adjustment strategy. According to the adjustment strategy output by the optimization model, the optimal operating evaporative cooling parameters and operating water temperature values ​​under the current environment are determined, and the initial evaporative cooling parameters and the current water temperature value of the condensing coil are dynamically adjusted: the water temperature of the condensing coil is adjusted to the target operating water temperature value, and the optimized evaporative cooling parameters are configured in the air-conditioning system; at the same time, the condensing efficiency index is monitored in real time. standards and evaporative cooling parameters for adjustment and optimization; the present application realizes dynamic configuration and real-time optimization of evaporative cooling technical parameters through the above method, effectively solving the problem that parameter setting in traditional technology relies on experience and lacks flexibility, and improves the accuracy of cooling demand prediction and the rationality of parameter setting by real-time capture and analysis of system operating parameters and outdoor environmental data, which not only makes full use of environmental resources, but also reduces the increase in energy consumption or decrease in comfort caused by cooling imbalance. At the same time, the present application also fully considers the influence of indoor personnel activity patterns and building characteristics, and realizes personalized optimization of evaporative cooling technical parameters and improves user satisfaction by integrating and analyzing key information such as user preferences, indoor activity conditions and building characteristics; in addition, the present application ensures that the water temperature can be accurately maintained in the optimal range by real-time monitoring and adjustment of the water temperature of the condensing coil, thereby improving the condensing efficiency and effectively improving the cooling efficiency and energy efficiency ratio of the entire air-conditioning system.

[0015] The second object of the present invention is achieved through the following technical solutions:

[0016] An intelligent pre-cooling device based on evaporative cooling technology of an air conditioning system, comprising:

[0017] The first input module is used to obtain the operating parameters of the air conditioning system and outdoor environmental data in real time, and input them into the pre-trained prediction model to predict cooling demand and environmental changes;

[0018] A first parameter determination module is used to determine initial evaporative cooling parameters of the air-conditioning system based on the cooling demand prediction information and the environmental change prediction information output by the prediction model;

[0019] The second input module is used to obtain indoor pattern data and building characteristic data, and input the indoor pattern data, building characteristic data and outdoor environment data into the pre-trained optimization model to formulate adjustment strategies;

[0020] The second parameter determination module is used to obtain the current water temperature value of the condensing coil in the air-conditioning system and determine the operating evaporative cooling parameters and the operating water temperature value based on the adjustment strategy output by the optimization model;

[0021] The monitoring module is used to adjust the current water temperature value of the condensing coil to the operating water temperature value, configure the operating evaporative cooling parameters in the air-conditioning system, and monitor and adjust the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters in real time.

[0022] In summary, this application includes at least one of the following beneficial technical effects:

[0023] 1. This application uses the above method to achieve dynamic configuration and real-time optimization of evaporative cooling technical parameters, effectively solving the problem of parameter setting relying on experience and lacking flexibility in traditional technologies. By real-time capture and analysis of system operating parameters and outdoor environmental data, the accuracy of cooling demand prediction and the rationality of parameter setting are improved, which not only fully utilizes environmental resources, but also reduces the increase in energy consumption or decrease in comfort caused by cooling imbalance. At the same time, this application also fully considers the influence of indoor personnel activity patterns and building characteristics, and realizes personalized optimization of evaporative cooling technical parameters and improves user satisfaction by integrating and analyzing key information such as user preferences, indoor activity conditions and building characteristics. In addition, this application ensures that the water temperature can be accurately maintained in the optimal range by real-time monitoring and adjustment of the water temperature of the condensing coil, thereby improving the condensing efficiency and effectively improving the cooling efficiency and energy efficiency ratio of the entire air-conditioning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of an embodiment of an intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system in the present application;

[0025] Figure 2 This is a flowchart for implementing step S10 in an embodiment of an intelligent pre-cooling method based on evaporative cooling technology of an air-conditioning system of the present application;

[0026] Figure 3 This is a flowchart for implementing step S30 in an embodiment of an intelligent pre-cooling method based on evaporative cooling technology of an air-conditioning system of the present application;

[0027] Figure 4 This is a flowchart before step S33 in an embodiment of an intelligent pre-cooling method based on evaporative cooling technology of an air-conditioning system of the present application;

[0028] Figure 5 This is a flowchart for implementing step S322 in an embodiment of an intelligent pre-cooling method based on evaporative cooling technology of an air-conditioning system in the present application. DETAILED DESCRIPTION

[0029] The following is combined with Figure 1-5 This application is described in further detail.

[0030] In one embodiment, if Figure 1 As shown, the present application discloses an intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system, which specifically includes the following steps:

[0031] S10: Acquire the operating parameters of the air conditioning system and outdoor environmental data in real time, and input them into the pre-trained prediction model to predict cooling demand and environmental changes;

[0032] In this embodiment, the air-conditioning system is a system for regulating indoor temperature, humidity, air flow and air quality, and is generally composed of a refrigeration system, a ventilation system, a control system, etc.; operating parameters are variables and states involved in the operation of the air-conditioning system, including temperature, humidity, wind speed, pressure, current, etc.; outdoor environmental data are data related to the external environment of the air-conditioning system, including outdoor temperature, humidity, wind speed, solar radiation intensity, etc., which have a direct impact on the cooling or heating demand of the air-conditioning system; a prediction model is a model established by mathematical or statistical methods based on historical data or known information, and is used to predict specific values ​​or trends at a certain moment or a certain period of time in the future, specifically, the cooling demand of the air-conditioning system and the prediction of environmental changes. The cooling demand prediction is to predict the cooling demand of the air-conditioning system in a future period of time through the prediction model based on the real-time acquired air-conditioning system operating parameters and outdoor environmental data; the environmental change prediction is to predict the environmental changes and their changing trends in a future period of time through the prediction model based on the real-time acquired outdoor environmental data;

[0033] Specifically, the operating parameters of the air-conditioning system and outdoor environmental data are obtained in real time and input into a pre-trained prediction model to accurately predict cooling demand and environmental changes.

[0034] S20: Determining initial evaporative cooling parameters of the air conditioning system based on the cooling demand prediction information and the environmental change prediction information output by the prediction model;

[0035] In this embodiment, the evaporative cooling parameters are parameters related to the evaporative cooling process in the air conditioning system, including evaporation temperature, evaporation pressure, cooling capacity, and COP (cost of performance) etc.;

[0036] Specifically, the initial evaporative cooling parameters of the air-conditioning system are determined by combining the cooling demand prediction information and the environmental change prediction information output by the prediction model.

[0037] S30: Acquire indoor pattern data and building characteristic data, and input the indoor pattern data, building characteristic data, and outdoor environment data into a pre-trained optimization model to formulate an adjustment strategy;

[0038] In this embodiment, the indoor pattern data is data reflecting the indoor environment pattern and user group behavior pattern and its cooling demand, including indoor temperature value, indoor humidity value, user activity status, wind speed selection, user preference, etc., wherein the indoor temperature value and indoor humidity value are obtained by temperature sensor and humidity sensor, user activity status is obtained by infrared sensor or camera, and wind speed selection and user preference are obtained by providing a user interface or mobile application; building characteristic data includes characteristic data such as the structure, material, thermal insulation performance, and spatial layout of the building, and the building characteristic data affects the load and energy consumption of the air-conditioning system; the optimization model is a model that matches strategy items and outputs adjustment strategies or solutions by inputting relevant data and constraints, specifically matching strategy items and outputting adjustment strategies based on indoor pattern data, building characteristic data, and outdoor environment data;

[0039] Specifically, indoor pattern data and building characteristic data, including user preferences, indoor activity conditions, building structure, spatial layout, etc., are further obtained and input into the optimization model together with outdoor environment data to formulate more refined adjustment strategies.

[0040] S40: obtaining a current water temperature value of a condensing coil in the air conditioning system, and determining an operating evaporative cooling parameter and an operating water temperature value based on an adjustment strategy output by the optimization model;

[0041] In this embodiment, the condensing coil is an important component of the air conditioning system, used to transfer the heat released by the refrigerant during the condensation process to the cooling water or air, thereby achieving condensation and circulation of the refrigerant; the current water temperature value is the current temperature of the cooling water in the condensing coil, which has a direct impact on the condensation effect and energy efficiency; the adjustment strategy is a scheme for adjusting the operating parameters of the air conditioning system and the water temperature of the condensing coil based on the output of the optimization model; the operating evaporative cooling parameter is the adjusted evaporative cooling parameter; and the operating water temperature value is the adjusted water temperature of the condensing coil.

[0042] Specifically, the optimal operating evaporative cooling parameters and operating water temperature values ​​under the current environment are determined according to the adjustment strategy output by the optimization model.

[0043] S50: adjusting the current water temperature of the condensing coil to the operating water temperature, configuring the operating evaporative cooling parameters in the air conditioning system, and monitoring and adjusting the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters in real time;

[0044] In this embodiment, the condensing efficiency index is an index used to evaluate the condensing efficiency of the condensing coil, including condensing temperature, condensing pressure, condensing efficiency, etc., which reflects the performance and operating status of the condensing coil;

[0045] Specifically, the water temperature of the condensing coil is adjusted to the target operating water temperature value, and the optimized evaporative cooling parameters are configured in the air conditioning system. At the same time, the condensing efficiency index and evaporative cooling parameters are monitored in real time for adjustment and optimization.

[0046] In one embodiment, the prediction model includes a feature extraction layer, a feature fusion layer, and a prediction output layer. Figure 2 As shown, step S10 includes the steps of:

[0047] S11: The feature extraction layer extracts operating features and environmental trend features based on the input operating parameters and outdoor environmental data;

[0048] S12: The feature fusion layer combines the operation features and the environmental trend features into a comprehensive feature vector, wherein the comprehensive feature vector includes a time series feature vector combined in time sequence and a non-time series feature vector not combined in time sequence;

[0049] S13: The prediction output layer analyzes the comprehensive feature vector output by the feature fusion layer and outputs cooling demand prediction information and environmental change prediction information;

[0050] In this embodiment, the feature extraction layer is the first layer of the prediction model, which is used to extract key feature information from the original data; the feature fusion layer is the second layer of the prediction model, which is used to combine and fuse the features extracted by the feature extraction layer to form a comprehensive feature vector including a time series feature vector combined in time sequence and a non-time series feature vector not combined in time sequence; the prediction output layer is the last layer of the prediction model, which is used to make predictions based on the fused feature vectors and output cooling demand prediction information and environmental change prediction information; the operating characteristics are the characteristics extracted from the operating parameters that can reflect the operating status of the air-conditioning system; the environmental trend characteristics are the energy characteristics extracted from the outdoor environmental data. Features that can reflect environmental change trends; comprehensive feature vectors are feature vectors formed by combining operating features and environmental trend features; time series feature vectors are vectors composed of features arranged in chronological order and aligned by timestamps, used to reflect the change trend of data over time and the correlation and change trend of features with the same timestamp; non-time series feature vectors are vectors composed of features not arranged in chronological order, including static or time-independent information; cooling demand forecast information is information output by the forecast model about the cooling demand in a certain time period in the future; environmental change forecast information is information output by the forecast model about the future change trend of the outdoor environment (such as temperature, humidity, etc.);

[0051] Specifically, after obtaining the operating parameters of the air-conditioning system and outdoor environmental data in real time, these data are input into the pre-trained prediction model. First, the feature extraction layer extracts the operating characteristics and environmental trend characteristics from these raw data. The feature fusion layer combines the features extracted by the feature extraction layer to form a comprehensive feature vector containing time series feature vectors and non-time series feature vectors, which not only retains the time series characteristics of the data, but also incorporates other important non-time series information. The prediction output layer outputs cooling demand prediction information and environmental change prediction information through in-depth analysis of the comprehensive feature vector; through the structured prediction model, accurate prediction of the cooling demand of the air-conditioning system and keen capture of environmental changes are achieved, which not only improves the accuracy of the prediction, but also enhances the model's adaptability to complex environmental changes: by real-time analysis of the operating status of the air-conditioning system and external environmental data, the prediction results are dynamically adjusted to ensure that the setting of evaporative cooling technical parameters is more reasonable, thereby effectively reducing the increase in energy consumption or decrease in comfort caused by cooling imbalance.

[0052] In one embodiment, step S13 includes the steps of:

[0053] S131: The prediction output layer decomposes the characteristic components of the time series feature vector at different frequencies based on wavelet transform, thereby analyzing the periodicity and trend of the time series feature vector;

[0054] S132: The prediction output layer performs importance evaluation and feature interaction analysis on non-time series feature vectors;

[0055] In this embodiment, wavelet transform is a mathematical transformation method used to decompose a signal (such as time series data) into components at different frequencies. This embodiment uses wavelet transform to analyze the characteristics of time series feature vectors at different frequencies, such as periodicity and trend. Periodicity analysis is the process of analyzing time series feature vectors to identify their periodic characteristics, and can obtain patterns of data (time series feature vectors) that repeat within fixed time intervals. Trend analysis is the process of analyzing time series feature vectors to identify their long-term trends or development directions, and can predict the future trends of data (time series feature vectors). Importance assessment is the process of ranking the importance or assigning weights to each feature in a non-time series feature vector, and can understand which features in the non-time series feature vector have a greater impact on the prediction results. Feature interaction analysis is the process of analyzing the interactions and influences between each feature in a non-time series feature vector, and can capture the correlation and dependency between the features of the non-time series feature vector.

[0056] Specifically, the prediction output layer uses wavelet transform to decompose the time series feature vector to explore its inherent periodicity and trend patterns. At the same time, the prediction output layer performs importance evaluation and feature interaction analysis on the non-time series feature vector to reveal the correlation and influence between different features. It not only considers the periodic changes of time series data, but also evaluates the impact of non-time series features on the prediction results, thereby improving the accuracy and reliability of the prediction.

[0057] In one embodiment, the optimization model includes a feature analysis layer, a data integration layer, and an optimization decision layer. Figure 3 As shown, step S30 includes the steps of:

[0058] S31: The feature parsing layer extracts indoor environment state features and user behavior pattern features based on the input indoor mode data, and extracts building structure features, thermal characteristics, and spatial layout features based on the input building characteristic data;

[0059] S32: The data integration layer integrates indoor environment state characteristics, user behavior pattern characteristics, and outdoor environment data to form an environment-user feature set, and integrates building structure characteristics, thermal characteristics, and spatial layout characteristics to form a building characteristic feature set;

[0060] S33: The optimization decision layer matches corresponding policy items from a pre-stored policy library based on the environment-user feature set and the building characteristic feature set, and integrates and outputs the corresponding policy items as an adjustment policy.

[0061] In this embodiment, the feature parsing layer is the first layer of the optimization model, which is used to extract key features from the input indoor pattern data; the data integration layer is the second layer of the optimization model, which is used to organically combine the features extracted by the feature parsing layer with other relevant data (such as outdoor environment data) to form a correlated environment-user feature set; the optimization decision layer is the final layer of the optimization model, which is used to formulate and output adjustment strategies based on the integrated environment-user feature set; the indoor environment state features are extracted from the indoor pattern data and can describe the current state of the indoor environment; the user behavior pattern features are extracted from the indoor pattern data and can reflect the typical behavior patterns of users and their demand states; the building structure features are extracted from the building characteristic data by the feature parsing layer, and describe the feature information of the building structure, including the number of floors, room layout, wall materials, etc. of the building; the thermal characteristic features are extracted by the feature parsing layer The feature parsing layer extracts characteristic information from building characteristic data, describing the building's thermal performance, including the building's thermal insulation performance, etc.; the spatial layout features are extracted from the building characteristic data by the feature parsing layer, describing the building's spatial layout, including the size, shape, and location of the room; the environment-user feature set is formed by integrating indoor environmental state characteristics, user behavior pattern characteristics, and outdoor environmental data, and contains a feature set of multi-dimensional information and association patterns; the building characteristic feature set is formed by integrating building structural characteristics, thermal characteristics, and spatial layout characteristics by the data integration layer, which integrates various characteristic information of the building; the strategy library is a pre-stored database containing multiple adjustment strategies; the adjustment strategy is a strategy that is integrated by matching several strategy items from the strategy library according to the environment-user feature set, and is used to guide the actual operation adjustment of equipment such as air-conditioning systems and condensing coils;

[0062] Furthermore, in the environment-user feature set, indoor environment status features include: indoor temperature: the actual temperature value of the current room, which may include the temperature of different areas or rooms; indoor humidity: the current indoor humidity level, reflecting the degree of wetness or dryness of the air; air quality: the concentration of pollutants in the indoor air, such as PM2.5 and CO2 concentration;

[0063] User behavior pattern features include: User activity level: the intensity of users' indoor activities, such as sitting quietly, light activity, and vigorous exercise; User location distribution: the distribution of users' locations in the indoor space, such as being concentrated in a certain area or scattered across various rooms; User preferences: the user's preferences for environmental parameters such as temperature, humidity, wind speed, and light intensity; User habits: the user's daily behavior habits, such as the time they wake up, when they return home, and the time they spend active; User feedback: the user's feedback on their satisfaction or discomfort with the current indoor environment;

[0064] Outdoor environmental data includes: outdoor temperature: the current outdoor temperature value, used for comparison and adjustment with the indoor temperature; outdoor humidity: the outdoor humidity level, which may affect the indoor humidity adjustment strategy; seasonal changes: spring, summer, autumn, and winter, different seasons have different requirements and adjustment strategies for the indoor environment;

[0065] The integrated environment-user feature set integrates the above-mentioned indoor environment state features, user behavior pattern features, and outdoor environment data to form an environment-user feature set containing multi-dimensional information and association patterns. For example, on a hot summer afternoon, the outdoor temperature is high and the humidity is high, the indoor user activity level is low, and users have a low temperature preference. In this case, the environment-user feature set includes the following features: high indoor temperature, moderate indoor humidity, low user activity level, user preference for low temperature, high outdoor temperature and humidity, and summer.

[0066] Specifically, after acquiring indoor pattern data and building characteristic data, they are input into a pre-trained optimization model along with outdoor environmental data. The feature parsing layer extracts indoor environmental state features and user behavior pattern features based on the input indoor pattern data, and extracts building structural features, thermal characteristics, and spatial layout features based on the building characteristic data. The data integration layer comprehensively integrates the features extracted by the feature parsing layer with the outdoor environmental data to construct an environment-user feature set covering multi-dimensional information of the environment and users, and a building characteristic feature set covering multi-dimensional characteristics of the building. The optimization decision layer intelligently matches the environment-user feature set and the building characteristic feature set in a pre-stored strategy library based on the pre-stored strategy library to quickly identify several strategy items that best fit the current situation. Through further integration and optimization, the matched strategy items are converted into specific adjustment strategies and output to guide the operation and adjustment of the air conditioning system and condensing coil. Through the optimization model, the indoor environmental state, user behavior patterns, building characteristics, and outdoor environmental data are comprehensively parsed and efficiently integrated, thereby formulating more practical and accurate adjustment strategies. This not only improves the operating efficiency and comfort of the air conditioning system, but also significantly enhances its adaptability to user behavior and environmental changes.

[0067] In one embodiment, the optimization model also includes a preference analysis layer, such as Figure 4 As shown, before step S33, the following steps are also included:

[0068] S321: The preference analysis layer performs a first behavior preference analysis based on the environment-user feature set and the pre-stored historical behavior feature set;

[0069] S322: The preference analysis layer performs a second behavior preference analysis based on the building characteristic feature set, the first behavior preference analysis result, and the pre-stored historical behavior feature set;

[0070] S323: The preference analysis layer updates the environment-user feature set based on the first behavior preference analysis result and the second behavior preference analysis result and outputs it to the optimization decision layer;

[0071] In this embodiment, the preference analysis layer is the layer before the optimization decision layer in the optimization model, which is used to analyze the user's behavioral preferences and update the environment-user feature set based on the behavioral preference analysis results; the first behavioral preference analysis is the first behavioral preference analysis performed by the preference analysis layer, which is mainly based on the environment-user feature set and the pre-stored historical behavioral feature set to identify the user's behavioral patterns and preferences under different environmental conditions; the second behavioral preference analysis is the second behavioral preference analysis performed by the preference analysis layer, which combines the building characteristic feature set, the first behavioral preference analysis results and the pre-stored historical behavioral feature set to deeply consider the impact of building characteristics on user behavioral preferences, as well as the changes in user behavior under different building environments; the historical behavioral feature set is a data feature set containing information such as the user's past behavioral patterns and selection preferences; updating the environment-user feature set is to update the original environment-user feature set based on the first behavioral preference analysis results and the second behavioral preference analysis results to ensure that it can more accurately reflect user needs and preferences;

[0072] Furthermore, the first behavioral preference analysis includes: data collection, feature extraction, behavioral pattern recognition, and preference inference, wherein data collection is to collect the user's historical behavioral data based on the pre-stored historical behavioral feature set, including the user's behavioral responses under different environmental conditions (such as adjusting the air conditioning temperature, opening windows, etc.), as well as contextual information such as the time and place of these behaviors; feature extraction is to extract features directly related to user behavior from the environment-user feature set, such as indoor temperature, humidity, user activity level, etc.; behavioral pattern recognition is to use machine learning algorithms (such as cluster analysis, association rule mining, etc.) to identify the user's behavioral patterns under different environmental conditions, that is, which environmental conditions are associated with the user's specific behavior (such as adjusting the air conditioning settings); preference inference is to infer the user's preference for specific environmental parameters based on the identified behavioral patterns to form the first behavioral preference analysis result;

[0073] Furthermore, the second behavioral preference analysis includes building characteristic analysis, pattern recognition, preference adjustment, and preference inference. Building characteristic analysis is to extract features directly related to indoor thermal comfort and energy efficiency from the building characteristic feature set, such as the building's thermal insulation performance, window shading effect, etc.; pattern recognition is to combine the first behavioral preference analysis results with the building characteristic feature set to identify new patterns or trends in user behavioral preferences under the influence of building characteristics, that is, to analyze how building characteristics affect user behavioral preferences. For example, if the building's thermal insulation performance is poor, users may be more inclined to increase the indoor temperature in cold weather; preference adjustment is to adjust user preferences accordingly based on the new patterns or trends obtained by pattern recognition; preference inference is to integrate the first behavioral preference analysis results, the identified behavioral patterns, and the adjusted preferences to form the second behavioral preference analysis results.

[0074] Specifically, the optimization model further includes a preference analysis layer on the original basis. The preference analysis layer performs a first behavioral preference analysis based on the environment-user feature set and the pre-stored historical behavior feature set to understand the user's behavioral habits and preferences under different environmental conditions; the preference analysis layer further combines the building characteristic feature set, the first behavioral preference analysis results, and the pre-stored historical behavior feature set to perform a second behavioral preference analysis, which not only considers the user's personal preferences, but also incorporates the possible impact of the building's own characteristics on user behavior, thereby ensuring that the adjustment strategy is in line with user habits and adapts to the building environment; the preference analysis layer updates the environment-user feature set based on the first behavioral preference analysis results and the second behavioral preference analysis results, and outputs the updated feature set to the optimization decision layer; by introducing the preference analysis layer and conducting a hierarchical and multi-dimensional behavioral preference analysis on it, accurate capture of user behavior and preferences is achieved, which not only improves the pertinence and effectiveness of the adjustment strategy, but also improves user experience and satisfaction.

[0075] In one embodiment, the preference analysis layer includes a correlation analysis sublayer, a weight adjustment sublayer, and an evaluation analysis sublayer. Figure 5 As shown, step S322 includes the following steps:

[0076] S3221: The association analysis sublayer performs association analysis on the building characteristic feature set and the first behavior preference analysis result, thereby obtaining a consistent feature set and a different feature set;

[0077] S3222: The association analysis sublayer extracts stable pattern features of user preferences in the consistent feature set and transition pattern features of user preferences in the differential feature set;

[0078] S3223: The weight adjustment sublayer assigns weights to the stable mode features and the transition mode features based on a preset weight assignment strategy;

[0079] S3224: The evaluation and analysis sublayer performs a second behavior preference analysis based on the weighted stable mode features and transition mode features in combination with the pre-stored historical behavior feature set;

[0080] In this embodiment, the association analysis sublayer is a component of the preference analysis layer, which is used to perform association analysis on the building characteristic feature set and the first behavior preference analysis result, and identify consistency features and difference features by analyzing the association relationship between the two; the consistency feature set is a data set that shows similar or consistent features in the building characteristic feature set and the first behavior preference analysis result in the association analysis, and the consistency feature set reflects the user's stable behavior pattern or preference under different building characteristics; the difference feature set is a data set that shows different or opposite features in the building characteristic feature set and the first behavior preference analysis result in the association analysis, and the difference feature set reveals the possible behavioral preference changes or differences of the user under different building characteristics; the stable pattern feature is a stable pattern or habit of user preference extracted from the consistency feature set, and the stable pattern feature represents The behavioral preferences that users continuously exhibit under specific building characteristics; the transition mode feature is the transition mode or change of user preferences extracted from the differential feature set, and the transition mode feature reflects the preference changes or adaptive behaviors that may occur in users under different building characteristics or environmental conditions; the weight adjustment sublayer is a component of the preference analysis layer, which is used to assign weights to stable mode features and transition mode features; the preset weight allocation strategy is the rule or method used in the weight adjustment sublayer to determine the weights of stable mode features and transition mode features, which considers factors such as the importance, relevance, and stability of the features to ensure the rationality and accuracy of the weight allocation; the evaluation analysis sublayer is a component of the preference analysis layer, which is used to perform a second behavioral preference analysis based on the stable mode features and transition mode features after the weights are assigned, combined with the pre-stored historical behavior feature set;

[0081] Specifically, in the optimization model, the preference analysis layer includes an association analysis sublayer, a weight adjustment sublayer, and an evaluation analysis sublayer. The association analysis sublayer receives the building characteristic feature set and the first behavior preference analysis result, and mines the intrinsic connection between the two through association analysis to identify the consistent feature set between user behavior and the environment and building characteristics, that is, to maintain a stable preference behavior pattern in different situations. At the same time, it identifies the differential feature set to reveal the transformation pattern of user behavior with changes in the environment or building characteristics, and further extracts the stable pattern features in the consistent feature set and the transformation pattern features in the differential feature set; the weight adjustment sublayer considers different features to determine the relationship between the characteristics of the building and the environment. The influence of user behavior preferences is determined by assigning reasonable weights to stable mode features and transition mode features according to the preset weight allocation strategy. The evaluation and analysis sublayer conducts a second behavior preference analysis based on the user's historical behavior habits, current environment, building characteristics, and changes in user behavior patterns based on the weighted stable mode features and transition mode features, combined with the pre-stored historical behavior feature set. By constructing a multi-level preference analysis layer that includes association analysis, weight adjustment, and evaluation analysis, in-depth mining and precise capture of user behavior preferences are achieved, which not only improves the accuracy and reliability of preference analysis, but also provides more comprehensive and detailed user preference information for the optimization decision-making layer.

[0082] In one embodiment, step S50 includes:

[0083] Dynamically adjust the condensing coil's condensing efficiency based on a pre-set adaptive PID control algorithm, thereby adjusting the current condensing coil water temperature to the operating water temperature. IoT devices are used to monitor the condensing coil's condensing efficiency, including condensing pressure, condensing temperature, and condensing water flow rate, as well as the air conditioning system's evaporative cooling parameters in real time.

[0084] In this embodiment, the adaptive PID control algorithm adds an adaptive mechanism to the traditional PID control algorithm. This algorithm automatically adjusts the PID parameters based on the system's real-time status or feedback information to improve the adaptability and robustness of the control system. PID stands for proportional, integral, and derivative control. In the control of the condensing coil, the adaptive PID control algorithm can dynamically adjust the control parameters based on the condensing efficiency index and the operating status of the air conditioning system, thereby more effectively adjusting the water temperature to the target value. IoT devices, or Internet of Things devices, can collect and transmit physical data in real time. In the air conditioning system, IoT devices are used to monitor the condensing efficiency index of the condensing coil and other relevant parameters of the air conditioning system.

[0085] Specifically, based on a preset adaptive PID control algorithm, the condensing efficiency index of the condensing coil is dynamically adjusted. The preset adaptive PID control algorithm can intelligently calculate the required adjustment amount according to the current system status and external conditions, thereby accurately and smoothly adjusting the current water temperature value of the condensing coil to the preset operating water temperature value; based on the IoT device, the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters of the air-conditioning system are monitored in real time. The condensing efficiency index covers several key parameters such as condensing pressure, condensing temperature and condensing water flow, which together reflect the working status and efficiency of the condensing coil. By real-time monitoring of the condensing efficiency index, possible deviations or abnormalities can be discovered and corrected in time to ensure that the condensing process always remains in the best state; by combining the adaptive PID control algorithm and IoT real-time monitoring technology, the condensing efficiency index of the condensing coil of the air-conditioning system can be accurately controlled and monitored, which not only improves the operating efficiency and stability of the air-conditioning system, but also significantly reduces energy consumption and maintenance costs.

[0086] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0087] The specific definition of an intelligent pre-cooling device based on evaporative cooling technology for air conditioning systems can be found in the definition of an intelligent pre-cooling method based on evaporative cooling technology for air conditioning systems described above, and will not be repeated here. The various modules in the above-mentioned intelligent pre-cooling device based on evaporative cooling technology for air conditioning systems can be implemented in whole or in part through software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above-mentioned modules.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system, characterized by: Including steps: Obtain the operating parameters of the air conditioning system and outdoor environmental data in real time, and input them into the pre-trained prediction model to predict cooling demand and environmental changes; Determining initial evaporative cooling parameters of the air conditioning system based on cooling demand forecast information and environmental change forecast information output by the forecast model; Obtain indoor pattern data and building characteristic data, and input the indoor pattern data, building characteristic data and outdoor environment data into the pre-trained optimization model to formulate adjustment strategies; Obtain the current water temperature of the condensing coil in the air conditioning system and determine the operating evaporative cooling parameters and operating water temperature based on the adjustment strategy output by the optimization model; Adjust the current water temperature of the condensing coil to the operating water temperature, configure the operating evaporative cooling parameters in the air conditioning system, and monitor and adjust the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters in real time; The optimization model includes a feature analysis layer, a data integration layer, and an optimization decision layer. The step of obtaining indoor pattern data and building characteristic data, and inputting the indoor pattern data, building characteristic data, and outdoor environment data into a pre-trained optimization model to formulate an adjustment strategy includes the following steps: The feature parsing layer extracts indoor environment state features and user behavior pattern features based on the input indoor mode data, and extracts building structure features, thermal characteristics, and spatial layout features based on the input building characteristic data; The data integration layer integrates indoor environmental state characteristics, user behavior pattern characteristics and outdoor environmental data to form an environment-user feature set, and integrates building structure characteristics, thermal characteristics and spatial layout characteristics to form a building characteristic feature set; The optimization decision layer matches several corresponding policy items from the pre-stored policy library based on the environment-user feature set and the building characteristic feature set, and integrates and outputs them as adjustment policies; The optimization model further includes a preference analysis layer. The optimization decision layer matches corresponding policy items from a pre-stored policy library based on the environment-user feature set and the building characteristic feature set, and integrates and outputs the corresponding policy items as the adjustment policy. The optimization model further includes the following steps: The preference analysis layer performs a first behavior preference analysis based on the environment-user feature set and the pre-stored historical behavior feature set; The preference analysis layer performs a second behavior preference analysis based on the building characteristic feature set, the first behavior preference analysis result, and the pre-stored historical behavior feature set; The preference analysis layer updates the environment-user feature set based on the first behavior preference analysis result and the second behavior preference analysis result and outputs it to the optimization decision layer; Among them, the first behavioral preference analysis includes data collection, feature extraction, behavioral pattern recognition and preference inference; The second behavioral preference analysis includes building characteristic analysis, pattern recognition, preference adjustment, and preference inference.

2. The intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system according to claim 1, characterized in that: The prediction model includes a feature extraction layer, a feature fusion layer, and a prediction output layer. The step of acquiring the operating parameters of the air-conditioning system and the outdoor environmental data in real time and inputting them into the pre-trained prediction model to perform cooling demand prediction and environmental change prediction includes the following steps: The feature extraction layer extracts operating features and environmental trend features based on the input operating parameters and outdoor environmental data; The feature fusion layer combines the operation features and environmental trend features into a comprehensive feature vector, where the comprehensive feature vector includes a time series feature vector combined in time sequence and a non-time series feature vector that is not combined in time sequence. The prediction output layer analyzes the comprehensive feature vector output by the feature fusion layer and outputs the cooling demand prediction information and the environmental change prediction information.

3. The intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system according to claim 2, characterized in that: The step of the prediction output layer analyzing the comprehensive feature vector output by the feature fusion layer and outputting cooling demand prediction information and environmental change prediction information includes the following steps: The prediction output layer decomposes the characteristic components of the time series feature vector at different frequencies based on wavelet transform, thereby analyzing the periodicity and trend of the time series feature vector; The prediction output layer performs importance evaluation and feature interaction analysis on non-time series feature vectors.

4. The intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system according to claim 1, characterized in that: The preference analysis layer includes an association analysis sublayer, a weight adjustment sublayer, and an evaluation analysis sublayer. The preference analysis layer performs a second behavior preference analysis based on the building characteristic feature set, the first behavior preference analysis result, and the pre-stored historical behavior feature set, including the following steps: The association analysis sublayer performs association analysis on the building characteristic feature set and the first behavior preference analysis results, thereby obtaining a consistent feature set and a different feature set; The association analysis sublayer extracts the stable pattern features of user preferences in the consistent feature set and the transition pattern features of user preferences in the differential feature set; The weight adjustment sublayer assigns weights to the stable mode features and the transition mode features based on a preset weight assignment strategy; The evaluation and analysis sublayer performs a second behavior preference analysis based on the weighted stable mode features and transition mode features combined with the pre-stored historical behavior feature set.

5. The intelligent pre-cooling method based on evaporative cooling technology of an air conditioning system according to claim 1, characterized in that: The present invention adjusts the current water temperature value of the condensing coil to the operating water temperature value, configures the operating evaporative cooling parameters in the air conditioning system, and monitors and adjusts the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters in real time, including: Based on a preset adaptive PID control algorithm, the condensing efficiency index of the condensing coil is dynamically adjusted, thereby adjusting the current water temperature value of the condensing coil to the operating water temperature value; based on the IoT device, the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters of the air conditioning system are monitored in real time. The condensing efficiency index includes condensing pressure, condensing temperature and condensing water flow.

6. An intelligent pre-cooling device based on evaporative cooling technology for air conditioning systems, used in the steps of an intelligent pre-cooling method based on evaporative cooling technology for air conditioning systems as claimed in any one of claims 1 to 5, characterized in that: include: The first input module is used to obtain the operating parameters of the air conditioning system and outdoor environmental data in real time, and input them into the pre-trained prediction model to predict cooling demand and environmental changes; A first parameter determination module is used to determine initial evaporative cooling parameters of the air-conditioning system based on the cooling demand prediction information and the environmental change prediction information output by the prediction model; The second input module is used to obtain indoor pattern data and building characteristic data, and input the indoor pattern data, building characteristic data and outdoor environment data into the pre-trained optimization model to formulate adjustment strategies; The second parameter determination module is used to obtain the current water temperature value of the condensing coil in the air-conditioning system and determine the operating evaporative cooling parameters and the operating water temperature value based on the adjustment strategy output by the optimization model; The monitoring module is used to adjust the current water temperature value of the condensing coil to the operating water temperature value, configure the operating evaporative cooling parameters in the air-conditioning system, and monitor and adjust the condensing efficiency index of the condensing coil and the operating evaporative cooling parameters in real time.

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