Intelligent temperature control method and device

Abstract

The invention relates to the technical field of equipment control, and provides an intelligent temperature control method and device. The method comprises the steps that environment parameters of a target place are collected, the environment parameters comprise the indoor temperature, the indoor humidity, the outdoor temperature, time information and the evaporation temperature and the maintaining temperature in a refrigeration house, the time information is the business time of the target place, and the refrigeration house is arranged in the target place; according to the environment parameters, a machine learning model is used for predicting the target refrigerating capacity, needing to be provided for the target site, of the refrigeration house, and the machine learning model is trained and can predict the refrigerating capacity needed by the site based on the environment parameters of the site; according to the refrigeration rule, operation data of refrigeration equipment in the refrigeration house are determined according to the target refrigeration capacity; and controlling the refrigeration equipment to refrigerate the target place according to the operation data. By means of the technical means, the problem that in the prior art, the temperature cannot be dynamically adjusted according to real-time environment changes of public places is solved.

Description

Technical Field

[0001] The present application relates to the field of equipment control technology, and in particular to a temperature intelligent control method and device. Background Art

[0002] In public places such as large shopping malls, where there is a large flow of people, how to provide a comfortable temperature for most customers is an urgent problem to be solved. At present, temperature control in public places relies on fixed settings or manual adjustments, and it is impossible to dynamically adjust the temperature according to real-time environmental changes, resulting in the indoor temperature being too high or too low at times, affecting customer comfort and operational efficiency. Summary of the invention

[0003] In view of this, the embodiments of the present 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] According to a first aspect of an embodiment of the present application, a temperature intelligent control method is provided, comprising: collecting environmental parameters of a target place, wherein the environmental parameters include: 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; based on the environmental parameters, using a machine learning model to predict a target cooling capacity that the cold storage needs to provide for the target place, wherein the machine learning model has been trained and can predict the cooling capacity required for the place based on the environmental parameters of the place; based on refrigeration rules, determining operating data of a refrigeration equipment in the cold storage according to the target cooling capacity; and controlling the refrigeration equipment to cool the target place according to the operating data.

[0005] According to a second aspect of an embodiment of the present application, there is provided a temperature intelligent control device, comprising: an acquisition module, configured to acquire environmental parameters of a target place, wherein the environmental parameters include: 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; a prediction module, configured to predict the target cooling capacity that the cold storage needs to provide for the target place based on the environmental parameters using a machine learning model, wherein the machine learning model has been trained and can predict the cooling capacity required for the place based on the environmental parameters of the place; a determination module, configured to determine the operating data of the refrigeration equipment in the cold storage according to the target cooling capacity based on the refrigeration rules; and a control module, configured to control the refrigeration equipment to cool the target place according to the operating data.

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

[0007] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0008] Compared with the prior art, the embodiments of the present application have the following beneficial effects: collecting environmental parameters of the target place, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target place, and the cold storage is set in the target place; based on the environmental parameters, using a machine learning model to predict the target cooling capacity that the cold storage needs to provide for the target place, wherein the machine learning model has been trained and can predict the cooling capacity required for the place based on the environmental parameters of the place; according to the refrigeration rules, determining the operating data of the refrigeration equipment in the cold storage according to the target cooling capacity; and controlling the refrigeration equipment to cool the target place according to the operating data. The above-mentioned technical means can solve the problem in the prior art that the temperature cannot be dynamically adjusted according to real-time environmental changes in public places, thereby improving customer comfort and the operational efficiency of public places, and optimizing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] Figure 1 It is a flow chart of a temperature intelligent control method provided in an embodiment of the present application;

[0011] Figure 2 It is a flow chart of an optimization method of a refrigeration rule and a machine learning model provided in an embodiment of the present application;

[0012] Figure 3 It is a structural schematic diagram of a temperature intelligent control device provided in an embodiment of the present application;

[0013] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also 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 prevent unnecessary details from obstructing the description of the present application.

[0015] A temperature intelligent control method and device according to an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0016] Figure 1 It is a flow chart of a temperature intelligent control method provided in an embodiment of the present application. Figure 1 The temperature intelligent control method can be executed by a computer or a server, or by software on the computer or the server. Figure 1 As shown, the temperature intelligent control method includes:

[0017] S101, collecting environmental parameters of the target location, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target location, and the cold storage is set in the target location;

[0018] S102, predicting a target refrigeration capacity that the cold storage needs to provide for the target location using a machine learning model based on the environmental parameters, wherein the machine learning model has been trained to predict the refrigeration capacity required for the location based on the environmental parameters of the location;

[0019] S103, determining the operation data of the refrigeration equipment in the cold storage according to the refrigeration rule and the target refrigeration capacity;

[0020] S104, controlling the refrigeration equipment to cool the target place according to the operation data.

[0021] The environmental parameters of the target location are collected by various sensors and other equipment installed in the target location. The evaporation temperature in the cold storage refers to the temperature reached by the refrigerant after absorbing heat when it changes from liquid to gas in the evaporator. The maintenance temperature in the cold storage is the set temperature that needs to be maintained inside the cold storage. The business hours of the target location include opening hours, closing hours, and business hours. The machine learning model can be a support vector machine, a random forest, or a neural network. The machine learning model is used to predict the cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters of the target location. 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.

[0022] In some embodiments, the operation data of the refrigeration equipment includes the startup time of the refrigeration equipment, and then the startup time of the refrigeration equipment is controlled according to the predicted refrigeration capacity.

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

[0024] For example, if the target cooling capacity is a, in the cooling rule, to provide the cooling capacity of a, 3 cooling towers, 2 refrigeration pumps and 1 chiller need to be operated. The working power of the chiller is b, and the working frequency of the refrigeration pump is c. According to the above operating data, the refrigeration equipment is controlled to cool the target place.

[0025] In some embodiments, the operation 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. The chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow are conventional parameters in cold storage and will not be described in detail here.

[0026] According to the technical solution provided in the embodiment of the present application, the environmental parameters of the target place are collected, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target place, and the cold storage is set in the target place; based on the environmental parameters, a machine learning model is used to predict the cooling capacity that the cold storage needs to provide for the target place, wherein the machine learning model has been trained and can predict the cooling capacity required for the place based on the environmental parameters of the place; based on the refrigeration rules, the operating data of the refrigeration equipment in the cold storage is determined according to the target cooling capacity; and the refrigeration equipment is controlled according to the operating data to cool the target place. The above-mentioned technical means can solve the problem in the prior art that the temperature cannot be dynamically adjusted according to the real-time environmental changes in public places, thereby improving the comfort of customers and the operational efficiency of public places, and optimizing energy consumption.

[0027] Furthermore, 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, including: calculating multiple temperature parameters based on the environmental parameters, wherein the multiple temperature parameters include: wet bulb temperature, indoor and outdoor temperature difference, and cold storage temperature difference; based on the environmental parameters and multiple temperature parameters, a machine learning model is used 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 the environmental parameters, including: calculating the wet bulb temperature based on the indoor temperature and indoor humidity; calculating the indoor and outdoor temperature difference based on the indoor temperature and outdoor temperature; and calculating the cold storage temperature difference based on the evaporation temperature and the maintenance temperature.

[0029] Generally, wet-bulb temperature is used to characterize the content of water vapor in the air, that is, the humidity of the air, which reflects the degree to which the water vapor in the air is close to saturation. In the embodiment of the present 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 temperature and outdoor temperature is used as the indoor and outdoor temperature difference; the difference between evaporation temperature and maintenance temperature is used as the cold storage temperature difference.

[0030] The machine learning model can predict the target cooling capacity that the cold storage needs to provide for the target location based on the indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor and outdoor temperature difference, and cold storage temperature difference. Time information is used to capture the relationship between the business hours of the target location and the required cooling capacity (cooling capacity is affected by time), because different business hours have different traffic flows and various environmental parameters are changing. The indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor and outdoor temperature difference, and cold storage temperature difference can be processed by conventional encoding methods and then spliced. The machine learning model then predicts the cooling capacity based on the spliced ​​features.

[0031] In an optional embodiment, the environmental parameters also include: human flow, wind speed, weather, etc. 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 and outdoor temperature difference, cold storage temperature difference, human flow, wind speed, and weather.

[0032] Before using a machine learning model to predict the target cooling capacity that the cold storage needs to provide for the target location based on environmental parameters, the method also 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 for each location at different times according to the space size of each location and the cooling temperature set by the user at each location at different times; using a machine learning model to predict the second cooling capacity required for each location at different times based on the environmental parameters of each location at different times; using a loss function to calculate the loss value between the first cooling capacity and the second cooling capacity required for each location at different times; 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 place at a moment: for environmental parameters, the user sets the cooling temperature, which is the target value to be reached by lowering the temperature in the place. The first cooling capacity required by the place at that moment is calculated based on the size of the space of the place and the difference between the actual temperature of the place at that moment and the cooling temperature (cooling capacity refers to the total amount of heat removed from the place when the actual temperature of the place is reduced to the cooling temperature). The second cooling capacity is predicted by the machine learning model based on the environmental parameters of the place at that moment. The first cooling capacity required by the place at that moment is used as the label of the environmental parameters of the place at that moment. The loss function is used to calculate the loss value between the first cooling capacity and the second cooling capacity, and the model parameters of the machine learning model are optimized based on the loss value. The loss function can be a common loss function such as cross entropy.

[0034] Before determining the operating data of the refrigeration equipment in the cold storage according to the target cooling capacity, the method also includes: obtaining the space size of each place, the refrigeration 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 for each place at different times according to the space size of each place and the refrigeration temperature set by the user at each place at different times; generating a refrigeration rule according to the operating data and the first cooling capacity corresponding to each place at different times.

[0035] For a place at a time: the first cooling capacity required by the place at the time is calculated according to the space size of the place and the difference between the actual temperature of the place at the time and the cooling temperature. The operation data of the refrigeration equipment in the cold storage of the place at the time is set by the user. According to the operation data and the first cooling capacity corresponding to each place at different times, the refrigeration rules are generated. In other words, the refrigeration rules are generated from the historical records of the user controlling the refrigeration equipment in the cold storage. If the cooling capacity is known, the corresponding operation data can be found from the refrigeration rules.

[0036] Figure 2 1 is a flow chart of a method for optimizing a refrigeration rule and a machine learning model provided in an embodiment of the present application. Figure 2 As shown, the method includes:

[0037] S201, after controlling the refrigeration equipment to cool the target place for a preset time according to the operation data, obtaining the actual temperature of the target place;

[0038] S202, calculating the actual cooling capacity provided by the refrigeration equipment for the target place according to the space size and actual temperature of the target place;

[0039] S203, optimizing cooling rules according to actual cooling capacity, target cooling capacity and operation data;

[0040] S204, optimizing model parameters of the machine learning model according to the actual temperature and the comfortable temperature.

[0041] The actual cooling capacity provided by the refrigeration equipment for the target place is calculated based on the space size of the target place and the difference between the temperature of the target place before the preset time and the actual temperature. If the difference between the actual cooling capacity and the target cooling capacity is greater than the threshold, it means that the correspondence between the cooling capacity and the operating data in the refrigeration rule is incorrect, and the refrigeration rule can be optimized. In the optimized refrigeration rule, the actual cooling capacity corresponds to the operating data of the refrigeration equipment. In addition, according to the size 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 threshold of the target cooling capacity, the cooling capacity or operating data of the refrigeration rule will be reduced.

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

[0043] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0044] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

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

[0046] The collection module 301 is configured to collect environmental parameters of the target location, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target location, and the cold storage is set in the target location;

[0047] A prediction module 302 is configured to predict a target refrigeration capacity that the cold storage needs to provide for a target location using a machine learning model based on the environmental parameters, wherein the machine learning model has been trained to predict the refrigeration capacity required for the location based on the environmental parameters of the location;

[0048] The determination module 303 is configured to determine the operation data of the refrigeration equipment in the cold storage according to the target refrigeration capacity based on the refrigeration rule;

[0049] The control module 304 is configured to control the refrigeration equipment to cool the target place according to the operation data.

[0050] The environmental parameters of the target location are collected by various sensors and other equipment installed in the target location. The evaporation temperature in the cold storage refers to the temperature reached by the refrigerant after absorbing heat when it changes from liquid to gas in the evaporator. The maintenance temperature in the cold storage is the set temperature that needs to be maintained inside the cold storage. The business hours of the target location include opening hours, closing hours, and business hours. The machine learning model can be a support vector machine, a random forest, or a neural network. The machine learning model is used to predict the cooling capacity that the cold storage needs to provide for the target location based on the environmental parameters of the target location. 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.

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

[0052] For example, if the target cooling capacity is a, in the cooling rule, to provide the cooling capacity of a, 3 cooling towers, 2 refrigeration pumps and 1 chiller need to be operated. The working power of the chiller is b, and the working frequency of the refrigeration pump is c. According to the above operating data, the refrigeration equipment is controlled to cool the target place.

[0053] In some embodiments, the operation 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. The chilled water supply and return water temperature, chilled water supply and return water pressure, and chilled water supply and return water flow are conventional parameters in cold storage and will not be described in detail here.

[0054] According to the technical solution provided in the embodiment of the present application, the environmental parameters of the target place are collected, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target place, and the cold storage is set in the target place; based on the environmental parameters, a machine learning model is used to predict the cooling capacity that the cold storage needs to provide for the target place, wherein the machine learning model has been trained and can predict the cooling capacity required for the place based on the environmental parameters of the place; based on the refrigeration rules, the operating data of the refrigeration equipment in the cold storage is determined according to the target cooling capacity; and the refrigeration equipment is controlled according to the operating data to cool the target place. The above-mentioned technical means can solve the problem in the prior art that the temperature cannot be dynamically adjusted according to the real-time environmental changes in public places, thereby improving the comfort of customers and the operational efficiency of public places, and optimizing energy consumption.

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

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

[0057] Generally, wet-bulb temperature is used to characterize the content of water vapor in the air, that is, the humidity of the air, which reflects the degree to which the water vapor in the air is close to saturation. In the embodiment of the present 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 temperature and outdoor temperature is used as the indoor and outdoor temperature difference; the difference between evaporation temperature and maintenance temperature is used as the cold storage temperature difference.

[0058] The machine learning model can predict the target cooling capacity that the cold storage needs to provide for the target location based on the indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor and outdoor temperature difference, and cold storage temperature difference. Time information is used to capture the relationship between the business hours of the target location and the required cooling capacity (cooling capacity is affected by time), because different business hours have different traffic flows and various environmental parameters are changing. The indoor temperature, outdoor temperature, time information, wet-bulb temperature, indoor and outdoor temperature difference, and cold storage temperature difference can be processed by conventional encoding methods and then spliced. The machine learning model then predicts the cooling capacity based on the spliced ​​features.

[0059] In an optional embodiment, the environmental parameters also include: human flow, wind speed, weather, etc. 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 and outdoor temperature difference, cold storage temperature difference, human flow, wind speed, and weather.

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

[0061] For a place at a moment: for environmental parameters, the user sets the cooling temperature, which is the target value to be reached by lowering the temperature in the place. The first cooling capacity required by the place at that moment is calculated based on the size of the space of the place and the difference between the actual temperature of the place at that moment and the cooling temperature (cooling capacity refers to the total amount of heat removed from the place when the actual temperature of the place is reduced to the cooling temperature). The second cooling capacity is predicted by the machine learning model based on the environmental parameters of the place at that moment. The first cooling capacity required by the place at that moment is used as the label of the environmental parameters of the place at that moment. The loss function is used to calculate the loss value between the first cooling capacity and the second cooling capacity, and the model parameters of the machine learning model are optimized based on the loss value. The loss function can be a common loss function such as cross entropy.

[0062] In some embodiments, the determination module 303 is also configured to obtain the space size of each place, the refrigeration temperature set by the user at that moment, and the operating data of the refrigeration equipment in the cold storage; calculate the first refrigeration capacity required for each place at different times according to the space size of each place and the refrigeration temperature set by the user at each place at different times; generate refrigeration rules according to the operating data and the first refrigeration capacity corresponding to each place at different times.

[0063] For a place at a time: the first cooling capacity required by the place at the time is calculated according to the space size of the place and the difference between the actual temperature of the place at the time and the cooling temperature. The operation data of the refrigeration equipment in the cold storage of the place at the time is set by the user. According to the operation data and the first cooling capacity corresponding to each place at different times, the refrigeration rules are generated. In other words, the refrigeration rules are generated from the historical records of the user controlling the refrigeration equipment in the cold storage. If the cooling capacity is known, the corresponding operation data can be found from the refrigeration rules.

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

[0065] The actual cooling capacity provided by the refrigeration equipment for the target place is calculated based on the space size of the target place and the difference between the temperature of the target place before the preset time and the actual temperature. If the difference between the actual cooling capacity and the target cooling capacity is greater than the threshold, it means that the correspondence between the cooling capacity and the operating data in the refrigeration rule is incorrect, and the refrigeration rule can be optimized. In the optimized refrigeration rule, the actual cooling capacity corresponds to the operating data of the refrigeration equipment. In addition, according to the size 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 threshold of the target cooling capacity, the cooling capacity or operating data of the refrigeration rule will be reduced.

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

[0067] 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 application.

[0068] Figure 4 Schematic diagram of an electronic device 4 provided in an embodiment of the present 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, 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.

[0069] 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 4 The 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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 computer-readable storage medium. Based on this understanding, the present application 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. The computer-readable 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. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased 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 electric carrier signals and telecommunication signals.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A temperature intelligent control method, characterized in that: include: Collecting environmental parameters of the target location, wherein the environmental parameters include: indoor temperature, indoor humidity, outdoor temperature, time information, evaporation temperature and maintenance temperature in the cold storage, the time information is the business hours of the target location, and the cold storage is set in the target location; Based on the environmental parameters, using a machine learning model to predict a target refrigeration capacity that the cold storage needs to provide for the target location, wherein the machine learning model has been trained to predict the refrigeration capacity required for the location based on the environmental parameters of the location; According to the refrigeration rule, the operating data of the refrigeration equipment in the cold storage is determined according to the target refrigeration capacity; The refrigeration equipment is controlled according to the operation data to cool the target place.

2. The method according to claim 1, characterized in that According to the environmental parameters, a machine learning model is used to predict the target refrigeration capacity that the cold storage needs to provide for the target location, including: Calculating a plurality of temperature parameters based on the environmental parameters, wherein the plurality of temperature parameters include: wet bulb temperature, indoor and 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.

3. The method according to claim 2, characterized in that Calculating a plurality of temperature parameters based on the environmental parameters, including: calculating the wet-bulb temperature based on the indoor temperature and the indoor humidity; Calculating the indoor and outdoor temperature difference based on the indoor temperature and the outdoor temperature; The cold storage temperature difference is calculated based on the evaporation temperature and the maintenance temperature.

4. The method according to claim 2, characterized in that: Before predicting the target refrigeration capacity that the cold storage needs to provide for the target location using the machine learning model based on the environmental parameters, the method further includes: Obtain environmental parameters of multiple places at different times and the cooling temperature set by the user at that time; Calculate the first cooling capacity required for each place at different times according to the space size of each place and the cooling temperature set by the user at each place at different times; According to the environmental parameters of each place at different times, using the machine learning model to predict the second cooling capacity required by each place at different times; Calculate the loss value between the first cooling capacity and the second cooling capacity required by each place at different times by using the loss function; Optimize the model parameters of the machine learning model according to the loss value to complete the training of the machine learning model.

5. The method according to claim 1, characterized in that The refrigeration equipment includes: a cooling tower, a refrigeration pump and a refrigeration machine; The operation data include: the number of operating cooling towers, the number of operating refrigeration pumps, the number of operating refrigerators, the operating power of the operating refrigerators and the operating frequency of the operating refrigeration pumps.

6. The method according to claim 1, characterized in that Before determining the operating data of the refrigeration equipment in the cold storage according to the target refrigeration capacity, the method further includes: Obtain the space size of each location, the refrigeration temperature set by users at different times in each location, and the operating data of the refrigeration equipment in the cold storage; Calculate the first cooling capacity required for each place at different times according to the space size of each place and the cooling temperature set by the user at each place at different times; The refrigeration rule is generated according to the operation data corresponding to each place at different times and the first refrigeration capacity.

7. The method according to claim 1, characterized in that The method further comprises: After controlling the refrigeration equipment to cool the target place for a preset period of time according to the operation data, obtaining the actual temperature of the target place; Calculating the actual cooling capacity provided by the refrigeration equipment for the target place according to the space size of the target place and the actual temperature; optimizing the refrigeration rule according to the actual refrigeration capacity, the target refrigeration capacity and the operation data; Optimize model parameters of the machine learning model based on the actual temperature and the comfortable temperature.

8. A temperature intelligent control device, characterized in that: include: A collection module is configured to collect 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, the time information is the business hours of the target location, and the cold storage is arranged in the target location; A prediction module is configured to predict the target refrigeration capacity that the cold storage needs to provide for the target location using a machine learning model based on the environmental parameters, wherein the machine learning model has been trained to predict the refrigeration capacity required for the location based on the environmental parameters of the location; a determination module configured to determine operation data of the refrigeration equipment in the cold storage according to the target refrigeration capacity based on a refrigeration rule; The control module is configured to control the refrigeration equipment to cool the target place according to the operation 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 computer-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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