Automatic energy-saving control method and device for refrigerating system

Abstract

The invention relates to the technical field of refrigeration control, and discloses an automatic energy-saving control method and device for a refrigeration system, and the method comprises the steps: generating a target space model for a target space, judging whether the current refrigeration equipment distribution can improve the cooling efficiency or not, simulating and generating a plurality of refrigeration schemes, and determining an optimal cooling scheme according to user habits. According to the automatic energy-saving control method and device of the refrigerating system, the indoor temperature is rapidly reduced to the target temperature, on the premise that the cooling requirement is met, energy consumption is minimized, cold air direct blowing is avoided, and the habits and the body feeling of a user are considered; through three-dimensional grid modeling, airflow and temperature field simulation and equipment operation strategy optimization, automatic and intelligent control over refrigeration equipment is achieved, invalid refrigeration is avoided, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of refrigeration control, and in particular to an automatic energy-saving control method and device for a refrigeration system. Background Art

[0002] Refrigeration systems are widely used in air conditioning, cold storage, freezing, industrial cooling and other fields. Their main function is to achieve the goal of lowering the temperature of the space by controlling the circulation of refrigerant. A typical refrigeration system includes a compressor, condenser, expansion valve (or throttle valve), evaporator and various sensors and control devices. With the energy crisis and the improvement of environmental protection requirements, energy saving has become an important development direction of refrigeration systems. In order to achieve intelligent, efficient and energy-saving control goals, researchers have gradually introduced automatic control, intelligent algorithms and information technology, and developed various advanced control methods. Therefore, the automatic energy-saving control methods and devices of refrigeration systems are of great significance in improving energy efficiency, reducing operating costs and protecting the environment. By introducing advanced control theory, intelligent algorithms and information technology, refrigeration control systems are developing towards intelligence, optimization and greenness, bringing broad application prospects to related industries. The existing automated energy-saving control methods and devices for refrigeration systems cannot simulate and generate space according to the conditions of the indoor space, cannot analyze the air flow conditions under the current space layout and the distribution of refrigeration equipment, cannot analyze the impact of the current space layout, object distribution and the distribution of refrigeration equipment on the energy consumption and cooling time of the refrigeration equipment, cannot simulate and generate several different cooling schemes and determine the best cooling scheme according to the user's habits, such as the inability to accept direct cold air, etc., which easily leads to energy waste, reduces the user experience, and affects the user's physical health. Its practicality has certain limitations. Summary of the Invention

[0003] The present invention provides an automatic energy-saving control method and device for a refrigeration system, which are used to promote the solution of the problems mentioned in the background technology.

[0004] The present invention provides the following technical solution: an automatic energy-saving control method for a refrigeration system, comprising: Get the target space; generating a target space model for the target space; According to the target space model, determine whether the current distribution of refrigeration equipment can improve the cooling efficiency; If it is determined that the current refrigeration equipment distribution can improve the cooling efficiency, a multi-equipment refrigeration simulation analysis and a comprehensive refrigeration simulation analysis are sequentially performed on the indoor refrigeration equipment to generate a multi-equipment refrigeration plan and a comprehensive refrigeration plan, respectively, to generate a first plan set; If it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency, single-device refrigeration simulation analysis, multi-device refrigeration simulation analysis, and comprehensive refrigeration simulation analysis are performed on the indoor refrigeration equipment in sequence, and a single-device refrigeration plan, a multi-device refrigeration plan, and a comprehensive refrigeration plan are generated respectively to generate a second plan set; Obtain user usage habits; Determine the best cooling solution based on the first solution set or the second solution set, combined with the user's usage habits, through solution comparison and analysis; According to the optimal cooling plan, the refrigeration equipment is controlled to operate with corresponding parameters.

[0005] As an automatic energy-saving control method for a refrigeration system according to the present invention, wherein: generating a target space model for the target space, specifically: obtaining first layout information in the target space, recorded as : ; wherein each first layout It can be expressed as ; Get the second layout information in the target space, recorded as : ; wherein each second layout It can be expressed as ; Get the third layout information in the target space, recorded as : ; where each third layout It can be expressed as ; Get the fourth layout information in the target space, recorded as : ; wherein each fourth layout It can be expressed as ; Get all the refrigeration equipment in the target space and form a device set, recorded as : ; For each device , get its three-dimensional coordinate position: ; For each device , get its parameter information: ;in, is the cooling capacity of the refrigeration equipment, is the air supply volume of the refrigeration equipment, is the power of the refrigeration equipment, The air supply angle of the refrigeration equipment is: ;in, is the horizontal air supply angle range, is the vertical air supply angle range; among them, The wind speed level of the refrigeration equipment is: ;in, For the Wind speed; for each device , get its current running status: ;in, The refrigeration equipment is on or off, marked , is the current set temperature of the refrigeration equipment, The current wind speed level of the cooling equipment is marked as ; Obtain all indoor item distribution information in the target space and form an indoor item distribution set, which is recorded as: ; For each item , get its item properties: ; Integrate the first layout information, the second layout information, the third layout information and the fourth layout information in the target space to generate an initial space model, which is recorded as : ; Each structure are represented as a three-dimensional geometric object, including its location, size, shape, material and other attributes; the initial space model Divide into uniform grids according to a given resolution to form the initial gridded space : ;in, To convert a 3D space model into , , Functions partitioned into a grid, 、 、 The grid sizes in the x, y, and z directions respectively; according to the device set and the initial grid space , forming a device grid space : ; For device grid space Each device in , analyze its impact range : ;in, Indicates that the air supply angle constraint is satisfied. The air supply angle constraint means that only the grids within the air supply angle range of the equipment are included in the affected range. For equipment The maximum effective radius of action; based on the indoor item distribution set and equipment grid space , forming an indoor grid space : ; For indoor grid space Each item in , analyze its airflow properties : ;in, is the original air velocity, is the airflow resistance function; According to the indoor grid space and the initial spatial model , update and generate the target space model.

[0006] As an automated energy-saving control method for a refrigeration system according to the present invention, the method of determining whether the current refrigeration equipment distribution can improve the cooling efficiency is specifically as follows: when no equipment is running, the average velocity of the initial airflow velocity field of the target space is calculated: ; For each device in the target space , within its air supply angle range Evenly select several air supply directions : ;in, is the angle step; for each direction , determine the airflow velocity distribution of the device in this direction through the airflow simulation function: ;in, Is a three-dimensional vector field, representing the airflow velocity distribution of the device in that direction; calculate the average airflow velocity of the device in that direction: ;in, represents the magnitude of the airflow velocity vector at the grid point, is the total number of grids; for each device in the target space , calculate its maximum airflow lift rate: ; Define an efficiency judgment function to determine whether the current refrigeration equipment distribution can improve the cooling efficiency: ;in, To increase the threshold, Indicates that there is any device that satisfies the ;like , then it is determined that the current refrigeration equipment distribution can improve the cooling efficiency; if , it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency.

[0007] As an automatic energy-saving control method for a refrigeration system according to the present invention, the single device refrigeration simulation analysis is specifically as follows: obtaining the device set ; For each device , set the parameter combination of simulation operation parameters: ; In the parameter combination, For All wind speed levels selected in From the angle range Several typical wind directions are selected, specifically: ; In the parameter combination, The outlet air temperature is selected from the set temperature set, specifically: ; In the parameter combination, The wind force is estimated based on wind speed and air volume, specifically: ;in, is the air density, Air supply volume for equipment; for each set of equipment operating parameter combinations , set up an airflow simulation function to simulate and calculate the airflow velocity field generated in the indoor space: ; In the airflow simulation function, Indicates that on the path, is the wind speed gear, is the unit direction vector, specifically: ; In the airflow simulation function, is the distance from the grid point to the device, specifically: ; In the airflow simulation function, is the distance decay function, specifically: ; In the airflow simulation function, is the angle deviation factor, specifically: ; In the airflow simulation function, is the obstacle blocking factor, specifically: ; For each set of equipment operating parameter combinations , set up a temperature simulation function to simulate and calculate the temperature field generated in the indoor space: ; In the temperature simulation function, Indicates that within the device's influence range, is the heat convection term, specifically: ; In the temperature simulation function, is the heat conduction term, specifically: ;in, is the thermal diffusion coefficient; in the temperature simulation function, is the thermal radiation term, specifically: Based on the spatial layout and item distribution, identify areas far from the equipment and obscured areas to form a set of difficult areas: In each area Set up several collection points ; For each region For each collection point in , obtain the collection point data: ;in, is the location of the collection point, marked as , is the temperature at the collection point, is the user accessibility of the collection point, For collection points and equipment The distance between them is: ; Among all the collection points that the user can reach, select the point farthest from the device and define it as the user's farthest point, which is recorded as: ; Collect the temperature at the user's farthest point and set it as the user's farthest temperature, recorded as: ; Among all the collection points, select the point farthest from the device and define it as the physical farthest point, which is recorded as: ; Collect the temperature of the physically farthest point and set it as the physically farthest temperature, recorded as: ; Define a point analysis function and determine the analysis collection points: ;Simulate equipment operation, record and analyze collection points The temperature from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ;Simulate equipment operation, calculate and analyze collection points The temperature from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption: ; Get all simulation scenarios to form a single simulation set: ; Get user preference data: ;like , then the cooling solution for a single device is determined as: ;like , then the cooling solution for a single device is determined as: .

[0008] As an automatic energy-saving control method for a refrigeration system according to the present invention, wherein: multi-device refrigeration simulation analysis, specifically: obtaining a device set ; Generate all possible device combinations: ; For each combination , set different operation modes and generate all possible operation mode sets: ; For each device , set parameters according to its operating mode; for each combination and operating mode , for each device , according to its operating status, the airflow velocity field generated by a single device in the indoor space is simulated and calculated through the airflow simulation function: ;in, For wind direction, is the wind speed; for each combination and operating mode , perform weighted superposition of the airflow fields of all devices and calculate the overall airflow velocity field generated in the indoor space: ;in, is the device weight, specifically , is the obstacle blocking factor, specifically ; Get combination The number of devices in cooling mode is defined as the cooling quantity; if the cooling quantity is ≥ 1, a temperature field simulation is performed to simulate the temperature field generated in the indoor space as a whole: ;in, is the temperature update function, and its specific formula is: ; In the temperature update function, is the heat convection term, specifically: ; In the temperature update function, is the heat conduction term, specifically: ;in, is the thermal diffusion coefficient; in the temperature update function, is the thermal radiation term, specifically: ;in, is the emissivity, is the Stefan-Boltzmann constant, is the surface temperature of the object; based on the spatial layout and object distribution, identify areas far away from the device and obscured areas to form a set of difficult areas: In each area Set up several collection points ; For each region For each collection point in the , record its temperature change process, which is recorded as ; For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ; For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption: ; Get all simulation scenarios to form a multi-simulation set: ; Get user preference data: ;like , then the multi-equipment cooling solution is determined as: ;like , then the multi-equipment cooling solution is determined as: .

[0009] As an automated energy-saving control method for a refrigeration system according to the present invention, the comprehensive refrigeration simulation analysis is specifically as follows: obtaining the temperature of the air output by the refrigeration equipment in the air supply mode and setting it as the air supply temperature: ; Get the temperature of the air output by the refrigeration equipment in cooling mode and set it as the cooling temperature: ; Define an analysis selection function to determine the simulation analysis to be performed at the current indoor and outdoor temperatures: ;like , then perform multi-device cooling simulation analysis, and extract the corresponding multi-device cooling scheme as the comprehensive cooling scheme when only considering the combination without air supply mode; if , then perform a single-device cooling simulation analysis and extract the corresponding single-device cooling solution: Perform multi-device cooling simulation analysis and extract the corresponding multi-device cooling solution when only considering the combination without air supply mode: ; Set up a scheme selection function to select the scheme with the lowest energy consumption as the comprehensive cooling scheme: As an automatic energy-saving control method for a refrigeration system according to the present invention, wherein: scheme comparison analysis, specifically: obtaining a first scheme set or a second scheme set, and setting it as a target analysis set ; For each solution in the target analysis set , extract its scheme information: ;in, is the simulation duration, To simulate energy consumption, For wind direction; obtain user usage habits: ;like , then the human body position is obtained, recorded as ; Define a wind direction analysis function to determine the solution Will it cause cold wind to blow directly on the human body? ;in, Wind direction With vector from device to human body Angle, is the set angle threshold, The specific formula is: ;like , then determine the solution It will cause cold wind to blow directly on the human body; if , then determine the solution It will not cause cold wind to blow directly on the human body; Perform matching analysis; extract the solution with the highest matching degree as the optimal cooling solution.

[0010] As an automatic energy-saving control method for a refrigeration system according to the present invention, wherein: Perform matching analysis, specifically: define a solution judgment function to determine whether the matching degree of the solution needs to be calculated: ;like , then there is no need to calculate the matching degree of the solution, and the matching degree of the solution can be directly determined. ;like , then calculate the maximum cooling time of all solutions in the target analysis set: ; Calculate the maximum energy consumption of all solutions in the target analysis set: ; For each solution in the target analysis set , calculate the normalized cooling time score: ; For each solution in the target analysis set , calculate the normalized energy consumption score: ; Get user preferences ;like , then set the weight set to ;like , then set the weight set to ; For each solution in the target analysis set , calculate its matching degree with user habits: .

[0011] The present invention also discloses a device for executing an automatic energy-saving control method for a refrigeration system, which includes: Data acquisition module: used to collect spatial layout information within the target space, the distribution location and equipment parameter information of the refrigeration equipment, and the distribution information of indoor items; Model generation module: used to generate a target space model of the target space based on the collected spatial layout information of the target space, the distribution location and equipment parameter information of the refrigeration equipment, and the distribution information of indoor items; Simulation analysis module: used to perform various cooling simulation analyses on the target space, generate different cooling solutions, and analyze and determine the most appropriate optimal cooling solution; Data analysis module: used to compare and analyze different cooling solutions and determine the most suitable cooling solution; Equipment control module: used to control the refrigeration equipment in the target space to operate with corresponding parameters according to the optimal cooling plan, so that the target space can be reduced to the required temperature.

[0012] The present invention has the following beneficial effects: 1. The automated energy-saving control method and device for this refrigeration system generates an indoor space model by acquiring the indoor spatial layout, including the layout of walls and windows, the distribution of all indoor refrigeration equipment, and the distribution of all indoor objects. This provides a precise spatial structure foundation, clarifies the equipment's impact range and obstacle distribution, and improves simulation accuracy.

[0013] 2. The automated energy-saving control method and device of the refrigeration system, when the indoor temperature needs to be cooled by refrigeration equipment, determines whether the distribution of the indoor refrigeration equipment can improve the cooling efficiency based on the distribution of all indoor refrigeration equipment. If the cooling efficiency cannot be improved, the indoor refrigeration equipment is subjected to single-device refrigeration simulation analysis, multi-device refrigeration simulation analysis, and comprehensive refrigeration simulation analysis based on indoor and outdoor environmental data. According to user usage habits, the most appropriate refrigeration solution is selected as the optimal cooling solution. According to the optimal cooling solution, the operation of the refrigeration equipment is controlled to achieve closed-loop control, dynamically adapt to environmental changes, avoid invalid equipment combinations, and improve simulation efficiency. Single-device simulation analysis can still find the optimal operation strategy when the equipment distribution is poor. Multi-device simulation analysis gives full play to the synergistic effect. Comprehensive simulation analysis combines the ambient temperature difference to further improve the energy-saving effect. At the same time, combined with user preferences, user habits and physical sensations are considered to enhance personalized experience and satisfaction.

[0014] 3. The automated energy-saving control method and device of the refrigeration system. When the indoor temperature needs to be cooled by refrigeration equipment, if the refrigeration efficiency can be improved, a multi-device refrigeration simulation analysis is performed on the indoor refrigeration equipment and a comprehensive refrigeration simulation analysis is performed on the indoor refrigeration equipment based on indoor and outdoor environmental data. According to the user's usage habits, the most suitable refrigeration solution is selected as the optimal cooling solution. According to the optimal cooling solution, the operation of the refrigeration equipment is controlled to achieve closed-loop control, dynamically adapt to environmental changes, avoid invalid equipment combinations, and improve simulation efficiency. When the equipment is well distributed, the multi-device simulation analysis gives full play to the synergistic effect. The comprehensive simulation analysis is combined with the ambient temperature difference to further improve the energy-saving effect. At the same time, combined with user preferences, direct blowing of cold air is avoided, user habits are considered, and physical comfort is improved, personalized experience and satisfaction are improved. On the premise of meeting the cooling needs, energy consumption is minimized, direct blowing of cold air is avoided, and user habits and physical sensations are considered. Through three-dimensional grid modeling, airflow and temperature field simulation, and equipment operation strategy optimization, automated and intelligent control of refrigeration equipment is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the automatic energy-saving control method for the refrigeration system of the present invention; Figure 2 This is a system block diagram of a device for executing the automatic energy-saving control method for a refrigeration system according to the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] Example 1: An automatic energy-saving control method for a refrigeration system, see Figure 1 ,include: Get the target space; generating a target space model for the target space; According to the target space model, determine whether the current distribution of refrigeration equipment can improve the cooling efficiency; If it is determined that the current refrigeration equipment distribution can improve the cooling efficiency, a multi-equipment refrigeration simulation analysis and a comprehensive refrigeration simulation analysis are sequentially performed on the indoor refrigeration equipment to generate a multi-equipment refrigeration plan and a comprehensive refrigeration plan, respectively, to generate a first plan set; If it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency, single-device refrigeration simulation analysis, multi-device refrigeration simulation analysis, and comprehensive refrigeration simulation analysis are performed on the indoor refrigeration equipment in sequence, and a single-device refrigeration plan, a multi-device refrigeration plan, and a comprehensive refrigeration plan are generated respectively to generate a second plan set; Obtain user usage habits; Determine the best cooling solution based on the first solution set or the second solution set, combined with the user's usage habits, through solution comparison and analysis; According to the optimal cooling plan, the refrigeration equipment is controlled to operate with corresponding parameters.

[0018] Among them, the target space model is generated for the target space, specifically: Obtain the first layout information in the target space, the first layout information is the wall layout information, recorded as : ; Among them, each first layout It can be expressed as: ; in, Indicates the location of the wall, specifically using three-dimensional coordinates to represent the starting and ending points of the wall, for example , Indicates the dimensions of the wall, specifically using length, width, and height, e.g. , Indicates the wall material, specifically expressed as a string, for example, ; Get the second layout information in the target space, the second layout information is the window layout information, recorded as : ; Among them, each second layout It can be expressed as: ; in, Indicates the window position, specifically the center position of the window using three-dimensional coordinates, for example , Indicates the window size, specifically expressed as width and height, for example , Indicates the window type, specifically represented by a string, for example , Indicates the direction of the window, specifically the direction of the window is expressed in degrees, for example , (north is 0°, clockwise); Get the third layout information in the target space, the third layout information is the door layout information, recorded as : ; Among them, each third layout It can be expressed as: ; in, Indicates the door position, specifically the center position of the door using three-dimensional coordinates, for example , Indicates the door size, specifically expressed in width and height, for example , Indicates the door type, specifically represented by a string, for example ; Obtain the fourth layout information in the target space, which is fixed structure layout information, such as columns, beams, etc., and is recorded as : ; Among them, each fourth layout It can be expressed as: ; in, Indicates the position of the fixed structure, specifically the center position of the fixed structure is represented by three-dimensional coordinates, for example , Indicates fixed structural dimensions, specifically expressed in length, width, and height, for example, ; Get all the cooling equipment in the target space and form a set of equipment, recorded as : ; For each device , through indoor positioning technologies such as UWB, Bluetooth beacons, RFID, etc. or equipment installation drawings, accurately obtain the three-dimensional coordinate position of the device indoors, and obtain its three-dimensional coordinate position: ; For each device , obtain its parameter information. The device parameter information is usually provided by the device manufacturer and stored in the device database or on the device nameplate. It can be obtained by reading the device database or scanning the device nameplate: ;in, is the cooling capacity of the refrigeration equipment, that is, the rated cooling capacity of the equipment, is the air supply volume of the refrigeration equipment, that is, the rated air supply volume of the equipment, is the power of the refrigeration equipment, that is, the rated power of the equipment; The air supply angle of the refrigeration equipment is: ;in, is the horizontal air supply angle range, It is the vertical air supply angle range; in, The wind speed level of the refrigeration equipment is: ;in, For the Wind speed; For each device , obtain its current operating status, where the device operating status information is obtained in real time through the device's own control system or sensor, for example, through the air conditioner's smart control panel or smart home system: ;in, The refrigeration equipment is on or off, marked , is the current set temperature of the refrigeration equipment, The current wind speed level of the cooling equipment is marked as ; Obtain the distribution information of all indoor items in the target space and form an indoor item distribution set, which is recorded as: ; For each item , get its item properties: ;in, is the three-dimensional coordinate position of the object in the room, denoted as , is the size of the item, recorded as ,in, Indicates the length of the item. Indicates the width of the item. Indicates the height of the item. is the geometric shape type of the item, denoted as , is the surface material type of the object, recorded as , is the current surface temperature of the object, recorded as ; Integrate the first layout information, the second layout information, the third layout information and the fourth layout information in the target space to generate an initial space model, which is recorded as : ; Each structure , such as walls, windows, doors, fixed structures, are all represented as a three-dimensional geometric object, including its location, size, shape and material properties. Each structure, such as walls, windows, doors, fixed structures, is converted into a geometric body in three-dimensional space, such as a cube, plane, polyhedron, etc., and a three-dimensional modeling technology is used, such as boundary representation B-Rep, constructive solid geometry CSG or triangular mesh, to construct the overall space model. All structures are combined in a unified coordinate system to form a complete initial indoor space model. ; The initial space model Divide into uniform grids according to a given resolution to form the initial gridded space , and represented as a collection of three-dimensional grids: ;in, To convert a 3D space model into , , Functions partitioned into a grid, 、 、 are the grid sizes in the x, y, and z directions, i.e., the resolution, i.e., the size of each grid unit is ; Based on the device collection and initial gridded space , forming a device grid space , that is, mapping the location and parameters of the refrigeration equipment to the corresponding grid of the indoor space model: ; Among them, the function The specific definition is: ; That is, for each device , according to its location and size, determine the grid range it occupies, and update the attributes of these grid units to device information, such as device ID, type, air supply angle, wind speed gear, etc. If multiple devices occupy the same grid, they can be processed according to priority or merging strategy, such as overwriting or merging, where the device Occupied grid; For device grid space Each device in , analyze its impact range : ; in, For equipment The maximum effective radius can be estimated based on the air volume, wind speed, etc. Indicates that the air supply angle constraint is satisfied. The air supply angle constraint refers to the air supply angle range of the device. Only grids within the horizontal and vertical directions are included in the influence range. With the device location as the center, all grid cells within its air supply angle range are calculated to determine whether each grid is within the device's effective radius and within the air supply angle coverage range. The grids within the device's effective radius and within the air supply angle coverage range are added to the influence range set. , that is, calculate the impact range of each device on the surrounding grid based on the air supply angle and distance of the device; Distribution of collections and equipment grid spaces based on indoor items , forming an indoor grid space , that is, mapping the location and attributes of the items to the corresponding grid of the indoor space model: ; Among them, the function The specific definition is: ; That is, for each item , according to its location and size , calculate the grid range it occupies, and update the attributes of these grid cells to item information, such as item ID, shape, material, surface temperature, etc. If the item overlaps with equipment or other items, it can be processed according to priority or merging strategies, such as marking it as "mixed occupancy" or "inaccessible", where it indicates the item Occupied grid; For indoor grid spaces Each item in , analyze its airflow properties , which represents the airflow velocity vector or resistance coefficient of each grid cell: ;in, is the original air velocity, set to 0, The airflow obstruction function is used to adjust the airflow speed or direction of the grid based on the shape, size, material and other properties of the object. In other words, based on the shape and size of the object, the degree of obstruction to the airflow is calculated and the airflow properties of the grid are updated. According to the indoor grid space and the initial spatial model , update and generate the target space model.

[0019] The above method generates an indoor space model by obtaining the indoor spatial layout, including the layout of walls, windows, and the distribution of all indoor refrigeration equipment, as well as the distribution of all indoor items. When the indoor space needs to be cooled by refrigeration equipment, the distribution of all indoor refrigeration equipment is used to determine whether it can improve the cooling efficiency. If the cooling efficiency cannot be improved, single-device cooling simulation analysis, multi-device cooling simulation analysis, and comprehensive cooling simulation analysis of the indoor refrigeration equipment based on indoor and outdoor environmental data are performed on the indoor refrigeration equipment. Based on user usage habits, the most appropriate cooling solution is selected as the optimal cooling solution. Based on the optimal cooling solution, the operation of the refrigeration equipment is controlled to quickly reduce the indoor temperature to the target temperature. While meeting the cooling requirements, energy consumption is minimized, cold air is avoided, and user habits and physical sensations are taken into consideration. Through three-dimensional mesh modeling, airflow and temperature field simulation, and equipment operation strategy optimization, automated and intelligent control of the refrigeration equipment is achieved.

[0020] Example 2: This example is an improvement on Example 1. The automatic energy-saving control method of the refrigeration system determines whether the current refrigeration equipment distribution can improve the cooling efficiency. Specifically: When no equipment is running, calculate the average velocity of the initial airflow velocity field in the target space: ;in, is the original airflow velocity at the grid point, set to 0; For each device in the target space , within its air supply angle range Evenly select several air supply directions , for example, taking an angle every 10°: ;in, is the angle step, such as 10°; For each direction , determine the airflow velocity distribution of the device in this direction through the airflow simulation function: ;in, It is a three-dimensional vector field, which represents the airflow velocity distribution of the device in that direction; Calculate the average airflow velocity of the device in this direction: ;in, Represents the modulus of the airflow velocity vector at the grid point, that is, the wind speed. is the total number of grid cells; For each device in the target space , calculate its maximum airflow lift rate: ; Define an efficiency judgment function to determine whether the current refrigeration equipment distribution can improve the cooling efficiency: ;in, To increase the threshold, for example, the threshold is 0.2, that is, 20%, which is used to determine whether the distribution of refrigeration equipment can improve the cooling efficiency. Indicates that there is any device that satisfies the ; like , it is determined that the current refrigeration equipment distribution can improve the cooling efficiency; like , it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency.

[0021] This embodiment also provides a single device cooling simulation analysis, specifically: obtain the device set ; For each device , set the parameter combination of simulation operation parameters: ; In the parameter combination, For All wind speed levels selected in From the angle range Several typical wind directions are selected, such as one every 15°, specifically: ; In the parameter combination, The outlet air temperature is selected from the set temperature set, specifically: ;in, 、 、 The selected set temperature is 16, 18, and 20 respectively; In the parameter combination, The wind force is estimated based on wind speed and air volume, specifically: ;in, is the air density, Air supply volume for equipment; For each set of equipment operating parameter combinations , set up an airflow simulation function to simulate and calculate the airflow velocity field generated in the indoor space: ; In the airflow simulation function, Indicates that on the path, is the wind speed gear, is the unit direction vector, specifically: ; In the airflow simulation function, is the distance from the grid point to the device, specifically: ; In the airflow simulation function, is the distance attenuation function, which is used to describe the phenomenon that wind speed gradually decreases with increasing distance. Specifically: ; In the airflow simulation function, is the angle deviation factor, which is used to describe the phenomenon that the wind speed decreases as the air supply angle deviates from the main direction. Specifically: ; In the airflow simulation function, is the obstacle blocking factor, which is used to quantify the degree to which obstacles weaken or block airflow and temperature, making the simulation results closer to the real indoor environment. 0 represents complete blocking. Specifically: ; For each set of equipment operating parameter combinations , set up a temperature simulation function to simulate and calculate the temperature field generated in the indoor space: ; In the temperature simulation function, Indicates that within the device's influence range, is the heat convection term, specifically: ;in, is the three-dimensional wind speed vector of the grid point, is the temperature gradient vector at the grid point, is the time step; In the temperature simulation function, is the heat conduction term, specifically: ;in, is the thermal diffusion coefficient, such as 0.01; In the temperature simulation function, is the thermal radiation term, specifically: ; Among them, is the emissivity, with a value of 0-1, is the Stefan-Boltzmann constant, is the surface temperature of objects, such as furniture, electrical appliances, etc. is the current grid temperature Based on the spatial layout and object distribution, identify areas far from the equipment and obstructed areas, such as corners and behind furniture, to form a collection of difficult areas: ; In each area Set up several collection points ; For each region For each collection point in , obtain the collection point data: ;in, is the location of the collection point, marked as , is the temperature at the collection point, The user accessibility of the collection point, that is, based on the user's historical data, determine whether the user will pass by or stay at the collection point. If the user will pass by or stay at the collection point, then =True, if the user will not pass or stay at the collection point, then , For collection points and equipment The distance between them is: ; Among all the collection points that the user can reach, the point farthest from the device is selected as the user's farthest point, which is recorded as: ; Collect the temperature at the user's farthest point and set it as the user's farthest temperature, recorded as: ; Among all the collection points, the point farthest from the device is selected as the physical farthest point, which is recorded as: ; Collect the temperature of the physically farthest point and set it as the physically farthest temperature, recorded as: ; Define a point analysis function and determine the analysis collection points: ; That is, the physical farthest point is used first, but if the user will never reach this point, the farthest point that the user can reach is used as a second choice, where indicates that the user will not pass through or stay at the physical farthest point; Simulate equipment operation and record analysis collection points The temperature from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ;in, A function to record the simulation duration; Simulate equipment operation and calculate and analyze collection points The temperature from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption: ; Get all simulation scenarios to form a single simulation set: ; Get user preference data: ; like , that is, the user prefers fast cooling, then the single-device cooling solution is determined as: ; like , that is, the user preference is energy saving, then the single-device cooling solution is determined as: .

[0022] This embodiment also provides a multi-device refrigeration simulation analysis, specifically: obtaining a device set ; Generate all possible equipment combinations, such as A={A1,A2,A3}, then ECO={{A1,A2},{A1,A3},{A2,A3},{A1,A2,A3}}: ; For each combination , set different operating modes and generate all possible operating mode sets. Each device can select cooling mode and air supply mode. The cooling mode outputs cold air, that is, set temperature + wind speed + wind direction. The air supply mode outputs normal air, that is, only wind speed + wind direction: ; Each is a function mapping: For example, for CO={A1,A2}, possible operation modes include: (A1 cooling, A2 air supply), (A1 air supply, A2 cooling), (A1 cooling, A2 cooling), (A1 air supply, A2 air supply); For each device , set the parameters according to its operating mode: If the device is in cooling mode, the wind direction is , wind speed is , the air outlet temperature is ; If the device is in air supply mode, the wind direction is , wind speed is , the air outlet temperature is the ambient temperature, that is, no cooling; For each combination and operating mode , for each device , according to its operating status, such as cooling or ventilation, the airflow velocity field generated by a single device in the indoor space is simulated and calculated through the airflow simulation function: ;in, For wind direction, is the wind speed; For each combination and operating mode , perform weighted superposition of the airflow fields of all devices and calculate the overall airflow velocity field generated in the indoor space: ;in, The default value is 1, which is used to determine the weight of the device on the airflow in the space. , is the obstacle blocking factor, which is used to quantify the degree of weakening or blocking of airflow and temperature by obstacles, so that the simulation results are closer to the real indoor environment. 0 means complete blocking. ; Get combination The number of devices in cooling mode is defined as the cooling quantity; If the cooling quantity is ≥1, a temperature field simulation is performed to simulate the temperature field generated in the entire indoor space: ;in, is the temperature update function, which is used to implement temperature update and jointly drive the evolution of the indoor temperature field. Its specific formula is: ; In the temperature update function, is the heat convection term, which describes the physical process of "wind taking away temperature", specifically: ;in, is the three-dimensional wind speed vector of the grid point, is the temperature gradient vector at the grid point, is the time step; In the temperature update function, is the heat conduction term, which describes the physical process of "heat diffusion to low temperature", specifically: ;in, is the thermal diffusion coefficient, such as 0.01; In the temperature update function, is the thermal radiation term, which describes the physical process of "high-temperature objects radiating heat", specifically: ;in, is the emissivity, with a value of 0-1, is the Stefan-Boltzmann constant, is the surface temperature of objects, such as furniture, electrical appliances, etc. is the current grid temperature; Based on the spatial layout and object distribution, identify areas far from the equipment and obstructed areas, such as corners and behind furniture, to form a collection of difficult areas: ; In each area Set up several collection points ; For each area For each collection point in the , record its temperature change process, which is recorded as , where t=0, 1, …, N; For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ; For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption, which is the power of all devices multiplied by the running time: ; Get all simulation scenarios to form a multi-simulation set: ; Get user preference data: ; like , that is, the user prefers fast cooling, then the multi-device cooling solution is determined as: ; in, Indicates the simulation duration for extracting the minimum value. It means extracting the simulation energy consumption with the smallest value from the data corresponding to several simulation durations with the smallest values; like , that is, the user preference is energy saving, then the multi-device cooling solution is determined as: .

[0023] in, Indicates the simulated energy consumption with the minimum extraction value, It means extracting the simulation duration with the smallest value from the data corresponding to several simulation energy consumptions with the smallest values; This embodiment also provides a comprehensive refrigeration simulation analysis, specifically: Get the temperature of the air output by the refrigeration equipment in the air supply mode and set it as the supply air temperature: ; That is, when the refrigeration equipment is in air supply mode, it does not cool but only delivers air, the temperature of which is equal to the outdoor temperature; Get the temperature of the air output by the refrigeration equipment in cooling mode and define it as the cooling temperature: ; That is, when the refrigeration equipment is in cooling mode, it outputs cold air, and its temperature is the user-set value or the default value, such as 16°C, 18°C, 20°C, etc.; Define an analysis selection function to determine the simulation analysis to be performed at the current indoor and outdoor temperatures: ; like , then perform multi-device cooling simulation analysis, and extract the corresponding multi-device cooling scheme as the comprehensive cooling scheme when only considering the combination without air supply mode; like , then perform a single-device cooling simulation analysis and extract the corresponding single-device cooling solution: ; in, To extract the function of the corresponding single-device cooling solution through single-device cooling simulation analysis; Perform a multi-device cooling simulation analysis and extract the corresponding multi-device cooling solution when only considering combinations without air supply mode: ; Set up a scheme selection function to select the scheme with the lowest energy consumption as the comprehensive cooling scheme: .

[0024] Example 3: This example is an improvement made on the basis of Example 2. In this example, the scheme comparison and analysis are as follows: Get the first solution set or the second solution set and set it as the target analysis set ; For each scenario in the target analysis set , extract its scheme information: ; in, is the simulation duration, To simulate energy consumption, The wind direction, i.e., the air supply direction, is included in the set of scheme information, including other parameters such as equipment combination, operation mode, etc. Obtain the user's usage habits, including whether the user accepts direct cold air blowing and user preferences: ; in, Indicates whether the user accepts direct cold air blowing. If the user does not accept direct cold air blowing, then , otherwise, , Indicates user preferences, marked as ,in, To cool down quickly, To save energy; like , then the human body position is obtained, recorded as ; Define a wind direction analysis function to determine the solution Will it cause cold wind to blow directly on the human body? ; in, Wind direction With vector from device to human body Angle, The angle threshold is set, such as 30°, which is used to judge the solution. Will it cause cold wind to blow directly on the human body? The specific formula is: ; like , then determine the solution It will cause cold wind to blow directly on the human body; like , then determine the solution Will not cause cold wind to blow directly on the human body; For the plan Conduct matching analysis; The solution with the highest matching degree is extracted as the optimal cooling solution.

[0025] Among them, the plan Perform matching analysis, specifically: Define a solution judgment function to determine whether the matching degree of the solution needs to be calculated: ; like , that is, the user does not accept direct cold air blowing and the solution will cause direct blowing, then there is no need to calculate the matching degree of the solution, and the matching degree of the solution can be directly determined. ; like , then calculate the maximum cooling time of all solutions in the target analysis set: ; Calculate the maximum energy consumption of all solutions in the target analysis set: ; For each solution in the target analysis set , calculate the normalized cooling time score: ; For each solution in the target analysis set , calculate the normalized energy consumption score: ; Among them, normalization is to map indicators of different dimensions to the [0,1] interval so that they can be compared and weighted on the same scale; obtain user preferences ;like , that is, the user prefers fast cooling, so the weight set is set to ;like , that is, the user preference is energy saving, then the weight set is set to ; For each solution in the target analysis set , calculate its matching degree with user habits: .

[0026] Example 4: This embodiment also discloses a device for executing an automatic energy-saving control method for a refrigeration system. Figure 2 ,include: Data acquisition module: used to collect spatial layout information within the target space, including wall layout information, window layout information, door layout information, fixed structure layout information, distribution location and equipment parameter information of refrigeration equipment, and indoor item distribution information; Model generation module: used to generate a target space model of the target space based on the collected spatial layout information of the target space, the distribution location and equipment parameter information of the refrigeration equipment, and the distribution information of indoor items; Simulation analysis module: used to perform various cooling simulation analyses on the target space, generate different cooling solutions, and analyze and determine the most appropriate optimal cooling solution; Data analysis module: used to compare and analyze different cooling solutions and determine the most suitable cooling solution; Equipment control module: used to control the refrigeration equipment in the target space to operate with corresponding parameters according to the optimal cooling plan, so that the target space can be reduced to the required temperature.

Claims

1. An automatic energy-saving control method for a refrigeration system, characterized by: include: Get the target space; generating a target space model for the target space; According to the target space model, determine whether the current distribution of refrigeration equipment can improve the cooling efficiency; If it is determined that the current refrigeration equipment distribution can improve the cooling efficiency, a multi-equipment refrigeration simulation analysis and a comprehensive refrigeration simulation analysis are sequentially performed on the indoor refrigeration equipment to generate a multi-equipment refrigeration plan and a comprehensive refrigeration plan, respectively, to generate a first plan set; If it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency, single-device refrigeration simulation analysis, multi-device refrigeration simulation analysis, and comprehensive refrigeration simulation analysis are performed on the indoor refrigeration equipment in sequence, and a single-device refrigeration plan, a multi-device refrigeration plan, and a comprehensive refrigeration plan are generated respectively to generate a second plan set; Obtain user usage habits; Determine the best cooling solution based on the first solution set or the second solution set, combined with the user's usage habits, through solution comparison and analysis; According to the optimal cooling plan, the refrigeration equipment is controlled to operate with corresponding parameters.

2. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Generate a target space model for the target space, specifically: Get the first layout information in the target space, recorded as : ； Among them, each first layout It can be expressed as: ； Get the second layout information in the target space, recorded as : ； Among them, each second layout It can be expressed as: ； Get the third layout information in the target space, recorded as : ； Among them, each third layout It can be expressed as: ； Get the fourth layout information in the target space, recorded as : ； Among them, each fourth layout It can be expressed as: ； Get all the cooling equipment in the target space and form a set of equipment, recorded as : ； For each device , get its three-dimensional coordinate position: ； For each device , get its parameter information: ； in, is the cooling capacity of the refrigeration equipment, is the air supply volume of the refrigeration equipment, is the power of the refrigeration equipment, The air supply angle of the refrigeration equipment is: ； in, is the horizontal air supply angle range, It is the vertical air supply angle range; in, The wind speed level of the refrigeration equipment is: ； in, For the Wind speed; For each device , get its current running status: ； in, The refrigeration equipment is on or off, marked , is the current set temperature of the refrigeration equipment, The current wind speed level of the cooling equipment is marked as ; Obtain the distribution information of all indoor items in the target space and form an indoor item distribution set, which is recorded as: ； For each item , get its item properties: ； Integrate the first layout information, the second layout information, the third layout information and the fourth layout information in the target space to generate an initial space model, which is recorded as : ； Among them, each structure They are all represented as a three-dimensional geometric object, including its properties such as position, size, shape and material; The initial space model Divide into uniform grids according to a given resolution to form the initial gridded space : ； in, To convert a 3D space model into , , Functions partitioned into a grid, 、 、 are the grid sizes in the x, y, and z directions respectively; Based on the device collection and initial gridded space , forming a device grid space : ； For device grid space Each device in , analyze its impact range : ； in, Indicates that the air supply angle constraint is satisfied. The air supply angle constraint means that only the grids within the air supply angle range of the equipment are included in the affected range. For equipment The maximum effective radius of action; Distribution of collections and equipment grid spaces based on indoor items , forming an indoor grid space : ； For indoor grid spaces Each item in , analyze its airflow properties : ； in, is the original air velocity, is the airflow resistance function; According to the indoor grid space and the initial spatial model , update and generate the target space model.

3. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Determine whether the current refrigeration equipment distribution can improve cooling efficiency, specifically: When no equipment is running, calculate the average velocity of the initial airflow velocity field in the target space: ； For each device in the target space , within its air supply angle range Evenly select several air supply directions : ； in, is the angle step; For each direction , determine the airflow velocity distribution of the device in this direction through the airflow simulation function: ； in, It is a three-dimensional vector field, which represents the airflow velocity distribution of the device in that direction; Calculate the average airflow velocity of the device in this direction: ； in, represents the magnitude of the airflow velocity vector at the grid point, is the total number of grid cells; For each device in the target space , calculate its maximum airflow lift rate: ； Define an efficiency judgment function to determine whether the current refrigeration equipment distribution can improve the cooling efficiency: ； in, To increase the threshold, Indicates that there is any device that satisfies the ; like , it is determined that the current refrigeration equipment distribution can improve the cooling efficiency; like , it is determined that the current refrigeration equipment distribution cannot improve the cooling efficiency.

4. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Single equipment refrigeration simulation analysis, specifically: Get device collection ; For each device , set the parameter combination of simulation operation parameters: ； In the parameter combination, For All wind speed levels selected in From the angle range Several typical wind directions are selected, specifically: ； In the parameter combination, The outlet air temperature is selected from the set temperature set, specifically: ； In the parameter combination, The wind force is estimated based on wind speed and air volume, specifically: ； in, is the air density, Air supply volume for equipment; For each set of equipment operating parameter combinations , set up an airflow simulation function to simulate and calculate the airflow velocity field generated in the indoor space: ； In the airflow simulation function, Indicates that on the path, is the wind speed gear, is the unit direction vector, specifically: ； In the airflow simulation function, is the distance from the grid point to the device, specifically: ； In the airflow simulation function, is the distance decay function, specifically: ； In the airflow simulation function, is the angle deviation factor, specifically: ； In the airflow simulation function, is the obstacle blocking factor, specifically: ； For each set of equipment operating parameter combinations , set up a temperature simulation function to simulate and calculate the temperature field generated in the indoor space: ； In the temperature simulation function, Indicates that within the device's influence range, is the heat convection term, specifically: ； In the temperature simulation function, is the heat conduction term, specifically: ； in, is the thermal diffusivity; In the temperature simulation function, is the thermal radiation term, specifically: ； Based on the spatial layout and object distribution, identify areas far from the equipment and obscured areas to form a set of difficult areas: ； In each area Set up several collection points ; For each area For each collection point in , obtain the collection point data: ； in, is the location of the collection point, marked as , is the temperature at the collection point, is the user accessibility of the collection point, For collection points and equipment The distance between them is: ； Among all the collection points that the user can reach, the point farthest from the device is selected as the user's farthest point, which is recorded as: ； The temperature at the farthest point from the user is collected and is defined as the farthest temperature of the user, which is recorded as: ； Among all the collection points, the point farthest from the device is selected as the physical farthest point, which is recorded as: ； The temperature of the physically farthest point is collected and defined as the physically farthest temperature, which is recorded as: ； Define a point analysis function and determine the analysis collection points: ； Simulate equipment operation and record analysis collection points The temperature from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ； Simulate equipment operation and calculate and analyze collection points The temperature from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption: ； Get all simulation scenarios to form a single simulation set: ； Get user preference data: ； like , then the cooling solution for a single device is determined as: ； like , then the cooling solution for a single device is determined as: 。 5. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Multi-equipment refrigeration simulation analysis, specifically: Get device collection ; Generate all possible device combinations: ； For each combination , set different operation modes and generate all possible operation mode sets: ； For each device , set parameters according to its operating mode; For each combination and operating mode , for each device , according to its operating status, the airflow velocity field generated by a single device in the indoor space is simulated and calculated through the airflow simulation function: ； in, For wind direction, is the wind speed; For each combination and operating mode , perform weighted superposition of the airflow fields of all devices and calculate the overall airflow velocity field generated in the indoor space: ； in, is the device weight, specifically , is the obstacle blocking factor, specifically ; Get combination The number of devices in cooling mode is defined as the cooling quantity; If the cooling quantity is ≥1, a temperature field simulation is performed to simulate the temperature field generated in the entire indoor space: ； in, is the temperature update function, and its specific formula is: ； In the temperature update function, is the heat convection term, specifically: ； In the temperature update function, is the heat conduction term, specifically: ； in, is the thermal diffusivity; In the temperature update function, is the thermal radiation term, specifically: ； in, is the emissivity, is the Stefan-Boltzmann constant, is the surface temperature of the object; Based on the spatial layout and object distribution, identify areas far from the equipment and obscured areas to form a set of difficult areas: ； In each area Set up several collection points ; For each area For each collection point in the , record its temperature change process, which is recorded as ; For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The time required is defined as the simulation duration: ； For each combination and operating mode , simulate the operation of the equipment, record the temperature of all collection points from the initial temperature Lower to target temperature The corresponding energy consumption is defined as the simulated energy consumption: ； Get all simulation scenarios to form a multi-simulation set: ； Get user preference data: ； like , then the multi-equipment cooling solution is determined as: ； like , then the multi-equipment cooling solution is determined as: 。 6. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Comprehensive refrigeration simulation analysis, specifically: Get the temperature of the air output by the refrigeration equipment in the air supply mode and set it as the supply air temperature: ； Get the temperature of the air output by the refrigeration equipment in cooling mode and define it as the cooling temperature: ； Define an analysis selection function to determine the simulation analysis to be performed at the current indoor and outdoor temperatures: ； like , then perform multi-device cooling simulation analysis, and extract the corresponding multi-device cooling scheme as the comprehensive cooling scheme when only considering the combination without air supply mode; like , then perform a single-device cooling simulation analysis and extract the corresponding single-device cooling solution: ； Perform a multi-device cooling simulation analysis and extract the corresponding multi-device cooling solution when only considering combinations without air supply mode: ； Set up a scheme selection function to select the scheme with the lowest energy consumption as the comprehensive cooling scheme: 。 7. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: Comparative analysis of the options, specifically: Get the first solution set or the second solution set and set it as the target analysis set ; For each scenario in the target analysis set , extract its scheme information: ； in, is the simulation duration, To simulate energy consumption, For wind direction; Get the user's usage habits: ； like , then the human body position is obtained, recorded as ; Define a wind direction analysis function to determine the solution Will it cause cold wind to blow directly on the human body? ； in, Wind direction With vector from device to human body Angle, is the set angle threshold, The specific formula is: ； like , then determine the solution It will cause cold wind to blow directly on the human body; like , then determine the solution Will not cause cold wind to blow directly on the human body; For the plan Conduct matching analysis; The solution with the highest matching degree is extracted as the optimal cooling solution.

8. The automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: For the plan Perform matching analysis, specifically: Define a solution judgment function to determine whether the matching degree of the solution needs to be calculated: ； like , then there is no need to calculate the matching degree of the solution, and the matching degree of the solution can be directly determined. ; like , then calculate the maximum cooling time of all solutions in the target analysis set: ； Calculate the maximum energy consumption among all solutions in the target analysis set: ； Analyze each solution in the set for the target , calculate the normalized cooling time score: ； Analyze each solution in the set for the target , calculate the normalized energy consumption score: ； Get user preferences ; like , then set the weight set to ; like , then set the weight set to ; Analyze each solution in the set for the target , calculate its matching degree with user habits: 。 9. A device for executing the automatic energy-saving control method for a refrigeration system according to claim 1, characterized in that: include: Data acquisition module: used to collect spatial layout information within the target space, the distribution location and equipment parameter information of the refrigeration equipment, and the distribution information of indoor items; Model generation module: used to generate a target space model of the target space based on the collected spatial layout information of the target space, the distribution location and equipment parameter information of the refrigeration equipment, and the distribution information of indoor items; Simulation analysis module: used to perform various cooling simulation analyses on the target space, generate different cooling solutions, and analyze and determine the most appropriate optimal cooling solution; Data analysis module: used to compare and analyze different cooling solutions and determine the most suitable cooling solution; Equipment control module: used to control the refrigeration equipment in the target space to operate with corresponding parameters according to the optimal cooling plan, so that the target space can be reduced to the required temperature.

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