Building energy consumption management system based on BIM building

Abstract

The invention discloses a building energy consumption management system based on a BIM building, and relates to the technical field of energy consumption management. According to the invention, the processing analysis module is arranged to comprehensively analyze the formatting processing duration and the processed data capacity of the energy consumption parameters of all the monitored cells to determine the processing evaluation quantity of the energy consumption parameters of all the monitored cells; a format distribution management unit is set to periodically analyze the data capacity of the monitoring data of all the stored monitoring parameters in combination with the processing evaluation quantity of the monitoring data to determine periodic cloud distribution parameters, and the monitoring data of the cloud distribution parameters are formatted by a cloud energy consumption format processing terminal; in this way, part of energy consumption parameter monitoring data which needs format processing and is large in data capacity and low in local processing efficiency are placed in the cloud energy consumption format processing terminal to be processed, and the situation that too many idle processing resources are wasted in the cloud energy consumption format processing terminal is avoided.

Description

Technical Field

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

[0002] With the rapid advancement of urbanization, building energy consumption accounts for an increasingly significant proportion of total energy consumption in society. Refined and efficient management of building energy consumption has become one of the core tasks of energy conservation, emission reduction, and sustainable development strategies.

[0003] Against this backdrop, building energy management systems based on BIM (Building Information Modeling) technology, with their powerful digital capabilities, have become an important support for accurate monitoring and intelligent regulation of building energy consumption. To gain a comprehensive understanding of the energy consumption trends of buildings within a region and achieve rational allocation and overall planning of energy resources, it has become an inevitable trend to aggregate energy consumption data from all residential areas in the region into the cloud for centralized management.

[0004] However, the energy consumption data collection equipment used by each residential community varies significantly. Earlier residential communities mostly use traditional mechanical metering devices that rely on manual reading, while newly built communities are widely equipped with advanced devices such as smart electricity and water meters that have automatic data collection and transmission capabilities. The diversity of energy supply and management models also leads to differences in equipment. Residential communities with centralized municipal energy supply configure metering equipment according to local standards, while those using distributed energy systems require dedicated monitoring equipment. These differences result in a complex and diverse format for energy consumption data uploaded to the cloud.

[0005] Some community data may be presented in a simple text format, containing only energy consumption values. Other community data is presented in a complex structured format, including device information, collection timestamps, and other rich content. Faced with such complex data formats, the cloud processes data with standardized formats and simple structures quickly. However, processing data with complex formats and lack of rules is slow. This seriously affects the overall efficiency and timeliness of energy consumption data analysis, making it difficult to quickly obtain effective data support for energy management decisions.

[0006] Currently, one strategy is to disperse the data processing process to each collection device. It is expected that each collection device will complete the data processing of energy consumption data locally and then transmit it to the cloud in a unified format. The data will be transmitted first and then transmitted later. However, in actual operation, due to the slow data processing speed of some collection devices, the computing resources of the cloud are idle and not properly utilized while waiting for these devices to complete processing and transmission. The powerful computing resources of the cloud are in a large surplus during this period, which not only wastes resources but also increases the operating cost of the energy consumption management system.

[0007] To solve the above problems, the present invention proposes a solution. Summary of the Invention

[0008] The object of the present invention is to provide a building energy consumption management system based on BIM architecture to solve the problems raised in the above background art.

[0009] The present invention provides a building energy consumption management system based on BIM architecture, including:

[0010] A local energy consumption format processing terminal for processing the energy consumption data of several monitoring communities in the target area, where the energy consumption data includes the monitoring data of several energy consumption parameters;

[0011] The local energy consumption format processing terminal includes several energy consumption processing modules, and one energy consumption processing module corresponds to one monitoring community in the target area;

[0012] The energy consumption processing module includes a format shunt management unit, several energy consumption collection units and a cloud interaction unit, and one energy consumption collection unit corresponds to one energy consumption parameter;

[0013] The format shunt management unit is used to store the monitoring data of the corresponding energy consumption parameters of several energy consumption collection cycles after receiving the transmission;

[0014] The format shunt management unit is also used to perform shunt processing on the monitoring data of all energy consumption parameters of the corresponding monitoring community stored in the format shunt cycle every other format shunt cycle according to a preset shunt processing rule to obtain all cloud shunt parameters under the corresponding format shunt cycle;

[0015] For all cloud shunt parameters obtained in one format shunt cycle, the format shunt management unit transmits the monitoring data of all cloud shunt parameters stored in the format shunt cycle to the cloud energy consumption format processing terminal.

[0016] Furthermore, a formatted processing script corresponding to the energy consumption parameter is pre-stored in the energy consumption collection unit, and a formatted processing script for all energy consumption parameters in the energy consumption data of all monitoring communities in the target area is pre-stored in the cloud energy consumption format processing terminal.

[0017] Furthermore, the cloud energy consumption format processing terminal is used to specify a corresponding formatted processing script to perform formatted processing on the monitoring data of the received corresponding cloud shunt parameters to obtain the formatted processing data of the corresponding cloud shunt parameters and store the formatted processing data.

[0018] Furthermore, the shunt processing rule for obtaining all cloud shunt parameters in one format shunt cycle is as follows:

[0019] S21: Obtain the processing evaluation quantities of all energy consumption parameters included in the format shunt management unit, and sequentially mark all energy consumption parameters included in the energy consumption data of the cell to be processed as G1, G2, ..., Gg in descending order of the processing evaluation quantity, where g≥1;

[0020] S22: Use the formula to calculate and obtain the comprehensive processing ratio index J1 of the energy consumption parameter G1, where P1 is the preset standard processing quantity of the energy consumption parameter G1, H1 is the data volume size of the monitoring data of the energy consumption parameter G1 stored during the format shunt period, I1 is the processing evaluation quantity of the energy consumption parameter G1 stored in the current processing unit, and β1 and β2 are the preset first and second comprehensive adjustment factors respectively;

[0021] S23: Compare the sizes of J1 and P2. If J1≥P2, mark the energy consumption parameter G1 as a cloud shunt parameter during the format shunt period; otherwise, do nothing. P2 is the preset standard screening shunt index;

[0022] S24: Sequentially calculate and obtain the comprehensive processing ratio indexes J2, J3, ..., Jg of the energy consumption parameters G2, G3, ..., Gg according to S21 to S22, and then sequentially compare the sizes of the comprehensive processing ratio indexes J2, J3, ..., Jg and P2 according to S23 to obtain all cloud shunt parameters during the format shunt period.

[0023] Furthermore, the format shunt management unit generates local processing instructions for the corresponding energy consumption parameters for all energy consumption parameters that are not marked as cloud energy consumption parameters within a format shunt period.

[0024] Compared with the prior art, the following beneficial effects are achieved:

[0025] The present invention collects the monitoring data of corresponding energy consumption parameters in the monitored communities by setting a number of energy consumption collection units, sets a processing and analysis module to comprehensively analyze the formatting processing duration and the size of the processed data volume of the energy consumption parameters of all monitored communities to determine the processing evaluation amount of the energy consumption parameters of all monitored communities. For each monitored community, a format diversion management unit is set to periodically analyze the data volume size of all the monitoring data of the stored monitoring parameters in combination with its processing evaluation amount to determine the periodic cloud diversion parameters. The monitoring data for the cloud diversion parameters is formatted by the cloud energy consumption format processing terminal executing the corresponding formatting processing script. In this way, the formatting processing process of the monitoring data of all energy consumption parameters is diverted. The monitoring data of some energy consumption parameters with a large data volume size that needs to be formatted and slow local processing efficiency is placed in the cloud energy consumption format processing terminal for processing, avoiding the waste of too much idle processing resources in the cloud energy consumption format processing terminal. Moreover, the monitoring data of energy consumption parameters with high processing efficiency and small data volume size is placed locally for processing. The two are carried out synchronously, which also speeds up the formatting processing rate of the monitoring data and further improves the efficiency of energy consumption analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1 , this application provides a building energy consumption management system based on BIM architecture, including a local energy consumption format processing terminal, a cloud energy consumption format processing terminal, and a processing and analysis module;

[0029] The local energy consumption format processing terminal is used to collect the energy consumption data of several monitored communities in the target area. The energy consumption data includes the monitoring data of several energy consumption parameters. In this application, the energy consumption parameters include equipment power consumption, total power consumption, area power consumption, total water consumption, area water consumption, total gas consumption, area gas consumption, total heat supply, heating temperature, area heat supply, ambient temperature, ambient humidity, light intensity, and equipment operation time;

[0030] The local energy consumption format processing terminal includes several energy consumption processing modules, and one energy consumption processing module corresponds to one monitored community in the target area;

[0031] The energy consumption processing module includes a format shunt management unit, a number of energy consumption collection units, and a cloud interaction unit. One energy consumption collection unit corresponds to one energy consumption parameter. A formatting processing script corresponding to the energy consumption parameter is pre-stored in the energy consumption collection unit, which is used to convert the monitoring data of the corresponding energy consumption parameter into data in a unified format for quick analysis;

[0032] The energy consumption collection unit collects the monitoring data of the corresponding energy consumption parameter according to the preset energy consumption collection period and transmits it to the format shunt management unit for storage. Among them, the energy consumption collection periods of a number of energy consumption parameters are set by the management personnel according to the importance of the energy consumption parameter for the energy consumption analysis of the corresponding monitoring community. In this application, the interval durations corresponding to the energy consumption collection periods of a number of energy consumption parameters are not the same;

[0033] After receiving the monitoring data of all the energy consumption parameters transmitted, the format shunt management unit stores it temporarily;

[0034] The format shunt management unit shunts the monitoring data of all the energy consumption parameters of the corresponding monitoring community stored during the format shunt period every other format shunt period. The interval duration of a format shunt period is set by the management personnel;

[0035] During the current format shunt period, the format shunt management unit shunts the monitoring data of all the energy consumption parameters of the corresponding monitoring community stored during the current format shunt period according to the preset shunt processing rules to obtain all the cloud shunt parameters under the current format shunt period, specifically as follows:

[0036] S21: Obtain the processing evaluation amounts of all the energy consumption parameters included in the format shunt management unit, and sequentially mark all the energy consumption parameters included in the energy consumption data of the community to be processed as G1, G2,..., Gg from largest to smallest processing evaluation amount, where g≥1;

[0037] S22: Use the formula to calculate and obtain the comprehensive processing ratio index J1 of the energy consumption parameter G1, where P1 is the preset standard processing amount of the energy consumption parameter G1, H1 is the data capacity size of the monitoring data of the energy consumption parameter G1 stored during the current format shunt period, I1 is the processing evaluation amount of the energy consumption parameter G1 stored in the current processing unit, and β1 and β2 are the preset first and second comprehensive adjustment factors respectively;

[0038] S23: Compare the sizes of J1 and P2. If J1≥P2, then mark the energy consumption parameter G1 as a cloud shunt parameter during the current format shunt period. Otherwise, do not perform any processing. P2 is the preset standard screening shunt index;

[0039] S24: Calculate and obtain the comprehensive processing ratio indicators J2, J3, ..., Jg of the energy consumption parameters G2, G3, ..., Gg in sequence according to S21 to S22, and then compare the comprehensive processing ratio indicators J2, J3, ..., Jg with P2 in sequence according to S23 to obtain all cloud splitting parameters in the current format splitting cycle;

[0040] The format splitting management unit transmits the monitoring data of all cloud splitting parameters stored during the current format splitting cycle to the cloud energy consumption format processing terminal;

[0041] The cloud energy consumption format processing terminal pre-stores formatting processing scripts for all energy consumption parameters in the energy consumption data of all monitored communities in the target area, so as to convert the monitoring data of the corresponding energy consumption parameters into data in a unified format for quick analysis;

[0042] After receiving the monitoring data of all cloud splitting parameters in the energy consumption data of all monitored communities in the target area transmitted, the cloud energy consumption format processing terminal designates the corresponding formatting processing script to perform formatting processing on the received monitoring data of the corresponding cloud splitting parameters to obtain the formatted processing data of the corresponding cloud splitting parameters, and stores the formatted processing data;

[0043] The format splitting management unit generates local processing instructions for the corresponding energy consumption parameters according to the remaining all energy consumption parameters stored during the current format splitting cycle, and transmits the local processing instructions to the corresponding energy consumption acquisition unit;

[0044] After receiving the transmitted local processing instructions, the energy consumption acquisition unit executes the pre-stored formatting processing script in it to obtain the formatted processing data of the corresponding energy consumption parameters in the current format splitting cycle, and transmits the formatted processing data to the cloud energy consumption format processing terminal for storage;

[0045] The data processing script performs structured processing on the monitoring data of the corresponding energy consumption parameters in the current format splitting cycle;

[0046] The processing analysis module is used to analyze the energy consumption processing data of all monitored communities in the target area. The energy consumption processing data contains processing log data of several energy consumption parameters, and the processing log data contains the data capacity size and processing duration. The analysis steps are as follows:

[0047] S11: First, randomly select a monitored community in the target area as the community to be processed, and randomly select one of all the energy consumption parameters included in the energy consumption data of the community to be processed as the parameter to be processed;

[0048] S12: Obtain all processed log data of the parameter to be processed from the energy consumption processed data of the cell to be processed stored in the processed analysis module, and mark them as A1, A2, ..., Aa respectively, where a≥1;

[0049] S13: Calculate and obtain the processing evaluation index E1 of the parameter to be processed under the processed log data A1 according to the preset calculation rule. The calculation rule is as follows:

[0050] S131: Calculate and obtain the data processing rate D1 of the parameter to be processed under the processed log data A1 by using the formula D1 = B1 / C1, where B1 and C1 are respectively the data capacity size and processing duration included in the processed log data A1;

[0051] S132: Calculate and obtain the processing evaluation index E1 of the parameter to be processed under the processed log data A1 by using the formula E1 = D1×ɑ1 + B1×ɑ2. It should be noted here that the processing evaluation index is defined artificially and is used to comprehensively evaluate the processing ability of the parameter to be processed based on the data capacity size and processing rate. In the formula, ɑ1 and ɑ2 are respectively the preset first and second dimension adjustment factors, which are used to adjust the characteristics of different calculation dimensions to the same calculation dimension for numerical calculation;

[0052] S14: Calculate and obtain the processing evaluation indexes E2, E3, ..., Ea of the parameter to be processed under the processed log data A2, A3, ..., Aa in sequence according to S13;

[0053] Use the discrete point filtering algorithm to process the processing evaluation indexes E1, E2, ..., Ea, and calculate the average value of all remaining processing evaluation indexes after data processing. Mark the average value as the processing evaluation quantity F1 of the parameter to be processed under the cell to be processed;

[0054] In this application, the discrete point filtering algorithm can be one of the Z-score filtering algorithm, IQR filtering algorithm, and density filtering algorithm;

[0055] S15: Select all energy consumption parameters included in the energy consumption data of the cell to be processed as the parameter to be processed in sequence, calculate and obtain the processing evaluation quantity of all the energy consumption parameters under the cell to be processed, and generate the energy consumption interaction reference data of the cell to be processed based on it;

[0056] S16; Select all monitored cells in the target area as the cells to be processed in sequence, and calculate and obtain the energy consumption interaction reference data of all monitored cells in the target area in sequence according to S12 to S15;

[0057] The processed analysis data transmits the energy consumption interaction reference data of all monitored cells in the target area to the local energy consumption format processing end;

[0058] After receiving the energy consumption interaction reference data of all monitored communities in the target area transmitted, the local energy consumption format processing terminal transmits the energy consumption interaction reference data of all monitored communities to the format shunt management unit of the corresponding energy consumption processing module for storage;

[0059] Some of the data in the above formula are numerically calculated after removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0060] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A building energy consumption management system based on BIM architecture, characterized in that, Including: A local energy consumption format processing terminal for processing the energy consumption data of several monitoring communities in the target area, where the energy consumption data includes the monitoring data of several energy consumption parameters; The local energy consumption format processing terminal includes several energy consumption processing modules, and one energy consumption processing module corresponds to one monitoring community in the target area; The energy consumption processing module includes a format shunt management unit, several energy consumption collection units and a cloud interaction unit, and one energy consumption collection unit corresponds to one energy consumption parameter; The format shunt management unit is used to store the monitoring data of the corresponding energy consumption parameters in several energy consumption collection cycles received in transmission; The format shunt management unit is also used to perform shunt processing on the monitoring data of all energy consumption parameters of the corresponding monitoring community stored in the format shunt cycle every other format shunt cycle according to a preset shunt processing rule to obtain all cloud shunt parameters in the corresponding format shunt cycle; For all cloud shunt parameters in a obtained format shunt cycle, the format shunt management unit transmits the monitoring data of all cloud shunt parameters stored in the format shunt cycle to the cloud energy consumption format processing terminal.

2. The building energy consumption management system based on BIM architecture according to claim 1, characterized in that, The energy consumption collection unit pre-stores the formatting processing scripts corresponding to the energy consumption parameters, and the cloud energy consumption format processing terminal pre-stores the formatting processing scripts of all energy consumption parameters in the energy consumption data of all monitoring communities in the target area.

3. An energy consumption management system for a BIM-based building according to claim 2, characterized in that, The cloud energy consumption format processing terminal is used to specify the corresponding formatting processing script to perform formatting processing on the monitoring data of the received corresponding cloud shunt parameters to obtain the formatted processing data of the corresponding cloud shunt parameters and store the formatted processing data.

4. A building energy consumption management system based on BIM architecture according to claim 1, characterized in that, The shunt processing rule for obtaining all cloud shunt parameters in a format shunt cycle is as follows: S21: Obtain the processing evaluation amounts of all energy consumption parameters included in the format shunt management unit, and sequentially mark all energy consumption parameters included in the energy consumption data of the community to be processed as G1, G2,..., Gg in descending order of the processing evaluation amount, where g≥1; S22: Use the formula to calculate and obtain the comprehensive processing ratio index J1 of the energy consumption parameter G1, where P1 is the preset standard processing volume of the energy consumption parameter G1, H1 is the data volume size of the monitoring data of the energy consumption parameter G1 stored during the format diversion period, I1 is the processing evaluation volume of the energy consumption parameter G1 stored in the current processing unit, and β1 and β2 are the preset first and second comprehensive adjustment factors respectively; S23: Compare the sizes of J1 and P2. If J1≥P2, then calibrate the energy consumption parameter G1 as a cloud shunt parameter in the format shunt cycle, otherwise do not perform any processing. P2 is a preset standard screening shunt index; S24: Calculate and obtain the comprehensive processing ratio indexes J2, J3,..., Jg of the energy consumption parameters G2, G3,..., Gg in sequence according to S21 to S22, and then compare the comprehensive processing ratio indexes J2, J3,..., Jg and P2 in sequence according to S23 to obtain all cloud shunt parameters in the format shunt cycle.

5. A building energy consumption management system based on BIM architecture according to claim 1, characterized in that, It also includes a processing analysis module for analyzing the energy consumption processing data of all monitoring communities in the target area. The energy consumption processing data includes the processing log data of several energy consumption parameters, and the processing log data includes the data capacity size and the processing duration. The analysis steps are as follows: S11: First, a monitoring cell in the target area is randomly selected as a cell to be processed, and one of all energy consumption parameters contained in the energy consumption data of the cell to be processed is randomly selected as a parameter to be processed; S12: Obtain all processed log data of the parameters to be processed from the energy consumption processing data of the cell to be processed stored in the processed analysis module, and mark them as A1, A2, ..., Aa, where a≥1; S13: Calculate and obtain the processing evaluation index E1 of the parameters to be processed under the processed log data A1 according to the preset calculation rules. The calculation rules are as follows: S131: Calculate the data processing rate D1 of the parameters to be processed under the processed log data A1 using the formula D1=B1 / C1, where B1 and C1 are the data capacity and processing time contained in the processed log data A1 respectively; S132: Calculate and obtain the processing evaluation index E1 of the parameter to be processed under the processed log data A1 using the formula E1 = D1 × ɑ1 + B1 × ɑ2, where ɑ1 and ɑ2 are preset first and second dimension adjustment factors, respectively, used to adjust features of different calculation dimensions to the same calculation dimension for numerical calculation; S14: Calculate and obtain processing evaluation indicators E2, E3, ..., Ea of the parameters to be processed under the processed log data A2, A3, ..., Aa in sequence according to S13; Use a discrete point filtering algorithm to process the processing evaluation indicators E1, E2, ..., Ea, and calculate the average value of all the processing evaluation indicators remaining after the data processing, and calibrate the average value as the processing evaluation amount F1 of the parameter to be processed in the cell to be processed; S15: sequentially selecting all energy consumption parameters included in the energy consumption data of the cell to be processed as parameters to be processed, calculating and obtaining processing evaluation quantities of all energy consumption parameters of the cell to be processed, and generating energy consumption interactive reference data of the cell to be processed based on the processing evaluation quantities; S16: Select all monitored cells in the target area as cells to be processed in sequence, and calculate and obtain energy consumption interactive reference data of all monitored cells in the target area in sequence according to S12 to S15.

6. The building energy consumption management system based on BIM architecture according to claim 4, characterized in that, The format diversion management unit generates local processing instructions corresponding to energy consumption parameters for all energy consumption parameters that are not calibrated as cloud energy consumption parameters within a format diversion cycle.

7. An energy consumption management system for a BIM-based building according to claim 6, characterized in that, After receiving the transmitted local processing instruction, the energy consumption collection unit executes the pre-stored formatting processing script to obtain the formatting processing data corresponding to the energy consumption parameters in the current format diversion cycle, and transmits the formatting processing data to the cloud energy consumption format processing terminal for storage.

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