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

Abstract

The invention relates to the technical field of temperature control, and provides an indoor temperature control method and device, electronic equipment and a readable storage medium. The method comprises the steps of adjusting current operation state data based on target temperature and indoor and outdoor environment characteristic data to obtain multiple pieces of first candidate operation state data of the cold station equipment; on the basis of the target temperature and the current indoor and outdoor environment characteristic data, screening from the first candidate operation state data to obtain target operation state data; wherein the difference value between the predicted temperature corresponding to the target operation state data and the target temperature is smaller than a first preset value, and the corresponding predicted energy consumption is the minimum value in the predicted energy consumption corresponding to the first candidate operation state data; according to the target operation state data, the cold station equipment is controlled, the defects of extensive temperature control and high energy consumption in a control method in the prior art are overcome, and the temperature control accuracy and the energy utilization efficiency of the cold station equipment are improved.

Description

Technical Field

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

[0002] As an important part of urban energy consumption, the improvement of energy efficiency of commercial parks and shopping malls is of great significance to achieving the above goals. With the increase of urbanization and commercial activities, the energy consumption of public places such as shopping malls has received increasing attention. As a key facility for regulating indoor temperature, the energy consumption of the cooling station system accounts for a large part of the total energy consumption of shopping malls. The existing cooling station system adopts a computer-based control system, which collects indoor and outdoor environmental data through sensors and automatically adjusts the operating status of the cooling station equipment according to the preset control logic. However, it lacks real-time data analysis and prediction, and cannot achieve refined and personalized temperature control, resulting in inaccurate temperature control and excessive energy consumption. Summary of the invention

[0003] In view of this, the 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] According to a first aspect of an embodiment of the present disclosure, there is provided an indoor temperature control method, comprising: obtaining current indoor and outdoor environmental characteristic data, current operating status data of a cold station device, and a target temperature; adjusting the current operating status data based on the target temperature and the indoor and outdoor environmental characteristic data to obtain a plurality of first candidate operating status data of the cold station device; filtering the target operating status data from the first candidate operating status data based on the target temperature and the current indoor and outdoor environmental characteristic data; wherein the difference between the predicted temperature corresponding to the target operating status data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is the minimum value among the predicted energy consumptions corresponding to the first candidate operating status data or the difference between the predicted energy consumption and the minimum value is less than a second preset value; and controlling the cold station device according to the target operating status data.

[0005] According to a second aspect of an embodiment of the present disclosure, there is provided an indoor temperature control device, comprising: a data acquisition module, for acquiring current indoor and outdoor environmental characteristic data, current operating status data of a cold station device, and a target temperature; an adjustment module, for adjusting the current operating status data based on the target temperature and the indoor and outdoor environmental characteristic data, to obtain a plurality of first candidate operating status data of the cold station device; a screening module, for screening the target operating status data from the first candidate operating status data based on the target temperature and the current indoor and outdoor environmental characteristic data; wherein the difference between the predicted temperature corresponding to the target operating status data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is the minimum value among the predicted energy consumptions corresponding to the first candidate operating status data, or the difference between the predicted energy consumption and the minimum value is less than a second preset value; and an execution module, for controlling the cold station device according to the target operating status data.

[0006] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including 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 an embodiment of the present disclosure, a readable storage medium is provided, which stores a computer program, and 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 embodiment has the following beneficial effects: by obtaining the current indoor and outdoor environmental characteristic data, the current operating state data of the cold station equipment and the target temperature, a basis is provided for subsequent analysis and decision-making. The indoor and outdoor environmental characteristic data and the current operating state data of the cold station equipment are important bases for formulating the control strategy of the cold station equipment, i.e., the candidate operating state data, and the target temperature is the indoor temperature to be reached. According to the target temperature and the indoor and outdoor environmental characteristic data, the current operating state of the cold station equipment is simulated and adjusted to generate multiple possible operating states (i.e., the first candidate operating state data), which provides multiple possibilities for subsequent screening. Based on the target temperature and the current indoor and outdoor environmental characteristic data, the target operating state data is screened from multiple first candidate operating state data to obtain the target operating state data, the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than the first preset value, and the corresponding predicted energy consumption is the minimum value of the predicted energy consumption corresponding to the first candidate operating state data or the difference between the minimum value and the minimum value is less than the second preset value, and among the multiple first candidate operating state data, the target operating state data that can both meet the temperature control requirements and achieve energy consumption optimization is screened to ensure the effectiveness and economy of the control strategy. The target operating state data finally selected is executed, and the cooling station equipment is controlled according to the target operating state data so that the indoor temperature reaches the target temperature. The temperature control method proposed in the present disclosure significantly improves the accuracy and energy efficiency of the temperature control of the cooling station equipment through real-time data collection, dual-objective optimization of energy consumption and temperature, and dynamic adjustment control, solves the defects of extensive temperature control and high energy consumption in the control method in the prior art, helps to better adapt to the changing environmental conditions and user needs, and realizes more sophisticated and efficient temperature management. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying 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 first candidate running status data result table provided by an embodiment of the present disclosure;

[0012] Figure 3 is another first candidate running status data result table provided by an embodiment of the present disclosure;

[0013] Figure 4is a structural schematic diagram of an indoor temperature control device provided by an embodiment of the present disclosure;

[0014] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear 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 obstructing the description of the present disclosure with unnecessary details.

[0016] A method and device for controlling indoor temperature according to an embodiment of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0017] 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:

[0018] Step 101, obtaining current indoor and outdoor environmental characteristic data, current operating status data of the cooling station equipment and target temperature.

[0019] Specifically, the current indoor and outdoor environmental characteristic data may include the current time, the current indoor temperature, the current outdoor temperature, the current indoor humidity, the current outdoor humidity, the pedestrian point data, the outdoor wind speed, the weather, etc. The current indoor and outdoor environmental characteristic data may be obtained in real time through various sensors installed indoors and outdoors. The current indoor and outdoor environmental characteristic data provides the environmental background conditions when the cold station equipment is running. The current operating status data of the cold station equipment may include the current time, the power of the cold machine, the number of cold machines, the number of cooling pumps, the frequency of the cooling pump, the chilled water supply temperature, the chilled water return temperature, the chilled water supply flow, the chilled water return flow, the operating status of the cold machine, etc. The current operating status data of the cold station equipment can reflect the operating status of the cold station system and is an important indicator that determines the performance of the cold station system. The current operating status data of the cold station equipment can be provided by the built-in sensor of the cold station equipment or an independent monitoring system. By acquiring indoor and outdoor environmental characteristic data and the operating status data of the cooling station equipment in real time, a basis is provided for subsequent data processing and analysis. By continuously monitoring and recording the indoor and outdoor environmental characteristic data and the operating status data of the cooling station equipment, the temperature control system of the cooling station equipment can more accurately understand the indoor environmental changes and the operating status of the cooling station equipment, and provide reliable data support for the intelligent control of the cooling station equipment. Specifically, the target temperature is the indoor temperature set point that the temperature control system of the cooling station equipment attempts to achieve or maintain, and is usually set by the user. The target temperature can be used as a reference point for the temperature control system of the cooling station equipment to guide the cooling station equipment to adjust its operating parameters to achieve the target temperature. By adjusting the indoor temperature to the target temperature, the comfort of the indoor environment is improved to meet the needs of indoor personnel.

[0020] Step 102: Based on the target temperature and the indoor and outdoor environment characteristic data, the current operation status data is adjusted to obtain a plurality of first candidate operation status data of the cold station equipment.

[0021] In some embodiments, the indoor and outdoor environmental characteristic data include the current indoor temperature, the target temperature is the indoor temperature that the user expects to achieve, and the current operating status data of the cold station equipment includes the operating parameters of the cold machine, cooling pump, cooling tower, cold machine set temperature, freezing pump frequency, etc. at the current moment. The current operating status data of the cold station equipment directly affects the current indoor temperature. Based on the target temperature and the current indoor temperature, the operating status of the current cold station equipment is adjusted to generate multiple possible operating status data sets, that is, multiple first candidate operating status data of the cold station equipment. By dynamically adjusting the operating status of the cold station equipment, the indoor temperature can be controlled more accurately to be close to the target temperature, and on the premise of meeting the temperature control requirements, the operating strategy with the lowest energy consumption is found to achieve energy conservation and emission reduction. Specifically, based on the target temperature and the current indoor temperature, the operating state of the current cold station equipment is adjusted, including: if the current indoor temperature is lower than the target temperature, indicating that there is an overcooling situation, the number of cold machines and / or the current freezing pump frequency and other operating parameters of the cold station equipment can be reduced, and at this time, the search space starts from the current operating state data of the cold station equipment and gradually decreases until the minimum number of cold machines that can be turned on or completely closed; if the current indoor temperature is higher than the target temperature, indicating that there is an overheating situation, the number of cold machines and / or the current freezing pump frequency and other operating parameters of the cold station equipment can be increased, and at this time, the search space can start from the current operating state data of the cold station equipment and gradually increase until the maximum number of cold machines that can be turned on is reached. The temperature control of the existing cold station equipment is mainly based on fixed rules and lacks flexibility. The operating state of the cold station equipment is dynamically adjusted according to the indoor and outdoor environmental characteristic data, and fine control can be performed according to actual needs to avoid overcooling or overheating, thereby improving the accuracy of temperature control. By generating multiple first candidate operating state data, a variety of options are provided for subsequent optimization decisions. By finding the operating strategy closest to the target temperature and with the lowest energy consumption, energy consumption can be effectively reduced and energy waste can be avoided.

[0022] Step 103, based on the target temperature and the current indoor and outdoor environmental characteristic data, the target operating status data is obtained by screening from the first candidate operating status data; wherein the difference between the predicted temperature corresponding to the target operating status data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is the minimum value among the predicted energy consumptions corresponding to the first candidate operating status data or the difference between the minimum value and the minimum value is less than a second preset value.

[0023] In some embodiments, operating state data that meets the following two conditions are selected from a plurality of first candidate operating state data as target operating state data: including the difference between the predicted temperature and the target temperature and the predicted energy consumption. Based on the current indoor and outdoor environmental characteristic data and each first candidate operating state data, the indoor temperature corresponding to each first candidate operating state data is predicted to obtain the predicted temperature corresponding to each first candidate operating state data; based on the current indoor and outdoor environmental characteristic data and each first candidate operating state data, the energy consumption corresponding to each first candidate operating state data is predicted to obtain the predicted energy consumption corresponding to each first candidate operating state data. It is determined that the difference between the predicted temperature corresponding to the selected target operating state data and the target temperature is less than a first preset value, so that the indoor temperature corresponding to the target operating state data (i.e., the predicted temperature) is close enough to the target temperature, and the corresponding predicted energy consumption is the minimum value of the predicted energy consumption corresponding to the first candidate operating state data or the difference between the predicted energy consumption and the minimum value is less than a second preset value, so that the target operating state data meets the temperature requirements while the energy consumption of the target operating state data is as low as possible. By screening the target operating status data that is closest to the target temperature and has the lowest energy consumption, the indoor temperature is always within a comfortable range, and energy consumption can be effectively reduced, energy waste can be avoided, and more precise indoor temperature control can be achieved.

[0024] Step 104, controlling the cold station equipment according to the target operating status data.

[0025] In some embodiments, the target operation status data may include: cold station equipment name: the name or identifier of the cold station system; project name: the name of the current project or shopping mall; target temperature: the target indoor temperature set; number of cooling towers: the number of cooling towers to be operated; number of freezing pumps: the number of freezing pumps to be operated; freezing pump frequency: the operating frequency of freezing pumps; number of chillers: the number of chillers to be operated; set temperature: the set temperature of the chiller (i.e., the chilled water supply temperature). Specifically, the chilled water supply temperature is the chilled water temperature coming out of the chiller evaporator, which is directly related to the refrigeration capacity of the chiller. A lower chilled water supply temperature indicates a greater refrigeration capacity, and a lower water supply temperature indicates that the chiller requires more energy to reduce the temperature of the chilled water. The cooling tower is used to cool the hot water returned from the chiller for recycling. Increasing the number of cooling towers can improve the cooling efficiency and indirectly improve the refrigeration capacity of the chiller, but it also increases the energy consumption of the cooling tower itself. The chiller pump is used to promote the circulation of chilled water in the cold station equipment. Increasing the number of chillers helps to increase the circulation flow of chilled water, thereby increasing the cooling capacity, but at the same time increasing the number of chillers will increase the energy consumption of the cold station equipment. The chiller pump frequency determines the flow rate of chilled water. Reducing the chiller pump frequency will reduce the flow of chilled water, thereby reducing the cooling capacity. At the same time, reducing the chiller pump frequency can reduce energy consumption. The power consumption of the chiller pump is positively correlated with its speed. The number of chillers determines the total cooling capacity of the cold station equipment. Increasing the number of chillers can increase the cooling capacity of the cold station equipment while increasing the energy consumption of the cold station equipment.

[0026] In some embodiments, the screened target operating state data is sent to the cold station equipment, so that the cold station equipment is adjusted according to the target operating state data, and intelligent control is realized to achieve precise control of the cold station equipment. By accurately controlling the operating state of the cold station equipment, the indoor temperature can always be kept within the set target temperature range, the accuracy and stability of temperature control can be improved, which helps to improve the comfort of the indoor environment and meet the user's high requirements for temperature control. At the same time, under the premise of meeting the temperature control requirements, the energy consumption of the cold station equipment is reduced.

[0027] In some embodiments, the cold station equipment continuously collects actual indoor and outdoor environmental characteristic data and operating status data during operation, and feeds these data back to the temperature control system of the cold station equipment for subsequent updating and optimization of parameters in the temperature control system. Specifically, the feedback adjustment mechanism includes: continuously collecting actual indoor and outdoor environmental characteristic data and operating status data of the cold station equipment; feeding back the collected indoor and outdoor environmental characteristic data and operating status data of the cold station equipment to the temperature control system of the cold station equipment for parameter updating and optimization of the temperature control system of the cold station equipment. According to the feedback data, the temperature control system of the cold station equipment is updated and adjusted to improve the accuracy and prediction ability of the temperature control system of the cold station equipment. According to the updated temperature control system of the cold station equipment, the generated control strategy (i.e., the target operating status data obtained by screening) is adjusted to adapt to the current environmental changes and energy consumption requirements, and the temperature control system of the cold station equipment is adjusted and optimized in time by continuously collecting actual indoor and outdoor environmental characteristic data and operating status data of the cold station equipment and feeding back to the temperature control system of the cold station equipment, thereby improving the accuracy and stability of prediction and control. The above feedback adjustment mechanism enables the temperature control system of the cold station equipment to continuously adapt to changes in the indoor and outdoor environment, and realize intelligent management and optimization of the cold station equipment.

[0028] The indoor temperature control method provided by the present disclosure provides a basis for subsequent analysis and decision-making by obtaining the current indoor and outdoor environmental characteristic data, the current operating state data of the cold station equipment and the target temperature. The indoor and outdoor environmental characteristic data and the current operating state data of the cold station equipment are important bases for formulating the control strategy of the cold station equipment, that is, the candidate operating state data, and the target temperature is the indoor temperature to be reached. According to the target temperature and the indoor and outdoor environmental characteristic data, the current operating state of the cold station equipment is simulated and adjusted to generate multiple possible operating states (i.e., the first candidate operating state data), which provides multiple possibilities for subsequent screening. Based on the target temperature and the current indoor and outdoor environmental characteristic data, the target operating state data is screened from multiple first candidate operating state data to obtain the target operating state data, the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than the first preset value, and the corresponding predicted energy consumption is the minimum value of the predicted energy consumption corresponding to the first candidate operating state data or the difference between the minimum value and the minimum value is less than the second preset value. Among the multiple first candidate operating state data, the target operating state data that can both meet the temperature control requirements and achieve energy consumption optimization is screened to ensure the effectiveness and economy of the control strategy. The target operating state data finally selected is executed, and the cooling station equipment is controlled according to the target operating state data so that the indoor temperature reaches the target temperature. The temperature control method proposed in the present disclosure significantly improves the accuracy and energy efficiency of the cooling station equipment in temperature control through real-time data collection, dual-objective optimization of energy consumption and temperature, and dynamic adjustment control, solves the defects of extensive temperature control and high energy consumption in the control method in the prior art, helps to better adapt to the changing environmental conditions and user needs, and realizes more sophisticated and efficient temperature management.

[0029] In some embodiments, based on the target temperature and the current indoor and outdoor environmental characteristic data, the target operating status data is obtained by screening from the first candidate operating status data, including: based on the current indoor and outdoor environmental characteristic data, predicting the energy consumption corresponding to each first candidate operating status data, and obtaining the predicted energy consumption corresponding to each first candidate operating status data; based on the current indoor and outdoor environmental characteristic data, predicting the indoor temperature corresponding to each first candidate operating status data, and obtaining the predicted temperature corresponding to each first candidate operating status data; based on the target temperature and each predicted temperature, screening each first candidate operating status data to obtain multiple second candidate operating status data; based on the predicted energy consumption corresponding to each second candidate operating status data, determining the target operating status data from each second candidate operating status data.

[0030] In some embodiments, combined with the current indoor and outdoor environmental characteristic data and each first candidate operating state data, the energy consumption of the first preset time period corresponding to each first candidate operating state data is predicted, and the predicted energy consumption of the next preset time period corresponding to each first candidate operating state data is obtained, and the energy consumption under different operating states is evaluated to provide a basis for subsequent screening. The next preset time period can be the next hour. Combined with the current indoor and outdoor environmental characteristic data and each first candidate operating state data, the median of the indoor temperature of each preset time period corresponding to each first candidate operating state data is predicted, and the median of the indoor temperature of the next preset time period corresponding to each first candidate operating state data is obtained. The change of indoor temperature under different operating states is evaluated to ensure whether the final selected operating state can meet the target temperature requirements. Compare each predicted temperature with the target temperature, and screen out the first candidate operating state data whose predicted temperature is close to the target temperature, so as to help the selected operating state to achieve the expected temperature control. Among the screened second candidate operating state data, the second candidate operating state data with the lowest energy consumption is selected as the target operating state data, so that while meeting the temperature control requirements, the energy consumption is as low as possible. By screening the target operating status data from each first candidate operating status data based on the target temperature and the current indoor and outdoor environmental characteristic data, the temperature control can be refined and the energy consumption can be effectively reduced. The method provided by the present invention improves the accuracy of temperature control, reduces energy consumption, and enhances the responsiveness and adaptability of the temperature control system of the cold station equipment, thereby solving the problem in the prior art that the cold station equipment cannot finely control the temperature and reduce energy consumption, and can better meet the needs of users, while improving energy utilization efficiency and achieving sustainable development goals.

[0031] In some embodiments, based on the current indoor and outdoor environmental characteristic data, the energy consumption corresponding to each first candidate operating status data is predicted to obtain the predicted energy consumption corresponding to each first candidate operating status data, including: inputting the current indoor and outdoor environmental characteristic data and each first candidate operating status data as a group of data into an energy consumption prediction model to obtain the predicted energy consumption in the next time period corresponding to each first candidate operating status data output by the energy consumption prediction model.

[0032] In some embodiments, the energy consumption prediction model is a prediction model obtained by training through a machine learning algorithm based on historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment. The energy consumption prediction model can fit the relationship between the indoor and outdoor environmental characteristic data and the operating status data of the cold station equipment and the actual energy consumption demand, and can predict the energy consumption in the next time period based on the input data, and provide prediction values ​​for energy consumption under different operating states. Each first candidate operating status data is combined with the current indoor and outdoor environmental characteristic data into a group of data, which is input into the energy consumption prediction model. The energy consumption prediction model outputs the predicted energy consumption of each first candidate operating status data in the next time period, and provides energy consumption prediction values ​​under different operating states, providing a basis for subsequent decision-making. By predicting the energy consumption under different operating states, the operating state closest to the target temperature and with the lowest energy consumption can be found.

[0033] In some embodiments, based on the current indoor and outdoor environmental characteristic data, the indoor temperature corresponding to each first candidate operating status data is predicted to obtain the predicted temperature corresponding to each first candidate operating status data, including: inputting the current indoor and outdoor environmental characteristic data and each first candidate operating status data as a group of data into a temperature prediction model, and obtaining the predicted indoor temperature in the next time period corresponding to each first candidate operating status data output by the temperature prediction model.

[0034] In some embodiments, the temperature prediction model is a prediction model obtained by training through a machine learning algorithm based on historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment. The temperature prediction model can fit the relationship between the indoor and outdoor environmental characteristic data and the operating status data of the cold station equipment and the actual temperature, and can predict the temperature in the next time period based on the input data, and provide prediction values ​​for the temperature under different operating states. Each first candidate operating status data is combined with the current indoor and outdoor environmental characteristic data into a group of data, which is input into the temperature prediction model. The temperature prediction model outputs the predicted temperature of each first candidate operating status data in the next time period, and provides temperature prediction values ​​under different operating states, providing a basis for subsequent decision-making. By predicting the indoor temperature under different operating states, the operating state closest to the target temperature can be found, and the temperature control can be refined.

[0035] In some embodiments, based on the target temperature and each predicted temperature, each first candidate operating status data is screened to obtain multiple second candidate operating status data, including: calculating the absolute value of the difference between each predicted temperature and the target temperature, and determining the first candidate operating status data corresponding to the absolute value of each difference that is less than a first preset value as the second candidate operating status data.

[0036] Specifically, the first preset value is an acceptable temperature deviation range, which is used to screen the operating state that is closer to the target temperature. The first preset value can be adjusted according to actual needs to achieve the best control effect. The difference between each predicted temperature and the target temperature is calculated, and the absolute value of the difference is taken. The gap between each first candidate operating state data and the target operating state data is quantified by the absolute value of the difference. The first candidate operating state data whose absolute value of the difference is less than the first preset value is determined as the second candidate operating state data. The second candidate operating state data whose predicted temperature is close to the target temperature is screened, which effectively narrows the selection range, retains the operating state that can meet the temperature control requirements, and lays the foundation for the subsequent selection of the operating state with the lowest energy consumption.

[0037] In some embodiments, based on the predicted energy consumption corresponding to each second candidate operating status data, the target operating status data is determined from each second candidate operating status data, including: according to the predicted energy consumption corresponding to each second candidate operating status data, the second candidate operating status data corresponding to the predicted energy consumption with the smallest numerical value is determined as the target operating status data.

[0038] In some embodiments, the predicted energy consumption corresponding to each second candidate operating state data is compared, and the second candidate operating state data corresponding to the predicted energy consumption with the smallest value is determined as the target operating state data, so that the selected target operating state data can both meet the temperature control requirements and achieve the lowest energy consumption.

[0039] In some embodiments, the current indoor and outdoor environmental characteristic data include the current indoor temperature, and the current operating status data include the current number of cold machines, the current cold machine set temperature and the current freezing pump frequency of the cold station equipment; based on the target temperature and the indoor and outdoor environmental characteristic data, the current operating status data is adjusted to obtain multiple first candidate operating status data of the cold station equipment, including: according to the current indoor temperature and the target temperature, determining the adjustment step corresponding to the current number of cold machines, the current cold machine set temperature and the current freezing pump frequency respectively; according to the pre-set adjustment step corresponding to the current number of cold machines, the current cold machine set temperature and the current freezing pump frequency respectively, adjusting at least one of the current number of cold machines, the current cold machine set temperature and the current freezing pump frequency to obtain multiple first candidate operating status data of the cold station equipment, so that the predicted temperature corresponding to the first candidate operating status data approaches the target temperature.

[0040] In some embodiments, according to the difference between the current indoor temperature and the target temperature, the adjustment step corresponding to the current number of refrigerators, the current set temperature of the refrigerator, and the current freezing pump frequency is determined, and the adjustment amplitude is quantified, which helps to make the adjusted operating state closer to the target temperature. According to the determined adjustment step, at least one of the current number of refrigerators, the current set temperature of the refrigerator, and the current freezing pump frequency is adjusted. It can be an adjustment of a single parameter or a combined adjustment of multiple parameters. Multiple first candidate operating state data are generated, and different operating state options are created for subsequent screening and evaluation, providing a variety of selection options for subsequent selection and optimization. The generation of multiple first candidate operating state data makes it possible to have more possibilities when selecting the optimal operating state, so that it is easier to find an operating state that meets the temperature control requirements and has low energy consumption. Multiple first candidate operating state data represent different operating states, and each state corresponds to a predicted temperature, which should be close to the target temperature. For example, the target temperature is 26 degrees, the current time is 9 o'clock, the current operating status data is that 2 refrigerators are turned on, the set temperature of the refrigerator is 7.6 degrees, the frequency conversion is 45, and the current indoor temperature of the mall is 25.6 degrees. This means that too many refrigerators are turned on. The corresponding strategy search space for the next hour is: refrigerator range, 2 units, 1.5 units, 1 unit, the set temperature of the refrigerator is 7.1-9.0 degrees, and the frequency conversion range is 40-50. The candidate operating status data is combined by the number of units, temperature, and frequency conversion, and the energy consumption prediction model and temperature prediction model are used to predict the temperature and energy consumption in the next hour, and the corresponding first candidate operating status data result table is generated. Reference Figure 2 and Figure 3 , Figure 2 and Figure 3 They are all first candidate operating status data result tables. Each row of the result table can be split into multiple rows according to the set temperature and frequency conversion number.

[0041] In some embodiments, obtaining current indoor and outdoor environmental characteristic data includes: obtaining multiple initial indoor temperatures in the current time period and outdoor weather in the next time period; selecting the median from each initial indoor temperature in the current time period as the current indoor temperature; encoding the outdoor weather in the next time period to obtain a characteristic vector of the outdoor weather in the next time period; and determining the characteristic vector of the current indoor temperature and the outdoor weather in the next time period as the indoor and outdoor environmental characteristic data.

[0042] In some embodiments, indoor temperature data within a set time period (such as 1 hour) can be collected by a temperature sensor installed indoors. The temperature sensor can collect data every 5 minutes to obtain multiple initial indoor temperatures. The initial indoor temperature in the previous time period can reflect the change of indoor temperature in the current time period. The multiple initial indoor temperature data collected are sorted, and the median is selected as the current indoor temperature according to the number of data. By selecting the median, the influence of extreme values ​​on indoor temperature evaluation can be effectively reduced, making the current indoor temperature more representative and stable, which helps the cold station equipment to more accurately judge the temperature condition of the current indoor environment, thereby performing more precise temperature control. The outdoor weather of the next time period can be obtained according to the weather forecast, and the outdoor weather of the next time period can be encoded. The outdoor weather (such as rain, snow, sunny, etc.) and weather parameters (such as temperature, humidity, wind speed, etc.) of the next time period can be converted into numerical or symbolic forms to obtain the characteristic vector of the outdoor weather of the next time period, and the complex weather information can be converted into a characteristic vector that is easy to process and analyze, which is helpful for subsequent input into the prediction model.

[0043] In some embodiments, multiple initial indoor temperatures in the current time period and outdoor weather in the next time period are all initial characteristic data of the current indoor and outdoor environment. To obtain the initial characteristic data of the current indoor and outdoor environment and the initial characteristic data of the current operating state of the refrigeration station equipment, the initial characteristic data needs to be preprocessed to ensure the quality and availability of the data, and to provide high-quality input data for subsequent prediction models. Only when the corresponding current indoor and outdoor environment characteristic data and the current operating state data of the refrigeration station equipment are obtained can they be input into the temperature prediction model and the energy consumption prediction model. Preprocessing may also include: adjusting the operating state of the refrigerator, processing the collected energy consumption data, and adjusting the refrigerator state to off when the refrigerator state is on and the energy consumption is negative or a small value, and vice versa, according to the distribution of the refrigerator switch and energy consumption, so that the operating state of the refrigerator is consistent with the energy consumption data; removing low-energy consumption samples, removing low-energy consumption samples caused by equipment failure, data collection errors or extreme atypical operating conditions (such as the refrigerator is turned on for a short time but consumes almost no energy), which helps to reduce noise data; constructing date and time features. Time-related features (such as whether it is a holiday, whether it is a holiday adjustment, the number of days to the holiday, the number of days to the summer solstice, business hours, opening and closing times of the store, etc.) have a significant impact on energy consumption. By constructing date and time features, it is helpful to predict energy consumption more accurately; calculating the wet-bulb temperature. The wet-bulb temperature can more comprehensively reflect the human body's thermal sensation of the environment. As an additional feature, it helps the prediction model to understand the indoor environmental status more accurately, thereby improving the prediction accuracy; Exploratory data analysis (EDA) is performed on indoor temperature and indoor humidity to identify outliers in the data, and outliers are filtered for data cleaning to ensure the accuracy and reliability of the data; indoor and outdoor temperature difference characteristics and cold station equipment temperature difference characteristics are constructed. The indoor and outdoor temperature difference characteristics and the cold station equipment temperature difference characteristics are directly related to the change in energy consumption. The indoor and outdoor temperature difference can reflect the size of the building's heat load, and the cold station equipment temperature difference characteristics can reflect the refrigeration efficiency. Inputting the indoor and outdoor temperature difference characteristics and the cold station equipment temperature difference characteristics into the prediction model helps the prediction model capture the direct impact of environmental changes on energy consumption and temperature; Processing tilted features. Tilt features (such as extreme values, skewed distribution, etc.) can have an adverse effect on the training and calculation of the prediction model. Through logarithmic transformation, normalization or truncation and other processing, the distribution of tilted features can be improved, making the prediction easier to understand and improving the stability and generalization ability of the prediction model. Through the above data preprocessing steps to clean the data, construct useful features, and deal with outliers and skewed features, the quality and availability of the data input into the prediction model can be effectively improved, and it helps to improve the prediction accuracy and robustness of the prediction model.

[0044] In some embodiments, the current indoor and outdoor environmental characteristic data and each first candidate operating status data are respectively input into the energy consumption prediction model as a group of data, and also include: obtaining historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment; using the historical indoor and outdoor environmental characteristic data and the historical operating status data as the first training sample, and using the historical energy consumption corresponding to the historical operating status data as the label of the first training sample to construct an energy consumption prediction training set and an energy consumption prediction test set; based on the energy consumption prediction training set, respectively train multiple candidate energy consumption prediction models to be trained to obtain multiple trained candidate energy consumption prediction models; based on the loss values ​​corresponding to the multiple candidate energy consumption prediction models, respectively select the candidate energy consumption prediction model with the smallest loss value from the multiple candidate energy consumption prediction models to determine it as the energy consumption prediction model.

[0045] In some embodiments, historical indoor and outdoor environmental characteristic data (such as temperature, humidity, light intensity, etc.) and historical operating status data of cold station equipment (such as equipment switch status, operating power, working hours, etc.) are obtained. Historical data is the basis for subsequent model training. The historical data is split into a training set energy consumption prediction training set and an energy consumption prediction test set. The energy consumption prediction training set is used to train the candidate energy consumption prediction model to be trained so that it can learn the inherent laws and patterns of the data; the energy consumption prediction test set is used to evaluate the performance of the trained candidate energy consumption prediction model on unseen data to test the generalization ability of the candidate energy consumption prediction model. The label (i.e., historical energy consumption) is the target value that the candidate energy consumption prediction model needs to predict. Based on the energy consumption prediction training set, multiple candidate energy consumption prediction models to be trained are trained separately. The candidate energy consumption prediction models to be trained can be linear regression, decision tree, random forest, neural network, etc. By training multiple candidate energy consumption prediction models, alternative solutions are provided for subsequent model selection to find the most suitable energy consumption prediction model for the current situation. By testing multiple candidate energy consumption prediction models through the energy consumption prediction test set, the loss values ​​corresponding to the multiple candidate energy consumption prediction models are obtained, and based on the loss values ​​corresponding to the multiple candidate energy consumption prediction models, the candidate energy consumption prediction model with the smallest loss value is selected from the multiple candidate energy consumption prediction models to be determined as the energy consumption prediction model, and the candidate energy consumption prediction model with the best prediction performance is selected as the energy consumption prediction model. Specifically, the above loss can be the root mean square error (RMSE) and the mean absolute error (MAE).

[0046] In some embodiments, when the historical indoor and outdoor environment characteristic data and the historical operation status data are used as the first training samples, the method of sequential floating forward selection (SFFS) can be used to construct each first training sample. Specifically, the historical indoor and outdoor environment characteristic data and the historical operation status data of the cold station equipment may include: historical indoor temperature, historical outdoor temperature, historical humidity, historical light intensity, historical number of cooling towers, historical number of refrigeration pumps, historical refrigeration pump frequency, historical number of cold machines, and historical cold machine set temperature. Initialization is performed, the feature set is empty, and a feature is selected from all features, such as the historical outdoor temperature, and a floating operation is performed. It can be judged by indicators such as cross-validation error whether removing the historical outdoor temperature can improve the performance of the energy consumption prediction model. If not, the historical outdoor temperature is retained. Forward selection is performed to select the next feature from the remaining features, such as the historical indoor temperature. It can be judged by indicators such as cross-validation error whether removing the historical indoor temperature or the historical outdoor temperature can improve the performance of the energy consumption prediction model. If not, it is retained. This process is continued until no feature can be added or removed to further improve the performance of the energy consumption prediction model. The best first training sample that can be determined by SFFS is the optimal energy consumption training set.

[0047] In some embodiments, before inputting the current indoor and outdoor environmental characteristic data and each first candidate operating status data into the temperature prediction model, it also includes: obtaining historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment; using the historical indoor and outdoor environmental characteristic data and the historical operating status data as the first training sample, and using the historical temperature corresponding to the historical operating status data as the label of the first training sample to construct a temperature prediction training set and a temperature prediction test set; based on the temperature prediction training set, respectively training multiple candidate temperature prediction models to be trained to obtain multiple trained candidate temperature prediction models; based on the loss values ​​corresponding to the multiple candidate temperature prediction models, respectively selecting the candidate temperature prediction model with the smallest loss value from the multiple candidate temperature prediction models to determine as the temperature prediction model.

[0048] 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.

[0049] The following are embodiments of the device disclosed herein, which can be used to execute the method embodiments disclosed herein. For details not disclosed in the device embodiments disclosed herein, please refer to the method embodiments disclosed herein.

[0050] Figure 4 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 comprises:

[0051] The data acquisition module 401 is used to acquire the current indoor and outdoor environmental characteristic data, the current operating status data of the cooling station equipment and the target temperature;

[0052] An adjustment module 402 is used to adjust the current operation status data based on the target temperature and the indoor and outdoor environment characteristic data to obtain a plurality of first candidate operation status data of the cold station equipment;

[0053] The screening module 403 is used to screen the target operating state data from the first candidate operating state data based on the target temperature and the current indoor and outdoor environment characteristic data; wherein the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is the minimum value among the predicted energy consumptions corresponding to the first candidate operating state data or the difference between the minimum value and the minimum value is less than a second preset value;

[0054] The execution module 404 is used to control the cold station equipment according to the target operation status data.

[0055] According to the technical solution provided by the embodiment of the present disclosure, by obtaining the current indoor and outdoor environmental characteristic data, the current operating state data of the cold station equipment and the target temperature, a basis is provided for subsequent analysis and decision-making. The indoor and outdoor environmental characteristic data and the current operating state data of the cold station equipment are important bases for formulating the control strategy of the cold station equipment, that is, the candidate operating state data, and the target temperature is the indoor temperature to be reached. According to the target temperature and the indoor and outdoor environmental characteristic data, the current operating state of the cold station equipment is simulated and adjusted to generate multiple possible operating states (i.e., the first candidate operating state data), which provides multiple possibilities for subsequent screening. Based on the target temperature and the current indoor and outdoor environmental characteristic data, the target operating state data is screened from multiple first candidate operating state data to obtain the target operating state data, the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than the first preset value, and the corresponding predicted energy consumption is the minimum value of the predicted energy consumption corresponding to the first candidate operating state data or the difference between the minimum value and the minimum value is less than the second preset value, and among the multiple first candidate operating state data, the target operating state data that can both meet the temperature control requirements and achieve energy consumption optimization is screened to ensure the effectiveness and economy of the control strategy. The target operating state data finally selected is executed, and the cold station equipment is controlled according to the target operating state data so that the indoor temperature reaches the target temperature. The temperature control method proposed in the present disclosure significantly improves the accuracy and energy efficiency of the temperature control of the cold station equipment through real-time data collection, multi-scheme simulation, dual-objective optimization of energy consumption and temperature, and dynamic adjustment control, solves the defects of extensive temperature control and high energy consumption in the control method in the prior art, helps to better adapt to the changing environmental conditions and user needs, and realizes more sophisticated and efficient temperature management.

[0056] In some embodiments, the screening module 403 is configured to predict the energy consumption corresponding to each first candidate operating status data based on the current indoor and outdoor environmental characteristic data, and obtain the predicted energy consumption corresponding to each first candidate operating status data; predict the indoor temperature corresponding to each first candidate operating status data based on the current indoor and outdoor environmental characteristic data, and obtain the predicted temperature corresponding to each first candidate operating status data; screen each first candidate operating status data based on the target temperature and each predicted temperature, and obtain multiple second candidate operating status data; determine the target operating status data from each second candidate operating status data based on the predicted energy consumption corresponding to each second candidate operating status data.

[0057] In some embodiments, the screening module 403 is configured to input the current indoor and outdoor environmental characteristic data and each first candidate operating status data as a group of data into the energy consumption prediction model to obtain the predicted energy consumption in the next time period corresponding to each first candidate operating status data output by the energy consumption prediction model.

[0058] In some embodiments, the screening module 403 is configured to input the current indoor and outdoor environmental characteristic data and each first candidate operating status data as a set of data into the temperature prediction model to obtain the predicted indoor temperature in the next time period corresponding to each first candidate operating status data output by the temperature prediction model.

[0059] In some embodiments, the screening module 403 is configured to calculate the absolute values ​​of the differences between each predicted temperature and the target temperature, and determine the first candidate operating status data corresponding to the absolute values ​​of each difference less than a first preset value as the second candidate operating status data.

[0060] In some embodiments, the screening module 403 is configured to determine the second candidate operating state data corresponding to the predicted energy consumption with the smallest value as the target operating state data according to the predicted energy consumption corresponding to each second candidate operating state data.

[0061] In some embodiments, the current indoor and outdoor environmental characteristic data include the current indoor temperature, and the current operating status data include the current number of cold machines, the current set temperature of the cold machines, and the current freezing pump frequency of the cold station equipment. The adjustment module 402 is configured to determine the adjustment step corresponding to the current number of cold machines, the current set temperature of the cold machines, and the current freezing pump frequency according to the current indoor temperature and the target temperature; according to the preset adjustment step corresponding to the current number of cold machines, the current set temperature of the cold machines, and the current freezing pump frequency, at least one of the current number of cold machines, the current set temperature of the cold machines, and the current freezing pump frequency is adjusted to obtain multiple first candidate operating status data of the cold station equipment, so that the predicted temperature corresponding to the first candidate operating status data approaches the target temperature.

[0062] In some embodiments, the data acquisition module 401 is configured to obtain multiple initial indoor temperatures within the current time period and the outdoor weather of the next time period; select the median from each initial indoor temperature within the current time period as the current indoor temperature; encode the outdoor weather of the next time period to obtain a feature vector of the outdoor weather of the next time period; determine the feature vector of the current indoor temperature and the outdoor weather of the next time period as indoor and outdoor environment feature data.

[0063] In some embodiments, before the current indoor and outdoor environmental characteristic data and each first candidate operating status data are respectively input into the energy consumption prediction model as a group of data, the indoor temperature control device is configured to obtain historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment; the historical indoor and outdoor environmental characteristic data and the historical operating status data are used as the first training samples, and the historical energy consumption corresponding to the historical operating status data is used as the label of the first training sample to construct an energy consumption prediction training set and an energy consumption prediction test set; based on the energy consumption prediction training set, multiple candidate energy consumption prediction models to be trained are respectively trained to obtain multiple trained candidate energy consumption prediction models; based on the loss values ​​corresponding to the multiple candidate energy consumption prediction models, the candidate energy consumption prediction model with the smallest loss value is selected from the multiple candidate energy consumption prediction models to be determined as the energy consumption prediction model.

[0064] 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.

[0065] Figure 5 Schematic diagram of an electronic device 5 provided in an embodiment of the present disclosure. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 501 executes the computer program 503, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0066] The electronic device 5 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 5 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that Figure 5 The electronic device 5 is merely an example and does not limit the electronic device 5 , and may include more or less components than those shown in the figure, or different components.

[0067] The processor 501 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.

[0068] The memory 502 may be an internal storage unit of the electronic device 5, for example, a hard disk or memory of the electronic device 5. The memory 502 may also be an external storage device of the electronic device 5, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. The memory 502 may also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0069] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be assigned to 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 unit can be implemented in the form of hardware or in the form of software functional units.

[0070] 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 (e.g., 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 capable of carrying 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.

[0071] 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 embodiments of the present disclosure, and should all be included in the protection scope of the present disclosure.

Claims

1. A method for controlling indoor temperature, characterized in that: include: Obtain current indoor and outdoor environmental characteristic data, current operating status data of cooling station equipment and target temperature; Based on the target temperature and the indoor and outdoor environment characteristic data, the current operation status data is adjusted to obtain a plurality of first candidate operation status data of the cold station equipment; Based on the target temperature and the current indoor and outdoor environment characteristic data, the target operating state data is obtained by screening from the first candidate operating state data; wherein the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is the minimum value among the predicted energy consumptions corresponding to the first candidate operating state data or the difference between the predicted energy consumption and the minimum value is less than a second preset value; The cold station equipment is controlled according to the target operating status data.

2. The method according to claim 1, characterized in that The step of selecting the target operating state data from the first candidate operating state data based on the target temperature and the current indoor and outdoor environment characteristic data includes: Based on the current indoor and outdoor environment characteristic data, predict the energy consumption corresponding to each of the first candidate operating state data to obtain the predicted energy consumption corresponding to each of the first candidate operating state data; Based on the current indoor and outdoor environment characteristic data, predict the indoor temperature corresponding to each of the first candidate operating state data to obtain the predicted temperature corresponding to each of the first candidate operating state data; Based on the target temperature and each of the predicted temperatures, screening each of the first candidate operating state data to obtain a plurality of second candidate operating state data; The target operating state data is determined from each second candidate operating state data based on the predicted energy consumption corresponding to each second candidate operating state data.

3. The method according to claim 2, characterized in that The predicting the energy consumption corresponding to each of the first candidate operating state data based on the current indoor and outdoor environment characteristic data to obtain the predicted energy consumption corresponding to each of the first candidate operating state data includes: Input the current indoor and outdoor environment characteristic data and each of the first candidate operating state data as a group of data into an energy consumption prediction model to obtain the predicted energy consumption in the next time period corresponding to each of the first candidate operating state data output by the energy consumption prediction model; The step of predicting the indoor temperature corresponding to each of the first candidate operating state data based on the current indoor and outdoor environment characteristic data to obtain the predicted temperature corresponding to each of the first candidate operating state data includes: The current indoor and outdoor environment characteristic data and each of the first candidate operating status data are respectively input into a temperature prediction model as a group of data to obtain the predicted indoor temperature in the next time period corresponding to each of the first candidate operating status data output by the temperature prediction model.

4. The method according to claim 2, characterized in that: The step of screening each of the first candidate operating state data based on the target temperature and each of the predicted temperatures to obtain a plurality of second candidate operating state data includes: calculating the absolute values ​​of the differences between each of the predicted temperatures and the target temperature, and determining the first candidate operating state data corresponding to the absolute values ​​of each difference that is smaller than the first preset value as the second candidate operating state data; The step of determining the target operating state data from each second candidate operating state data based on the predicted energy consumption corresponding to each second candidate operating state data includes: According to the predicted energy consumption corresponding to each of the second candidate operating state data, the second candidate operating state data corresponding to the predicted energy consumption with the smallest value is determined as the target operating state data.

5. The method according to claim 1, characterized in that The current indoor and outdoor environment characteristic data include the current indoor temperature, and the current operation status data include the current number of refrigerators, the current refrigerator setting temperature and the current refrigeration pump frequency of the refrigeration station equipment; The adjusting the current operating state data based on the target temperature and the indoor and outdoor environment characteristic data to obtain a plurality of first candidate operating state data of the cold station equipment includes: According to the current indoor temperature and the target temperature, determining the adjustment step lengths corresponding to the current number of refrigerators, the current set temperature of the refrigerators, and the current refrigeration pump frequency respectively; According to the preset adjustment steps corresponding to the current number of refrigeration machines, the current set temperature of the refrigeration machines and the current refrigeration pump frequency respectively, at least one of the current number of refrigeration machines, the current set temperature of the refrigeration machines and the current refrigeration pump frequency is adjusted to obtain multiple first candidate operating status data of the cold station equipment, so that the predicted temperature corresponding to the first candidate operating status data approaches the target temperature.

6. The method according to claim 1, characterized in that The obtaining of current indoor and outdoor environment characteristic data includes: Get multiple initial indoor temperatures in the current time period and outdoor weather in the next time period; Selecting a median from each of the initial indoor temperatures within the current time period as the current indoor temperature; Encoding the outdoor weather in the next time period to obtain a feature vector of the outdoor weather in the next time period; The feature vectors of the current indoor temperature and the outdoor weather in the next time period are determined as the indoor and outdoor environment feature data.

7. The method according to claim 3, characterized in that Before inputting the current indoor and outdoor environment characteristic data and each of the first candidate operating state data as a group of data into the energy consumption prediction model, the method further includes: Acquiring historical indoor and outdoor environmental characteristic data and historical operating status data of the cold station equipment; The historical indoor environment characteristic data and the historical operation status data are used as the first training sample, and the historical energy consumption corresponding to the historical operation status data is used as the label of the first training sample to construct an energy consumption prediction training set and an energy consumption prediction test set; Based on the energy consumption prediction training set, a plurality of candidate energy consumption prediction models to be trained are trained respectively to obtain a plurality of trained candidate energy consumption prediction models; Based on the loss values ​​respectively corresponding to a plurality of candidate energy consumption prediction models, a candidate energy consumption prediction model with the smallest loss value is selected from the plurality of candidate energy consumption prediction models and determined as the energy consumption prediction model.

8. 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, the current operating status data of the cooling station equipment and the target temperature; An adjustment module, configured to adjust the current operation status data based on the target temperature and the indoor and outdoor environment characteristic data to obtain a plurality of first candidate operation status data of the cold station equipment; A screening module, configured to screen the target operating state data from the first candidate operating state data based on the target temperature and the current indoor and outdoor environment characteristic data; wherein the difference between the predicted temperature corresponding to the target operating state data and the target temperature is less than a first preset value, and the corresponding predicted energy consumption is a minimum value among the predicted energy consumptions corresponding to the first candidate operating state data or the difference between the minimum value and the minimum value is less than a second preset value; An execution module is used to control the cold station equipment according to the target operating status data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 7 are implemented.

Images