CN115470566B - Intelligent building energy consumption control method and system based on BIM - Google Patents
Intelligent building energy consumption control method and system based on BIM Download PDFInfo
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Abstract
The invention provides a building intelligent energy consumption control method and system based on BIM (building information modeling), and relates to the technical field of intelligent temperature regulation, wherein a building BIM (building information modeling) model is constructed based on design drawing information of a target building, multi-level space division of the target building is carried out based on historical traffic data, the combination somatosensory demand temperature data is input into the building BIM model to obtain initial control data, internal and external environment influence data is obtained to regulate the initial control data, and intelligent control data are generated.
Description
Technical Field
The invention relates to the technical field of intelligent temperature regulation and control, in particular to a BIM-based intelligent building energy consumption control method and system.
Background
The energy is as the basis of social development, it is the basic support of maintenance society normal operating, the accessible carries out the rational planning of energy utilization and realizes resource maximize utilization, in order to carry out energy-conservation and reduce consumption, when carrying out the air conditioner temperature control in the commercial site, accessible rational control body feels the temperature and improves control intelligence, effectively reduce the control energy consumption under the prerequisite of guaranteeing user experience, now, carry out the control process of indoor temperature, need the user to operate heating source or refrigeration source, generally speaking mostly overall control, control method universality is too strong, can't ensure passerby's comfort level, still there is certain promotion space.
In the prior art, the energy consumption control method for the building cannot be intelligently regulated and controlled based on the inside operation condition of the building, the analysis depth of control influence factors is insufficient, the control result is not accurate enough, the fit degree with actual requirements is insufficient, and energy waste can be caused.
Disclosure of Invention
The application provides an intelligent building energy consumption control method and system based on BIM for solve the energy consumption control method of the building that exists among the prior art and can't carry out intelligent regulation and control based on building inside operation live, be not enough to the analysis degree of depth of control influence factor for the control result is accurate inadequately, and is not enough with the degree of agreeing with of actual demand, can cause the extravagant technical problem of energy simultaneously.
In view of the above problems, the present application provides a method and a system for controlling energy consumption of an intelligent building based on BIM.
In a first aspect, the present application provides a BIM-based intelligent building energy consumption control method, including: acquiring design drawing information of a target building through the data interaction device; building a BIM (building information modeling) model based on the design drawing information; acquiring historical traffic data of a target building, and performing multi-level space division on the target building based on the historical traffic data to obtain multi-level space division results; setting somatosensory required temperature data, and inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model to obtain initial control data; acquiring an external environment through the environment acquisition device, and acquiring external environment influence data according to external environment information; acquiring the passing real-time image of the target building through the image acquisition device, and acquiring internal environment influence data according to an image acquisition result; and adjusting the initial control data through the external environment influence data and the internal environment influence data to obtain intelligent control data, and performing intelligent control on the target building through the intelligent control data.
In a second aspect, the present application provides a BIM-based intelligent building energy consumption control system, the system includes: the information acquisition module is used for acquiring design drawing information of a target building through the data interaction device; the model construction module is used for constructing a building BIM model based on the design drawing information; the space division module is used for acquiring historical traffic data of a target building, and performing multi-level space division on the target building based on the historical traffic data to obtain multi-level space division results; the model analysis module is used for setting somatosensory required temperature data, inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model and obtaining initial control data; the external environment data acquisition module is used for acquiring an external environment through the environment acquisition device and acquiring external environment influence data according to external environment information; the internal environment data acquisition module is used for acquiring a passing real-time image of the target building through the image acquisition device and acquiring internal environment influence data according to an image acquisition result; and the data adjusting module is used for adjusting the initial control data through the external environment influence data and the internal environment influence data to obtain intelligent control data, and intelligently controlling the target building through the intelligent control data.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the intelligent building energy consumption control method based on the BIM, the design drawing information of a target building is acquired through the data interaction device, and then a building BIM model is constructed; obtaining historical traffic data of a target building, carrying out multi-level space division on the target building to obtain multi-level space division results and set somatosensory required temperature data, inputting the multi-level space division results into the building BIM model to obtain initial control data, and collecting external environment information to obtain external environment influence data; the method comprises the steps of collecting a real-time passing image of a target building to obtain internal environment influence data, adjusting initial control data through the external environment influence data and the internal environment influence data to obtain intelligent control data to carry out intelligent control on the target building, solving the technical problems that an energy consumption control method of the building in the prior art cannot carry out intelligent regulation and control based on the internal operation condition of the building, the analysis depth of control influence factors is insufficient, the control result is not accurate enough, the degree of agreement with actual requirements is insufficient, and energy waste can be caused at the same time, carrying out analysis and evaluation based on multiple dimensions, combining the actual requirements to carry out intelligent and accurate regulation and control on the temperature in the building, and realizing energy conservation and consumption reduction on the basis of ensuring the comfort level.
Drawings
Fig. 1 is a schematic flow chart of a method for controlling energy consumption of an intelligent building based on BIM according to the present application;
fig. 2 is a schematic diagram illustrating a flow of obtaining multi-level space division results in a BIM-based intelligent building energy consumption control method according to the present application;
FIG. 3 is a schematic diagram illustrating an internal environment influence data acquisition process in a BIM-based intelligent building energy consumption control method according to the present application;
fig. 4 is a schematic structural diagram of an intelligent building energy consumption control system based on BIM according to the present application.
Description of reference numerals: the system comprises an information acquisition module 11, a model construction module 12, a space division module 13, a model analysis module 14, an external environment data acquisition module 15, an internal environment data acquisition module 16 and a data adjustment module 17.
Detailed Description
The application provides a building intelligent energy consumption control method and system based on BIM, a building BIM model is constructed based on design drawing information of a target building, multi-level space division of the target building is carried out based on historical traffic data, initial control data are obtained by inputting combined somatosensory demand temperature data into the building BIM model, internal and external environment influence data are obtained to adjust the initial control data, intelligent control data are generated to carry out intelligent control on the target building, the method is used for solving the problem that the building energy consumption control method existing in the prior art cannot carry out intelligent regulation and control based on the building internal operation condition, the analysis depth of control influence factors is insufficient, the control result is not accurate enough, the degree of engagement with the actual demand is insufficient, and meanwhile, the technical problem of energy waste can be caused.
Example one
As shown in fig. 1, the present application provides a smart building energy consumption control method based on BIM, the method is applied to a smart building energy consumption control system, the smart building energy consumption control system is in communication connection with an environment acquisition device, a data interaction device and an image acquisition device, the method includes:
step S100: acquiring design drawing information of a target building through the data interaction device;
specifically, energy can be reasonably planned and utilized to achieve maximum utilization of energy, so as to achieve energy saving and consumption reduction, and when air conditioner temperature control is performed in a commercial site, control intelligence can be improved by reasonably controlling sensible temperature, and control energy consumption can be effectively reduced on the premise of guaranteeing user experience.
Step S200: building a BIM (building information modeling) model based on the design drawing information;
step S300: acquiring historical traffic data of a target building, and performing multi-level space division on the target building based on the historical traffic data to obtain multi-level space division results;
specifically, a unified size measurement standard, a plurality of splitting modules and model accuracy are determined based on the design drawing information and are used as modeling requirements, model construction is carried out on the target building according to the design drawing information based on a three-dimensional modeling platform and the modeling requirements, the building BIM model is generated, the three-dimensional modeling platform is an auxiliary modeling platform, the building BIM model can be regarded as geometric mapping of the target building, and temperature control live analysis in the building is carried out based on the building BIM model, so that final optimized control data are determined to carry out intelligent control on the target building.
Furthermore, a preset time interval is set, historical traffic data of the target building are collected based on the preset time interval, and then the historical traffic data are divided, illustratively, when a mall is subjected to temperature control analysis, temperature demands corresponding to different time intervals are different, temperature control parameters are determined based on actual temperature demands, resource waste can be avoided on the basis of guaranteeing the temperature control demands, for example, a plurality of division periods are determined based on Monday to Sunday, the historical traffic data are classified and integrated according to the plurality of division periods, a plurality of groups of traffic data are determined, time interval aggregation is further performed on the plurality of groups of traffic data, a plurality of division periods are determined based on traffic flow, for example, the difference of the traffic data corresponding to the different time intervals is large, the target building is divided into regions based on the traffic data of the different time intervals, the traffic data of the same region are similar, the plurality of division spaces are determined as space division results, the space division results determined in the different periods are different, the multi-level space division results are determined, a consideratization analysis is performed based on the multi-level space division results, the effective temperature control analysis can be performed, and the accuracy of the real-time control in the building can be improved, and the control accuracy of guaranteeing the target building can be guaranteed.
Further, as shown in fig. 2, step S300 of the present application further includes:
step S310: acquiring merchant activity planning data and merchant position information;
step S320: generating cycle segmentation constraint data through the merchant activity planning data and the merchant position information;
step S330: carrying out data period segmentation on the historical traffic data through the period segmentation constraint data to obtain period segmentation data;
step S340: and performing time interval aggregation on each segmentation group in the periodic segmentation data, and obtaining the multi-level space division result based on a time interval aggregation result.
Specifically, the historical traffic data of the target building is obtained, planning activities may exist in a merchant for a holiday of a special section, the traffic volume is larger than that of a common date, a targeted analysis is required to ensure the temperature control accuracy in the target building, the activity planning data and the merchant position information of the merchant are collected, activity limit ranges of a plurality of merchant areas are determined based on the merchant activity planning data and the merchant position information, and further the period division constraint data is determined, exemplarily, a holiday of a special section, such as national day, valentine day and the like, is subjected to independent division constraint, for a common date, monday to sunday can be divided into 7 periods, all monday is taken as a division group, and so on, and further, the historical traffic data is divided based on the period division constraint data, dividing the historical traffic data into corresponding periodic data based on a plurality of segmentation groups as the periodic segmentation data, wherein the segmentation groups are associated and correspond to a plurality of periodic data in the periodic segmentation data, performing time interval aggregation on the periodic segmentation data corresponding to each segmentation group, for example, analyzing the periodic segmentation data corresponding to the one segmentation group based on traffic flow based on location areas of merchants, further determining a plurality of traffic time intervals, such as the morning, the evening and the evening, and large differences exist in the traffic data corresponding to different traffic time intervals, obtaining time interval aggregation results, including the traffic data of different time intervals in different segmentation periods of the location areas of each merchant, dividing the target building based on the time interval aggregation results, and generating the multi-level space division results, and carrying out targeted real-time temperature regulation and control based on the multi-level space division result so as to control the temperature as required.
Step S400: setting somatosensory required temperature data, and inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model to obtain initial control data;
specifically, the target building is spatially divided, the multi-level space division result is determined, the most comfortable ambient temperature of the human body is further determined, the most comfortable ambient temperature is set as the somatosensory required temperature data, for example, 23 ℃, the somatosensory required temperature and the multi-level space division result are input into the building BIM model, temperature control data corresponding to a plurality of different divided areas of the target building in the current time period are determined and used as the initial control data, wherein the corresponding temperature control requirement is high for the time period and the area with a large traffic flow, comfort is maximally ensured, the control requirement can be appropriately reduced for the unnecessary control time period and the unnecessary control area, and the corresponding control data can be determined as required.
Step S500: acquiring an external environment through the environment acquisition device, and acquiring external environment influence data according to external environment information;
step S600: acquiring the passing real-time image of the target building through the image acquisition device, and acquiring internal environment influence data according to an image acquisition result;
specifically, the temperature control parameters in the target building are influenced by the live conditions of the internal environment and the external environment, the temperature control parameters are respectively analyzed to ensure the final control precision, information acquisition is carried out on the external environment based on the environment acquisition device, wherein the information acquisition comprises the external environment temperature, the material data and the building thickness data of the target building are further determined, the distance measurement is carried out on a plurality of divided space areas and wall bodies, the parameter data are comprehensively evaluated, and the temperature influence degree of the real-time external environment temperature on different areas in the target building is determined to serve as the external environment influence data; further, the target building is subjected to real-time traffic image acquisition based on the image acquisition device, an image acquisition result is obtained, real-time traffic quantity and traffic personnel source information are determined based on the image acquisition result, a source influence weight value is determined based on external environment influence data, for example, the current influence factors are the largest in time intervals and areas which just enter the target building and are in the highest traffic quantity, weighting calculation is carried out on the plurality of influence factors to determine corresponding temperature control influence degrees, information corresponding integration is further carried out to determine the internal environment influence data, and basic data are provided for follow-up adjustment and correction of control data by acquiring the external environment influence data and the internal environment influence data.
Further, step S500 of the present application further includes:
step S510: obtaining the building material data and the building thickness data of the target building according to the design drawing information;
step S520: performing wall distance correlation according to the multi-level space division result and the design drawing information to obtain a distance correlation result;
step S530: constructing an external environment temperature influence model, and inputting the building material data, the building thickness data, the distance correlation result and the external environment information into the external environment temperature influence model to obtain a temperature influence result;
step S540: and obtaining the external environment influence data according to the temperature influence result.
Specifically, the external environment temperature of the target building may affect the temperature in the building to a certain extent, the building material data and the building thickness data of the target building are extracted based on the design drawing information, wherein the temperature transfer benefits corresponding to different building materials are different, for example, the building iron and the building steel have different heat conduction capacities, the thickness of the building wall is inversely proportional to the temperature transfer benefits, and further, the distance between each divided area and the building wall is measured based on the multi-level space division result and the design drawing information to perform wall distance correlation analysis, and when the distance is large, the influence degree of the external environment temperature on the area is small, and the distance correlation result is obtained.
The method comprises the steps of establishing an external environment temperature influence model based on a machine learning algorithm, wherein the external environment temperature influence model is embedded in a BIM (building information modeling) model and used for carrying out temperature influence analysis, calling historical associated influence data as sample data based on big data, dividing the sample data into a training set and a verification set, carrying out model training and verification on the external environment temperature influence model until the output precision of the external environment temperature influence model reaches a preset accuracy rate, further inputting the building material data, the building thickness data, the distance association result and the external environment information into the external environment temperature influence model, outputting the temperature influence result by carrying out data comparison reference and model simulation analysis, determining the influence degree of an external environment on different areas in a target building based on the temperature influence result, generating the external environment influence data, and carrying out external environment influence analysis through the established model, so that the accuracy and objectivity of the analysis result can be effectively guaranteed.
Further, as shown in fig. 3, step S600 of the present application further includes:
step S610: carrying out image recognition on the image acquisition result, and acquiring real-time traffic quantity data based on the image recognition result;
step S620: performing source identification of a passer on the image identification result to generate source identification data;
step S630: generating a source influence weight value through the external environment influence data, and performing weighted calculation on the source identification data through the source influence weight value to obtain a weighted calculation result;
step S640: and performing data replacement of the real-time traffic quantity data according to the weighting calculation result, and acquiring the internal environment influence data based on the data replacement result.
Specifically, the traffic data in the target building is an influence factor for temperature control, when the traffic quantity is too large, sensitivity and accuracy of temperature control need to be effectively guaranteed, when the traffic quantity is small, a control requirement can be properly reduced, image acquisition is performed on the basis of the image acquisition device on the real-time traffic condition in the target building, image information identification is performed on an image acquisition result to determine real-time traffic quantity data of different space regions, further, traffic time, trend and the like corresponding to the specific traffic quantity are determined on the basis of an image identification result, exemplarily, when a passer directly enters from the outside, the sensible temperature is greatly influenced by the external environment, so that temperature control limits are different, the passer is identified on the basis of the traffic time and a source to generate source identification data, further, weighted calculation is performed on the basis of the source influence weighted value to determine weighted values corresponding to different influence factors on the basis of the external environment influence data, such as the external environment temperature, an entry time interval, a current time period and the like, the weighted calculation result is further, the source identification data is integrated with the traffic quantity data, the corresponding to determine the effective influence data of the traffic quantity data, and the internal influence data is analyzed on the real-time.
Step S700: and adjusting the initial control data through the external environment influence data and the internal environment influence data to obtain intelligent control data, and performing intelligent control on the target building through the intelligent control data.
Specifically, the initial control data is current control data of the temperature control device in the target building, the control accuracy of the initial control data may not meet an expected requirement, temperature acquisition devices are respectively arranged in a plurality of divided areas, real-time temperature data of the target building is acquired, namely a control result of the initial control data, the temperature acquisition data is acquired, deviation comparison calculation is carried out on the somatosensory requirement temperature data, a corresponding temperature deviation value, namely a temperature interval needing adjustment control, is determined, the initial control data is adjusted and corrected by further combining the external environment influence data and the internal environment influence data, the intelligent control data is determined by optimizing the initial control data, the target building is intelligently controlled based on the intelligent control data, the internal environment temperature of the target building reaches the somatosensory requirement temperature data, targeted adjustment can be carried out based on real-time dynamic flow, and maximum utilization of energy is achieved on the basis of guaranteeing the comfort level of general staff.
Further, step S700 of the present application further includes:
step S710: distributing the temperature acquisition devices based on the multi-level space division result;
step S720: calling the temperature acquisition device according to the time interval data to obtain temperature acquisition data;
step S730: generating adjustment control data based on the deviation values of the temperature acquisition data and the somatosensory required temperature data;
step S740: and optimizing the intelligent control data through the adjustment control data.
Specifically, the multi-level space division result is determined by performing space division on the target building, the arrangement positions of the temperature acquisition devices in different areas are determined according to the space division result, the arrangement number of the devices is further determined according to the space division result, the temperature acquisition devices are used for acquiring the environmental temperatures of different areas in the target building, the calling number and the positions of the temperature acquisition devices are further determined according to the time period data, namely the current division time period and the current area, exemplarily, the device calling frequency can be set, the device calling frequency can be increased and gradually decreased in the time period in which the number of the passing persons is dense, meanwhile, the temperature control requirement is low in the non-necessary calling time period, for example, when the passing persons are not detected for a long time, the device calling number can be appropriately reduced, the real-time temperature acquisition is performed according to the called temperature acquisition devices, the temperature acquisition data is acquired, the deviation value calculation is further performed on the temperature acquisition data and the somatosensory requirement temperature, the adjustment control data, the adjustment control scale, namely the temperature adjustment control data is determined according to the temperature deviation value, and the intelligent control data is optimized according to the adjustment control data, so that the control precision of the intelligent control data is improved.
Further, step S730 of the present application further includes:
step S731: constructing a time interval sensitivity set through big data;
step S732: performing sensitivity matching of the time interval sensitivity set based on the time interval data to obtain a sensitivity matching result;
step S733: and obtaining the adjustment control data through the sensitivity matching result and the deviation value.
Specifically, the equipment operation sensitivity requirements corresponding to different division periods are different, the equipment sensitivity requirements corresponding to periods with more passing personnel are higher, the comfort level of the passing personnel is enhanced, the multilevel sensitivity is determined based on big data and is associated and corresponding with a plurality of division periods, the associated and corresponding result is identified to generate a period sensitivity set so as to carry out identification and matching, the current division period and the division space region are used as period data and are subjected to sensitivity matching with the period sensitivity set, the data with the highest fitness with the period data is determined to be used as the sensitivity matching result so as to determine the sensitivity requirements corresponding to the period data, further, the sensitivity matching result and the deviation value are used as control requirements to carry out comprehensive evaluation to generate the adjustment control data, the adjustment control data are parameters for optimizing and adjusting the intelligent control data, and the control accuracy of the intelligent control parameters can be further improved.
Further, the present application further includes step S750, which includes:
step S751: determining whether the deviation value meets an expected deviation threshold;
step S752: when the deviation value cannot meet the expected deviation threshold value, generating an equipment maintenance instruction;
step S753: and carrying out temperature control equipment maintenance of the target building through the equipment maintenance instruction.
Particularly, through right temperature data collection with body is felt demand temperature data and is carried out the temperature deviation and calculate, acquires the deviant, sets up the expected deviant threshold value, promptly the controllable critical value of deviant value judges whether the deviant value satisfies the expected deviant value, when satisfying indicates that equipment operation is normal, based on adjustment analysis step carry out deviant value adjustment correction can, work as the deviant value does not satisfy when prefetching deviant threshold value, indicate the deviant value is too high, is in abnormal state, and there may be certain equipment trouble to lead to, generates the equipment overhauls instruction, carries out temperature control equipment troubleshooting's start instruction promptly, when receiving when the equipment overhauls the instruction, right the target building temperature control equipment carries out the troubleshooting to the normal operating condition of maintenance equipment.
Example two
Based on the same inventive concept as the intelligent building energy consumption control method based on the BIM in the foregoing embodiment, as shown in fig. 4, the present application provides an intelligent building energy consumption control system based on the BIM, and the system includes:
the information acquisition module 11 is used for acquiring design drawing information of a target building through the data interaction device;
the model building module 12 is used for building a BIM (building information modeling) model based on the design drawing information;
the space dividing module 13 is configured to collect and obtain historical traffic data of a target building, perform multi-level space division on the target building based on the historical traffic data, and obtain a multi-level space division result;
the model analysis module 14 is used for setting somatosensory required temperature data, inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model, and obtaining initial control data;
the external environment data acquisition module 15 is configured to perform external environment acquisition by the environment acquisition device, and acquire external environment influence data according to external environment information;
an internal environment data acquisition module 16, where the internal environment data acquisition module 16 is configured to perform real-time image acquisition of passing of the target building through the image acquisition device, and obtain internal environment influence data according to an image acquisition result;
a data adjusting module 17, wherein the data adjusting module 17 is configured to adjust the initial control data according to the external environment influence data and the internal environment influence data to obtain intelligent control data, and perform intelligent control on the target building according to the intelligent control data.
Further, the system further comprises:
the data acquisition module is used for acquiring the building material data and the building thickness data of the target building according to the design drawing information;
the distance correlation module is used for performing wall distance correlation according to the multi-level space division result and the design drawing information to obtain a distance correlation result;
the data analysis module is used for constructing an external environment temperature influence model, inputting the building material data, the building thickness data, the distance correlation result and the external environment information into the external environment temperature influence model and obtaining a temperature influence result;
and the influence data acquisition module is used for acquiring the external environment influence data according to the temperature influence result.
Further, the system further comprises:
the data acquisition module is used for acquiring and obtaining merchant activity planning data and merchant position information;
the constraint data acquisition module is used for generating cycle segmentation constraint data through the merchant activity planning data and the merchant position information;
the data segmentation module is used for carrying out data period segmentation on the historical traffic data through the period segmentation constraint data to obtain period segmentation data;
and the space division result acquisition module is used for carrying out time interval aggregation on each division group in the periodic division data and acquiring the multi-level space division result based on a time interval aggregation result.
Further, the system further comprises:
the traffic data acquisition module is used for carrying out image identification on the image acquisition result and acquiring real-time traffic quantity data based on the image identification result;
the data identification module is used for identifying the source of the passer to the image identification result to generate source identification data;
the data calculation module is used for generating a source influence weight value through the external environment influence data, and performing weighted calculation on the source identification data through the source influence weight value to obtain a weighted calculation result;
and the data replacement module is used for performing data replacement on the real-time traffic quantity data according to the weighting calculation result and obtaining the internal environment influence data based on the data replacement result.
Further, the system further comprises:
a device layout module for laying out the temperature acquisition devices based on the multi-level spatial division result;
the temperature acquisition module is used for calling the temperature acquisition device according to time interval data to obtain temperature acquisition data;
the data generation module is used for generating adjustment control data based on the deviation values of the temperature acquisition data and the somatosensory required temperature data;
and the control data optimization module is used for optimizing the intelligent control data through the adjustment control data.
Further, the system further comprises:
an information set construction module for constructing a time period sensitivity set by big data;
the sensitivity matching module is used for carrying out sensitivity matching on the time interval sensitivity set based on the time interval data to obtain a sensitivity matching result;
and the adjustment control data acquisition module is used for acquiring the adjustment control data according to the sensitivity matching result and the deviation value.
Further, the system further comprises:
a threshold judgment module for judging whether the deviation value meets an expected deviation threshold;
the instruction generation module is used for generating an equipment maintenance instruction when the deviation value cannot meet an expected deviation threshold value;
and the equipment maintenance module is used for carrying out temperature control equipment maintenance of the target building through the equipment maintenance instruction.
In the present specification, through the foregoing detailed description of the intelligent building energy consumption control method based on the BIM, those skilled in the art can clearly know that the intelligent building energy consumption control method and system based on the BIM in the present embodiment, for the apparatus disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the relevant points, refer to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A BIM-based intelligent building energy consumption control method is applied to an intelligent building energy consumption control system, the intelligent building energy consumption control system is in communication connection with an environment acquisition device, a data interaction device and an image acquisition device, and the method comprises the following steps:
acquiring design drawing information of a target building through the data interaction device;
building a BIM (building information modeling) model based on the design drawing information;
acquiring historical traffic data of a target building, dividing multi-level space of the target building based on the historical traffic data, and acquiring multi-level space division results, wherein the multi-level space division results comprise: setting a preset time interval, determining a plurality of division periods based on the preset time interval, classifying and integrating the historical traffic data according to the division periods, and determining a plurality of groups of traffic data; performing time interval aggregation on the multiple groups of traffic data; determining a plurality of division time periods based on traffic flow of passers, performing regional division on a target building through traffic data in different time periods, determining a plurality of division spaces as space division results, determining that the space division results are different in different periods, and obtaining a multi-level space division result;
setting somatosensory required temperature data, and inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model to obtain initial control data;
acquiring an external environment through the environment acquisition device, and acquiring external environment influence data according to external environment information;
acquiring a passing real-time image of the target building through the image acquisition device, and acquiring internal environment influence data according to an image acquisition result;
respectively arranging temperature acquisition devices for the plurality of divided areas, and acquiring real-time temperature of the target building through the temperature acquisition devices to acquire temperature acquisition data; obtaining a corresponding temperature deviation value by performing deviation comparison calculation on the temperature acquisition data and the somatosensory required temperature data, wherein the temperature deviation value is a temperature interval which needs to be adjusted and controlled; and adjusting the initial control data by combining the external environment influence data and the internal environment influence data to obtain intelligent control data, and performing intelligent control on the target building through the intelligent control data.
2. The method of claim 1, wherein the method further comprises:
obtaining the building material data and the building thickness data of the target building according to the design drawing information;
performing wall distance correlation according to the multi-level space division result and the design drawing information to obtain a distance correlation result;
constructing an external environment temperature influence model, and inputting the building material data, the building thickness data, the distance correlation result and the external environment information into the external environment temperature influence model to obtain a temperature influence result;
and obtaining the external environment influence data according to the temperature influence result.
3. The method of claim 1, wherein the method further comprises:
acquiring merchant activity planning data and merchant position information;
generating cycle segmentation constraint data through the merchant activity planning data and the merchant position information;
carrying out data cycle segmentation on the historical traffic data through the cycle segmentation constraint data to obtain cycle segmentation data, wherein the data cycle segmentation data comprises the following steps: carrying out independent segmentation constraint on the special holidays, dividing the common dates into 7 periods from Monday to Sunday, taking all Mondays as a segmentation group, and so on to obtain a plurality of segmentation groups; dividing the historical traffic data into corresponding periodic data based on a plurality of segmentation groups to obtain the periodic segmentation data;
and performing time interval aggregation on each segmentation group in the periodic segmentation data, and obtaining the multi-level space division result based on a time interval aggregation result.
4. The method of claim 1, wherein the method comprises:
carrying out image recognition on the image acquisition result, and acquiring real-time traffic quantity data based on the image recognition result;
performing source identification of a passer on the image identification result to generate source identification data;
generating a source influence weight value through the external environment influence data, and performing weighted calculation on the source identification data through the source influence weight value to obtain a weighted calculation result;
and performing data replacement of the real-time traffic quantity data according to the weighting calculation result, and acquiring the internal environment influence data based on the data replacement result.
5. The method of claim 1, wherein the intelligent building energy consumption control system is communicatively coupled to a temperature acquisition device, the method further comprising:
distributing the temperature acquisition devices based on the multi-level space division result;
calling the temperature acquisition device according to the time interval data to obtain temperature acquisition data;
generating adjustment control data based on the deviation values of the temperature acquisition data and the somatosensory required temperature data;
and optimizing the intelligent control data through the adjustment control data.
6. The method of claim 5, wherein the method further comprises:
constructing a time interval sensitivity set through big data, wherein the time interval sensitivity set is a time interval sensitivity set operated by equipment;
performing sensitivity matching of the time interval sensitivity set based on the time interval data to obtain a sensitivity matching result;
and obtaining the adjustment control data through the sensitivity matching result and the deviation value.
7. The method of claim 5, wherein the method comprises:
determining whether the deviation value meets an expected deviation threshold;
when the deviation value cannot meet the expected deviation threshold value, generating an equipment maintenance instruction;
and carrying out temperature control equipment maintenance of the target building through the equipment maintenance instruction.
8. The utility model provides an wisdom building energy consumption control system based on BIM, its characterized in that, system and environment collection system, data interaction device, image acquisition device communication connection, the system includes:
the information acquisition module is used for acquiring design drawing information of a target building through the data interaction device;
a model construction module for constructing a building BIM model based on the design drawing information;
the space division module is used for acquiring historical traffic data of a target building, performing multi-level space division on the target building based on the historical traffic data, and acquiring multi-level space division results, and comprises: setting a preset time interval, determining a plurality of division periods based on the preset time interval, classifying and integrating the historical traffic data according to the division periods, and determining a plurality of groups of traffic data; performing time interval aggregation on the multiple groups of traffic data; determining a plurality of division time periods based on traffic flow of passers, performing regional division on a target building through traffic data in different time periods, determining a plurality of division spaces as space division results, determining that the space division results are different in different periods, and obtaining a multi-level space division result;
the model analysis module is used for setting somatosensory required temperature data, inputting the somatosensory required temperature data and the multi-level space division result into the building BIM model and obtaining initial control data;
the external environment data acquisition module is used for acquiring an external environment through the environment acquisition device and acquiring external environment influence data according to external environment information;
the internal environment data acquisition module is used for acquiring a passing real-time image of the target building through the image acquisition device and acquiring internal environment influence data according to an image acquisition result;
the data adjusting module is used for respectively arranging temperature acquisition devices for the plurality of divided areas, acquiring the real-time temperature of the target building through the temperature acquisition devices and acquiring temperature acquisition data; obtaining a corresponding temperature deviation value by performing deviation comparison calculation on the temperature acquisition data and the somatosensory required temperature data, wherein the temperature deviation value is a temperature interval which needs to be adjusted and controlled; and combining the external environment influence data and the internal environment influence data to adjust the initial control data to obtain intelligent control data, and performing intelligent control on the target building through the intelligent control data.
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