CN117371872A - Intelligent management method and platform for intelligent building - Google Patents
Intelligent management method and platform for intelligent building Download PDFInfo
- Publication number
- CN117371872A CN117371872A CN202311565318.XA CN202311565318A CN117371872A CN 117371872 A CN117371872 A CN 117371872A CN 202311565318 A CN202311565318 A CN 202311565318A CN 117371872 A CN117371872 A CN 117371872A
- Authority
- CN
- China
- Prior art keywords
- building
- data
- building area
- environmental quality
- intelligent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000007726 management method Methods 0.000 title claims abstract description 43
- 230000007613 environmental effect Effects 0.000 claims abstract description 73
- 238000012544 monitoring process Methods 0.000 claims abstract description 51
- 230000009471 action Effects 0.000 claims abstract description 34
- 238000004458 analytical method Methods 0.000 claims abstract description 26
- 238000004088 simulation Methods 0.000 claims abstract description 22
- 238000012549 training Methods 0.000 claims abstract description 13
- 238000003062 neural network model Methods 0.000 claims abstract description 4
- 239000000779 smoke Substances 0.000 claims description 36
- 238000005265 energy consumption Methods 0.000 claims description 33
- 238000005286 illumination Methods 0.000 claims description 16
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 14
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 13
- 229910052760 oxygen Inorganic materials 0.000 claims description 13
- 239000001301 oxygen Substances 0.000 claims description 13
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 7
- 239000001569 carbon dioxide Substances 0.000 claims description 7
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 6
- 229910002091 carbon monoxide Inorganic materials 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 239000007789 gas Substances 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000012360 testing method Methods 0.000 claims description 4
- 238000009423 ventilation Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 150000002500 ions Chemical class 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 230000005622 photoelectricity Effects 0.000 claims description 3
- 230000000241 respiratory effect Effects 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 4
- WSFSSNUMVMOOMR-UHFFFAOYSA-N Formaldehyde Chemical compound O=C WSFSSNUMVMOOMR-UHFFFAOYSA-N 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005520 cutting process Methods 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 239000013618 particulate matter Substances 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000012502 risk assessment Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000004378 air conditioning Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
- G06Q50/265—Personal security, identity or safety
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Computer Security & Cryptography (AREA)
Abstract
The invention discloses an intelligent management method and a platform for an intelligent building, which relate to the technical field of intelligent buildings, wherein data acquisition is carried out in a building area, a Bp neural network model is used, and a state prediction model of the building is built after training; tracking the user entering the building, and identifying the current action of the user, wherein the current action is used as an input condition; monitoring and evaluating the environmental quality in the building area to generate an environmental quality coefficient Kxs, and sending out early warning information if the environmental quality coefficient Kxs is larger than a corresponding environmental quality threshold; according to the current state and movement of the user, generating an adjustment strategy after simulation analysis, so that the environmental quality coefficient Kxs is lower than a preset environmental quality threshold; and the state prediction model of the building gives an adjustment strategy for each piece of equipment in the building area and executes the adjustment strategy. By adjusting various devices in the building area, the fire risk coefficient Hsx is reduced as much as possible, and the fire risk in the building area is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent buildings, in particular to an intelligent management method and platform for intelligent buildings.
Background
The intelligent building management platform depends on artificial intelligence technology, including natural language understanding, image recognition, voice recognition and the like; by natural language interaction with the user, recognition of images or voice instructions, the platform can more intelligently understand and respond to the user's needs, providing more convenient and efficient services. In order to meet the daily work needs and the demands of people on working and living environments, the number of monitored objects in a building is gradually increased, the number of various functional subsystems is continuously increased, and the content of the building intelligent system is increasingly abundant.
In the chinese application of the invention with application number 201711436601.7, an intelligent management platform for intelligent building is disclosed, which is remotely connected with a plurality of mobile devices, and comprises: the label management module is used for storing label information of each prefabricated member, wherein the label information comprises label data, and the labels are arranged on the corresponding prefabricated members; the prefabricated member management module is used for storing management files of each prefabricated member; the user identification module is connected with the tag management module and the prefabricated member management module and is used for acquiring the user identity sent by the mobile equipment and giving the mobile equipment corresponding permission for inquiring and editing the management file based on the user identity; each mobile device is used for respectively identifying the label on the prefabricated member to obtain label data, and remotely inquiring and editing the management file according to the label data.
However, the intelligent building management platform also has a plurality of problems and challenges, the normal operation of the intelligent building management platform depends on the data acquisition of various sensors and devices, and the quality and reliability of the acquired data are critical to the normal operation of the platform; however, the existing platform is difficult to intelligently adjust the running states of various devices in a building area according to the actions of users, which may cause higher energy consumption level, and also cannot quickly find emergency situations such as building fire and the like in the building, and timely take measures, thereby bringing a certain potential safety hazard.
Therefore, the invention provides an intelligent management method and platform for intelligent buildings.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides an intelligent management method and platform for intelligent buildings, which aim to solve the problems that the existing platform is difficult to intelligently adjust the running states of various devices in a building area according to the actions of users, so that a higher energy consumption level is generated, emergency situations such as building fires and the like in the buildings cannot be found quickly, and potential safety hazards exist.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme: an intelligent management method for intelligent building,
when a user exists in a building area, data acquisition is carried out in the building area, wherein the data comprises environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, a Bp neural network model is used, and a state prediction model of a building is built after training by combining the data;
setting a safety management system in a building, wherein the safety management system comprises a video monitoring system and an access control system, tracks a user entering the building, and recognizes the current action of the user, and takes the current action as an input condition; after a user enters a building area, monitoring and evaluating the environmental quality in the building area to generate an environmental quality coefficient Kxs, and if the environmental quality coefficient Kxs is greater than a corresponding environmental quality threshold value, sending out early warning information;
after a user enters a building area, according to the current state and action of the user, generating an adjustment strategy by a state prediction model of the building after simulation analysis, executing the adjustment strategy and adjusting equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the fire and smoke conditions in the building are monitored in real time through the temperature and smoke monitoring system, the smoke concentration and the carbon monoxide concentration are obtained, the risk of fire hazards in the building area is judged, and after simulation analysis, the adjustment strategy for all equipment in the building area is given out and executed by the state prediction model of the building.
Further, collecting environmental condition data includes: temperature, humidity data, illumination intensity data and air quality data are summarized, and then environmental condition data are established; the operation and energy consumption data of the equipment are collected, and the operation and energy consumption data specifically comprise: and collecting information such as the running state, energy consumption, equipment faults and the like of each piece of equipment, and establishing equipment state data after summarizing.
Further, acquiring safety monitoring data, including monitoring videos around the building, and further acquiring position information and activity tracks of personnel; taking the Bp neural model as a basis, combining environmental condition data, equipment state data, personnel position and action data, and combining structural parameters in the building, and building a state prediction model of the building after training and testing.
Further, the personnel identity verification information is read through an access control card reader or scanning equipment, and an access control controller is used for processing the identity verification information and controlling the opening and closing of the access control equipment; and acquiring the position information of the user in the building in real time, and identifying the current action of the user through the action sensor.
Further, CO is used 2 The sensor monitors the carbon dioxide concentration Nc and knows the ventilation condition of indoor air and the respiratory load of indoor personnel; the indoor temperature Kc in the building area is monitored and obtained through a temperature sensor, and the illumination condition in the building area is monitored through a light sensor to obtain indoor illumination Qc; the above parameters are summarized as an environmental quality dataset within the building area.
Further, an environmental quality coefficient Kxs is generated from the environmental quality data set as an important index for evaluating the air quality; wherein, the environmental quality coefficient Kxs is generated as follows,
after dimensionless treatment is carried out on the carbon dioxide concentration Nc, the indoor temperature Kc and the indoor illumination Qc, the following formula is adopted:
wherein, the parameter meaning is: CO 2 Factors of，/>Temperature factor->，/>Light factor->，/>，/>A constant correction coefficient greater than 0; and presetting an environment quality threshold, and if the environment quality coefficient Kxs is larger than the environment quality threshold, sending out early warning information.
Further, using a trained state prediction model of the building, and adjusting starting and working states of various devices in the building area according to movement and actions of a user until an environment quality coefficient Kxs is lower than a preset environment quality threshold; setting an energy consumption threshold, using a state prediction model of a building according to actions and state data of users in the building area, and generating an adjustment strategy for the running states of various devices in the building area after simulation analysis; outputting the adjustment strategy, and adjusting the running states of all the devices in the building area according to the adjustment strategy.
Further, monitoring the residual oxygen concentration in the building area by using a sensor to generate an oxygen concentration Yn; detecting the generation of smoke in a building through sensors such as gas sensors, ions, photoelectricity and the like, and acquiring smoke concentration Wn if the smoke is generated;
from the oxygen concentration Yn and the smoke concentration Wn, a fire risk factor Hsx is generated in the following manner:
wherein, the parameter meaning is: oxygen factorSmoke factor->，/>，/>A constant correction factor greater than 0.
Further, when the fire risk coefficient Hsx is greater than a preset risk threshold, generating an adjustment strategy for the running states of all equipment in the building area by the state prediction model of the building after simulation analysis, so that the fire risk coefficient Hsx is reduced according to the adjustment strategy; if not, an alarm message is sent outwards.
An intelligent building management platform comprising:
the model construction unit is used for collecting data in a building area, including environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, and building a state prediction model of the building after training;
the monitoring unit is used for tracking the user entering the building and identifying the current action of the user, monitoring and evaluating the environmental quality in the building area after the user enters the building area to generate an environmental quality coefficient Kxs, and sending out early warning information if the environmental quality coefficient Kxs is larger than a corresponding environmental quality threshold;
the analysis unit generates an adjustment strategy by the state prediction model of the building after simulation analysis according to the current state and action of the user, executes the adjustment strategy and adjusts equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the output unit monitors fire and smoke conditions in the building, acquires smoke concentration and carbon monoxide concentration, judges the risk of fire hazards in the building area, and gives out an adjustment strategy for each device in the building area and executes the adjustment strategy after simulation analysis by the state prediction model of the building after simulation analysis.
(III) beneficial effects
The invention provides an intelligent management method and platform for intelligent buildings, which have the following beneficial effects:
1. the fire and smoke monitoring system has the advantages that fire and smoke are timely found, real-time alarm is provided, emergency measures are quickly taken, life safety of personnel is guaranteed, data analysis and early warning functions are provided to support fire risk assessment and fire prevention measures, fire and smoke monitoring data are comprehensively recorded and stored to be recorded and analyzed in the future, further, various devices in a building area are adjusted by generating an adjustment strategy, fire risk coefficient Hsx is reduced as much as possible, and fire risk in the building area is reduced.
2. Through the intelligent building management platform, a user can know the energy consumption condition of a building in real time, and the problems of energy consumption peak and energy waste are identified; the platform can provide energy-saving optimization suggestions according to data analysis, helps users reduce energy consumption, improves energy efficiency and reduces influence on environment.
Drawings
FIG. 1 is a schematic flow chart of an intelligent building management method according to the invention;
fig. 2 is a schematic structural diagram of the intelligent building management platform according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an intelligent management method for intelligent buildings, comprising the following steps:
step one, when a user exists in a building area, data acquisition is carried out in the building area, wherein the data comprises environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, a Bp neural network model is used, and a state prediction model of a building is built after training by combining the data;
the first step comprises the following steps:
step 101: the environmental condition data is collected, specifically: measuring indoor and outdoor temperature and humidity data by using a temperature and humidity sensor, and monitoring the temperature and humidity change conditions inside and outside the building in real time; measuring indoor and outdoor illumination intensity data by using a light sensor, and monitoring indoor illumination intensity and outdoor illumination intensity of a building;
using CO 2 A sensor, a particulate matter sensor, a harmful gas sensor measures indoor and outdoor air quality data, comprising: monitoring the air quality conditions inside and outside a building by data such as carbon dioxide concentration, particulate matter concentration, oxygen content, formaldehyde concentration and the like;
after the data are summarized, establishing environmental condition data;
step 102: the operation and energy consumption data of the equipment are collected, and the operation and energy consumption data specifically comprise: collecting information of running states, energy consumption, equipment faults and the like of various equipment including heating equipment, ventilation equipment, air conditioning equipment and the like; the monitoring, management and optimization of the remote real-time energy consumption are realized through the front-end intelligent ammeter, the intelligent water meter, the energy consumption collector and the data instrument;
summarizing equipment operation data and energy consumption data thereof, and establishing equipment state data;
step 103: the method for acquiring the safety monitoring data comprises the following steps: the method comprises the steps of acquiring video monitoring data of each area of a building in real time through cameras arranged in the building, wherein the video monitoring data comprise monitoring videos of the periphery of the building, monitoring videos of different areas and the like, and further acquiring position information and activity tracks of personnel;
104, taking a Bp nerve model as a basis, combining environmental condition data, equipment state data, personnel position and action data, combining building internal structure parameters, respectively establishing a training set and a testing set after feature recognition, after an initial model is established after a network structure is selected, training and testing the initial model, and outputting the trained initial model as a state prediction model of a building;
in use, the contents of steps 201 to 204 are combined:
by using various sensors to monitor the temperature, humidity, illumination intensity, air quality, energy consumption data of building equipment and video monitoring data in real time inside and outside the building and combining the structural parameters in the building by taking the Bp neural model as a basis, the twin model is built, so that the relevant data of the building can be observed more intuitively and in real time, the analysis of the building data is facilitated, and the management and optimization are facilitated.
Setting a safety management system in the building, wherein the safety management system comprises a video monitoring system and an access control system, tracks a user entering the building, and recognizes the current action of the user, and takes the current action as an input condition;
the second step comprises the following steps:
step 201: the video monitoring system is integrated by ONVIF protocol, RS232 interface and video security monitoring hardware, a server or streaming media server supporting RTSP protocol is arranged, RTSP protocol related settings including IP address, port number, user name and password information of a camera are configured in the server, python language or open source library is used for analyzing RTSP video stream, and video frame data are extracted;
setting a time interval or a triggering condition according to requirements, and performing high-frequency image cutting operation on video frame data by using an image processing algorithm to obtain a key frame image cutting picture; the key frame cut map is subjected to safety comparison through a big data algorithm, and early warning is sent to the front end when abnormal conditions or suspicious mark personnel information is found, security personnel are notified to patrol the abnormal information, and hidden danger is eliminated in time;
step 202: the system for managing and controlling personnel to enter and exit the building by using the access control system specifically comprises the following steps: the personnel identity verification information such as card swiping, fingerprint identification, face recognition and the like is read through an access control card reader or scanning equipment;
the method comprises the steps of processing identity verification information by using an access controller and controlling the opening and closing of access equipment; the door access equipment comprises door locks, gate machines, electronic door access and the like, and is used for actually controlling personnel to enter and exit an access area; based on authority setting of a software management system manager, recording personnel access time and area, and providing various access control configuration and reporting functions;
the condition that the personnel enter and exit the building is monitored in real time by setting and managing the access authority and time limit of the personnel, and access time and identity information are recorded; therefore, after the user enters the building, the position information of the user in the building is obtained in real time, and the current action of the user is identified through the action sensor, so that the tracking of the user is realized.
Step three, after a user enters a building area, monitoring and evaluating the environmental quality in the building area to generate an environmental quality coefficient Kxs, and if the environmental quality coefficient Kxs is greater than a corresponding environmental quality threshold value, sending out early warning information;
the third step comprises the following steps:
step 301, monitoring air quality of a building in real time: environmental condition data acquired by the data acquisition unit are acquired, various pollutants and indexes in indoor and outdoor air are received, and the quality and health condition of the air are estimated and monitored: using CO 2 The sensor monitors the carbon dioxide concentration Nc and knows the ventilation condition of indoor air and the respiratory load of indoor personnel; the indoor temperature Kc in the building area is monitored and obtained through a temperature sensor, and the illumination condition in the building area is monitored through a light sensor to obtain indoor illumination Qc;
step 302, summarizing the parameters to be used as an environmental quality data set in a building area, and generating an environmental quality coefficient Kxs by the environmental quality data set to be used as an important index for evaluating air quality;
the environmental quality coefficient Kxs is generated by performing dimensionless treatment on the carbon dioxide concentration Nc, the indoor temperature Kc and the indoor illumination Qc according to the following formula:
wherein, the parameter meaning is: CO 2 Factors of，/>Temperature factor->，/>Light factor->，/>，/>A constant correction coefficient greater than 0;
and presetting an environment quality threshold, and if the environment quality coefficient Kxs is larger than the environment quality threshold, sending out early warning information.
In use, the contents of steps 201 and 202 are combined:
the indoor environment quality is evaluated by monitoring the air quality, the indoor temperature and the indoor illumination of the building in real time, the environment quality coefficient is generated and is compared with the preset environment quality threshold, and when the environment quality coefficient is larger than the preset environment quality threshold, an early warning is sent out, so that indoor personnel can be timely informed, and life health of the personnel is threatened when the indoor pollution degree is serious.
Step four, after a user enters a building area, generating an adjustment strategy by a state prediction model of the building after simulation analysis according to the current state and action of the user, executing the adjustment strategy and adjusting equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the fourth step comprises the following steps:
step 401: the energy consumption monitoring device, such as a front-end intelligent ammeter, an intelligent water meter, an energy consumption collector and a data instrument, is used for monitoring, managing and optimizing various devices in a building, such as lighting equipment, air conditioners and the like, so that the water and electricity are comprehensively managed by comprehensively considering the water and electricity saving system of a property management center when the energy is managed, the allocation of resources among departments is optimized, and the resource utilization efficiency is improved;
step 402, setting an energy consumption threshold according to an average value of energy consumption of each piece of equipment in the last year, wherein the energy consumption threshold is used for starting or closing each piece of equipment in a building area when no user exists in the building area; each device in the building area is monitored, controlled and optimally managed, and the specific content is as follows:
using a state prediction model of the building after training, and adjusting starting and working states of various devices in a building area according to movement and actions of a user until an environment quality coefficient Kxs is lower than a preset environment quality threshold;
acquiring an energy consumption threshold, using a state prediction model of a building according to the action and state data of a user in a building area, and generating an adjustment strategy for the running states of various devices in the building area after simulation analysis; outputting the adjustment strategy, and adjusting the running states of all the devices in the building area according to the adjustment strategy.
In use, the contents of steps 401 and 402 are combined:
by monitoring the energy consumption of the building equipment and managing various equipment in the building area, the environmental quality coefficient is lower than a preset environmental quality threshold, so that the allocation of resources among departments is optimized, the resource utilization efficiency is improved, the environmental quality in the building area is improved, and the environmental pollution is avoided.
As a further improvement, there is also after step four:
step five, monitoring fire and smoke conditions in a building in real time through a temperature and smoke monitoring system, acquiring smoke concentration and carbon monoxide concentration, judging fire hidden danger risks of a building area, and giving out an adjustment strategy for each piece of equipment in the building area and executing the adjustment strategy after simulation analysis by a state prediction model of the building;
the fifth step comprises the following steps:
step 501, monitoring the residual oxygen concentration in a building area by using a sensor to generate an oxygen concentration Yn; detecting the generation of smoke in a building through sensors such as gas sensors, ions, photoelectricity and the like, and acquiring smoke concentration Wn if the smoke is generated;
from the oxygen concentration Yn and the smoke concentration Wn, a fire risk factor Hsx is generated in the following manner:
wherein, the parameter meaning is: oxygen factorSmoke factor->，/>，/>A constant correction coefficient greater than 0;
step 502, when the fire risk coefficient Hsx is greater than a preset risk threshold, generating an adjustment strategy for the running states of all equipment in a building area by a state prediction model of the building after simulation analysis, so that the fire risk coefficient Hsx is reduced according to the adjustment strategy; if not, sending out alarm information;
in use, the contents of steps 501 and 502 are combined:
the fire and smoke monitoring system has the advantages that fire and smoke are timely found, real-time alarm is provided, emergency measures are quickly taken, life safety of personnel is guaranteed, data analysis and early warning functions are provided to support fire risk assessment and fire prevention measures, fire and smoke monitoring data are comprehensively recorded and stored to be recorded and analyzed in the future, further, various devices in a building area are adjusted by generating an adjustment strategy, fire risk coefficient Hsx is reduced as much as possible, and fire risk in the building area is reduced.
Referring to fig. 2, the present invention provides an intelligent management platform for intelligent buildings, comprising:
the model construction unit is used for collecting data in a building area, including environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, and building a state prediction model of the building after training;
the monitoring unit is used for tracking the user entering the building and identifying the current action of the user, monitoring and evaluating the environmental quality in the building area after the user enters the building area to generate an environmental quality coefficient Kxs, and sending out early warning information if the environmental quality coefficient Kxs is larger than a corresponding environmental quality threshold;
the analysis unit generates an adjustment strategy by the state prediction model of the building after simulation analysis according to the current state and action of the user, executes the adjustment strategy and adjusts equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the output unit monitors fire and smoke conditions in the building, acquires smoke concentration and carbon monoxide concentration, judges the risk of fire hazards in the building area, and gives out an adjustment strategy for each device in the building area and executes the adjustment strategy after simulation analysis by the state prediction model of the building after simulation analysis.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on 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.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (10)
1. An intelligent management method for intelligent buildings is characterized in that: comprising the following steps:
when a user exists in a building area, data acquisition is carried out in the building area, wherein the data comprises environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, a Bp neural network model is used, and a state prediction model of a building is built after training by combining the data;
setting a safety management system in a building, wherein the safety management system comprises a video monitoring system and an access control system, tracks a user entering the building, and recognizes the current action of the user, and takes the current action as an input condition; after a user enters a building area, monitoring and evaluating the environmental quality in the building area to generate an environmental quality coefficient Kxs, and if the environmental quality coefficient Kxs is greater than a corresponding environmental quality threshold value, sending out early warning information;
after a user enters a building area, according to the current state and action of the user, generating an adjustment strategy by a state prediction model of the building after simulation analysis, executing the adjustment strategy and adjusting equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the fire and smoke conditions in the building are monitored in real time through the temperature and smoke monitoring system, the smoke concentration and the carbon monoxide concentration are obtained, the risk of fire hazards in the building area is judged, and after simulation analysis, the adjustment strategy for all equipment in the building area is given out and executed by the state prediction model of the building.
2. The intelligent building management method according to claim 1, wherein:
collecting environmental condition data, including: temperature, humidity data, illumination intensity data and air quality data are summarized, and then environmental condition data are established; the operation and energy consumption data of the equipment are collected, and the operation and energy consumption data specifically comprise: and collecting information such as the running state, energy consumption, equipment faults and the like of each piece of equipment, and establishing equipment state data after summarizing.
3. The intelligent building management method according to claim 2, wherein:
acquiring safety monitoring data, wherein the safety monitoring data comprise monitoring videos around a building, and further acquiring position information and activity tracks of personnel; taking the Bp neural model as a basis, combining environmental condition data, equipment state data, personnel position and action data, and combining structural parameters in the building, and building a state prediction model of the building after training and testing.
4. The intelligent building management method according to claim 1, wherein:
the personnel identity verification information is read through an entrance guard card reader or scanning equipment, and an entrance guard controller is used for processing the identity verification information and controlling the entrance guard equipment to be opened and closed; and acquiring the position information of the user in the building in real time, and identifying the current action of the user through the action sensor.
5. The intelligent building management method according to claim 1, wherein:
using CO 2 The sensor monitors the carbon dioxide concentration Nc and knows the ventilation condition of indoor air and the respiratory load of indoor personnel; the indoor temperature Kc in the building area is monitored and obtained through a temperature sensor, and the illumination condition in the building area is monitored through a light sensor to obtain indoor illumination Qc; will be as followsAnd summarizing the above parameters as an environmental quality data set in the building area.
6. The intelligent building management method according to claim 5, wherein:
generating an environmental quality coefficient Kxs from the environmental quality dataset as an important indicator for assessing air quality; wherein, the environmental quality coefficient Kxs is generated as follows,
after dimensionless treatment is carried out on the carbon dioxide concentration Nc, the indoor temperature Kc and the indoor illumination Qc, the following formula is adopted:
；
wherein, the parameter meaning is: CO 2 Factors of，/>Temperature factor->，/>Light factor->，/>，/>A constant correction coefficient greater than 0;
and presetting an environment quality threshold, and if the environment quality coefficient Kxs is larger than the environment quality threshold, sending out early warning information.
7. The intelligent building management method according to claim 1, wherein:
using a state prediction model of the building after training, and adjusting starting and working states of various devices in a building area according to movement and actions of a user until an environment quality coefficient Kxs is lower than a preset environment quality threshold;
setting an energy consumption threshold, using a state prediction model of a building according to actions and state data of users in the building area, and generating an adjustment strategy for the running states of various devices in the building area after simulation analysis;
outputting the adjustment strategy, and adjusting the running states of all the devices in the building area according to the adjustment strategy.
8. The intelligent building management method according to claim 7, wherein:
monitoring the residual oxygen concentration in the building area by using a sensor to generate oxygen concentration Yn; detecting the generation of smoke in a building through sensors such as gas sensors, ions, photoelectricity and the like, and acquiring smoke concentration Wn if the smoke is generated;
from the oxygen concentration Yn and the smoke concentration Wn, a fire risk factor Hsx is generated in the following manner:
；
wherein, the parameter meaning is: oxygen factorSmoke factor->，/>，/>A constant correction factor greater than 0.
9. The intelligent building management method according to claim 8, wherein:
when the fire risk coefficient Hsx is larger than a preset risk threshold, after simulation analysis, a state prediction model of the building generates an adjustment strategy for the running states of all equipment in the building area, so that the fire risk coefficient Hsx is reduced according to the adjustment strategy; if not, an alarm message is sent outwards.
10. An intelligent management platform for intelligent buildings, which is characterized in that: comprising the following steps:
the model construction unit is used for collecting data in a building area, including environmental condition data, energy consumption data, equipment operation data, monitoring data and personnel flow data, and building a state prediction model of the building after training;
the monitoring unit is used for tracking the user entering the building and identifying the current action of the user, monitoring and evaluating the environmental quality in the building area after the user enters the building area to generate an environmental quality coefficient Kxs, and sending out early warning information if the environmental quality coefficient Kxs is larger than a corresponding environmental quality threshold;
the analysis unit generates an adjustment strategy by the state prediction model of the building after simulation analysis according to the current state and action of the user, executes the adjustment strategy and adjusts equipment in the building area until the environmental quality coefficient Kxs is lower than a preset environmental quality threshold;
the output unit monitors fire and smoke conditions in the building, acquires smoke concentration and carbon monoxide concentration, judges the risk of fire hazards in the building area, and gives out an adjustment strategy for each device in the building area and executes the adjustment strategy after simulation analysis by the state prediction model of the building after simulation analysis.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311565318.XA CN117371872A (en) | 2023-11-22 | 2023-11-22 | Intelligent management method and platform for intelligent building |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311565318.XA CN117371872A (en) | 2023-11-22 | 2023-11-22 | Intelligent management method and platform for intelligent building |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117371872A true CN117371872A (en) | 2024-01-09 |
Family
ID=89402477
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311565318.XA Withdrawn CN117371872A (en) | 2023-11-22 | 2023-11-22 | Intelligent management method and platform for intelligent building |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117371872A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117910517A (en) * | 2024-01-25 | 2024-04-19 | 河海大学 | Dyke empty hidden danger identification method and system based on physical information neural network |
| CN117910811A (en) * | 2024-03-18 | 2024-04-19 | 深圳原世界科技有限公司 | Intelligent fire control management method and system based on multi-mode AI large model |
| CN117993687A (en) * | 2024-03-18 | 2024-05-07 | 西安建筑科大工程技术有限公司 | Multi-energy scheduling method for building energy conservation with real-time energy efficiency |
| CN118393977A (en) * | 2024-07-01 | 2024-07-26 | 浙江省药品信息宣传和发展服务中心(浙江省药品监督管理局行政受理中心) | Pharmaceutical production line data supervision system and method |
| CN118426311A (en) * | 2024-04-26 | 2024-08-02 | 吉林建筑大学 | Environment control method and system for public building |
-
2023
- 2023-11-22 CN CN202311565318.XA patent/CN117371872A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117910517A (en) * | 2024-01-25 | 2024-04-19 | 河海大学 | Dyke empty hidden danger identification method and system based on physical information neural network |
| CN117910811A (en) * | 2024-03-18 | 2024-04-19 | 深圳原世界科技有限公司 | Intelligent fire control management method and system based on multi-mode AI large model |
| CN117993687A (en) * | 2024-03-18 | 2024-05-07 | 西安建筑科大工程技术有限公司 | Multi-energy scheduling method for building energy conservation with real-time energy efficiency |
| CN117910811B (en) * | 2024-03-18 | 2024-05-28 | 深圳原世界科技有限公司 | Intelligent fire control management method and system based on multi-mode AI large model |
| CN118426311A (en) * | 2024-04-26 | 2024-08-02 | 吉林建筑大学 | Environment control method and system for public building |
| CN118393977A (en) * | 2024-07-01 | 2024-07-26 | 浙江省药品信息宣传和发展服务中心(浙江省药品监督管理局行政受理中心) | Pharmaceutical production line data supervision system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117371872A (en) | Intelligent management method and platform for intelligent building | |
| CN111443609B (en) | Laboratory environment self-adaptive adjustment method based on Internet of things | |
| CN118072255B (en) | Intelligent park multisource data dynamic monitoring and real-time analysis system and method | |
| CN110246074A (en) | A kind of community security protection management system and method based on multidimensional acquisition | |
| CN111131478A (en) | Light steel villa monitoring management system | |
| CN110428349A (en) | A kind of Regional Management System | |
| CN113409538A (en) | Intelligent remote monitoring and early warning management system | |
| CN114139735A (en) | Moving ring monitoring platform | |
| CN112785765A (en) | Intelligent home remote control user authorization method based on big data analysis and intelligent home cloud control platform | |
| CN114660982A (en) | Laboratory safety inspection method and device based on Internet of things | |
| CN117690245A (en) | Intelligent monitoring and early warning method and system for building fire control | |
| CN117714910B (en) | Building intercom control system based on Internet of things | |
| CN110930632A (en) | Early warning system based on artificial intelligence | |
| CN112102583A (en) | Intelligent building system based on big data | |
| CN117912186A (en) | Intelligent security linkage early warning system based on big data service | |
| CN115065812B (en) | Real-time monitoring method based on user behavior and related equipment | |
| KR102606526B1 (en) | System for multi functional monitoring of unmanned stations based on Artificial Intelligence | |
| CN205942830U (en) | Intelligent security access control system | |
| CN115762046A (en) | Early warning and intervention device for building top light life person | |
| CN109959124A (en) | A kind of office environment monitoring system based on cloud | |
| CN115330115A (en) | Special operation safety detection system and detection method for closed place | |
| CN114119297A (en) | Intelligent community management system based on Internet of things | |
| CN115685859A (en) | Visual monitoring method for information machine room | |
| CN112258702A (en) | Intelligent access control monitoring system for power environment and monitoring method thereof | |
| CN220545056U (en) | Intelligent monitoring system for power distribution room |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20240109 |