Method for evaluating power consumption of refrigeration equipment based on BIM simulation system

Abstract

A method for evaluating the power consumption of refrigeration equipment based on a BIM simulation system, which relates to the technical field of equipment evaluation; establishing a three-dimensional model of a building and performing operation simulation on the refrigeration equipment, setting data monitoring points and obtaining internal temperature data, generating a temperature visual model in combination with the three-dimensional model, obtaining refrigeration equipment points and the refrigeration distance and refrigeration coefficient of the data monitoring points, constructing a first evaluation model and performing deployment adjustment on the refrigeration equipment points, setting refrigeration temperature control standards and obtaining actual external temperature data and actual temperature change coefficients, inputting them into a preset second evaluation model to obtain the theoretical refrigeration power and theoretical power consumption, performing power consumption adjustment on the refrigeration equipment, monitoring the actual power consumption to determine whether there are abnormal power consumption components, and performing operation and maintenance on the abnormal power consumption components, which can make the refrigeration effects on different areas inside the building more balanced, improve the flexibility of the refrigeration equipment for refrigeration, and reduce the power consumption of the refrigeration equipment.

Description

Technical Field

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

[0002] Evaluating the power consumption of refrigeration equipment using BIM technology is a method for 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 layouts, etc. This method provides an effective tool for the design and optimization of the building refrigeration system, which is beneficial to improving the energy efficiency and energy-saving performance of the refrigeration system;

[0003] In the prior art, due to the unreasonable design of the air outlet of the refrigeration equipment, the indoor temperature change in the building is unbalanced, resulting in an extended refrigeration time for the refrigeration equipment to reach the desired room temperature, and finally often causing a high power consumption situation. Moreover, in the prior art, since the outdoor temperature of the building is in dynamic change, if the refrigeration equipment cannot adjust its refrigeration power in a timely manner according to the outdoor temperature, it will also lead to a high power consumption situation. In view of the deficiencies of the prior art, the present invention provides a method for evaluating the power consumption of refrigeration equipment based on a BIM simulation system. Summary of the Invention

[0004] The purpose of the present invention is 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 through the following technical solutions: A method for evaluating the power consumption of refrigeration equipment based on a BIM simulation system includes the following steps:

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

[0007] Step S2: Obtain the refrigeration equipment points in the temperature-visual model, obtain the refrigeration distance of the data monitoring points according to the refrigeration equipment points, obtain the refrigeration coefficient of the data monitoring points according to the internal temperature data, construct a first evaluation model based on the refrigeration distance and the refrigeration coefficient, and use the first evaluation model to adjust the deployment of the refrigeration equipment points;

[0008] Step S3: Set the refrigeration temperature control standard, obtain the refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different outdoor temperature data and temperature change coefficients, obtain the actual outdoor temperature data and the actual temperature change coefficient, and input them into a preset second evaluation model to obtain the theoretical refrigeration power and the 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] Further, the process of establishing a three-dimensional model of a building using BIM technology and performing operation simulation on the refrigeration equipment in the three-dimensional model includes:

[0011] Collect the building information of the building, collect the equipment information of the refrigeration equipment, and establish a three-dimensional model of the building using BIM technology according to the building information;

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

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

[0014] Set a number of data monitoring points and their monitoring areas in the three-dimensional model. During the operation simulation process, obtain the internal temperature data of each data monitoring point in real time, and divide the internal temperature data into different numerical intervals;

[0015] Set a color corresponding to a wavelength for each numerical interval, and render each monitoring area as the color corresponding to the internal temperature data of its data monitoring point to obtain the temperature visualization model of the building.

[0016] Further, the process of obtaining the refrigeration equipment points in the temperature visualization model, obtaining the refrigeration distance of the data monitoring points according to the refrigeration equipment points, and obtaining the refrigeration coefficient of the data monitoring points according to the internal temperature data includes:

[0017] The refrigeration equipment points refer to the respective air outlets when the refrigeration equipment in the building performs refrigeration. Obtain the relevant refrigeration equipment points of each data monitoring point, and take the average value of the straight-line distances between each relevant refrigeration equipment point and its data monitoring point as the refrigeration distance of its data monitoring point;

[0018] Obtain the temperature drop amplitude of each data monitoring point within a preset fixed time period after the start of the operation simulation, and take the ratio of the temperature drop amplitude to the preset fixed time period as the refrigeration coefficient of the data monitoring point.

[0019] Further, the process of constructing a first evaluation model according to the refrigeration distance and the refrigeration coefficient and using the first evaluation model to deploy and adjust the refrigeration equipment points includes:

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

[0021] Construct a first neural network. Use the coefficient of performance in the first training set as the input data of the first neural network, and use the refrigeration distance in the first training set as the output data of the first neural network. Train the first neural network to obtain an initial first neural network;

[0022] Use the first test set to verify the model of the initial first neural network, and output the initial first neural network with an error less than or equal to the preset first test error threshold as the first evaluation model;

[0023] Set a coefficient of performance standard, input the coefficient of performance standard into the first evaluation model to obtain a corresponding refrigeration distance standard, compare the refrigeration distance of each data monitoring point with the refrigeration distance standard, and deploy and adjust the relevant refrigeration equipment points for the data monitoring points with a refrigeration distance 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 generate deployment adjustment information according to the deployment positions of all refrigeration equipment points that meet this condition.

[0025] Further, the process of setting a 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 change coefficients includes:

[0026] Set a refrigeration temperature control standard, input different external temperature data into the temperature view model, and construct a real-time external temperature change curve graph with the external temperature data as the vertical coordinate and the corresponding time as the horizontal coordinate, and obtain the temperature change coefficient of different external temperature data according to the external temperature change curve graph;

[0027] Under the conditions of different external temperature data and temperature change coefficients, perform operation simulation on the refrigeration equipment. During the operation simulation process, record the refrigeration power and power consumption of the refrigeration equipment in real time, and bind the refrigeration power and power consumption under different external temperature data and temperature change coefficients respectively.

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

[0029] According to the deployment adjustment information, deploy and adjust the refrigeration equipment points in the actual application scenario, monitor the actual external temperature data and construct a corresponding external temperature change curve graph, and obtain the latest actual temperature change coefficient according to the external temperature change curve graph;

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

[0031] Generate a temperature control data set based on the refrigeration power and power consumption under different external temperature data and temperature change coefficients, and divide the temperature control data set into a second training set and a second test set;

[0032] Construct a second neural network, use the external temperature data and temperature change coefficients in the second training set as the input data of the second neural network, use the refrigeration power and power consumption in the second training set as the output data of the second neural network, and train the second neural network to obtain an initial second neural network;

[0033] Use the second test set to verify the model of the initial second neural network, and output the initial second neural network whose error is less than or equal to the preset second test error threshold as the second evaluation model;

[0034] Input the latest actual temperature change coefficient and its actual external temperature data into the second evaluation model to obtain the corresponding theoretical refrigeration power and theoretical power consumption.

[0035] Furthermore, the process of adjusting the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitoring the actual power consumption, judging whether there are abnormal power consumption components, and performing operation and maintenance on the abnormal power consumption components includes:

[0036] Continuously adjust the refrigeration equipment to the latest theoretical refrigeration power in the actual application scenario to achieve power consumption adjustment, and monitor the actual power consumption of the refrigeration equipment after deployment adjustment and power consumption adjustment;

[0037] Compare the actual power consumption with the theoretical power consumption. If the actual power consumption is greater than the theoretical power consumption, respectively obtain the actual local power consumption and theoretical local power consumption of each equipment component, 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 beneficial effects of the present invention are:

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

[0040] 2. By inputting different external temperature data and temperature change coefficients into the temperature-visualization model, the present invention can theoretically obtain the cooling power and power consumption that maintain the cooling temperature control standard under different external temperature data and temperature change coefficients, which is beneficial to obtaining the optimal theoretical cooling power and theoretical power consumption of the refrigeration equipment according to the actual external temperature data and actual temperature change coefficients. By continuously adjusting the power consumption of the refrigeration equipment using the theoretical cooling power, the flexibility of the refrigeration equipment for refrigeration can be effectively improved, and the power consumption of the refrigeration equipment can be reduced. By using the obtained theoretical power consumption to judge whether there are abnormal power consumption components, it is beneficial to timely discover the abnormal equipment components and perform operation and maintenance on them, which can effectively reduce the power consumption of the entire refrigeration equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

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

[0044] Step S2: Obtain the refrigeration equipment points in the temperature-visualization model, obtain the refrigeration distance of the data monitoring points according to the refrigeration equipment points, obtain the refrigeration coefficient of the data monitoring points according to the internal temperature data, construct a first evaluation model according to the refrigeration distance and the refrigeration coefficient, and use the first evaluation model to adjust the deployment of the refrigeration equipment points;

[0045] Step S3: Set the refrigeration temperature control standard, obtain the cooling power and power consumption that maintain the refrigeration temperature control standard under different external temperature data and temperature change coefficients, obtain the actual external temperature data and actual temperature change coefficients, and input them into a preset second evaluation model to obtain the theoretical cooling power and theoretical power consumption;

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

[0047] It should be further noted that, in the specific implementation process, the process of using BIM technology to establish a three-dimensional model of a building and performing an operation simulation on the refrigeration equipment in the three-dimensional model includes:

[0048] Collect the building information of the building, where the building information refers to the three-dimensional spatial structure of the building, such as the dimensions, positions, shapes, etc. of structures such as walls, floors, beams, columns, and pipes;

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

[0050] Use BIM technology to establish a three-dimensional model of the building based on the collected building information, upload the collected device information to the constructed three-dimensional model for synchronization, import the three-dimensional model containing the device information into the energy consumption simulation software, simulate the operation of the refrigeration equipment, and then obtain the power consumption of the refrigeration equipment under different conditions and the room temperature change situation in the building.

[0051] It should be further noted 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 the building in combination with the three-dimensional model includes:

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

[0053] Divide the internal temperature data into different numerical intervals according to the numerical size, set a color with a corresponding wavelength for each numerical interval. The larger the value, the larger the wavelength. Render each monitoring area as the color corresponding to its internal temperature data to realize the visualization processing of the three-dimensional model. Mark the three-dimensional model after the visualization processing as the temperature-visual model of the building. The temperature-visual model can intuitively reflect the room temperature change situation inside the building during the operation simulation process.

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

[0055] The refrigeration equipment points refer to the air outlets of the refrigeration equipment in the building when refrigerating. Each refrigeration equipment point is displayed in the temperature-visual model, and the refrigeration equipment points and the data monitoring points exist independently of each other in the temperature-visual model;

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

[0057] Obtain the temperature drop amplitude of the data monitoring point within a preset fixed duration after the start of the running simulation. The temperature drop amplitude refers to the magnitude of the change in the internal temperature data within the preset fixed duration. Take the ratio of the obtained temperature drop amplitude to the preset fixed duration as the refrigeration coefficient of the data monitoring point, and use the same method to obtain the refrigeration coefficients of each data monitoring point.

[0058] It should be further noted that in the specific implementation process, the process of constructing the first evaluation model based on the refrigeration distance and the refrigeration coefficient and using the first evaluation model to deploy and adjust the refrigeration equipment points includes:

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

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

[0061] Use the first test set to verify the model of the initial first neural network, and output the initial first neural network with an error less than or equal to the preset first test error threshold as the corresponding first evaluation model;

[0062] Set a refrigeration coefficient standard, which is used to reflect the expected temperature drop amplitude within a preset fixed duration. Input the set refrigeration coefficient standard into the first evaluation model to obtain the corresponding refrigeration distance standard, which is used to reflect that only within this refrigeration distance standard can the preset refrigeration coefficient standard be achieved;

[0063] Compare the refrigeration distances of each data monitoring point with the refrigeration distance standard, and deploy and adjust the relevant refrigeration equipment points of the data monitoring points greater than or equal to the refrigeration distance standard. The deployment adjustment includes two methods: moving the relevant refrigeration equipment points closer and adding relevant refrigeration equipment points, until the refrigeration distances of the data monitoring points are all less than the refrigeration distance standard;

[0064] Use the same method to deploy and adjust the relevant refrigeration equipment points of each data monitoring point until the refrigeration distances of all data monitoring points are less than the refrigeration distance standard, and generate deployment adjustment information according to the deployment positions of all refrigeration equipment points that meet this condition.

[0065] It should be further noted that in the specific implementation process, set a refrigeration temperature control standard. The process of obtaining the refrigeration power and power consumption for maintaining the refrigeration temperature control standard under different external temperature data and temperature change coefficients includes:

[0066] Set the refrigeration temperature control standard, which is used to reflect the temperature range that needs to be maintained inside the building. Input different external temperature data into the temperature view model, and the external temperature data corresponds to the internal temperature data.

[0067] According to the input external temperature data, construct a curve graph of external temperature change in real time with the external temperature data on the vertical axis and the corresponding time on the horizontal axis. Mark the external temperature data at a preset fixed interval, denoted as W i , where i = 1, 2,..., n, and n is the number of marked external temperature data. Obtain the temperature change coefficients of different external temperature data, denoted as B i ;

[0068]

[0069] Obtain the average value of all external temperature data in the curve graph of external temperature change and mark it as the external temperature standard W 0 , compare the external temperature data with the external temperature standard. If W i >W 0 , then mark its corresponding temperature change coefficient as +B i , if W i <W 0 , then mark its corresponding temperature change coefficient as -B i ;

[0070] Since the temperature view model is a theoretical model, it is possible to simulate the operation of the refrigeration equipment under different external temperature data and temperature change coefficients with the aim of maintaining the refrigeration temperature control standard. During the operation simulation process, 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 change coefficients are respectively bound.

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

[0072] According to the obtained deployment adjustment information, deploy and adjust the refrigeration equipment points in the actual application scenario, monitor the actual external temperature data, and construct a corresponding curve graph of external temperature change. In the same way as obtaining the temperature change coefficient, obtain the latest actual temperature change coefficient according to the curve graph of external temperature change;

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

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

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

[0076] Use the second test set to verify the model of the initial second neural network, and output the initial second neural network with an error less than or equal to the preset second test error threshold as the corresponding second evaluation model;

[0077] Input the latest actual temperature change coefficient and its actual external temperature data into the constructed second evaluation model to obtain the corresponding theoretical refrigeration power and theoretical power consumption.

[0078] It should be further noted that in the specific implementation process, the process of adjusting the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitoring the actual power consumption, judging whether there are abnormal power consumption components, and performing operation and maintenance on the abnormal power consumption components includes:

[0079] Continuously adjust the refrigeration equipment to the latest theoretical refrigeration power in the actual application scenario to achieve the corresponding power consumption adjustment. The power consumption adjustment can keep the refrigeration equipment at the best refrigeration power all the time on the premise of keeping the room temperature stable;

[0080] Monitor the actual power consumption of the refrigeration equipment after deployment adjustment and power consumption adjustment, compare the obtained actual power consumption with the theoretical power consumption. If the actual power consumption is less than or equal to the theoretical power consumption, do not perform any other operations on it;

[0081] 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 equipment component of the refrigeration equipment in the actual application scenario and the temperature view model respectively, compare them respectively, mark the equipment components with actual local power consumption greater than the theoretical local power consumption as abnormal power consumption components, and perform operation and maintenance on the abnormal power consumption components, 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 of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced 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: Set the refrigeration temperature control standard, input different external temperature data into the temperature vision model, and construct a real-time external temperature change curve chart with the ordinate being the external temperature data and the abscissa being the corresponding time, and obtain the temperature change coefficient of different external temperature data according to the external temperature change curve chart; Obtain the cooling power and power consumption to maintain the cooling temperature control standard under different external temperature data and temperature variation coefficients, obtain the actual external temperature data and the actual temperature variation coefficient, and input them into the preset second evaluation model to obtain the theoretical cooling power and theoretical power consumption; Step S4: adjusting the power consumption of the refrigeration equipment according to the theoretical refrigeration power, monitoring the actual power consumption, comparing the actual power consumption with the theoretical power consumption, determining whether there are abnormal power consumption components, and performing operation and maintenance on the abnormal power consumption components; 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 value interval, and each monitoring area is rendered with the color corresponding to the internal temperature data of its data monitoring point to obtain the temperature visual model of the building; 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.

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 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.

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 cooling power and power consumption to maintain the cooling temperature control standard under different external temperature data and temperature variation coefficients includes: 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.

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 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.

6. The method for evaluating power consumption of refrigeration equipment based on a BIM simulation system according to claim 5 is characterized in that: Adjust the power consumption of the refrigeration equipment, monitor the actual power consumption, compare the actual power consumption with the theoretical power consumption, determine whether there are abnormal power consumption components, and perform maintenance on abnormal power consumption components. The process 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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