CN116246404A

CN116246404A - Distributed optical fiber fire disaster identification method and system based on underground comprehensive pipe rack

Info

Publication number: CN116246404A
Application number: CN202310012250.6A
Authority: CN
Inventors: 宋文斌; 张守亮; 何雨衡; 袁荣楠; 包元锋; 陈振华
Original assignee: Suzhou Utility Tunnel Development Co ltd; Suzhou Sicui Integrated Infrastructure Technology Research Institute Co ltd
Current assignee: Suzhou Utility Tunnel Development Co ltd; Suzhou Sicui Integrated Infrastructure Technology Research Institute Co ltd
Priority date: 2023-01-05
Filing date: 2023-01-05
Publication date: 2023-06-09

Abstract

The embodiment of the specification provides a distributed optical fiber fire disaster identification method and system based on a underground utility tunnel, wherein the method comprises the following steps: determining a plurality of fire initiation factors associated with the fire determination; establishing and training an identification classifier based on a plurality of fire initiation factors associated with fire judgment; determining a fire risk assessment system based on a plurality of fire initiation factors associated with the fire judgment; acquiring real-time information of an underground utility tunnel, wherein the real-time information comprises information related to a plurality of fire initiation factors; the comprehensive risk level value of the fire disaster of the underground comprehensive pipe rack is determined according to the real-time information through the identification classifier and the fire disaster risk evaluation system, and the comprehensive risk level value has the advantages of being beneficial to preliminary judgment and positioning during risk accident occurrence, grasping the law of risk development and controlling the risk, and ensuring effective avoidance of the risk.

Description

Distributed optical fiber fire disaster identification method and system based on underground comprehensive pipe rack

Technical Field

The specification relates to the field of fire disaster identification, in particular to a distributed optical fiber fire disaster identification method and system based on an underground comprehensive pipe rack.

Background

The utility tunnel, namely, the utility tunnel of the utility city integrated with electric pipeline, gas pipeline, water supply and drainage pipeline, communication pipeline, heat supply pipeline, etc. is a safe operation of the utility tunnel, and the inside of the tunnel needs to be provided with a plurality of disaster early warning systems. Fire is used as one of main monitoring objects of comprehensive pipe rack environmental safety, and a fire early warning system with early warning capability and high accuracy is required to be established. Most of traditional fire detectors use single fire characteristic parameters such as gas concentration, smoke concentration, temperature and the like as fire judgment basis, and are extremely easy to be interfered by the outside to cause the problems of false alarm and untimely early warning. For example, a fire alarm device using temperature monitoring only sends an alarm signal after the temperature exceeds a threshold value, only monitors a single fire characteristic parameter, cannot be associated with other parameters, and is easy to cause the problems of false alarm and missing alarm.

Therefore, a distributed optical fiber fire disaster identification method and system based on the underground utility tunnel are needed to be provided, and the method and system have the advantage of timely early warning fire risks of the underground utility tunnel.

Disclosure of Invention

One of the embodiments of the present disclosure provides a distributed optical fiber fire identification method based on an underground utility tunnel, the method including: determining a plurality of fire initiation factors associated with the fire determination; establishing and training an identification classifier based on the plurality of fire initiation factors related to fire judgment; determining a fire risk assessment system based on the plurality of fire initiation factors associated with fire judgment; acquiring real-time information of an underground utility tunnel, wherein the real-time information comprises information related to the plurality of fire initiation factors; and determining the fire comprehensive risk level value of the underground utility tunnel according to the real-time information through the identification classifier and the fire risk evaluation system.

In some embodiments, the identification classifier comprises a BP neural network or a correlation vector machine.

In some embodiments, the fire risk assessment system includes a plurality of primary indicators, wherein the plurality of primary indicators includes at least a pipeline self fault primary indicator, an artifact primary indicator, a combustible material leakage primary indicator, and an environmental factor primary indicator, each of the primary indicators including at least two secondary indicators.

In some embodiments, the primary indicators of the pipeline self faults at least comprise a short circuit secondary indicator, a poor contact secondary indicator, a line overload secondary indicator and a line leakage secondary indicator; the human factor primary index at least comprises an open fire operation secondary index and a heat source improper management secondary index; the combustible substance leakage primary index at least comprises a fuel gas leakage secondary index and a sewage pipe methane leakage secondary index; the environmental factor primary index at least comprises a heavy rain weather secondary index and a surrounding surface fire secondary index.

In some embodiments, the determining, by the identification classifier and the fire risk assessment system, a fire risk level value of the utility tunnel according to the real-time information includes: judging whether an abnormal event occurs to the underground utility tunnel or not according to the real-time information through the identification classifier; and when judging that the underground comprehensive pipe gallery has an abnormal event, determining a fire comprehensive risk level value of the underground comprehensive pipe gallery according to the real-time information through the fire risk evaluation system.

In some embodiments, the determining, by the fire risk assessment system, a fire comprehensive risk level value of the utility tunnel according to the real-time information includes: determining the weight of each secondary index based on an analytic hierarchy process; and determining the comprehensive risk level value of the underground utility tunnel according to the real-time information and the weight of each secondary index based on a fuzzy comprehensive evaluation method.

In some embodiments, the method further comprises: an emergency measure rule is formulated in advance, wherein the emergency measure rule comprises a plurality of fire risk classes, and early warning response measures and emergency treatment measures corresponding to each fire risk class; determining the real-time fire risk level of the underground utility tunnel according to the fire comprehensive risk level value of the underground utility tunnel; and determining a real-time early warning response measure and a real-time emergency treatment measure according to the real-time fire risk level and the emergency measure rule.

One of the embodiments of the present specification provides a distributed optical fiber fire identification system based on an underground utility tunnel, comprising: a factor determination module for determining a plurality of fire initiation factors associated with the fire judgment; the classifier building module is used for building and training a recognition classifier based on the plurality of fire initiation factors related to fire judgment; the system determining module is used for determining a fire risk evaluation system based on the plurality of fire initiation factors related to fire judgment; the information acquisition module is used for acquiring real-time information of the underground comprehensive pipe rack, wherein the real-time information comprises information related to the plurality of fire initiation factors; and the risk determining module is used for determining the comprehensive risk level value of the underground utility tunnel according to the real-time information through the identification classifier and the fire risk evaluation system.

In some embodiments, the risk determination module is further to: judging whether an abnormal event occurs to the underground utility tunnel or not according to the real-time information through the identification classifier; and when judging that the underground comprehensive pipe gallery has an abnormal event, determining a fire comprehensive risk level value of the underground comprehensive pipe gallery according to the real-time information through the fire risk evaluation system.

In some embodiments, the system further comprises: the measure determining module is used for pre-formulating an emergency measure rule, wherein the emergency measure rule comprises a plurality of fire risk levels, and early warning response measures and emergency treatment measures corresponding to each fire risk level; the measure determining module is further used for determining the real-time fire risk level of the underground comprehensive pipe rack according to the fire comprehensive risk level value of the underground comprehensive pipe rack, and determining real-time early warning response measures and real-time emergency treatment measures according to the real-time fire risk level and the emergency measure rules.

Drawings

The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

FIG. 1 is a schematic block diagram of an exemplary utility tunnel-based distributed fiber optic fire identification system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow chart of a distributed fiber optic fire identification method based on utility tunnel, according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram of an identification classifier shown in accordance with some embodiments of the present description;

FIG. 4 is a schematic diagram of an identification classifier according to further embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

FIG. 1 is a schematic block diagram of an exemplary utility tunnel-based distributed fiber optic fire identification system according to some embodiments of the present disclosure. As shown in fig. 1, the distributed optical fiber fire disaster identification system based on the underground utility tunnel can comprise a factor determination module, a classifier establishing module, a system determination module, an information acquisition module, a risk determination module and a measure determination module.

The factor determination module may be configured to determine a plurality of fire initiation factors associated with a fire determination.

The classifier creation module may be configured to create and train the identification classifier based on a plurality of fire initiation factors associated with the fire judgment.

The system determination module may be configured to determine a fire risk assessment system based on a plurality of fire initiation factors associated with the fire determination.

The information acquisition module can be used for acquiring real-time information of the underground comprehensive pipe gallery.

Wherein the real-time information includes information related to a plurality of fire initiation factors.

The risk determination module can be used for determining the comprehensive risk level value of the fire disaster of the underground comprehensive pipe rack according to the real-time information through the identification classifier and the fire disaster risk evaluation system. In some embodiments, the risk determination module may also be configured to: judging whether an abnormal event occurs in the underground comprehensive pipe gallery or not according to the real-time information through the identification classifier; and when judging that the underground comprehensive pipe rack has an abnormal event, determining a fire comprehensive risk level value of the underground comprehensive pipe rack according to the real-time information through a fire risk evaluation system. In some embodiments, the risk determination module may also be configured to: determining the weight of each secondary index based on an analytic hierarchy process; and determining the fire comprehensive risk level value of the underground comprehensive pipe rack according to the real-time information and the weight of each secondary index based on the fuzzy comprehensive evaluation method.

The measure determination module may be used to pre-formulate emergency measure rules. The emergency measure rule comprises early warning response measures and emergency treatment measures corresponding to various fire risk classes. In some embodiments, the measure determination module may also be configured to: and determining the real-time fire risk level of the underground comprehensive pipe rack according to the fire comprehensive risk level value of the underground comprehensive pipe rack, and determining real-time early warning response measures and real-time emergency treatment measures according to the real-time fire risk level and the emergency measure rules.

FIG. 2 is an exemplary flow chart of a distributed fiber optic fire identification method based on utility tunnel, according to some embodiments of the present description. As shown in fig. 2, the utility tunnel-based distributed fiber optic fire identification method may include the following steps. In some embodiments, the utility tunnel-based distributed fiber optic fire identification method may be performed by a utility tunnel-based distributed fiber optic fire identification system.

At step 210, a plurality of fire initiation factors associated with the fire determination are determined. In some embodiments, step 210 may be performed by a factor determination module.

In some embodiments, the plurality of fire initiation factors may include factors related to the pipeline itself, related to artifacts, related to combustible material leakage, or related to the ambient environment. Factors associated with the pipeline itself may include shorts, poor contact, line overload, and line leakage, where shorts refer to: if the circuit is in a working state for a long time, the insulation layer outside the cable is aged and damaged, so that the protection function of the cable is lost, the temperature of the equipment is rapidly increased when the cable is in a short circuit, and a fire disaster is caused when the cable reaches a certain temperature; poor contact refers to: if contact is poor at the circuit joint, contact resistance can appear, and fire can be caused by overlarge contact resistance, and if contact looseness exists at the joint of an electric circuit, voltage between the contacts is enough to break down an air gap to form an electric arc, and the electric arc enters and exits from a spark to ignite combustible materials nearby to form fire; line overload refers to: when the current passing through the electric wire exceeds the safe current-carrying capacity of the electric wire, more heat is generated than normal, the temperature of the electric wire is increased, the circuit can run in overload, and long time, the aging and damage of the insulating skin of the power supply can generate short circuit to cause fire disaster; the line leakage is: when the cable protective layer ages, poor contact or moisture, etc., it may cause a short circuit of the line, causing a fire. The human factors can include fire caused by open fire when the maintenance equipment is used, manual invasion of carried fire sources, the condition that open fire is generated when non-standardized operation is performed by patrol and maintenance personnel or some other heat sources are not properly managed, and the like, so that the fire is caused. Combustible substances such as leaked fuel gas, biogas overflowed from sewage pipes and the like cause fire disasters. Natural gas itself has inflammable and explosive and poisonous characteristics, and can cause pipeline facilities to leak under the influence of factors such as improper setting, damage by external force, improper operation, pipeline breakage, corrosion and the like, and the leaked fuel gas mixture and the like can cause fire, explosion and poisoning accidents when encountering any fire source and the gas concentration of the natural gas reaches a certain degree. The organic matters in the sewage pipeline are decomposed to generate a large amount of inflammable gases such as methane, CO and the like, and the inflammable gases can explode under the condition of meeting a certain concentration. The surrounding environment is also a big factor of fire accidents of the urban underground utility tunnel. For example, in stormy weather, rain water penetrates into the cabin and the cable is soaked with the rain water; or the fire on the peripheral surface spreads to the pipe gallery buried ground and generates gases such as H2S, CH4 and the like in the soil layer, so that fire accidents can be easily caused.

At step 220, an identification classifier is established and trained based on a plurality of fire initiation factors associated with the fire determination. In some embodiments, step 220 may be performed by a classifier setup module.

The identification classifier can be used for judging whether the underground utility tunnel is abnormal or not. In some embodiments, the recognition classifier may be a machine learning model, which may include, but is not limited to, a Neural Network (NN), a Convolutional Neural Network (CNN), a Deep Neural Network (DNN), a Recurrent Neural Network (RNN), etc., or any combination thereof, e.g., the machine learning model may be a model formed by a combination of a convolutional neural network and a deep neural network.

Fig. 3 is a schematic diagram of an identification classifier, as shown in fig. 3, according to some embodiments of the present description, which may include a BP (Back Propagation) neural network in some embodiments.

FIG. 4 is a schematic diagram of an identification classifier, as shown in FIG. 4, according to other embodiments of the present description, which may include a RVM (Relevance Vector Machine) regression prediction model in some embodiments.

At step 230, a fire risk assessment system is determined based on a plurality of fire initiation factors associated with the fire determination. In some embodiments, step 230 may be performed by the hierarchy determination module.

In some embodiments, the fire risk assessment system includes a plurality of primary indicators, wherein the plurality of primary indicators includes at least a pipeline self failure primary indicator, a human factor primary indicator, a combustible material leakage primary indicator, and an environmental factor primary indicator, each primary indicator including at least two secondary indicators. The first-level fault index of the pipeline at least comprises a short-circuit second-level index, a poor contact second-level index, a line overload second-level index and a line leakage second-level index; the human factor primary index at least comprises an open fire operation secondary index and a heat source improper management secondary index; the primary index of combustible substance leakage at least comprises a secondary index of gas leakage and a secondary index of sewage pipe methane leakage; the environmental factor primary index at least comprises a heavy rain weather secondary index and a surrounding surface fire secondary index.

For example, the fire risk assessment system may be as shown in table 1.

TABLE 1

In some embodiments, in order to quantitatively evaluate the fire risk of the underground utility tunnel, the relative importance of each index, namely the weight of each index, should be determined, and the weight illustrates the position of each index in the whole index system, and the influence degree of each index on the fire risk evaluation result is described. For the calculation of the weight, a more mature method is a hierarchical analysis method (AHP (Analytic Hierarchy Process) method). Therefore, combining with the Delphi method, inviting a plurality of experts to judge the relative importance of the indexes by adopting a 1-9 scale method according to the principle of the AHP method, and calculating to obtain the weight of each index on the basis of qualified consistency test.

For example, the weights of the various secondary indicators included in the pipeline's own failed primary indicator may be as shown in Table 2.

TABLE 2

For another example, the weights of the secondary indicators included in the primary indicators of the artifacts may be as shown in table 3.

TABLE 3 Table 3

For another example, the weights of the respective secondary indicators included in the combustible substance leakage primary indicator may be as shown in table 4.

TABLE 4 Table 4

For another example, the weights of the secondary indicators included in the surrounding primary indicators may be as shown in table 5.

TABLE 5

For another example, the weights of the respective primary metrics may be as shown in table 6.

TABLE 6

In some embodiments, after determining the weights of the various indicators of the fire risk assessment system, a consistency check may be performed until the consistency check passes.

In some embodiments, the indexes of the fire risk evaluation system of a certain comprehensive pipe rack can be scored according to the actual engineering condition of the pipe rack by inviting a plurality of experts related to main responsible persons of the industry and enterprises, the scoring is performed according to the Likett scale method, the grade standard of each index is divided into lower, middle, higher and extremely high 5 grades, and the corresponding scoring values are respectively 1, 2, 3, 4 and 5.

For example, 5 experts may be invited, and the expert scoring table is shown in Table 7.

TABLE 7

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In some embodiments, the membership matrix for each indicator to the fire risk level is established according to an expert scoring table, as shown in table 8.

TABLE 8

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In some embodiments, the first-level evaluation index fuzzy matrix R may be calculated first, and then the first-level evaluation index evaluation set B may be calculated based on the membership matrix of each index to the fire risk level and the weight of each index.

Step 240, obtaining real-time information of the underground utility tunnel. In some embodiments, step 240 may be performed by an information acquisition module.

In some embodiments, the real-time information of the utility tunnel may include information related to pipelines, artifacts, combustible materials, and the surrounding environment. For example, the information related to the pipeline may include picture information of the pipeline, voltage, current, temperature, arc, etc.; the information related to the human factors can comprise information such as picture information of personnel in the underground utility tunnel; the information related to combustible substances may include information such as the concentration of flammable gases (e.g., methane, CO, etc.) in the utility tunnel; the information related to the surrounding environment may include weather information, rainfall information, specific gases (e.g., H ₂ S、CH ₄ Etc.) concentration, etc.

And step 250, determining the comprehensive risk level value of the underground utility tunnel according to the real-time information by identifying the classifier and the fire risk evaluation system. In some embodiments, step 250 may be performed by a risk determination module.

The comprehensive risk level value of the fire disaster can represent the severity of the fire disaster happening in the underground comprehensive pipe gallery, and the greater the comprehensive risk level value of the fire disaster, the more serious the fire disaster happening in the underground comprehensive pipe gallery.

In some embodiments, the risk determination module determines the fire risk level value of the utility tunnel from the real-time information by identifying a classifier and a fire risk assessment system, and may include:

judging whether an abnormal event occurs in the underground comprehensive pipe gallery or not according to the real-time information through the identification classifier;

and when judging that the underground comprehensive pipe rack has an abnormal event, determining a fire comprehensive risk level value of the underground comprehensive pipe rack according to the real-time information through a fire risk evaluation system.

In some embodiments, the risk determination module may calculate the fire integrated risk level value for the utility tunnel based on the first level evaluation index panel B.

For example, the utility tunnel's comprehensive risk of fire level value D may be calculated based on the following formula:

i.e. to characterize a fire occurring in the utility tunnel at a high risk level, wherein G ^T Is the transpose of the defined evaluation set.

In some embodiments, the action determination module may pre-formulate emergency action rules prior to performing step 240. The emergency measure rule comprises early warning response measures and emergency treatment measures corresponding to various fire risk classes.

In some embodiments, after step 250 is performed, the measure determination module may determine a real-time early warning response measure and a real-time emergency treatment measure according to the real-time fire risk level and the emergency measure rule.

In some embodiments, the distributed optical fiber fire disaster identification method and system based on the underground utility tunnel provided by the specification enable parallel utilization of risk assessment and identification classifier, are more beneficial to preliminary judgment and positioning during risk accident occurrence, master rules of risk development and control technology of risks, and ensure that risks can be effectively avoided.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. A distributed optical fiber fire disaster identification method based on an underground comprehensive pipe rack is characterized by comprising the following steps:

determining a plurality of fire initiation factors associated with the fire determination;

establishing and training an identification classifier based on the plurality of fire initiation factors related to fire judgment;

determining a fire risk assessment system based on the plurality of fire initiation factors associated with fire judgment;

acquiring real-time information of an underground utility tunnel, wherein the real-time information comprises information related to the plurality of fire initiation factors;

and determining the fire comprehensive risk level value of the underground utility tunnel according to the real-time information through the identification classifier and the fire risk evaluation system.

2. The method for identifying the distributed optical fiber fire based on the underground utility tunnel according to claim 1, wherein the identifying classifier comprises a BP neural network or a related vector machine.

3. The method for identifying the distributed optical fiber fire based on the underground utility tunnel according to claim 1, wherein the fire risk evaluation system comprises a plurality of primary indexes, wherein the plurality of primary indexes at least comprise a pipeline self fault primary index, a human factor primary index, a combustible substance leakage primary index and an environmental factor primary index, and each primary index comprises at least two secondary indexes.

4. The method for identifying the distributed optical fiber fire based on the underground utility tunnel according to claim 3, wherein the primary fault index of the pipeline comprises at least a short circuit secondary index, a poor contact secondary index, a line overload secondary index and a line leakage secondary index;

the human factor primary index at least comprises an open fire operation secondary index and a heat source improper management secondary index;

the combustible substance leakage primary index at least comprises a fuel gas leakage secondary index and a sewage pipe methane leakage secondary index;

the environmental factor primary index at least comprises a heavy rain weather secondary index and a surrounding surface fire secondary index.

5. The method for identifying a distributed optical fiber fire on the basis of an underground utility tunnel according to claim 3 or 4, wherein the determining the fire comprehensive risk level value of the underground utility tunnel according to the real-time information through the identification classifier and the fire risk evaluation system comprises:

judging whether an abnormal event occurs to the underground utility tunnel or not according to the real-time information through the identification classifier;

and when judging that the underground comprehensive pipe gallery has an abnormal event, determining a fire comprehensive risk level value of the underground comprehensive pipe gallery according to the real-time information through the fire risk evaluation system.

6. The method for identifying the distributed optical fiber fire disaster based on the utility tunnel according to claim 5, wherein the determining the comprehensive risk level value of the utility tunnel according to the real-time information through the fire risk evaluation system comprises the following steps:

determining the weight of each secondary index based on an analytic hierarchy process;

and determining the comprehensive risk level value of the underground utility tunnel according to the real-time information and the weight of each secondary index based on a fuzzy comprehensive evaluation method.

7. The method for identifying a distributed optical fiber fire on the basis of an underground utility tunnel according to any one of claims 1 to 4, further comprising:

an emergency measure rule is formulated in advance, wherein the emergency measure rule comprises a plurality of fire risk classes, and early warning response measures and emergency treatment measures corresponding to each fire risk class;

determining the real-time fire risk level of the underground utility tunnel according to the fire comprehensive risk level value of the underground utility tunnel;

and determining a real-time early warning response measure and a real-time emergency treatment measure according to the real-time fire risk level and the emergency measure rule.

8. Distributed optical fiber fire identification system based on utility tunnel, characterized in that includes:

a factor determination module for determining a plurality of fire initiation factors associated with the fire judgment;

the classifier building module is used for building and training a recognition classifier based on the plurality of fire initiation factors related to fire judgment;

the system determining module is used for determining a fire risk evaluation system based on the plurality of fire initiation factors related to fire judgment;

the information acquisition module is used for acquiring real-time information of the underground comprehensive pipe rack, wherein the real-time information comprises information related to the plurality of fire initiation factors;

and the risk determining module is used for determining the comprehensive risk level value of the underground utility tunnel according to the real-time information through the identification classifier and the fire risk evaluation system.

9. The utility tunnel-based distributed fiber optic fire identification method of claim 8, wherein the risk determination module is further configured to:

10. The method for identifying a distributed optical fiber fire based on an underground utility tunnel according to claim 8 or 9, further comprising:

the measure determining module is used for pre-formulating an emergency measure rule, wherein the emergency measure rule comprises a plurality of fire risk levels, and early warning response measures and emergency treatment measures corresponding to each fire risk level;

the measure determining module is further used for determining the real-time fire risk level of the underground comprehensive pipe rack according to the fire comprehensive risk level value of the underground comprehensive pipe rack, and determining real-time early warning response measures and real-time emergency treatment measures according to the real-time fire risk level and the emergency measure rules.