CN113255717A - Piping lane fire detection method and system - Google Patents
Piping lane fire detection method and system Download PDFInfo
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Abstract
The invention provides a pipe gallery fire detection method, which comprises the following steps: acquiring a pipe gallery sample and a neural network for pipe gallery fire detection, wherein the pipe gallery sample comprises a training set and a testing set; inputting the training set into the neural network for training to obtain a training model, inputting the test set into the training model, and determining training parameters and a detection model; through whether real-time piping lane sample conflagration takes place detects detection model. The detection model is determined through the establishment of the neural network and the data training, and relevant characteristic data are analyzed, so that whether the fire of the pipe gallery occurs can be predicted more accurately and rapidly, the fire prevention work is made in advance, and a proposal of feasibility can be provided for the safety and the operation maintenance of the comprehensive pipe gallery, so that the safety of the comprehensive pipe gallery is improved, and casualties and property loss are reduced.
Description
Technical Field
The invention relates to the technical field of detection, in particular to a pipe gallery fire detection method and system.
Background
The utility tunnel is generally constructed on the main road that pipeline facilities highly concentrated, and its affiliated engineering system is huge, and the degree of difficulty of construction and later operation maintenance is higher, and the problem that the design of utility tunnel project, construction, completion, operation maintenance etc. stage expose is outstanding day by day, and simultaneously, interior pipeline of utility tunnel and equipment are of a great variety, and the pipe gallery space is narrow and small, ventilation condition is limited, in case the fire incident takes place, causes bigger secondary disaster easily.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention aims to provide a pipe gallery fire detection method and system, which is used for solving the problem of inconvenient pipe gallery fire detection in the prior art.
To achieve the above and other related objects, the present invention provides a fire detection method for a pipe rack, including:
acquiring a pipe gallery sample and a neural network for pipe gallery fire detection, wherein the pipe gallery sample comprises a training set and a testing set;
inputting the training set into the neural network for training to obtain a training model, inputting the test set into the training model, and determining training parameters and a detection model;
through whether real-time piping lane sample conflagration takes place detects detection model.
Optionally, the step of obtaining a tube lane sample comprises:
collecting design drawings and geographic information data of a pipe gallery to obtain a pipe gallery model;
and carrying out three-dimensional transformation on the pipe gallery model to obtain the pipe gallery sample.
Optionally, the training set includes pipe gallery samples for n months of m years, and the test set includes pipe gallery samples for n +1 months of m years.
Optionally, the step of obtaining a tube lane sample further comprises: through piping lane sample collection piping lane characteristic information, wherein, piping lane characteristic information includes one of following at least: temperature, humidity, location, time.
Optionally, inputting the training set into the neural network for training, and the step of obtaining a training model includes:
obtaining the training set, wherein the training set is as follows:
X＝{(xi,yi),i＝1,2,…,n}，xi＝[xi1,xi2,…,xim]
wherein m is the number of features, i is the sample serial number, x is the sample, and y is the true value;
minimizing a loss function of an objective function by an optimization function, the mathematical expression of the optimization function being:
L(y,f(x))＝exp[-yf(x)]
wherein the content of the first and second substances,
for the optimization function, f (x) is an objective function, L (y, f (x)) is a loss function, Ey，XTo be expected, argmin is an exponential function with a minimum function, based on a natural constant e.
Optionally, the mathematical expression of the objective function is:
wherein, giAnd hiFirst order and second order gradient statistics of the loss function, j is the number of leaf nodes of the decision tree, gamma is the coefficient of the leaf nodes of the decision tree, and lambda is L2Coefficient of regularization, wjA vector representing the sample weight of the jth leaf node.
Optionally, the mathematical expression of the splitting yield of each leaf node is as follows:
wherein G is the fission yield, I is the father sample set, and I ═ IL∪IR，ILAnd IRSample sets for the left and right branches, respectively.
A piping lane fire detection system, comprising:
the network module is used for acquiring a pipe gallery sample and a neural network for detecting the fire of the pipe gallery, wherein the pipe gallery sample comprises a training set and a testing set;
a training module, configured to input the training set into the neural network for training, obtain a training model, input the test set into the training model, and determine a training parameter and a detection model, where the training parameter at least includes one of: accuracy, recall rate and comprehensive evaluation indexes;
and the detection module is used for detecting whether the real-time pipe gallery sample is in fire or not through the detection model.
An electronic device, comprising:
one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the electronic device to perform one or more of the methods.
One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform one or more of the described methods.
As described above, the pipe gallery fire detection method and system of the present invention have the following beneficial effects: .
The detection model is determined through the establishment of the neural network and the data training, and relevant characteristic data are analyzed, so that whether the fire of the pipe gallery occurs can be predicted more accurately and rapidly, the fire prevention work is made in advance, and a proposal of feasibility can be provided for the safety and the operation maintenance of the comprehensive pipe gallery, so that the safety of the comprehensive pipe gallery is improved, and casualties and property loss are reduced.
Drawings
Fig. 1 is a schematic structural view of a belt conveying mechanism according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a discharging device according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention.
It should be noted that the drawings provided in the present embodiment are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated. The structures, proportions, sizes, and other dimensions shown in the drawings and described in the specification are for understanding and reading the present disclosure, and are not intended to limit the scope of the present disclosure, which is defined in the claims, and are not essential to the art, and any structural modifications, changes in proportions, or adjustments in size, which do not affect the efficacy and attainment of the same are intended to fall within the scope of the present disclosure. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
Referring to fig. 1, the present invention provides a method for detecting fire in a pipe rack, including:
s1: acquiring a pipe gallery sample and a neural network for pipe gallery fire detection, wherein the pipe gallery sample comprises a training set and a testing set;
s2: inputting the training set into the neural network for training to obtain a training model, inputting the test set into the training model, and determining training parameters and a detection model;
s3: through whether real-time piping lane sample conflagration takes place detects detection model. For example, can be through the design drawing and the data information data of pipe gallery, based on certain coding rule, combine spatial data and parameter data, form pipe gallery model or pipe gallery sample, and confirm the detection model through the buildding and the data training of neural network, the relevant characteristic data of analysis, thereby whether more accurate, the prediction pipe gallery conflagration takes place fast, make the work of fire prevention in advance, and can provide the suggestion of feasibility for utility tunnel safety and operation maintenance, improve utility tunnel's security, reduce casualties and loss of property.
In order to facilitate the modeling and management of the pipe gallery sample, the step of obtaining the pipe gallery sample includes:
collecting design drawings and geographic information data of a pipe gallery to obtain a pipe gallery model;
and carrying out three-dimensional transformation on the pipe gallery model to obtain the pipe gallery sample. Form and use utility tunnel comprehensive monitoring, fortune dimension, emergency command, data analysis etc. function as an organic whole utility tunnel monitoring platform. Can also utilize GIS + BIM technique, with BIM piping lane data import to GIS spatial model in, on mapping to GIS through real-time supervision data, more audio-visual real-time running state who masters the piping lane, realize that digital twin's wisdom piping lane can carry out effective integration and directly perceived the display with data such as the design of utility tunnel project, the construction, completion, operation and maintenance, scrap and demolish, combine the internet of things, techniques such as artificial intelligence, realize sound and static combination, strengthen piping lane information management, improve work efficiency, supplementary leader decision-making. The utility model discloses a construct utility tunnel monitoring platform based on GIS + BIM and neural network algorithm, mainly include basic function subsystem, comprehensive monitoring subsystem, operation maintenance subsystem, emergent subsystem, data analysis subsystem, operation and maintenance APP, large-size screen visual cockpit, piping lane GIS application platform etc. and specific content is as follows:
1) the basic function platform is used as a basic common module of the comprehensive pipe rack monitoring platform, and unified authority, workflow and main data management are provided for service systems such as pipe rack monitoring, operation and maintenance management and emergency command.
2) The comprehensive monitoring subsystem can acquire various key indexes such as temperature and humidity, harmful gas and equipment faults in real time through electromechanical equipment monitoring, environment monitoring, video monitoring and the like, and can give an alarm according to set parameters to realize the functions of unified monitoring and centralized management and control of the comprehensive pipe rack.
3) The operation maintenance subsystem utilizes an informatization means to carry out rapid and standard processing on daily business activities, dynamically masters the real-time conditions of the inside of the pipe gallery and the conditions of personnel movement and duty, and realizes the operation service and full closed-loop management of pipe gallery resources and personnel.
4) The emergency subsystem carries out all-round real time monitoring on the key area of the corridor, realizes the real-time overview of emergency resources and the remote dispatching of emergency commands, provides technical support for the dynamic identification, monitoring, risk assessment and the like of major events, establishes a unified emergency command center, and enables the events to be found more timely, processed more efficiently and commanded and dispatched more freely.
5) The data analysis subsystem carries out deep analysis and mining on big data based on mass data generated in the daily monitoring and operation and maintenance process of the pipe gallery, analyzes and displays various data in the modes of abundant visual reports, self-defined reports, figures, models and the like, and provides valuable decision suggestions for safe, economic and efficient operation of the pipe gallery.
6) The operation and maintenance APP application enables pipe gallery workers, managers and the like to process any things related to business at any time and any place. The method has the advantages of low cost, information safety, accelerated informatization process, high-efficiency and quick work, standard and automatic work flow, enhanced communication and cooperation, improved competitiveness and the like.
7) The large-screen visual cockpit is used as a display platform of the pipe gallery management and control system, important data are displayed by combining a model and space geographic information, and information such as pipe gallery equipment maintenance condition, alarm quantity trend, risk potential monitoring and flow monitoring is contained in visual display.
8) The utility model discloses a pipe gallery GIS application platform is used for the utility tunnel monitoring platform to provide geographic information service, through spatial information and service data's stack, shows the monitoring information, the warning information and the daily maintenance information of patrolling and examining of pipe gallery collection directly perceivedly on pipe gallery GIS application platform, and the user can directly look over the real-time situation of pipe gallery at pipe gallery GIS application platform, promotes pipe gallery management efficiency, realizes that the high-efficient accurate operation of pipe gallery controls and manages.
To facilitate management of sample data and training of models, the training set includes pipe gallery samples for the month n m years, and the test set includes pipe gallery samples for the month n +1 m years. The step of obtaining the tube lane sample further comprises: through piping lane sample collection piping lane characteristic information, wherein, piping lane characteristic information includes one of following at least: temperature, humidity, location, time.
Further, the step of inputting the training set into the neural network for training to obtain a training model includes:
obtaining the training set, wherein the training set is as follows:
X＝{(xi,yi),i＝1,2,…,n}，xi＝[xi1,xi2,…,xim]
wherein m is the number of features, i is the sample serial number, x is the sample, and y is the true value;
minimizing a loss function of an objective function by an optimization function, the mathematical expression of the optimization function being:
L(y，f(x))＝exp[-yf(x)]
wherein the content of the first and second substances,
for the optimization function, f (x) is an objective function, L (y, f (x)) is a loss function, Ey，XFor expectation, argmin is a minimum function, and is obtained by optimizing the functionThe minimum of the loss function is an exponential function with a natural constant e as the base.
Optionally, the mathematical expression of the objective function is:
wherein, giAnd hiFirst order and second order gradient statistics of the loss function, j is the number of leaf nodes of the decision tree, gamma is the coefficient of the leaf nodes of the decision tree, and lambda is L2Coefficient of regularization, wjA vector representing the sample weight of the jth leaf node.
Optionally, the mathematical expression of the splitting yield of each leaf node is as follows:
wherein G is the fission yield, I is the father sample set, and I ═ IL∪IR，ILAnd IRSample sets for the left and right branches, respectively.
Referring to fig. 2, the present invention also provides a pipe rack fire detection system, which includes:
the network module is used for acquiring a pipe gallery sample and a neural network for detecting the fire of the pipe gallery, wherein the pipe gallery sample comprises a training set and a testing set;
a training module, configured to input the training set into the neural network for training, obtain a training model, input the test set into the training model, and determine a training parameter and a detection model, where the training parameter at least includes one of: accuracy, recall rate and comprehensive evaluation indexes;
and the detection module is used for detecting whether the real-time pipe gallery sample is in fire or not through the detection model.
An embodiment of the present invention provides an electronic device, including: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the electronic device to perform one or more of the methods. The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
Embodiments of the invention also provide one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform one or more of the methods described herein. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (10)
1. A method for fire detection in a pipe rack, comprising:
acquiring a pipe gallery sample and a neural network for pipe gallery fire detection, wherein the pipe gallery sample comprises a training set and a testing set;
inputting the training set into the neural network for training to obtain a training model, inputting the test set into the training model, and determining training parameters and a detection model;
through whether real-time piping lane sample conflagration takes place detects detection model.
2. The pipe rack fire detection method of claim 1, wherein the step of obtaining pipe rack samples comprises:
collecting design drawings and geographic information data of a pipe gallery to obtain a pipe gallery model;
and carrying out three-dimensional transformation on the pipe gallery model to obtain the pipe gallery sample.
3. The piping lane fire detection method of claim 1, wherein the training set comprises m-year n-month piping lane samples and the test set comprises m-year n + 1-month piping lane samples.
4. The pipe rack fire detection method of claim 1 or 2, wherein the step of obtaining pipe rack samples further comprises: through piping lane sample collection piping lane characteristic information, wherein, piping lane characteristic information includes one of following at least: temperature, humidity, location, time.
5. The piping lane fire detection method of claim 1, wherein the training set is input into the neural network for training, and the step of obtaining a training model comprises:
obtaining the training set, wherein the training set is as follows:
X＝{(xi,yi),i＝1,2,…,n}，xi＝[xi1,xi2,…,xim]
wherein m is the number of features, i is the sample serial number, X is the training set, X is the sample, and y is the true value;
minimizing a loss function of an objective function by an optimization function, the mathematical expression of the optimization function being:
L(y,f(x))＝exp[-yf(x)]
wherein the content of the first and second substances,
for the optimization function, f (x) is an objective function, L (y, f (x)) is a loss function, Ey,XTo be expected, argmin is a minimum function, exp is an exponential function with a natural constant e as the base.
6. A pipe gallery fire detection method as claimed in claim 5, characterized in that the mathematical expression of its objective function is:
wherein, giAnd hiFirst order and second order gradient statistics of the loss function, j is the number of leaf nodes of the decision tree, gamma is the coefficient of the leaf nodes of the decision tree, and lambda is L2Coefficient of regularization, wjA vector representing the sample weight of the jth leaf node.
7. The piping lane fire detection method of claim 6, wherein the mathematical expression of the division yield of each leaf node is:
wherein G is the fission yield, I is the father sample set, and I ═ IL∪IR，ILAnd IRSample sets for the left and right branches, respectively.
8. A piping lane fire detection system, comprising:
the network module is used for acquiring a pipe gallery sample and a neural network for detecting the fire of the pipe gallery, wherein the pipe gallery sample comprises a training set and a testing set;
a training module, configured to input the training set into the neural network for training, obtain a training model, input the test set into the training model, and determine a training parameter and a detection model, where the training parameter at least includes one of: accuracy, recall rate and comprehensive evaluation indexes;
and the detection module is used for detecting whether the real-time pipe gallery sample is in fire or not through the detection model.
9. An electronic device, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the electronic device to perform the method recited by one or more of claims 1-7.
10. One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform the method recited by one or more of claims 1-7.
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Application publication date: 20210813 |