Construction gas leakage detection device and method for tunnel explosion prevention

Abstract

The invention discloses a construction gas leakage detection device and method for tunnel explosion prevention, and relates to the technical field of leakage detection. The device comprises an information acquisition module which is used for acquiring tunnel construction area distribution information; the distance identification module is used for carrying out distance identification and outputting an identification construction area group; the equipment identification module is provided with a gas leakage sensing device; the data acquisition module is used for acquiring a real-time gas detection data group corresponding to the identification construction area group; and the early warning module is used for inputting the real-time gas detection data group into an explosion risk probability calculation model, obtaining an explosion risk probability between the identification construction area groups, and outputting a construction early warning signal when the explosion risk probability is greater than a preset explosion risk probability. The technical problem of low construction safety caused by inaccurate gas leakage detection and untimely early warning in the tunnel construction process in the prior art is solved, and the technical effect of improving the tunnel construction safety is achieved by accurately identifying the gas leakage condition of the construction area and early warning the explosion risk in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of leakage detection, and in particular to a construction gas leakage detection device and method for tunnel explosion protection. Background Art

[0002] During tunnel construction, due to the airtightness and complexity of the underground environment, the operation of construction equipment and changes in geological conditions may lead to the leakage of harmful gases. Especially when gas leaks occur simultaneously in two adjacent construction areas, the gases mix with each other after diffusion, which may lead to the accumulation of flammable and explosive gases, thereby significantly increasing the risk of explosion. If not detected and handled in time, it may cause serious explosion accidents and threaten the safety of construction workers. Therefore, how to effectively detect and warn of gas leaks has become a key issue in tunnel construction safety management. Traditional gas leak detection methods often rely on only a single sensor or fixed monitoring point, which cannot adapt to the complex and changing construction environment. Especially when operations are carried out in multiple construction areas at the same time, the risk assessment and response speed caused by gas diffusion and mixing are insufficient, resulting in limited accuracy and timeliness of early warning. Summary of the invention

[0003] The embodiments of the present application provide a construction gas leakage detection device and method for tunnel explosion protection, which solves the technical problems in the prior art of inaccurate gas leakage detection and untimely warning during tunnel construction, resulting in low construction safety.

[0004] In view of the above problems, the embodiments of the present application provide a construction gas leakage detection device and method for tunnel explosion protection.

[0005] In a first aspect of an embodiment of the present application, a construction gas leakage detection device for tunnel explosion prevention is provided, the device comprising:

[0006] An information acquisition module, the information acquisition module is used to acquire the distribution information of the tunnel construction area; a distance identification module, the distance identification module is used to perform distance identification according to the construction area distribution information, and output an identified construction area group, wherein the identified construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance; an equipment identification module, the equipment identification module is used to identify the construction equipment included in each construction area, and record the gas leakage sample of each construction equipment, and configure the gas leakage sensor device according to the gas leakage sample of each construction equipment; a data acquisition module, the data acquisition module is used to obtain the real-time gas detection data group corresponding to the identified construction area group according to the gas leakage sensor device; an early warning module, the early warning module is used to input the real-time gas detection data group into an explosion risk probability calculation model, obtain the explosion risk probability between the identified construction area groups, and output a construction early warning signal when the explosion risk probability is greater than the preset explosion risk probability.

[0007] A second aspect of the embodiments of the present application provides a method for detecting gas leakage in tunnel explosion-proof construction, the method comprising:

[0008] Obtain tunnel construction area distribution information; perform distance identification based on the construction area distribution information, and output a marked construction area group, wherein the marked construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance; identify the construction equipment included in each construction area, and record the gas leakage sample of each construction equipment, and configure a gas leakage sensor device according to the gas leakage sample of each construction equipment; obtain a real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device; input the real-time gas detection data group into an explosion risk probability calculation model to obtain the explosion risk probability between the marked construction area groups, and when the explosion risk probability is greater than the preset explosion risk probability, output a construction warning signal.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The information acquisition module acquires the distribution information of the tunnel construction area; the distance identification module performs distance identification based on the construction area distribution information and outputs a marked construction area group, wherein the marked construction area group includes two construction areas, and the distance between the two construction areas is less than the preset distance; the equipment identification module identifies the construction equipment included in each construction area, and records the gas leakage samples of each construction equipment, and configures the gas leakage sensor device according to the gas leakage samples of each construction equipment; the data acquisition module acquires the real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device; the early warning module inputs the real-time gas detection data group into the explosion risk probability calculation model, obtains the explosion risk probability between the marked construction area groups, and outputs a construction early warning signal when the explosion risk probability is greater than the preset explosion risk probability. The technical problem of inaccurate gas leakage detection and untimely early warning during tunnel construction in the prior art is solved, and the technical effect of improving the safety of tunnel construction is achieved by accurately identifying the gas leakage in the construction area and warning the explosion risk in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1A schematic diagram of the structure of a gas leakage detection device for tunnel explosion-proof construction provided in an embodiment of the present application;

[0013] Figure 2 A schematic flow chart of a method for detecting gas leakage for tunnel explosion protection construction provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: information acquisition module 11 , distance identification module 12 , device identification module 13 , data acquisition module 14 , early warning module 15 . DETAILED DESCRIPTION

[0015] The embodiments of the present application provide a construction gas leakage detection device and method for tunnel explosion protection, thereby solving the technical problems in the prior art of inaccurate gas leakage detection and untimely warning during tunnel construction, which lead to low construction safety.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a construction gas leakage detection device for tunnel explosion protection, wherein the device comprises:

[0019] The information acquisition module 11 is used to acquire the tunnel construction area distribution information.

[0020] The information acquisition module 11 acquires the tunnel construction area distribution information through the Geographic Information System (GIS), including the location information of each construction area in the current tunnel, the area division and its corresponding construction scope.

[0021] The distance identification module 12 is used to perform distance identification according to the construction area distribution information and output an identified construction area group, wherein the identified construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance.

[0022] The function of the distance identification module 12 is to analyze and identify the distances between different construction areas based on the tunnel construction area distribution information provided by the information acquisition module 11. The distance identification module 12 determines the adjacent areas by calculating the relative distances between the construction areas, and outputs a group of identified construction areas. Specifically, when the distance between two construction areas is less than a preset safety distance, these areas are marked as a group of identified construction areas, wherein the preset distance is determined based on factors such as safety specifications for tunnel construction, gas diffusion characteristics, and the minimum safety spacing of construction equipment.

[0023] In a possible implementation, the distance identification module 12 is used to: obtain multiple construction area group samples and multiple test distance samples; construct a gas diffusion model, test each construction area group at different test distance samples, and output gas diffusion test data, wherein the gas diffusion test data is the gas diffusion time corresponding to when gas mixing is achieved; obtain a first safety time according to the gas diffusion test data; configure the preset distance according to the first safety time, and output an identification of the construction area group.

[0024] Specifically, multiple construction area group samples and multiple different test distance samples are collected, and these samples provide basic data for analyzing the distance between different areas and the specific conditions of gas diffusion; based on the construction environment and gas characteristics, a gas diffusion model is constructed to simulate the diffusion behavior of gas between different construction areas; using the gas diffusion model, gas diffusion tests are performed on samples of different test distances to simulate the diffusion of gas between different construction area groups, and gas diffusion test data is output. The gas diffusion test data records the diffusion time required for the gas to reach a mixed state between two construction areas, that is, the length of time it takes for the gas to diffuse from one construction area to another until the gas is mixed; due to gas leakage in the two construction areas, the gas is mixed after diffusion, resulting in the risk of explosion. Therefore, the first safe time can be determined based on the gas diffusion test data, that is, the shortest safe time for the gas to diffuse to mix between the two areas; by combining the safe time for gas diffusion with the distance between the areas, the preset safe distance is adjusted and configured to ensure that when the gas diffusion time between the two areas is less than the safe time, the potential risk area can be identified; based on the first safe time and the preset distance, the construction area group is finally output and identified.

[0025] The equipment identification module 13 is used to identify the construction equipment included in each construction area, record the gas leakage samples of each construction equipment, and configure the gas leakage sensor device according to the gas leakage samples of each construction equipment.

[0026] The equipment identification module 13 determines the various types of construction equipment being used in each construction area through equipment tags, RFID, image recognition, etc. Different types of equipment may have different gas leakage risks during the construction process. By collecting the historical gas leakage data of each construction equipment, including the type of gas, leakage rate, leakage time and location, a gas leakage sample is formed. According to the gas leakage samples and characteristics of each construction equipment, an appropriate gas leakage sensor device is selected for installation to ensure timely detection and early warning when a gas leak occurs in the equipment. For example, for equipment that may leak flammable gas, gas sensors with high sensitivity and fast response time will be configured first.

[0027] In a possible implementation, the equipment identification module 13 is used to: obtain N construction equipment in each construction area, and N gas leakage samples corresponding to the N construction equipment, where N is a positive integer greater than or equal to 1; cluster the N gas leakage samples according to gas type, and output multiple representative gases for each construction area; configure a gas leakage sensor for each construction area according to the multiple representative gases, and integrate multiple gas leakage sensors from multiple construction areas to obtain a gas leakage sensing device.

[0028] Specifically, N construction equipment in each construction area are collected, and gas leakage sample data of these N equipment are obtained. These sample data include gas leakage characteristics of different equipment under different working conditions (such as the type of leaked gas, leakage rate, etc.), where N is a positive integer greater than or equal to 1, which means that each construction area has at least one equipment and corresponding gas leakage data.

[0029] After obtaining N gas leakage samples, cluster analysis is performed based on the type of gas. K-means, hierarchical clustering and other algorithms are used to divide the samples into different categories according to the similarity of gas types, and obtain multiple representative gases. These representative gases cover the gas leakage risks of all construction equipment in the area. Based on the multiple representative gases in each construction area, a suitable gas leakage sensor will be selected for the area. Different types of gas leaks require different sensors to detect. For example, some sensors are specifically used to detect toxic gases, while others are suitable for detecting combustible gases. By selecting and configuring sensors for representative gases, accurate and comprehensive leakage monitoring is ensured for each construction area.

[0030] After configuration is completed, the gas leakage sensors in multiple construction areas are integrated into a complete gas leakage sensing device, which can monitor gas leakage in multiple areas at the same time and send the detected data to the data acquisition module for further analysis and early warning.

[0031] In a possible implementation, the device identification module 13 is used to: classify the multiple representative gases according to gas concentration, obtain a first type of representative gas with a concentration greater than a preset concentration, and a second type of representative gas with a concentration less than the preset concentration; wherein the gas leakage sensor includes at least one main sensor and at least one auxiliary sensor, the main sensor is used to detect the leakage of the first type of representative gas, and the auxiliary sensor is used to detect the leakage of the second type of representative gas.

[0032] After identifying multiple representative gases in each construction area, they will be further classified according to the concentration of each gas. Specifically, based on the collected gas leakage sample data, the concentration of each representative gas in the construction area is calculated; at the same time, a preset gas concentration threshold is set, and the representative gases are divided into the first type of representative gases with a gas concentration greater than the preset concentration, and the second type of representative gases with a gas concentration less than the preset concentration.

[0033] According to different types of representative gases, the gas leakage sensors are divided into main sensors and auxiliary sensors to achieve more accurate gas monitoring. The main sensor is used to detect the leakage of the first type of representative gas (high-concentration gas). The concentration of the first type of representative gas leakage is high. The main sensor has a high sensitivity and a fast response speed, which can detect the leakage of high-concentration gas in time and provide a rapid early warning response. The auxiliary sensor is used to detect the leakage of the second type of representative gas (low-concentration gas). The auxiliary sensor has a low sensitivity and is suitable for detecting gases with low concentrations but still need to be monitored. The main sensor and the auxiliary sensor are configured in the construction area at the same time to ensure that the leakage of gases of different concentrations can be fully monitored. When the first type of representative gas leaks, the main sensor responds quickly and issues an alarm; when the second type of representative gas leaks, the auxiliary sensor can provide additional monitoring support. Through this collaborative method of main and auxiliary sensors, the device can respond accordingly according to the leakage of gases of different concentrations, improving the accuracy and safety of gas leakage monitoring.

[0034] The data acquisition module 14 is used to acquire a real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device.

[0035] The data acquisition module 14 continuously receives real-time gas detection data from each construction area through the configured gas leakage sensor device. Specifically, the detection data obtained from each gas leakage sensor in the identified construction area group is classified and summarized according to the area and sensor type to generate a real-time gas detection data group, which reflects the gas leakage situation of each construction area at the current moment.

[0036] The early warning module 15 is used to input the real-time gas detection data group into the explosion risk probability calculation model, obtain the explosion risk probability between the identified construction area groups, and output a construction early warning signal when the explosion risk probability is greater than the preset explosion risk probability.

[0037] The early warning module 15 receives the real-time gas detection data group from the data acquisition module 14, which includes the gas leakage concentration, type and other related environmental parameters in each marked construction area group. The acquired real-time gas detection data group is input into the preset explosion risk probability calculation model. The explosion risk probability calculation model calculates the explosion risk probability between the marked construction area groups based on parameters such as gas flammability, leakage concentration, gas mixing ratio, construction environment (such as temperature, ventilation conditions), etc. Through calculation, the explosion risk probability value between each marked construction area group is obtained, and this probability represents the risk of gas leakage causing explosion in the current construction area environment. The calculated explosion risk probability is compared with the preset explosion risk probability threshold; if the calculated explosion risk probability is greater than the preset threshold, it means that there is a greater explosion risk in the construction area. When the explosion risk probability exceeds the preset value, a construction early warning signal will be automatically output, and the construction early warning signal can trigger the alarm system to notify the construction personnel to take emergency response measures, such as immediate evacuation, stopping construction or starting the leakage emergency treatment procedure.

[0038] In a possible implementation, the early warning module 15 is used for: wherein the explosion risk probability calculation model is obtained through a Bayesian network; time synchronization processing is performed on the real-time gas detection data group corresponding to the identified construction area group, and the real-time gas detection data group after synchronization processing is output; the Bayesian network performs Bayesian calculation on the real-time gas detection data group after synchronization processing, and outputs the explosion risk probability between the identified construction area groups.

[0039] Specifically, the Bayesian network is used as a computational model. The Bayesian network is a model based on probabilistic reasoning that can deduce the probability of occurrence of unknown events from known data. Through this model, the explosion risk probability between the identified construction area groups can be deduced based on the real-time gas detection data group.

[0040] The real-time gas detection data group corresponding to the marked construction area group is time-synchronized to ensure that all data is analyzed in the same time dimension. After time synchronization, the synchronized real-time gas detection data group is input into the Bayesian network. The Bayesian network performs Bayesian calculations based on the gas detection data of each construction area, including gas concentration, type, leakage trend, etc. Through calculation, the explosion risk probability of each construction area group will be derived. The calculation result of the Bayesian network is the explosion risk probability of each marked construction area group, which reflects the possibility of an explosion in the current environment.

[0041] In a possible implementation, the early warning module 15 is used to: use the real-time gas detection data of each construction area as a variable node, and construct an initial Bayesian network with the distribution information of the construction area; collect historical explosion event data sets, learn the initial Bayesian network based on the historical explosion event data sets, and output a learning conditional probability table; when the calculation accuracy of the learning conditional probability table is greater than the preset accuracy, a Bayesian network that has been learned is obtained, and the explosion risk probability calculation model is generated.

[0042] Specifically, the real-time gas detection data of each construction area is used as the variable node of the Bayesian network, and each node represents the gas leakage situation in the construction area, such as gas type, concentration, leakage rate, etc. At the same time, the distribution information of the construction area is used as the structural basis to construct an initial Bayesian network, which reflects the relationship between different construction areas and the mutual influence between gas leakage data. The data set of historical explosion events is collected and sorted, which includes key information such as gas concentration, environmental conditions, and equipment status when the explosion occurred. The initial Bayesian network is trained by using the historical explosion event data set; the learning process is completed by adjusting the conditional probability table (CPT) in the Bayesian network, that is, the dependency and probability distribution between different nodes in the network are updated through historical data. During the learning process, a new conditional probability table is output, which records the probability of gas leakage leading to explosion under different conditions. Through learning, the conditional probability table is gradually adjusted to improve the accuracy of the model. After the learning is completed, the calculation results of the conditional probability table will be verified to evaluate its accuracy in predicting explosion risks. If the calculation accuracy of the learning conditional probability table is greater than the preset accuracy threshold, it means that the prediction effect of the model has reached the required accuracy. When the learning effect meets the preset accuracy requirement, a Bayesian network optimized by learning will be generated, which can more accurately predict the explosion risk in the construction area and become the final explosion risk probability calculation model.

[0043] In a possible implementation, the early warning module 15 is also used to: take the event of explosion risk between the identified construction area groups as a state node; obtain the ventilation environment information of the tunnel; the explosion risk probability calculation model uses the ventilation environment information as an influencing variable, calculates the probability of the state node under the real-time gas detection data group, and obtains the explosion risk probability, wherein each real-time gas detection data includes a gas type and a gas concentration.

[0044] In the Bayesian network, the explosion risk events that may occur between the construction area groups are identified as state nodes. The state nodes indicate whether the explosion event will occur in a specific construction area group, which is the final output of the risk assessment. At the same time, the ventilation environment information in the tunnel is obtained, including wind speed, wind direction, air flow path, working status of ventilation facilities, etc.; the ventilation environment information is used as an important influencing variable in the Bayesian network and input into the explosion risk probability calculation model. After obtaining the real-time gas detection data group (including gas type and gas concentration), the Bayesian network will combine these data with the ventilation environment information for joint calculation, so as to calculate a more accurate explosion risk probability.

[0045] In summary, the embodiments of the present application have at least the following technical effects:

[0046] The information acquisition module acquires the distribution information of the tunnel construction area; the distance identification module performs distance identification based on the construction area distribution information and outputs a marked construction area group, wherein the marked construction area group includes two construction areas, and the distance between the two construction areas is less than the preset distance; the equipment identification module identifies the construction equipment included in each construction area, and records the gas leakage samples of each construction equipment, and configures the gas leakage sensor device according to the gas leakage samples of each construction equipment; the data acquisition module acquires the real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device; the early warning module inputs the real-time gas detection data group into the explosion risk probability calculation model, obtains the explosion risk probability between the marked construction area groups, and outputs a construction early warning signal when the explosion risk probability is greater than the preset explosion risk probability. The technical problem of inaccurate gas leakage detection and untimely early warning during tunnel construction in the prior art is solved, and the technical effect of improving the safety of tunnel construction is achieved by accurately identifying the gas leakage in the construction area and warning the explosion risk in advance.

[0047] Embodiment 2, as Figure 2 As shown, the embodiment of the present application provides a method for detecting gas leakage in tunnel explosion-proof construction, wherein the method comprises:

[0048] Obtain tunnel construction area distribution information; perform distance identification based on the construction area distribution information, and output a marked construction area group, wherein the marked construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance; identify the construction equipment included in each construction area, and record the gas leakage sample of each construction equipment, and configure a gas leakage sensor device according to the gas leakage sample of each construction equipment; obtain a real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device; input the real-time gas detection data group into an explosion risk probability calculation model to obtain the explosion risk probability between the marked construction area groups, and when the explosion risk probability is greater than the preset explosion risk probability, output a construction warning signal.

[0049] Further, a gas leakage sensor device is configured according to the gas leakage sample of each construction equipment, and the method includes:

[0050] Obtain N construction equipment in each construction area, and N gas leakage samples corresponding to the N construction equipment, wherein N is a positive integer greater than or equal to 1; cluster the N gas leakage samples according to gas type, and output multiple representative gases for each construction area; configure a gas leakage sensor for each construction area according to the multiple representative gases, and integrate multiple gas leakage sensors from multiple construction areas to obtain a gas leakage sensing device.

[0051] Further, according to the plurality of representative gases, a gas leakage sensor is configured in each construction area, and the method includes:

[0052] The multiple representative gases are divided according to gas concentration to obtain a first type of representative gas with a concentration greater than a preset concentration and a second type of representative gas with a concentration less than the preset concentration; wherein the gas leakage sensor includes at least one main sensor and at least one auxiliary sensor, the main sensor is used to detect the leakage of the first type of representative gas, and the auxiliary sensor is used to detect the leakage of the second type of representative gas.

[0053] Further, distance recognition is performed according to the construction area distribution information, and a construction area group is output and identified. The method includes:

[0054] Acquire multiple construction area group samples and multiple test distance samples; construct a gas diffusion model, test each construction area group at different test distance samples, and output gas diffusion test data, wherein the gas diffusion test data is the gas diffusion time corresponding to when gas mixing is achieved; obtain a first safety time according to the gas diffusion test data; configure the preset distance according to the first safety time, and output an identified construction area group.

[0055] Further, the real-time gas detection data group is input into an explosion risk probability calculation model to obtain the explosion risk probability between the identified construction area groups, the method comprising:

[0056] Among them, the explosion risk probability calculation model is obtained through a Bayesian network; the real-time gas detection data group corresponding to the identified construction area group is time synchronized, and the real-time gas detection data group after synchronization is output; the Bayesian network performs Bayesian calculation on the real-time gas detection data group after synchronization, and outputs the explosion risk probability between the identified construction area groups.

[0057] Furthermore, the explosion risk probability calculation model is obtained through a Bayesian network, and the method includes:

[0058] The real-time gas detection data of each construction area is used as a variable node, and an initial Bayesian network is constructed with the distribution information of the construction area; a historical explosion event data set is collected, and the initial Bayesian network is learned according to the historical explosion event data set, and a learning conditional probability table is output; when the calculation accuracy of the learning conditional probability table is greater than the preset accuracy, a Bayesian network with complete learning is obtained, and the explosion risk probability calculation model is generated.

[0059] Furthermore, the Bayesian network performs Bayesian calculation on the synchronously processed real-time gas detection data set, and the method further includes:

[0060] The event of explosion risk between the identified construction area groups is taken as a state node; the ventilation environment information of the tunnel is obtained; the explosion risk probability calculation model uses the ventilation environment information as an influencing variable, calculates the probability of the state node under the real-time gas detection data group, and obtains the explosion risk probability, wherein each real-time gas detection data includes a gas type and a gas concentration.

[0061] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0063] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A construction gas leakage detection device for tunnel explosion protection, characterized in that: The device comprises: An information acquisition module, wherein the information acquisition module is used to acquire tunnel construction area distribution information; A distance identification module, the distance identification module is used to perform distance identification according to the construction area distribution information, and output an identified construction area group, wherein the identified construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance; An equipment identification module, the equipment identification module is used to identify the construction equipment included in each construction area, record the gas leakage sample of each construction equipment, and configure the gas leakage sensor device according to the gas leakage sample of each construction equipment; A data acquisition module, the data acquisition module is used to acquire a real-time gas detection data group corresponding to the marked construction area group according to the gas leakage sensor device; An early warning module is used to input the real-time gas detection data group into an explosion risk probability calculation model to obtain the explosion risk probability between the identified construction area groups, and output a construction early warning signal when the explosion risk probability is greater than a preset explosion risk probability.

2. The tunnel explosion-proof construction gas leakage detection device according to claim 1, characterized in that: The device identification module is used for: Obtain N construction equipment in each construction area, and N gas leakage samples corresponding to the N construction equipment, where N is a positive integer greater than or equal to 1; Clustering the N gas leakage samples according to gas type, and outputting multiple representative gases in each construction area; According to the multiple representative gases, a gas leakage sensor is configured in each construction area, and multiple gas leakage sensors in multiple construction areas are integrated to obtain a gas leakage sensor device.

3. The tunnel explosion-proof construction gas leakage detection device according to claim 2, characterized in that: The device identification module is used for: Classify the plurality of representative gases according to gas concentrations to obtain a first type of representative gas having a concentration greater than a preset concentration and a second type of representative gas having a concentration less than the preset concentration; The gas leakage sensor comprises at least one main sensor and at least one auxiliary sensor, wherein the main sensor is used to detect leakage of the first type representative gas, and the auxiliary sensor is used to detect leakage of the second type representative gas.

4. The tunnel explosion-proof construction gas leakage detection device according to claim 1, characterized in that: The distance recognition module is used for: Obtain multiple construction area group samples and multiple test distance samples; Construct a gas diffusion model, test each construction area group with samples at different test distances, and output gas diffusion test data, wherein the gas diffusion test data is the gas diffusion time corresponding to when gas mixing is achieved; Obtaining a first safety time according to the gas diffusion test data; The preset distance is configured according to the first safety time, and a construction area group is output and marked.

5. The tunnel explosion-proof construction gas leakage detection device according to claim 1, characterized in that: The early warning module is used for: Wherein, the explosion risk probability calculation model is obtained through a Bayesian network; Performing time synchronization processing on the real-time gas detection data group corresponding to the marked construction area group, and outputting the real-time gas detection data group after synchronization processing; The Bayesian network performs Bayesian calculation on the synchronously processed real-time gas detection data group, and outputs the explosion risk probability between the identified construction area groups.

6. The tunnel explosion-proof construction gas leakage detection device according to claim 5, characterized in that: The early warning module is used for: The real-time gas detection data of each construction area is used as a variable node, and an initial Bayesian network is constructed with the distribution information of the construction area; Collecting a historical explosion event data set, learning the initial Bayesian network according to the historical explosion event data set, and outputting a learning conditional probability table; When the calculation accuracy of the learning conditional probability table is greater than the preset accuracy, a Bayesian network that has completed learning is obtained, and the explosion risk probability calculation model is generated.

7. The tunnel explosion-proof construction gas leakage detection device according to claim 5, characterized in that: The early warning module is also used for: The event that identifies the explosion risk between the construction area groups is used as a state node; Acquiring ventilation environment information of the tunnel; The explosion risk probability calculation model uses the ventilation environment information as an influencing variable, calculates the probability of the state node under the real-time gas detection data group, and obtains the explosion risk probability, wherein each real-time gas detection data includes a gas type and a gas concentration.

8. A method for detecting gas leakage in construction for tunnel explosion prevention, characterized in that: The method is applied to the tunnel explosion-proof construction gas leakage detection device according to any one of claims 1 to 7, and the method comprises: Obtaining tunnel construction area distribution information; Performing distance recognition according to the construction area distribution information, and outputting a marked construction area group, wherein the marked construction area group includes two construction areas, and the distance between the two construction areas is less than a preset distance; Identify the construction equipment included in each construction area, record the gas leakage samples of each construction equipment, and configure the gas leakage sensing device according to the gas leakage samples of each construction equipment; According to the gas leakage sensor device, a real-time gas detection data group corresponding to the marked construction area group is obtained; The real-time gas detection data group is input into an explosion risk probability calculation model to obtain the explosion risk probability between the identified construction area groups, and when the explosion risk probability is greater than a preset explosion risk probability, a construction warning signal is output.

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