Building energy consumption prediction method and system based on BIM

Abstract

The present invention relates to the technical field of building energy consumption prediction, and in particular to a building energy consumption prediction method and system based on BIM, comprising a building data acquisition module, a building material data analysis module, an equipment data analysis module, a BIM building equipment construction module and an energy consumption control module. The BIM building equipment construction module constructs a BIM equipment energy consumption three-dimensional model according to the obtained data information of each device, the equipment data analysis module predicts the building energy consumption according to the analysis of the power consumption information of various types of equipment in the building, and the building material data analysis module predicts the energy consumption of the building under different environments according to the analysis of various data information of building materials. The present invention aims to predict the energy consumption of the building in advance, and perform maintenance processing on various high-energy-consuming equipment, so as to improve the equipment operation stability and reduce the building energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption prediction, and in particular to a building energy consumption prediction method and system based on BIM. Background Art

[0002] In terms of energy consumption, building energy consumption accounts for a considerable proportion of the global total energy consumption, and building energy consumption is mainly concentrated in heating, cooling, lighting, ventilation, and the operation of various equipment. In terms of heating and cooling, due to the insufficient thermal insulation performance of the building envelope and the limited energy efficiency of equipment such as air conditioners, a large amount of energy is wasted in the process of indoor temperature regulation. In the building operation and management stage, the monitoring and control technology of building energy consumption is relatively backward, and usually only post-statistics can be carried out. It is difficult to grasp the energy consumption status in real time and take effective energy-saving measures in time. When it is found that the energy consumption in a certain month is too high, it is difficult to quickly locate which equipment or links cause the problem, and it is impossible to make adjustments in time. Under the current society's strong demand for energy conservation, emission reduction and sustainable development, there is an urgent need for a technical method and system that can accurately predict building energy consumption, monitor and control energy consumption in real time, and reduce building energy consumption. The building energy consumption prediction method and system based on BIM technology in this application provide new ideas and effective ways to solve the problem of building energy consumption. Summary of the invention

[0003] In order to solve the technical problems existing in the background technology, the present invention provides a building energy consumption prediction method and system based on BIM.

[0004] The technical solution adopted by the present invention to solve the above technical problems is:

[0005] A first aspect of the present invention provides a building energy consumption prediction system based on BIM, including a building data acquisition module, a building material data analysis module, an equipment data analysis module, a BIM building equipment construction module, an energy consumption control module and a BIM data center.

[0006] The building data acquisition module collects data information about building materials and internal equipment of the building and sends it to each module for analysis. The specific acquisition process is as follows:

[0007] The input end of the building data acquisition module is connected to the output end of the BIM data center to extract the building material data of the current building in the BIM data center. The building material data includes structural materials, wall materials and material specific heat capacity, and obtains the building material parameter value of the current analyzed building;

[0008] Collect the equipment types and power in the current building in real time, and obtain the various types of equipment in the building and the corresponding power parameter values ​​of each type of equipment, including air conditioning, lighting, elevators, etc.;

[0009] The output end of the building data acquisition module is connected to the input end of the building material data analysis module, and the output end of the building data acquisition module is connected to the input end of the equipment data analysis module. The obtained building material parameter values ​​are sent to the material data analysis module, and the equipment type in the building and the corresponding equipment power parameter values ​​are sent to the equipment data analysis module.

[0010] The building materials data analysis module analyzes the data information of building materials and predicts the energy consumption of buildings under different environments. The specific process is as follows:

[0011] The input end of the building material data analysis module is connected to the output end of the BIM data center, and the output end of the building material data analysis module is connected to the input end of the energy consumption control module. 24 hours are marked as specific time periods, and the specific heat capacity of the building materials in the current specific time period is monitored to obtain the specific heat capacity value of the building materials in the specific time period, which is marked as GD. The current building is divided into several building areas, and the thermal conductivity coefficient of each building area in the current specific time period is monitored to obtain the thermal conductivity value of each building area. The average value of each thermal conductivity coefficient value is calculated to obtain the average value of the thermal conductivity coefficient, which is marked as GH. The thermal conductivity coefficient values ​​are arranged from large to small to obtain the maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value. The maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value are subtracted to obtain the thermal conductivity difference value, which is marked as GJ. The current solar radiation absorption coefficient is set, which is marked as GS. The specific heat capacity value, the average value of the thermal conductivity coefficient, the thermal conductivity difference value and the solar radiation absorption coefficient are calculated using the formula The temperature control influence index GT of the current building material is obtained, where R1, R2, R3 and R4 are preset proportional coefficients. The preset temperature control influence index of the BIM data center is extracted, and the actual temperature control influence index is compared with the preset temperature control influence index. If the actual temperature control influence index is less than the preset temperature control influence index, a control signal is generated and sent to the energy consumption control module.

[0012] The equipment data analysis module analyzes the power consumption information of various types of equipment in the building and predicts the building energy consumption. The specific acquisition process is as follows:

[0013] The input end of the equipment data analysis module is connected to the output end of the BIM data center. The 24 hours of working days and the 24 hours of weekends are marked as working hours and rest hours respectively. The power consumption time of each type of equipment in the working hours of the building is monitored in real time, and the working power consumption time of each type of equipment is obtained. The working power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the working hours, which is marked as the total working time. The power consumption time of each type of equipment in the rest period of the building is monitored in real time, and the rest power consumption time of each type of equipment is obtained. The rest power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the rest period, which is marked as the total rest time. The total rest time and the total working time are statistically obtained. According to the total rest time and the total working time, the total monthly energy consumption time is analyzed and marked as ;

[0014] Monitor the power consumption of various types of equipment during the working hours of the building in real time, obtain the power consumption of various types of equipment during the working hours, arrange the power consumption of various types of equipment from large to small and number them in sequence, j=1, 2, ..., n, j represents the number of each type of equipment, n represents the total number of each type of equipment, add the power consumption of various types of equipment during the working hours to obtain the total power consumption during the working hours, monitor the power consumption of various types of equipment during the rest period, obtain the power consumption of various types of equipment during the rest period, add the power consumption of various types of equipment to obtain the total power consumption during the rest period, add the total power consumption of the working period and the total power consumption of the rest period to obtain the total monthly power consumption, marked as ;

[0015] Obtain the power parameter values ​​of various types of equipment obtained by the building data acquisition module, extract the standard power values ​​corresponding to various types of equipment in the BIM data center, compare each power parameter value with the corresponding standard power value, mark the corresponding equipment with a power parameter value greater than the standard power parameter value as abnormal equipment, and obtain the number of abnormal equipment by counting and marking it as GF. Subtract the power parameter value corresponding to the number of abnormal equipment from the standard power value to obtain the abnormal power value GD. The abnormal power value is a positive number. Obtain the abnormal power duration GK and calculate it using the formula Get the abnormal power correction coefficient , where F1, F2 and F3 are preset proportional coefficients; the acquired total monthly energy consumption duration, total monthly power consumption and abnormal power correction coefficient are sent to the BIM building equipment construction module.

[0016] The BIM building equipment construction module constructs a BIM equipment energy consumption three-dimensional model based on the data information of each device. The specific acquisition process is as follows:

[0017] S1: The output end of the BIM building equipment construction module is connected to the input end of the energy consumption control module. The number of holidays corresponding to each month is set, and the holidays of each month are matched with the corresponding working hours and rest periods. If the holiday corresponds to a working period, the working period is modified to a rest period. If it is a rest period, it remains unchanged. According to the number of holidays corresponding to the month, the monthly energy consumption duration compensation coefficient is analyzed and marked as ;

[0018] S2: The total monthly energy consumption duration, total monthly power consumption, monthly energy consumption duration compensation coefficient and abnormal power correction coefficient are used to construct the BIM equipment energy consumption three-dimensional model of the current building. The BIM equipment energy consumption three-dimensional model is: , where DYP represents the predicted energy consumption value of the equipment, K1, K2, K3 and K4 are preset proportional coefficients, i represents each month, y represents the total number of months, extracts the preset equipment predicted energy consumption value of the BIM data center, compares the actual equipment predicted energy consumption value with the preset equipment predicted energy consumption value, and if the actual equipment predicted energy consumption value is greater than the preset equipment predicted energy consumption value, generates an energy-saving signal and sends it to the energy consumption control module.

[0019] The energy consumption control module is set according to the current building energy consumption, and obtains each abnormal energy consumption device for processing. The specific acquisition process is as follows:

[0020] The input end of the energy consumption control module is connected to the BIM data center. After receiving the energy-saving signal and the control signal, it performs comprehensive processing on the energy consumption, sets the total power consumption of the current building, extracts the total power consumption of each historical month of the BIM data center, calculates the average of each historical monthly power consumption, and sets the average of the historical monthly power consumption as the total power consumption of this month. When the total power consumption of this month exceeds the average of the historical monthly power consumption, it generates an energy over-consumption signal and sends it to the energy consumption monitoring system. The energy consumption monitoring system receives the energy over-consumption signal and monitors the power consumption of various types of equipment in the current building in real time. Consumption, extract the preset power consumption of the data center, compare the power consumption of each type of equipment in the current building with the preset power consumption, mark each type of equipment with a power consumption higher than the preset power consumption as a high-power consumption equipment, take each high-power consumption equipment as the center point, obtain the mobile phone terminal of each maintenance personnel within the radius, connect the high-power consumption equipment and the corresponding mobile phone terminal of the maintenance personnel, obtain each maintenance distance, select the nearest maintenance distance, mark the maintenance personnel at the corresponding distance as the selected personnel, generate a maintenance signal and send it to the selected personnel, the selected personnel receive the maintenance signal and obtain the location of the corresponding high-power consumption equipment for repair.

[0021] A second aspect of the present invention provides a method for predicting building energy consumption based on BIM, comprising the following steps:

[0022] Z1: Based on the data information collected from building materials and internal equipment of the building;

[0023] Z2: Based on the analysis of various data information of building materials, predict the energy consumption of buildings in different environments;

[0024] Z3: Predict building energy consumption based on analysis of electricity consumption information of various types of equipment in the building;

[0025] Z4: Construct a three-dimensional BIM equipment energy consumption model based on the data information of each device;

[0026] Z5: Set it according to the current energy consumption of the building, and obtain the equipment with abnormal energy consumption for processing.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention comprehensively collects building materials and equipment information through the building data acquisition module, the building material data analysis module deeply analyzes material properties, the equipment data analysis module accurately counts equipment power consumption data, and the BIM building equipment construction module constructs an accurate energy consumption three-dimensional model, thereby realizing accurate prediction of building energy consumption; in practical applications, it can accurately predict energy consumption in different periods of each month, and the error can be controlled within a small range, providing reliable data support for building energy management, so that energy supply strategies can be scientifically adjusted according to actual needs to avoid energy waste. The equipment data analysis module can monitor the power consumption time and power consumption of the equipment in real time, detect abnormal power equipment in time, and calculate the abnormal power correction coefficient, so as to more accurately predict energy consumption. When the actual equipment predicted energy consumption value exceeds the preset value, it can quickly generate an energy-saving signal and send it to the energy consumption control module. The energy consumption control module determines whether the total power consumption is abnormal by comparing it with historical data. Once an abnormality is found, it can quickly locate high-power consumption equipment and promptly notify maintenance personnel for processing. This mechanism of timely detection and processing of abnormal energy consumption equipment effectively avoids increased energy consumption and production interruptions caused by equipment failure, ensures the stable operation of building equipment, extends equipment service life, reduces equipment maintenance costs, and also ensures the normal development of various activities in the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a principle block diagram of the present invention;

[0029] Figure 2 It is a diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments.

[0031] like Figure 1As shown, the first aspect of the present invention provides a BIM-based building energy consumption prediction system, including a building data acquisition module, a building material data analysis module, an equipment data analysis module, a BIM building equipment construction module, an energy consumption control module and a BIM data center.

[0032] The building data acquisition module collects data information about building materials and internal equipment of the building and sends it to each module for analysis. The specific acquisition process is as follows:

[0033] The input end of the building data acquisition module is connected to the output end of the BIM data center to extract the building material data of the current building in the BIM data center. The building material data includes structural materials, wall materials and material specific heat capacity, and obtains the building material parameter value of the current analyzed building;

[0034] Collect the equipment types and power in the current building in real time, and obtain the various types of equipment in the building and the corresponding power parameter values ​​of each type of equipment, including air conditioning, lighting, elevators, etc.;

[0035] The output end of the building data acquisition module is connected to the input end of the building material data analysis module, and the output end of the building data acquisition module is connected to the input end of the equipment data analysis module. The obtained building material parameter values ​​are sent to the material data analysis module, and the equipment type in the building and the corresponding equipment power parameter values ​​are sent to the equipment data analysis module.

[0036] The building materials data analysis module analyzes the data information of building materials and predicts the energy consumption of buildings under different environments. The specific process is as follows:

[0037] The input end of the building material data analysis module is connected to the output end of the BIM data center, and the output end of the building material data analysis module is connected to the input end of the energy consumption control module. 24 hours are marked as specific time periods, and the specific heat capacity of the building materials in the current specific time period is monitored to obtain the specific heat capacity value of the building materials in the specific time period, which is marked as GD. The current building is divided into several building areas, and the thermal conductivity coefficient of each building area in the current specific time period is monitored to obtain the thermal conductivity value of each building area. The average value of each thermal conductivity coefficient value is calculated to obtain the average value of the thermal conductivity coefficient, which is marked as GH. The thermal conductivity coefficient values ​​are arranged from large to small to obtain the maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value. The maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value are subtracted to obtain the thermal conductivity difference value, which is marked as GJ. The current solar radiation absorption coefficient is set, which is marked as GS. The specific heat capacity value, the average value of the thermal conductivity coefficient, the thermal conductivity difference value and the solar radiation absorption coefficient are calculated using the formula The temperature control influence index GT of the current building material is obtained, where R1, R2, R3 and R4 are preset proportional coefficients. The preset temperature control influence index of the BIM data center is extracted, and the actual temperature control influence index is compared with the preset temperature control influence index. If the actual temperature control influence index is less than the preset temperature control influence index, a control signal is generated and sent to the energy consumption control module.

[0038] The equipment data analysis module analyzes the power consumption information of various types of equipment in the building and predicts the building energy consumption. The specific acquisition process is as follows:

[0039] The input end of the equipment data analysis module is connected to the output end of the BIM data center. The 24 hours of working days and the 24 hours of weekends are marked as working hours and rest hours respectively. The power consumption time of each type of equipment in the working hours of the building is monitored in real time, and the working power consumption time of each type of equipment is obtained. The working power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the working hours, which is marked as the total working time. The power consumption time of each type of equipment in the rest period of the building is monitored in real time, and the rest power consumption time of each type of equipment is obtained. The rest power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the rest period, which is marked as the total rest time. The total rest time and the total working time are statistically obtained. According to the total rest time and the total working time, the total monthly energy consumption time is analyzed and marked as ;

[0040] Monitor the power consumption of various types of equipment during the working hours of the building in real time, obtain the power consumption of various types of equipment during the working hours, arrange the power consumption of various types of equipment from large to small and number them in sequence, j=1, 2, ..., n, j represents the number of each type of equipment, n represents the total number of each type of equipment, add the power consumption of various types of equipment during the working hours to obtain the total power consumption during the working hours, monitor the power consumption of various types of equipment during the rest period, obtain the power consumption of various types of equipment during the rest period, add the power consumption of various types of equipment to obtain the total power consumption during the rest period, add the total power consumption of the working period and the total power consumption of the rest period to obtain the total monthly power consumption, marked as ;

[0041] Obtain the power parameter values ​​of various types of equipment obtained by the building data acquisition module, extract the standard power values ​​corresponding to various types of equipment in the BIM data center, compare each power parameter value with the corresponding standard power value, mark the corresponding equipment with a power parameter value greater than the standard power parameter value as abnormal equipment, and obtain the number of abnormal equipment by counting and marking it as GF. Subtract the power parameter value corresponding to the number of abnormal equipment from the standard power value to obtain the abnormal power value GD. The abnormal power value is a positive number. Obtain the abnormal power duration GK and calculate it using the formula Get the abnormal power correction coefficient , where F1, F2 and F3 are preset proportional coefficients; the acquired total monthly energy consumption duration, total monthly power consumption and abnormal power correction coefficient are sent to the BIM building equipment construction module.

[0042] The BIM building equipment construction module constructs a BIM equipment energy consumption three-dimensional model based on the data information of each device. The specific acquisition process is as follows:

[0043] S1: The output end of the BIM building equipment construction module is connected to the input end of the energy consumption control module. The number of holidays corresponding to each month is set, and the holidays of each month are matched with the corresponding working hours and rest periods. If the holiday corresponds to a working period, the working period is modified to a rest period. If it is a rest period, it remains unchanged. According to the number of holidays corresponding to the month, the monthly energy consumption duration compensation coefficient is analyzed and marked as ;

[0044] S2: The total monthly energy consumption duration, total monthly power consumption, monthly energy consumption duration compensation coefficient and abnormal power correction coefficient are used to construct the BIM equipment energy consumption three-dimensional model of the current building. The BIM equipment energy consumption three-dimensional model is: , where DYP represents the predicted energy consumption value of the equipment, K1, K2, K3 and K4 are preset proportional coefficients, i represents each month, y represents the total number of months, extracts the preset equipment predicted energy consumption value of the BIM data center, compares the actual equipment predicted energy consumption value with the preset equipment predicted energy consumption value, and if the actual equipment predicted energy consumption value is greater than the preset equipment predicted energy consumption value, generates an energy-saving signal and sends it to the energy consumption control module.

[0045] The energy consumption control module is set according to the current building energy consumption, and obtains each abnormal energy consumption device for processing. The specific acquisition process is as follows:

[0046] The input end of the energy consumption control module is connected to the BIM data center. After receiving the energy-saving signal and the control signal, it performs comprehensive processing on the energy consumption, sets the total power consumption of the current building, extracts the total power consumption of each historical month of the BIM data center, calculates the average of each historical monthly power consumption, and sets the average of the historical monthly power consumption as the total power consumption of this month. When the total power consumption of this month exceeds the average of the historical monthly power consumption, it generates an energy over-consumption signal and sends it to the energy consumption monitoring system. The energy consumption monitoring system receives the energy over-consumption signal and monitors the power consumption of various types of equipment in the current building in real time. Consumption, extract the preset power consumption of the data center, compare the power consumption of each type of equipment in the current building with the preset power consumption, mark each type of equipment with a power consumption higher than the preset power consumption as a high-power consumption equipment, take each high-power consumption equipment as the center point, obtain the mobile phone terminal of each maintenance personnel within the radius, connect the high-power consumption equipment and the corresponding mobile phone terminal of the maintenance personnel, obtain each maintenance distance, select the nearest maintenance distance, mark the maintenance personnel at the corresponding distance as the selected personnel, generate a maintenance signal and send it to the selected personnel, the selected personnel receive the maintenance signal and obtain the location of the corresponding high-power consumption equipment for repair.

[0047] like Figure 2 As shown, the second aspect of the present invention provides a building energy consumption prediction method based on BIM, comprising the following steps:

[0048] Z1: Building data collection is based on collecting data information of building materials and internal equipment of buildings;

[0049] Z2: The building materials data analysis module analyzes the data information of building materials and predicts the energy consumption of buildings under different environments;

[0050] Z3: Equipment data analysis predicts building energy consumption based on the analysis of electricity consumption information of various types of equipment in the building;

[0051] Z4: BIM building equipment construction builds a three-dimensional model of BIM equipment energy consumption based on the data information of each equipment;

[0052] Z5: Energy consumption control is set according to the current energy consumption of the building, and the equipment with abnormal energy consumption is obtained for processing.

[0053] Although exemplary embodiments of the present invention have been described, those skilled in the art should understand that the above embodiments are merely illustrative of the present invention rather than limiting thereof, and that modifications and substitutions made without departing from the essence and advantages of the present invention should be within the scope of protection of the present invention.

Claims

1. A BIM-based building energy consumption prediction system, including a building data acquisition module, a building material data analysis module, an equipment data analysis module, an energy consumption control module and a BIM data center, characterized in that: Also included is the BIM building equipment building module; The BIM building equipment construction module constructs a three-dimensional model of BIM equipment energy consumption based on the data information of each device, specifically: S1: The output end of the BIM building equipment construction module is connected to the input end of the energy consumption control module. The number of holidays corresponding to each month is set, and the holidays of each month are matched with the corresponding working hours and rest periods. If the holiday corresponds to a working period, the working period is modified to a rest period. If it is a rest period, it remains unchanged. According to the number of holidays corresponding to the month, the monthly energy consumption duration compensation coefficient is analyzed and marked as TN. 系数 ; S2: The total monthly energy consumption duration TY 总时长 , Monthly total power consumptionTU 电量 , Monthly energy consumption duration compensation coefficient TN 系数 and abnormal power correction coefficient GC 系数 The BIM equipment energy consumption three-dimensional model of the current building is constructed. The BIM equipment energy consumption three-dimensional model is: , where DYP represents the predicted energy consumption value of the equipment, K1, K2, K3 and K4 are preset proportional coefficients, i represents each month, y represents the total number of months, j=1, 2, ..., n, j represents the number of each type of equipment, n represents the total number of each type of equipment, extract the preset equipment predicted energy consumption value of the BIM data center, compare the actual equipment predicted energy consumption value with the preset equipment predicted energy consumption value, and if the actual equipment predicted energy consumption value is greater than the preset equipment predicted energy consumption value, generate an energy-saving signal and send it to the energy consumption control module.

2. A BIM-based building energy consumption prediction system according to claim 1, characterized in that: The equipment data analysis module analyzes the power consumption information of various types of equipment in the building and predicts the building energy consumption. The specific process is as follows: The input end of the equipment data analysis module is connected to the output end of the BIM data center. The 24 hours of working days and the 24 hours of weekends are marked as working hours and rest hours respectively. The power consumption time of each type of equipment in the building working hours is monitored in real time, and the working power consumption time of each type of equipment is obtained. The working power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the working hours, which is marked as the total working time. The power consumption time of each type of equipment in the building rest period is monitored in real time, and the rest power consumption time of each type of equipment is obtained. The rest power consumption time of each type of equipment is added up to obtain the total power consumption time of each type of equipment in the rest period, which is marked as the total rest time. The total rest time and the total working time are obtained by statistics. According to the total rest time and the total working time, the total monthly energy consumption time is analyzed and marked as TY 总时长 ; Monitor the power consumption of various types of equipment during the working hours of the building in real time, obtain the power consumption of various types of equipment during the working hours, arrange the power consumption of various types of equipment from large to small and number them in sequence, add the power consumption of various types of equipment during the working hours to obtain the total power consumption during the working hours, monitor the power consumption of various types of equipment during the rest period, obtain the power consumption of various types of equipment during the rest period, add the power consumption of various types of equipment to obtain the total power consumption during the rest period, add the total power consumption of the working period and the total power consumption of the rest period to obtain the total monthly power consumption, marked as TU 电量 ; Get the power parameter values ​​of each type of equipment obtained by the building data acquisition module, extract the standard power value corresponding to each type of equipment, compare each power parameter value with the corresponding standard power value, mark the corresponding equipment with a power parameter value greater than the standard power parameter value as abnormal equipment, and count the number of abnormal equipment, marked as GF, and make a difference between the power parameter value corresponding to the number of abnormal equipment and the standard power value to obtain the abnormal power value GD. The abnormal power value is a positive number, and the abnormal power duration GK is obtained. Calculate using the formula Get the abnormal power correction coefficient GC 系数 , where F1, F2 and F3 are preset proportional coefficients; the acquired total monthly energy consumption duration, total monthly power consumption and abnormal power correction coefficient are sent to the BIM building equipment construction module.

3. The BIM-based building energy consumption prediction system according to claim 1 is characterized in that: The building materials data analysis module analyzes the data information of building materials and predicts the energy consumption of buildings under different environments. The specific process is as follows: The input end of the building material data analysis module is connected to the output end of the BIM data center, and the output end of the building material data analysis module is connected to the input end of the energy consumption control module. 24 hours are marked as specific time periods, and the specific heat capacity of the building materials in the current specific time period is monitored to obtain the specific heat capacity value of the building materials in the specific time period, which is marked as GD. The current building is divided into several building areas, and the thermal conductivity coefficient of each building area in the current specific time period is monitored to obtain the thermal conductivity value of each building area. The average value of each thermal conductivity coefficient value is calculated to obtain the average value of the thermal conductivity coefficient, which is marked as GH. The thermal conductivity coefficient values ​​are arranged from large to small to obtain the maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value. The maximum thermal conductivity coefficient value and the minimum thermal conductivity coefficient value are subtracted to obtain the thermal conductivity difference value, which is marked as GJ. The current solar radiation absorption coefficient is set, which is marked as GS. The specific heat capacity value, the average value of the thermal conductivity coefficient, the thermal conductivity difference value and the solar radiation absorption coefficient are calculated using the formula The temperature control influence index GT of the current building material is obtained, where R1, R2, R3 and R4 are preset proportional coefficients. The preset temperature control influence index of the BIM data center is extracted, and the actual temperature control influence index is compared with the preset temperature control influence index. If the actual temperature control influence index is less than the preset temperature control influence index, a control signal is generated and sent to the energy consumption control module.

4. The BIM-based building energy consumption prediction system according to claim 1 is characterized in that: The energy consumption control module is set according to the current building energy consumption, and obtains each abnormal energy consumption device for processing. The specific process is as follows: The input end of the energy consumption control module is connected to the BIM data center. After receiving the energy-saving signal and the control signal, it performs comprehensive processing on the energy consumption, sets the total power consumption of the current building, extracts the total power consumption of each historical month of the BIM data center, calculates the average of each historical monthly power consumption, and sets the average of the historical monthly power consumption as the total power consumption of this month. When the total power consumption of this month exceeds the average of the historical monthly power consumption, it generates an energy over-consumption signal and sends it to the energy consumption monitoring system. The energy consumption monitoring system receives the energy over-consumption signal and monitors the power consumption of various types of equipment in the current building in real time. Consumption, extract the preset power consumption of the data center, compare the power consumption of each type of equipment in the current building with the preset power consumption, mark each type of equipment with a power consumption higher than the preset power consumption as a high-power consumption equipment, take each high-power consumption equipment as the center point, obtain the mobile phone terminal of each maintenance personnel within the radius, connect the high-power consumption equipment and the corresponding mobile phone terminal of the maintenance personnel, obtain each maintenance distance, select the nearest maintenance distance, mark the maintenance personnel at the corresponding distance as the selected personnel, generate a maintenance signal and send it to the selected personnel, the selected personnel receive the maintenance signal and obtain the location of the corresponding high-power consumption equipment for repair.

5. The BIM-based building energy consumption prediction system according to claim 1 is characterized in that: The building data collection module collects data information about building materials and internal equipment of the building and sends it to each module for analysis. The specific process is as follows: The input end of the building data acquisition module is connected to the output end of the BIM data center to extract the building material data of the current building in the BIM data center. The building material data includes structural materials, wall materials and material specific heat capacity, and obtains the building material parameter value of the current analyzed building; Collect the equipment types and power in the current building in real time, and obtain the various types of equipment in the building and the corresponding power parameter values ​​of each type of equipment, including air conditioning, lighting and elevators; The output end of the building data acquisition module is connected to the input end of the building material data analysis module, and the output end of the building data acquisition module is connected to the input end of the equipment data analysis module. The obtained building material parameter values ​​are sent to the material data analysis module, and the equipment type in the building and the corresponding equipment power parameter values ​​are sent to the equipment data analysis module.

6. A BIM-based building energy consumption prediction system according to any one of claims 1 to 5, characterized in that: The prediction method of the system includes the following steps: Z1: Building data collection is based on collecting data information of building materials and internal equipment of buildings; Z2: The building materials data analysis module analyzes the data information of building materials and predicts the energy consumption of buildings under different environments; Z3: Equipment data analysis predicts building energy consumption based on the analysis of electricity consumption information of various types of equipment in the building; Z4: BIM building equipment construction builds a three-dimensional model of BIM equipment energy consumption based on the data information of each equipment; Z5: Energy consumption control is set according to the current energy consumption of the building, and the equipment with abnormal energy consumption is obtained for processing.

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