A high-efficiency water storage intelligent temperature control and regulation method, system and medium

Abstract

The present application provides a method, system and medium for intelligent temperature control and regulation of efficient water cold storage. The method includes: obtaining the historical operating equipment data and real-time operating equipment data of the factory, calculating the cold change index of the operating equipment, calculating the cold demand index of environmental changes according to the historical environmental data and the real-time environmental data, and then obtaining the cold demand correction factor of the production equipment according to the product data, the historical order scheduling data and the real-time order scheduling data, combining the cold change index of the operating equipment, the cold demand index of environmental changes and the cold demand correction factor of the production equipment with the preset historical cold demand to calculate the real-time cold demand data, and finally obtaining the cold storage state through threshold comparison and adjusting the cold storage; thereby realizing the technology of accurate adjustment of cold storage through the calculation of the cold change index of the operating equipment, the cold demand index of environmental changes, the cold demand correction factor of the production equipment and the real-time cold demand data.

Description

Technical Field

[0001] The present application relates to the field of intelligent temperature control, and more specifically, to a method, system and medium for efficient intelligent temperature control and regulation of water-cooled storage. Background Art

[0002] With the continuous development of industrial enterprises and the continuous improvement of factory requirements for working environment, more and more factories rely on water storage to achieve temperature control of factory environment during operation. In the temperature control process, cold water is mainly stored in the cold storage tank, and then different cold water is transported to different workshops or offices according to actual conditions, so as to achieve the purpose of temperature regulation. Cold storage capacity is an important part of achieving temperature control, but due to changes in order production volume and environmental factors, there will be different demands for cold storage capacity during the temperature control process, which will cause insufficient or excessive cold storage capacity. How to ensure the temperature control effect while taking into account environmental protection requirements and electricity cost factors, so as to better achieve dynamic and precise adjustment of cold storage capacity, has become a problem that needs to be solved at present.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a method, system and medium for efficient water storage and intelligent temperature control and regulation, which can realize the technology of efficient water storage and intelligent temperature control and regulation by calculating the real-time production rhythm data of effective products in the product production line.

[0005] The present application also provides a highly efficient water storage cooling intelligent temperature control and regulation method, comprising the following steps:

[0006] Obtain the historical and real-time operating equipment information of the factory, and extract the historical and real-time operating equipment data;

[0007] According to the historical operating equipment data and the real-time operating equipment data, a cold change index of the operating equipment is obtained by processing the cold change evaluation model of the preset operating equipment;

[0008] Obtain the historical and real-time environmental data of the factory, and process them through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index;

[0009] Obtain product data, historical order scheduling data, and real-time order scheduling data;

[0010] Obtaining a correction factor for the cooling demand of production equipment according to the product data, historical order scheduling data and real-time order scheduling data;

[0011] The real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand;

[0012] The real-time cooling demand data is compared with the real-time cooling storage capacity to obtain a cooling demand relative value, and the cooling demand relative value is compared with a preset cooling demand relative threshold value to obtain a cooling storage capacity state;

[0013] The cold storage capacity is adjusted according to the cold storage capacity state, including increasing the cold storage capacity, maintaining the cold storage capacity, or reducing the cold storage capacity.

[0014] Optionally, in the efficient water storage cooling intelligent temperature control and regulation method described in the present application, the obtaining of historical operating equipment information and real-time operating equipment information of the factory, and extracting historical operating equipment data and real-time operating equipment data, specifically includes:

[0015] Obtain the factory's historical operating equipment information and extract historical operating equipment data, including the number of historical operating equipment, historical operating equipment working hours, and historical operating equipment average heat release rate;

[0016] Obtain the real-time operating equipment information of the factory and extract the real-time operating equipment data, including the number of real-time operating equipment, the working hours of real-time operating equipment and the average heat release rate of real-time operating equipment.

[0017] Optionally, in the efficient water storage cooling intelligent temperature control and regulation method described in the present application, the historical operating equipment data and the real-time operating equipment data are processed by a preset operating equipment cooling change evaluation model to obtain the operating equipment cooling change index, specifically including:

[0018] Input the historical number of operating equipment, the historical operating equipment working hours, the historical average heat release rate of operating equipment, the real-time number of operating equipment, the real-time operating equipment working hours and the real-time average heat release rate of operating equipment into a preset operating equipment use cold change evaluation model for processing, and obtain an operating equipment use cold change index;

[0019] The calculation formula of the running equipment cooling change index in the running equipment cooling change evaluation model is:

[0020] ;

[0021] in, To operate the equipment with the cold change index, is the number of historically running devices, The working hours of historical equipment. is the average heat release rate of the historically operated equipment, To run the number of devices in real time, To measure the working time of the real-time running equipment, The average heat release rate of the real-time running equipment. , , is the preset characteristic coefficient.

[0022] Optionally, in the efficient water storage cooling intelligent temperature control and regulation method described in the present application, the acquisition of historical environmental data and real-time environmental data of the factory, and processing through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index specifically includes:

[0023] Obtain the factory's historical environmental data, including historical personnel data and historical temperature data;

[0024] Obtain real-time environmental data of the factory, including real-time personnel data and real-time predicted temperature data;

[0025] Inputting the historical personnel data, historical temperature data, real-time personnel data and real-time predicted temperature data into a preset environment change cooling demand assessment model for processing to obtain an environment change cooling demand index;

[0026] The calculation formula of the environmental change cooling demand index in the environmental change cooling demand assessment model is:

[0027] ;

[0028] in, Use cooling demand index for environmental changes, For real-time personnel data, To predict temperature data in real time, For historical personnel data, is the historical temperature data, , is the preset characteristic coefficient.

[0029] Optionally, in the efficient water storage cooling intelligent temperature control and regulation method described in the present application, the obtaining of product data, historical order scheduling data and real-time order scheduling data specifically includes:

[0030] Obtain product data, including product model data and corresponding product model heat release data;

[0031] Obtain historical order production scheduling data, including historical production scheduling product model data and historical production scheduling quantities of corresponding models;

[0032] Obtain real-time order scheduling data, including real-time scheduling product model data and real-time scheduling quantity of corresponding models.

[0033] Optionally, in the efficient water storage cooling intelligent temperature control and regulation method described in the present application, the processing of obtaining the cooling demand correction factor of the production equipment according to the product data, historical order scheduling data and real-time order scheduling data specifically includes:

[0034] The historical production schedule product model data, the historical production schedule quantity, the product model data and the product model heat release data are processed to obtain the historical production schedule product heat release average value;

[0035] According to the real-time production scheduling product model data, the real-time production scheduling quantity combined with the product model data and the product model heat release data, the real-time production scheduling product heat release average value is obtained;

[0036] The average heat release of the real-time scheduled products is compared with the average heat release of the historically scheduled products to obtain a correction factor for the cooling demand of the production equipment.

[0037] Optionally, in the efficient water-cooled intelligent temperature control and regulation method described in the present application, the real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand, specifically including:

[0038] Input the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, the cooling demand correction factor of the production equipment and the preset historical cooling demand into a preset real-time cooling demand assessment model for processing to obtain real-time cooling demand data;

[0039] ;

[0040] in, To obtain real-time cooling demand data, For historical cooling demand, To operate the equipment with the cold change index, Use cooling demand index for environmental changes, is the correction factor for the cooling demand of production equipment, For the strictness data of quality inspection standards, The duration data of the quality inspection process. , , , , is the preset characteristic coefficient.

[0041] Optionally, in the efficient water cooling intelligent temperature control and regulation method described in the present application, the real-time cooling demand data is compared with the real-time cooling storage capacity to obtain a relative cooling demand value, and the relative cooling demand value is compared with a preset relative cooling demand threshold to obtain a cooling storage capacity state, specifically including:

[0042] Dividing the real-time cooling demand data by the real-time cooling storage capacity to obtain a relative cooling demand value;

[0043] Comparing the relative value of cooling demand with a preset relative threshold of cooling demand to obtain a cooling capacity state;

[0044] Extracting a first threshold and a second threshold according to the relative threshold of the cooling demand, wherein the first threshold is greater than the second threshold;

[0045] comparing the relative value of cooling demand with the first threshold and the second threshold;

[0046] If the relative value of the cooling demand is greater than the first threshold, the cooling capacity state is a cooling capacity insufficient state;

[0047] If the relative value of the cooling demand is greater than the second threshold value and less than or equal to the first threshold value, the cooling capacity state is a moderate cooling capacity state;

[0048] If the relative value of the cooling demand is less than or equal to the second threshold, the cooling capacity state is an excessive cooling capacity state.

[0049] In a second aspect, the present application provides a highly efficient water storage intelligent temperature control and regulation system, the system comprising:

[0050] Data collection module, used to collect data of offices, production workshops, dormitories and production equipment cooling, including historical operating equipment data, real-time operating equipment data, historical environment data, real-time environment data, product data, historical order scheduling data and real-time order scheduling data;

[0051] A data processing module is used to obtain the operating equipment cooling change index, environmental change cooling demand index, production equipment cooling demand correction factor, real-time cooling demand data and cooling demand relative value based on the collected historical operating equipment data, real-time operating equipment data, historical environmental data, real-time environmental data, product data, historical order scheduling data and real-time order scheduling data;

[0052] A judgment and evaluation module, used for judging the state of the cold storage capacity according to the relative value of the cold demand;

[0053] The regulating execution module is used to adjust the electric proportional valve according to the state of the cold storage capacity to adjust the cold storage capacity.

[0054] In the third aspect, the present application also provides a readable storage medium, which includes a high-efficiency water storage intelligent temperature control and regulation method program. When the high-efficiency water storage intelligent temperature control and regulation method program is executed by a processor, the steps of the high-efficiency water storage intelligent temperature control and regulation method as described in any one of the above items are implemented.

[0055] As can be seen from the above, the present application provides an efficient water-cooled intelligent temperature control and regulation method, system and medium. The method obtains the historical operating equipment data and real-time operating equipment data of the factory, calculates the operating equipment cooling change index, calculates the environmental change cooling demand index according to the historical environmental data and the real-time environmental data, and then obtains the production equipment cooling demand correction factor according to the product data, the historical order scheduling data and the real-time order scheduling data, and calculates the operating equipment cooling change index, the environmental change cooling demand index and the production equipment cooling demand correction factor in combination with the preset historical cooling demand to obtain the real-time cooling demand data, and finally obtains the cooling capacity state through threshold comparison and adjusts the cooling capacity; thus, the technology of precise adjustment of cooling capacity is realized through the calculation of the operating equipment cooling change index, the environmental change cooling demand index, the production equipment cooling demand correction factor and the real-time cooling demand data.

[0056] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0058] Figure 1 A flow chart of a highly efficient water storage cooling intelligent temperature control method provided in an embodiment of the present application;

[0059] Figure 2 A flow chart of obtaining historical operating equipment data and real-time operating equipment data for the efficient water storage cooling intelligent temperature control and regulation method provided in an embodiment of the present application;

[0060] Figure 3 A flow chart of obtaining an environmental change cooling demand index for a high-efficiency water cooling intelligent temperature control method provided in an embodiment of the present application;

[0061] Figure 4 A flowchart of obtaining product data, historical order scheduling data and real-time order scheduling data for the efficient water storage cooling intelligent temperature control and regulation method provided in an embodiment of the present application;

[0062] Figure 5 A schematic diagram of the structure of a high-efficiency water cooling intelligent temperature control and regulation system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0064] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0065] Please refer to Figure 1 , Figure 1 Flow chart of a high-efficiency water storage intelligent temperature control and regulation method in some embodiments of the present application. The high-efficiency water storage intelligent temperature control and regulation method is used in a terminal device, such as a computer. The high-efficiency water storage intelligent temperature control and regulation method comprises the following steps:

[0066] S11, obtaining historical operation equipment information and real-time operation equipment information of the factory, and extracting historical operation equipment data and real-time operation equipment data;

[0067] S12, processing the historical operating equipment data and the real-time operating equipment data through a preset operating equipment use cold change evaluation model to obtain an operating equipment use cold change index;

[0068] S13, obtaining historical environmental data and real-time environmental data of the factory, and processing them through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index;

[0069] S14, obtaining product data, historical order scheduling data and real-time order scheduling data;

[0070] S15, obtaining a correction factor for the cooling demand of production equipment according to the product data, historical order scheduling data and real-time order scheduling data;

[0071] S16, obtaining real-time cooling demand data according to the cooling demand change index of the operating equipment, the cooling demand index of the environmental change and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand calculation;

[0072] S17, comparing the real-time cooling demand data with the real-time cooling storage capacity to obtain a cooling demand relative value, and comparing the cooling demand relative value with a preset cooling demand relative threshold to obtain a cooling storage capacity state;

[0073] S18. Adjusting the cold storage capacity according to the cold storage capacity state, including increasing the cold storage capacity, maintaining the cold storage capacity, or reducing the cold storage capacity.

[0074] It should be noted that the water-cooled temperature control and regulation technology can achieve temperature control in various scenes of the factory, including offices, dormitories, production workshops and production equipment. There are many factors affecting the temperature control of water-cooled temperature, including factors to ensure the normal operation of the factory equipment, production equipment and environmental factors. The storage capacity must also be adjusted according to changes in actual conditions. Only in this way can intelligent temperature control be achieved and the storage capacity can be accurately adjusted. When performing intelligent temperature control, the historical operating equipment data and real-time operating equipment data of the factory must be obtained first, and the operating equipment cold change index is obtained after processing. The operating equipment cold change index can reflect the changes in the operating equipment's demand for cold storage. Then the historical environmental data and real-time environmental data are obtained, and the cold demand evaluation model for environmental changes is used to evaluate the cold demand. The cold demand index of environmental changes is obtained through type processing, which is also an influencing factor affecting the demand for cold storage capacity. The demand for cold storage capacity of production equipment is also an important link. By obtaining product data, historical order scheduling data and real-time order scheduling data, the cold demand correction factor of production equipment can be obtained. On the basis of historical cold demand, the cold storage capacity is calculated according to the cold change index of operating equipment, the cold demand index of environmental changes and the cold demand correction factor of production equipment to obtain real-time cold demand data. The real-time cold demand data is compared with the real-time cold storage capacity to obtain the relative value of cold demand, and then the cold storage capacity state is obtained through threshold comparison. The cold storage capacity is adjusted according to the cold storage capacity state. Increasing the cold storage capacity means adding part of the cold storage capacity on the basis of the original cold storage capacity.

[0075] Please refer to Figure 2 , Figure 2 The flowchart of the method for obtaining historical operation equipment data and real-time operation equipment data of the efficient water storage intelligent temperature control and regulation method provided in the embodiment of the present application is as follows. According to the embodiment of the present invention, the acquisition of the historical operation equipment information and real-time operation equipment information of the factory and the extraction of the historical operation equipment data and the real-time operation equipment data specifically include:

[0076] S21. Obtain the historical operating equipment information of the factory, and extract the historical operating equipment data, including the number of historical operating equipment, the working hours of historical operating equipment, and the average heat release rate of historical operating equipment;

[0077] S22. Acquire the real-time operating equipment information of the factory and extract the real-time operating equipment data, including the number of real-time operating equipment, the working time of the real-time operating equipment and the average heat release rate of the real-time operating equipment.

[0078] It should be noted that water storage temperature control mainly involves the amount of cold storage, and the amount of cold storage consumed is affected by the operating equipment. The operating equipment refers to the equipment used to keep the factory running during operation, which does not include production equipment. It mainly refers to the air conditioners in the office, computer rooms, and dormitory air conditioners and lighting systems. Changes in the operating equipment affect the amount of cold storage demand. The historical operating equipment data is obtained. The historical average heat release rate of the operating equipment refers to the average heat release rate of the operating equipment in the past. The equipment will generate and release heat when it is running. Taking the average value can more stably feedback the speed of heat release. The real-time operating equipment data refers to the data of the factory's current operating equipment. The real-time average heat release rate of the operating equipment refers to the average heat release rate of the currently operating equipment.

[0079] According to an embodiment of the present invention, the processing of the historical operating equipment data and the real-time operating equipment data by a preset operating equipment use cold change evaluation model to obtain the operating equipment use cold change index specifically includes:

[0080] Input the historical number of operating equipment, the historical operating equipment working hours, the historical average heat release rate of operating equipment, the real-time number of operating equipment, the real-time operating equipment working hours and the real-time average heat release rate of operating equipment into a preset operating equipment use cold change evaluation model for processing, and obtain an operating equipment use cold change index;

[0081] The calculation formula of the running equipment cooling change index in the running equipment cooling change evaluation model is:

[0082] ;

[0083] in, To operate the equipment with the cold change index, is the number of historically running devices, The working hours of historical equipment. is the average heat release rate of the historically operated equipment, To run the number of devices in real time, To measure the working time of the real-time running equipment, The average heat release rate of the real-time running equipment. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0084] It should be noted that the cold usage change index of operating equipment refers to a situation that can reflect the cold storage capacity demand of the operating equipment to a certain extent. During the operation of the operating equipment, due to the influence of power or other factors, the data of heat generation or heat release changes. Therefore, the demand for cold capacity during temperature control also changes. In order to better adjust the cold storage capacity, it is necessary to calculate the cold usage change index of the operating equipment based on the historical cold usage of the operating equipment. The preset cold usage change evaluation model of the operating equipment is obtained through training and analysis of a large amount of historical data, and the cold usage change index of the operating equipment can be calculated.

[0085] Please refer to Figure 3 , Figure 3 The flowchart of the method for obtaining the cooling demand index for environmental changes in the efficient water storage intelligent temperature control and regulation method provided in the embodiment of the present application is as follows. According to the embodiment of the present invention, the historical environmental data and real-time environmental data of the factory are obtained, and the cooling demand index for environmental changes is obtained by processing the data through a preset environmental change cooling demand assessment model, specifically including:

[0086] S31. Obtain historical environmental data of the factory, including historical personnel data and historical temperature data;

[0087] S32, obtaining real-time environmental data of the factory, including real-time personnel data and real-time predicted temperature data;

[0088] S33, inputting the historical personnel data, historical temperature data, real-time personnel data and real-time predicted temperature data into a preset environment change cooling demand assessment model for processing, and obtaining an environment change cooling demand index;

[0089] The calculation formula of the environmental change cooling demand index in the environmental change cooling demand assessment model is:

[0090] ;

[0091] in, Use cooling demand index for environmental changes, For real-time personnel data, To predict temperature data in real time, For historical personnel data, is the historical temperature data, , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0092] It should be noted that in addition to the impact of running equipment, environmental factors should also be considered to obtain the factory's historical and real-time environmental data. Historical personnel data refers to the number of personnel in the factory in the past. If there are more personnel, dormitories, office areas, and workshops will be affected. The more personnel there are, the more dormitories are activated, the more energy consumption and heat release there are, and the heat released by personnel in office areas and workshops will be more. Historical temperature data refers to the previous ambient temperature, and the ambient temperature will also affect the demand for cooling. Real-time predicted temperature refers to the temperature data for tomorrow or a future day obtained based on weather conditions. The historical personnel data, historical temperature data, real-time personnel data, and real-time predicted temperature data are processed through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index. The environmental change cooling demand index refers to data that can reflect the changes in cooling demand to a certain extent. The environmental change cooling demand assessment model is obtained through training and analysis of a large amount of historical data, and the environmental change cooling demand index can be calculated.

[0093] Please refer to Figure 4 , Figure 4 The flowchart of the method for obtaining product data, historical order scheduling data and real-time order scheduling data of the efficient water storage cooling intelligent temperature control and regulation method provided in the embodiment of the present application is as follows. According to the embodiment of the present invention, the obtaining of product data, historical order scheduling data and real-time order scheduling data specifically includes:

[0094] S41, obtaining product data, including product model data and corresponding product model heat release data;

[0095] S42, obtaining historical order production scheduling data, including historical production scheduling product model data and historical production scheduling quantities of corresponding models;

[0096] S43. Obtain real-time order production scheduling data, including real-time production scheduling product model data and real-time production scheduling quantity of corresponding models.

[0097] It should be noted that the production workshop is a major component of a factory, and the production equipment will generate a lot of heat, which in turn requires more cold storage. The changes in the cold storage demand of the workshop production equipment affect the temperature control effect. The production workshop is produced according to the order scheduling plan. The product data obtained includes product model data and the corresponding product model heat release data. The product model heat release data refers to the heat data generated when producing this model of product. The more heat generated, the more cold storage is required; obtain historical order scheduling data. The historical order scheduling data refers to the product model and the number of scheduled products in the previous production. The real-time order scheduling data refers to the latest production schedule data in the factory. The real-time production quantity refers to the number of a certain model of products that need to be produced in the production schedule arranged by the factory.

[0098] According to an embodiment of the present invention, the obtaining of the correction factor of the cold demand of production equipment according to the product data, the historical order scheduling data and the real-time order scheduling data specifically includes:

[0099] The historical production schedule product model data, the historical production schedule quantity, the product model data and the product model heat release data are processed to obtain the historical production schedule product heat release average value;

[0100] According to the real-time production scheduling product model data, the real-time production scheduling quantity combined with the product model data and the product model heat release data, the real-time production scheduling product heat release average value is obtained;

[0101] The average heat release of the real-time scheduled products is compared with the average heat release of the historically scheduled products to obtain a correction factor for the cooling demand of the production equipment.

[0102] It should be noted that the average heat release of historical scheduled products refers to the average heat release of scheduled products in previous production, that is, the quantity of products of different models is multiplied by the heat release data of the product model and then added to obtain the total heat release data, and then the total heat release data is divided by the total production quantity obtained according to the historical order scheduling data to obtain the average heat release of historical scheduled products; the heat release data of the product model refers to the heat release data of the product model during the reproduction process; the average heat release of real-time scheduled products refers to the average heat release of the scheduled products calculated according to the current real-time order scheduling data. value; multiply the production quantity corresponding to the product model by the heat release data of the product model and then sum them up to get the total heat release data of the real-time order production scheduling, and then divide the total heat release data by the total production quantity obtained according to the real-time order production scheduling data to get the average heat release of the real-time production scheduling products; divide the average heat release of the real-time production scheduling products by the average heat release of the historical production scheduling products to get the correction factor for the cooling demand of the production equipment. The correction factor for the cooling demand of the production equipment reflects the changes in the cooling demand of the production equipment. Correcting the cooling capacity according to different cooling demand correction factors of the production equipment can be more accurate.

[0103] According to an embodiment of the present invention, the real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand, specifically including:

[0104] Input the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, the cooling demand correction factor of the production equipment and the preset historical cooling demand into a preset real-time cooling demand assessment model for processing to obtain real-time cooling demand data;

[0105] ;

[0106] in, To obtain real-time cooling demand data, For historical cooling demand, To operate the equipment with the cold change index, Use cooling demand index for environmental changes, is the correction factor for the cooling demand of production equipment, For the strictness data of quality inspection standards, The duration data of the quality inspection process. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0107] It should be noted that the operating equipment cooling change index, environmental change cooling demand index, and production equipment cooling demand correction factor are all factors that affect the cooling demand quantity. Comprehensive consideration of the above factors can better reflect the changes in cooling demand and better adjust the cooling capacity; therefore, the real-time cooling demand data can be calculated through the real-time cooling demand evaluation model.

[0108] According to an embodiment of the present invention, comparing the real-time cooling demand data with the real-time cooling storage capacity to obtain a cooling demand relative value, and comparing the cooling demand relative value with a preset cooling demand relative threshold to obtain a cooling storage capacity state specifically includes:

[0109] Dividing the real-time cooling demand data by the real-time cooling storage capacity to obtain a relative cooling demand value;

[0110] Comparing the relative value of cooling demand with a preset relative threshold of cooling demand to obtain a cooling capacity state;

[0111] Extracting a first threshold and a second threshold according to the relative threshold of the cooling demand, wherein the first threshold is greater than the second threshold;

[0112] comparing the relative value of cooling demand with the first threshold and the second threshold;

[0113] If the relative value of the cooling demand is greater than the first threshold, the cooling capacity state is a cooling capacity insufficient state;

[0114] If the relative value of the cooling demand is greater than the second threshold value and less than or equal to the first threshold value, the cooling capacity state is a moderate cooling capacity state;

[0115] If the relative value of the cooling demand is less than or equal to the second threshold, the cooling capacity state is an excessive cooling capacity state.

[0116] It should be noted that the real-time cooling demand data directly reflects the amount of cold storage. The relative value of cooling demand is obtained by dividing the real-time cooling demand data by the real-time cold storage, which reflects the relationship between the cooling demand and the cold storage. Therefore, the cold storage state can be obtained by comparing the relative value of cooling demand with the preset relative threshold of cooling demand. The cold storage state includes insufficient cold storage state, moderate cold storage state or excessive cold storage state. The insufficient cold storage state means that the cold storage cannot meet the cooling demand and the cold storage needs to be increased. The moderate cold storage state means that the cold storage can The cooling demand is met with a moderate amount and no energy waste is caused, and the current cooling capacity needs to be maintained; the excessive cooling capacity state means that the cooling capacity is too much and there is a certain degree of energy waste, which is an unreasonable state and the cooling capacity needs to be reduced; in this embodiment, the relative threshold value of the cooling demand is set as follows: when the relative value of the cooling demand is greater than 1, it is an insufficient cooling capacity state; [0.95, 1] ​​is a moderate cooling capacity state; (0, 0.95) is an excessive cooling capacity state; when the calculated relative value of the cooling demand is 0.96, the cooling capacity state is a moderate cooling capacity state.

[0117] like Figure 5 As shown, the present invention also discloses a high-efficiency water storage intelligent temperature control and regulation system, including a data acquisition module 51, a data processing module 52, a judgment and evaluation module 53 and a regulation execution module 54, specifically:

[0118] Data collection module, used to collect data of offices, production workshops, dormitories and production equipment cooling, including historical operating equipment data, real-time operating equipment data, historical environment data, real-time environment data, product data, historical order scheduling data and real-time order scheduling data;

[0119] A data processing module is used to obtain the operating equipment cooling change index, environmental change cooling demand index, production equipment cooling demand correction factor, real-time cooling demand data and cooling demand relative value based on the collected historical operating equipment data, real-time operating equipment data, historical environmental data, real-time environmental data, product data, historical order scheduling data and real-time order scheduling data;

[0120] A judgment and evaluation module, used for judging the state of the cold storage capacity according to the relative value of the cold demand;

[0121] The regulating execution module is used to adjust the electric proportional valve according to the state of the cold storage capacity to adjust the cold storage capacity;

[0122] When the efficient water storage intelligent temperature control and regulation method is executed by the efficient water storage intelligent temperature control and regulation system, the following steps are implemented:

[0123] Obtain the historical and real-time operating equipment information of the factory, and extract the historical and real-time operating equipment data;

[0124] According to the historical operating equipment data and the real-time operating equipment data, a cold change index of the operating equipment is obtained by processing the cold change evaluation model of the preset operating equipment;

[0125] Obtain the historical and real-time environmental data of the factory, and process them through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index;

[0126] Obtain product data, historical order scheduling data, and real-time order scheduling data;

[0127] Obtaining a correction factor for the cooling demand of production equipment according to the product data, historical order scheduling data and real-time order scheduling data;

[0128] The real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand;

[0129] The real-time cooling demand data is compared with the real-time cooling storage capacity to obtain a cooling demand relative value, and the cooling demand relative value is compared with a preset cooling demand relative threshold value to obtain a cooling storage capacity state;

[0130] The cold storage capacity is adjusted according to the cold storage capacity state, including increasing the cold storage capacity, maintaining the cold storage capacity, or reducing the cold storage capacity.

[0131] It should be noted that the water-cooled temperature control and regulation technology can achieve temperature control in various scenes of the factory, including offices, dormitories, production workshops and production equipment. There are many factors affecting the temperature control of water-cooled temperature, including factors to ensure the normal operation of the factory equipment, production equipment and environmental factors. The storage capacity must also be adjusted according to changes in actual conditions. Only in this way can intelligent temperature control be achieved and the storage capacity can be accurately adjusted. When performing intelligent temperature control, the historical operating equipment data and real-time operating equipment data of the factory must be obtained first, and the operating equipment cold change index is obtained after processing. The operating equipment cold change index can reflect the changes in the operating equipment's demand for cold storage. Then the historical environmental data and real-time environmental data are obtained, and the cold demand evaluation model for environmental changes is used to evaluate the cold demand. The cold demand index of environmental changes is obtained through type processing, which is also an influencing factor affecting the demand for cold storage capacity. The demand for cold storage capacity of production equipment is also an important link. By obtaining product data, historical order scheduling data and real-time order scheduling data, the cold demand correction factor of production equipment can be obtained. On the basis of historical cold demand, the cold storage capacity is calculated according to the cold change index of operating equipment, the cold demand index of environmental changes and the cold demand correction factor of production equipment to obtain real-time cold demand data. The real-time cold demand data is compared with the real-time cold storage capacity to obtain the relative value of cold demand, and then the cold storage capacity state is obtained through threshold comparison. The cold storage capacity is adjusted according to the cold storage capacity state. Increasing the cold storage capacity means adding part of the cold storage capacity on the basis of the original cold storage capacity.

[0132] According to an embodiment of the present invention, the obtaining of historical operating equipment information and real-time operating equipment information of a factory, and extracting historical operating equipment data and real-time operating equipment data, specifically includes:

[0133] Obtain the factory's historical operating equipment information and extract historical operating equipment data, including the number of historical operating equipment, historical operating equipment working hours, and historical operating equipment average heat release rate;

[0134] Obtain the real-time operating equipment information of the factory and extract the real-time operating equipment data, including the number of real-time operating equipment, the working hours of real-time operating equipment and the average heat release rate of real-time operating equipment.

[0135] It should be noted that water storage temperature control mainly involves the amount of cold storage, and the amount of cold storage consumed is affected by the operating equipment. The operating equipment refers to the equipment used to keep the factory running during operation, which does not include production equipment. It mainly refers to the air conditioners in the office, computer rooms, and dormitory air conditioners and lighting systems. Changes in the operating equipment affect the amount of cold storage demand. The historical operating equipment data is obtained. The historical average heat release rate of the operating equipment refers to the average heat release rate of the operating equipment in the past. The equipment will generate and release heat when it is running. Taking the average value can more stably feedback the speed of heat release. The real-time operating equipment data refers to the data of the factory's current operating equipment. The real-time average heat release rate of the operating equipment refers to the average heat release rate of the currently operating equipment.

[0136] According to an embodiment of the present invention, the processing of the historical operating equipment data and the real-time operating equipment data by a preset operating equipment use cold change evaluation model to obtain the operating equipment use cold change index specifically includes:

[0137] Input the historical number of operating equipment, the historical operating equipment working hours, the historical average heat release rate of operating equipment, the real-time number of operating equipment, the real-time operating equipment working hours and the real-time average heat release rate of operating equipment into a preset operating equipment use cold change evaluation model for processing, and obtain an operating equipment use cold change index;

[0138] The calculation formula of the running equipment cooling change index in the running equipment cooling change evaluation model is:

[0139] ;

[0140] in, To operate the equipment with the cold change index, is the number of historically running devices, The working hours of historical equipment. is the average heat release rate of the historically operated equipment, To run the number of devices in real time, To measure the working time of the real-time running equipment, The average heat release rate of the real-time running equipment. , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0141] It should be noted that the cold usage change index of operating equipment refers to a situation that can reflect the cold storage capacity demand of the operating equipment to a certain extent. During the operation of the operating equipment, due to the influence of power or other factors, the data of heat generation or heat release changes. Therefore, the demand for cold capacity during temperature control also changes. In order to better adjust the cold storage capacity, it is necessary to calculate the cold usage change index of the operating equipment based on the historical cold usage of the operating equipment. The preset cold usage change evaluation model of the operating equipment is obtained through training and analysis of a large amount of historical data, and the cold usage change index of the operating equipment can be calculated.

[0142] According to an embodiment of the present invention, the acquisition of historical environmental data and real-time environmental data of the factory, and processing by a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index specifically includes:

[0143] Obtain the factory's historical environmental data, including historical personnel data and historical temperature data;

[0144] Obtain real-time environmental data of the factory, including real-time personnel data and real-time predicted temperature data;

[0145] Inputting the historical personnel data, historical temperature data, real-time personnel data and real-time predicted temperature data into a preset environment change cooling demand assessment model for processing to obtain an environment change cooling demand index;

[0146] The calculation formula of the environmental change cooling demand index in the environmental change cooling demand assessment model is:

[0147] ;

[0148] in, Use cooling demand index for environmental changes, For real-time personnel data, To predict temperature data in real time, For historical personnel data, is the historical temperature data, , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0149] It should be noted that in addition to the impact of running equipment, environmental factors should also be considered to obtain the factory's historical and real-time environmental data. Historical personnel data refers to the number of personnel in the factory in the past. If there are more personnel, dormitories, office areas, and workshops will be affected. The more personnel there are, the more dormitories are activated, the more energy consumption and heat release there are, and the heat released by personnel in office areas and workshops will be more. Historical temperature data refers to the previous ambient temperature, and the ambient temperature will also affect the demand for cooling. Real-time predicted temperature refers to the temperature data for tomorrow or a future day obtained based on weather conditions. The historical personnel data, historical temperature data, real-time personnel data, and real-time predicted temperature data are processed through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index. The environmental change cooling demand index refers to data that can reflect the changes in cooling demand to a certain extent. The environmental change cooling demand assessment model is obtained through training and analysis of a large amount of historical data, and the environmental change cooling demand index can be calculated.

[0150] According to an embodiment of the present invention, the obtaining of product data, historical order scheduling data and real-time order scheduling data specifically includes:

[0151] Obtain product data, including product model data and corresponding product model heat release data;

[0152] Obtain historical order production scheduling data, including historical production scheduling product model data and historical production scheduling quantities of corresponding models;

[0153] Obtain real-time order scheduling data, including real-time scheduling product model data and real-time scheduling quantity of corresponding models.

[0154] It should be noted that the production workshop is a major component of a factory, and the production equipment will generate a lot of heat, which in turn requires more cold storage. The changes in the cold storage demand of the workshop production equipment affect the temperature control effect. The production workshop is produced according to the order scheduling plan. The product data obtained includes product model data and the corresponding product model heat release data. The product model heat release data refers to the heat data generated when producing this model of product. The more heat generated, the more cold storage is required; obtain historical order scheduling data. The historical order scheduling data refers to the product model and the number of scheduled products in the previous production. The real-time order scheduling data refers to the latest production schedule data in the factory. The real-time production quantity refers to the number of a certain model of products that need to be produced in the production schedule arranged by the factory.

[0155] According to an embodiment of the present invention, the obtaining of the correction factor of the cold demand of production equipment according to the product data, the historical order scheduling data and the real-time order scheduling data specifically includes:

[0156] The historical production schedule product model data, the historical production schedule quantity, the product model data and the product model heat release data are processed to obtain the historical production schedule product heat release average value;

[0157] According to the real-time production scheduling product model data, the real-time production scheduling quantity combined with the product model data and the product model heat release data, the real-time production scheduling product heat release average value is obtained;

[0158] The average heat release of the real-time scheduled products is compared with the average heat release of the historically scheduled products to obtain a correction factor for the cooling demand of the production equipment.

[0159] It should be noted that the average heat release of historical scheduled products refers to the average heat release of scheduled products in previous production, that is, the quantity of products of different models is multiplied by the heat release data of the product model and then added to obtain the total heat release data, and then the total heat release data is divided by the total production quantity obtained according to the historical order scheduling data to obtain the average heat release of historical scheduled products; the heat release data of the product model refers to the heat release data of the product model during the reproduction process; the average heat release of real-time scheduled products refers to the average heat release of the scheduled products calculated according to the current real-time order scheduling data. value; multiply the production quantity corresponding to the product model by the heat release data of the product model and then sum them up to get the total heat release data of the real-time order production scheduling, and then divide the total heat release data by the total production quantity obtained according to the real-time order production scheduling data to get the average heat release of the real-time production scheduling products; divide the average heat release of the real-time production scheduling products by the average heat release of the historical production scheduling products to get the correction factor for the cooling demand of the production equipment. The correction factor for the cooling demand of the production equipment reflects the changes in the cooling demand of the production equipment. Correcting the cooling capacity according to different cooling demand correction factors of the production equipment can be more accurate.

[0160] According to an embodiment of the present invention, the real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand, specifically including:

[0161] Input the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, the cooling demand correction factor of the production equipment and the preset historical cooling demand into a preset real-time cooling demand assessment model for processing to obtain real-time cooling demand data;

[0162] ;

[0163] in, To obtain real-time cooling demand data, For historical cooling demand, To operate the equipment with the cold change index, Use cooling demand index for environmental changes, is the correction factor for the cooling demand of production equipment, For the strictness data of quality inspection standards, The duration data of the quality inspection process. , , , , is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset high-efficiency water storage cooling intelligent temperature control and regulation platform).

[0164] It should be noted that the operating equipment cooling change index, environmental change cooling demand index, and production equipment cooling demand correction factor are all factors that affect the cooling demand quantity. Comprehensive consideration of the above factors can better reflect the changes in cooling demand and better adjust the cooling capacity; therefore, the real-time cooling demand data can be calculated through the real-time cooling demand evaluation model.

[0165] According to an embodiment of the present invention, comparing the real-time cooling demand data with the real-time cooling storage capacity to obtain a cooling demand relative value, and comparing the cooling demand relative value with a preset cooling demand relative threshold to obtain a cooling storage capacity state specifically includes:

[0166] Dividing the real-time cooling demand data by the real-time cooling storage capacity to obtain a relative cooling demand value;

[0167] Comparing the relative value of cooling demand with a preset relative threshold of cooling demand to obtain a cooling capacity state;

[0168] Extracting a first threshold and a second threshold according to the relative threshold of the cooling demand, wherein the first threshold is greater than the second threshold;

[0169] comparing the relative value of cooling demand with the first threshold and the second threshold;

[0170] If the relative value of the cooling demand is greater than the first threshold, the cooling capacity state is a cooling capacity insufficient state;

[0171] If the relative value of the cooling demand is greater than the second threshold value and less than or equal to the first threshold value, the cooling capacity state is a moderate cooling capacity state;

[0172] If the relative value of the cooling demand is less than or equal to the second threshold, the cooling capacity state is an excessive cooling capacity state.

[0173] It should be noted that the real-time cooling demand data directly reflects the amount of cold storage. The relative value of cooling demand is obtained by dividing the real-time cooling demand data by the real-time cold storage, which reflects the relationship between the cooling demand and the cold storage. Therefore, the cold storage state can be obtained by comparing the relative value of cooling demand with the preset relative threshold of cooling demand. The cold storage state includes insufficient cold storage state, moderate cold storage state or excessive cold storage state. The insufficient cold storage state means that the cold storage cannot meet the cooling demand and the cold storage needs to be increased. The moderate cold storage state means that the cold storage can The cooling demand is met with a moderate amount and no energy waste is caused, and the current cooling capacity needs to be maintained; the excessive cooling capacity state means that the cooling capacity is too much and there is a certain degree of energy waste, which is an unreasonable state and the cooling capacity needs to be reduced; in this embodiment, the relative threshold value of the cooling demand is set as follows: when the relative value of the cooling demand is greater than 1, it is an insufficient cooling capacity state; [0.95, 1] ​​is a moderate cooling capacity state; (0, 0.95) is an excessive cooling capacity state; when the calculated relative value of the cooling demand is 0.96, the cooling capacity state is a moderate cooling capacity state.

[0174] A third aspect of the present invention provides a readable storage medium, which includes a high-efficiency water storage intelligent temperature control and regulation method program. When the high-efficiency water storage intelligent temperature control and regulation method program is executed by a processor, the steps of the high-efficiency water storage intelligent temperature control and regulation method as described in any one of the above items are implemented.

[0175] The high-efficiency water-cooled intelligent temperature control and regulation method, system and medium disclosed in the present invention obtain equipment status data, personnel status data, parts status data, quality control data and product assembly process data of a product production line, obtain the production equipment operation index and equipment operation status data after processing the equipment status data, and then calculate the first influencing factor of production operation in combination with the personnel status data and the assembly process data, and then obtain the second influencing factor of production operation in combination with the parts status data and the quality control data, and calculate the production rhythm correction index, and then obtain the real-time production rhythm data of effective products in combination with the preset product standard production rhythm data, and then adjust the comprehensive operation speed of the product production line; thereby, by calculating the real-time production rhythm data of effective products in the product production line, the technology of high-efficiency water-cooled intelligent temperature control and regulation is realized.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0180] 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 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 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 described in 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.

Claims

1. A highly efficient water storage intelligent temperature control and regulation method, characterized in that: include: Obtain the historical and real-time operating equipment information of the factory, and extract the historical and real-time operating equipment data; According to the historical operating equipment data and the real-time operating equipment data, a cold change index of the operating equipment is obtained by processing the cold change evaluation model of the preset operating equipment; Obtain the historical and real-time environmental data of the factory, and process them through a preset environmental change cooling demand assessment model to obtain an environmental change cooling demand index; Obtain product data, historical order scheduling data, and real-time order scheduling data; Obtaining a correction factor for the cooling demand of production equipment according to the product data, historical order scheduling data and real-time order scheduling data; The real-time cooling demand data is obtained by calculating the cooling demand change index of the operating equipment, the cooling demand index of the environmental change and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand; The real-time cooling demand data is compared with the real-time cooling storage capacity to obtain a cooling demand relative value, and the cooling demand relative value is compared with a preset cooling demand relative threshold value to obtain a cooling storage capacity state; The cold storage capacity is adjusted according to the cold storage capacity state, including increasing the cold storage capacity, maintaining the cold storage capacity, or reducing the cold storage capacity.

2. The high-efficiency water cooling intelligent temperature control and regulation method according to claim 1 is characterized in that: The obtaining of the historical operation equipment information and the real-time operation equipment information of the factory, and extracting the historical operation equipment data and the real-time operation equipment data, specifically includes: Obtain the factory's historical operating equipment information and extract historical operating equipment data, including the number of historical operating equipment, historical operating equipment working hours, and historical operating equipment average heat release rate; Obtain the real-time operating equipment information of the factory and extract the real-time operating equipment data, including the number of real-time operating equipment, the working hours of real-time operating equipment and the average heat release rate of real-time operating equipment.

3. The high-efficiency water storage intelligent temperature control and regulation method according to claim 2 is characterized in that: The step of obtaining the cold change index of the operating equipment by processing the historical operating equipment data and the real-time operating equipment data through a preset cold change evaluation model of the operating equipment specifically includes: Input the historical number of operating equipment, the historical operating equipment working hours, the historical average heat release rate of operating equipment, the real-time number of operating equipment, the real-time operating equipment working hours and the real-time average heat release rate of operating equipment into a preset operating equipment use cold change evaluation model for processing, and obtain an operating equipment use cold change index; The calculation formula of the running equipment cooling change index in the running equipment cooling change evaluation model is: ； in, To operate the equipment with the cold change index, is the number of historically running devices, The working hours of historical equipment. is the average heat release rate of the historically operated equipment, To run the number of devices in real time, To measure the working time of the real-time running equipment, is the average heat release rate of the real-time running equipment. , , is the preset characteristic coefficient.

4. The high-efficiency water storage intelligent temperature control and regulation method according to claim 3 is characterized in that: The process of obtaining the historical environmental data and the real-time environmental data of the factory and processing the data through a preset environmental change cooling demand evaluation model to obtain the environmental change cooling demand index specifically includes: Obtain the factory's historical environmental data, including historical personnel data and historical temperature data; Obtain real-time environmental data of the factory, including real-time personnel data and real-time predicted temperature data; Inputting the historical personnel data, historical temperature data, real-time personnel data and real-time predicted temperature data into a preset environment change cooling demand assessment model for processing to obtain an environment change cooling demand index; The calculation formula of the environmental change cooling demand index in the environmental change cooling demand assessment model is: ； in, Use cooling demand index for environmental changes, For real-time personnel data, To predict temperature data in real time, For historical personnel data, is the historical temperature data, , is the preset characteristic coefficient.

5. The efficient water cooling intelligent temperature control method according to claim 4 is characterized in that: The acquisition of product data, historical order scheduling data and real-time order scheduling data specifically includes: Obtain product data, including product model data and corresponding product model heat release data; Obtain historical order production scheduling data, including historical production scheduling product model data and historical production scheduling quantities of corresponding models; Obtain real-time order scheduling data, including real-time scheduling product model data and real-time scheduling quantity of corresponding models.

6. The efficient water storage intelligent temperature control and regulation method according to claim 5 is characterized in that: The obtaining of the correction factor of the cooling demand of production equipment according to the product data, the historical order scheduling data and the real-time order scheduling data specifically includes: The historical production schedule product model data, the historical production schedule quantity, the product model data and the product model heat release data are processed to obtain the historical production schedule product heat release average value; According to the real-time production scheduling product model data, the real-time production scheduling quantity combined with the product model data and the product model heat release data, the real-time production scheduling product heat release average value is obtained; The average heat release of the real-time scheduled products is compared with the average heat release of the historically scheduled products to obtain a correction factor for the cooling demand of the production equipment.

7. The efficient water cooling intelligent temperature control method according to claim 6 is characterized in that: The step of calculating and obtaining the real-time cooling demand data according to the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, and the cooling demand correction factor of the production equipment in combination with the preset historical cooling demand specifically includes: Input the cooling demand change index of the operating equipment, the cooling demand index of the environmental change, the cooling demand correction factor of the production equipment and the preset historical cooling demand into a preset real-time cooling demand assessment model for processing to obtain real-time cooling demand data; ； in, To obtain real-time cooling demand data, For historical cooling demand, To operate the equipment with the cold change index, Use cooling demand index for environmental changes, is the correction factor for the cooling demand of production equipment, For the strictness data of quality inspection standards, The duration data of the quality inspection process. , , , , is the preset characteristic coefficient.

8. The efficient water storage intelligent temperature control and regulation method according to claim 7 is characterized in that: The comparing the real-time cooling demand data with the real-time cooling storage capacity to obtain a cooling demand relative value, and comparing the cooling demand relative value with a preset cooling demand relative threshold to obtain a cooling storage capacity state specifically includes: Dividing the real-time cooling demand data by the real-time cooling storage capacity to obtain a relative cooling demand value; Comparing the relative value of cooling demand with a preset relative threshold of cooling demand to obtain a cooling capacity state; Extracting a first threshold and a second threshold according to the relative threshold of the cooling demand, wherein the first threshold is greater than the second threshold; comparing the relative value of cooling demand with the first threshold and the second threshold; If the relative value of the cooling demand is greater than the first threshold, the cooling capacity state is a cooling capacity insufficient state; If the relative value of the cooling demand is greater than the second threshold value and less than or equal to the first threshold value, the cooling capacity state is a moderate cooling capacity state; If the relative value of the cooling demand is less than or equal to the second threshold, the cooling capacity state is an excessive cooling capacity state.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a high-efficiency water cooling intelligent temperature control and regulation method program. When the high-efficiency water cooling intelligent temperature control and regulation method program is executed by a processor, the steps of the high-efficiency water cooling intelligent temperature control and regulation method as described in any one of claims 1 to 8 are implemented.

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