Intelligent control method of intelligent temperature control system

Abstract

The invention relates to an intelligent control method of an intelligent temperature control system, which belongs to the technical field of intelligent temperature control, and comprises the following steps of: tracking and identifying real-time heating characteristic data of each sub-region in a target region, obtaining a sub-region to be refrigerated, obtaining a refrigeration demand of the sub-region to be refrigerated, and obtaining the refrigeration demand of the sub-region to be refrigerated; refrigeration performance data of each sub-region temperature intelligent control system are obtained, feasibility analysis is carried out according to the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration requirement of the sub-region to be refrigerated, and a feasibility analysis result is obtained; and finally, refrigerating the to-be-refrigerated sub-region or generating related warning information according to a feasibility analysis result. Effective evaluation is performed according to the refrigeration performance data of the intelligent temperature control system and the heating condition of the area needing refrigeration, so that a more accurate temperature control process can be formulated, and reduction of power consumption and accurate temperature control are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to an intelligent control method of a temperature intelligent control system. Background Art

[0002] The temperature intelligent control system is an intelligent system that can perform dynamic control based on the temperature characteristics of the target object or target area. The temperature intelligent control system includes refrigeration equipment or heating equipment such as air conditioners, fans, and heaters. However, nowadays, after a certain number of years of service, the refrigeration or heating performance of the equipment related to the temperature intelligent control system will decline to a certain extent, which may not meet the temperature control requirements. The existing technology does not consider this situation at all, resulting in a decrease in the control accuracy of the temperature intelligent control system and failure to meet the control requirements of the temperature control. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an intelligent control method for a temperature intelligent control system.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides an intelligent control method for a temperature intelligent control system, comprising the following steps: Acquire environmental characteristic data of the target area, build a temperature characteristic model based on the environmental characteristic data of the target area, and build a temperature tracking model based on a deep neural network; Track and identify the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtain the sub-area to be cooled, and obtain the cooling demand of the sub-area to be cooled; Acquire refrigeration performance data of each sub-region temperature intelligent control system, and perform feasibility analysis based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and obtain feasibility analysis results; The sub-area to be cooled is cooled or related warning information is generated according to the feasibility analysis result.

[0005] Furthermore, in this method, environmental characteristic data of the target area is obtained, and a temperature characteristic model is constructed according to the environmental characteristic data of the target area, specifically: Acquire layout data in the target area, and construct a three-dimensional model of the target area through three-dimensional modeling software according to the layout data in the target area, by setting a sensor in each sub-area of ​​the target area; Acquire environmental characteristic data of the target area through a sensor, set a temperature gradient data graph, and display the temperature characteristics of each sub-area in the target area according to the temperature gradient data graph and the environmental characteristic data of the target area, and build a temperature characteristic model of the current timestamp based on the three-dimensional model graph of the target area; The temperature characteristic model of each time stamp is obtained, and the temperature characteristic model of each time stamp is combined and displayed in a preset manner to form a dynamic temperature characteristic model, which is output as the final temperature characteristic model.

[0006] Furthermore, in this method, a temperature tracking model is constructed based on a deep neural network, specifically: Building a temperature tracking model based on a deep neural network, initializing the learning rate of the temperature tracking model and the mean square error of the stopping condition, obtaining a dynamic temperature feature model, and extracting model change characteristics of the dynamic temperature feature model; Taking the model change characteristics of each of the dynamic temperature characteristic models as a state vector, calculating the transition probability value of the state vector transferring to another state vector, and setting a transition probability threshold; When the transition probability value of the state vector transferring to another state vector is greater than the transition probability threshold, the state vector is updated to another state vector, and the model change characteristics of the dynamic temperature characteristic model are updated; When the transition probability value of the state vector transferring to another state vector is not greater than the transition probability threshold, the current state vector is maintained unchanged, and the model change characteristics of the dynamic temperature characteristic model are input into the temperature tracking model for learning based on the learning. When the stopping condition of the model is less than the mean square error of the stopping condition, the temperature tracking model training is completed.

[0007] Furthermore, in this method, the real-time heating characteristic data of each sub-region in the target region is tracked and identified according to the temperature tracking model and the temperature characteristic model to obtain the sub-region to be cooled, specifically: Acquire a temperature characteristic model within a preset time, and input the temperature characteristic model within the preset time into the temperature tracking model for identification and status update; By identifying and updating the state, a temperature characteristic model after the state is updated is obtained, and real-time heating characteristic data of each sub-area in the target area is obtained according to the temperature characteristic model after the state is updated; Setting a fever characteristic data threshold, and determining whether the real-time fever characteristic data of the sub-area is greater than the fever characteristic data threshold, and if so, treating the corresponding area as a normal fever area; When the real-time heating characteristic data of the sub-region is greater than the heating characteristic data threshold, the corresponding region is used as the sub-region to be cooled.

[0008] Furthermore, in the present method, refrigeration performance data of each sub-region temperature intelligent control system is obtained, and a feasibility analysis is performed based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and a feasibility analysis result is obtained, specifically: Acquire refrigeration performance data of each sub-region temperature intelligent control system, and acquire maximum refrigeration performance data of each sub-region temperature intelligent control system according to the refrigeration performance data of each sub-region temperature intelligent control system; Determining whether the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated; When the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-region to be refrigerated, the sub-region with the maximum refrigeration performance data greater than the refrigeration demand of the sub-region to be refrigerated is regarded as an adjustable region; When the maximum refrigeration performance data of the temperature intelligent control system is not greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area whose maximum refrigeration performance data is not greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an uncontrollable area; A feasibility analysis result is constructed based on the controllable area and the uncontrollable area, and the feasibility analysis result is output.

[0009] Furthermore, in this method, the sub-area to be cooled is cooled or related warning information is generated according to the feasibility analysis result, specifically: When the feasibility analysis result is an uncontrollable area, the location of the temperature intelligent control system corresponding to the sub-area is obtained, and relevant warning information is generated according to the location of the temperature intelligent control system corresponding to the sub-area; When the feasibility analysis result is a controllable area, the refrigeration working parameters of the temperature intelligent control system are initialized, and each sub-area is instructed based on the refrigeration working parameters of the temperature intelligent control system to obtain the cooling characteristic data of the target area; Setting a temperature drop characteristic data threshold, when the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold, outputting the refrigeration working parameters of the temperature intelligent control system, and performing temperature control according to the refrigeration working parameters of the temperature intelligent control system; When the temperature drop characteristic data of the target area is not greater than the temperature drop characteristic data threshold, the refrigeration working parameters of the temperature intelligent control system are readjusted until the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold.

[0010] A second aspect of the present invention provides an intelligent control device for a temperature intelligent control system, comprising a memory and a processor, wherein the memory comprises an intelligent control method program for the temperature intelligent control system, and when the intelligent control method program for the temperature intelligent control system is executed by the processor, the steps of any one of the intelligent control methods for the temperature intelligent control system are implemented.

[0011] A third aspect of the present invention provides a temperature intelligent control terminal, comprising: The temperature tracking model building module is responsible for acquiring the environmental characteristic data of the target area, building a temperature characteristic model according to the environmental characteristic data of the target area, and building a temperature tracking model based on a deep neural network; A cooling analysis module is responsible for tracking and identifying the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtaining the sub-area to be cooled, and obtaining the cooling demand of the sub-area to be cooled; A feasibility analysis module is responsible for obtaining the refrigeration performance data of each sub-region temperature intelligent control system, and performing a feasibility analysis based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, to obtain a feasibility analysis result; The control analysis module is responsible for cooling the sub-area to be cooled or generating relevant warning information according to the feasibility analysis result.

[0012] The fourth aspect of the present invention provides a computer-readable storage medium, including an intelligent control method program for a temperature intelligent control system. When the intelligent control method program for the temperature intelligent control system is executed by a processor, the steps of any one of the intelligent control methods for the temperature intelligent control system are implemented.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention obtains environmental characteristic data of the target area, builds a temperature characteristic model based on the environmental characteristic data of the target area, and builds a temperature tracking model based on a deep neural network, and then tracks and identifies the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtains the sub-area to be refrigerated, obtains the refrigeration demand of the sub-area to be refrigerated, thereby obtaining the refrigeration performance data of the temperature intelligent control system of each sub-area, and performs a feasibility analysis based on the refrigeration performance data of the temperature intelligent control system of each sub-area and the refrigeration demand of the sub-area to be refrigerated, obtains the feasibility analysis result, and finally refrigerates the sub-area to be refrigerated or generates relevant warning information based on the feasibility analysis result. The present invention evaluates the effectiveness according to the refrigeration performance data of the temperature intelligent control system and the heating situation of the area to be refrigerated, so as to formulate a more accurate temperature control process, achieve power consumption reduction and precise temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The overall flow chart of the intelligent control method of the temperature intelligent control system is shown; Figure 2 A partial method flow chart of an intelligent control method of a temperature intelligent control system is shown; Figure 3 A schematic diagram of an intelligent control device of a temperature intelligent control system is shown; Figure 4 A schematic diagram of a temperature intelligent control terminal is shown. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] like Figure 1As shown, the first aspect of the present invention provides an intelligent control method for a temperature intelligent control system, comprising the following steps: S102: Acquire environmental characteristic data of the target area, build a temperature characteristic model according to the environmental characteristic data of the target area, and build a temperature tracking model based on a deep neural network; S104: Tracking and identifying the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtaining the sub-area to be cooled, and obtaining the cooling demand of the sub-area to be cooled; S106: Acquire refrigeration performance data of the temperature intelligent control system of each sub-region, and perform a feasibility analysis based on the refrigeration performance data of the temperature intelligent control system of each sub-region and the refrigeration demand of the sub-region to be refrigerated, and obtain a feasibility analysis result; S108: Cool the sub-area to be cooled or generate relevant warning information according to the feasibility analysis result.

[0019] It should be noted that the present invention evaluates the effectiveness based on the refrigeration performance data of the temperature intelligent control system and the heating conditions of the area that needs refrigeration, so as to formulate a more accurate temperature control process, achieve power consumption reduction and precise temperature control.

[0020] Furthermore, in this method, environmental characteristic data of the target area is obtained, and a temperature characteristic model is constructed according to the environmental characteristic data of the target area, specifically: Acquire layout data in the target area, and construct a three-dimensional model of the target area through three-dimensional modeling software according to the layout data in the target area, by setting a sensor in each sub-area of ​​the target area; Acquire environmental characteristic data of the target area through sensors, set a temperature gradient data graph, and display the temperature characteristics of each sub-area in the target area according to the temperature gradient data graph and the environmental characteristic data of the target area, and build a temperature characteristic model of the current timestamp based on the three-dimensional model graph of the target area; The temperature characteristic model of each timestamp is obtained, and the temperature characteristic models of each timestamp are combined and displayed in a preset manner to form a dynamic temperature characteristic model, which is output as the final temperature characteristic model.

[0021] It should be noted that the target area can be a building or a device, or an indoor area, etc. The 3D modeling software includes 3ds Max, Maya, Rhino, ZBrush, SketchUp, Blender, C4D software, etc., so as to construct a 3D model diagram or device model diagram of the target area. The temperature gradient data diagram shows different colors for different temperature gradients, such as green at 25 degrees Celsius and red at 45 degrees Celsius, etc. By rendering in the 3D model diagram of the target area, the temperature situation in the target area is displayed dynamically and in 3D mode.

[0022] Furthermore, in this method, a temperature tracking model is constructed based on a deep neural network, specifically: A temperature tracking model is constructed based on a deep neural network, and the learning rate of the temperature tracking model and the mean square error of the stopping condition are initialized to 0.001, a dynamic temperature feature model is obtained, and the model change characteristics of the dynamic temperature feature model are extracted; Taking the model change characteristics of each dynamic temperature characteristic model as a state vector, calculating the transition probability value of the state vector transferring to another state vector, and setting the transition probability threshold; When the transfer probability value of the state vector transferring to another state vector is greater than the transfer probability threshold, the state vector is updated to another state vector, and the model change characteristics of the dynamic temperature characteristic model are updated; When the transfer probability value of a state vector to another state vector is not greater than the transfer probability threshold, the current state vector is maintained unchanged. Based on learning, the model change characteristics of the dynamic temperature feature model are input into the temperature tracking model for learning. When the stopping condition of the model is less than the mean square error of the stopping condition, the temperature tracking model training is completed.

[0023] It should be noted that the temperature characteristic model is dynamically learned through deep neural networks and machine learning to complete the intelligent tracking of temperature data. The transition probability value of a state vector transferring to another state vector is calculated through the Markov model, and the transition probability threshold is set to update the state in time and improve the accuracy of intelligent tracking of temperature.

[0024] Furthermore, in this method, the real-time heating characteristic data of each sub-region in the target region is tracked and identified according to the temperature tracking model and the temperature characteristic model to obtain the sub-region to be cooled, specifically: Obtaining a temperature characteristic model within a preset time, and inputting the temperature characteristic model within the preset time into a temperature tracking model for identification and status update; By identifying and updating the state, a temperature characteristic model after the state is updated is obtained, and real-time heating characteristic data of each sub-area in the target area is obtained according to the temperature characteristic model after the state is updated; Set a fever characteristic data threshold, and determine whether the real-time fever characteristic data of the sub-area is greater than the fever characteristic data threshold. If it is less than the threshold, the corresponding area is regarded as a normal fever area. When the real-time heating characteristic data of the sub-region is greater than the heating characteristic data threshold, the corresponding region is used as the sub-region to be cooled.

[0025] It should be noted that this method can be used to perform real-time evaluation on the target area, thereby identifying abnormal areas.

[0026] Furthermore, in this method, the refrigeration performance data of each sub-region temperature intelligent control system is obtained, and a feasibility analysis is performed based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and the feasibility analysis results are obtained, specifically: Acquire the refrigeration performance data of each sub-region temperature intelligent control system, and acquire the maximum refrigeration performance data of each sub-region temperature intelligent control system according to the refrigeration performance data of each sub-region temperature intelligent control system; Determine whether the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated; When the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area with the maximum refrigeration performance data greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an adjustable area; When the maximum refrigeration performance data of the temperature intelligent control system is not greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area whose maximum refrigeration performance data is not greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an uncontrollable area; Construct feasibility analysis results based on controllable areas and uncontrollable areas, and output the feasibility analysis results.

[0027] It should be noted that the refrigeration performance of each device can be predicted or directly obtained, and the refrigeration performance data includes the temperature drop within a unit time, the heat drop within a unit time, the refrigeration temperature within a unit time and other data. The refrigeration demand of the sub-area to be refrigerated includes the required refrigeration temperature within a unit time and the temperature drop within a unit time. In fact, when some areas generate more heat, higher refrigeration requirements are required, otherwise the temperature environment cannot be maintained within the predetermined area range. When the maximum refrigeration performance data of the temperature intelligent control system is not greater than the refrigeration demand of the sub-area to be refrigerated, it means that the refrigeration does not meet the requirements. On the contrary, it meets the requirements. This method can analyze abnormal areas.

[0028] like Figure 2 As shown, further, in this method, the sub-area to be cooled is cooled or related warning information is generated according to the feasibility analysis result, specifically: S202: When the feasibility analysis result is an uncontrollable area, the location of the temperature intelligent control system corresponding to the sub-area is obtained, and relevant warning information is generated according to the location of the temperature intelligent control system corresponding to the sub-area; S204: When the feasibility analysis result is a controllable area, the refrigeration working parameters of the temperature intelligent control system are initialized, and each sub-area is instructed based on the refrigeration working parameters of the temperature intelligent control system to obtain the cooling characteristic data of the target area; S206: Setting a temperature drop characteristic data threshold, when the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold, outputting the refrigeration working parameters of the temperature intelligent control system, and performing temperature control according to the refrigeration working parameters of the temperature intelligent control system; S208: When the cooling characteristic data of the target area is not greater than the cooling characteristic data threshold, the refrigeration working parameters of the temperature intelligent control system are readjusted until the cooling characteristic data of the target area is greater than the cooling characteristic data threshold.

[0029] It should be noted that the refrigeration working parameters of the temperature intelligent control system include the cold air supply volume per unit time, the cold water flow volume per unit time, etc. The method can dynamically adjust the refrigeration working parameters so that the refrigeration working parameters meet the predetermined requirements and improve the rationality of the temperature control system.

[0030] In addition, the method specifically includes: Acquire historical refrigeration performance change data of the temperature intelligent control system, construct a timestamp, sort the refrigeration performance change data based on the sequence of the timestamps, and acquire the historical refrigeration performance change data based on the time series; Building a refrigeration performance characteristic prediction model based on a deep neural network, inputting the refrigeration performance change into the refrigeration performance characteristic prediction model for training, and obtaining a trained refrigeration performance characteristic prediction model; Acquire the refrigeration performance change data of each temperature intelligent control system within a preset time, and input the refrigeration performance change data of the temperature intelligent control system within the preset time into the trained refrigeration performance characteristic prediction model for prediction; The refrigeration performance requirements of each area are obtained, and through prediction, the time nodes at which the refrigeration performance of each temperature intelligent control system is lower than the refrigeration performance requirements are calculated, and an early warning is issued according to the time nodes at which the refrigeration performance of the temperature intelligent control system is lower than the refrigeration performance requirements, and displayed in a preset manner.

[0031] It should be noted that, through this method, the refrigeration performance data of each area can be known in advance, so as to prevent in advance the time node when the refrigeration performance is lower than the refrigeration performance requirement, and to replace or analyze the temperature intelligent control system in advance.

[0032] In addition, the method further comprises: Acquire temperature characteristic data of each sub-area of ​​the target area at the current timestamp, acquire temperature demand information of the user, introduce a genetic algorithm, and set a genetic generation based on the genetic algorithm; Acquire the driving position required by the user, and initialize the recommended driving route for the user according to the temperature characteristic data of each sub-area of ​​the target area at the current timestamp and the driving position required by the user; Acquire temperature characteristic data of each user's recommended driving route, and determine whether the temperature characteristic data of the user's recommended driving route meets the user's temperature requirement information; When the temperature characteristic data of the user's recommended driving route meets the user's temperature requirement information, the user's recommended driving route is output and displayed in a preset manner; When the temperature characteristic data of the user's recommended driving route does not meet the user's temperature requirement information, inheritance is performed based on genetic algebra to replan the user's recommended driving route until it meets the user's temperature requirement information and is displayed in a preset manner.

[0033] It should be noted that when the target area is some public places and the user needs a cool route, when the temperature in some areas is not suitable, a genetic algorithm is used to plan a driving route suitable for the user to improve the user experience in public places.

[0034] like Figure 3As shown, the second aspect of the present invention provides an intelligent control device 4 for a temperature intelligent control system, comprising a memory 41 and a processor 42. The memory 41 comprises an intelligent control method program for the temperature intelligent control system. When the intelligent control method program for the temperature intelligent control system is executed by the processor 42, any step of the intelligent control method for the temperature intelligent control system is implemented.

[0035] like Figure 4 As shown, the third aspect of the present invention provides a temperature intelligent control terminal, comprising: The temperature tracking model building module 10 is responsible for acquiring the environmental characteristic data of the target area, building a temperature characteristic model according to the environmental characteristic data of the target area, and building a temperature tracking model based on a deep neural network; The cooling analysis module 20 is responsible for tracking and identifying the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtaining the sub-area to be cooled, and obtaining the cooling demand of the sub-area to be cooled; The feasibility analysis module 30 is responsible for obtaining the refrigeration performance data of each sub-region temperature intelligent control system, and performing a feasibility analysis based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and obtaining a feasibility analysis result; The control analysis module 40 is responsible for cooling the sub-area to be cooled or generating relevant warning information according to the feasibility analysis result.

[0036] Furthermore, in this device, environmental characteristic data of the target area is obtained, and a temperature characteristic model is constructed according to the environmental characteristic data of the target area, specifically: Acquire layout data in the target area, and construct a three-dimensional model of the target area through three-dimensional modeling software according to the layout data in the target area, by setting a sensor in each sub-area of ​​the target area; Acquire environmental characteristic data of the target area through sensors, set a temperature gradient data graph, and display the temperature characteristics of each sub-area in the target area according to the temperature gradient data graph and the environmental characteristic data of the target area, and build a temperature characteristic model of the current timestamp; The temperature characteristic model of each timestamp is obtained, and the temperature characteristic models of each timestamp are combined and displayed in a preset manner to form a dynamic temperature characteristic model, which is output as the final temperature characteristic model.

[0037] Furthermore, in this device, a temperature tracking model is constructed based on a deep neural network, specifically: A temperature tracking model is constructed based on a deep neural network, and the learning rate of the temperature tracking model and the mean square error of the stopping condition are initialized to obtain a dynamic temperature feature model and extract the model change characteristics of the dynamic temperature feature model; Taking the model change characteristics of each dynamic temperature characteristic model as a state vector, calculating the transition probability value of the state vector transferring to another state vector, and setting the transition probability threshold; When the transfer probability value of the state vector transferring to another state vector is greater than the transfer probability threshold, the state vector is updated to another state vector, and the model change characteristics of the dynamic temperature characteristic model are updated; When the transfer probability value of a state vector to another state vector is not greater than the transfer probability threshold, the current state vector is maintained unchanged. Based on learning, the model change characteristics of the dynamic temperature feature model are input into the temperature tracking model for learning. When the stopping condition of the model is less than the mean square error of the stopping condition, the temperature tracking model training is completed.

[0038] Furthermore, in this device, the real-time heating characteristic data of each sub-region in the target region is tracked and identified according to the temperature tracking model and the temperature characteristic model, and the sub-region to be cooled is obtained, specifically: Obtaining a temperature characteristic model within a preset time, and inputting the temperature characteristic model within the preset time into a temperature tracking model for identification and status update; By identifying and updating the state, a temperature characteristic model after the state is updated is obtained, and real-time heating characteristic data of each sub-area in the target area is obtained according to the temperature characteristic model after the state is updated; Set a fever characteristic data threshold, and determine whether the real-time fever characteristic data of the sub-area is greater than the fever characteristic data threshold. If it is less than the threshold, the corresponding area is regarded as a normal fever area. When the real-time heating characteristic data of the sub-region is greater than the heating characteristic data threshold, the corresponding region is used as the sub-region to be cooled.

[0039] Furthermore, in this device, the refrigeration performance data of each sub-region temperature intelligent control system is obtained, and a feasibility analysis is performed based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and the feasibility analysis results are obtained, specifically: Acquire the refrigeration performance data of each sub-region temperature intelligent control system, and acquire the maximum refrigeration performance data of each sub-region temperature intelligent control system according to the refrigeration performance data of each sub-region temperature intelligent control system; Determine whether the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated; When the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area with the maximum refrigeration performance data greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an adjustable area; When the maximum refrigeration performance data of the temperature intelligent control system is not greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area whose maximum refrigeration performance data is not greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an uncontrollable area; Construct feasibility analysis results based on controllable areas and uncontrollable areas, and output the feasibility analysis results.

[0040] Furthermore, in this device, the sub-area to be cooled is cooled or related warning information is generated according to the feasibility analysis result, specifically: When the feasibility analysis result is an uncontrollable area, the location of the temperature intelligent control system corresponding to the sub-area is obtained, and relevant warning information is generated according to the location of the temperature intelligent control system corresponding to the sub-area; When the feasibility analysis result is a controllable area, the refrigeration working parameters of the temperature intelligent control system are initialized, and instructions are given to each sub-area based on the refrigeration working parameters of the temperature intelligent control system to obtain the cooling characteristic data of the target area; A temperature drop characteristic data threshold is set. When the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold, the cooling working parameters of the temperature intelligent control system are output, and the temperature is controlled according to the cooling working parameters of the temperature intelligent control system. When the temperature drop characteristic data of the target area is not greater than the temperature drop characteristic data threshold, the refrigeration working parameters of the temperature intelligent control system are readjusted until the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold.

[0041] The fourth aspect of the present invention provides a computer-readable storage medium, including an intelligent control method program for a temperature intelligent control system. When the intelligent control method program for a temperature intelligent control system is executed by a processor, the steps of any item of the intelligent control method for a temperature intelligent control system are implemented.

[0042] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0043] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0044] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0045] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0046] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0047] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent control method for a temperature intelligent control system, characterized in that: The following steps are involved: Acquire environmental characteristic data of the target area, build a temperature characteristic model based on the environmental characteristic data of the target area, and build a temperature tracking model based on a deep neural network; Track and identify the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtain the sub-area to be cooled, and obtain the cooling demand of the sub-area to be cooled; Acquire refrigeration performance data of each sub-region temperature intelligent control system, and perform feasibility analysis based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, and obtain feasibility analysis results; The sub-area to be cooled is cooled or related warning information is generated according to the feasibility analysis result.

2. The intelligent control method of a temperature intelligent control system according to claim 1, characterized in that: Acquire the environmental characteristic data of the target area, and construct a temperature characteristic model according to the environmental characteristic data of the target area, specifically: Acquire layout data in the target area, and construct a three-dimensional model of the target area through three-dimensional modeling software according to the layout data in the target area, by setting a sensor in each sub-area of ​​the target area; Acquire environmental characteristic data of the target area through a sensor, set a temperature gradient data graph, and display the temperature characteristics of each sub-area in the target area according to the temperature gradient data graph and the environmental characteristic data of the target area, and build a temperature characteristic model of the current timestamp based on the three-dimensional model graph of the target area; The temperature characteristic model of each time stamp is obtained, and the temperature characteristic model of each time stamp is combined and displayed in a preset manner to form a dynamic temperature characteristic model, which is output as the final temperature characteristic model.

3. The intelligent control method of a temperature intelligent control system according to claim 1, characterized in that: A temperature tracking model is built based on a deep neural network, specifically: Building a temperature tracking model based on a deep neural network, initializing the learning rate of the temperature tracking model and the mean square error of the stopping condition, obtaining a dynamic temperature feature model, and extracting model change characteristics of the dynamic temperature feature model; Taking the model change characteristics of each of the dynamic temperature characteristic models as a state vector, calculating the transition probability value of the state vector transferring to another state vector, and setting a transition probability threshold; When the transition probability value of the state vector transferring to another state vector is greater than the transition probability threshold, the state vector is updated to another state vector, and the model change characteristics of the dynamic temperature characteristic model are updated; When the transition probability value of the state vector transferring to another state vector is not greater than the transition probability threshold, the current state vector is maintained unchanged, and the model change characteristics of the dynamic temperature characteristic model are input into the temperature tracking model for learning based on the learning. When the stopping condition of the model is less than the mean square error of the stopping condition, the temperature tracking model training is completed.

4. The intelligent control method of a temperature intelligent control system according to claim 1, characterized in that: According to the temperature tracking model and the temperature characteristic model, the real-time heating characteristic data of each sub-area in the target area is tracked and identified to obtain the sub-area to be cooled, specifically: Acquire a temperature characteristic model within a preset time, and input the temperature characteristic model within the preset time into the temperature tracking model for identification and status update; By identifying and updating the state, a temperature characteristic model after the state is updated is obtained, and real-time heating characteristic data of each sub-area in the target area is obtained according to the temperature characteristic model after the state is updated; Setting a fever characteristic data threshold, and determining whether the real-time fever characteristic data of the sub-area is greater than the fever characteristic data threshold, and if so, treating the corresponding area as a normal fever area; When the real-time heating characteristic data of the sub-region is greater than the heating characteristic data threshold, the corresponding region is used as the sub-region to be cooled.

5. The intelligent control method of a temperature intelligent control system according to claim 1, characterized in that: The refrigeration performance data of the temperature intelligent control system of each sub-region is obtained, and a feasibility analysis is performed according to the refrigeration performance data of the temperature intelligent control system of each sub-region and the refrigeration demand of the sub-region to be refrigerated, and the feasibility analysis result is obtained, specifically: Acquire refrigeration performance data of each sub-region temperature intelligent control system, and acquire maximum refrigeration performance data of each sub-region temperature intelligent control system according to the refrigeration performance data of each sub-region temperature intelligent control system; Determining whether the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-area to be refrigerated; When the maximum refrigeration performance data of the temperature intelligent control system is greater than the refrigeration demand of the sub-region to be refrigerated, the sub-region with the maximum refrigeration performance data greater than the refrigeration demand of the sub-region to be refrigerated is regarded as an adjustable region; When the maximum refrigeration performance data of the temperature intelligent control system is not greater than the refrigeration demand of the sub-area to be refrigerated, the sub-area whose maximum refrigeration performance data is not greater than the refrigeration demand of the sub-area to be refrigerated is regarded as an uncontrollable area; A feasibility analysis result is constructed based on the controllable area and the uncontrollable area, and the feasibility analysis result is output.

6. The intelligent control method of a temperature intelligent control system according to claim 1, characterized in that: According to the feasibility analysis result, the sub-area to be cooled is cooled or related warning information is generated, specifically: When the feasibility analysis result is an uncontrollable area, the location of the temperature intelligent control system corresponding to the sub-area is obtained, and relevant warning information is generated according to the location of the temperature intelligent control system corresponding to the sub-area; When the feasibility analysis result is a controllable area, the refrigeration working parameters of the temperature intelligent control system are initialized, and each sub-area is instructed based on the refrigeration working parameters of the temperature intelligent control system to obtain the cooling characteristic data of the target area; Setting a temperature drop characteristic data threshold, when the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold, outputting the refrigeration working parameters of the temperature intelligent control system, and performing temperature control according to the refrigeration working parameters of the temperature intelligent control system; When the temperature drop characteristic data of the target area is not greater than the temperature drop characteristic data threshold, the refrigeration working parameters of the temperature intelligent control system are readjusted until the temperature drop characteristic data of the target area is greater than the temperature drop characteristic data threshold.

7. An intelligent control device for a temperature intelligent control system, characterized in that: It includes a memory and a processor, wherein the memory includes an intelligent control method program for a temperature intelligent control system, and when the intelligent control method program for the temperature intelligent control system is executed by the processor, the steps of the intelligent control method for the temperature intelligent control system as described in any one of claims 1-6 are implemented.

8. A temperature intelligent control terminal, characterized in that: include: The temperature tracking model building module is responsible for acquiring the environmental characteristic data of the target area, building a temperature characteristic model according to the environmental characteristic data of the target area, and building a temperature tracking model based on a deep neural network; A cooling analysis module is responsible for tracking and identifying the real-time heating characteristic data of each sub-area in the target area according to the temperature tracking model and the temperature characteristic model, obtaining the sub-area to be cooled, and obtaining the cooling demand of the sub-area to be cooled; A feasibility analysis module is responsible for obtaining the refrigeration performance data of each sub-region temperature intelligent control system, and performing a feasibility analysis based on the refrigeration performance data of each sub-region temperature intelligent control system and the refrigeration demand of the sub-region to be refrigerated, to obtain a feasibility analysis result; The control analysis module is responsible for cooling the sub-area to be cooled or generating relevant warning information according to the feasibility analysis result.

9. A computer-readable storage medium, characterized in that: It includes an intelligent control method program for a temperature intelligent control system. When the intelligent control method program for the temperature intelligent control system is executed by a processor, the steps of the intelligent control method for the temperature intelligent control system as described in any one of claims 1 to 6 are implemented.

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