Temperature intelligent control method and device

Abstract

The application relates to the technical field of device control, and provides a temperature intelligent control method and device. The method comprises the following steps: collecting environment parameters of a target place, wherein the environment parameters comprise indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in a cold storage, the time information is the business hours of the target place, and the cold storage is arranged in the target place; predicting a target refrigerating capacity required by the cold storage for the target place by using a machine learning model according to the environment parameters, wherein the machine learning model has been trained and can predict the refrigerating capacity required by a place based on the environment parameters of the place; determining the operation data of refrigerating equipment in the cold storage according to the target refrigerating capacity based on refrigerating rules; and controlling the refrigerating equipment to refrigerate the target place according to the operation data. By using the technical means, the problem that the temperature cannot be dynamically adjusted according to the real-time environment change of a public place in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of equipment control technology, and in particular to a method and apparatus for intelligent temperature control. Background Technology

[0002] In large shopping malls and other public places with high foot traffic, providing a comfortable temperature for most customers is a pressing issue. Currently, temperature control in public places relies on fixed settings or manual adjustments, which cannot dynamically adjust the temperature according to real-time environmental changes. This results in indoor temperatures that are sometimes too high and sometimes too low, affecting customer comfort and operational efficiency. Summary of the Invention

[0003] In view of this, embodiments of this application provide a temperature intelligent control method, device, electronic device, and computer-readable storage medium to solve the problem in the prior art that the temperature cannot be dynamically adjusted according to real-time environmental changes in public places.

[0004] A first aspect of this application provides a temperature intelligent control method, comprising: collecting environmental parameters of a target location, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in a cold storage facility, and the time information is the operating hours of the target location, and the cold storage facility is located within the target location; predicting the target cooling capacity required by the cold storage facility for the target location based on the environmental parameters using a machine learning model, wherein the machine learning model has been trained and is capable of predicting the required cooling capacity of the location based on the environmental parameters; determining the operating data of the refrigeration equipment in the cold storage facility according to refrigeration rules and the target cooling capacity; and controlling the refrigeration equipment to cool the target location according to the operating data.

[0005] A second aspect of this application provides a temperature intelligent control device, comprising: a data acquisition module configured to acquire environmental parameters of a target location, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in a cold storage facility, and the time information is the operating hours of the target location, and the cold storage facility is located within the target location; a prediction module configured to predict the target cooling capacity required by the cold storage facility for the target location based on the environmental parameters using a machine learning model, wherein the machine learning model has been trained and is capable of predicting the required cooling capacity of the location based on the environmental parameters; a determination module configured to determine the operating data of the refrigeration equipment in the cold storage facility according to refrigeration rules and the target cooling capacity; and a control module configured to control the refrigeration equipment to cool the target location according to the operating data.

[0006] A third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0008] The beneficial effects of this application embodiment compared with the prior art are as follows: Environmental parameters of the target location are collected, including indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, and the time information refers to the operating hours of the target location. The cold storage is located within the target location. Based on the environmental parameters, a machine learning model is used to predict the target cooling capacity that the cold storage needs to provide to the target location. This machine learning model has been trained and can predict the required cooling capacity based on the location's environmental parameters. Based on cooling rules, the operating data of the refrigeration equipment in the cold storage is determined according to the target cooling capacity. The refrigeration equipment is then controlled to cool the target location according to the operating data. By employing the above technical means, the problem of not being able to dynamically adjust the temperature according to real-time environmental changes in public places in the prior art can be solved, thereby improving customer comfort and the operational efficiency of public places, and optimizing energy consumption. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of a temperature intelligent control method provided in an embodiment of this application;

[0011] Figure 2 This is a flowchart illustrating an optimization method for cooling rules and machine learning models provided in an embodiment of this application.

[0012] Figure 3 This is a schematic diagram of the structure of a temperature intelligent control device provided in an embodiment of this application;

[0013] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0015] A temperature intelligent control method and apparatus according to embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0016] Figure 1 This is a schematic flowchart of a temperature intelligent control method provided in an embodiment of this application. Figure 1 Intelligent temperature control methods can be implemented by a computer or server, or by software on a computer or server. For example... Figure 1 As shown, the intelligent temperature control method includes:

[0017] S101, Collect environmental parameters of the target location, including: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, and time information is the operating hours of the target location. The cold storage is located within the target location.

[0018] S102, Based on environmental parameters, use a machine learning model to predict the target cooling capacity that the cold storage needs to provide for the target location. The machine learning model has been trained and can predict the required cooling capacity of the location based on the environmental parameters of the location.

[0019] S103, Based on the refrigeration rules, determine the operating data of the refrigeration equipment in the cold storage according to the target refrigeration capacity;

[0020] S104 controls the refrigeration equipment to refrigerate the target location according to the operating data.

[0021] Environmental parameters of the target location are collected through various sensors and other devices installed within the location. The evaporation temperature in the cold storage refers to the temperature reached when the refrigerant absorbs heat as it changes from a liquid to a gaseous state in the evaporator. The maintenance temperature in the cold storage is the set temperature that needs to be maintained inside the cold storage. The operating hours of the target location include opening time, closing time, and operating duration. The machine learning model can be a support vector machine, random forest, or neural network. Based on the environmental parameters of the target location, the machine learning model predicts the required cooling capacity of the cold storage. The operating data of the refrigeration equipment in the cold storage is determined based on the cooling capacity, and then the refrigeration equipment is controlled accordingly.

[0022] In some embodiments, the operating data of the refrigeration equipment includes the start-up time of the refrigeration equipment, and then controls when to start the refrigeration equipment based on the predicted cooling capacity.

[0023] Refrigeration equipment, including: cooling towers, chilled pumps, and chillers; operating data, including: the number of operating cooling towers, the number of operating chilled pumps, the number of operating chillers, the operating power of the operating chillers, and the operating frequency of the operating chilled pumps.

[0024] For example, if the target cooling capacity is 'a', the cooling rules require the operation of 3 cooling towers, 2 chilled pumps, and 1 chiller to provide the cooling capacity 'a'. The chiller's operating power is 'b', and the chilled pumps' operating frequency is 'c'. The cooling equipment is controlled to cool the target location according to the above operating data.

[0025] In some embodiments, the operating data also includes: chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow rate. Chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow rate are standard parameters in cold storage facilities and will not be described in detail here.

[0026] According to the technical solution provided in this application, environmental parameters of the target location are collected. These environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, and the time information refers to the operating hours of the target location. The cold storage is located within the target location. Based on the environmental parameters, a machine learning model is used to predict the required cooling capacity of the cold storage for the target location. The machine learning model has been trained and can predict the required cooling capacity based on the environmental parameters of the location. Based on refrigeration rules, the operating data of the refrigeration equipment in the cold storage is determined according to the target cooling capacity. The refrigeration equipment is then controlled to cool the target location according to the operating data. By employing the above technical means, the problem of not being able to dynamically adjust the temperature according to real-time environmental changes in public places in existing technologies can be solved, thereby improving customer comfort and the operational efficiency of public places, and optimizing energy consumption.

[0027] Furthermore, based on environmental parameters, a machine learning model is used to predict the target cooling capacity that the cold storage needs to provide for the target location. This includes: calculating multiple temperature parameters based on environmental parameters, including wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference; and using a machine learning model based on the environmental parameters and the multiple temperature parameters to predict the target cooling capacity that the cold storage needs to provide for the target location.

[0028] Furthermore, multiple temperature parameters are calculated based on environmental parameters, including: wet-bulb temperature based on indoor temperature and humidity; indoor-outdoor temperature difference based on indoor temperature and outdoor temperature; and cold storage temperature difference based on evaporation temperature and maintenance temperature.

[0029] Generally, wet-bulb temperature is used to characterize the water vapor content in the air, i.e., the humidity of the air, reflecting the degree to which the air is close to saturation with water vapor. In this application, the ratio of indoor temperature to indoor humidity can be directly used as the wet-bulb temperature to measure the relationship between indoor temperature and humidity. The difference between indoor and outdoor temperatures is used as the indoor-outdoor temperature difference; the difference between evaporation temperature and maintenance temperature is used as the cold storage temperature difference.

[0030] Machine learning models can predict the target cooling capacity that a cold storage facility needs to provide for a target location based on indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference. Time information is used to capture the relationship between the target location's operating hours and the required cooling capacity (cooling capacity is affected by time), as different operating hours result in varying foot traffic and changes in various environmental parameters. The indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference can be processed using conventional encoding methods, then concatenated. The machine learning model then predicts the cooling capacity based on this concatenated feature set.

[0031] In one alternative embodiment, environmental parameters also include: foot traffic, wind speed, and weather. The machine learning model can predict the target cooling capacity that the cold storage needs to provide for the target location based on indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, cold storage temperature difference, foot traffic, wind speed, and weather.

[0032] Before using a machine learning model to predict the target cooling capacity that the cold storage needs to provide to the target location based on environmental parameters, the method further includes: obtaining environmental parameters of multiple locations at different times and the cooling temperature set by the user at that time; calculating the first cooling capacity required by each location at different times based on the space size of each location and the cooling temperature set by the user at each location at different times; predicting the second cooling capacity required by each location at different times based on the environmental parameters of each location at different times using a machine learning model; calculating the loss value between the first and second cooling capacities required by each location at different times using a loss function; and optimizing the model parameters of the machine learning model based on the loss value to complete the training of the machine learning model.

[0033] For a given location at a given time: The user sets a cooling temperature based on environmental parameters; this cooling temperature is the target value to be achieved by lowering the temperature within the location. The first required cooling capacity (cooling capacity refers to the total heat removed from the location to lower its actual temperature to the cooling temperature) is calculated based on the location's size and the difference between the actual temperature and the target cooling temperature at that time. The second required cooling capacity is predicted by a machine learning model based on the location's environmental parameters at that time. The first required cooling capacity serves as a label for the location's environmental parameters at that time. A loss function is used to calculate the loss between the first and second required cooling capacities, and the model parameters of the machine learning model are optimized based on this loss value. Common loss functions include cross-entropy.

[0034] Before determining the operating data of the refrigeration equipment in the cold storage based on the target cooling capacity, the method also includes: obtaining the space size of each location, the cooling temperature set by the user at that moment, and the operating data of the refrigeration equipment in the cold storage; calculating the first cooling capacity required by each location at different times based on the space size of each location and the cooling temperature set by the user at each location at different times; and generating refrigeration rules based on the operating data and the first cooling capacity corresponding to each location at different times.

[0035] For a given location at a given moment: the initial cooling capacity required for that location at that moment is calculated based on the space size of the location and the difference between the actual temperature and the refrigeration temperature at that moment. The operating data of the refrigeration equipment in the cold storage at that moment is set by the user. Cooling rules are generated based on the operating data and initial cooling capacity corresponding to each location at different times. In other words, the cooling rules are generated from the historical records of the refrigeration equipment in the cold storage controlled by the user. If the cooling capacity is known, the corresponding operating data can be found from the cooling rules.

[0036] Figure 2 This is a flowchart illustrating an optimization method for cooling rules and machine learning models provided in an embodiment of this application. Figure 2 As shown, the method includes:

[0037] S201, after controlling the refrigeration equipment to refrigerate the target location for a preset duration according to the operating data, obtain the actual temperature of the target location;

[0038] S202, calculate the actual cooling capacity provided by the refrigeration equipment to the target location based on the size of the target location and the actual temperature;

[0039] S203 optimizes the cooling rules based on the actual cooling capacity, target cooling capacity, and operating data;

[0040] S204 optimizes the model parameters of the machine learning model based on the actual temperature and the comfort temperature.

[0041] The actual cooling capacity provided by the refrigeration equipment to the target location is calculated based on the difference between the target location's temperature before the preset time period and the actual temperature. If the difference between the actual cooling capacity and the target cooling capacity is greater than a threshold, it indicates that the correspondence between the cooling capacity and the operating data in the refrigeration rules is incorrect, and the refrigeration rules can be optimized. In the optimized refrigeration rules, the actual cooling capacity corresponds to the operating data controlling the refrigeration equipment. Furthermore, based on the relationship between the actual cooling capacity and the target cooling capacity, the operating data can be adjusted. For example, if the actual cooling capacity is twice the target cooling capacity, the refrigeration capacity or operating data in the refrigeration rules will be reduced.

[0042] The machine learning model parameters can also be optimized based on the actual temperature and the comfort temperature. The comfort temperature is set based on customer feedback. For example, if the actual temperature is lower than the comfort temperature, it means that the cooling capacity predicted by the machine learning model is inaccurate and the predicted cooling capacity is too high. The model parameters can be optimized based on this feedback.

[0043] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0044] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0045] Figure 3 This is a schematic diagram of a temperature intelligent control device provided in an embodiment of this application. Figure 3 As shown, the intelligent temperature control device includes:

[0046] The data acquisition module 301 is configured to collect environmental parameters of the target location. The environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, and the time information is the operating hours of the target location. The cold storage is located in the target location.

[0047] The prediction module 302 is configured to predict the target cooling capacity that the cold storage needs to provide to the target location based on environmental parameters using a machine learning model. The machine learning model has been trained to predict the required cooling capacity of the location based on the environmental parameters of the location.

[0048] Module 303 is configured to determine the operating data of the refrigeration equipment in the cold storage based on the refrigeration rules and the target refrigeration capacity.

[0049] Control module 304 is configured to control the refrigeration equipment to refrigerate the target location according to the operating data.

[0050] Environmental parameters of the target location are collected through various sensors and other devices installed within the location. The evaporation temperature in the cold storage refers to the temperature reached when the refrigerant absorbs heat as it changes from a liquid to a gaseous state in the evaporator. The maintenance temperature in the cold storage is the set temperature that needs to be maintained inside the cold storage. The operating hours of the target location include opening time, closing time, and operating duration. The machine learning model can be a support vector machine, random forest, or neural network. Based on the environmental parameters of the target location, the machine learning model predicts the required cooling capacity of the cold storage. The operating data of the refrigeration equipment in the cold storage is determined based on the cooling capacity, and then the refrigeration equipment is controlled accordingly.

[0051] Refrigeration equipment, including: cooling towers, chilled pumps, and chillers; operating data, including: the number of operating cooling towers, the number of operating chilled pumps, the number of operating chillers, the operating power of the operating chillers, and the operating frequency of the operating chilled pumps.

[0052] For example, if the target cooling capacity is 'a', the cooling rules require the operation of 3 cooling towers, 2 chilled pumps, and 1 chiller to provide the cooling capacity 'a'. The chiller's operating power is 'b', and the chilled pumps' operating frequency is 'c'. The cooling equipment is controlled to cool the target location according to the above operating data.

[0053] In some embodiments, the operating data also includes: chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow rate. Chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow rate are standard parameters in cold storage facilities and will not be described in detail here.

[0054] According to the technical solution provided in this application, environmental parameters of the target location are collected. These environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, and the time information refers to the operating hours of the target location. The cold storage is located within the target location. Based on the environmental parameters, a machine learning model is used to predict the required cooling capacity of the cold storage for the target location. The machine learning model has been trained and can predict the required cooling capacity based on the environmental parameters of the location. Based on refrigeration rules, the operating data of the refrigeration equipment in the cold storage is determined according to the target cooling capacity. The refrigeration equipment is then controlled to cool the target location according to the operating data. By employing the above technical means, the problem of not being able to dynamically adjust the temperature according to real-time environmental changes in public places in existing technologies can be solved, thereby improving customer comfort and the operational efficiency of public places, and optimizing energy consumption.

[0055] In some embodiments, the prediction module 302 is further configured to calculate multiple temperature parameters based on environmental parameters, wherein the multiple temperature parameters include: wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference; and to predict the target cooling capacity that the cold storage needs to provide to the target location using a machine learning model based on the environmental parameters and the multiple temperature parameters.

[0056] In some embodiments, the prediction module 302 is further configured to calculate the wet-bulb temperature based on indoor temperature and indoor humidity; calculate the indoor-outdoor temperature difference based on indoor temperature and outdoor temperature; and calculate the cold storage temperature difference based on evaporation temperature and maintenance temperature.

[0057] Generally, wet-bulb temperature is used to characterize the water vapor content in the air, i.e., the humidity of the air, reflecting the degree to which the air is close to saturation with water vapor. In this application, the ratio of indoor temperature to indoor humidity can be directly used as the wet-bulb temperature to measure the relationship between indoor temperature and humidity. The difference between indoor and outdoor temperatures is used as the indoor-outdoor temperature difference; the difference between evaporation temperature and maintenance temperature is used as the cold storage temperature difference.

[0058] Machine learning models can predict the target cooling capacity that a cold storage facility needs to provide for a target location based on indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference. Time information is used to capture the relationship between the target location's operating hours and the required cooling capacity (cooling capacity is affected by time), as different operating hours result in varying foot traffic and changes in various environmental parameters. The indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference can be processed using conventional encoding methods, then concatenated. The machine learning model then predicts the cooling capacity based on this concatenated feature set.

[0059] In one alternative embodiment, environmental parameters also include: foot traffic, wind speed, and weather. The machine learning model can predict the target cooling capacity that the cold storage needs to provide for the target location based on indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor-outdoor temperature difference, cold storage temperature difference, foot traffic, wind speed, and weather.

[0060] In some embodiments, the prediction module 302 is further configured to acquire environmental parameters of multiple locations at different times and the cooling temperature set by the user at that time; calculate the first cooling capacity required by each location at different times based on the space size of each location and the cooling temperature set by the user at each location at different times; predict the second cooling capacity required by each location at different times using a machine learning model based on the environmental parameters of each location at different times; calculate the loss value between the first cooling capacity and the second cooling capacity required by each location at different times using a loss function; and optimize the model parameters of the machine learning model based on the loss value to complete the training of the machine learning model.

[0061] For a given location at a given time: The user sets a cooling temperature based on environmental parameters; this cooling temperature is the target value to be achieved by lowering the temperature within the location. The first required cooling capacity (cooling capacity refers to the total heat removed from the location to lower its actual temperature to the cooling temperature) is calculated based on the location's size and the difference between the actual temperature and the target cooling temperature at that time. The second required cooling capacity is predicted by a machine learning model based on the location's environmental parameters at that time. The first required cooling capacity serves as a label for the location's environmental parameters at that time. A loss function is used to calculate the loss between the first and second required cooling capacities, and the model parameters of the machine learning model are optimized based on this loss value. Common loss functions include cross-entropy.

[0062] In some embodiments, the determining module 303 is further configured to acquire the space size of each location, the cooling temperature set by the user at that moment, and the operating data of the refrigeration equipment in the cold storage; calculate the first cooling capacity required by each location at different times based on the space size of each location and the cooling temperature set by the user at each location at different times; and generate refrigeration rules based on the operating data and the first cooling capacity corresponding to each location at different times.

[0063] For a given location at a given moment: the initial cooling capacity required for that location at that moment is calculated based on the space size of the location and the difference between the actual temperature and the refrigeration temperature at that moment. The operating data of the refrigeration equipment in the cold storage at that moment is set by the user. Cooling rules are generated based on the operating data and initial cooling capacity corresponding to each location at different times. In other words, the cooling rules are generated from the historical records of the refrigeration equipment in the cold storage controlled by the user. If the cooling capacity is known, the corresponding operating data can be found from the cooling rules.

[0064] In some embodiments, the control module 304 is further configured to: after controlling the refrigeration equipment to refrigerate the target location for a preset time according to the operating data, obtain the actual temperature of the target location; calculate the actual cooling capacity provided by the refrigeration equipment to the target location based on the size of the target location and the actual temperature; optimize the refrigeration rules based on the actual cooling capacity, the target cooling capacity and the operating data; and optimize the model parameters of the machine learning model based on the actual temperature and the comfort temperature.

[0065] The actual cooling capacity provided by the refrigeration equipment to the target location is calculated based on the difference between the target location's temperature before the preset time period and the actual temperature. If the difference between the actual cooling capacity and the target cooling capacity is greater than a threshold, it indicates that the correspondence between the cooling capacity and the operating data in the refrigeration rules is incorrect, and the refrigeration rules can be optimized. In the optimized refrigeration rules, the actual cooling capacity corresponds to the operating data controlling the refrigeration equipment. Furthermore, based on the relationship between the actual cooling capacity and the target cooling capacity, the operating data can be adjusted. For example, if the actual cooling capacity is twice the target cooling capacity, the refrigeration capacity or operating data in the refrigeration rules will be reduced.

[0066] The machine learning model parameters can also be optimized based on the actual temperature and the comfort temperature. The comfort temperature is set based on customer feedback. For example, if the actual temperature is lower than the comfort temperature, it means that the cooling capacity predicted by the machine learning model is inaccurate and the predicted cooling capacity is too high. The model parameters can be optimized based on this feedback.

[0067] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0068] Figure 4 This is a schematic diagram of the electronic device 4 provided in an embodiment of this application. 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, it implements the steps in the various method embodiments described above. Alternatively, when the processor 401 executes the computer program 403, it implements the functions of each module / unit in the various device embodiments described above.

[0069] Electronic device 4 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 4 may include, but is not limited to, processor 401 and memory 402. Those skilled in the art will understand that... Figure 4 This is merely an example of electronic device 4 and does not constitute a limitation on electronic device 4. It may include more or fewer components than shown, or different components.

[0070] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0071] The memory 402 can be an internal storage unit of the electronic device 4, such as a hard disk or RAM of the electronic device 4. The memory 402 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 4. The memory 402 can also include both internal and external storage units of the electronic device 4. The memory 402 is used to store computer programs and other programs and data required by the electronic device.

[0072] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above 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 integrated unit can be implemented in hardware or as a software functional unit.

[0073] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0074] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for intelligent temperature control, characterized in that, include: Collect environmental parameters of the target location, including: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, traffic flow, wind force and wind speed, and weather. The time information refers to the business hours of the target location, and the cold storage is located within the target location. Based on the environmental parameters, a machine learning model is used to predict the target cooling capacity that the cold storage needs to provide for the target location. The machine learning model has been trained to predict the required cooling capacity of the location based on the environmental parameters of the location. Based on the refrigeration rules, the operating data of the refrigeration equipment in the cold storage are determined according to the target refrigeration capacity; The refrigeration equipment is controlled to refrigerate the target location according to the operating data; The method of predicting the target cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters using a machine learning model includes: Based on the environmental parameters, multiple temperature parameters are calculated, including: wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference; Based on the environmental parameters and multiple temperature parameters, the machine learning model is used to predict the target cooling capacity that the cold storage needs to provide for the target location; Before determining the operating data of the refrigeration equipment in the cold storage based on the target cooling capacity, the method further includes: Acquire the space size of each location, the cooling temperature set by users at different times, and the operating data of the refrigeration equipment in the cold storage; Calculate the initial cooling capacity required by each location at different times based on the size of the space and the cooling temperature set by the user at each location at different times. The cooling rules are generated based on the operating data and initial cooling capacity of each location at different times.

2. The method according to claim 1, characterized in that, Based on the aforementioned environmental parameters, multiple temperature parameters are calculated, including: The wet-bulb temperature is calculated based on the indoor temperature and the indoor humidity. The indoor-outdoor temperature difference is calculated based on the indoor temperature and the outdoor temperature. The temperature difference of the cold storage is calculated based on the evaporation temperature and the maintenance temperature.

3. The method according to claim 1, characterized in that, Before predicting the target cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters using a machine learning model, the method further includes: Acquire environmental parameters for multiple locations at different times, as well as the cooling temperature set by the user at that time; Calculate the initial cooling capacity required by each location at different times based on the size of the space and the cooling temperature set by the user at each location at different times. Based on the environmental parameters of each location at different times, the machine learning model is used to predict the second cooling capacity required by each location at different times. The loss function is used to calculate the loss value between the first and second cooling capacities required by each location at different times. The model parameters of the machine learning model are optimized based on the loss value to complete the training of the machine learning model.

4. The method according to claim 1, characterized in that, The refrigeration equipment includes: a cooling tower, a refrigeration pump, and a chiller; The operational data includes: the number of operating cooling towers, the number of operating chilled pumps, the number of operating chillers, the operating power of the operating chillers, and the operating frequency of the operating chilled pumps.

5. The method according to claim 1, characterized in that, The method further includes: After controlling the refrigeration equipment to refrigerate the target location for a preset duration according to the operating data, the actual temperature of the target location is obtained; The actual cooling capacity provided by the refrigeration equipment to the target location is calculated based on the size of the target location and the actual temperature. The cooling rules are optimized based on the actual cooling capacity, the target cooling capacity, and the operating data. The model parameters of the machine learning model are optimized based on the actual temperature and the comfort temperature.

6. A temperature intelligent control device, characterized in that, include: The data acquisition module is configured to collect environmental parameters of the target location, including: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, traffic flow, wind force and wind speed, and weather. The time information is the business hours of the target location, and the cold storage is located within the target location. The prediction module is configured to predict the target cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters using a machine learning model, wherein the machine learning model has been trained to predict the required cooling capacity of the location based on the location's environmental parameters. The determination module is configured to determine the operating data of the refrigeration equipment in the cold storage based on the refrigeration rules and the target refrigeration capacity. The control module is configured to control the refrigeration equipment to refrigerate the target location according to the operating data; The method of predicting the target cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters using a machine learning model includes: Based on the environmental parameters, multiple temperature parameters are calculated, including: wet-bulb temperature, indoor-outdoor temperature difference, and cold storage temperature difference; Based on the environmental parameters and multiple temperature parameters, the machine learning model is used to predict the target cooling capacity that the cold storage needs to provide for the target location; Before determining the operating data of the refrigeration equipment in the cold storage based on the target cooling capacity, the method further includes: Acquire the space size of each location, the cooling temperature set by users at different times, and the operating data of the refrigeration equipment in the cold storage; Calculate the initial cooling capacity required by each location at different times based on the size of the space and the cooling temperature set by the user at each location at different times. The cooling rules are generated based on the operating data and initial cooling capacity of each location at different times.

7. 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, it implements the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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