Method for evaluating power consumption of refrigeration equipment based on BIM (Building Information Modeling) simulation system

Abstract

The invention discloses a refrigeration equipment power consumption evaluation method based on a BIM simulation system, and relates to the technical field of equipment evaluation. The method comprises the following steps: establishing a three-dimensional model of a building and carrying out operation simulation on refrigeration equipment; setting a data monitoring point and obtaining internal temperature data; generating a temperature vision model by combining the three-dimensional model; obtaining a refrigeration equipment point and obtaining a refrigeration distance and a refrigeration coefficient of the data monitoring point; setting a refrigeration temperature control standard, obtaining actual external temperature data and an actual temperature change coefficient, inputting the actual external temperature data and the actual temperature change coefficient into a preset second evaluation model to obtain theoretical refrigeration power and theoretical power consumption, performing power consumption adjustment on the refrigeration equipment, monitoring the actual power consumption to judge whether an abnormal power consumption component exists or not, and judging whether the abnormal power consumption component exists or not. And the abnormal power consumption assembly is operated and maintained, so that the refrigeration influence on different areas in the building can be more balanced, the refrigeration flexibility of the refrigeration equipment is improved, and the power consumption of the refrigeration equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment evaluation, and in particular to a method for evaluating power consumption of refrigeration equipment based on a BIM simulation system. Background Art

[0002] Using BIM technology to evaluate the power consumption of refrigeration equipment is a method of simulating and analyzing the refrigeration system of a building to predict the power consumption of refrigeration equipment. By analyzing the power consumption results of refrigeration equipment, the design of the refrigeration system can be optimized, such as adjusting equipment parameters, changing pipeline layout, etc. This method provides an effective tool for the design and optimization of building refrigeration systems, which is conducive to improving the energy efficiency and energy-saving performance of refrigeration systems.

[0003] In the prior art, due to the unreasonable design of the air outlet of the refrigeration equipment, the room temperature inside the building changes unbalanced, which prolongs the refrigeration time for the refrigeration equipment to reach the desired room temperature, and finally causes high power consumption. In the prior art, since the temperature outside the building is in dynamic change, if the refrigeration equipment cannot adjust its own refrigeration power according to the external temperature in time, it will also lead to high power consumption. In view of the shortcomings of the prior art, the present invention provides a refrigeration equipment power consumption evaluation method based on a BIM simulation system. Summary of the invention

[0004] The present invention aims to provide a method for evaluating the power consumption of refrigeration equipment based on a BIM simulation system.

[0005] The purpose of the present invention can be achieved by the following technical solution: A method for evaluating the power consumption of refrigeration equipment based on a BIM simulation system comprises the following steps:

[0006] Step S1: Use BIM technology to build a three-dimensional model of the building, simulate the operation of the refrigeration equipment in the three-dimensional model, set data monitoring points and obtain internal temperature data, and generate a temperature model of the building in combination with the three-dimensional model;

[0007] Step S2: obtaining a refrigeration equipment point in the temperature vision model, obtaining a refrigeration distance of the data monitoring point according to the refrigeration equipment point, obtaining a refrigeration coefficient of the data monitoring point according to the internal temperature data, constructing a first evaluation model according to the refrigeration distance and the refrigeration coefficient, and using the first evaluation model to adjust the deployment of the refrigeration equipment point;

[0008] Step S3: setting a refrigeration temperature control standard, obtaining refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different external temperature data and temperature variation coefficients, obtaining actual external temperature data and actual temperature variation coefficient, and inputting them into a preset second evaluation model to obtain theoretical refrigeration power and theoretical power consumption;

[0009] Step S4: adjust the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitor the actual power consumption, determine whether there are abnormal power consumption components, and perform operation and maintenance on the abnormal power consumption components.

[0010] Furthermore, the three-dimensional model of the building is established using BIM technology. The process of simulating the operation of the refrigeration equipment in the three-dimensional model includes:

[0011] Collect architectural information of the building, collect equipment information of the refrigeration equipment, and use BIM technology to establish a three-dimensional model of the building based on the architectural information;

[0012] Upload the equipment information to the 3D model for synchronization, import the 3D model containing the equipment information into the energy consumption simulation software, and simulate the operation of the refrigeration equipment.

[0013] Furthermore, the process of setting data monitoring points and obtaining internal temperature data, and generating a temperature-visual model of the building in combination with the three-dimensional model includes:

[0014] Several data monitoring points and their monitoring areas are set in the three-dimensional model. During the simulation, the internal temperature data of each data monitoring point is obtained in real time, and the internal temperature data is divided into different value intervals.

[0015] A color of corresponding wavelength is set for each numerical interval, and each monitoring area is rendered as the color corresponding to the internal temperature data of its data monitoring point to obtain the temperature visual model of the building.

[0016] Furthermore, the process of obtaining the refrigeration equipment point in the temperature vision model, obtaining the refrigeration distance of the data monitoring point according to the refrigeration equipment point, and obtaining the refrigeration coefficient of the data monitoring point according to the internal temperature data includes:

[0017] The refrigeration equipment point refers to each air outlet of the refrigeration equipment in the building when the refrigeration equipment is cooling, and the relevant refrigeration equipment point of each data monitoring point is obtained, and the average of the straight-line distances between each relevant refrigeration equipment point and its data monitoring point is taken as the refrigeration distance of its data monitoring point;

[0018] The temperature drop amplitude of each data monitoring point within a preset fixed time after the simulation starts is obtained, and the ratio of the temperature drop amplitude to the preset fixed time is used as the cooling coefficient of the data monitoring point.

[0019] Furthermore, a first evaluation model is constructed according to the cooling distance and the cooling coefficient, and a process of using the first evaluation model to adjust the deployment of the cooling equipment point includes:

[0020] Generate a refrigeration data set according to the refrigeration coefficient and refrigeration distance of each data monitoring point, and divide the refrigeration data set into a first training set and a first test set;

[0021] Constructing a first neural network, taking the refrigeration coefficient in the first training set as input data of the first neural network, taking the refrigeration distance in the first training set as output data of the first neural network, and training the first neural network to obtain an initial first neural network;

[0022] Using the first test set to perform model verification on the initial first neural network, outputting the initial first neural network that is less than or equal to a preset first test error threshold as a first evaluation model;

[0023] Setting a refrigeration coefficient standard, inputting the refrigeration coefficient standard into the first evaluation model to obtain a corresponding refrigeration distance standard, comparing the refrigeration distance of each data monitoring point with the refrigeration distance standard, and performing deployment adjustment on related refrigeration equipment points of data monitoring points that are greater than or equal to the refrigeration distance standard;

[0024] The deployment adjustment includes two methods: moving closer to the relevant refrigeration equipment points and adding relevant refrigeration equipment points, until the refrigeration distance of all data monitoring points is less than the refrigeration distance standard, and the deployment adjustment information is generated according to the deployment positions of all refrigeration equipment points that meet the condition.

[0025] Furthermore, the process of setting the refrigeration temperature control standard and obtaining the refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different external temperature data and temperature variation coefficients includes:

[0026] Set the refrigeration temperature control standard, input different external temperature data in the temperature vision model, and build a real-time external temperature change curve with the vertical axis being the external temperature data and the horizontal axis being the corresponding time, and obtain the temperature change coefficient of different external temperature data according to the external temperature change curve;

[0027] Under the conditions of different external temperature data and temperature variation coefficients, the operation of the refrigeration equipment is simulated. During the operation simulation, the refrigeration power and power consumption of the refrigeration equipment are recorded in real time, and the refrigeration power and power consumption under different external temperature data and temperature variation coefficients are bound respectively.

[0028] Furthermore, the process of obtaining actual external temperature data and actual temperature variation coefficient and inputting them into a preset second evaluation model to obtain theoretical cooling power and theoretical power consumption includes:

[0029] According to the deployment adjustment information, the refrigeration equipment points are deployed and adjusted in the actual application scenario, the actual external temperature data is monitored and the corresponding external temperature change curve is constructed, and the latest actual temperature change coefficient is obtained according to the external temperature change curve;

[0030] The preset process of the second evaluation model is as follows:

[0031] Generate a temperature control data set according to the cooling power and power consumption under different external temperature data and temperature variation coefficients, and divide the temperature control data set into a second training set and a second test set;

[0032] Constructing a second neural network, taking the external temperature data and the temperature variation coefficient in the second training set as input data of the second neural network, taking the refrigeration power and the power consumption in the second training set as output data of the second neural network, and training the second neural network to obtain an initial second neural network;

[0033] Using the second test set to perform model verification on the initial second neural network, outputting an initial second neural network that is less than or equal to a preset second test error threshold as a second evaluation model;

[0034] The latest actual temperature variation coefficient and its actual external temperature data are input into the second evaluation model to obtain the corresponding theoretical cooling power and theoretical power consumption.

[0035] Furthermore, the power consumption of the refrigeration equipment is adjusted according to the theoretical refrigeration power, the actual power consumption is monitored, and it is determined whether there are abnormal power consumption components. The process of operating and maintaining the abnormal power consumption components includes:

[0036] In actual application scenarios, the cooling equipment is continuously adjusted to the latest theoretical cooling power to achieve power consumption adjustment, and the actual power consumption of the cooling equipment that has been deployed and adjusted is monitored;

[0037] Compare the actual power consumption with the theoretical power consumption. If the actual power consumption is greater than the theoretical power consumption, obtain the actual local power consumption and theoretical local power consumption of each device component respectively, compare the two to obtain abnormal power consumption components, and repair and replace the abnormal power consumption components.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. The present invention uses BIM technology to construct a temperature-visual model of a building, which can simulate the operation of refrigeration equipment in a digital twin space and obtain the temperature changes inside the building. By obtaining the refrigeration coefficients of different data monitoring points during the operation simulation, it can reflect the refrigeration impact of the deployment of refrigeration equipment points on different areas in the building. By adjusting the deployment of refrigeration equipment points, the degree of refrigeration impact on different areas inside the building can be more balanced, thereby shortening the refrigeration time and reducing the power consumption of refrigeration equipment;

[0040] 2. The present invention can theoretically obtain the refrigeration power and power consumption that maintain the refrigeration temperature control standard under different external temperature data and temperature variation coefficients by inputting different external temperature data and temperature variation coefficients into the temperature vision model, which is beneficial to obtaining the best theoretical refrigeration power and theoretical power consumption of the refrigeration equipment according to the actual external temperature data and the actual temperature variation coefficient, and continuously using the theoretical refrigeration power to adjust the power consumption of the refrigeration equipment, which can effectively improve the refrigeration flexibility of the refrigeration equipment and reduce the power consumption of the refrigeration equipment. The obtained theoretical power consumption is used to judge whether there are abnormal power consumption components, which is beneficial to timely discover abnormal equipment components and carry out operation and maintenance on them, and can effectively reduce the power consumption of the entire refrigeration equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0042] like Figure 1 As shown, the power consumption evaluation method of refrigeration equipment based on the BIM simulation system includes the following steps:

[0043] Step S1: Use BIM technology to build a three-dimensional model of the building, simulate the operation of the refrigeration equipment in the three-dimensional model, set data monitoring points and obtain internal temperature data, and generate a temperature model of the building in combination with the three-dimensional model;

[0044] Step S2: obtaining a refrigeration equipment point in the temperature vision model, obtaining a refrigeration distance of the data monitoring point according to the refrigeration equipment point, obtaining a refrigeration coefficient of the data monitoring point according to the internal temperature data, constructing a first evaluation model according to the refrigeration distance and the refrigeration coefficient, and using the first evaluation model to adjust the deployment of the refrigeration equipment point;

[0045] Step S3: setting a refrigeration temperature control standard, obtaining refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different external temperature data and temperature variation coefficients, obtaining actual external temperature data and actual temperature variation coefficient, and inputting them into a preset second evaluation model to obtain theoretical refrigeration power and theoretical power consumption;

[0046] Step S4: adjust the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitor the actual power consumption, determine whether there are abnormal power consumption components, and perform operation and maintenance on the abnormal power consumption components.

[0047] It should be further explained that, in the specific implementation process, the three-dimensional model of the building is established using BIM technology, and the process of simulating the operation of the refrigeration equipment in the three-dimensional model includes:

[0048] Collecting architectural information of the building, which refers to the three-dimensional structure of the building, such as the size, position, shape and other information of the wall, floor, beam, column, pipe and other structures;

[0049] Collecting equipment information of refrigeration equipment, where the equipment information refers to equipment parameters of refrigeration equipment, including technical parameters and operating parameters, such as cooling capacity, energy efficiency ratio, cooling power, energy consumption efficiency, etc., as well as voltage, current, temperature, pressure, flow, etc.;

[0050] BIM technology is used to build a three-dimensional model of the building based on the collected building information, the collected equipment information is uploaded to the constructed three-dimensional model for synchronization, and the three-dimensional model containing the equipment information is imported into the energy consumption simulation software to simulate the operation of the refrigeration equipment, thereby obtaining the power consumption of the refrigeration equipment under different conditions and the changes in room temperature in the building.

[0051] It should be further explained that, in the specific implementation process, the process of setting data monitoring points and obtaining internal temperature data, and generating a temperature-visual model of a building in combination with a three-dimensional model includes:

[0052] Several data monitoring points are set at equal intervals in the three-dimensional model. Each data monitoring point has a certain monitoring area. The monitoring areas of all data monitoring points can cover the entire three-dimensional model. During the simulation, the temperature value of each data monitoring point is obtained in real time and marked as internal temperature data. The obtained internal temperature data is bound to its monitoring area.

[0053] The internal temperature data is divided into different numerical intervals according to the numerical size, and a color corresponding to the wavelength is set for each numerical interval. The larger the numerical value, the larger the wavelength. Each monitoring area is rendered in the color corresponding to its internal temperature data to realize the visualization of the three-dimensional model. The three-dimensional model that has completed the visualization is marked as the temperature vision model of the building. The temperature vision model can intuitively reflect the changes in room temperature inside the building during the running simulation process.

[0054] It should be further explained that, in the specific implementation process, the process of obtaining the refrigeration equipment point in the temperature vision model, obtaining the refrigeration distance of the data monitoring point according to the refrigeration equipment point, and obtaining the refrigeration coefficient of the data monitoring point according to the internal temperature data includes:

[0055] The refrigeration equipment point refers to each air outlet of the refrigeration equipment in the building when refrigeration is performed. Each refrigeration equipment point is displayed in the temperature vision model. The refrigeration equipment point and the data monitoring point exist independently of each other in the temperature vision model.

[0056] Taking any data monitoring point as an example, obtain the three refrigeration equipment points with the closest straight-line distance to the data monitoring point, mark the three refrigeration equipment points as the relevant refrigeration equipment points of the data monitoring point, and take the average of the straight-line distances between each relevant refrigeration equipment point and the data monitoring point as the refrigeration distance of the data monitoring point;

[0057] The temperature drop amplitude of the data monitoring point within the preset fixed time after the simulation starts is obtained. The temperature drop amplitude refers to the numerical change of the internal temperature data within the preset fixed time. The ratio of the obtained temperature drop amplitude to the preset fixed time is used as the cooling coefficient of the data monitoring point. The same method is adopted to obtain the cooling coefficient of each data monitoring point.

[0058] It should be further explained that, in a specific implementation process, a first evaluation model is constructed according to the cooling distance and the cooling coefficient, and a process of using the first evaluation model to adjust the deployment of the cooling equipment point includes:

[0059] Generate a refrigeration data set according to the refrigeration coefficient of each data monitoring point and its corresponding refrigeration distance, and divide the obtained refrigeration data set into a first training set and a first test set;

[0060] Constructing a first neural network, taking the refrigeration coefficients of different data monitoring points in the first training set as input data of the first neural network, taking the corresponding refrigeration distances in the first training set as output data of the first neural network, and training the first neural network to obtain a corresponding initial first neural network;

[0061] Using the first test set to perform model verification on the initial first neural network, outputting the initial first neural network that is less than or equal to a preset first test error threshold as the corresponding first evaluation model;

[0062] Setting a refrigeration coefficient standard, the refrigeration coefficient standard is used to reflect the expected temperature reduction range within a preset fixed time, inputting the set refrigeration coefficient standard into the first evaluation model, and obtaining a corresponding refrigeration distance standard, the refrigeration distance standard is used to reflect that only within this refrigeration distance standard can the preset refrigeration coefficient standard be achieved;

[0063] Compare the cooling distance of each data monitoring point with the cooling distance standard, and perform deployment adjustment on the relevant cooling equipment points of the data monitoring points that are greater than or equal to the cooling distance standard, wherein the deployment adjustment includes two methods: moving the relevant cooling equipment points closer and adding relevant cooling equipment points, until the cooling distances of the data monitoring points are all less than the cooling distance standard;

[0064] The same method is adopted to adjust the deployment of the relevant refrigeration equipment points of each data monitoring point until the refrigeration distance of all data monitoring points is less than the refrigeration distance standard, and the deployment adjustment information is generated according to the deployment positions of all refrigeration equipment points that meet the condition.

[0065] It should be further explained that, in the specific implementation process, the process of setting the refrigeration temperature control standard and obtaining the refrigeration power and power consumption to maintain the refrigeration temperature control standard under different external temperature data and temperature variation coefficient includes:

[0066] Setting a refrigeration temperature control standard, which is used to reflect the temperature range that needs to be maintained inside the building, and inputting different external temperature data into the temperature-viewing model, wherein the external temperature data corresponds to the internal temperature data;

[0067] According to the input external temperature data, a vertical axis is the external temperature data, and the horizontal axis is the external temperature change curve graph corresponding to the time. The external temperature data is marked at a preset fixed interval, which is recorded as W i , where i = 1, 2, ..., n, n is the number of marked external temperature data, and the temperature variation coefficient of different external temperature data is obtained, denoted as B i ;

[0068]

[0069] Get the mean of all external temperature data in the external temperature change curve and mark it as the external temperature standard W0. Compare the external temperature data with the external temperature standard. If W i >W0, the corresponding temperature coefficient is marked as +B i , if W i <W0, then the corresponding temperature coefficient is marked as -B i ;

[0070] Since the temperature-dependent model is a theoretical model, it is possible to simulate the operation of the refrigeration equipment under different external temperature data and temperature variation coefficients for the purpose of maintaining the refrigeration temperature control standard. During the operation simulation, the refrigeration power and power consumption of the refrigeration equipment are recorded in real time, and the refrigeration power and power consumption under different external temperature data and temperature variation coefficients are bound separately.

[0071] It should be further explained that, in the specific implementation process, the process of obtaining the actual external temperature data and the actual temperature variation coefficient and inputting them into the preset second evaluation model to obtain the theoretical cooling power and the theoretical power consumption includes:

[0072] According to the obtained deployment adjustment information, the refrigeration equipment points are deployed and adjusted in the actual application scenario, the actual external temperature data is monitored, and the corresponding external temperature change curve is constructed. The latest actual temperature change coefficient is obtained according to the external temperature change curve in the same way as the temperature change coefficient is obtained;

[0073] The preset process of the second evaluation model is as follows:

[0074] Generate a temperature control data set according to the cooling power and power consumption under different external temperature data and temperature variation coefficients, and divide the obtained temperature control data set into a second training set and a second test set;

[0075] Constructing a second neural network, taking the external temperature data and the temperature variation coefficient in the second training set as input data of the second neural network, taking the corresponding refrigeration power and power consumption in the second training set as output data of the second neural network, and training the second neural network to obtain a corresponding initial second neural network;

[0076] Using the second test set to perform model verification on the initial second neural network, outputting an initial second neural network that is less than or equal to a preset second test error threshold as a corresponding second evaluation model;

[0077] The latest actual temperature variation coefficient and its actual external temperature data are input into the constructed second evaluation model to obtain the corresponding theoretical cooling power and theoretical power consumption.

[0078] It should be further explained that, in the specific implementation process, the power consumption of the refrigeration equipment is adjusted according to the theoretical refrigeration power, the actual power consumption is monitored, and it is determined whether there are abnormal power consumption components. The process of operating and maintaining the abnormal power consumption components includes:

[0079] In actual application scenarios, the refrigeration equipment is continuously adjusted to the latest theoretical refrigeration power to achieve corresponding power consumption adjustment. The power consumption adjustment can keep the refrigeration equipment at the optimal refrigeration power while keeping the room temperature stable.

[0080] The actual power consumption of the refrigeration equipment after deployment adjustment and power consumption adjustment is monitored, and the actual power consumption obtained is compared with the theoretical power consumption. If the actual power consumption is less than or equal to the theoretical power consumption, no other operation is performed on it.

[0081] If the actual power consumption is greater than the theoretical power consumption, the actual local power consumption and the theoretical local power consumption of each device component of the refrigeration equipment are obtained in the actual application scenario and the temperature vision model, and compared respectively. The device components whose actual local power consumption is greater than the theoretical local power consumption are marked as abnormal power consumption components, and the abnormal power consumption components are operated and maintained, including inspection and replacement.

[0082] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for evaluating power consumption of refrigeration equipment based on a BIM simulation system, characterized in that: The following steps are involved: Step S1: Use BIM technology to build a three-dimensional model of the building, simulate the operation of the refrigeration equipment in the three-dimensional model, set data monitoring points and obtain internal temperature data, and generate a temperature model of the building in combination with the three-dimensional model; Step S2: obtaining a refrigeration equipment point in the temperature vision model, obtaining a refrigeration distance of the data monitoring point according to the refrigeration equipment point, obtaining a refrigeration coefficient of the data monitoring point according to the internal temperature data, constructing a first evaluation model according to the refrigeration distance and the refrigeration coefficient, and using the first evaluation model to adjust the deployment of the refrigeration equipment point; Step S3: setting a refrigeration temperature control standard, obtaining refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different external temperature data and temperature variation coefficients, obtaining actual external temperature data and actual temperature variation coefficient, and inputting them into a preset second evaluation model to obtain theoretical refrigeration power and theoretical power consumption; Step S4: adjust the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitor the actual power consumption, determine whether there are abnormal power consumption components, and perform operation and maintenance on the abnormal power consumption components.

2. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 1 is characterized in that: The process of building a 3D model and simulating the operation of the refrigeration equipment includes: Collect architectural information of the building, collect equipment information of the refrigeration equipment, and use BIM technology to establish a three-dimensional model of the building based on the architectural information; Upload the equipment information to the 3D model for synchronization, import the 3D model containing the equipment information into the energy consumption simulation software, and simulate the operation of the refrigeration equipment.

3. The method for evaluating power consumption of refrigeration equipment based on the BIM simulation system according to claim 2 is characterized in that: The process of obtaining internal temperature data and generating a temperature-sensing model includes: Several data monitoring points and their monitoring areas are set in the three-dimensional model. During the simulation, the internal temperature data of each data monitoring point is obtained in real time, and the internal temperature data is divided into different value intervals. A color of corresponding wavelength is set for each numerical interval, and each monitoring area is rendered as the color corresponding to the internal temperature data of its data monitoring point to obtain the temperature visual model of the building.

4. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 3 is characterized in that: The process of obtaining the refrigeration distance of the data monitoring point according to the refrigeration equipment point and the refrigeration coefficient of the data monitoring point according to the internal temperature data includes: The refrigeration equipment point refers to each air outlet of the refrigeration equipment in the building when the refrigeration equipment is cooling, and the relevant refrigeration equipment point of each data monitoring point is obtained, and the average of the straight-line distances between each relevant refrigeration equipment point and its data monitoring point is taken as the refrigeration distance of its data monitoring point; The temperature drop amplitude of each data monitoring point within a preset fixed time after the simulation starts is obtained, and the ratio of the temperature drop amplitude to the preset fixed time is used as the cooling coefficient of the data monitoring point.

5. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 4 is characterized in that: The process of constructing the first evaluation model and adjusting the deployment of refrigeration equipment points includes: Generate a refrigeration data set according to the refrigeration coefficient and refrigeration distance of each data monitoring point, and divide the refrigeration data set into a first training set and a first test set; Constructing a first neural network, taking the refrigeration coefficient in the first training set as input data of the first neural network, taking the refrigeration distance in the first training set as output data of the first neural network, and training the first neural network to obtain an initial first neural network; Using the first test set to perform model verification on the initial first neural network, outputting the initial first neural network that is less than or equal to a preset first test error threshold as a first evaluation model; Setting a refrigeration coefficient standard, inputting the refrigeration coefficient standard into the first evaluation model to obtain a corresponding refrigeration distance standard, comparing the refrigeration distance of each data monitoring point with the refrigeration distance standard, and performing deployment adjustment on related refrigeration equipment points of data monitoring points that are greater than or equal to the refrigeration distance standard; The deployment adjustment includes two methods: moving closer to the relevant refrigeration equipment points and adding relevant refrigeration equipment points, until the refrigeration distance of all data monitoring points is less than the refrigeration distance standard, and the deployment adjustment information is generated according to the deployment positions of all refrigeration equipment points that meet the condition.

6. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 5 is characterized in that: The process of obtaining the cooling power and power consumption to maintain the cooling temperature control standard under different external temperature data and temperature variation coefficients includes: Set the refrigeration temperature control standard, input different external temperature data in the temperature vision model, and build a real-time external temperature change curve with the vertical axis being the external temperature data and the horizontal axis being the corresponding time, and obtain the temperature change coefficient of different external temperature data according to the external temperature change curve; Under the conditions of different external temperature data and temperature variation coefficients, the operation of the refrigeration equipment is simulated. During the operation simulation, the refrigeration power and power consumption of the refrigeration equipment are recorded in real time, and the refrigeration power and power consumption under different external temperature data and temperature variation coefficients are bound respectively.

7. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 6 is characterized in that: The process of obtaining the theoretical cooling power and theoretical power consumption includes: According to the deployment adjustment information, the refrigeration equipment points are deployed and adjusted in the actual application scenario, the actual external temperature data is monitored and the corresponding external temperature change curve is constructed, and the latest actual temperature change coefficient is obtained according to the external temperature change curve; The preset process of the second evaluation model is as follows: Generate a temperature control data set according to the cooling power and power consumption under different external temperature data and temperature variation coefficients, and divide the temperature control data set into a second training set and a second test set; Constructing a second neural network, taking the external temperature data and the temperature variation coefficient in the second training set as input data of the second neural network, taking the refrigeration power and the power consumption in the second training set as output data of the second neural network, and training the second neural network to obtain an initial second neural network; Using the second test set to perform model verification on the initial second neural network, outputting an initial second neural network that is less than or equal to a preset second test error threshold as a second evaluation model; The latest actual temperature variation coefficient and its actual external temperature data are input into the second evaluation model to obtain the corresponding theoretical cooling power and theoretical power consumption.

8. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 7 is characterized in that: The process of adjusting the power consumption of the refrigeration equipment, monitoring the actual power consumption, and determining whether there are abnormal power consumption components and performing maintenance on abnormal power consumption components includes: In actual application scenarios, the cooling equipment is continuously adjusted to the latest theoretical cooling power to achieve power consumption adjustment, and the actual power consumption of the cooling equipment that has been deployed and adjusted is monitored; Compare the actual power consumption with the theoretical power consumption. If the actual power consumption is greater than the theoretical power consumption, obtain the actual local power consumption and theoretical local power consumption of each device component respectively, compare the two to obtain abnormal power consumption components, and repair and replace the abnormal power consumption components.

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