CN117319451A - Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof - Google Patents
Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof Download PDFInfo
- Publication number
- CN117319451A CN117319451A CN202311595411.5A CN202311595411A CN117319451A CN 117319451 A CN117319451 A CN 117319451A CN 202311595411 A CN202311595411 A CN 202311595411A CN 117319451 A CN117319451 A CN 117319451A
- Authority
- CN
- China
- Prior art keywords
- data
- fire
- fire control
- mode
- supervision
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000012544 monitoring process Methods 0.000 claims abstract description 76
- 230000010354 integration Effects 0.000 claims abstract description 53
- 230000004927 fusion Effects 0.000 claims abstract description 27
- 238000013499 data model Methods 0.000 claims abstract description 13
- 238000007781 pre-processing Methods 0.000 claims description 22
- 238000005065 mining Methods 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 17
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
- 238000007726 management method Methods 0.000 claims description 13
- 238000000547 structure data Methods 0.000 claims description 13
- 230000006870 function Effects 0.000 claims description 11
- 239000011159 matrix material Substances 0.000 claims description 11
- 230000004044 response Effects 0.000 claims description 11
- 238000003062 neural network model Methods 0.000 claims description 10
- 230000000007 visual effect Effects 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 7
- 238000013500 data storage Methods 0.000 claims description 6
- 230000006855 networking Effects 0.000 claims description 6
- 238000003908 quality control method Methods 0.000 claims description 6
- 230000007246 mechanism Effects 0.000 claims description 5
- 230000004913 activation Effects 0.000 claims description 4
- 238000004140 cleaning Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000013524 data verification Methods 0.000 claims description 2
- 238000001303 quality assessment method Methods 0.000 claims description 2
- 238000012502 risk assessment Methods 0.000 abstract description 7
- 238000013135 deep learning Methods 0.000 abstract description 6
- 230000002265 prevention Effects 0.000 abstract description 4
- 238000004891 communication Methods 0.000 description 3
- 238000013136 deep learning model Methods 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000009792 diffusion process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000007500 overflow downdraw method Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000013441 quality evaluation Methods 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000012098 association analyses Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000011088 calibration curve Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013502 data validation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/251—Fusion techniques of input or preprocessed data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/26—Discovering frequent patterns
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder networks
-
- 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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/0985—Hyperparameter optimisation; Meta-learning; Learning-to-learn
-
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/20—Analytics; Diagnosis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/50—Safety; Security of things, users, data or systems
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Human Resources & Organizations (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Computer Security & Cryptography (AREA)
- Educational Administration (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Probability & Statistics with Applications (AREA)
- Primary Health Care (AREA)
- Quality & Reliability (AREA)
- Emergency Management (AREA)
- Signal Processing (AREA)
Abstract
The invention discloses a city-level fire-fighting Internet of things supervision system based on multi-mode big data and a method thereof. The invention utilizes a deep learning algorithm to construct a unified fire data model for fusion and integration of multi-source heterogeneous and multi-mode data in the urban fire Internet of things system. Meanwhile, risk monitoring and prediction are carried out, and timely risk assessment and early warning are provided, so that corresponding measures are taken for prevention and treatment.
Description
Technical Field
The invention relates to the technical field of Internet of things, in particular to a city-level fire-fighting Internet of things supervision system based on multi-mode big data and a method thereof.
Background
Along with the accelerated development of the internet of things technology, important revolution is brought to the urban fire control field, and through the urban-level internet of things supervision system, data communication from the bottom layer to the upper layer and from the scene to the cloud can be realized, so that a new solution is brought for fire emergency response, fire control real-time monitoring, fire control risk early warning and fire control operation management.
The city-level fire-fighting Internet of things supervision system mainly comprises the following parts: (1) a sensor device: including temperature sensor, smoke sensor, flammable gas sensor etc. for real-time supervision conflagration risk and environmental parameter. (2) electricity consumption monitoring equipment: including monitoring devices for leakage current, load, electrical device temperature, etc. (3) monitoring camera: the fire-fighting device is used for monitoring the fire-fighting area in real time and detecting the occurrence of fire and the diffusion condition of fire. (4) fire alarm: the alarm device is used for sending out audible and visual alarm signals to remind people to evacuate in time. (5) The fire control water supply monitoring comprises a fire hydrant real-time under-voltage alarm device, a fire control water storage real-time liquid level water shortage alarm device and the like. (6) The intelligent patrol terminal comprises basic functions of NFC communication, code scanning, shooting, patrol record filling and uploading and the like. (7) data acquisition and transmission equipment: the system is used for collecting data of devices such as a sensor, a monitoring device, an alarm, a camera and the like, and transmitting the data to a data center or a cloud platform, and specifically comprises network devices such as an internet of things gateway and the like. (8) data center/cloud platform: the system is used for storing and processing the acquired data and carrying out data analysis and decision support. (9) a control center: the monitoring system is used for monitoring and managing the whole fire-fighting Internet of things supervision system and comprises the functions of equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management, command scheduling and the like.
The data center and the cloud platform of the urban fire-fighting Internet of things supervision system acquire massive multi-source heterogeneous and multi-mode data from front-end equipment such as the sensors, the monitoring equipment, the alarm, the cameras and the like, the difficulty of data integration and fusion is high, and the monitoring and prediction of fire-fighting risks are not easy to realize by combining machine learning. In the prior art, common data fusion methods include a weighted average method, a feature fusion method and the like, but the methods have certain limitations in processing multi-source heterogeneous and multi-mode data. In addition, the existing fire risk monitoring and predicting method often depends on manual experience and rules, and lacks accuracy and instantaneity. In particular, different urban areas and building bodies have obvious differences in space forms, surrounding environments, software and hardware facilities and personnel and object distribution, and the effective assessment and prediction of fire risk are more difficult to adapt to the diversity factors.
Disclosure of Invention
(one) object of the invention:
in view of the above problems, the invention aims to provide a city-level fire-fighting Internet of things supervision system based on multi-mode big data and a method thereof. The invention aims to solve the difficult problem of data fusion and integration in the urban fire-fighting Internet of things system, realize fusion and integration of multi-source heterogeneous and multi-mode data in the urban fire-fighting Internet of things system and improve the efficiency and accuracy of data processing. On the other hand, the invention constructs a unified fire data model by utilizing a deep learning algorithm. Meanwhile, the model is used for risk monitoring and prediction, and timely risk assessment and early warning are provided, so that corresponding measures are taken for prevention and treatment.
(II) technical scheme:
in order to solve the technical problems, the invention discloses the following technical scheme.
As a first aspect of the present invention, the present invention discloses a city-level fire-fighting internet of things supervision system based on multi-mode big data, which is characterized by comprising:
the fire control thing networking supervision front end is used for gathering and generating fire control supervision related multi-mode data on site, fire control supervision related multi-mode data includes: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data;
the internet of things data centralized interface is used for intensively acquiring the fire control supervision related multi-mode data from the fire control internet of things supervision front end through the internet of things network and transmitting the data to a fire control big data center;
the fire-fighting big data center is used for acquiring the fire-fighting supervision related multi-mode data from the internet of things data set interface, carrying out data preprocessing, multi-mode data fusion integration and carrying out risk monitoring prediction on a fire-fighting data model based on an LTSM or RNN structure;
the control center is used for monitoring and managing the whole fire-fighting Internet of things supervision system and comprises the functions of equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management, command scheduling and the like;
wherein, fire control big data center includes: the system comprises a data preprocessing unit, a multi-mode data fusion integration unit and a risk monitoring prediction unit; the data preprocessing unit performs data preprocessing on the multi-mode data transferred by the data set interface of the Internet of things; the multimode data fusion integration unit integrates the preprocessed fire control supervision related multimode data into a unified data storage system and executes the associated integration of the data; and the risk monitoring and predicting unit inputs a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and performs monitoring and predicting on fire control risks.
Preferably, the data preprocessing unit further introduces a data quality control mechanism to perform quality evaluation and correction on the multi-mode data; the quality control mechanism includes at least one of: data correction and calibration, data verification, data documentation, and metadata management.
Preferably, the association integration of the data performed by the multi-modal data fusion integration unit includes: aiming at the spatial integration of the building space of the fire control supervision object, further carrying out association rule mining on the multi-mode data associated with the same spatial position ID, and carrying out fire control supervision related multi-mode data clustering integrated under the same spatial position ID based on the association rule.
It is further preferred that the multi-mode data fusion integration unit specifically includes, for spatial integration of fire supervision object building spaces: according to the structure and layout of the fire control supervision object building, the collected data are associated with the space dimension of the building, and the space position ID of the building is used as the basis of the association, so that the data are associated with the space dimension.
It is further preferred that, on the basis of spatial integration, the multi-mode data associated to the same spatial location ID is subjected to further association rule mining, which specifically includes: expressing the multi-mode data integrated under the same space position ID as a graph structure formed by nodes and edges, wherein the nodes express the multi-mode data, and the edges express the association relation among the multi-mode data; the graph neural network model is trained and learned by utilizing a multi-mode data sample set of the graph structure; and performing feature extraction and representation learning on actual fire control supervision related multi-modal data by using the trained graph neural network model, so as to realize association rule mining of the multi-modal data, and further performing integrated fire control supervision related multi-modal data clustering under the same spatial position ID based on the association rule.
Preferably, the graph neural network model for association rule mining between the multi-modal data, which is adopted by the multi-modal data fusion integration unit, comprises: graph network encoder, graph network decoder, and association rule prediction mining Softmax classification layer.
Preferably, the graph network encoder is represented asWherein->Representing the graph structure data input to the encoder, i.e., the graph structure data integrated with the multi-mode data integrated under the same spatial position ID; />Is a parameter vector formed by all parameter matrixes of all network layers of the graph network encoder, and the graph network encoder is totally +.>Layer network layers, the parameter matrix columns of each layer network layer are expressed as +.>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the The graph network encoder is directed to graph structure data +.>Is a node of each representing an entity +.> In the->The layer network layer calculates the characteristic table of the nodeThe illustration is:the method comprises the steps of carrying out a first treatment on the surface of the Here->Is node->Is a graph network encoder of->The output characteristics of the layer are such that,representing an activation function->Representation and node->Has association relation->Is defined by a set of nodes of the set,representation set->Number of elements.
Preferably, the graph network decoder is represented asWherein->Representation network encoderMatrix of all output features of the last network layer of (a), i.e.)>Wherein->Is all parameter matrix of each network layer of the graph network decoder +.>A constructed parameter vector; the graph network decoder is used for integrating any two nodes in graph structure data under the same spatial position ID>And->The matching of the two nodes is carried out, namely, the characteristic output of the graph network encoder generated aiming at the 2 nodes is +.>And->According to the parameter matrix of the graph network decoder +.>Performing pairwise combination to calculate node combination characteristics +.>。
Preferably, the Softmax classification layer calculationRepresenting the node->Andprobability of whether the association relation exists between the two; and establishing association rules among the integrated multi-mode data under the same spatial position ID according to the probability of association relation among any 2 nodes output by the Softmax classification layer.
The invention discloses a city-level fire-fighting Internet of things supervision method based on multi-mode big data, which comprises the following steps:
a fire control supervision related multi-modal data acquisition step for acquiring and generating fire control supervision related multi-modal data on site, the fire control supervision related multi-modal data comprising: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data;
a data centralization step for centralizing the fire control supervision related multi-modal data;
a data preprocessing step, wherein the data preprocessing step comprises data cleaning, denoising, missing value processing and outlier filtering on the multi-mode data;
a multi-mode data fusion and integration step, wherein the preprocessed fire control supervision related multi-mode data is integrated into a unified data storage system, and the associated integration of the data is executed; the associated integration of the data comprises the following steps: aiming at the spatial integration of the building space of the fire control supervision object, carrying out further association rule mining on the multi-mode data associated with the same spatial position ID, and carrying out the fire control supervision related multi-mode data clustering integrated under the same spatial position ID based on the association rule;
a risk monitoring and predicting step, which is used for inputting a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and executing monitoring and predicting of fire control risk;
and a control response step, which is used for performing equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management and command scheduling according to the monitoring prediction of the fire-fighting risk.
(III) beneficial effects:
the method and the device realize data fusion and prediction by using a deep learning algorithm, so that the accuracy and the instantaneity of fire risk monitoring are improved; through the deep learning algorithm, the multi-mode data from different data sources can be fused and integrated, and the difficult problem of data fusion and integration in the urban fire-fighting Internet of things system is solved. Meanwhile, risk monitoring and prediction are carried out, and timely risk assessment and early warning can be provided, so that corresponding measures can be taken for prevention and treatment. Through the improvement of this application, can realize fire control thing networking data's high-efficient integration and integration, improve data processing's efficiency and accuracy. Meanwhile, the monitoring and prediction of the fire risk are realized, timely risk assessment and early warning are provided for the fire unit, and the urban fire safety is guaranteed.
Drawings
The embodiments described below with reference to the drawings are exemplary and intended to illustrate and describe the invention and should not be construed as limiting the scope of the invention.
Fig. 1 is a diagram of a supervision system of the urban fire control internet of things based on multi-mode big data.
Fig. 2 is a schematic diagram of a specific structure of a fire fighting big data center disclosed in the present invention.
Fig. 3 is a flowchart of a supervision method of the urban fire control internet of things based on multi-mode big data.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention become more apparent, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention.
It should be noted that: in the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the invention, and the embodiments and features of the embodiments in this application may be combined with each other without conflict. 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.
In view of the above problems, the invention aims to provide a city-level fire-fighting Internet of things supervision system based on multi-mode big data. Referring now to fig. 1, the system essentially comprises the following:
the fire control thing networking supervision front end is used for gathering and generating fire control supervision related multi-mode data on site, fire control supervision related multi-mode data includes: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data. The above data is multi-modal data including various format field data, semi-format or non-format text data, time series sequence data, image video data.
Specifically, the specific types of the fire-fighting internet of things supervision front end include: (1) a sensor device: including temperature sensor, smoke sensor, flammable gas sensor etc. for real-time supervision conflagration risk and environmental parameter. (2) electricity consumption monitoring equipment: including monitoring devices for leakage current, load, electrical device temperature, etc. (3) monitoring camera: the fire-fighting device is used for monitoring the fire-fighting area in real time and detecting the occurrence of fire and the diffusion condition of fire. (4) fire alarm: the alarm device is used for sending out audible and visual alarm signals to remind people to evacuate in time. (5) The fire control water supply monitoring comprises a fire hydrant real-time under-voltage alarm device, a fire control water storage real-time liquid level water shortage alarm device and the like. (6) The intelligent patrol terminal comprises basic functions of NFC communication, code scanning, shooting, patrol record filling and uploading and the like.
The internet of things data centralized interface is used for intensively obtaining the fire control supervision related multi-mode data from the fire control internet of things supervision front end through the internet of things network and transmitting the data to a fire control big data center. The internet of things data centralized interface takes the internet of things gateway as a hardware basis and is responsible for executing functions of front end data centralization, caching, analysis, transfer and internet of things network maintenance, and comprises the functions of internet of things protocol analysis, internet of things link management, internet of things interface support and the like.
And the fire-fighting big data center is used for acquiring the fire-fighting supervision related multi-mode data from the internet of things data set interface, carrying out data preprocessing, multi-mode data fusion integration and carrying out risk monitoring prediction on a fire-fighting data model based on an LTSM or RNN structure.
The control center is used for monitoring and managing the whole fire-fighting Internet of things supervision system and comprises the functions of equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management, command scheduling and the like.
Referring specifically to fig. 2, the fire fighting big data center includes: the system comprises a data preprocessing unit, a multi-mode data fusion integration unit and a risk monitoring prediction unit.
The data preprocessing unit performs data preprocessing on the multi-mode data transferred by the data set interface of the Internet of things, and comprises data cleaning, denoising, missing value processing, outlier filtering and the like.
The data preprocessing unit also introduces a data quality control mechanism to perform quality evaluation and correction on the multi-mode data. Through data quality control, the accuracy, reliability and usability of the data can be improved. The method for controlling the data quality can comprise the following steps: (1) data correction and calibration: for the sensor data and the power consumption monitoring data, possible errors can be corrected by means of a correction and calibration process, and for the sensor data calibration curves or sensor calibration methods can be used to adjust the measured values. (2) data validation: for fire alarm data and fire water supply monitoring data, in order to prevent false alarm errors in the fire alarm data and the fire water supply monitoring data, the accuracy and consistency of the data can be verified by comparing the fire alarm data with other reliable data sources or verifying the fire water supply monitoring data by using knowledge of domain experts. In addition, cross-validation or repeated validation may also be used to verify the reliability of the data. (3) data document and metadata management: for each data source, a document and metadata management system is built to record the source of the data, the acquisition process, the processing method and the quality assessment results. This helps track the history and quality of the data and provides traceability.
And integrating the preprocessed fire control supervision related multi-mode data into a unified data storage system by the multi-mode data fusion integration unit so as to carry out the subsequent associated integration of the data.
The association integration of the data comprises spatial integration aiming at the space of the fire control supervision object building, specifically, the acquired data is associated with the spatial dimension of the building according to the structure and layout of the fire control supervision object building, the spatial position ID of the building can be used as an association basis, and the association analysis of the data of the same building unit can be realized by associating the data with the spatial dimension.
Furthermore, on the basis of space integration, further association rule mining is carried out on the multi-mode data associated with the same space position ID, so that association relations among different mode data are mined, fusion among the multi-mode data is established, and a foundation is laid for subsequent risk monitoring prediction based on a fire data model. Association rule mining between multimodal data may employ a graph neural network (Graph Neural Network, GNN) model. The graph neural network model is a deep learning model capable of processing graph structure data, and complex association relations between the data can be fully mined. The method comprises the following specific steps: expressing the multi-mode data integrated under the same space position ID as a graph structure formed by nodes and edges, wherein the nodes express the multi-mode data, and the edges express the association relation among the multi-mode data; the graph neural network model is trained and learned by utilizing a multi-mode data sample set of the graph structure; and performing feature extraction and representation learning on actual fire control supervision related multi-modal data by using the trained graph neural network model, so as to realize association rule mining of the multi-modal data, and further performing integrated fire control supervision related multi-modal data clustering under the same spatial position ID based on the association rule.
Specifically, the graph neural network model for association rule mining between multimodal data, which is adopted by the multimodal data fusion integration unit, comprises a graph network encoder and a graph network decoder. The graph network encoder is represented asWhereinRepresenting the graph structure data input to the encoder, i.e., the graph structure data integrated with the multi-mode data integrated under the same spatial position ID;is a parameter vector formed by all parameter matrixes of all network layers of the graph network encoder, and the graph network encoder is in totalLayer network layers, each of which has a matrix array of parameters expressed asWhereinThe method comprises the steps of carrying out a first treatment on the surface of the Graph network encoder targets graph structure dataEach of which represents a node of an entity At its first stageThe layer network layer computes a feature representation of the node:the method comprises the steps of carrying out a first treatment on the surface of the Here, theIs a nodeGraph network encoder of (1)The output characteristics of the layer are such that,the activation function is represented as a function of the activation,representation and nodeHas association relationIs defined by a set of nodes of the set,representing a collectionNumber of elements. The output of the graph network encoder, i.e. its firstThe output of the layer network layer is connected to a graph network decoder.
The graph network decoder is represented asWherein->Representing a diagram network encoder->Matrix of all output features of the last network layer of (a), i.e.)>Wherein->Is all parameter matrix of each network layer of the graph network decoder +.>A constructed parameter vector; the graph network decoder is used for integrating any two nodes in graph structure data under the same spatial position ID>And->The matching of the two nodes is carried out, namely, the characteristic output of the graph network encoder generated aiming at the 2 nodes is +.>And->According to the parameter matrix of the graph network decoder +.>Performing pairwise combination to calculate node combination characteristics +.>。
The output of the graph network decoder is connected to a Softmax classification layer of association rule prediction mining; the Softmax classification layer calculationRepresenting the node->And->Probability of whether the association relation exists between the two; and establishing association rules among the integrated multi-mode data under the same spatial position ID according to the probability of association relation among any 2 nodes output by the Softmax classification layer. Therefore, based on the association rule, the fire control supervision related multi-mode data integrated under the same spatial position ID are clustered into a data cluster, and the association rule integration of the fire control supervision related multi-mode data is further realized on the basis of spatial integration.
The risk monitoring prediction unit inputs a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and performs monitoring prediction on fire control risk; the fire data model may be a deep learning model of an LTSM or RNN structure, which is trained using a training dataset and the weights and bias of the model are optimized by a back propagation algorithm. Through iterative training, the accuracy and generalization capability of the model are improved. And further, performing risk prediction on the message unit by using the trained deep learning model. According to the real-time data input model, real-time assessment and early warning of fire risk are realized through the reasoning and prediction capability of the model. The control center can take corresponding measures to conduct risk treatment and control according to the risk assessment and early warning results.
Furthermore, as shown in fig. 3, the urban fire-fighting internet of things supervision method based on the multi-mode big data disclosed by the invention comprises the following steps:
s1, a fire control supervision related multi-mode data acquisition step, which is used for acquiring and generating fire control supervision related multi-mode data on site, wherein the fire control supervision related multi-mode data comprises the following steps: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data;
s2, a data centralization step, which is used for centralizing and obtaining the fire control supervision related multi-mode data;
s3, data preprocessing, namely performing data preprocessing on the multi-mode data, wherein the data preprocessing comprises data cleaning, denoising, missing value processing and outlier filtering;
s4, integrating the preprocessed fire control supervision related multi-mode data into a unified data storage system, and executing associated integration of the data; the associated integration of the data comprises the following steps: aiming at the spatial integration of the building space of the fire control supervision object, carrying out further association rule mining on the multi-mode data associated with the same spatial position ID, and carrying out the fire control supervision related multi-mode data clustering integrated under the same spatial position ID based on the association rule;
s5, a risk monitoring and predicting step, which is used for inputting a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and executing monitoring and predicting on fire control risk;
and S6, a control response step, which is used for performing equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting job task allocation and management and command scheduling according to the monitoring prediction of the fire-fighting risk.
The method and the device realize data fusion and prediction by using a deep learning algorithm, so that the accuracy and the instantaneity of fire risk monitoring are improved; through the deep learning algorithm, the multi-mode data from different data sources can be fused and integrated, and the difficult problem of data fusion and integration in the urban fire-fighting Internet of things system is solved. Meanwhile, risk monitoring and prediction are carried out, and timely risk assessment and early warning can be provided, so that corresponding measures can be taken for prevention and treatment. Through the improvement of this application, can realize fire control thing networking data's high-efficient integration and integration, improve data processing's efficiency and accuracy. Meanwhile, the monitoring and prediction of the fire risk are realized, timely risk assessment and early warning are provided for the fire unit, and the urban fire safety is guaranteed.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. Urban fire control thing networking supervisory systems based on multi-mode big data, characterized by comprising:
the fire control thing networking supervision front end is used for gathering and generating fire control supervision related multi-mode data on site, fire control supervision related multi-mode data includes: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data;
the internet of things data centralized interface is used for intensively acquiring the fire control supervision related multi-mode data from the fire control internet of things supervision front end through the internet of things network and transmitting the data to a fire control big data center;
the fire-fighting big data center is used for acquiring the fire-fighting supervision related multi-mode data from the internet of things data set interface, carrying out data preprocessing, multi-mode data fusion integration and carrying out risk monitoring prediction on a fire-fighting data model based on an LTSM or RNN structure;
the control center is used for monitoring and managing the whole fire-fighting Internet of things supervision system and comprises the functions of equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management, command scheduling and the like;
wherein, fire control big data center includes: the system comprises a data preprocessing unit, a multi-mode data fusion integration unit and a risk monitoring prediction unit; the data preprocessing unit performs data preprocessing on the multi-mode data transferred by the data set interface of the Internet of things; the multimode data fusion integration unit integrates the preprocessed fire control supervision related multimode data into a unified data storage system and executes the associated integration of the data; and the risk monitoring and predicting unit inputs a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and performs monitoring and predicting on fire control risks.
2. The urban fire control internet of things supervision system based on multi-mode big data according to claim 1, wherein the data preprocessing unit further introduces a data quality control mechanism to perform quality assessment and correction on the multi-mode data; the quality control mechanism includes at least one of: data correction and calibration, data verification, data documentation, and metadata management.
3. The multi-modal big data based city level fire protection internet of things supervisory system of claim 2 wherein the associated integration of data by the multi-modal data fusion integration unit comprises: aiming at the spatial integration of the building space of the fire control supervision object, further carrying out association rule mining on the multi-mode data associated with the same spatial position ID, and carrying out fire control supervision related multi-mode data clustering integrated under the same spatial position ID based on the association rule.
4. The urban fire control internet of things supervisory system based on multi-mode big data according to claim 3, wherein the spatial integration of the multi-mode data fusion integration unit for the fire control supervisory object building space specifically comprises: according to the structure and layout of the fire control supervision object building, the collected data are associated with the space dimension of the building, and the space position ID of the building is used as the basis of the association, so that the data are associated with the space dimension.
5. The urban fire control internet of things supervision system based on multi-mode big data according to claim 4, wherein the further association rule mining is performed on multi-mode data associated to the same spatial location on the basis of spatial integration, and specifically comprising: expressing the multi-mode data integrated under the same space position ID as a graph structure formed by nodes and edges, wherein the nodes express the multi-mode data, and the edges express the association relation among the multi-mode data; the graph neural network model is trained and learned by utilizing a multi-mode data sample set of the graph structure; and performing feature extraction and representation learning on actual fire control supervision related multi-modal data by using the trained graph neural network model, so as to realize association rule mining of the multi-modal data, and further performing integrated fire control supervision related multi-modal data clustering under the same spatial position based on the association rule.
6. The urban fire control internet of things supervision system based on multi-modal big data according to claim 5, wherein the graph neural network model for association rule mining between multi-modal data, which is adopted by the multi-modal data fusion integration unit, comprises: graph network encoder, graph network decoder, and association rule prediction mining Softmax classification layer.
7. The multi-modal big data based city level fire protection internet of things supervisory system of claim 6 wherein the graph network encoder is represented asWherein->Representing the graph structure data input to the encoder, i.e., the graph structure data integrated with the multi-mode data integrated under the same spatial position ID; />Is a parameter vector formed by all parameter matrixes of all network layers of the graph network encoder, and the graph network encoder is totally +.>Layer network layers, the parameter matrix columns of each layer network layer are expressed as +.>Wherein->The method comprises the steps of carrying out a first treatment on the surface of the The graph network encoder is directed to graph structure data +.>Is a node of each representing an entity +.> In the->The layer network layer computes a feature representation of the node: />The method comprises the steps of carrying out a first treatment on the surface of the Here->Is node->Is a graph network encoder of->Output characteristics of layer->Representing an activation function->Representation and node->Has association relation->Node set of->Representation set->Number of elements.
8. The multi-modal big data based city level fire protection internet of things supervisory system of claim 7 wherein the graph network decoder is represented asWherein->Representing a diagram network encoder->Matrix of all output features of the last network layer of (a), i.e.)>Wherein->Is graph network decodingAll parameter matrices of the network layers of the device>A constructed parameter vector; the graph network decoder is used for integrating any two nodes in graph structure data under the same spatial position ID>And->The matching of the two nodes is carried out, namely, the characteristic output of the graph network encoder generated aiming at the 2 nodes is +.>And->According to the parameter matrix of the graph network decoder +.>Performing pairwise combination to calculate node combination characteristics +.>。
9. The multi-modal big data based urban fire protection internet of things supervisory system of claim 8, wherein the Softmax classification layer calculationRepresenting the node->And->Probability of whether the association relation exists between the two; any 2 nodes output by the classifying layer according to Softmax have association relationsProbability of the system, and establishing association rules between the integrated multi-mode data under the same spatial position.
10. The urban fire control Internet of things supervision method based on the multi-mode big data is characterized by comprising the following steps of:
a fire control supervision related multi-modal data acquisition step for acquiring and generating fire control supervision related multi-modal data on site, the fire control supervision related multi-modal data comprising: sensor data, electricity consumption monitoring data, monitoring camera data, fire alarm data, fire water supply monitoring data and patrol record data;
a data centralization step for centralizing the fire control supervision related multi-modal data;
a data preprocessing step, wherein the data preprocessing step comprises data cleaning, denoising, missing value processing and outlier filtering on the multi-mode data;
a multi-mode data fusion and integration step, wherein the preprocessed fire control supervision related multi-mode data is integrated into a unified data storage system, and the associated integration of the data is executed; the associated integration of the data comprises the following steps: aiming at the spatial integration of the building space of the fire control supervision object, carrying out further association rule mining on the multi-mode data associated with the same spatial position ID, and carrying out the fire control supervision related multi-mode data clustering integrated under the same spatial position ID based on the association rule;
a risk monitoring and predicting step, which is used for inputting a data cluster of the fire control supervision related multi-mode data cluster based on the fire control data model, and executing monitoring and predicting of fire control risk;
and a control response step, which is used for performing equipment state monitoring, alarm emergency response processing, risk early warning visual display, fire-fighting operation task allocation and management and command scheduling according to the monitoring prediction of the fire-fighting risk.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311595411.5A CN117319451B (en) | 2023-11-28 | 2023-11-28 | Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311595411.5A CN117319451B (en) | 2023-11-28 | 2023-11-28 | Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117319451A true CN117319451A (en) | 2023-12-29 |
| CN117319451B CN117319451B (en) | 2024-02-27 |
Family
ID=89250172
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311595411.5A Active CN117319451B (en) | 2023-11-28 | 2023-11-28 | Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117319451B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117828280A (en) * | 2024-03-05 | 2024-04-05 | 山东新科建工消防工程有限公司 | Intelligent fire information acquisition and management method based on Internet of things |
| CN117893385A (en) * | 2024-03-18 | 2024-04-16 | 四川银谷碳汇再生资源有限公司 | Fire-fighting early warning method and system for guaranteeing warehouse safety |
| CN117932368A (en) * | 2024-03-22 | 2024-04-26 | 潍坊市平安消防工程有限公司 | Fire-fighting equipment operator real-operation management system and method |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113111581A (en) * | 2021-04-09 | 2021-07-13 | 重庆邮电大学 | LSTM trajectory prediction method combining space-time factors and based on graph neural network |
| US20210374143A1 (en) * | 2020-05-29 | 2021-12-02 | Rn Technologies, Llc | Real-time processing of a data stream using a graph-based data model |
| US20220138568A1 (en) * | 2020-11-01 | 2022-05-05 | Nvidia Corporation | Model-based reinforcement learning for behavior prediction |
| CN114548751A (en) * | 2022-02-21 | 2022-05-27 | 山东大学 | Intelligent deduction method, system, equipment and storage medium for emergency under supervision scene |
| CN114693058A (en) * | 2022-01-19 | 2022-07-01 | 应急管理部上海消防研究所 | Internet of things big data driven high-rise building fire risk sensing and early warning system |
| CN115018021A (en) * | 2022-08-08 | 2022-09-06 | 广东电网有限责任公司肇庆供电局 | Machine room abnormity detection method and device based on graph structure and abnormity attention mechanism |
| CN116576852A (en) * | 2023-03-17 | 2023-08-11 | 中科海慧（北京）科技有限公司 | Forest rescue intelligent navigation system integrating multisource road network data |
| CN116609055A (en) * | 2023-06-14 | 2023-08-18 | 华北电力大学 | Method for diagnosing wind power gear box fault by using graph convolution neural network |
| CN116743804A (en) * | 2023-06-09 | 2023-09-12 | 杭州市保密科技测评中心(杭州市专用通信与保密技术服务中心) | Visual supervisory systems of computer lab |
| CN116887212A (en) * | 2023-09-07 | 2023-10-13 | 北京航天常兴科技发展股份有限公司 | Fire situation information processing and transmitting method based on wireless communication network |
| CN117113206A (en) * | 2023-09-08 | 2023-11-24 | 中国科学院计算技术研究所 | Lightweight large-scale multi-element time sequence prediction model and training method thereof |
-
2023
- 2023-11-28 CN CN202311595411.5A patent/CN117319451B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210374143A1 (en) * | 2020-05-29 | 2021-12-02 | Rn Technologies, Llc | Real-time processing of a data stream using a graph-based data model |
| US20220138568A1 (en) * | 2020-11-01 | 2022-05-05 | Nvidia Corporation | Model-based reinforcement learning for behavior prediction |
| CN113111581A (en) * | 2021-04-09 | 2021-07-13 | 重庆邮电大学 | LSTM trajectory prediction method combining space-time factors and based on graph neural network |
| CN114693058A (en) * | 2022-01-19 | 2022-07-01 | 应急管理部上海消防研究所 | Internet of things big data driven high-rise building fire risk sensing and early warning system |
| CN114548751A (en) * | 2022-02-21 | 2022-05-27 | 山东大学 | Intelligent deduction method, system, equipment and storage medium for emergency under supervision scene |
| CN115018021A (en) * | 2022-08-08 | 2022-09-06 | 广东电网有限责任公司肇庆供电局 | Machine room abnormity detection method and device based on graph structure and abnormity attention mechanism |
| CN116576852A (en) * | 2023-03-17 | 2023-08-11 | 中科海慧（北京）科技有限公司 | Forest rescue intelligent navigation system integrating multisource road network data |
| CN116743804A (en) * | 2023-06-09 | 2023-09-12 | 杭州市保密科技测评中心(杭州市专用通信与保密技术服务中心) | Visual supervisory systems of computer lab |
| CN116609055A (en) * | 2023-06-14 | 2023-08-18 | 华北电力大学 | Method for diagnosing wind power gear box fault by using graph convolution neural network |
| CN116887212A (en) * | 2023-09-07 | 2023-10-13 | 北京航天常兴科技发展股份有限公司 | Fire situation information processing and transmitting method based on wireless communication network |
| CN117113206A (en) * | 2023-09-08 | 2023-11-24 | 中国科学院计算技术研究所 | Lightweight large-scale multi-element time sequence prediction model and training method thereof |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117828280A (en) * | 2024-03-05 | 2024-04-05 | 山东新科建工消防工程有限公司 | Intelligent fire information acquisition and management method based on Internet of things |
| CN117828280B (en) * | 2024-03-05 | 2024-06-07 | 山东新科建工消防工程有限公司 | Intelligent fire information acquisition and management method based on Internet of things |
| CN117893385A (en) * | 2024-03-18 | 2024-04-16 | 四川银谷碳汇再生资源有限公司 | Fire-fighting early warning method and system for guaranteeing warehouse safety |
| CN117893385B (en) * | 2024-03-18 | 2024-06-04 | 四川银谷碳汇再生资源有限公司 | Fire-fighting early warning method and system for guaranteeing warehouse safety |
| CN117932368A (en) * | 2024-03-22 | 2024-04-26 | 潍坊市平安消防工程有限公司 | Fire-fighting equipment operator real-operation management system and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117319451B (en) | 2024-02-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117319451B (en) | Urban fire-fighting Internet of things supervision system based on multi-mode big data and method thereof | |
| Wu et al. | An intelligent tunnel firefighting system and small-scale demonstration | |
| CN110264672A (en) | A kind of early warning system of geological disaster | |
| CN103246265B (en) | Electromechanical equipment detection maintaining method | |
| CN109146093A (en) | A kind of electric power equipment on-site exploration method based on study | |
| US20230419425A1 (en) | Method for monitoring operation of liquefied natural gas (lng) storage and internet of things system (iot) thereof | |
| CN110555617A (en) | Real-time dynamic quantitative assessment method for building fire risk based on Internet of things | |
| CN107240216A (en) | Based on 3DGIS+BIM technologies and artificial intelligence O＆M emergent alarm and fast response method | |
| CN112966714A (en) | Edge time sequence data anomaly detection and network programmable control method | |
| CN114665608B (en) | Intelligent sensing inspection system and method for transformer substation | |
| CN105915402A (en) | Industrial control network security protection system | |
| CN112270429A (en) | Cloud edge cooperation-based power battery pole piece manufacturing equipment maintenance method and system | |
| CN111932816B (en) | Fire alarm management method and device for offshore wind farm and island microgrid | |
| CN115865992A (en) | Wisdom water conservancy on-line monitoring system | |
| CN116187740A (en) | Mountain fire monitoring method and system along power transmission line | |
| CN116027146A (en) | Multi-state distributed passive sensing monitoring method, device and equipment for distribution cable network | |
| CN115964503A (en) | Safety risk prediction method and system based on community equipment facilities | |
| CN113625097A (en) | Big data analysis method for running state of power distribution network | |
| CN117156386A (en) | Intelligent positioning, calling for help and alarming intercom equipment for fire control | |
| CN116052300A (en) | Digital twinning-based power inspection system and method | |
| CN114693058A (en) | Internet of things big data driven high-rise building fire risk sensing and early warning system | |
| CN115115117A (en) | Intelligent processing and analyzing system based on block chain | |
| CN111857015B (en) | Power transmission and transformation cloud intelligent controller | |
| CN114462974A (en) | Intelligent fire-fighting full-chain prevention and control system and multi-source information fusion decision-making method | |
| CN109780451B (en) | Power plant speed regulator oil circuit inspection method based on machine vision |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |