CN117557970A - Intelligent building management method, system and storage medium based on digital twin - Google Patents
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Abstract
The invention relates to an intelligent building management method, a system and a storage medium based on digital twinning, which belong to the building management technology. The method can timely detect the air environment data in the monitored area, timely early warning and environment regulation can be performed when the air environment related to the pathogen exists, the event can be timely found, the transmission risk of the pathogen or the disease in the indoor environment in the public occasion area is reduced, and the health of people is guaranteed.
Description
Technical Field
The invention relates to the technical field of building management, in particular to an intelligent building management method, system and storage medium based on digital twinning.
Background
The intelligent communication, computer and other technologies are emerging, developed and popularized, so that a new power support is provided for various industries, the income level of people is greatly increased, the requirements on the safety of the building are higher on the premise that people are satisfied with temperature saturation, and green high-rise buildings are generated, so that the focus of attention of the present country and people is focused. In recent years, the fusion application of technologies such as the Internet of things and the artificial intelligence in high-rise buildings is gradually increased, wherein the technology belongs to the most representative of the intelligent Internet of things, the influence of the technology on the green building industry is gradually deepened, and the technology becomes one of key application technologies in the intelligent building industry nowadays. At present, the internet of things technology is widely applied to the intelligent building industry, but the environment monitoring aspect is still weak, the intelligent building safety level is still low, when the air data in the environment has preset type data (such as smell released by living environment favored by germs), if the event cannot be found in time, a certain pathogen or disease transmission risk exists in a public occasion area, and the health of people is endangered.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent building management method, system and storage medium based on digital twinning.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides an intelligent building management method based on digital twinning, which comprises the following steps:
building drawing information of a target area is obtained, a building model diagram is built according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated;
acquiring real-time air data information of each monitoring area through a digital twin monitoring network, and evaluating the real-time air data information of the monitoring areas to acquire the monitoring areas with pathogenic infection risks and normal areas;
acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
and generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environment regulation and control on the monitored area with the pathogenic infection based on the related early warning information.
Further, in the method, building drawing information of the target area is obtained, a building model diagram is constructed according to the building drawing information of the target area, and the monitoring area is screened based on the building model diagram, which specifically comprises the following steps:
building drawing information of a target area is obtained, building model diagrams are built through three-dimensional modeling software according to the building drawing information of the target area, and the building model diagrams are divided into areas to obtain building model diagrams of a plurality of sub-areas;
identifying the building model diagram of the sub-area, judging whether the sub-area is a preset area, taking the corresponding sub-area as a monitoring area when the sub-area is the preset area, and outputting the building model diagram of the monitoring area;
and when the sub-area is not the preset area, taking the corresponding area as a non-monitoring area, and eliminating the building model diagram of the non-monitoring area.
Further, in the method, initializing a monitoring network for a monitoring area to generate a digital twin monitoring network, specifically comprising:
acquiring a building model diagram of a monitoring area, initializing the quantity information and the installation position information of the air quality monitoring devices, acquiring the monitoring range of each air quality monitoring device, and calculating an estimated monitoring range according to the monitoring range of the air quality monitoring device, the quantity information and the installation position information of the air quality monitoring device;
Acquiring an actual monitoring range required by a building model diagram of a monitoring area, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and judging whether the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area;
if the estimated monitoring range is smaller than the actual monitoring range required by the building model diagram of the monitoring area, inheriting according to the inheritance algebra, and adjusting the quantity information and the installation position information of the air quality monitoring equipment;
when the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area, a digital twin monitoring network is constructed according to the quantity information and the installation position information of the air quality monitoring equipment.
Further, in the method, the real-time air data information of each monitoring area is obtained through the digital twin monitoring network, the real-time air data information of the monitoring area is evaluated, and the monitoring area and the normal area with pathogenic infection risk are obtained, specifically comprising:
acquiring and generating air data information corresponding to each pathogen type through big data, introducing a graph neural network, taking the pathogen type as a first graph node, taking the air data information as a second graph node, and constructing a pathogen type identification model based on the deep neural network;
Constructing a topological structure diagram according to the first graph node and the second graph node, generating an adjacent matrix based on the topological structure diagram, introducing a local outlier detection algorithm, calculating an outlier of the first graph node in the adjacent matrix through the local outlier detection algorithm, and judging whether the outlier is not larger than a preset outlier;
removing the first graph nodes with the outliers not larger than the preset outliers, updating the adjacent matrix, obtaining an updated adjacent matrix, and inputting the updated adjacent matrix into a pathogen type recognition model for coding learning;
the real-time air data information of each monitoring area is obtained through a digital twin monitoring network, the real-time air data information of the monitoring area is input into a pathogen type identification model for prediction, and the monitoring area with pathogen infection risk and the normal area are obtained.
Further, in the method, the monitoring image data information in each monitoring area is acquired, the identification result is acquired by identifying the monitoring image data information, and the transmission priori probability value of the pathogenic infection is acquired according to the monitoring area with the pathogenic infection risk and the identification result, specifically including:
Acquiring monitoring image data information in each monitoring area, identifying pathogen transmission organisms by the aid of the monitoring image data information, acquiring pathogen transmission organism quantity change characteristic data information within preset time, and presetting a pathogen transmission organism quantity change characteristic threshold;
when the pathogen transmission biological quantity change characteristic data information is larger than the pathogen transmission biological quantity change characteristic threshold value within the preset time, generating a recognition result according to the pathogen transmission biological quantity change characteristic data information within the preset time;
calculating a transmission priori probability value of each pathogenic type infection occurring in a monitoring area with pathogenic infection risk under each identification result through a Markov chain, and constructing a knowledge graph according to the transmission priori probability value of each pathogenic type infection occurring in the monitoring area with pathogenic infection risk under each identification result;
inputting the identification result into the knowledge graph, and acquiring a transmission priori probability value of each pathogenic type infection in the monitoring area with pathogenic infection risk under the current identification result.
Further, in the method, related early warning information is generated according to the propagation priori probability value of each pathogenic type infection, and the environment of the monitored area with the pathogenic infection is regulated and controlled based on the related early warning information, which specifically comprises the following steps:
Judging whether the propagation priori probability value of each pathogenic type infection is larger than a preset propagation priori probability value, and if so, acquiring the corresponding pathogenic type and the monitoring area;
generating related early warning information according to the corresponding pathogenic type and the monitoring area, acquiring the position of the monitoring area with pathogenic infection, and generating a retrieval tag according to the position of the monitoring area with pathogenic infection;
searching the environment control equipment of the monitored area with pathogenic infection based on the search tag, acquiring the communication protocol information of the environment control equipment of the monitored area with pathogenic infection, and presetting environment regulation parameters;
and generating relevant control information according to the communication protocol information of the environmental control equipment of the monitored area with pathogenic infection and the environmental regulation parameters, carrying out early warning based on the relevant early warning information, and simultaneously carrying out environmental regulation on the monitored area with pathogenic infection according to the relevant control information.
The second aspect of the present invention provides a digital twin-based intelligent building management system, which includes a memory and a processor, wherein the memory includes a digital twin-based intelligent building management method program, and when the digital twin-based intelligent building management method program is executed by the processor, the following steps are implemented:
Building drawing information of a target area is obtained, a building model diagram is built according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated;
acquiring real-time air data information of each monitoring area through a digital twin monitoring network, and evaluating the real-time air data information of the monitoring areas to acquire the monitoring areas with pathogenic infection risks and normal areas;
acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
and generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environment regulation and control on the monitored area with the pathogenic infection based on the related early warning information.
A third aspect of the present invention provides a computer readable storage medium including a digital twinning-based intelligent building management method program, which when executed by a processor, implements the steps of any one of the digital twinning-based intelligent building management methods.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
according to the method, building drawing information of a target area is obtained, a building model diagram is constructed according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized to the monitoring area, a digital twin monitoring network is generated, real-time air data information of each monitoring area is obtained through the digital twin monitoring network, the real-time air data information of the monitoring area is evaluated, a monitoring area with pathogenic infection risk and a normal area are obtained, therefore, through obtaining monitoring image data information of each monitoring area and identifying the monitoring image data information, an identification result is obtained, a transmission priori probability value of each pathogenic type infection is obtained according to the monitoring area with pathogenic infection risk and the identification result, relevant early warning information is generated according to the transmission priori probability value of each pathogenic type infection, and environment regulation is carried out on the monitoring area with pathogenic infection based on the relevant early warning information. The method can timely detect the air environment data in the monitored area, timely early warning and environment regulation can be performed when the air environment related to the pathogen exists, the event can be timely found, the transmission risk of the pathogen or the disease in the indoor environment in the public occasion area is reduced, and the health of people is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a digital twinning-based intelligent building management method;
FIG. 2 shows a first method flow diagram of a digital twinning-based intelligent building management method;
FIG. 3 shows a second method flow diagram of a digital twinning-based intelligent building management method;
fig. 4 shows a system block diagram of a digital twinning-based intelligent building management system.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
It should be noted that, as shown in fig. 1, the first aspect of the present invention provides an intelligent building management method based on digital twinning, which includes the following steps:
s102, acquiring building drawing information of a target area, constructing a building model diagram according to the building drawing information of the target area, screening a monitoring area based on the building model diagram, initializing a monitoring network for the monitoring area, and generating a digital twin monitoring network;
s104, acquiring real-time air data information of each monitoring area through a digital twin monitoring network, and evaluating the real-time air data information of the monitoring area to acquire a monitoring area with pathogenic infection risk and a normal area;
s106, acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
S108, generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and carrying out environment regulation and control on the monitored area with the pathogenic infection based on the related early warning information.
It is to be noted that, through the method, the air environment data in the monitored area can be detected in time, when the air environment related to the pathogen exists, the early warning and the environment regulation can be performed in time, the event can be found in time, the spreading risk of the pathogen or the disease in the indoor environment in the public occasion area is reduced, and the health of the crowd is ensured.
Further, in the method, building drawing information of the target area is obtained, a building model diagram is constructed according to the building drawing information of the target area, and the monitoring area is screened based on the building model diagram, which specifically comprises the following steps:
building drawing information of a target area is obtained, building model diagrams are built through three-dimensional modeling software according to the building drawing information of the target area, and the building model diagrams are divided into areas to obtain building model diagrams of a plurality of sub-areas;
identifying the building model diagram of the sub-area, judging whether the sub-area is a preset area, taking the corresponding sub-area as a monitoring area when the sub-area is the preset area, and outputting the building model diagram of the monitoring area;
And when the sub-area is not the preset area, taking the corresponding area as a non-monitoring area, and eliminating the building model diagram of the non-monitoring area.
It should be noted that, the building drawing information includes data such as a building drawing of a pipeline engineering and a building drawing of a drainage engineering, where the preset area is mainly a public area, such as a public area of a hospital, an office building, a library, or a private monitoring area, and is not limited excessively in this embodiment.
As shown in fig. 2, further, in the method, the monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated, which specifically includes:
s202, acquiring a building model diagram of a monitoring area, initializing quantity information and installation position information of air quality monitoring devices, acquiring a monitoring range of each air quality monitoring device, and calculating an estimated monitoring range according to the monitoring range of the air quality monitoring device, the quantity information and the installation position information of the air quality monitoring device;
s204, acquiring an actual monitoring range required by a building model diagram of a monitoring area, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and judging whether the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area;
S206, if the estimated monitoring range is smaller than the actual monitoring range required by the building model diagram of the monitoring area, inheriting according to the inheritance algebra, and adjusting the quantity information and the installation position information of the air quality monitoring equipment;
and S208, when the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area, constructing a digital twin monitoring network according to the quantity information and the installation position information of the air quality monitoring equipment.
The air data of the monitoring area is dynamically displayed through the digital twin monitoring network, and red is displayed in a preset area when abnormal gas types exist. In the method, the monitoring range may not be fully contained, and the quantity information and the installation position information of the air quality monitoring equipment are optimized through a genetic algorithm, so that the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area, and the monitoring rationality is improved. The air quality monitoring equipment comprises various gas type wireless detection sensors, air detectors and the like.
As shown in fig. 3, in the method, further, the real-time air data information of each monitoring area is obtained through a digital twin monitoring network, and the real-time air data information of the monitoring area is evaluated to obtain a monitoring area with pathogenic infection risk and a normal area, which specifically includes:
S302, acquiring and generating air data information corresponding to each pathogen type through big data, introducing a graph neural network, taking the pathogen type as a first graph node, taking the air data information as a second graph node, and constructing a pathogen type identification model based on the deep neural network;
s304, constructing a topological structure diagram according to the first graph node and the second graph node, generating an adjacent matrix based on the topological structure diagram, introducing a local outlier detection algorithm, calculating an outlier of the first graph node in the adjacent matrix through the local outlier detection algorithm, and judging whether the outlier is not more than a preset outlier;
s306, eliminating the first graph nodes with the outliers not larger than the preset outliers, updating the adjacent matrix, acquiring an updated adjacent matrix, and inputting the updated adjacent matrix into a pathogen type recognition model for coding learning;
s308, acquiring real-time air data information of each monitoring area through a digital twin monitoring network, inputting the real-time air data information of the monitoring area into a pathogen type identification model for prediction, and acquiring a monitoring area with pathogen infection risk and a normal area.
It should be noted that, the local outlier detection algorithm is a local outlier factor algorithm, abbreviated as LOF algorithm, and when the outlier is not greater than the preset outlier, it is indicated that similar data does not exist between the first graph nodes, so that the first graph nodes can be prevented from being repeated, the calculation amount of the model is prevented from being increased, and the calculation complexity of the pathogen type recognition model is optimized. In fact, the environment in which different pathogen types are suitable is inconsistent, and thus inconsistent gas types or gas concentration data may be generated, by which the monitored areas at risk of pathogenic infection as well as normal areas can be more accurately identified. Wherein the air data includes a gas type, a gas concentration corresponding to the gas type, and the like.
Further, in the method, the monitoring image data information in each monitoring area is acquired, the identification result is acquired by identifying the monitoring image data information, and the transmission priori probability value of the pathogenic infection is acquired according to the monitoring area with the pathogenic infection risk and the identification result, specifically including:
acquiring monitoring image data information in each monitoring area, identifying pathogen transmission organisms by the aid of the monitoring image data information, acquiring pathogen transmission organism quantity change characteristic data information within preset time, and presetting a pathogen transmission organism quantity change characteristic threshold;
when the pathogen transmission biological quantity change characteristic data information is larger than the pathogen transmission biological quantity change characteristic threshold value within the preset time, generating a recognition result according to the pathogen transmission biological quantity change characteristic data information within the preset time;
calculating a transmission priori probability value of each pathogenic type infection occurring in a monitoring area with pathogenic infection risk under each identification result through a Markov chain, and constructing a knowledge graph according to the transmission priori probability value of each pathogenic type infection occurring in the monitoring area with pathogenic infection risk under each identification result;
Inputting the identification result into the knowledge graph, and acquiring a transmission priori probability value of each pathogenic type infection in the monitoring area with pathogenic infection risk under the current identification result.
It should be noted that, by the method, the propagation priori probability value of each pathogenic type infection occurring in the monitored area with the pathogenic infection risk under the current identification result can be obtained. Since pathogen transmission may be transmitted through a certain transmission medium, such as micro-animals including mosquitoes, flies, worms, etc., the pathogen transmission risk in the monitored area is great when the characteristic data information of the variation in the quantity of the pathogen transmission organism is greater than the characteristic threshold value of the variation in the quantity of the pathogen transmission organism within a preset time. And calculating the transmission priori probability value of each pathogenic infection in the monitoring area with pathogenic infection risk under each identification result through a Markov chain, so as to predict the transmission priori probability value of each pathogenic infection in each monitoring area.
Further, in the method, related early warning information is generated according to the propagation priori probability value of each pathogenic type infection, and the environment of the monitored area with the pathogenic infection is regulated and controlled based on the related early warning information, which specifically comprises the following steps:
Judging whether the propagation priori probability value of each pathogenic type infection is larger than a preset propagation priori probability value, and if so, acquiring the corresponding pathogenic type and the monitoring area;
generating related early warning information according to the corresponding pathogenic type and the monitoring area, acquiring the position of the monitoring area with pathogenic infection, and generating a retrieval tag according to the position of the monitoring area with pathogenic infection;
searching the environment control equipment of the monitored area with pathogenic infection based on the search tag, acquiring the communication protocol information of the environment control equipment of the monitored area with pathogenic infection, and presetting environment regulation parameters;
and generating relevant control information according to the communication protocol information of the environmental control equipment of the monitored area with pathogenic infection and the environmental regulation parameters, carrying out early warning based on the relevant early warning information, and simultaneously carrying out environmental regulation on the monitored area with pathogenic infection according to the relevant control information.
It should be noted that, if the transmission priori probability value is greater than the preset transmission priori probability value, it indicates that the infection risk corresponding to the pathogen type is large. The environment control equipment comprises an exhaust fan, an air conditioner, air purifying equipment and the like. By performing environmental regulations on the monitored area where pathogenic infection exists according to the relevant control information, environmental regulation parameters including temperature, humidity, etc., such as by adsorbing gas away in the environmental regulation apparatus, the risk of infection is reduced.
In addition, the method can further comprise the following steps:
acquiring real-time parameter data information of a monitoring area with pathogenic infection in each time stamp, and rendering a building model diagram of the monitoring area according to the real-time parameter data information of the monitoring area with pathogenic infection in each time stamp to generate an air quality-building dynamic digital twin body;
displaying the air quality-building dynamic digital twin on a display device according to a preset mode, acquiring crowd real-time positioning data information in a target area, and calculating Euclidean distance value from the crowd real-time positioning data information in the target area to a monitoring area with pathogenic infection in the air quality-building dynamic digital twin;
judging whether the Euclidean distance value is not greater than a preset Euclidean distance value, and when the Euclidean distance value is not greater than the preset Euclidean distance value, performing dangerous marking on a monitored area with pathogenic infection to generate related marking information;
and displaying the related marking information in a display device, acquiring related counter measures through big data, sending the related counter measures to a user terminal according to a preset mode, and updating a monitoring area with pathogenic infection in real time.
When people exist in a preset area, calculating real-time positioning data information of the people in the target area to a Euclidean distance value of a monitoring area with pathogenic infection in the air quality-building dynamic digital twin body, and when the Euclidean distance value is not larger than the preset Euclidean distance value, performing danger marking on the monitoring area with pathogenic infection so as to remind the people of the dangerous area, wherein certain precautionary measures are needed.
In addition, the method can further comprise the following steps:
acquiring the installation position information of communication equipment in a digital twin monitoring network, testing through electromagnetic interference equipment, acquiring electromagnetic interference characteristic distribution data information of a current monitoring area within a preset range, and introducing a particle swarm algorithm;
acquiring electromagnetic interference characteristic data information corresponding to the installation position information of the current communication equipment based on the electromagnetic interference characteristic distribution data information of the current monitoring area within a preset range, setting iteration algebra according to the particle swarm algorithm, and setting electromagnetic interference characteristic threshold value data;
judging whether the electromagnetic interference characteristic data information is larger than the electromagnetic interference characteristic threshold value data or not, and when the electromagnetic interference characteristic data information is larger than the electromagnetic interference characteristic threshold value data, iterating according to the iteration algebra to adjust the installation position information of the communication equipment;
And outputting the installation position information of the communication equipment when the electromagnetic interference characteristic data information is not more than the electromagnetic interference characteristic threshold value data, and adjusting the digital twin monitoring network according to the installation position information of the communication equipment.
It should be noted that, the communication device includes WIFI devices, antenna devices, etc., due to the existence of electromagnetic interference, an electromagnetic field (electromagnetic interference characteristic data information) may be generated, and the electromagnetic field may affect data transmission, so as to affect the authenticity and timeliness of the digital twin monitoring network for obtaining the monitoring data, and by using the method, the arrangement rationality of the digital twin monitoring network can be further improved.
As shown in fig. 4, the second aspect of the present invention provides a digital twinning-based intelligent building management system 4, which includes a memory 41 and a processor 42, wherein the memory 41 includes a digital twinning-based intelligent building management method program, and when the digital twinning-based intelligent building management method program is executed by the processor 42, the following steps are implemented:
building drawing information of a target area is obtained, a building model diagram is built according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated;
Acquiring real-time air data information of each monitoring area through a digital twin monitoring network, and evaluating the real-time air data information of the monitoring areas to acquire the monitoring areas with pathogenic infection risks and normal areas;
acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
and generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environment regulation and control on the monitored area with the pathogenic infection based on the related early warning information.
Further, in the system, building drawing information of the target area is obtained, a building model diagram is constructed according to the building drawing information of the target area, and the monitoring area is screened based on the building model diagram, which specifically comprises:
building drawing information of a target area is obtained, building model diagrams are built through three-dimensional modeling software according to the building drawing information of the target area, and the building model diagrams are divided into areas to obtain building model diagrams of a plurality of sub-areas;
Identifying the building model diagram of the sub-area, judging whether the sub-area is a preset area, taking the corresponding sub-area as a monitoring area when the sub-area is the preset area, and outputting the building model diagram of the monitoring area;
and when the sub-area is not the preset area, taking the corresponding area as a non-monitoring area, and eliminating the building model diagram of the non-monitoring area.
Further, in the system, the monitoring network is initialized for the monitoring area, and the digital twin monitoring network is generated, which specifically comprises:
acquiring a building model diagram of a monitoring area, initializing the quantity information and the installation position information of the air quality monitoring devices, acquiring the monitoring range of each air quality monitoring device, and calculating an estimated monitoring range according to the monitoring range of the air quality monitoring device, the quantity information and the installation position information of the air quality monitoring device;
acquiring an actual monitoring range required by a building model diagram of a monitoring area, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and judging whether the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area;
if the estimated monitoring range is smaller than the actual monitoring range required by the building model diagram of the monitoring area, inheriting according to the inheritance algebra, and adjusting the quantity information and the installation position information of the air quality monitoring equipment;
When the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area, a digital twin monitoring network is constructed according to the quantity information and the installation position information of the air quality monitoring equipment.
Further, in the system, the digital twin monitoring network is used for acquiring real-time air data information of each monitoring area, and evaluating the real-time air data information of the monitoring area to acquire the monitoring area with pathogenic infection risk and the normal area, and the system specifically comprises the following steps:
acquiring and generating air data information corresponding to each pathogen type through big data, introducing a graph neural network, taking the pathogen type as a first graph node, taking the air data information as a second graph node, and constructing a pathogen type identification model based on the deep neural network;
constructing a topological structure diagram according to the first graph node and the second graph node, generating an adjacent matrix based on the topological structure diagram, introducing a local outlier detection algorithm, calculating an outlier of the first graph node in the adjacent matrix through the local outlier detection algorithm, and judging whether the outlier is not larger than a preset outlier;
removing the first graph nodes with the outliers not larger than the preset outliers, updating the adjacent matrix, obtaining an updated adjacent matrix, and inputting the updated adjacent matrix into a pathogen type recognition model for coding learning;
The real-time air data information of each monitoring area is obtained through a digital twin monitoring network, the real-time air data information of the monitoring area is input into a pathogen type identification model for prediction, and the monitoring area with pathogen infection risk and the normal area are obtained.
Further, in the present system, the monitoring image data information in each monitoring area is acquired, and the identification result is acquired by identifying the monitoring image data information, and the propagation priori probability value of the pathogenic infection is acquired according to the monitoring area with the pathogenic infection risk and the identification result, which specifically includes:
acquiring monitoring image data information in each monitoring area, identifying pathogen transmission organisms by the aid of the monitoring image data information, acquiring pathogen transmission organism quantity change characteristic data information within preset time, and presetting a pathogen transmission organism quantity change characteristic threshold;
when the pathogen transmission biological quantity change characteristic data information is larger than the pathogen transmission biological quantity change characteristic threshold value within the preset time, generating a recognition result according to the pathogen transmission biological quantity change characteristic data information within the preset time;
Calculating a transmission priori probability value of each pathogenic type infection occurring in a monitoring area with pathogenic infection risk under each identification result through a Markov chain, and constructing a knowledge graph according to the transmission priori probability value of each pathogenic type infection occurring in the monitoring area with pathogenic infection risk under each identification result;
inputting the identification result into the knowledge graph, and acquiring a transmission priori probability value of each pathogenic type infection in the monitoring area with pathogenic infection risk under the current identification result.
Further, in the system, related early warning information is generated according to the propagation priori probability value of each pathogenic type infection, and the environment of the monitored area with the pathogenic infection is regulated and controlled based on the related early warning information, which specifically comprises the following steps:
judging whether the propagation priori probability value of each pathogenic type infection is larger than a preset propagation priori probability value, and if so, acquiring the corresponding pathogenic type and the monitoring area;
generating related early warning information according to the corresponding pathogenic type and the monitoring area, acquiring the position of the monitoring area with pathogenic infection, and generating a retrieval tag according to the position of the monitoring area with pathogenic infection;
Searching the environment control equipment of the monitored area with pathogenic infection based on the search tag, acquiring the communication protocol information of the environment control equipment of the monitored area with pathogenic infection, and presetting environment regulation parameters;
and generating relevant control information according to the communication protocol information of the environmental control equipment of the monitored area with pathogenic infection and the environmental regulation parameters, carrying out early warning based on the relevant early warning information, and simultaneously carrying out environmental regulation on the monitored area with pathogenic infection according to the relevant control information.
A third aspect of the present invention provides a computer readable storage medium including a digital twinning-based intelligent building management method program, which when executed by a processor, implements the steps of any one of the digital twinning-based intelligent building management methods.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The intelligent building management method based on digital twinning is characterized by comprising the following steps of:
building drawing information of a target area is obtained, a building model diagram is built according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated;
acquiring real-time air data information of each monitoring area through the digital twin monitoring network, and evaluating the real-time air data information of the monitoring areas to acquire the monitoring areas with pathogenic infection risks and normal areas;
acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
and generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environment regulation and control on a monitored area with the pathogenic infection based on the related early warning information.
2. The intelligent building management method based on digital twinning according to claim 1, wherein the method comprises the steps of obtaining building drawing information of a target area, constructing a building model diagram according to the building drawing information of the target area, and screening a monitoring area based on the building model diagram, and specifically comprises the following steps:
building drawing information of a target area is obtained, a building model diagram is built through three-dimensional modeling software according to the building drawing information of the target area, and the building model diagram is divided into areas to obtain building model diagrams of a plurality of sub-areas;
identifying the building model diagram of the subarea, judging whether the subarea is a preset area, taking the corresponding subarea as a monitoring area when the subarea is the preset area, and outputting the building model diagram of the monitoring area;
and when the subarea is not the preset area, taking the corresponding area as a non-monitoring area, and eliminating the building model diagram of the non-monitoring area.
3. The intelligent building management method based on digital twinning according to claim 1, wherein the monitoring area is initialized with a monitoring network, and a digital twinning monitoring network is generated, specifically comprising:
Acquiring a building model diagram of the monitoring area, initializing the quantity information and the installation position information of the air quality monitoring devices, acquiring the monitoring range of each air quality monitoring device, and calculating the estimated monitoring range according to the monitoring range of the air quality monitoring device, the quantity information and the installation position information of the air quality monitoring device;
acquiring an actual monitoring range required by a building model diagram of the monitoring area, introducing a genetic algorithm, setting a genetic algebra according to the genetic algorithm, and judging whether the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area;
if the estimated monitoring range is smaller than the actual monitoring range required by the building model diagram of the monitoring area, inheriting according to the inheritance algebra, and adjusting the quantity information and the installation position information of the air quality monitoring equipment;
and when the estimated monitoring range is not smaller than the actual monitoring range required by the building model diagram of the monitoring area, constructing a digital twin monitoring network according to the quantity information and the installation position information of the air quality monitoring equipment.
4. The intelligent building management method based on digital twinning according to claim 1, wherein the digital twinning monitoring network is used for acquiring real-time air data information of each monitoring area, evaluating the real-time air data information of the monitoring area, and acquiring a monitoring area with pathogenic infection risk and a normal area, and the method specifically comprises the following steps:
Acquiring and generating air data information corresponding to each pathogen type through big data, introducing a graph neural network, taking the pathogen type as a first graph node, taking the air data information as a second graph node, and constructing a pathogen type identification model based on a deep neural network;
constructing a topological structure diagram according to the first graph node and the second graph node, generating an adjacent matrix based on the topological structure diagram, introducing a local outlier detection algorithm, calculating an outlier of the first graph node in the adjacent matrix through the local outlier detection algorithm, and judging whether the outlier is not larger than a preset outlier;
removing the first graph node with the outlier not larger than a preset outlier, updating the adjacency matrix, acquiring an updated adjacency matrix, and inputting the updated adjacency matrix into the pathogen type recognition model for coding learning;
and acquiring real-time air data information of each monitoring area through the digital twin monitoring network, inputting the real-time air data information of the monitoring area into the pathogen type identification model for prediction, and acquiring a monitoring area with pathogen infection risk and a normal area.
5. The intelligent building management method based on digital twinning according to claim 1, wherein the steps of obtaining the monitoring image data information in each monitoring area, and obtaining the identification result by identifying the monitoring image data information, and obtaining the transmission priori probability value of the occurrence of the pathogenic infection according to the monitoring area with the pathogenic infection risk and the identification result comprise:
acquiring monitoring image data information in each monitoring area, identifying pathogen transmission organisms by the monitoring image data information, acquiring pathogen transmission organism quantity change characteristic data information within preset time, and presetting a pathogen transmission organism quantity change characteristic threshold;
when the pathogen transmission biological quantity change characteristic data information within the preset time is larger than the pathogen transmission biological quantity change characteristic threshold value, generating a recognition result according to the pathogen transmission biological quantity change characteristic data information within the preset time;
calculating a transmission priori probability value of each pathogenic type infection occurring in a monitoring area with pathogenic infection risk under each identification result through a Markov chain, and constructing a knowledge graph according to the transmission priori probability value of each pathogenic type infection occurring in the monitoring area with pathogenic infection risk under each identification result;
And inputting the identification result into the knowledge graph, and acquiring a transmission priori probability value of each pathogenic type infection in a monitoring area with pathogenic infection risk under the current identification result.
6. The intelligent building management method based on digital twinning according to claim 1, wherein the method is characterized by generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environmental regulation on a monitored area with pathogenic infection based on the related early warning information, and specifically comprises the following steps:
judging whether the propagation priori probability value of each pathogenic type infection is larger than a preset propagation priori probability value, and if the propagation priori probability value is larger than the preset propagation priori probability value, acquiring a corresponding pathogenic type and a monitoring area;
generating related early warning information according to the corresponding pathogenic type and the monitoring area, acquiring the position of the monitoring area with pathogenic infection, and generating a retrieval tag according to the position of the monitoring area with pathogenic infection;
searching the environment control equipment of the monitoring area with pathogenic infection based on the search tag, acquiring the communication protocol information of the environment control equipment of the monitoring area with pathogenic infection, and presetting environment regulation parameters;
And generating relevant control information according to the communication protocol information and the environment regulation parameters of the environment control equipment of the monitored area with the pathogenic infection, carrying out early warning based on the relevant early warning information, and simultaneously carrying out environment regulation on the monitored area with the pathogenic infection according to the relevant control information.
7. The intelligent building management system based on digital twinning is characterized by comprising a memory and a processor, wherein the memory comprises an intelligent building management method program based on digital twinning, and when the intelligent building management method program based on digital twinning is executed by the processor, the following steps are realized:
building drawing information of a target area is obtained, a building model diagram is built according to the building drawing information of the target area, a monitoring area is screened based on the building model diagram, a monitoring network is initialized for the monitoring area, and a digital twin monitoring network is generated;
acquiring real-time air data information of each monitoring area through the digital twin monitoring network, and evaluating the real-time air data information of the monitoring areas to acquire the monitoring areas with pathogenic infection risks and normal areas;
Acquiring monitoring image data information in each monitoring area, identifying the monitoring image data information to acquire an identification result, and acquiring a transmission priori probability value of each pathogenic type infection according to the monitoring area with pathogenic infection risk and the identification result;
and generating related early warning information according to the propagation priori probability value of each pathogenic type infection, and performing environment regulation and control on a monitored area with the pathogenic infection based on the related early warning information.
8. A computer readable storage medium, characterized in that it comprises a digital twinning based intelligent building management method program, which, when executed by a processor, implements the steps of the digital twinning based intelligent building management method according to any one of claims 1-6.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113793234A (en) * | 2021-11-16 | 2021-12-14 | 中通服建设有限公司 | Wisdom garden platform based on digit twin technique |
| CN116308302A (en) * | 2023-05-19 | 2023-06-23 | 深圳抛物线科技有限公司 | Digital twinning-based advanced warning method and system for potential insulation hazards |
| CN116558082A (en) * | 2023-06-16 | 2023-08-08 | 广东新拓展建筑工程有限公司 | Energy-saving ventilation system |
| CN117171842A (en) * | 2023-08-04 | 2023-12-05 | 华南理工大学 | Urban slow-moving bridge health monitoring and digital twin system |
| CN117272247A (en) * | 2023-11-17 | 2023-12-22 | 沧州师范学院 | Data integration method and system applied to digital twin intelligent village |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113793234A (en) * | 2021-11-16 | 2021-12-14 | 中通服建设有限公司 | Wisdom garden platform based on digit twin technique |
| CN116308302A (en) * | 2023-05-19 | 2023-06-23 | 深圳抛物线科技有限公司 | Digital twinning-based advanced warning method and system for potential insulation hazards |
| CN116558082A (en) * | 2023-06-16 | 2023-08-08 | 广东新拓展建筑工程有限公司 | Energy-saving ventilation system |
| CN117171842A (en) * | 2023-08-04 | 2023-12-05 | 华南理工大学 | Urban slow-moving bridge health monitoring and digital twin system |
| CN117272247A (en) * | 2023-11-17 | 2023-12-22 | 沧州师范学院 | Data integration method and system applied to digital twin intelligent village |
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