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 following steps: based on indoor and outdoor environment characteristic data and a target temperature, predicting an indoor heat demand in a next time period to obtain a target demand heat; based on the target demand heat and the corresponding relation between the preset operation state data and the heat generated by the heat station equipment, initial operation state data are obtained through screening from the multiple pieces of operation state data; generating target operation state data of the heat station equipment based on the initial operation state data, the target demand heat, the current operation characteristic data and the indoor and outdoor environment characteristic data; and controlling the heat station equipment according to the target operation state data. The problem that in the prior art, indoor temperature control is not accurate is solved, the indoor temperature is kept within the set comfortable range all the time, cold station equipment is finely controlled, and the accuracy and efficiency of temperature control 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] In order to ensure a comfortable and safe shopping and working environment, large shopping malls are equipped with heating stations to control the indoor temperature of the mall. Existing heating stations collect data such as indoor and outdoor temperature and humidity through sensors, and automatically adjust the operating status of the heating station equipment according to the preset control logic. However, they rely on fixed control strategies and cannot accurately predict heat demand. They lack the ability to deeply analyze and predict real-time data, and cannot achieve refined and personalized temperature control of indoor temperature. 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 a method for controlling indoor temperature, comprising:

[0005] Obtain indoor and outdoor environmental characteristic data and indoor target temperature;

[0006] Based on the indoor and outdoor environmental characteristic data and the target temperature, the indoor heat demand for the next time period is predicted to obtain the indoor target heat demand for the next time period;

[0007] Based on the target required heat and the correspondence between each operating state data in the pre-set multiple operating state data of the heat station equipment and the heat generated by the heat station equipment, initial operating state data is obtained from the multiple operating state data;

[0008] Acquire current operation characteristic data of the heat station equipment, and generate target operation status data of the heat station equipment based on initial operation status data, target required heat, current operation characteristic data and indoor and outdoor environment characteristic data;

[0009] Control the thermal station equipment according to the target operating status data.

[0010] According to a second aspect of the embodiments of the present disclosure, there is provided an indoor temperature control device, comprising:

[0011] An acquisition module is configured to acquire indoor and outdoor environmental characteristic data and indoor target temperature;

[0012] A prediction module is configured to predict the indoor heat demand for the next time period based on the indoor and outdoor environmental characteristic data and the target temperature, and obtain the indoor target heat demand for the next time period;

[0013] A screening module is configured to screen the initial operating state data from the multiple operating state data based on the target required heat and the correspondence between each operating state data of the multiple operating state data of the heat station equipment and the heat generated by the heat station equipment;

[0014] A generating module is configured to obtain current operating characteristic data of the heat station equipment, and generate target operating status data of the heat station equipment based on the initial operating status data, the target required heat, the current operating characteristic data and the indoor and outdoor environment characteristic data;

[0015] The control module is configured to control the thermal station equipment according to the target operating status data.

[0016] 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.

[0017] 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.

[0018] Compared with the prior art, the disclosed embodiments have the following beneficial effects: by acquiring indoor and outdoor environmental characteristic data and indoor target temperature, a basis is provided for subsequent data processing and analysis, so that the indoor temperature control method is based on real-time environmental data and actual needs of users. The indoor target heat demand in the next time period is predicted based on the indoor and outdoor environmental characteristic data and the target temperature, thereby improving the predictability and accuracy of temperature control. Based on the predicted target heat demand, combined with the corresponding relationship between the operating status of the thermal station equipment and the heat generation, the initial operating status data of the thermal station equipment that can meet the target heat demand is preliminarily determined. The initial operating status data is supplemented by combining the current operating characteristic data of the thermal station equipment, the predicted target heat demand, and the indoor and outdoor environmental characteristic data, generating more accurate and efficient target operating status data of the thermal station equipment, and controlling the thermal station equipment based on the target operating status data, thereby realizing dynamic control of the thermal station equipment, and facilitating timely response to changing environmental conditions and needs. The indoor temperature control method proposed in the present invention introduces a heat demand prediction mechanism and a dynamic adjustment mechanism. It no longer relies on fixed control strategies, conducts in-depth analysis and prediction of real-time data, and generates target operating status data of the thermal station equipment based on real-time data. This can achieve more refined and personalized temperature control based on current environmental conditions and user needs, dynamically adjust the operating status of the thermal station equipment, solve the limitations of traditional fixed strategy control methods, and improve the level of refinement of indoor temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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.

[0020] Figure 1 is a flow chart of an internal temperature control method provided by an embodiment of the present disclosure;

[0021] Figure 2 is a flow chart of another internal temperature control method provided by an embodiment of the present disclosure;

[0022] Figure 3 is a structural schematic diagram of an internal temperature control device provided by an embodiment of the present disclosure;

[0023] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0024] 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.

[0025] 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.

[0026] 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:

[0027] Step 101, obtaining indoor and outdoor environmental characteristic data and indoor target temperature.

[0028] In some embodiments, the indoor and outdoor environment characteristic data is the data after preprocessing the collected original data (i.e., the indoor and outdoor environment initial characteristic data). The indoor and outdoor environment characteristic data may include the current time characteristics, the indoor wet bulb temperature in the current time period, the indoor average temperature in the current time period, the indoor humidity in the current time period, the indoor and outdoor temperature difference in the current time period, the outdoor temperature in the next time period, the characteristic vector of the outdoor weather in the current time period, and the characteristic vector of the outdoor weather in the next time period. The indoor and outdoor environment characteristic data may reflect the state of the indoor and outdoor environment in the current time period and the outdoor weather in the next time period. The indoor and outdoor environment characteristic data directly affects the indoor comfort and the direction of the regulation of the heat station boiler, and is an important factor in predicting the indoor target demand heat in the next time period. Acquiring the indoor and outdoor environment characteristic data can provide reliable data support for the intelligent control of the heat station equipment. The indoor target temperature may be the desired indoor temperature set by the user, which is the target value of the temperature control. The target temperature indicates the direction for the heat station equipment, that is, the indoor temperature set point that the heat station equipment attempts to reach or maintain. The target temperature can be used as a reference point for the thermal station equipment to guide the thermal 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 occupants.

[0029] Step 102, based on the indoor and outdoor environmental characteristic data and the target temperature, predict the indoor heat demand for the next time period to obtain the indoor target heat demand for the next time period.

[0030] In some embodiments, the indoor and outdoor environment characteristic data include current time characteristics, indoor wet bulb temperature in the current time period, indoor average temperature in the current time period, indoor humidity in the current time period, indoor and outdoor temperature difference in the current time period, outdoor temperature in the next time period, characteristic vector of outdoor weather in the current time period, and characteristic vector of outdoor weather in the next time period, etc. The target temperature is the indoor temperature that the user sets and expects to reach. According to the current indoor and outdoor environment characteristic data and the target temperature, the heat that needs to be provided to the room in the future is predicted, that is, the target demand heat of the room in the next time period, and the next time period can be the next hour. Specifically, the current time can be ten o'clock, the indoor and outdoor environment characteristic data are the indoor and outdoor environment characteristics of the time period from nine to ten o'clock and the outdoor weather from ten to eleven o'clock obtained through the weather forecast, and the next time period is the time period from ten to eleven o'clock. The machine learning model can be used to process the indoor and outdoor environment characteristic data and the target temperature, and the indoor heat demand in the next time period (such as the next hour) is predicted with reference to various factors such as the indoor and outdoor environment characteristic data and the target temperature. The machine learning model outputs the target demand heat of the room in the next time period, and the value reflects the heat required to meet the indoor target temperature. The predicted target heat demand helps the thermal station equipment allocate resources more efficiently, such as adjusting the operating status of the boiler and the frequency conversion of the heat pump to meet the predicted demand. At the same time, it can reduce unnecessary energy consumption, improve the overall efficiency of the thermal station equipment, and refer to the target temperature to better meet the personalized needs of users. Through in-depth analysis of real-time environmental data, the operating status of the thermal station equipment can be adjusted more accurately to meet the current environmental conditions and the personalized needs of users.

[0031] Step 103 , based on the target required heat and the correspondence between each of the plurality of pre-set operating status data of the heat station equipment and the heat generated by the heat station equipment, initial operating status data is obtained by screening the plurality of operating status data.

[0032] Specifically, the target heat demand is the heat output target that the heat station equipment needs to achieve, which is determined according to the indoor and outdoor environmental characteristic data and the target temperature. The operating status data of the heat station equipment includes operating parameters such as the number of boilers, hot water pumps, set temperatures, and hot water pump frequency. The current operating status data of the heat station equipment directly reflects the operating status and heat generation capacity of the heat station equipment, so that the current operating status data of the heat station equipment can directly affect the current indoor temperature. The correspondence between the operating status data of the heat station equipment and the heat generated by the heat station equipment is the relationship between each operating status data and the heat that can be generated by the heat station equipment. Each operating status data has a certain correlation with the heat generated by the heat station equipment. For example, increasing the operating power of the heat station equipment can increase the generated heat; adjusting the flow rate and set temperature can also affect the transfer and distribution of heat. The above correspondence is constructed based on multiple historical operating status data of the heat station equipment. Select an operating status data whose generated heat is closest to the target required heat from multiple operating status data as the initial operating status data. For example, the corresponding relationship between the operating status data and the heat generated by the heat station equipment can be that when the number of boilers is 2, the number of hot water pumps is 2, and the frequency of the hot water pump is 40Hz, the corresponding generated heat can be 95kW; when the number of boilers is 2, the number of hot water pumps is 2, and the frequency of the hot water pump is 50Hz, the corresponding generated heat can be 105kW; when the number of boilers is 3, the number of hot water pumps is 3, and the frequency of the hot water pump is 40Hz, the corresponding generated heat can be 120kW; when the target required heat is determined to be 104kW, the operating status data of the heat station equipment closest to the required heat of 104kW is selected. Among the above operating status data, the heat output when the number of boilers is 2, the number of hot water pumps is 2, and the frequency of the hot water pump is 50Hz is closest to 104kW, and this operating status data is selected as the initial operating status data. Based on the initial operating status data obtained by screening the predicted target required heat, the control strategy of the heat station equipment is adjusted according to the real-time data to achieve more precise control of the indoor temperature.

[0033] Step 104, obtaining current operation characteristic data of the heat station equipment, and generating target operation status data of the heat station equipment based on the initial operation status data, the target required heat, the current operation characteristic data and the indoor and outdoor environment characteristic data.

[0034] Specifically, the current operation characteristic data of the heat station equipment is the data after preprocessing the collected original data (i.e., the current operation initial characteristic data). The current operation characteristic data of the heat station equipment may include the supply and return water temperature difference, the hot water main supply temperature, the hot water main return temperature, the flow rate, the hot water pump frequency, the number of boilers, the hot water pump, and the set temperature. The current operation characteristic data is the operation characteristic data of the current time period. For example, the current time period may be the time period from nine to ten o'clock. The current operation characteristic data can not only reflect the current working state of the heat station equipment, but also imply the changing trend and potential problems of the performance of the heat station equipment. By continuously monitoring and recording the operation characteristic data of the heat station equipment, the operation state of the heat station equipment inside the mall can be more accurately understood, providing reliable data support for the intelligent control of the heat station equipment. Based on the target demand heat, the current operation characteristic data, and the indoor and outdoor environment characteristic data, the target set temperature for the next time period is generated, and the target set temperature is combined with the initial operation state data to generate the target operation state data of the heat station equipment.

[0035] In some embodiments, the current operation characteristic data, the predicted target demand heat, the indoor and outdoor environmental characteristic data and the target temperature are combined to generate more refined target operation state data of the heat station equipment, so that the target operation state data of the heat station equipment can not only meet the predicted demand heat, but also be optimized according to the current environmental conditions to improve efficiency and reduce energy consumption. By comprehensively considering the current operation state of the heat station equipment and the indoor and outdoor environmental conditions, the operation state of the heat station equipment is dynamically adjusted to cope with changing needs, and the in-depth analysis and prediction capabilities of real-time data are enhanced to achieve more refined and personalized temperature control.

[0036] Step 105: Control the thermal station equipment according to the target operating status data.

[0037] In some embodiments, the target operating status data includes specific operating parameters of each device that should be set to achieve the target temperature. The target operating status data may include: project name: identifying the name of the current project or shopping mall; target temperature: the set target indoor temperature; number of boilers: setting the number of boilers that need to be run; hot water pump frequency: setting the operating frequency of the hot water pump; number of hot water pumps: setting the number of hot water pumps that need to be run; set temperature: setting the hot water supply set temperature of the boiler.

[0038] The heating station equipment sends instructions to each device in the heating station according to the target operating status data to accurately control the heating station equipment, ensuring that the heating station equipment operates in the optimal state to meet the expected heat demand. The heating station equipment is controlled according to the target operating status data to achieve temperature control of the indoor environment and intelligent adjustment of the indoor temperature. The cooling station equipment is controlled based on the target operating status data, which can automatically adjust the operating status of the heating station equipment, realize automatic control, reduce dependence on manual operation, and improve the response speed and operating efficiency of the heating station temperature control system.

[0039] In some embodiments, the heat station equipment continuously collects actual indoor and outdoor environmental characteristic data and operating status data during operation, and feeds these data back to the heat station equipment for subsequent updating and optimization of parameters in the heat station equipment. Specifically, the feedback adjustment mechanism includes: continuously collecting actual indoor and outdoor environmental characteristic data and operating status data of the heat station equipment; feeding back the collected indoor and outdoor environmental characteristic data and operating status data of the heat station equipment to the temperature control system of the heat station equipment for parameter updating and optimization of the temperature control system of the heat station equipment. According to the feedback data, the temperature control system of the heat station equipment is updated and adjusted to improve the accuracy and prediction ability of the temperature control system of the heat station equipment. According to the updated temperature control system of the heat 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 heat station equipment is adjusted and optimized in time by continuously collecting actual indoor and outdoor environmental characteristic data and operating status data of the heat station equipment and feeding back to the temperature control system of the heat station equipment, thereby improving the accuracy and stability of prediction and control. The above feedback adjustment mechanism enables the temperature control system of the heat station equipment to continuously adapt to changes in the indoor and outdoor environment, and realize intelligent management and optimization of the heat station equipment.

[0040] The indoor temperature control method provided by the present disclosure provides a basis for subsequent data processing and analysis by acquiring indoor and outdoor environmental characteristic data and indoor target temperature, so that the indoor temperature control method is based on real-time environmental data and actual needs of users. The indoor target heat demand in the next time period is predicted based on the indoor and outdoor environmental characteristic data and the target temperature, thereby improving the predictability and accuracy of temperature control. Based on the predicted target heat demand, combined with the corresponding relationship between the operating status of the thermal station equipment and the heat generation, the initial operating status data of the thermal station equipment that can meet the target heat demand is preliminarily determined. The initial operating status data is supplemented by combining the current operating characteristic data of the thermal station equipment, the predicted target heat demand, and the indoor and outdoor environmental characteristic data to generate more accurate and efficient target operating status data of the thermal station equipment, and the thermal station equipment is controlled based on the target operating status data to achieve dynamic control of the thermal station equipment, which helps to respond to changing environmental conditions and needs in a timely manner. The indoor temperature control method proposed in the present invention introduces a heat demand prediction mechanism and a dynamic adjustment mechanism. It no longer relies on fixed control strategies, conducts in-depth analysis and prediction of real-time data, and generates target operating status data of the thermal station equipment based on real-time data. This can achieve more refined and personalized temperature control based on current environmental conditions and user needs, dynamically adjust the operating status of the thermal station equipment, solve the limitations of traditional fixed strategy control methods, and improve the level of refinement of indoor temperature control.

[0041] In some embodiments, based on indoor and outdoor environmental characteristic data and a target temperature, the indoor heat demand for the next time period is predicted to obtain a target indoor heat demand for the next time period, including: inputting the indoor and outdoor environmental characteristic data into a heat prediction model to obtain the predicted indoor heat for the next time period output by the heat prediction model; inputting the predicted heat and the indoor and outdoor environmental characteristic data as a set of data into a temperature regulation heat model to obtain an initial predicted temperature corresponding to the predicted heat output by the temperature regulation heat model; correcting the initial predicted temperature based on the target temperature to obtain a target predicted temperature, until the absolute value of the difference between the target predicted temperature and the target temperature is less than or equal to a first preset value, and determining the heat corresponding to the target predicted temperature as the target heat demand.

[0042] Specifically, the heat prediction model can fit the indoor and outdoor parameters of the current time period and the required heat in the next time period. The heat prediction model is trained based on the historical indoor and outdoor environmental characteristic data and the corresponding historical generated heat. The historical generated heat is calculated based on the historical operation characteristic data of the heat station equipment. For example, the historical operation characteristic data of the heat station equipment includes flow rate, hot water main water supply temperature and hot water main return water temperature. The corresponding generated heat can be calculated based on the flow rate, hot water main water supply temperature and hot water main return water temperature. The indoor and outdoor environmental characteristic data are input into the heat prediction model, and the indoor heat demand in the future period is predicted based on the indoor and outdoor environmental characteristic data. The predicted indoor heat in the next time period output by the heat prediction model is obtained, which provides a data basis for subsequent steps.

[0043] Specifically, the temperature regulating heat model can be used to fit the indoor average temperature of the room in the next time period according to the indoor and outdoor environmental characteristic data and the predicted indoor heat in the next time period. The predicted heat and the indoor and outdoor environmental characteristic data are input into the temperature regulating heat model as a set of data. According to the predicted heat and environmental characteristics, the indoor temperature in the current environment and when the predicted heat is generated is simulated to obtain the initial predicted temperature corresponding to the predicted heat output by the temperature regulating heat model. The initial predicted temperature is the indoor average temperature. By evaluating whether the predicted indoor temperature under the conditions of the current environment and the generation of the predicted heat can meet the requirements of the target temperature, a basis is provided for subsequent corrections.

[0044] In some embodiments, when constructing a heat prediction model, indoor and outdoor environmental feature data are used as original data sets, and the indoor and outdoor environmental feature data include time features, indoor wet-bulb temperature in the current time period, indoor average temperature in the current time period, indoor humidity in the current time period, indoor and outdoor temperature difference in the current time period, outdoor temperature in the next time period, feature vector of outdoor weather in the current time period, and feature vector of outdoor weather in the next time period, etc., and the optimal feature subset is obtained by sequential floating forward selection (SFFS). The features in the optimal feature subset are the most informative for the heat prediction model. During the training process, a multi-fold cross-dataset splitting method CustomTimeSeriesSplit is constructed. The CustomTimeSeriesSplit tool class can be constructed based on the base class BaseCrossValidator of all cross-validators of sklearn to realize the custom training set and test set splitting interval, and construct a multi-fold time series data set. For example, a prediction set of 3 days totaling from 2023-02-01 to 2023-02-03 is reserved for the test set, and a 5-fold cross-validation data set is constructed with a training set interval of 7 days and a validation set interval of 1 day as shown in the following table.

[0045] discount Training set period (7 days interval) Validation set period 1 2023-01-24～2023-01-30 2023-01-31 2 2023-01-23～2023-01-29 2023-01-30 3 2023-01-22～2023-01-28 2023-01-29 4 2023-01-21～2023-01-27 2023-01-28 5 2023-01-20～2023-01-26 2023-01-29

[0046] The heat prediction model to be trained can be a Light Gradient Boosting Machine (LightGBM) or a Ridge Regression Ridge model, and the model hyperparameters are determined using Bayesian search, and the mean absolute percentage error (MAPE) is used to evaluate the model, and the optimal model is selected and saved to obtain a trained heat prediction model. When constructing a temperature-regulating heat model, the predicted heat and indoor and outdoor environmental feature data are used as original data sets. The indoor and outdoor environmental feature data and the predicted heat include multiple sub-features, and the optimal feature subset can be obtained by SFFS screening. The features in the optimal feature subset are the most informative for the temperature-regulating heat model. The temperature-regulating heat model to be trained can be a LightGBM or a Ridge model, and the model hyperparameters are determined using Bayesian search. The model is evaluated using MAPE, and the optimal model is selected and saved to obtain a trained temperature-regulating heat model.

[0047] Further, the initial predicted temperature is corrected based on the target temperature to obtain the target predicted temperature, until the absolute value of the difference between the target predicted temperature and the target temperature is less than or equal to the first preset value, and the predicted indoor temperature is close enough to the target temperature through iterative adjustment, which helps the indoor temperature to accurately reach the target temperature expected by the user. The heat corresponding to the target predicted temperature is determined as the target required heat, and the heat value that the heat station equipment needs to provide in the end is determined so that the indoor temperature reaches the target temperature, providing a clear heat demand value as the basis for the subsequent control strategy generation. By introducing the heat prediction model and the temperature adjustment heat model, accurate prediction of future heat demand is achieved to avoid the blindness and lag in traditional temperature control methods. Comprehensive reference is made to the indoor and outdoor environmental characteristic data and the target temperature, and dynamic adjustment is made according to real-time data to make temperature control more refined and personalized to meet the needs of different users and scenarios. By accurately predicting and adjusting heat demand, unnecessary energy consumption and waste can be avoided, and energy utilization efficiency can be improved. By acquiring data in real time, it is possible to respond quickly and make adjustments in the face of complex and changing environmental conditions to ensure the stability and comfort of the indoor temperature, and enhance the adaptability and stability of the temperature control of the heat station equipment.

[0048] In some embodiments, the initial predicted temperature is corrected based on the target temperature to obtain the target predicted temperature, including: when the absolute value of the difference between the initial predicted temperature and the target temperature is greater than a first preset value, the predicted heat is adjusted according to a preset heat adjustment step to obtain multiple candidate heats; each candidate heat is input into a temperature regulation heat model as a set of data together with indoor and outdoor environmental characteristic data to obtain an initial predicted temperature corresponding to each candidate heat output by the temperature regulation heat model; the absolute value of the difference between the initial predicted temperature corresponding to each candidate heat and the target temperature is calculated, and the initial predicted temperature corresponding to each candidate heat that meets the preset requirements is determined as the target predicted temperature, and the preset requirement is less than or equal to the first preset value and the absolute value of the difference with the target temperature is the smallest.

[0049] Specifically, the target temperature is the ideal temperature expected to be reached indoors, and the initial predicted temperature is the predicted indoor temperature obtained based on the predicted heat and indoor and outdoor environmental characteristic data. The absolute value of the difference between the initial predicted temperature and the target temperature is calculated to clarify the gap between the current predicted temperature and the target temperature, and to determine whether the absolute value of the difference is greater than the first preset value. If the absolute value of the difference is less than or equal to the first preset value, it means that the current prediction is close enough to the target and no substantial adjustment is required; if the absolute value of the difference is greater than the first preset value, it is necessary to adjust to narrow the gap. The predicted heat is adjusted by the preset heat adjustment step to generate multiple candidate heats. The multiple candidate heats represent possible adjustment directions and amplitudes, providing a basis for subsequent screening. By generating multiple candidate heats, different generated heats can be explored to find the solution closest to the target temperature. Each candidate heat and the indoor and outdoor environmental characteristic data are input into the temperature regulation heat model to obtain the initial predicted temperature corresponding to each candidate heat output by the temperature regulation heat model, and the indoor temperature changes under different heat values ​​are simulated to evaluate whether the target temperature requirements can be met under different candidate heats. By calculating the difference between the initial predicted temperature corresponding to the candidate heat and the target temperature, the gap between the predicted temperature corresponding to each candidate heat and the target temperature is evaluated, and the initial predicted temperature corresponding to each candidate heat that meets the preset requirements is determined as the target predicted temperature. The preset requirement is that the initial predicted temperature is less than or equal to the first preset value and the absolute value of the difference with the target temperature is the smallest, so that the adjusted target predicted temperature is close to the target temperature without being over-adjusted.

[0050] For example, when the initial predicted temperature is 25°C, the target temperature is 26°C, the first preset value is 0.5, and the predicted heat is 103kW, the absolute value of the difference between the initial predicted temperature and the target temperature is 1. The absolute value of the difference between the initial predicted temperature and the target temperature is greater than the first preset value, and the predicted heat needs to be adjusted. Multiple candidate heats are generated with a step size of 1kW: 104kW, 105kW, and 106kW. Each candidate heat and indoor and outdoor environmental characteristic data are input into the temperature regulation heat model. The temperature regulation heat model is used to simulate the indoor temperature changes under different heat values, and the result of 104kW for The initial predicted temperature of 104kW is 25.4℃, the initial predicted temperature of 105kW is 25.9℃, and the initial predicted temperature of 106kW is 26.4℃. The difference between the initial predicted temperature and the target temperature corresponding to the candidate heat is calculated. The difference for 104kW is 0.6, the difference for 105kW is 0.1, and the difference for 106kW is 0.4. The difference of 105kW (0.1) is less than the first preset value of 0.5, and the absolute value of the difference with the target temperature of 26 is the smallest. The initial predicted temperature of 25.9℃ corresponding to 105kW can be determined as the target predicted temperature.

[0051] In some embodiments, reference Figure 2 , step 103, i.e., before filtering and obtaining the initial operating state data from the multiple operating state data based on the target required heat and the correspondence between each operating state data in the multiple operating state data of the pre-set heat station equipment and the heat generated by the heat station equipment, it also includes:

[0052] Step 201, obtaining multiple historical operation status data of the heat station equipment and the flow rate corresponding to each historical operation status data, wherein the historical operation status data includes the historical number of boilers, the historical number of hot water pumps, and the historical frequency of hot water pumps;

[0053] Step 202, clustering each historical running status data based on the traffic corresponding to each historical running status data, to obtain processed running status data corresponding to a plurality of cluster centers after clustering and the traffic corresponding to the processed running status data;

[0054] Step 203, determining the adjustment step length corresponding to the hot water pump frequency according to the hot water pump frequency and the hot water pump frequency range in the processed operating status data;

[0055] Step 204, adjusting the hot water pump frequency in each processed operation status data according to the adjustment step corresponding to the hot water pump frequency, to obtain a plurality of operation status data of the heat station equipment;

[0056] Step 205, based on each running status data and each processed running status data, determining the flow corresponding to each running status data;

[0057] Step 206, inputting the flow corresponding to each running state data into the flow heat prediction model, and obtaining the heat corresponding to each running state data output by the flow heat prediction model;

[0058] Step 207: establishing a corresponding relationship based on each piece of operating status data and the amount of heat corresponding to each piece of operating status data.

[0059] Specifically, multiple historical operating status data of the thermal station equipment and the flow corresponding to each historical operating status data are obtained to understand the behavioral characteristics of the thermal station equipment under different operating states, and to obtain the performance of the thermal station equipment under different operating states in the past, so as to provide a basis for further analysis and optimization. The historical operating status data includes the historical number of boilers, the historical number of hot water pumps, and the historical frequency of hot water pumps. The historical flow is the flow data corresponding to the historical operating status data. The historical number of boilers is the number of boilers running in the thermal station equipment in the corresponding historical time period. The number of boilers directly affects the heat generation capacity of the thermal station equipment; the historical number of hot water pumps is the number of hot water pumps running in the thermal station equipment in the corresponding historical time period. The hot water pump delivers hot water to where it is needed, and the number of hot water pumps affects the delivery efficiency of hot water; the historical frequency of hot water pumps is the operating frequency in the corresponding historical time period. The high or low frequency of the hot water pump determines the speed of the hot water pump, which in turn affects the water flow and heat transfer. For each historical operating status data, there is a corresponding flow. The flow is the carrier of heat transfer, and its size is closely related to the amount of heat transfer. Kmeans can be used to cluster historical operation status data, identify operation status groups with similar behavior patterns, and obtain processed operation status data corresponding to multiple cluster centers and the flow corresponding to the processed operation status data. The processed operation status data corresponding to each cluster center represents a group of operation status data of thermal station equipment with similar flow characteristics. The processed operation status data corresponding to multiple cluster centers after clustering can be sorted from small to large according to the corresponding flow. The sorted cluster centers can be used to represent different flow patterns or demand levels and provide reference for subsequent optimization scheduling.

[0060] In some embodiments, within the frequency range of the hot water pump, the adjustment step of the hot water pump frequency is determined. The adjustment step of the hot water pump frequency can be 1 Hz, and the frequency range of the hot water pump can be 35 Hz to 50 Hz. The adjustment step of the hot water pump frequency is used to adjust the hot water pump frequency in the processed running state data. By adjusting the step, different hot water pump frequency configurations can be explored. According to the adjustment step corresponding to the hot water pump frequency, the hot water pump frequency in each processed running state data is adjusted. On the basis of each processed running state data, the hot water pump frequency is gradually adjusted based on the determined adjustment step. Each adjustment generates a new running state data. Through multiple adjustments, a series of running state data of the heat station equipment under different hot water pump frequencies are obtained. Flow rate is a key parameter for heat transfer. When the number of boilers and the number of hot water pumps in the running state data are the same, the ratio of the flow rate corresponding to the running state is equal to the ratio of the hot water pump frequency in the running state. According to the processed running state data and the hot water pump frequency in the running state data, when the flow rate corresponding to the processed running state data is known, the flow rate corresponding to each running state data can be calculated. By determining the flow rate, necessary data support can be provided for subsequent heat prediction.

[0061] The flow corresponding to each operating state data is input into the flow heat prediction model to predict the heat output under different flow rates. The flow heat prediction model uses internal algorithms and parameters to calculate based on the input flow rate to obtain the heat corresponding to each operating state data output by the flow heat prediction model. The above-mentioned flow heat model can fit the heat according to the flow. When training the flow heat model, the flow corresponding to each historical operating state data is used as a training sample, and the heat corresponding to each historical operating state data is used as the label corresponding to the sample to construct a flow heat training set. The flow heat model to be trained can be a LightGBM or Ridge model, and the model hyperparameters are determined using Bayesian search. MAPE is used to evaluate the model performance, and the optimal model is selected and saved to obtain the trained flow heat model. Through the flow heat prediction model, the heat output under different operating states can be quantified, providing a basis for the subsequent screening of initial operating state data from multiple operating state data.

[0062] Furthermore, according to different operating status data of the heat station equipment and the heat corresponding to the operating status data, a mapping table or database can be established to record the relationship between each operating status data and the heat corresponding to it. By establishing the corresponding relationship between the operating status and the heat, the operating status data closest to the target heat demand can be quickly found under a given target heat demand, that is, the initial operating status data can be screened to meet the predicted target heat demand, thereby realizing accurate control of the heat station equipment.

[0063] In some embodiments, target operating status data of the thermal station equipment is generated based on initial operating status data, target demand heat, current operating characteristic data and indoor and outdoor environmental characteristic data, including: inputting the target demand heat, current operating characteristic data and indoor and outdoor environmental characteristic data as a set of data into a set temperature prediction model to obtain the target set temperature for the next time period output by the set temperature prediction model; and generating the target operating status data of the thermal station equipment based on the target set temperature and the initial operating status data.

[0064] Specifically, the set temperature prediction model can fit the target set temperature of the next time period according to the target demand heat, the current operation characteristic data and the indoor and outdoor environment characteristic data, and the target set temperature is the boiler water supply temperature setting value. The indoor and outdoor environment characteristic data can be the outdoor temperature of the next time period, the indoor and outdoor temperature difference in the current time period, and the characteristic vector of the outdoor weather in the next time period. The current operation characteristic data can be the supply and return water temperature difference. The outdoor temperature of the next time period, the indoor and outdoor temperature difference in the current time period, the characteristic vector of the outdoor weather in the next time period, the supply and return water temperature difference and the target demand heat are input into the set temperature prediction model as a set of data. Through the set temperature prediction model, the target set temperature of the next time period is predicted according to the current environmental conditions, operation status and demand heat, providing a basis for subsequent operation status adjustment. The target operation status data of the heat station equipment is generated based on the target set temperature and the initial operation status data. For example, the initial operation status data can be the number of boilers is 2, the number of hot water pumps is 2, the frequency of the hot water pump is 50Hz, and the target set temperature is 60℃. The generated target operation status data is the number of boilers is 2, the number of hot water pumps is 2, the frequency of the hot water pump is 50Hz, and the target set temperature is 60℃. By generating the target operating status data of the thermal station equipment through the initial operating status data, target required heat, current operating characteristic data and indoor and outdoor environmental characteristic data, more refined control can be performed according to the current environmental conditions and user needs to meet heat needs and improve user experience and satisfaction.

[0065] In addition, when constructing the set temperature prediction model, the target demand heat, current operation characteristic data and indoor and outdoor environmental characteristic data are used as the original data set. The indoor and outdoor environmental characteristic data include time characteristics, indoor wet bulb temperature in the current time period, indoor average temperature in the current time period, indoor humidity in the current time period, indoor and outdoor temperature difference in the current time period, outdoor temperature in the next time period, characteristic vector of outdoor weather in the current time period and characteristic vector of outdoor weather in the next time period, etc. The current operation characteristic data include supply and return water temperature difference, hot water main supply water temperature, hot water main return water temperature, flow rate, hot water pump frequency, number of boilers, hot water pump, set temperature and other sub-features. The optimal feature subset can be obtained by SFFS screening. The features in the optimal feature subset are the most informative for the set temperature prediction model. Specifically, the optimal feature subset can include outdoor temperature in the next time period, indoor and outdoor temperature difference in the current time period, characteristic vector of outdoor weather in the next time period, supply and return water temperature difference and target demand heat. The set temperature prediction model to be trained can be a LightGBM or Ridge model, and the model hyperparameters are determined using Bayesian search, and the MAPE evaluation model is used to select the optimal model for saving, thereby obtaining a trained set temperature prediction model.

[0066] In some embodiments, obtaining indoor and outdoor environmental characteristic data includes: obtaining initial characteristic data of the indoor and outdoor environment, the initial characteristic data of the indoor and outdoor environment including the average indoor temperature in the current time period, the outdoor temperature in the current time period, the indoor humidity in the current time period, the outdoor weather in the current time period, the outdoor temperature in the next time period, the outdoor weather in the next time period and the current time; constructing a time feature based on the current time period; determining the corresponding indoor wet-bulb temperature in the current time period based on the average indoor temperature in the current time period and the indoor humidity in the current time period; determining the corresponding indoor and outdoor temperature difference in the current time period based on the average indoor temperature in the current time period and the outdoor temperature in the current time period; encoding the outdoor weather in the current time period and the outdoor weather in the next time period to obtain a characteristic vector of the outdoor weather in the current time period and a characteristic vector of the outdoor weather in the next time period; determining the time feature, the indoor wet-bulb temperature in the current time period, the average indoor temperature in the current time period, the indoor humidity in the current time period, the indoor and outdoor temperature difference in the current time period, the outdoor temperature in the next time period, the characteristic vector of the outdoor weather in the current time period and the characteristic vector of the outdoor weather in the next time period as indoor and outdoor environmental characteristic data.

[0067] Specifically, the initial characteristic data of the indoor and outdoor environment is the basic data directly obtained from the environmental monitoring equipment or data source. The average indoor temperature in the current time period is the average indoor temperature obtained by multiple indoor temperature sensors in the current time period. The current time period can be 1 hour, and each temperature sensor can send back temperature data every five minutes. The outdoor temperature in the current time period is the average outdoor temperature obtained by multiple outdoor temperature sensors in the current time period. The current time period can be 1 hour, and each outdoor temperature sensor can send back temperature data every five minutes. The indoor humidity in the current time period is the average humidity level of the indoor environment, which can be measured by a humidity sensor. The outdoor weather in the current time period can be sunny, rainy, cloudy, etc. The outdoor temperature in the next time period and the outdoor weather in the next time period can be obtained according to the weather forecast. The current time is the specific time point for recording the data, which is helpful for the time series analysis of the data. In addition, the initial characteristic data of the indoor and outdoor environment can also include CO 2 Content, wind speed, etc. Based on the current time period, construct time features, including whether it is a holiday, whether it is adjusted, the number of days to the holiday, the number of days to the east solstice, business hours, store opening and closing times, etc. The above features can help the model better capture time-related influences. After filtering abnormal sensors, exploratory data analysis (EDA) can be performed on the indoor temperature and humidity data, and based on the indoor average temperature in the current time period and the indoor humidity in the current time period, the corresponding indoor wet bulb temperature in the current time period is determined. The indoor wet bulb temperature is crucial for evaluating indoor comfort. The above indoor and outdoor temperature difference is the difference between the indoor average temperature and the outdoor temperature in the current time period. It is important to understand the heat exchange efficiency of the building and capture the impact of ambient temperature changes on heat consumption. Encode the outdoor weather in the current time period and the outdoor weather in the next time period. The encoding can be OneHot encoding, mapping the weather type to a numerical value, and converting the text description of the weather (such as sunny and rainy days) into a vector form, so that the model can recognize and learn the impact of weather on the indoor and outdoor environment. Through the above preprocessing steps, the original data has been effectively cleaned and converted, providing reliable input data for the subsequent model and improving the accuracy and generalization ability of the model. By integrating the above processed feature data to form complete indoor and outdoor environmental feature data, a data set that fully reflects the environmental conditions can be obtained to predict the target demand heat and target set temperature for the next time period.

[0068] In some embodiments, obtaining current operating characteristic data of the thermal station equipment includes: obtaining current initial operating characteristic data of the thermal station equipment, the current initial operating characteristic data including a hot water main supply water temperature and a hot water main return water temperature; determining a corresponding supply and return water temperature difference based on the hot water main supply water temperature and the hot water main return water temperature; and determining the supply and return water temperature difference as the current operating characteristic data.

[0069] In some embodiments, the current operation initial characteristic data is the basic data that can directly reflect the operation status of the heat station equipment at a certain moment or time period. The current operation initial characteristic data may include the hot water main supply temperature, the hot water main return temperature, the boiler operation status, the hot water pump operation status, the hot water pump frequency, etc. The hot water main supply temperature is the temperature of the hot water provided by the heat station equipment to the system, which can reflect the heating capacity and efficiency of the heat station equipment. The hot water main return temperature is the temperature when the hot water returns to the heat station equipment after being used by the system, which can reflect the heat consumption and the heat loss of the system. Supply and return water temperature difference = hot water main supply temperature - hot water main return temperature. The supply and return water temperature difference is an important indicator for evaluating the thermal efficiency, heat loss and user heat demand satisfaction of the heat station equipment. The larger the supply and return water temperature difference, the more heat is absorbed by the indoor air, or the heat loss is small. The supply and return water temperature difference can comprehensively reflect the operation status, thermal efficiency and heat demand of the heat station equipment, and can be one of the current operation characteristic data of the heat station equipment. By incorporating the supply and return water temperature difference into the operating characteristic data set, we can have a more comprehensive understanding of the operating status of the thermal station equipment, and then carry out performance evaluation, fault diagnosis or optimized scheduling and other operations.

[0070] 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.

[0071] 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.

[0072] Figure 3 Schematic diagram of an indoor temperature control device provided by an embodiment of the present disclosure. Figure 3 As shown, the indoor temperature control device comprises:

[0073] An acquisition module 301 is configured to acquire indoor and outdoor environment characteristic data and indoor target temperature;

[0074] The prediction module 302 is configured to predict the indoor heat demand for the next time period based on the indoor and outdoor environment characteristic data and the target temperature, and obtain the indoor target heat demand for the next time period;

[0075] The screening module 303 is configured to screen the initial operating state data from the multiple operating state data based on the target required heat and the correspondence between each operating state data of the multiple operating state data of the heat station equipment and the heat generated by the heat station equipment;

[0076] The generating module 304 is configured to obtain the current operating characteristic data of the heat station equipment, and generate the target operating state data of the heat station equipment based on the initial operating state data, the target required heat, the current operating characteristic data and the indoor and outdoor environment characteristic data;

[0077] The control module 305 is configured to control the thermal station equipment according to the target operating state data.

[0078] According to the technical solution provided by the embodiment of the present disclosure, by obtaining indoor and outdoor environmental characteristic data and indoor target temperature, a basis is provided for subsequent data processing and analysis, so that the indoor temperature control method is based on real-time environmental data and actual needs of users. The indoor target heat demand in the next time period is predicted based on the indoor and outdoor environmental characteristic data and the target temperature, thereby improving the predictability and accuracy of temperature control. Based on the predicted target heat demand, combined with the corresponding relationship between the operating status of the thermal station equipment and the heat generation, the initial operating status data of the thermal station equipment that can meet the target heat demand is preliminarily determined. The initial operating status data is supplemented by combining the current operating characteristic data of the thermal station equipment, the predicted target heat demand, and the indoor and outdoor environmental characteristic data to generate more accurate and efficient target operating status data of the thermal station equipment, and the thermal station equipment is controlled based on the target operating status data to achieve dynamic control of the thermal station equipment, which helps to respond to changing environmental conditions and needs in a timely manner. The indoor temperature control method proposed in the present invention introduces a heat demand prediction mechanism and a dynamic adjustment mechanism. It no longer relies on fixed control strategies, conducts in-depth analysis and prediction of real-time data, and generates target operating status data of the thermal station equipment based on real-time data. This can achieve more refined and personalized temperature control based on current environmental conditions and user needs, dynamically adjust the operating status of the thermal station equipment, solve the limitations of traditional fixed strategy control methods, and improve the level of refinement of indoor temperature control.

[0079] In some embodiments, the prediction module 302 is configured to input indoor and outdoor environmental characteristic data into a heat prediction model to obtain the predicted indoor heat in the next time period output by the heat prediction model; input the predicted heat and indoor and outdoor environmental characteristic data as a set of data into a temperature regulation heat model to obtain an initial predicted temperature corresponding to the predicted heat output by the temperature regulation heat model; correct the initial predicted temperature based on the target temperature to obtain a target predicted temperature, until the absolute value of the difference between the target predicted temperature and the target temperature is less than or equal to a first preset value, and determine the heat corresponding to the target predicted temperature as the target required heat.

[0080] In some embodiments, the prediction module 302 is configured to adjust the predicted heat according to a preset heat adjustment step to obtain multiple candidate heats when the absolute value of the difference between the initial predicted temperature and the target temperature is greater than a first preset value; input each candidate heat and the indoor and outdoor environmental characteristic data into the temperature regulation heat model as a set of data to obtain the initial predicted temperature corresponding to each candidate heat output by the temperature regulation heat model; calculate the absolute value of the difference between the initial predicted temperature corresponding to each candidate heat and the target temperature, and determine the initial predicted temperature corresponding to each candidate heat that meets the preset requirements as the target predicted temperature, and the preset requirement is that the initial predicted temperature is less than or equal to the first preset value and the absolute value of the difference with the target temperature is the smallest.

[0081] In some embodiments, based on the target demand heat and the correspondence between each operating status data in the multiple operating status data of the pre-set thermal station equipment and the heat generated by the thermal station equipment, before filtering out the initial operating status data from the multiple operating status data, the screening module 303 is configured to obtain multiple historical operating status data of the thermal station equipment and the flow rate corresponding to each historical operating status data, wherein the historical operating status data includes the historical number of boilers, the historical number of hot water pumps, and the historical frequency of hot water pumps; based on the flow rate corresponding to each historical operating status data, cluster each historical operating status data to obtain the processed operating status data corresponding to the multiple cluster centers after clustering and the processed operating status data; The flow rate corresponding to the operating status data; according to the hot water pump frequency and the hot water pump frequency range in the processed operating status data, determine the adjustment step corresponding to the hot water pump frequency; according to the adjustment step corresponding to the hot water pump frequency, adjust the hot water pump frequency in each processed operating status data to obtain multiple operating status data of the thermal station equipment; based on each operating status data and each processed operating status data, determine the flow rate corresponding to each operating status data; input the flow rate corresponding to each operating status data into the flow heat prediction model to obtain the heat corresponding to each operating status data output by the flow heat prediction model; establish a corresponding relationship based on each operating status data and the heat corresponding to each operating status data.

[0082] In some embodiments, the generation module 304 is configured to input the target demand heat, current operating characteristic data and indoor and outdoor environmental characteristic data as a set of data into a set temperature prediction model to obtain the target set temperature for the next time period output by the set temperature prediction model; and generate the target operating status data of the thermal station equipment based on the target set temperature and the initial operating status data.

[0083] In some embodiments, the acquisition module 301 is configured to acquire initial characteristic data of the indoor and outdoor environment, the initial characteristic data of the indoor and outdoor environment including the average indoor temperature in the current time period, the outdoor temperature in the current time period, the indoor humidity in the current time period, the outdoor weather in the current time period, the outdoor temperature in the next time period, the outdoor weather in the next time period and the current time; construct a time feature based on the current time period; determine the corresponding indoor wet-bulb temperature in the current time period based on the average indoor temperature in the current time period and the indoor humidity in the current time period; determine the corresponding indoor and outdoor temperature difference in the current time period based on the average indoor temperature in the current time period and the outdoor temperature in the current time period; encode the outdoor weather in the current time period and the outdoor weather in the next time period to obtain a feature vector of the outdoor weather in the current time period and a feature vector of the outdoor weather in the next time period; determine the time feature, the indoor wet-bulb temperature in the current time period, the average indoor temperature in the current time period, the indoor humidity in the current time period, the indoor and outdoor temperature difference in the current time period, the outdoor temperature in the next time period, the feature vector of the outdoor weather in the current time period and the feature vector of the outdoor weather in the next time period as indoor and outdoor environment characteristic data.

[0084] In some embodiments, the generation module 304 is configured to obtain the current initial operating characteristic data of the thermal station equipment, the current initial operating characteristic data including the hot water main supply water temperature and the hot water main return water temperature; based on the hot water main supply water temperature and the hot water main return water temperature, determine the corresponding supply and return water temperature difference; determine the supply and return water temperature difference as the current operating characteristic data.

[0085] 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.

[0086] Figure 4 Schematic diagram of an electronic device 4 provided in an embodiment of the present disclosure. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 401, a memory 402, and a computer program 403 stored in the memory 402 and executable on the processor 401. When the processor 401 executes the computer program 403, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0087] The electronic device 4 may be a desktop computer, a notebook, a PDA, a cloud server, or other electronic device. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that Figure 4The electronic device 4 is merely an example and does not limit the electronic device 4 , and may include more or less components than those shown in the figure, or different components.

[0088] The processor 401 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.

[0089] The memory 402 may be an internal storage unit of the electronic device 4, for example, a hard disk or memory of the electronic device 4. The memory 402 may also be an external storage device of the electronic device 4, 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 4. The memory 402 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0090] 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.

[0091] 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.

[0092] 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 indoor and outdoor environmental characteristic data and indoor target temperature; Based on the indoor and outdoor environment characteristic data and the target temperature, predicting the indoor heat demand for the next time period, and obtaining the indoor target heat demand for the next time period; Based on the target required heat and the correspondence between each operating status data of the pre-set multiple operating status data of the heat station equipment and the heat generated by the heat station equipment, initial operating status data is obtained from the multiple operating status data; Acquire current operation characteristic data of the heat station device, and generate target operation status data of the heat station device based on the initial operation status data, the target required heat, the current operation characteristic data and the indoor and outdoor environment characteristic data; The heat station equipment is controlled according to the target operating status data.

2. The method according to claim 1, characterized in that The predicting of the indoor heat demand in the next time period based on the indoor and outdoor environment characteristic data and the target temperature to obtain the indoor target heat demand in the next time period includes: Inputting the indoor and outdoor environmental characteristic data into a heat prediction model to obtain the predicted indoor heat in the next time period output by the heat prediction model; Input the predicted heat and the indoor and outdoor environment characteristic data as a set of data into a temperature regulating heat model to obtain an initial predicted temperature corresponding to the predicted heat output by the temperature regulating heat model; The initial predicted temperature is corrected based on the target temperature to obtain a target predicted temperature, until the absolute value of the difference between the target predicted temperature and the target temperature is less than or equal to a first preset value, and the heat corresponding to the target predicted temperature is determined as the target required heat.

3. The method according to claim 2, characterized in that The correcting the initial predicted temperature based on the target temperature to obtain the target predicted temperature includes: When the absolute value of the difference between the initial predicted temperature and the target temperature is greater than the first preset value, adjusting the predicted heat according to a preset heat adjustment step to obtain a plurality of candidate heats; Input each candidate heat and the indoor and outdoor environment characteristic data as a set of data into the temperature adjustment heat model to obtain an initial predicted temperature corresponding to each candidate heat output by the temperature adjustment heat model; Calculate the absolute value of the difference between the initial predicted temperature corresponding to each of the candidate heats and the target temperature, and determine the initial predicted temperature that meets the preset requirements among the initial predicted temperatures corresponding to each of the candidate heats as the target predicted temperature, wherein the preset requirements are that the initial predicted temperature is less than or equal to the first preset value and the absolute value of the difference with the target temperature is the smallest.

4. The method according to claim 1, characterized in that Before the initial operation status data is obtained by screening the operation status data from the plurality of operation status data based on the target required heat and the correspondence between each operation status data in the plurality of operation status data preset for the heat station equipment and the heat generated by the heat station equipment, the method further includes: Acquire multiple historical operation status data of the heat station equipment and the flow rate corresponding to each historical operation status data, wherein the historical operation status data includes the historical number of boilers, the historical number of hot water pumps, and the historical frequency of hot water pumps; Based on the traffic corresponding to each historical running status data, each historical running status data is clustered to obtain processed running status data corresponding to multiple cluster centers after clustering and the traffic corresponding to the processed running status data; Determine the adjustment step length corresponding to the hot water pump frequency according to the hot water pump frequency and the hot water pump frequency range in the processed operating status data; According to the adjustment step corresponding to the hot water pump frequency, the hot water pump frequency in each of the processed operating status data is adjusted to obtain a plurality of operating status data of the heat station equipment; Based on each of the operating status data and each of the processed operating status data, determining a flow rate corresponding to each of the operating status data; Inputting the flow corresponding to each of the operating status data into a flow heat prediction model to obtain the heat corresponding to each of the operating status data output by the flow heat prediction model; The corresponding relationship is established based on each piece of the operating status data and the amount of heat corresponding to each piece of the operating status data.

5. The method according to claim 1, characterized in that The generating target operating state data of the heat station device based on the initial operating state data, the target required heat, the current operating characteristic data and the indoor and outdoor environment characteristic data comprises: Input the target required heat, the current operation characteristic data and the indoor and outdoor environment characteristic data as a set of data into a set temperature prediction model to obtain a target set temperature for the next time period output by the set temperature prediction model; The target operating state data of the heat station device is generated based on the target set temperature and the initial operating state data.

6. The method according to claim 1, characterized in that The obtaining of indoor and outdoor environment characteristic data includes: Acquire initial characteristic data of the indoor and outdoor environment, wherein the initial characteristic data of the indoor and outdoor environment includes the average indoor temperature in the current time period, the outdoor temperature in the current time period, the indoor humidity in the current time period, the outdoor weather in the current time period, the outdoor temperature in the next time period, the outdoor weather in the next time period, and the current time; constructing a time feature based on the current time period; Determine the indoor wet-bulb temperature in the current time period based on the indoor average temperature in the current time period and the indoor humidity in the current time period; Determine the indoor and outdoor temperature difference within the current time period based on the average indoor temperature within the current time period and the outdoor temperature within the current time period; Encoding the outdoor weather in the current time period and the outdoor weather in the next time period to obtain a feature vector of the outdoor weather in the current time period and a feature vector of the outdoor weather in the next time period; The time characteristics, the indoor wet-bulb temperature in the current time period, the indoor average temperature in the current time period, the indoor humidity in the current time period, the indoor and outdoor temperature difference in the current time period, the outdoor temperature in the next time period, the characteristic vector of the outdoor weather in the current time period and the characteristic vector of the outdoor weather in the next time period are determined as the indoor and outdoor environment characteristic data.

7. The method according to claim 1, characterized in that The obtaining of current operation characteristic data of the heat station equipment includes: Acquire the current operation initial characteristic data of the heat station equipment, wherein the current operation initial characteristic data includes the hot water main supply water temperature and the hot water main return water temperature; Determining a corresponding supply and return water temperature difference based on the hot water main supply water temperature and the hot water main return water temperature; The supply and return water temperature difference is determined as the current operation characteristic data.

8. An indoor temperature control device, characterized in that: include: An acquisition module is configured to acquire indoor and outdoor environmental characteristic data and indoor target temperature; A prediction module is configured to predict the indoor heat demand for the next time period based on the indoor and outdoor environment characteristic data and the target temperature, and obtain the indoor target heat demand for the next time period; a screening module configured to screen the initial operating status data from the plurality of operating status data based on the target required heat and a correspondence between each operating status data of the plurality of operating status data of the pre-set thermal station equipment and the heat generated by the thermal station equipment; A generating module is configured to obtain current operating characteristic data of the heat station device, and generate target operating status data of the heat station device based on the initial operating status data, the target required heat, the current operating characteristic data and the indoor and outdoor environment characteristic data; The control module is configured to control the thermal station equipment according to the target operating state 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.

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