Building energy consumption data processing control method and system based on 3D modeling

Abstract

The invention relates to the technical field of data processing, in particular to a building energy consumption data processing control method and system based on 3D modeling, and the system comprises a model construction analysis module, a parameter collection and calculation module, a heat supply monitoring module and a heat supply prediction adjustment module. The method comprises the following steps: determining a heat supply source of a heat supply object and a plurality of corresponding heat supply nodes, analyzing and processing historical heat supply data of the heat supply nodes, establishing a load standard curve and a historical situation curve, and performing composite processing on the load standard curve and the historical situation curve to obtain a load prediction curve. The future adjustable load capacity is predicted through the load prediction curve, the intelligent prediction effect of the load of the heat supply source is improved, and the load of the heat supply source can be conveniently controlled and adjusted based on decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a building energy consumption data processing and control method and system based on 3D modeling. Background Art

[0002] Under the traditional model, power plants have low levels of automation and rely heavily on manual control, resulting in an inaccurate match between supply and demand, exposing problems of energy waste and environmental pollution. Facing pressure from various sources, the traditional energy supply industry is actively seeking a solution. In recent years, the rapid development of new-generation information technologies, represented by the internet, big data, cloud computing, and artificial intelligence, has accelerated their penetration into various sectors and driven the transformation and upgrading of traditional industries.

[0003] While the traditional heating industry remains mired in a period of fierce competition, driven by societal and business demands, smart energy supply has become a trend. Advances in technology and advanced management are essential to expanding market share, reducing energy costs, and increasing corporate profits.

[0004] The heating load is a measure of the ability to provide heat to users. In some buildings, such as factories and workshops, during the operation of the building energy supply system, due to inadequate energy consumption data processing and regulation, there are still imbalances in supply and demand. For example, in terms of water flow distribution, the heat source outputs different temperatures at different nodes under the same load, resulting in a contradiction between supply and demand and increased energy consumption costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose a building energy consumption data processing and control method and system based on 3D modeling.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a building energy consumption data processing and control method and system based on 3D modeling, comprising the following steps: S1. Obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; S2. Obtain safe operating parameters of the heating equipment and determine the maximum heating load of the heating equipment; S3. Obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, and calculate the heating load of several heating sources to obtain the total heating load; S4. Obtain the heating node that needs to be load-regulated and use it as the target node. Calculate the future predicted load based on the historical heating load data and the real-time heating load of the corresponding heating source. Regulate the load of the target node in combination with the total heating load and the maximum heating load.

[0007] As a further solution of the present invention, for S3, the method for calculating the total heating load is: Get the preset temperature of different heating nodes under the same heat source, through the formula, Calculate the preset temperature of the heat source ; Where Vi is the spatial volume of the heating node, Ti is the preset temperature of the heating node, Li is the distance from the heating node to the heating source, is the average volume of several heating nodes under the heat source, is the average value of the heating network pipe distances of several heating nodes under the heat source, i is the heating node number, i is a positive integer, i∈[1,n], n is the total number of heating nodes corresponding to the heat source; and are the weight coefficients of spatial volume and network management distance respectively; Get the external temperature and space volume of the heating node, and use the formula The total heating load Q is calculated; where a is the correction coefficient, qv is the volume heat index, is the average outdoor temperature of the heating node.

[0008] As a further solution of the present invention, with respect to S4, a method for regulating the load of a target node includes the following steps: Collect and analyze historical load data of heat sources, establish load forecast curves, and dynamically calculate adjustable loads; Determining whether the adjustable load quantity satisfies the load application quantity, and calculating the load change of the heat source caused by adjusting the load application quantity, obtaining an analysis and calculation result, and determining whether to generate a first adjustment instruction based on the analysis and calculation result; Obtaining the real-time temperature and personnel status of the heating node, calculating the adjustment parameters of the heating source based on the real-time temperature and personnel status, analyzing the adjustment parameters, and generating a second adjustment instruction; The first adjustment instruction and the second adjustment instruction are analyzed by adjusting the perception model, and the load of the target node is adjusted according to the analysis result.

[0009] As a further embodiment of the present invention, a method for establishing a load forecast curve includes: Obtain historical heating load data of the heating source, analyze the historical load data, and establish a standard load curve; Obtain real-time load and select historical trend curve based on real-time load and historical load data; The standard load curve and the historical situation curve are compositely processed to determine the load forecast curve.

[0010] As a further embodiment of the present invention, a method for determining whether to generate a first adjustment instruction includes the following steps: S401. Mark the heat source to which the target node belongs as an actively adjusted heat source, and mark other heat sources as passively adjusted heat sources; mark the load change of the heat source caused by the load adjustment request of the target node as the load request amount; obtain the load proportion of the actively adjusted heat source and the passively adjusted heat source, and the lower limit temperature value of the corresponding heating node respectively; S402: Determine whether the adjustable load quantity meets the load application quantity. If the adjustable load quantity meets the load application quantity, generate a pre-adjustment tag; if the adjustable load quantity does not meet the load application quantity, generate a calculation tag. S403: Obtain the heating load of the actively adjusted heat source based on the pre-adjustment tag and sum it with the load application amount to obtain a pre-calculated load; calculate the load proportion of the pre-calculated load and determine whether it exceeds the load threshold of the actively adjusted heat source; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, generate a first adjustment instruction; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, do not generate the first adjustment instruction; S404: Determine, based on the calculation tags, whether any passive heat source has a load ratio exceeding a corresponding load threshold; if any passive heat source has a load ratio exceeding the corresponding load threshold, mark it as a target passive heat source; count the number of target passive heat sources and record it as the total number of target heat sources; If the total number of target heat sources is greater than 1, the target passive heat source is compared with the heating load to determine the minimum value of the heating load, and the heating node corresponding to the target passive heat source with the minimum heating load is marked as an adjustment node; if the total number of target heat sources is 1, the heating node corresponding to the target passive heat source is marked as an adjustment node; the real-time temperature of the adjustment node is obtained, and it is determined whether there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value; if there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value, the first adjustment instruction is not generated; If there is no adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit, the heating load of the passive adjustment heat source is reduced, and the real-time temperature of the adjustment node is monitored until there is an adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit; S405 , obtaining the heating load reduced by passively adjusting the heat source, summing the sum with the adjustable load, updating the sum as the adjustable load, and executing S402 to S405 again.

[0011] As a further solution of the present invention, the method for calculating the adjustment parameters of the heat source is: Collect the real-time temperature of the heating node and mark it as TZi, the number of air outlets of the heating node and mark it as Ni, and the number of people at the air outlet of the heating node and mark it as Ri; By formula The adjustment parameter F is calculated; C0 is the standard reference parameter, which is calculated based on historical heating load big data; The adjustment parameter F is analyzed. If the adjustment parameter F is not greater than 0, a second adjustment instruction is generated; if the adjustment parameter F is greater than 0, no second adjustment instruction is generated.

[0012] As a further solution of the present invention, the expression for adjusting the perception model is: ; GT is the adjustment consent value; GT=1 means that the load adjustment is performed on the active adjustment heat source where the target node is located; GT=-1 means that the load adjustment is not performed on the active adjustment heat source where the target node is located.

[0013] In addition, the present invention also discloses a building energy consumption data processing and control system based on 3D modeling, which includes a model construction and analysis module, a parameter acquisition and calculation module, a heating monitoring module, and a heating prediction and adjustment module; The model construction analysis module is used to obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; Parameter acquisition and calculation module, used to obtain safe operating parameters of heating equipment and determine the maximum heating load of heating equipment; The heating monitoring module is used to obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, count the heating loads of several heating sources, and obtain the total heating load; The heating forecasting and adjustment module is used to obtain the heating node that needs to be load-regulated and use it as the target node. It calculates the future forecast load based on the historical heating load data and the real-time heating load of the corresponding heating source, and regulates the load of the target node in combination with the total heating load and the maximum heating load.

[0014] Compared with the existing technology, the advantages of the present invention are: by determining the heat source of the heating object and the corresponding multiple heating nodes, by analyzing and processing the historical heating data of the heating nodes, a load standard curve and a historical situation curve are established, and the load standard curve and the historical situation curve are compositely processed to obtain a load forecast curve. The load forecast curve is used to predict the future adjustable load, thereby improving the intelligent forecast effect of the heat source load and facilitating the load adjustment of the heat source based on the decision; By obtaining the heating demand of the heating node, calculating the load application amount, judging whether the adjustable load amount meets the load application amount, and calculating the load change of the heating source caused by adjusting the load application amount, a first adjustment instruction is generated, and the adjustment parameters of the heating source are calculated. Based on the analysis of the adjustment parameters, a second adjustment instruction is generated, and the first adjustment instruction and the second adjustment instruction are input into the adjustment perception model to obtain the adjustment agreement value. Based on the adjustment agreement value, the heating load of the heating source is intelligently adjusted, which effectively solves the problem of uneven distribution of water flow in the heating network pipes, resulting in different temperatures output by the heating source to different nodes under the same load, reduces the demand that causes the contradiction between supply and demand, and improves the heating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0017] Example 1, refer to Figure 1 , a building energy consumption data processing and control method and system based on 3D modeling, comprising the following steps: S1. Obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; For example, the building structure and scale of the heating target are obtained, and the type of heating equipment of the heating target is clarified, including the models of equipment such as boilers, heat exchangers, and circulating pumps; the installation route of the heating pipeline includes the layout and direction of the heating network pipes, and the connection method between the main heating network pipes and the branch heating network pipes; the number and location of the heating sources, the pressure monitoring equipment and thermometer monitoring equipment in the heating network pipes at the heating sources, etc.; 3D models can be implemented using existing software such as 3DMax and Blender; heating nodes are relatively independent heating spaces, and one heating source can be connected to multiple heating nodes; S2. Obtain safe operating parameters of the heating equipment and determine the maximum heating load of the heating equipment; Specifically, determining a power range of the heating equipment to ensure safety, determining a maximum power of the heating equipment based on the power range, and determining a maximum heating load of the heating equipment based on the maximum power of the heating equipment and a heating load parameter; S3. Obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, and calculate the heating load of several heating sources to obtain the total heating load; For S3, the total heating load is calculated as follows: Get the preset temperature of different heating nodes under the same heat source, through the formula, Calculate the preset temperature of the heat source ; Where Vi is the spatial volume of the heating node, Ti is the preset temperature of the heating node, Li is the distance from the heating node to the heating source, is the average volume of several heating nodes under the heat source, is the average value of the heating network pipe distances of several heating nodes under the heat source, i is the heating node number, i is a positive integer, i∈[1,n], n is the total number of heating nodes corresponding to the heat source; and are the weight coefficients of space volume and network management distance respectively; it should be noted that, and All are calculated based on the 3D model simulation of the heating object; Get the external temperature and space volume of the heating node, and use the formula The total heating load Q is calculated; Where a is the correction coefficient, qv is the volume heat index, is the average outdoor temperature of the heating node; S4. Obtain the heating node that needs to be load-regulated and use it as the target node. Calculate the future predicted load based on the historical heating load data and the real-time heating load of the corresponding heating source. Regulate the load of the target node in combination with the total heating load and the maximum heating load.

[0018] For S4, the method for regulating the load of the target node includes the following steps: Collect and analyze historical load data of heat sources, establish load forecast curves, and dynamically calculate adjustable loads; Methods for establishing load forecast curves include: Obtain historical heating load data of the heating source, analyze the historical load data, and establish a standard load curve; The method for analyzing historical load data is: Based on the historical heating load data, determine the historical heating load and the corresponding time point, establish the historical heating load coordinate points, use time as the horizontal axis and the historical heating load as the vertical axis to establish a time-load two-dimensional coordinate system, input the historical heating load coordinate points into the coordinate system, establish a historical single-day real-time load curve, and establish a load curve set based on different dates; Set the sampling period, and sample several historical single-day real-time load curves in the load curve set according to the sampling period to obtain several historical heating loads corresponding to the sampling period, mark them as target loads, calculate the average of several target loads, and use the calculated result as the standard sampling value of the sampling period; the sampling period can be set to 5 minutes; Count the cycle duration corresponding to the standard sampling value, use the cycle duration as the horizontal coordinate and the standard sampling value as the vertical coordinate to establish the standard load coordinate point, and establish the standard load curve based on the standard load coordinate point; Obtain real-time load and select historical trend curve based on real-time load and historical load data; Use the derivative formula to establish the historical rate of change curve of the historical single-day real-time load curve, and establish the historical change curve set of the historical rate of change curve; Draw a real-time load curve, calculate the real-time load change rate, amplify the real-time load change rate to obtain a change rate screening range, mark the real-time time point as the target time point, input the target time point into the historical change curve set, filter the obtained historical change rates using the change rate screening range as the screening target, mark the historical change rate curve corresponding to the historical change rate within the change rate screening range as the target change rate curve, and mark the historical single-day real-time load curve corresponding to the target change rate curve as the historical situation curve; Composite processing of standard load curve and historical situation curve is performed to determine load forecast curve; The historical situation curve is separated according to the target time point, and the curve segments within the prediction period after the target curve point are retained. The curve segments are subjected to the same sampling process to obtain several historical situation loads corresponding to the sampling period; By formula Calculate the predicted load YCk for the future h-th sampling period; where Lh is the historical load of the h-th sampling period, Bk is the standard load corresponding to the h-th sampling period in the standard load curve, h is the historical load number, h is a positive integer, h∈[1,p], and p is the total number of historical loads; 、 is the weight coefficient, which is obtained based on the fitting of historical load and standard load big data; The predicted time point th corresponding to the predicted load is calculated according to the formula th=t0+h×tc, where t0 is the target time point and tc is the sampling period; Based on the predicted time point and the predicted load, a predicted load coordinate point is established. Based on the h predicted load coordinate points, a load forecast curve for the next h sampling periods is established. In this embodiment, an interpolation algorithm is used to perform spline interpolation on the historical heating load coordinate points, the standard load coordinate points, and the predicted load coordinate points to generate the corresponding smooth historical single-day real-time load curve, the standard load curve, and the load forecast curve. The adjustable load is calculated by taking the difference between the maximum heating load and the total heating load, and the adjustable load is updated in real time; Determining whether the adjustable load quantity satisfies the load application quantity, and calculating the load change of the heat source caused by adjusting the load application quantity, obtaining an analysis and calculation result, and determining whether to generate a first adjustment instruction based on the analysis and calculation result; Specifically, the method for determining whether to generate a first adjustment instruction includes the following steps: S401. Mark the heat source to which the target node belongs as an actively adjusted heat source, and mark other heat sources as passively adjusted heat sources; mark the load change of the heat source caused by the load adjustment request of the target node as the load request amount; obtain the load proportion of the actively adjusted heat source and the passively adjusted heat source, and the lower limit temperature value of the corresponding heating node respectively; S402: Determine whether the adjustable load quantity meets the load application quantity. If the adjustable load quantity meets the load application quantity, generate a pre-adjustment tag; if the adjustable load quantity does not meet the load application quantity, generate a calculation tag. S403: Obtain the heating load of the actively adjusted heat source based on the pre-adjustment tag and sum it with the load application amount to obtain a pre-calculated load; calculate the load proportion of the pre-calculated load and determine whether it exceeds the load threshold of the actively adjusted heat source; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, generate a first adjustment instruction; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, do not generate the first adjustment instruction; S404: Determine, based on the calculation tags, whether any passive heat source has a load ratio exceeding a corresponding load threshold; if any passive heat source has a load ratio exceeding the corresponding load threshold, mark it as a target passive heat source; count the number of target passive heat sources and record it as the total number of target heat sources; If the total number of target heat sources is greater than 1, the target passive heat source is compared with the heating load to determine the minimum value of the heating load, and the heating node corresponding to the target passive heat source with the minimum heating load is marked as an adjustment node; if the total number of target heat sources is 1, the heating node corresponding to the target passive heat source is marked as an adjustment node; the real-time temperature of the adjustment node is obtained, and it is determined whether there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value; if there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value, the first adjustment instruction is not generated; If there is no adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit, the heating load of the passive adjustment heat source is reduced, and the real-time temperature of the adjustment node is monitored until there is an adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit; S405, obtaining the heating load reduced by the passive adjustment of the heat source, summing the sum with the adjustable load, updating the sum as the adjustable load, and executing S402 to S405 again; Obtaining the real-time temperature and personnel status of the heating node, calculating the adjustment parameters of the heating source based on the real-time temperature and personnel status, analyzing the adjustment parameters, and generating a second adjustment instruction; Specifically, the method for calculating the adjustment parameters of the heat source is: Collect the real-time temperature of the heating node and mark it as TZi, the number of air outlets of the heating node and mark it as Ni, and the number of people at the air outlet of the heating node and mark it as Ri; By formula The adjustment parameter F is calculated; C0 is the standard reference parameter, which is calculated based on historical heating load big data; Analyze the adjustment parameter F, and if the adjustment parameter F is not greater than 0, generate a second adjustment instruction; if the adjustment parameter F is greater than 0, do not generate the second adjustment instruction; Analyzing the first adjustment instruction and the second adjustment instruction by adjusting the perception model, and adjusting the load of the target node according to the analysis result; Specifically, the expression for adjusting the perception model is: ; GT is the adjustment consent value; GT=1 means that the load adjustment is performed on the active adjustment heat source where the target node is located; GT=-1 means that the load adjustment is not performed on the active adjustment heat source where the target node is located.

[0019] Embodiment 2, a building energy consumption data processing and control system based on 3D modeling, comprising a model construction and analysis module, a parameter acquisition and calculation module, a heat supply monitoring module, and a heat supply prediction and adjustment module; The model construction analysis module is used to obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; Parameter acquisition and calculation module, used to obtain safe operating parameters of heating equipment and determine the maximum heating load of heating equipment; The heating monitoring module is used to obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, count the heating loads of several heating sources, and obtain the total heating load; The heating forecasting and adjustment module is used to obtain the heating node that needs to be load-regulated and use it as the target node. It calculates the future forecast load based on the historical heating load data and the real-time heating load of the corresponding heating source, and regulates the load of the target node in combination with the total heating load and the maximum heating load.

[0020] Based on the present invention, a pilot renovation was carried out on a certain community, and the energy consumption performance is shown in the following table: Table 1: Energy consumption comparison table

[0021] Table 1 shows the comparative data of energy consumption before and after the use of this building energy consumption data processing and control system in this community; The system of the present invention can monitor the operating parameters of each heating network branch in real time, and timely analyze and control the production operation status. It improves the refined management of workers' production, realizes rapid response, rapid processing, and full-process supervision of business, improves service reputation, and achieves good social benefits.

[0022] In the future, with the continuous advancement of technology and the continuous expansion of application scenarios, the system will play an important role in more fields and promote the construction of smart cities to new heights.

[0023] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A building energy consumption data processing and control method based on 3D modeling, characterized by: The following steps are involved: S1. Obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; S2. Obtain safe operating parameters of the heating equipment and determine the maximum heating load of the heating equipment; S3. Obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, and calculate the heating load of several heating sources to obtain the total heating load; S4. Obtain a heating node that requires load regulation and use it as the target node. Calculate the future predicted load based on historical heating load data and the real-time heating load of the corresponding heating source. Regulate the load of the target node based on the total heating load and the maximum heating load. S4 includes: S401. Mark the load change of the heat source caused by the load adjustment application of the target node as the load application amount; S402: Determine whether the adjustable load meets the load application amount; if the adjustable load meets the load application amount, generate a pre-adjustment tag; if the adjustable load does not meet the load application amount, generate a calculation tag; S403: Obtain a pre-calculated load based on the pre-adjustment tag, calculate the load ratio of the pre-calculated load, and determine whether it exceeds the load threshold for actively adjusting the heat source; S404: Determine, based on the calculation tag, whether the load ratio of the passively adjusted heat source exceeds the corresponding load threshold.

2. The method for processing and controlling building energy consumption data based on 3D modeling according to claim 1, characterized in that: For S3, the total heating load is calculated as follows: Get the preset temperature of different heating nodes under the same heat source, through the formula, Calculate the preset temperature of the heat source ; Where Vi is the spatial volume of the heating node, Ti is the preset temperature of the heating node, Li is the distance from the heating node to the heating source, is the average volume of several heating nodes under the heat source, is the average value of the heating network pipe distances of several heating nodes under the heat source, i is the heating node number, i is a positive integer, i∈[1,n], n is the total number of heating nodes corresponding to the heat source; and are the weight coefficients of spatial volume and network management distance respectively; Get the external temperature and space volume of the heating node, and use the formula The total heating load Q is calculated; where a is the correction coefficient, qv is the volume heat index, is the average outdoor temperature of the heating node.

3. The building energy consumption data processing and control method based on 3D modeling according to claim 2 is characterized by: For S4, the method for regulating the load of the target node includes the following steps: Collect and analyze historical load data of heat sources, establish load forecast curves, and dynamically calculate adjustable loads; Determining whether the adjustable load quantity satisfies the load application quantity, and calculating the load change of the heat source caused by adjusting the load application quantity, obtaining an analysis and calculation result, and determining whether to generate a first adjustment instruction based on the analysis and calculation result; Obtaining the real-time temperature and personnel status of the heating node, calculating the adjustment parameters of the heating source based on the real-time temperature and personnel status, analyzing the adjustment parameters, and generating a second adjustment instruction; The first adjustment instruction and the second adjustment instruction are analyzed by adjusting the perception model, and the load of the target node is adjusted according to the analysis result.

4. The method for processing and controlling building energy consumption data based on 3D modeling according to claim 3, characterized in that: Methods for establishing load forecast curves include: Obtain historical heating load data of the heating source, analyze the historical load data, and establish a standard load curve; Obtain real-time load and select historical trend curve based on real-time load and historical load data; The standard load curve and the historical situation curve are compositely processed to determine the load forecast curve.

5. The building energy consumption data processing and control method based on 3D modeling according to claim 4 is characterized in that: S401 also includes: marking the heat source to which the target node belongs as an active adjustment heat source, and marking other heat sources as passive adjustment heat sources; respectively obtaining the load proportions of the active adjustment heat source and the passive adjustment heat source and the lower limit temperature value of the corresponding heating node; S403 further includes: obtaining the heating load of the actively adjusted heat source according to the pre-adjustment tag and summing it with the load application amount to obtain a pre-calculated load; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, generating a first adjustment instruction; if the pre-calculated load does not exceed the load threshold of the actively adjusted heat source, not generating the first adjustment instruction; S404 further includes: if there is a passive heat source whose load ratio exceeds the corresponding load threshold, marking it as a target passive heat source; counting the number of target passive heat sources and recording it as the total number of target heat sources; If the total number of target heat sources is greater than 1, the target passive heat source is compared with the heating load to determine the minimum value of the heating load, and the heating node corresponding to the target passive heat source with the minimum heating load is marked as an adjustment node; if the total number of target heat sources is 1, the heating node corresponding to the target passive heat source is marked as an adjustment node; the real-time temperature of the adjustment node is obtained, and it is determined whether there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value; if there is an adjustment node whose real-time temperature does not exceed the corresponding temperature lower limit value, the first adjustment instruction is not generated; If there is no adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit, the heating load of the passive adjustment heat source is reduced, and the real-time temperature of the adjustment node is monitored until there is an adjustment node whose real-time temperature does not exceed the corresponding lower temperature limit; S405 , obtaining the heating load reduced by passively adjusting the heat source, summing the sum with the adjustable load, updating the sum as the adjustable load, and executing S402 to S405 again.

6. The building energy consumption data processing and control method based on 3D modeling according to claim 5 is characterized by: The method for calculating the adjustment parameters of the heat source is: Collect the real-time temperature of the heating node and mark it as TZi, the number of air outlets of the heating node and mark it as Ni, and the number of people at the air outlet of the heating node and mark it as Ri; By formula The adjustment parameter F is calculated; C0 is the standard reference parameter, which is calculated based on historical heating load big data; The adjustment parameter F is analyzed. If the adjustment parameter F is not greater than 0, a second adjustment instruction is generated; if the adjustment parameter F is greater than 0, no second adjustment instruction is generated.

7. The building energy consumption data processing and control method based on 3D modeling according to claim 6 is characterized by: The expression for adjusting the perception model is: ; GT is the adjustment consent value; GT=1 means that the load adjustment is performed on the active adjustment heat source where the target node is located; GT=-1 means that the load adjustment is not performed on the active adjustment heat source where the target node is located.

8. A building energy consumption data processing and control system based on 3D modeling, applying the building energy consumption data processing and control method based on 3D modeling according to any one of claims 1 to 7, characterized in that: It includes model building and analysis module, parameter acquisition and calculation module, heating monitoring module and heating forecasting and adjustment module; The model construction analysis module is used to obtain the heating object of the heating equipment, collect the building data of the heating object, build a 3D model based on the building data, and determine the heating source of the heating object and multiple heating nodes under the heating source; Parameter acquisition and calculation module, used to obtain safe operating parameters of heating equipment and determine the maximum heating load of heating equipment; The heating monitoring module is used to obtain the heating demand of the heating node, calculate the heating load of the corresponding heating source, count the heating loads of several heating sources, and obtain the total heating load; The heating forecasting and adjustment module is used to obtain the heating node that needs to be load-regulated and use it as the target node. It calculates the future forecast load based on the historical heating load data and the real-time heating load of the corresponding heating source, and regulates the load of the target node in combination with the total heating load and the maximum heating load.

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