A municipal building energy efficiency intelligent analysis method based on BIM

Abstract

The application discloses a kind of municipal building energy efficiency intelligent analysis methods based on BIM, specifically related to BIM field, including S1: building energy efficiency model construction, S2: energy consumption monitoring, S3: passenger flow analysis, S4: spatial aggregation analysis, S5: energy consumption analysis, S6: abnormal extraction, S7: energy efficiency optimization early warning.The present application realizes the digital image of the geometric characteristics and equipment parameters of municipal building by BIM model, and constructs building energy efficiency model according to model deviation, extracts abnormal energy efficiency timestamp by correlation curve analysis, and provides guidance for the corresponding countermeasures of different levels of early warning according to abnormal energy efficiency timestamp and correlation analysis results when there is risk, reduces the frequency of manual intervention, reduces the operation and maintenance labor cost, provides whole life cycle cost optimization and management support for operation and maintenance party, to a certain extent, guarantees the continuity of municipal building energy efficiency analysis, achieves the goal of improving quality, convenient economy, green environmental protection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of BIM, more particularly, the present application relates to a municipal building energy efficiency intelligent analysis method based on BIM. BACKGROUND

[0002] Global energy consumption continues to grow, and environmental problems are becoming increasingly prominent. Buildings consume a large proportion of energy during their life cycle. Municipal buildings, as an important part of urban infrastructure, are numerous and large in size. The improvement of their energy efficiency is of great significance to energy saving and emission reduction. Therefore, effective energy efficiency analysis methods are urgently needed to analyze energy efficiency and reduce energy consumption based on energy efficiency analysis results to achieve sustainable development. In recent years, BIM technology has provided technical support for municipal building energy efficiency intelligent analysis methods by creating and managing digital information of buildings, realizing three-dimensional visualization and data integration of building projects throughout their life cycle.

[0003] Traditional building energy saving analysis relies on experience estimation and simplified calculation, which is difficult to fully consider the complexity and variability of buildings and cannot meet the needs of fine and personalized energy saving design. For municipal buildings, which are diverse in function and complex in structure, it is more difficult for traditional methods to accurately assess their energy efficiency. However, there are still some shortcomings in actual use. First, traditional building energy saving analysis usually relies on manual field measurement to obtain building basic data, which is low in efficiency and easily affected by human error. For large municipal buildings, manual measurement is difficult to cover the entire range, and the data integrity is poor. At the same time, energy consumption analysis is mostly based on empirical formulas or simplified models, ignoring the actual impact of building complex structure and material characteristics, resulting in large deviation between analysis results and actual energy consumption.

[0004] Second, with the promotion of green buildings, near-zero energy consumption buildings and other standards, the rough calculation of traditional methods cannot meet the quantitative evaluation needs of mandatory energy efficiency indicators. For example, when municipal buildings declare green star level, life cycle energy consumption simulation is required, and traditional methods cannot meet the data precision requirements. On the other hand, existing methods focus on a single target, but municipal buildings often need to consider comfort, carbon emission reduction, operating cost and other multi-dimensional needs for energy consumption adjustment. SUMMARY

[0005] Therefore, the embodiments of the present application provide a municipal building energy efficiency intelligent analysis method based on BIM, which effectively solves the problems raised in the background art through the following solutions.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] S1: Building energy efficiency model construction: constructing a building energy efficiency model through BIM, and verifying the model to determine whether there is deviation, and adding dynamic parameters required for energy efficiency analysis, wherein the dynamic parameters include envelope parameters and heating and ventilation parameters;

[0008] S2: Energy consumption monitoring: Obtain the building energy consumption of the building equipment, and then obtain N building energy consumption texts according to the building energy consumption, and associate the building energy consumption texts with the building energy efficiency model in a predefined manner through the Internet of Things gateway, thereby obtaining the text timestamp;

[0009] S3: People flow analysis: According to the text timestamp, the people flow of the target municipal building is counted, and the peak people flow of the target municipal building is called, and then the duration thereof is extracted from the peak people flow;

[0010] S4: Spatial aggregation analysis: Based on the duration of the peak people flow and the people flow analysis of the target municipal building, the spatial aggregation degree corresponding to the text timestamp is analyzed;

[0011] S5: Energy consumption analysis: Based on the association relationship between the building energy consumption text and the building energy efficiency model, the energy consumption of each text timestamp is analyzed, and the equipment energy consumption degree corresponding to each text timestamp is obtained;

[0012] S6: Abnormal extraction: Based on the spatial aggregation degree and the equipment energy consumption degree corresponding to each text timestamp, an association curve is constructed, and then the abnormal energy efficiency timestamp is extracted according to the association curve;

[0013] S7: Energy efficiency optimization and early warning: Based on the abnormal energy efficiency timestamp extracted in S6 and the association analysis result, the building energy efficiency model and the Internet of Things system are combined to develop and execute targeted optimization strategies, and early warning is performed when there is a risk.

[0014] The technical effects and advantages of the present application are as follows:

[0015] 1. The present application realizes the digital mirror image of the geometric characteristics and equipment parameters of the municipal building through the BIM model, and combines the enclosure parameters and the heating and ventilation parameters into the building energy efficiency model according to the model deviation, which on the one hand optimizes the building energy efficiency model, overcomes the energy efficiency changes caused by environmental factors to a certain extent, improves the stability of energy efficiency data collection and analysis, and on the other hand, through the BIM building energy efficiency model, the geometric parameters, material parameters and equipment parameters required for energy efficiency analysis can be integrated into a single model, which can improve the predictability and controllability of building energy saving performance, and also provide full life cycle cost optimization and management support for the operation and maintenance party, achieving the goals of improving quality, convenient economy and green environmental protection;

[0016] 2、The present application acquires building equipment energy consumption data in real time, forms a building energy consumption text, and obtains a text timestamp after associating the building energy consumption text with the building energy efficiency model in a predefined manner, providing a time reference for energy efficiency comparison across devices and time periods, not limited to single data energy efficiency analysis, and analyzing the degree of spatial aggregation and the degree of equipment energy consumption through the text timestamp, thereby ensuring the continuity of municipal building energy efficiency analysis to a certain extent, and providing accurate data basis for subsequent associated curve construction;

[0017] 3、The present application extracts abnormal energy efficiency timestamps through associated curve analysis, and provides guidance for targeted measures to deal with different levels of early warning, reduces the frequency of manual intervention, reduces the cost of operation and maintenance personnel, and avoids the confusion of early warning caused by inconsistent human judgment standards. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The present application is a schematic diagram of the overall structure.

[0019] Figure 2 The present application is a building energy efficiency model construction step flow chart.

[0020] Figure 3 The present application is a construction step flow chart of the associated curve.

[0021] Figure 4 The present application is an energy efficiency optimization warning flow chart. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0023] As shown in the accompanying drawings, a BIM-based municipal building energy efficiency intelligent analysis method, the specific implementation includes the following steps: Figure 1

[0024] S1: Building energy efficiency model construction: build a building energy efficiency model through BIM, and check the model to determine whether the model has deviation, and add dynamic parameters required for energy efficiency analysis, wherein the dynamic parameters include enclosure parameters and heating and ventilation parameters.

[0025] In this embodiment, it needs to be specifically pointed out that the building energy efficiency model construction refers to Figure 2 , and the specific implementation is as follows: ​

[0026] A1: Import the BIM geometric model and determine the municipal building type, which includes but is not limited to government buildings, public entertainment facilities, urban transportation facilities, educational and scientific and cultural institution buildings, and medical and health institution buildings;

[0027] A2: Load the parameter template according to the municipal building type to generate an initial energy efficiency model. For example, when the municipal building type is determined to be a government building, load the power parameter template to construct the initial energy efficiency model, and when the municipal building type is determined to be a medical and health institution building, load the constant temperature and humidity template to construct the initial energy efficiency model;

[0028] A3: Detect whether the space division of the initial energy efficiency model is complete, such as rooms, floors, and functional areas. If there is a deviation in the model, return to A1 to correct the BIM geometric model;

[0029] A4: According to the municipal building type, detect whether the parameter template loaded in the initial model matches the building function requirement. If there is a deviation in the model, return to A2 to adjust the parameter template;

[0030] A5: If there is no deviation in the model, add the envelope parameters and the heating and ventilation parameters, wherein the envelope parameters include the heat transfer coefficient and the shading coefficient, and the heating and ventilation parameters include the equipment running time and the equipment power, thereby constructing the building energy efficiency model.

[0031] It needs to be explained that the envelope parameters reflect the heat transfer characteristics of the envelope structure under different climate conditions, the heat transfer coefficient is the heat transfer capacity of the wall, roof, and floor, and the shading coefficient is the blocking ability of the external window glass or shading facility to solar radiation. The acquisition method can be to extract the fixed value of the envelope parameter from the BIM model attribute and to dynamically correct the envelope parameter through machine learning training based on historical data; the heating and ventilation parameters reflect the running state and energy efficiency performance of the heating and ventilation air conditioning system under different loads, the equipment running time is the duration of the equipment in the target municipal building, and the equipment power is the rated power of the equipment in the target municipal building; in complex municipal building energy efficiency analysis, data collection will be disturbed by many factors. By combining the envelope parameters and the heating and ventilation parameters to construct the building energy efficiency model, the energy efficiency changes caused by environmental factors can be overcome to a certain extent, and the stability of energy efficiency data collection and analysis can be improved.

[0032] It needs to be further explained that BIM is a building information model, which can integrate geometric parameters, material parameters and equipment parameters required for energy efficiency analysis into a single model as a building full-element database. Building energy efficiency model can be built through BIM technology, which can improve the predictability and controllability of building energy saving performance, and also provide whole life cycle cost optimization and management support for owners, designers and operation parties, so as to achieve the goal of improving quality, convenient economy and green environmental protection.

[0033] S2: Energy consumption monitoring: obtaining the building energy consumption of the building equipment, and then obtaining N building energy consumption texts according to the building energy consumption, and associating the building energy consumption texts and the building energy efficiency model in a predefined manner through the Internet of Things gateway, so as to obtain the text timestamp.

[0034] In this embodiment, it needs to be specifically explained that the building energy consumption of the building equipment is obtained by installing energy consumption metering equipment in the target municipal building, wherein the energy consumption metering equipment can be a smart meter and an ultrasonic heat meter. The ultrasonic heat meter can obtain the cooling and heating capacity of the air conditioning system, and the smart meter can perform sub-metering on different building equipment, such as lighting system, air conditioning system and power system. The lighting system is used to install an electric meter at the outlet end of the lighting distribution box to measure the energy consumption of lamps and emergency lighting. The air conditioning system is used to measure the power consumption of air conditioning terminals, cold and heat source equipment and distribution systems independently. The power system is used to independently monitor large power equipment such as elevators, escalators and water supply and drainage pumps, and convert the original data corresponding to different equipment into a unified format.

[0035] It needs to be supplemented that N building energy consumption texts are obtained by removing abnormal data through edge computing according to the building energy consumption of the building equipment, and calculating the derived parameters of each building equipment. The derived parameters are integrated as N municipal building energy consumption texts. For example, through edge computing, the negative value of the electric meter is marked as invalid and removed. The derived parameters are energy efficiency ratio, unit area energy consumption, power density and load rate. The energy efficiency ratio is used to evaluate the operating efficiency of the water chiller unit and is calculated according to the power consumption of the air conditioning system. Specifically, it is obtained by comparing the cooling and heating capacity of the air conditioning system with the power consumption. The unit area energy consumption is obtained by comparing the total energy consumption of the target municipal building with the building area. The power density is obtained by comparing the lighting power of the lighting system with the area of the system. The load rate is obtained by comparing the actual power of the power system with the design power.

[0036] It needs to be further explained that the building energy consumption texts and the building energy efficiency model are associated in a predefined manner, which includes: one-to-one correspondence between the building energy consumption texts and the equipment family instances in the building energy efficiency model; in the building energy efficiency model, each equipment family instance, such as a smart meter, is assigned a globally unique identifier, and the building energy consumption texts are associated with the building energy efficiency model through the same ID.

[0037] Adopting the same hierarchical naming as the BIM spatial tree: The building energy efficiency model is constructed into a spatial tree structure by building, floor, area, and room. The building energy consumption text uses the same hierarchical path naming, and the spatial nodes corresponding to each path are matched one by one to establish spatial association.

[0038] Associating Model Components with GUIDs from the IFC Standard: The IFC standard is an open data format used for exchanging data in BIM. In the IFC standard, GUIDs are used to uniquely identify various elements in the model. These elements can be different parts of a building, such as walls, windows, floors, or other information related to the building. Building energy consumption text references GUIDs through fields, associating them with components in the model.

[0039] It should be added that the association between equipment, space, and model and building energy consumption text is mapped according to a predefined method, and the building energy consumption text is matched with the building energy efficiency model area. By combining the three methods, data sources are provided for municipal buildings, so that energy consumption data can be associated with equipment, space, and model, reducing the phenomenon of data silos and providing underlying support for the intelligent management of municipal buildings.

[0040] It should be added that, based on the correlation between building energy consumption text and building energy efficiency model, the text timestamp is directly generated for energy consumption metering devices with time synchronization function, and for energy consumption metering devices without time synchronization function, the IoT gateway adds timestamps during data collection according to its own clock accuracy. The timestamps are integrated and sorted according to the chronological order to obtain the text timestamp.

[0041] It should be added that the text timestamp refers to the point in time when the building energy consumption text is actually collected or recorded. It records the time when the data is generated at the source and reflects the moment when the data begins to be processed and analyzed in energy efficiency analysis.

[0042] S3: Pedestrian Flow Analysis: Based on the text timestamp, count the pedestrian flow of the target municipal building, retrieve the peak pedestrian flow of the target municipal building, and then extract the duration of the peak pedestrian flow.

[0043] In this embodiment, it should be specifically explained that the pedestrian flow is based on the number of people in the target municipal building recorded by card swiping or facial recognition at the text timestamp. The peak pedestrian flow is calculated by the rolling average of the pedestrian flow in the text timestamp. The pedestrian flow corresponding to the current text timestamp is compared with 1.5 times the average value corresponding to the text timestamps before the current text timestamp. When the pedestrian flow corresponding to the current text timestamp is greater than 1.5 times the average value corresponding to the text timestamps before the current text timestamp, the pedestrian flow corresponding to the current text timestamp is determined to be the peak pedestrian flow.

[0044] Further, if the interval between the two peak flow rates corresponding to the text timestamps is less than the preset interval, they are set as the same peak flow rate, and the total duration is combined to calculate the duration of the peak flow rate.

[0045] For example, the flow rate of the target municipal building corresponding to each text timestamp is recorded and counted by face recognition as Fp, and the flow rate corresponding to the current text timestamp is Fp I At this time, the rolling average of the flow rate corresponding to the first I-1 text timestamps is If Fp I is greater than , the flow rate corresponding to the current text timestamp is a peak flow rate.

[0046] S4: Spatial aggregation analysis: based on the duration of the peak flow rate and the flow rate of the target municipal building, the spatial aggregation degree corresponding to the text timestamp is analyzed.

[0047] In this embodiment, it needs to be specifically explained that the spatial aggregation degree corresponding to the text timestamp is analyzed according to Figure 3 , and the specific process is as follows:

[0048] B1: Extract the historical flow rate of the target municipal building at the text timestamp, and perform weighted calculation on the historical flow rate of each text timestamp to obtain the predicted flow rate of the target municipal building, and mark it as Z(t), t represents the time corresponding to the text timestamp, wherein the weight corresponding to the historical flow rate of each text timestamp is matched according to the flow rate of different text timestamps, and the greater the flow rate at the text timestamp, the greater the corresponding weight;

[0049] B2: According to the correlation between the building energy consumption text and the building energy efficiency model, combined with the text timestamp, the use area of each spatial node is extracted, and the flow rate of each text timestamp corresponding to N building energy consumption texts is marked as Fp i , i is the text timestamp number, i=1, 2, …, N;

[0050] B3: The flow rate of each text timestamp is averaged to obtain the average flow rate of the target municipal building, which is marked as Fp a ;

[0051] B4: Based on the flow rate of each text timestamp, the average flow rate and the predicted flow rate, the spatial aggregation degree corresponding to the text timestamp is calculated, which is specifically represented as:

[0052] ,

[0053] Wherein D represents the spatial aggregation corresponding to each text timestamp, R1 and R2 represent the set coefficient, t1 and t2 represent the starting time and ending time of the peak passenger flow respectively, and N represents the number of text timestamp numbers.

[0054] It needs to be added that, The degree of fluctuation of the passenger flow in each text timestamp of the target municipal building is represented, and the dispersion degree of the passenger flow is obtained by calculating the sum of squares of the difference between the passenger flow in each text timestamp and the average passenger flow and then taking the square root, reflecting the distribution of the passenger flow in different text timestamps. The greater the fluctuation of the passenger flow in different text timestamps, the more uneven the distribution. The integral of the product of the set coefficient and the predicted passenger flow in the time period from the starting text timestamp to the current text timestamp is represented, and the influence of the accumulation of the predicted passenger flow with time on the energy efficiency analysis of the target municipal building is obtained.

[0055] It needs to be added that the determination of R1 takes the passenger flow gradient difference related factors as the energy efficiency analysis influencing factors, such as the current weather, workday regularity, municipal activities, etc. These factors may indirectly affect the passenger flow distribution, and further affect the relationship between the passenger flow gradient difference and the energy efficiency of the municipal building; the determination of R2 is automatically retrieved through the room function or device configuration of the building energy efficiency model. For example, with the air conditioning system, the increase of passenger flow will increase the cooling load through human body heat dissipation, wherein the human body heat dissipation is 100W / person, and the set coefficient can be represented as the ratio of the cooling load per unit passenger flow increase to the energy efficiency ratio of the air conditioning system.

[0056] S5: Energy consumption analysis: based on the correlation between the building energy consumption text and the building energy efficiency model, the energy consumption of the building energy consumption text in each text timestamp is analyzed to obtain the degree of equipment energy consumption corresponding to each text timestamp.

[0057] In this embodiment, it needs to be specifically explained that the degree of equipment energy consumption corresponding to each text timestamp is specifically analyzed as follows:

[0058] C1: through the corresponding relationship between the building energy consumption text and the device family in the building energy efficiency model, the building energy consumption text energy efficiency ratio, unit area energy consumption, power density and load rate of each text timestamp are extracted, and then normalized processing is carried out, which specifically includes: comparing the unit area energy consumption with the total energy consumption of the target municipal building to obtain the cumulative energy consumption proportion, comparing the difference between the power density and the expected power density with the expected power density to obtain the power density deviation rate, and comparing the difference between the energy efficiency ratio and the historical energy efficiency ratio of the corresponding text timestamp with the historical energy efficiency ratio to obtain the energy efficiency growth rate;

[0059] C2: remove the negative value of each text timestamp, and calculate the average value of the normalized data as the filling after removal, and then weight and sum the energy efficiency growth rate, the cumulative energy consumption proportion, the power density deviation rate and the load rate, so as to obtain the device health degree;

[0060] C3: calculate the energy consumption increment based on the actual building energy consumption and the device health degree, and then sum the actual building energy consumption and the energy consumption increment to obtain the device total energy consumption of each text timestamp, which is specifically expressed as:

[0061] ,

[0062] Wherein Ea represents the device total energy consumption corresponding to each text timestamp, H represents the device health degree, E0 represents the actual building energy consumption, and k represents the energy consumption degradation coefficient. The larger the device health degree is, the smaller the energy consumption increment is, and the smaller the device total energy consumption is.

[0063] It should be noted that the energy consumption degradation coefficient represents the maximum energy consumption increment when the device is completely disabled, which is related to the device type. The setting method of k can be to select the energy consumption degradation coefficient corresponding to the device use time through the performance attenuation curve in the device manual. Exemplarily, k = 0.85.

[0064] S6: anomaly extraction: based on the spatial aggregation degree and the device energy consumption degree corresponding to each text timestamp, an association curve is constructed, and then the abnormal energy efficiency timestamp is extracted according to the association curve.

[0065] In this embodiment, it should be noted that the association curve is constructed as follows:

[0066] Based on the spatial aggregation degree analysis corresponding to each text timestamp, the spatial aggregation amount is extracted and compared with the preset spatial aggregation amount, wherein the preset spatial aggregation amount is the flow of people when the target municipal building reaches saturation. Specifically, the spatial aggregation amount is compared with the preset spatial aggregation amount after the difference is taken, and then compared with the preset spatial aggregation amount, so as to calculate the spatial aggregation difference degree;

[0067] Based on the device energy consumption degree analysis corresponding to each text timestamp, the device total energy consumption is extracted. Similarly, the device total energy consumption is compared with the preset device total energy consumption, and the preset device total energy consumption is the theoretical energy consumption benchmark value of all devices in the target municipal building under normal operation. Thus, the device energy consumption difference degree is calculated;

[0068] A two-dimensional coordinate system is constructed with the text timestamp as the horizontal coordinate and the spatial aggregation difference degree as the vertical coordinate. Thus, the text timestamp corresponding to the target municipal building and the spatial aggregation difference degree are labeled in the constructed two-dimensional coordinate system to form a spatial aggregation change curve.

[0069] A two-dimensional coordinate system is constructed with the text timestamp as the horizontal coordinate and the device energy consumption difference as the vertical coordinate. Similarly, the points in the constructed two-dimensional coordinate system are labeled to form an energy consumption change curve.

[0070] It should be noted that by constructing a two-dimensional coordinate system with the same horizontal coordinate and different vertical coordinates, the abnormal change trend of spatial aggregation and device energy consumption under the same time axis can be displayed, which facilitates intuitive observation of the synchronization, lag or correlation between the two. At the same time, under normal circumstances, there should be a certain positive correlation between the degree of spatial aggregation and the amount of energy consumption, i.e. the more people, the more energy consumption. If the degree of spatial aggregation changes little but the degree of energy consumption abnormally increases at a certain time point, or the degree of spatial aggregation increases but the degree of energy consumption abnormally decreases, the hyperbolic curve can quickly identify the abnormal energy efficiency timestamp.

[0071] Further, the abnormal energy efficiency timestamp is extracted as follows:

[0072] The correlation coefficient is calculated according to the spatial aggregation change curve and the energy consumption change curve, and then the correlation coefficient is taken as the abnormal energy efficiency correlation degree;

[0073] The abnormal energy efficiency correlation degree is compared with the preset same direction correlation degree. For example, the same direction correlation degree is 0.8. If the abnormal energy efficiency correlation degree is greater than or equal to the same direction correlation degree, it indicates that the spatial aggregation change and the energy consumption change present a significant same direction change trend, and the energy efficiency change is in a normal correlation state, and the abnormal timestamp extraction mechanism does not need to be triggered. The system continues to collect and store data according to the normal period, and continuously monitors the dynamic correlation change of the two;

[0074] When the abnormal energy efficiency correlation degree is less than the preset same direction correlation degree threshold, it is determined that the same direction correlation between the spatial aggregation change and the energy consumption is weakened, and the energy efficiency change is in an abnormal correlation state.

[0075] An extreme point detection algorithm is used to identify the inflection points existing in the spatial aggregation change curve and the energy consumption change curve, and the inflection points are labeled as spatial aggregation inflection points and energy consumption inflection points, respectively. The text timestamps corresponding to the spatial aggregation inflection points and the energy consumption inflection points are extracted;

[0076] The text timestamps corresponding to the spatial aggregation inflection points and the energy consumption inflection points are calculated for intersection. If there is an intersection result, the intersection result is taken as the abnormal energy efficiency timestamp. If there is no intersection result, the priority of spatial aggregation and energy consumption is determined as the first level of energy consumption and the second level of spatial aggregation. The text timestamps corresponding to the inflection points are labeled as abnormal energy efficiency timestamps.

[0077] It needs to be explained that the correlation coefficient is Pearson correlation coefficient, which can measure the strength and direction of linear relationship between two variables. If the correlation coefficient is close to 1, it indicates that there is a strong positive correlation between the two variables, that is, the same direction changes. If the correlation coefficient is close to -1, it indicates that there is a strong negative correlation between the two variables, that is, the opposite direction changes. If the correlation coefficient is close to 0, it indicates that the two variables are not related.

[0078] It needs to be added that the inflection point is an extreme point or a second derivative mutation point, which usually corresponds to the transition of system state. If the change curve of both appears an inflection point when the correlation weakens, such as the spatial aggregation difference decreases but the equipment energy consumption difference suddenly rises, it indicates that the normal correlation trend is broken. At the same time, the inflection point itself represents the mutation of system state, which is the key node of data pattern deviating from the expectation, so it is naturally reasonable to take it as a candidate of abnormal timestamp.

[0079] It needs to be added that the priority of spatial aggregation and energy consumption is the first level of energy consumption and the second level of spatial aggregation because the goal of energy efficiency analysis is to reduce energy waste and improve energy utilization efficiency, and energy consumption data is the most direct evaluation index. Even if the correlation between spatial aggregation and energy consumption weakens or disappears, abnormal fluctuations in energy consumption itself may still reflect equipment failure or abnormal operation and energy waste or management loopholes. The degree of spatial aggregation is chosen secondly, which is an indirect manifestation of energy efficiency problems.

[0080] S7: Energy efficiency optimization warning: based on the abnormal energy efficiency timestamp and correlation analysis results extracted in S6, combined with the building energy efficiency model and the Internet of Things system, formulate and execute targeted optimization strategies, and give early warning when there is risk.

[0081] In this embodiment, it needs to be specifically explained that the optimization process of S7 refers to Figure 4 , as follows: the difference between the spatial aggregation difference corresponding to the abnormal energy efficiency timestamp and the building energy efficiency threshold is recorded as the spatial aggregation safety deviation value; compare the spatial aggregation safety deviation value with the spatial aggregation safety deviation warning value stored in the database; if the spatial aggregation safety deviation value is greater than or equal to the spatial aggregation safety deviation warning value, issue a spatial secondary warning for the abnormal energy efficiency timestamp, at which time the spatial shunt plan is automatically triggered, such as guiding people to low aggregation areas through broadcast; otherwise, issue a spatial primary warning for the abnormal energy efficiency timestamp, at which time the video of the period is retrieved, the aggregation degree data authenticity is manually reviewed, and the alarm is issued.

[0082] According to the device energy consumption difference, the device energy consumption safety deviation value is obtained, if the device energy consumption safety deviation value is greater than or equal to the device energy consumption safety deviation early warning value, the energy consumption secondary early warning is sent to the abnormal energy efficiency timestamp, at this time, the device emergency shutdown process is triggered, such as the air conditioning unit in the non-safety state is turned off; otherwise, the energy consumption first early warning is sent to the abnormal energy efficiency timestamp, at this time, the device is marked as a maintenance state, and is included in the next day's inspection plan.

[0083] Secondly: the drawings in the disclosed embodiments of the application only involve the structures involved in the disclosed embodiments of the application, other structures can refer to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the application can be combined with each other;

[0084] Finally: the above only describes the preferred embodiments of the application and is not used to limit the application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A BIM-based intelligent analysis method for energy efficiency of municipal buildings, characterized in that, The method comprises the following steps: S1: building energy efficiency model construction: building a building energy efficiency model through BIM, and checking the model to determine whether there is deviation, and adding dynamic parameters required for energy efficiency analysis, wherein the dynamic parameters include envelope parameters and HVAC parameters; S2: energy consumption monitoring: obtaining building energy consumption of building equipment, and then obtaining N building energy consumption texts according to the building energy consumption, and associating the building energy consumption texts with the building energy efficiency model in a predefined manner through an Internet of Things gateway, thereby obtaining text timestamps; S3: people flow analysis: according to the text timestamps, the people flow of the target municipal building is counted, and the peak people flow of the target municipal building is called, and then the duration of the peak people flow is extracted; S4: spatial aggregation analysis: based on the duration of the peak people flow and the people flow analysis of the target municipal building, the spatial aggregation degree of the text timestamps is analyzed; S5: energy consumption analysis: based on the association between the building energy consumption texts and the building energy efficiency model, the energy consumption of each text timestamp is analyzed, and the equipment energy consumption degree corresponding to each text timestamp is obtained; S6: abnormality extraction: based on the spatial aggregation degree and the equipment energy consumption degree corresponding to each text timestamp, an association curve is constructed, and then the abnormal energy efficiency timestamp is extracted according to the association curve; S7: energy efficiency optimization and early warning: based on the abnormal energy efficiency timestamp extracted in S6 and the association analysis result, the building energy efficiency model and the Internet of Things system are combined to develop and implement targeted optimization strategies, and early warning is performed when there is a risk.

2. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The building energy efficiency model construction is as follows: A1: import the BIM geometric model, and determine the type of municipal building; A2: load the parameter template according to the type of municipal building, thereby generating an initial energy efficiency model; A3: detect whether the space division of the initial energy efficiency model is complete, if the model has deviation, return to A1 to modify the BIM geometric model; A4: according to the type of municipal building, detect whether the parameter template loaded by the initial model matches the building function requirement, if the model has deviation, return to A2 to adjust the parameter template; A5: if the model has no deviation, add the envelope parameters and the HVAC parameters, wherein the envelope parameters include the heat transfer coefficient and the shading coefficient, and the HVAC parameters include the equipment running time and the equipment power, thereby constructing the building energy efficiency model.

3. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The building energy consumption of the building equipment is obtained by installing energy consumption metering equipment in the target municipal building, and the building energy consumption texts and the building energy efficiency model are associated in a predefined manner, which includes: one-to-one correspondence between the building energy consumption texts and the equipment family instances in the building energy efficiency model, consistent hierarchical naming with the BIM space tree, and association of model components through the GUID of the IFC standard.

4. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The human flow is recorded according to the number of personnel in the target municipal building by card swiping or face recognition at the text timestamp, and the peak human flow is obtained by calculating the rolling average value of the human flow at the text timestamp, comparing the human flow corresponding to the current text timestamp with 1.5 times the average value corresponding to the text timestamp before the current text timestamp, and determining that the human flow corresponding to the current text timestamp is the peak human flow when the human flow corresponding to the current text timestamp is greater than 1.5 times the average value corresponding to the text timestamp before the current text timestamp; if the interval between the text timestamps corresponding to the two peak human flows is less than the preset interval, the two peak human flows are determined as the same peak human flow, and the total duration is calculated as the duration of the peak human flow.

5. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The spatial aggregation degree corresponding to the text timestamp is analyzed as follows: B1: Extract the historical human flow of the target municipal building at the text timestamp, and calculate the weighted value of the historical human flow at each text timestamp to obtain the predicted human flow of the target municipal building, denoted as Z(t), t represents the time corresponding to the text timestamp, wherein the weight of the historical human flow at each text timestamp is matched according to the human flow at different text timestamps; B2: Through the correlation between building energy consumption text and building energy efficiency model, combined with the text timestamp, the use area of each space node is extracted, and the passenger flow corresponding to N building energy consumption texts and N text timestamps is recorded as Fp i , i is the text timestamp number; B3: Calculate the average of the flow of people of each text timestamp, and get the average flow of people of the target municipal building, denoted as Fp a ; B4: Calculate the spatial aggregation amount corresponding to the text timestamp based on the human flow, average human flow and predicted human flow of each text timestamp.

6. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The device energy consumption degree corresponding to each text timestamp is analyzed as follows: C1: Extract the building energy consumption text energy efficiency ratio, unit area energy consumption, power density and load rate of each text timestamp through the corresponding relationship between the building energy consumption text and the device family in the building energy efficiency model, and then normalize the building energy consumption text energy efficiency ratio, unit area energy consumption and power density. The normalization process includes: comparing the unit area energy consumption with the total energy consumption of the target municipal building to obtain the cumulative energy consumption ratio, comparing the difference between the power density and the expected power density with the expected power density to obtain the power density deviation rate, and comparing the difference between the energy efficiency ratio and the historical energy efficiency ratio corresponding to the text timestamp with the historical energy efficiency ratio to obtain the energy efficiency growth rate; C2: Remove the negative values of each text timestamp, and calculate the average value of the normalized data as the filling after removal, and then weight and sum the energy efficiency growth rate, cumulative energy consumption ratio, power density deviation rate and load rate to obtain the device health degree; C3: Calculate the energy consumption increment based on the actual building energy consumption and the device health degree, and then sum the actual building energy consumption and the energy consumption increment to obtain the total device energy consumption of each text timestamp.

7. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The correlation curve is constructed as follows: Based on the spatial aggregation degree analysis corresponding to each text timestamp, the spatial aggregation amount is extracted and compared with the preset spatial aggregation amount, which includes taking the absolute value of the difference between the spatial aggregation amount and the preset spatial aggregation amount and comparing it with the preset spatial aggregation amount, thereby calculating the spatial aggregation difference degree; Based on the device energy consumption degree analysis corresponding to each text timestamp, the total device energy consumption is extracted, and the total device energy consumption is compared with the preset total device energy consumption in the same way, thereby calculating the device energy consumption difference degree. A two-dimensional coordinate system is constructed with the text timestamp as the horizontal coordinate and the spatial aggregation difference degree as the vertical coordinate, and a spatial aggregation change curve is formed in the two-dimensional coordinate system by marking points corresponding to the target municipal building and the text timestamp and the spatial aggregation difference degree. A two-dimensional coordinate system is constructed with the text timestamp as the horizontal coordinate and the device energy consumption difference degree as the vertical coordinate, and an energy consumption change curve is formed in the two-dimensional coordinate system by marking points.

8. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The abnormal energy efficiency timestamp extraction is as follows: The correlation coefficient is calculated according to the spatial aggregation change curve and the energy consumption change curve, and the correlation coefficient is used as the abnormal energy efficiency correlation degree; The abnormal energy efficiency correlation degree is compared with the preset same direction correlation degree. If the abnormal energy efficiency correlation degree is greater than or equal to the same direction correlation degree, the energy efficiency change is in a normal correlation state, and the abnormal timestamp extraction mechanism does not need to be triggered. The system continues to collect and store data according to the normal cycle, and continuously monitors the dynamic correlation change of the two; When the abnormal energy efficiency correlation degree is less than the preset same direction correlation degree threshold, it is determined that there is an abnormal energy efficiency phenomenon; The extreme point detection algorithm is used to identify the inflection points of the spatial aggregation change curve and the energy consumption change curve, and the spatial aggregation inflection points and the energy consumption inflection points are marked in sequence. The text timestamps corresponding to the spatial aggregation inflection points and the energy consumption inflection points are extracted; The intersection of the text timestamps corresponding to the spatial aggregation inflection points and the energy consumption inflection points is calculated. If there is an intersection result, the intersection result is used as the abnormal energy efficiency timestamp. If there is no intersection result, the priority of the spatial aggregation and the energy consumption is determined as the first level of energy consumption and the second level of spatial aggregation. The text timestamps corresponding to the inflection points are marked as abnormal energy efficiency timestamps.

9. The BIM-based municipal building energy efficiency intelligent analysis method according to claim 1, characterized in that: The energy efficiency optimization warning is as follows: the difference between the spatial aggregation difference degree corresponding to the abnormal energy efficiency timestamp and the building energy efficiency threshold is recorded as the spatial aggregation safety deviation value; the spatial aggregation safety deviation value is compared with the spatial aggregation safety deviation warning value stored in the database; If the spatial aggregation safety deviation value is greater than or equal to the spatial aggregation safety deviation warning value, a spatial secondary warning is issued for the abnormal energy efficiency timestamp; Conversely, a spatial primary warning is issued for the abnormal energy efficiency timestamp; Similarly, the device energy consumption safety deviation value is obtained according to the device energy consumption difference degree. If the device energy consumption safety deviation value is greater than or equal to the device energy consumption safety deviation warning value, a secondary energy consumption warning is issued for the abnormal energy efficiency timestamp; Conversely, a primary energy consumption warning is issued for the abnormal energy efficiency timestamp.

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