Indoor temperature control method, device, electronic device and readable storage medium

Abstract

The present disclosure relates to the field of temperature control technology, and provides an indoor temperature control method, device, electronic device and readable storage medium. The method includes: inputting the current indoor and outdoor environmental characteristic data into a target cooling capacity prediction model, processing the processed characteristic data of the current indoor and outdoor environment based on the target cooling capacity prediction model, and obtaining the candidate cooling capacity for the next time period indoors; predicting the operating parameters of the cooling station equipment based on the candidate cooling capacity, the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment, and generating the target operating status data of the cooling station equipment; controlling the cooling station equipment according to the target operating status data so as to control the temperature of the indoor environment. This method solves the problem of inaccurate indoor temperature control in the prior art, maintains the indoor temperature always within the set comfortable range, performs fine control on the cooling station equipment, and improves the accuracy and efficiency of temperature control.

Description

Technical Field

[0001] The present disclosure relates to the field of temperature control technology, and in particular to an indoor temperature control method, device, electronic device, and readable storage medium. Background Art

[0002] With the increase in urbanization and commercial activity, energy consumption in public spaces like shopping malls is a growing concern. As key components for regulating indoor temperatures, cooling systems account for a significant portion of a mall's total energy consumption. Existing cooling systems utilize computer-based control systems that collect indoor and outdoor environmental data through sensors and automatically adjust the cooling equipment's operating status based on pre-set control logic. However, these systems lack real-time data analysis and forecasting, making it impossible to accurately predict cooling load demand within a mall. This results in inaccurate temperature control and excessive energy consumption. Summary of the Invention

[0003] In view of this, embodiments of the present disclosure provide an indoor temperature control method, device, electronic device, and readable storage medium to solve the problem of inaccurate indoor temperature control in the prior art.

[0004] In a first aspect of an embodiment of the present disclosure, a method for controlling indoor temperature is provided, comprising: obtaining characteristic data of current indoor and outdoor environments and characteristic data of current operating status of a cooling station device; preprocessing the characteristic data of the current indoor and outdoor environments to obtain processed characteristic data of the current indoor and outdoor environments, and preprocessing the characteristic data of the current operating status to obtain processed characteristic data of the current operating status; inputting the processed characteristic data of the current indoor and outdoor environments into a trained target cooling capacity prediction model, processing the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model, and obtaining candidate cooling capacity for the room in the next time period; predicting operating parameters of the cooling station device based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status, and generating target operating status data of the cooling station device; and controlling the cooling station device according to the target operating status data to control the temperature of the indoor environment.

[0005] According to a second aspect of an embodiment of the present disclosure, an indoor temperature control device is provided, comprising: a data acquisition module for acquiring current indoor and outdoor environmental characteristic data and current operating status characteristic data of a cooling station device; a preprocessing module for preprocessing the current indoor and outdoor environmental characteristic data to obtain processed characteristic data of the current indoor and outdoor environments, and preprocessing the current operating status characteristic data to obtain processed characteristic data of the current operating status; a cooling capacity prediction module for inputting the processed characteristic data of the current indoor and outdoor environments into a trained target cooling capacity prediction model, processing the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model, and obtaining candidate cooling capacity for the indoor environment in the next time period; a target operating status data generation module for predicting the operating parameters of the cooling station device based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status, and generating target operating status data of the cooling station device; and an execution module for controlling the cooling station device according to the target operating status data, so as to control the temperature of the indoor environment.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0007] According to a fourth aspect of the embodiments of the present disclosure, a readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the above method are implemented.

[0008] Compared with the prior art, the disclosed embodiments have the following advantages: by acquiring current indoor and outdoor environmental characteristic data and current operating status characteristic data of the cooling station equipment, a basis is provided for subsequent data analysis and model prediction. By continuously monitoring and recording the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment, indoor environmental changes and the operating status of the cooling station equipment can be more accurately understood, providing reliable data support for the intelligent control of the cooling station system. The current indoor and outdoor environmental characteristic data are preprocessed to obtain processed indoor and outdoor environmental characteristic data, and the current operating status characteristic data are preprocessed to obtain processed operating status characteristic data. The collected raw data is preprocessed to remove outliers and convert the data format to make it suitable for the target cooling capacity prediction model. The preprocessed indoor and outdoor environmental characteristic data and the processed operating status characteristic data can more accurately reflect the actual situation, provide reliable input data for the subsequent target cooling capacity prediction model, and improve the accuracy and generalization ability of the target cooling capacity prediction model. Based on the trained target cooling capacity prediction model, the current processed indoor and outdoor environmental characteristic data are input to predict the indoor cooling capacity required for the next time period, providing basic data for generating target operating status data. The operating parameters of the cooling station equipment are predicted by combining the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status to meet the expected cooling capacity demand. This helps the cooling station equipment operate in an optimal state, meeting the temperature control requirements while minimizing energy consumption, and generates the target operating status data for the cooling station equipment. The generated target operating status data is applied to the actual control of the cooling station equipment. Through refined control of the cooling station equipment, effective management of indoor temperature is achieved, solving the problem of inaccurate indoor temperature control in existing technologies, helping to maintain the indoor temperature within the set comfortable range. Refined control of the cooling station equipment improves the accuracy and efficiency of temperature control, reducing energy consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0010] Figure 1 is a flow chart of an indoor temperature control method provided by an embodiment of the present disclosure;

[0011] Figure 2 is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure;

[0012] Figure 3 1 is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure;

[0013] Figure 4 is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure;

[0014] Figure 5 is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure;

[0015] Figure 6 is a structural schematic diagram of an indoor temperature control device provided by an embodiment of the present disclosure;

[0016] Figure 7 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0018] An indoor temperature control method and device according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0019] Figure 1 FIG. 1 is a flow chart of an indoor temperature control method provided by an embodiment of the present disclosure. Figure 1 As shown, the indoor temperature control method includes:

[0020] Step 101: Acquire current indoor and outdoor environment characteristic data and current operating status characteristic data of cooling station equipment.

[0021] In some embodiments, current indoor and outdoor environmental characteristic data may include the current time, current indoor temperature, current indoor humidity, current outdoor temperature, current outdoor humidity, foot traffic, wind force, wind speed, weather, CO2 concentration, etc. This current indoor and outdoor environmental characteristic data can directly affect indoor comfort and the indoor cooling demand over a period of time. By collecting this current indoor and outdoor environmental characteristic data in real time, the current environmental status can be understood in real time, providing a basis for subsequent decision-making.

[0022] In some embodiments, the current operating status characteristic data includes the current time, chiller power, water pump frequency, chilled water supply temperature, chilled water return temperature, chilled water supply pressure, chilled water return pressure, chilled water supply flow rate, chilled water return flow rate, and the operating status of the cooling tower. The current operating status characteristic data can reflect the operating status of the cooling station system and is an important indicator of the cooling station system's performance. The real-time current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment can provide a foundation for subsequent data processing and analysis. By continuously monitoring and recording the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment, indoor environmental changes and the operating status of the cooling station equipment can be more accurately understood, providing reliable data support for the intelligent control of the cooling station system.

[0023] Step 102 , pre-processing the current indoor and outdoor environment characteristic data to obtain the processed characteristic data of the current indoor and outdoor environment, and pre-processing the current operating state characteristic data to obtain the processed characteristic data of the current operating state.

[0024] In some embodiments, preprocessing the current indoor and outdoor environmental characteristic data may include eliminating noise and outliers, converting the current indoor and outdoor environmental characteristic data into a unified format or range, constructing environmental factor characteristics, etc., to obtain processed characteristic data of the current indoor and outdoor environment, which helps to improve the accuracy and reliability of the data in subsequent analysis and modeling. Preprocessing the current operating status characteristic data may include eliminating noise and outliers, converting the data into a format suitable for subsequent model input, and obtaining processed characteristic data of the current operating status. This can more accurately reflect the current operating status of the cold station equipment, providing a basis for subsequent prediction and control. At the same time, it can eliminate noise and outliers caused by equipment characteristics or measurement errors, thereby improving the accuracy of prediction.

[0025] Step 103: Input the processed characteristic data of the current indoor and outdoor environments into the trained target cooling capacity prediction model, process the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model, and obtain candidate indoor cooling capacity for the next time period.

[0026] In some embodiments, the target cooling capacity prediction model can be an algorithmic model learned and optimized based on historical data, capable of capturing the complex relationship between indoor and outdoor environmental characteristics and cooling demand. By inputting the processed feature data of the current indoor and outdoor environments into the trained target cooling capacity prediction model, the model can apply mathematical operations and learned patterns to analyze the relationships and underlying patterns between the processed feature data. Based on the input, the model can then infer a target output, namely, a candidate cooling capacity for the next time period. The candidate cooling capacity for the next time period is an estimate of the cooling capacity that the cooling station system may need to provide over a period of time based on current environmental conditions. The next time period can be the next hour. By inputting the processed feature data of the current indoor and outdoor environments, the target cooling capacity prediction model can predict the cooling demand for the next time period in real time. The accuracy of the prediction can influence the subsequent setting of cooling station equipment operating parameters and the effectiveness of indoor temperature control. After obtaining the candidate cooling capacity, the operating parameters of the cooling station equipment can be fine-tuned based on the candidate cooling capacity, thereby achieving precise control of the indoor temperature, helping to avoid energy waste and improve energy efficiency.

[0027] Step 104 : predicting the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status, and generating target operating status data of the cooling station equipment.

[0028] In some embodiments, a comprehensive input dataset is formed by combining candidate cooling capacities for the next time period predicted by a target cooling capacity prediction model, processed feature data of the current indoor and outdoor environments, and processed feature data of the current operating state. This dataset serves as a foundation for subsequent optimization calculations. Based on this dataset, a data analysis algorithm or machine learning model can be employed to predict target operating state data to which the cooling plant should be adjusted, taking into account the stability and efficiency of the cooling plant equipment to avoid frequent power on / off and over-adjustment that could lead to increased energy consumption or equipment damage. The target operating state data is an optimized, recommended value that serves as a control strategy for optimal energy conservation while meeting demand. This helps the cooling plant equipment respond quickly to future load changes and maintain optimal operating efficiency while ensuring stable indoor temperatures. The target operating state data may include: cooling source system name (identifying the name or identifier of the cooling plant system); project name (identifying the name of the current project or mall); desired temperature (target indoor temperature); number of cooling towers (number of cooling towers to be operated); number of chiller pumps (number of chiller pumps to be operated); chiller pump frequency (frequency); number of chillers (number of chillers to be operated); and set temperature (set temperature of the chillers). By predicting and optimizing cooling plant equipment operating status data, we can maintain cooling capacity while minimizing energy consumption and operating costs, helping to improve energy efficiency and reduce waste. The predicted target operating status data helps maintain indoor comfort, keeping parameters like temperature and humidity within set ranges.

[0029] Step 105: Control the cooling station equipment according to the target operating status data to control the temperature of the indoor environment.

[0030] In some embodiments, the indoor temperature control method disclosed herein can be applied to a cold station temperature control system, which receives and parses target operating status data. The target operating status data includes specific operating parameters of each device that should be set to achieve the target indoor temperature, such as the number of cooling towers, the number of refrigeration pumps, and the frequency of the refrigeration pumps. The cold station temperature control system sends instructions to each device in the cold station based on the operating status data to accurately control the cold station equipment, ensuring that the cold station equipment operates in the optimal state to meet the expected cooling demand. Controlling the cold station equipment based on the target operating status data to achieve temperature control of the indoor environment and intelligent adjustment of the indoor temperature helps to improve energy efficiency and enhance user satisfaction. By controlling the cold station equipment based on the target operating status data, the operating status of the cold station equipment can be automatically adjusted, automatic control can be achieved, dependence on manual operation can be reduced, and the response speed and operating efficiency of the cold station temperature control system can be improved.

[0031] The indoor temperature control method proposed in this disclosure obtains current indoor and outdoor environmental characteristic data and current operating status characteristic data of the cooling station equipment, providing a foundation for subsequent data analysis and model prediction. By continuously monitoring and recording the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment, indoor environmental changes and the operating status of the cooling station equipment can be more accurately understood, providing reliable data support for the intelligent control of the cooling station system. The current indoor and outdoor environmental characteristic data are preprocessed to obtain processed indoor and outdoor environmental characteristic data, and the current operating status characteristic data are preprocessed to obtain processed operating status characteristic data. The collected raw data is preprocessed to remove outliers and convert the data format to make it suitable for the target cooling capacity prediction model. The preprocessed indoor and outdoor environmental characteristic data and the processed operating status characteristic data more accurately reflect the actual situation, providing reliable input data for the subsequent target cooling capacity prediction model, thereby improving the accuracy and generalization ability of the target cooling capacity prediction model. Based on the trained target cooling capacity prediction model, the current processed indoor and outdoor environmental characteristic data are input to predict the indoor cooling capacity required for the next time period, providing basic data for generating target operating status data. The operating parameters of the cooling station equipment are predicted by combining the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status to meet the expected cooling capacity demand. This helps the cooling station equipment operate in an optimal state, meeting the temperature control requirements while minimizing energy consumption, and generates the target operating status data for the cooling station equipment. The generated target operating status data is applied to the actual control of the cooling station equipment. Through refined control of the cooling station equipment, effective management of indoor temperature is achieved, solving the problem of inaccurate indoor temperature control in existing technologies, helping to maintain the indoor temperature within the set comfortable range. Refined control of the cooling station equipment improves the accuracy and efficiency of temperature control, reducing energy consumption and operating costs.

[0032] Figure 2 1 is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure. In some embodiments, the current indoor and outdoor environment characteristic data includes the current time, the current indoor temperature, the current indoor humidity, the current outdoor temperature, the current outdoor humidity, the current indoor and outdoor category characteristic data, and the current indoor and outdoor tilt characteristic data.

[0033] like Figure 2 As shown, step 102 may specifically include steps 201 to 207.

[0034] Step 201, constructing a time feature based on the current time;

[0035] Step 202: Perform exploratory data analysis on the current indoor temperature, the current indoor humidity, the current outdoor temperature, and the current outdoor humidity to obtain the analyzed current indoor temperature, the analyzed current indoor humidity, the analyzed current outdoor temperature, and the analyzed current outdoor humidity.

[0036] Step 203, determining the corresponding indoor wet-bulb temperature based on the analyzed current indoor temperature and the analyzed current indoor humidity;

[0037] Step 204, determining a corresponding indoor and outdoor temperature difference based on the analyzed current indoor temperature and the analyzed current outdoor temperature;

[0038] Step 205, normalizing the current indoor and outdoor tilt feature data to obtain current indoor and outdoor normalized feature data;

[0039] Step 206, encoding the current indoor and outdoor category feature data to obtain the current indoor and outdoor category feature vector;

[0040] Step 207, the time feature, the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis, the current outdoor humidity after analysis, the indoor wet-bulb temperature, the indoor and outdoor temperature difference, the current indoor and outdoor normalized feature data and the current indoor and outdoor category feature vector are determined as the processed feature data of the current indoor and outdoor environment.

[0041] In some embodiments, based on the current time, constructed time features can include whether it is a holiday, whether it is a holiday adjustment, the number of days until the holiday, the number of days until the summer solstice, business hours, store opening and closing times, etc. Time features help the target cooling capacity prediction model better capture the impact of time-related features on cooling capacity demand and capture time dependence. Exploratory data analysis is an important step in understanding data distribution, identifying outliers, and understanding the relationship between variables. Exploratory data analysis can be performed by cleaning (e.g., removing outliers), transforming (e.g., logarithmic transformation), or standardizing. By performing exploratory data analysis on the current indoor temperature, current indoor humidity, current outdoor temperature, and current outdoor humidity, the analyzed current indoor temperature, analyzed current indoor humidity, analyzed current outdoor temperature, and analyzed current outdoor humidity can be obtained. This can better understand their characteristics and potential patterns, improve data accuracy and reliability, and provide a basis for subsequent cooling capacity prediction. Wet-bulb temperature is a parameter that represents the relative humidity of air. It integrates information on temperature and humidity. Based on the analyzed current indoor temperature and analyzed current indoor humidity, the corresponding indoor wet-bulb temperature can be determined, helping to more comprehensively describe the thermal and humid state of the indoor environment. Based on the analyzed current indoor and outdoor temperatures, the corresponding indoor and outdoor temperature difference is determined. This indoor and outdoor temperature difference can represent the degree of difference between the indoor and outdoor environments, and the magnitude of the indoor and outdoor temperature difference can directly affect the comfort and energy efficiency of the indoor environment. Based on the chilled water supply and return temperatures, the corresponding cold source system temperature difference is determined. The magnitude of the cold source system temperature difference can indicate the efficiency of the cooling plant equipment. A larger temperature difference may indicate that the cooling plant equipment is operating well and has high cooling efficiency. The current indoor and outdoor tilted feature data is unevenly distributed or has outliers. Processing can be performed on the current indoor and outdoor tilted feature data to obtain the current indoor and outdoor category feature vector. This processing method can include performing logarithmic transformation, normalization, or truncation on the current indoor and outdoor tilted feature data to obtain the current indoor and outdoor normalized feature data to ensure the stability and usability of the feature data. The indoor and outdoor category feature data can be converted into a feature vector using one-hot encoding to obtain the current indoor and outdoor category feature vector. This allows the non-numeric data to be understood and processed by the target cooling capacity prediction model, thereby increasing the accuracy of the target cooling capacity prediction. All the processed features mentioned above are integrated with the unprocessed feature data (such as pedestrian flow) to obtain a structured set of "processed feature data of the current indoor and outdoor environment". This set contains multi-dimensional information reflecting the status of the indoor and outdoor environment, which is helpful for further analysis, prediction or control decision-making.

[0042] In some embodiments, the current operating status characteristic data includes the chilled water supply temperature and the chilled water return temperature. The current operating status characteristic data is preprocessed to obtain the processed characteristic data of the current operating status, including: determining the corresponding chilled water temperature difference based on the chilled water supply temperature and the chilled water return temperature; and determining the chilled water temperature difference as the processed characteristic data of the current operating status.

[0043] In some embodiments, the chilled water supply temperature is the temperature of the chilled water flowing out of the refrigeration unit and before entering the terminal equipment (such as fan coil units, air conditioning units, etc.), which can indicate the refrigeration capacity of the refrigeration unit and the heat loss of the water supply pipe. The chilled water return temperature is the temperature of the chilled water flowing back from the terminal equipment to the refrigeration unit, which can indicate the heat exchange efficiency and load of the terminal equipment. The chilled water supply temperature and chilled water return temperature collected by the sensor are obtained and cleaned. Based on the chilled water supply temperature and chilled water return temperature, the chilled water temperature difference is calculated. Chilled water temperature difference = chilled water supply temperature - chilled water return temperature. The chilled water temperature difference can indicate the actual load borne by the cold station equipment and the operating efficiency of the refrigeration unit. The chilled water temperature difference is an important indicator for evaluating the performance of the cold station equipment. Preprocessing the current operating status characteristic data and extracting the chilled water temperature difference feature can help the cold station equipment perform performance analysis, fault prediction, optimization control, and load prediction.

[0044] Figure 3 This is a flow chart of another indoor temperature control method provided by an embodiment of the present disclosure. In some embodiments, step 104, which predicts the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating state, to generate target operating state data for the cooling station equipment, may specifically include steps 301 to 305.

[0045] Step 301: Input the indoor and outdoor temperature difference and the chilled water temperature difference into a temperature prediction model, process the indoor and outdoor temperature difference and the chilled water temperature difference based on the temperature prediction model, and obtain a predicted set temperature of the cooling station equipment;

[0046] Step 302: Input the predicted cooling capacity, indoor wet-bulb temperature, and analyzed current outdoor temperature into a cooling tower quantity prediction model. The predicted cooling capacity, indoor wet-bulb temperature, and analyzed current outdoor temperature are processed based on the cooling tower quantity prediction model to obtain a predicted number of cooling towers for the cooling station.

[0047] Step 303: Input the predicted number of cooling towers and the processed characteristic data of the current operating status into the supply cooling capacity prediction model, process the predicted number of cooling towers and the processed characteristic data of the current operating status, and obtain a second candidate cooling capacity of the cooling station equipment;

[0048] Step 304: Correct the candidate cooling capacity based on the second candidate cooling capacity to obtain a target cooling capacity for the indoor environment, and obtain a target set temperature for the cooling station equipment and a target number of cooling towers for the cooling station equipment.

[0049] Step 305 : generating target operating status data of the cooling station equipment based on the target cooling capacity, the target set temperature, and the target number of cooling towers.

[0050] In some embodiments, the temperature prediction model is a linear regression model trained based on historical data (including historical indoor and outdoor temperature differences and historical chilled water temperature differences). The model can be processed based on the indoor and outdoor temperature differences and chilled water temperature differences to predict the predicted set temperature of the cooling station equipment. The predicted set temperature of the cooling station equipment can both meet the comfort requirements of the indoor environment and optimize energy consumption and equipment operating efficiency. The predicted cooling capacity, indoor wet-bulb temperature, and analyzed current outdoor temperature output by the target cooling capacity prediction model are input into a cooling tower quantity prediction model to predict the required number of cooling towers, resulting in a predicted number of cooling towers for the cooling station equipment. This helps ensure that there is sufficient cooling capacity to support the predicted cooling capacity demand while avoiding overuse or underuse of cooling towers. Cooling towers are important heat dissipation components in the cooling station system, and the number of cooling towers directly affects heat dissipation efficiency. By comprehensively analyzing the predicted cooling capacity, indoor wet-bulb temperature, and analyzed current outdoor temperature, the most cost-effective cooling tower configuration is determined, resulting in a predicted number of cooling towers for the cooling station equipment to meet current and expected cooling load requirements. The cooling tower quantity prediction model comprehensively considers the impact of environmental conditions on cooling efficiency to ensure that the system can effectively dissipate heat under different environments. The predicted number of cooling towers and the processed characteristic data of the current operating status are input into the cooling capacity supply prediction model. Based on the predicted number of cooling towers and the current operating status of the cooling station equipment, the cooling capacity that the cooling station equipment can actually supply is predicted, that is, the second candidate cooling capacity of the cooling station equipment. This can provide basic data for subsequent cooling capacity correction and target set temperature and the target number of cooling towers of the cooling station equipment, ensuring that the predicted cooling capacity matches the actual cooling capacity supplied. If the difference between the second candidate cooling capacity of the cooling station equipment and the candidate cooling capacity is within a preset range, the target operating status data of the cooling station equipment is generated based on the candidate cooling capacity, the predicted set temperature and the predicted number of cooling towers; if the difference between the second candidate cooling capacity of the cooling station equipment and the candidate cooling capacity exceeds the preset range, the candidate cooling capacity is corrected based on the second candidate cooling capacity until the difference between the corrected second candidate cooling capacity and the predicted cooling capacity is within the preset range, thereby obtaining the target cooling capacity of the indoor environment, the target set temperature of the cooling station equipment, and the target number of cooling towers of the cooling station equipment. Based on the target cooling capacity, target set temperature, and target number of cooling towers, a relationship table is constructed regarding the number of units activated, frequency, and cooling capacity. Using optimization algorithms or rule engines, an optimal startup strategy—the target operating status data for the cooling station equipment—is generated to meet demand while maximizing energy savings. This target operating status data directly guides the regulation of cooling station equipment to achieve efficient and stable cooling results. Using predictive models to predict and optimize cooling station equipment operating parameters ensures optimal operation, meeting indoor environmental requirements while reducing energy consumption and improving equipment efficiency.

[0051] Figure 4 This is a flow chart of another indoor temperature control method provided by the embodiment of the present disclosure. In some embodiments, reference Figure 4 Step 305 , namely, generating target operating status data of the cooling station equipment based on the target cooling capacity, the target set temperature and the target number of cooling towers, may specifically include: steps 401 to 404 .

[0052] Step 401: generating a plurality of first candidate operating status data of cooling station equipment that meets the target cooling capacity based on the target cooling capacity, the target set temperature, and the target number of cooling towers;

[0053] Step 402: Filter the plurality of first candidate running status data based on a preset rule to obtain a plurality of second candidate running status data;

[0054] Step 403: Based on each second candidate operating state data, obtain the energy consumption corresponding to each second candidate operating state data;

[0055] Step 404 : determining target operating state data from each second candidate operating state data based on each energy consumption.

[0056] In some embodiments, based on the three core indicators of target cooling capacity, target set temperature, and target number of cooling towers, multiple possible operating configuration schemes are generated, namely multiple first candidate operating state data. Each first candidate operating state data takes into account different equipment combinations and operating parameters (such as the number of cooling towers, the number of cooling pumps, etc.) to meet the established cooling capacity requirements and environmental regulation goals. The multiple first candidate operating state data can provide a broad option base for subsequent screening, ensuring that all configurations that can achieve the target cooling capacity and set temperature are covered. Based on preset rules, the multiple first candidate operating state data can be screened to eliminate infeasible or economically inefficient first candidate operating state data, thereby obtaining multiple second candidate operating state data. The preset rules can be cold station equipment operating constraints, safety thresholds, economic principles, etc. For each second candidate operating state that passes the screening, the energy consumption corresponding to each second candidate operating state data is calculated. Based on the calculated energy consumption, the second candidate operating state with the lowest energy consumption or the highest energy efficiency ratio is selected as the final target operating state. In addition, the state that meets other optimization conditions (such as lowest cost, shortest operating time, etc.) can also be selected as the target operating state data. Through screening and optimization, the cooling station equipment can be operated in an energy-saving manner while meeting the cooling demand and environmental regulation. At the same time, the stability and efficiency of the cooling station equipment can be taken into consideration to avoid frequent power on and off and excessive adjustment that may lead to increased energy consumption or equipment damage.

[0057] Figure 5 A flow chart of another indoor temperature control method provided in the embodiment of the present disclosure, in some embodiments, reference is made to Figure 5Step 103, that is, before inputting the processed characteristic data of the current indoor and outdoor environment into the trained target cooling capacity prediction model, can also include steps 501 to 506.

[0058] Step 501: Acquire historical indoor environment characteristic data and historical operating status characteristic data of the cooling station equipment, wherein the historical operating status characteristic data includes historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow rate, and historical chilled water return flow rate;

[0059] Step 502, pre-processing the historical indoor and outdoor environment characteristic data to obtain processed historical indoor and outdoor environment characteristic data;

[0060] Step 503, determining the corresponding historical cooling capacity based on the historical chilled water supply temperature, the historical chilled water return temperature, the historical chilled water supply flow rate, and the historical chilled water return flow rate;

[0061] Step 504: Using the processed feature data of historical indoor and outdoor environments as training samples and the historical cooling capacity as labels of the training samples, a cooling capacity prediction training set is constructed;

[0062] Step 505: Input the cooling capacity prediction training set into a plurality of candidate cooling capacity prediction models to be trained respectively, and train each candidate cooling capacity prediction model to be trained based on the cooling capacity prediction training set to obtain a plurality of candidate cooling capacity prediction models;

[0063] Step 506: Select a target cooling capacity prediction model from multiple candidate cooling capacity prediction models.

[0064] In some embodiments, historical indoor environment characteristic data may include historical time, historical indoor temperature, historical indoor humidity, historical outdoor temperature, historical outdoor humidity, historical foot traffic, historical wind speed, historical weather, etc., and historical operating status characteristic data may include historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow rate, and historical chilled water return flow rate. The historical indoor and outdoor environment characteristic data are preprocessed. The preprocessing may include cleaning, screening, conversion, and standardization to eliminate noise, missing values, and inconsistencies, improve data quality, and obtain processed historical indoor and outdoor environment characteristic data. The processed historical indoor and outdoor environment characteristic data is then made suitable for subsequent data analysis and model training. By analyzing the historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow rate, and historical chilled water return flow rate, the corresponding historical cooling capacity is calculated in combination with thermodynamic principles and calculation formulas. If the calculated historical cooling capacity contains negative values, the negative values ​​are truncated to zero to ensure the rationality and accuracy of the cooling capacity data. The pre-processed historical indoor and outdoor environmental feature data are used as training samples, and the corresponding historical cooling capacity is used as the label of the training sample to construct a cooling capacity prediction training set, which will be used for subsequent model training. Use different algorithms (such as linear regression, decision tree, random forest, support vector machine, etc.) to construct multiple candidate cooling capacity prediction models to be trained, and train them with the cooling capacity prediction training set to obtain multiple candidate cooling capacity prediction models, verify the performance of different algorithms on the cooling capacity prediction training set, and screen out the target cooling capacity prediction model that is most suitable for predicting cooling capacity. The results can be obtained through cross-validation, mean square error, and determination coefficient R. 2 The prediction performance of candidate cooling capacity prediction models is compared using evaluation indicators such as [number of indicators], and the target cooling capacity prediction model with the best prediction performance is selected. The resulting target cooling capacity prediction model has the best cooling capacity prediction capability, accurately predicting future cooling demand and providing a reliable basis for intelligent scheduling and energy-saving control of cooling station equipment. Furthermore, the cooling capacity prediction model training can be deployed to a cloud server, enabling centralized model training and optimization across projects. This leverages the cloud's massive data and computing resources to further improve the accuracy and generalization of the cooling capacity prediction model.

[0065] In some embodiments, after controlling the cooling station equipment, it also includes: collecting real-time indoor and outdoor environmental characteristic data and real-time operating status characteristic data of the cooling station equipment; updating the parameters of the target cooling capacity prediction model according to the real-time indoor and outdoor environmental characteristic data and the real-time operating status characteristic data.

[0066] In some embodiments, actual indoor and outdoor real-time environmental characteristic data and real-time operating status characteristic data of the cooling station equipment are continuously collected, and the collected indoor and outdoor real-time environmental characteristic data and real-time operating status characteristic data of the cooling station equipment are fed back to the target cooling capacity prediction model for parameter updating and optimization of the target cooling capacity prediction model. Based on the feedback data, the target cooling capacity prediction model is updated and adjusted to improve the accuracy and prediction ability of the target cooling capacity prediction model. Based on the updated target cooling capacity prediction model, the generated control strategy is adjusted to adapt to current environmental changes and energy consumption requirements. By continuously collecting indoor and outdoor real-time environmental characteristic data and real-time operating status characteristic data of the cooling station equipment and feeding them back to the target cooling capacity prediction model, the target cooling capacity prediction model can be adjusted and optimized in a timely manner, thereby improving the accuracy and stability of prediction and control, helping the cooling station equipment system to continuously adapt to changes in the indoor environment, and realizing intelligent management and optimization of the cooling station equipment.

[0067] In some embodiments, in addition to automatically adjusting the operating parameters of cooling station equipment, this can also be extended to intelligent coordinated control of other indoor equipment, such as lighting and ventilation. Integrated coordinated control of various indoor subsystems can further improve overall energy efficiency.

[0068] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present disclosure, and will not be described in detail here.

[0069] The following are embodiments of the apparatus disclosed herein, which can be used to implement the method embodiments disclosed herein. For details not disclosed in the apparatus embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0070] Figure 6 Schematic diagram of an indoor temperature control device provided by an embodiment of the present disclosure. Figure 4 As shown, the indoor temperature control device includes:

[0071] The data acquisition module 601 is used to obtain the current indoor and outdoor environment characteristic data and the current operating status characteristic data of the cooling station equipment;

[0072] The preprocessing module 602 is used to preprocess the current indoor and outdoor environment feature data to obtain the processed feature data of the current indoor and outdoor environment, and to preprocess the current operating state feature data to obtain the processed feature data of the current operating state;

[0073] The cooling capacity prediction module 603 is used to input the processed characteristic data of the current indoor and outdoor environments into the trained target cooling capacity prediction model, and process the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model to obtain the candidate indoor cooling capacity for the next time period;

[0074] The target operating state data generating module 604 is configured to predict the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating state, and generate the target operating state data of the cooling station equipment;

[0075] The execution module 605 is used to control the cooling station equipment according to the target operating state data so as to control the temperature of the indoor environment.

[0076] According to the technical solutions provided by the embodiments of the present disclosure, by acquiring current indoor and outdoor environmental characteristic data and current operating status characteristic data of the cooling station equipment, a foundation is provided for subsequent data analysis and model prediction. By continuously monitoring and recording the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment, indoor environmental changes and the operating status of the cooling station equipment can be more accurately understood, providing reliable data support for the intelligent control of the cooling station system. The current indoor and outdoor environmental characteristic data are preprocessed to obtain processed indoor and outdoor environmental characteristic data, and the current operating status characteristic data are preprocessed to obtain processed operating status characteristic data. The collected raw data is preprocessed to remove outliers and convert the data format to make it suitable for the target cooling capacity prediction model. The preprocessed indoor and outdoor environmental characteristic data and the processed operating status characteristic data can more accurately reflect the actual situation, providing reliable input data for the subsequent target cooling capacity prediction model, thereby improving the accuracy and generalization ability of the target cooling capacity prediction model. Based on the trained target cooling capacity prediction model, the current processed indoor and outdoor environmental characteristic data are input to predict the indoor cooling capacity required for the next time period, providing basic data for generating target operating status data. The operating parameters of the cooling station equipment are predicted by combining the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating status to meet the expected cooling capacity demand. This helps the cooling station equipment operate in an optimal state, meeting the temperature control requirements while minimizing energy consumption, and generates the target operating status data for the cooling station equipment. The generated target operating status data is applied to the actual control of the cooling station equipment. Through refined control of the cooling station equipment, effective management of indoor temperature is achieved, solving the problem of inaccurate indoor temperature control in existing technologies, helping to maintain the indoor temperature within the set comfortable range. Refined control of the cooling station equipment improves the accuracy and efficiency of temperature control, reducing energy consumption and operating costs.

[0077] In some embodiments, the current indoor and outdoor environmental characteristic data include the current time, the current indoor temperature, the current indoor humidity, the current outdoor temperature, the current outdoor humidity, the current indoor and outdoor category characteristic data, and the current indoor and outdoor tilt characteristic data. The preprocessing module 602 is configured to construct a time feature based on the current time; perform exploratory data analysis on the current indoor temperature, the current indoor humidity, the current outdoor temperature, and the current outdoor humidity, respectively, to obtain the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis, and the current outdoor humidity after analysis; determine the current indoor temperature after analysis and the current indoor humidity after analysis based on the current indoor temperature after analysis and the current indoor humidity after analysis. Determine the corresponding indoor wet-bulb temperature; determine the corresponding indoor and outdoor temperature difference based on the current indoor temperature after analysis and the current outdoor temperature after analysis; normalize the current indoor and outdoor tilt feature data to obtain the current indoor and outdoor normalized feature data; encode the current indoor and outdoor category feature data to obtain the current indoor and outdoor category feature vector; determine the time feature, the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis, the current outdoor humidity after analysis, the indoor wet-bulb temperature, the indoor and outdoor temperature difference, the current indoor and outdoor normalized feature data and the current indoor and outdoor category feature vector as the processed feature data of the current indoor and outdoor environment.

[0078] In some embodiments, the current operating status characteristic data includes the chilled water supply temperature and the chilled water return temperature. The preprocessing module 602 is configured to determine the corresponding chilled water temperature difference based on the chilled water supply temperature and the chilled water return temperature; and determine the chilled water temperature difference as the processed characteristic data of the current operating status.

[0079] In some embodiments, the target operating status data generation module 604 is configured to input the indoor and outdoor temperature difference and the chilled water temperature difference into a temperature prediction model, process the indoor and outdoor temperature difference and the chilled water temperature difference based on the temperature prediction model, and obtain the predicted set temperature of the cooling station equipment; input the predicted cooling capacity, indoor wet-bulb temperature and the current outdoor temperature after analysis into a cooling tower quantity prediction model, process the predicted cooling capacity, indoor wet-bulb temperature and the current outdoor temperature after analysis based on the cooling tower quantity prediction model, and obtain the predicted number of cooling towers of the cooling station equipment; input the predicted number of cooling towers and the processed characteristic data of the current operating status into the supply cooling capacity prediction model, process the predicted number of cooling towers and the processed characteristic data of the current operating status, and obtain a second candidate cooling capacity of the cooling station equipment; correct the candidate cooling capacity based on the second candidate cooling capacity to obtain the target cooling capacity of the indoor environment, and obtain the target set temperature of the cooling station equipment and the target number of cooling towers of the cooling station equipment; generate the target operating status data of the cooling station equipment based on the target cooling capacity, target set temperature and target number of cooling towers.

[0080] In some embodiments, the target operating status data generation module 604 is configured to generate multiple first candidate operating status data for cold station equipment that meets the target cooling capacity based on the target cooling capacity, the target set temperature and the target number of cooling towers; filter the multiple first candidate operating status data based on preset rules to obtain multiple second candidate operating status data; obtain the energy consumption corresponding to each second candidate operating status data based on each second candidate operating status data; and determine the target operating status data from each second candidate operating status data based on each energy consumption.

[0081] In some embodiments, before inputting the processed characteristic data of the current indoor and outdoor environments into the trained target cooling capacity prediction model, the indoor temperature control device is configured to obtain historical indoor environmental characteristic data and historical operating status characteristic data of the cooling station equipment, the historical operating status characteristic data including historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow, and historical chilled water return flow; preprocessing the historical indoor and outdoor environmental characteristic data to obtain the processed characteristic data of the historical indoor and outdoor environments; determining the corresponding historical cooling capacity based on the historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow, and historical chilled water return flow; constructing a cooling capacity prediction training set using the processed characteristic data of the historical indoor and outdoor environments as training samples and the historical cooling capacity as labels of the training samples; inputting the cooling capacity prediction training set into a plurality of candidate cooling capacity prediction models to be trained respectively, and training each candidate cooling capacity prediction model to be trained based on the cooling capacity prediction training set to obtain a plurality of candidate cooling capacity prediction models; and selecting a target cooling capacity prediction model from the plurality of candidate cooling capacity prediction models.

[0082] In some embodiments, after controlling the cooling station equipment, the indoor temperature control device is configured to collect real-time indoor and outdoor environmental characteristic data and real-time operating status characteristic data of the cooling station equipment; and update the parameters of the target cooling capacity prediction model based on the real-time indoor and outdoor environmental characteristic data and the real-time operating status characteristic data.

[0083] 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 the present disclosure.

[0084] Figure 7 FIG. 7 is a schematic diagram of an electronic device 7 provided in an embodiment of the present disclosure. Figure 7As shown, the electronic device 7 of this embodiment includes: a processor 701, a memory 702, and a computer program 703 stored in the memory 702 and executable by the processor 701. When the processor 701 executes the computer program 703, the steps of the above-mentioned method embodiments are implemented. Alternatively, when the processor 701 executes the computer program 703, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0085] The electronic device 7 may be a desktop computer, a notebook, a PDA, a cloud server or other electronic device. The electronic device 7 may include but is not limited to a processor 701 and a memory 702. Those skilled in the art will understand that Figure 7 This is merely an example of the electronic device 7 and does not limit the electronic device 7 . The electronic device 7 may include more or fewer components than shown in the figure, or different components.

[0086] The processor 701 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0087] The memory 702 can be an internal storage unit of the electronic device 7, such as a hard disk or memory of the electronic device 7. The memory 702 can also be an external storage device of the electronic device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 7. The memory 702 can also include both an internal storage unit of the electronic device 7 and an external storage device. The memory 702 is used to store computer programs and other programs and data required by the electronic device.

[0088] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0089] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium (such as a computer-readable storage medium). Based on this understanding, the present disclosure implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. The computer program may include computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0090] The above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure 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 disclosure, and should all be included in the scope of protection of the present disclosure.

Claims

1. A method for controlling indoor temperature, characterized in that: include: Obtain current indoor and outdoor environmental characteristic data and current operating status characteristic data of the cooling station equipment; Preprocessing the current indoor and outdoor environment characteristic data to obtain processed characteristic data of the current indoor and outdoor environment, and preprocessing the current operating state characteristic data to obtain processed characteristic data of the current operating state; Inputting the processed characteristic data of the current indoor and outdoor environments into a trained target cooling capacity prediction model, processing the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model to obtain candidate indoor cooling capacity for the next time period; Predicting the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating state, to generate target operating state data of the cooling station equipment; controlling the cooling station equipment according to the target operating state data so as to control the temperature of the indoor environment; The current indoor and outdoor environment characteristic data includes the current time, the current indoor temperature, the current indoor humidity, the current outdoor temperature, the current outdoor humidity, the current indoor and outdoor category characteristic data, and the current indoor and outdoor tilt characteristic data. The preprocessing of the current indoor and outdoor environment characteristic data to obtain the processed characteristic data of the current indoor and outdoor environment includes: constructing a time feature based on the current time; Performing exploratory data analysis on the current indoor temperature, the current indoor humidity, the current outdoor temperature, and the current outdoor humidity, respectively, to obtain the analyzed current indoor temperature, the analyzed current indoor humidity, the analyzed current outdoor temperature, and the analyzed current outdoor humidity; determining a corresponding indoor wet-bulb temperature based on the analyzed current indoor temperature and the analyzed current indoor humidity; Determining a corresponding indoor and outdoor temperature difference based on the analyzed current indoor temperature and the analyzed current outdoor temperature; Normalizing the current indoor and outdoor tilt feature data to obtain current indoor and outdoor normalized feature data; Encoding the current indoor and outdoor category feature data to obtain a current indoor and outdoor category feature vector; Determining the time feature, the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis, the current outdoor humidity after analysis, the indoor wet-bulb temperature, the indoor and outdoor temperature difference, the current indoor and outdoor normalized feature data, and the current indoor and outdoor category feature vector as the processed feature data of the current indoor and outdoor environment; The current operating state characteristic data includes the chilled water supply temperature and the chilled water return temperature. The preprocessing of the current operating state characteristic data to obtain the processed characteristic data of the current operating state includes: Determining a corresponding chilled water temperature difference based on the chilled water supply temperature and the chilled water return temperature; Determining the chilled water temperature difference as the processed characteristic data of the current operating state; The predicting of the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating state to generate target operating state data of the cooling station equipment includes: Inputting the indoor and outdoor temperature difference and the chilled water temperature difference into a temperature prediction model, processing the indoor and outdoor temperature difference and the chilled water temperature difference based on the temperature prediction model to obtain a predicted set temperature of the cooling station equipment; Inputting the candidate cooling capacity, the indoor wet-bulb temperature, and the analyzed current outdoor temperature into a cooling tower quantity prediction model, and processing the candidate cooling capacity, the indoor wet-bulb temperature, and the analyzed current outdoor temperature based on the cooling tower quantity prediction model to obtain a predicted number of cooling towers for the cooling station equipment; Inputting the predicted number of cooling towers and the processed characteristic data of the current operating status into a supply cooling capacity prediction model, processing the predicted number of cooling towers and the processed characteristic data of the current operating status to obtain a second candidate cooling capacity of the cooling station equipment; Correcting the candidate cooling capacity based on the second candidate cooling capacity to obtain a target cooling capacity for the indoor environment, and obtaining a target set temperature for the cooling station equipment and a target number of cooling towers for the cooling station equipment; Target operating status data of the cooling station equipment is generated based on the target cooling capacity, the target set temperature, and the target number of cooling towers.

2. The method according to claim 1, characterized in that Generating the target operating status data of the cooling station equipment based on the target cooling capacity, the target set temperature, and the target number of cooling towers includes: generating a plurality of first candidate operating state data of the cooling station equipment that meets the target cooling capacity based on the target cooling capacity, the target set temperature, and the target number of cooling towers; Filtering the plurality of first candidate operating status data based on a preset rule to obtain a plurality of second candidate operating status data; Based on each piece of the second candidate operating state data, obtaining energy consumption corresponding to each piece of the second candidate operating state data; The target operating state data is determined from each of the second candidate operating state data based on the respective energy consumptions.

3. The method according to claim 1, characterized in that Before inputting the processed characteristic data of the current indoor and outdoor environment into the trained target cooling capacity prediction model, the method further includes: Acquire historical indoor environment characteristic data and historical operating status characteristic data of the cooling station equipment, wherein the historical operating status characteristic data includes historical chilled water supply temperature, historical chilled water return temperature, historical chilled water supply flow rate, and historical chilled water return flow rate; Preprocessing the historical indoor and outdoor environment characteristic data to obtain processed historical indoor and outdoor environment characteristic data; Determining corresponding historical cooling capacity based on the historical chilled water supply temperature, the historical chilled water return temperature, the historical chilled water supply flow rate, and the historical chilled water return flow rate; The processed feature data of the historical indoor and outdoor environments are used as training samples, and the historical cooling capacity is used as a label of the training samples to construct a cooling capacity prediction training set; Inputting the cooling capacity prediction training set into a plurality of candidate cooling capacity prediction models to be trained respectively, and training each candidate cooling capacity prediction model to be trained based on the cooling capacity prediction training set to obtain a plurality of candidate cooling capacity prediction models; A target cooling capacity prediction model is selected from the plurality of candidate cooling capacity prediction models.

4. The method according to claim 1, wherein After controlling the cold station equipment, the method further includes: Collecting real-time indoor and outdoor environmental characteristic data and real-time operating status characteristic data of the cooling station equipment; The parameters of the target cooling capacity prediction model are updated according to the real-time indoor and outdoor environmental characteristic data and the real-time operating status characteristic data.

5. An indoor temperature control device, characterized in that: include: The data acquisition module is used to obtain the current indoor and outdoor environmental characteristic data and the current operating status characteristic data of the cooling station equipment; A preprocessing module, configured to preprocess the current indoor and outdoor environment characteristic data to obtain processed characteristic data of the current indoor and outdoor environments, and to preprocess the current operating state characteristic data to obtain processed characteristic data of the current operating state; a cooling capacity prediction module, configured to input the processed characteristic data of the current indoor and outdoor environments into a trained target cooling capacity prediction model, and process the processed characteristic data of the current indoor and outdoor environments based on the target cooling capacity prediction model to obtain a candidate indoor cooling capacity for the next time period; a target operating state data generating module, configured to predict the operating parameters of the cooling station equipment based on the candidate cooling capacity, the processed characteristic data of the current indoor and outdoor environments, and the processed characteristic data of the current operating state, and generate target operating state data of the cooling station equipment; an execution module, configured to control the cooling station equipment according to the target operating state data so as to control the temperature of the indoor environment; Among them, the current indoor and outdoor environmental characteristic data include the current time, the current indoor temperature, the current indoor humidity, the current outdoor temperature, the current outdoor humidity, the current indoor and outdoor category characteristic data, and the current indoor and outdoor tilt characteristic data. The preprocessing module is specifically used to: construct a time feature based on the current time; perform exploratory data analysis on the current indoor temperature, the current indoor humidity, the current outdoor temperature and the current outdoor humidity respectively to obtain the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis and the current outdoor humidity after analysis; determine the corresponding indoor wet-bulb temperature based on the current indoor temperature after analysis and the current indoor humidity after analysis. degree; based on the current indoor temperature after analysis and the current outdoor temperature after analysis, determining the corresponding indoor and outdoor temperature difference; normalizing the current indoor and outdoor tilt feature data to obtain current indoor and outdoor normalized feature data; encoding the current indoor and outdoor category feature data to obtain a current indoor and outdoor category feature vector; determining the time feature, the current indoor temperature after analysis, the current indoor humidity after analysis, the current outdoor temperature after analysis, the current outdoor humidity after analysis, the indoor wet-bulb temperature, the indoor and outdoor temperature difference, the current indoor and outdoor normalized feature data and the current indoor and outdoor category feature vector as the processed feature data of the current indoor and outdoor environment; The current operating state characteristic data includes a chilled water supply temperature and a chilled water return temperature, and the pre-processing module is further configured to: determine a corresponding chilled water temperature difference based on the chilled water supply temperature and the chilled water return temperature; and determine the chilled water temperature difference as the processed characteristic data of the current operating state; The target operating status data generation module is specifically used to: input the indoor and outdoor temperature difference and the chilled water temperature difference into a temperature prediction model, process the indoor and outdoor temperature difference and the chilled water temperature difference based on the temperature prediction model, and obtain the predicted set temperature of the cooling station equipment; input the candidate cooling capacity, the indoor wet-bulb temperature, and the analyzed current outdoor temperature into a cooling tower quantity prediction model, process the candidate cooling capacity, the indoor wet-bulb temperature, and the analyzed current outdoor temperature based on the cooling tower quantity prediction model, and obtain the predicted number of cooling towers of the cooling station equipment; input the predicted number of cooling towers and the processed characteristic data of the current operating status into a supply cooling capacity prediction model, process the predicted number of cooling towers and the processed characteristic data of the current operating status, and obtain a second candidate cooling capacity of the cooling station equipment; correct the candidate cooling capacity based on the second candidate cooling capacity to obtain the target cooling capacity of the indoor environment, and obtain the target set temperature of the cooling station equipment and the target number of cooling towers of the cooling station equipment; generate the target operating status data of the cooling station equipment based on the target cooling capacity, the target set temperature, and the target number of cooling towers.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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