Tunnel safety monitoring system based on big data

Abstract

The invention discloses a tunnel safety monitoring system based on big data, and relates to the technical field of tunnel safety monitoring. A tunnel is divided into a tunnel active area and a tunnel idle area, the dust concentration, harmful gas and temperature and humidity of the tunnel active area are subjected to safety assessment, then an assessment result and a corresponding threshold value are calculated to construct a right triangle environment assessment model, and the assessment result is compared with a standard assessment model to calculate an active area assessment value. In the process, through layered monitoring and data analysis, multi-dimensional environmental parameters are converted into unified quantitative indexes, and the problem that generally, only the space in the whole tunnel can be monitored, the tunnel cannot be divided into sub-regions for targeted monitoring, and the monitoring efficiency of the tunnel is greatly improved is solved. And the intelligent degree is low.

Description

Technical Field

[0001] This invention relates to the field of tunnel safety monitoring technology, and in particular to a tunnel safety monitoring system based on big data. Background Technology

[0002] Tunnels are closely related to people's travel, and as an important part of transportation infrastructure construction, their safety is of paramount importance;

[0003] However, existing tunnel safety monitoring systems still have the following shortcomings in practical applications:

[0004] Although existing technologies can monitor various environmental parameters within a tunnel, they typically can only monitor the entire space within the tunnel and cannot divide the tunnel into sub-regions for targeted monitoring, resulting in a low level of intelligence.

[0005] Furthermore, when tunnel safety monitoring results show abnormalities, the alarm is often triggered in a single instance, and management personnel are notified to formulate a handling plan. This approach is inefficient because it cannot quickly develop solutions for handling tunnel anomalies by combining historical data.

[0006] To address the aforementioned issues, a tunnel safety monitoring system based on big data has been developed. Summary of the Invention

[0007] In view of this, the present invention provides a tunnel safety monitoring system based on big data to solve the problems mentioned in the background art.

[0008] The objective of this invention can be achieved through the following technical solution: a tunnel safety monitoring system based on big data, characterized in that it comprises:

[0009] Data acquisition module: Collects environmental parameters for each zone of the tunnel, including dust concentration, harmful gases, temperature and humidity; the zones include the active zone and the idle zone.

[0010] Data processing module: Conducts safety assessments on environmental parameters of each divided area within the tunnel to obtain the active zone rating and the idle zone rating of the tunnel.

[0011] Monitoring and processing module: Receives the active zone assessment value of the active area and the idle zone assessment value of the idle area of ​​the tunnel, selectively triggers alarm signals and executes corresponding steps.

[0012] In some embodiments, the process of conducting a safety assessment of dust concentration in the tunnel activity area specifically includes:

[0013] The tunnel activity area is divided into various height zones according to a preset height division ratio; each height zone includes a high-altitude zone, a mid-altitude zone, and a low-altitude zone. Dust concentration data for each height zone within a set time window is acquired. For the dust concentration data of each height zone at each time point within the set time window, the average value is calculated to obtain the dust concentration performance values ​​corresponding to the high-altitude, mid-altitude, and low-altitude zones within the tunnel activity area, denoted as f1, f2, and f3, respectively. The preset standard value corresponding to the dust concentration is represented by Ka, and is obtained through the formula... The dust concentration assessment index of the tunnel activity area was calculated. ,in , , These are the weighting factors corresponding to the high-altitude, mid-altitude, and low-altitude regions within the tunnel's activity area, respectively.

[0014] Let Gi, Zi, and Di represent the dust concentration data at each time point within the set time window for the high-altitude, mid-altitude, and low-altitude regions, respectively, where i represents the number of each time point, and i = 1, 2, 3, ..., n, where n is the total number of time points within the set time window; for the high-altitude region, use formula (1). Calculate the average rate of change of dust concentration in the high-altitude zone of the tunnel; for the mid-altitude and low-altitude zones, calculate the corresponding average rate of change according to formula (1), and mark them as follows. and The system pre-determines weighting coefficients for the average rate of change corresponding to the high-altitude, mid-altitude, and low-altitude regions. It then multiplies each of these average rates of change by its corresponding pre-determined weighting coefficient and sums the results to obtain the powder concentration change value. Set the dust concentration assessment value for the tunnel activity zone. Changes in powder concentration The corresponding weighting coefficients, for the powder concentration evaluation value Changes in powder concentration After normalization, the dust concentration is multiplied by the corresponding set weight coefficients, and then summed to obtain the dust assessment index P1.

[0015] In some embodiments, the process of conducting a safety assessment of the concentration of harmful gases in the tunnel activity area specifically includes:

[0016] Obtain the concentration of harmful gases in each height area of ​​the tunnel activity zone within a set time window. Let j represent the type number of the harmful gas, j=1,2,...,u, where u represents the total number of types of harmful gases. Take the highest value from the gas average of different harmful gas concentrations in each height area as the degree of harm of the corresponding harmful gas in the tunnel activity zone, denoted as Mj.

[0017] Set the maximum allowable value for different harmful gases in the tunnel, denoted by Hj; according to the formula... A comprehensive calculation was performed to obtain the gas assessment index P2 for the active zone of the tunnel; whereby... This represents the weighting coefficients corresponding to different harmful gases;

[0018] In some embodiments, the process of conducting a temperature safety assessment of the tunnel activity area specifically includes:

[0019] The tunnel activity area is divided into an equipment area and a construction area according to its function. Temperature parameters of the equipment area and the construction area are extracted at each time point within a set time window.

[0020] The highest value among the temperature parameters of each area is taken as the temperature threat value of that area. The temperature threat values ​​of the equipment area and the construction area are N1 and N2, respectively.

[0021] Set the maximum allowable temperatures for the equipment area and construction area to T1 and T2 respectively, and use the formula... The temperature assessment index P3 of the tunnel activity zone was calculated, where , These are the weighting coefficients for the equipment area and the construction area, respectively.

[0022] In some embodiments, the process of conducting a humidity safety assessment of the tunnel activity area specifically includes:

[0023] The tunnel activity area is divided into an equipment area and a construction area according to its function. The humidity parameters of the equipment area and the construction area are extracted at each time point within a set time window. The maximum and minimum values ​​of the humidity parameters in the equipment area and the construction area are extracted and the difference is calculated to obtain the equipment humidity difference and the construction humidity difference. The allowable humidity deviations corresponding to the equipment area and the construction area are preset. The equipment humidity difference and the construction humidity difference are used as the numerators and the corresponding allowable humidity deviations are used as the denominators to calculate the ratio to obtain the equipment deviation index and the construction deviation index corresponding to the equipment area and the construction area, respectively.

[0024] The weighting coefficients corresponding to the pre-defined equipment area and construction area are used to multiply the humidity deviation index of the corresponding area by the weighting coefficient of each area and sum them up to obtain the humidity assessment index P4 of the tunnel operation area.

[0025] In some embodiments, the liveness assessment of the tunnel active zone is processed as follows:

[0026] Set thresholds corresponding to P1, P2, P3, and P4; calculate the ratios between each pair of P1, P2, P3, and P4 as numerators and the corresponding thresholds as denominators to obtain four sets of ratios Q1, Q2, Q3, and Q4. The sum of Q1 and Q2 is recorded as the first value, and the sum of Q3 and Q4 is recorded as the second value.

[0027] Using the constructed single-value and constructed double-value as the two legs of a right triangle, the constructed right triangle is used as the environmental assessment model of the tunnel activity area; the stored standard assessment model is retrieved from the database, and the area of ​​the environmental assessment model is subtracted from the area of ​​the standard assessment model to obtain the activity area assessment value;

[0028] In some embodiments, the idle area assessment of the tunnel idle area is processed as follows:

[0029] Similarly, the safety assessment process for dust and harmful gas concentrations in the tunnel activity area is carried out by assessing the dust and harmful gas concentrations in the tunnel idle area, and obtaining the dust concentration assessment index and gas concentration assessment index corresponding to the dust concentration and harmful gas concentration in the tunnel idle area, respectively.

[0030] The weighting factors for the dust concentration assessment index and the gas concentration assessment index are preset. The dust concentration assessment index and the gas concentration assessment index are multiplied by their respective weighting factors, added together and divided by two to calculate the idle area assessment value of the tunnel idle area.

[0031] In some embodiments, the selective triggering of the alarm signal and the execution of the corresponding steps specifically include:

[0032] The active zone value of the tunnel is compared with the preset active zone threshold. If it is less than the preset threshold, an alarm signal for the active zone is triggered. The idle zone value of the tunnel is compared with the preset idle zone threshold. If it is less than the preset threshold, an alarm signal for the idle zone is triggered.

[0033] After an alarm signal is triggered, historical alarm cases corresponding to the active or idle areas of the tunnel are retrieved from the database. Each set of historical alarm cases includes the alarm trigger time, subsequent processing method, and processing time. The case fit index of each set of historical alarm cases is analyzed, and the historical alarm case with the highest case fit index is selected as a reference case and sent to the management personnel.

[0034] In some embodiments, the screening case fit index The highest historical alarm case is used as a reference case, specifically:

[0035] Based on the specific regions where the alarm signal is triggered, the Euclidean distance algorithm is used to calculate the similarity between the current alarm signal and each group of historical alarm cases. M1, M2, M3, and M4 represent the dust assessment index, gas assessment index, temperature assessment index, and humidity assessment index, respectively, when the alarm signal was triggered for each group of historical alarm cases.

[0036] The historical alarm cases in each group are sorted from smallest to largest according to their similarity; the top five historical alarm cases from left to right are selected as candidate cases.

[0037] Extract the similarity and processing time of each group of candidate cases, normalize them, and then input them into the formula. The case fit index of each group of candidate cases was calculated. ; where a1 and a2 are the weight coefficients of similarity and processing time in each group of candidate cases, respectively.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention divides a tunnel into an active zone and an idle zone, conducts a safety assessment of dust concentration, harmful gases, and temperature in the active zone, and then calculates a right-angled triangle environmental assessment model based on the assessment results and corresponding thresholds. The assessment results are then compared with a standard assessment model to calculate the active zone's rating. For the idle zone, the assessment of dust and gas concentrations is simplified, and the idle zone's rating is calculated using a weighted average. This process, through layered monitoring and data analysis, transforms multi-dimensional environmental parameters into unified quantitative indicators. This solves the problem that monitoring can usually only be done on the entire space within the tunnel, and cannot be done by dividing the tunnel into sub-regions for targeted monitoring, resulting in a low level of intelligence.

[0040] This invention, after triggering an alarm, uses the Euclidean distance algorithm to calculate the similarity between the current risk and historical cases, filters the top five groups of cases, calculates the case fit index by combining the similarity and processing time, and selects the candidate case with the highest case fit to send to the management personnel. This replaces the traditional manual analysis and decision-making mode, significantly shortens the response time, and optimizes the handling plan based on historical cases, improving accuracy and efficiency.

[0041] This invention utilizes a historical case database of big data and the Euclidean distance algorithm to quickly match similar cases when risks occur, automatically generate and activate relevant matching strategies, and significantly shorten the response time compared to manual decision-making. It solves the problem in existing technologies where, when tunnel safety monitoring results show abnormalities, a single alarm is often triggered and management personnel are notified to formulate a handling plan. This approach cannot combine historical data to quickly formulate solutions for handling tunnel anomalies, resulting in low efficiency. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the principle of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Please see Figure 1 As shown, a tunnel safety monitoring system based on big data includes a data acquisition module, a data processing module, and a monitoring and processing module.

[0045] The data acquisition module is used to collect environmental parameters for each zone of the tunnel, including dust concentration, harmful gases, temperature and humidity; the zones include the active zone and the idle zone.

[0046] By default, the collected data is stored and backed up, then integrated and packaged into a data packet.

[0047] The specific process of collecting environmental parameters:

[0048] Prepare high-precision dust concentration detectors (such as laser scattering dust meters, which can accurately measure dust concentration in the range of 0.01mg / m³-1000mg / m³), composite harmful gas detectors (which can simultaneously detect common harmful gases such as carbon monoxide, hydrogen sulfide, and methane, with detection accuracy at the ppm level), and temperature and humidity recorders (measurement accuracy ±0.3℃, ±2%RH), and pre-deploy them in various designated areas within the tunnel;

[0049] The basis for dividing the regions is:

[0050] Based on the tunnel design drawings and actual usage, the specific scope of the tunnel activity area (construction area, traffic area, and other areas where personnel and equipment frequently move) and the tunnel idle area (areas not in use or with extremely low usage frequency) should be clearly defined.

[0051] The data processing module is used to comprehensively process the environmental parameters of each divided area within the tunnel, thereby obtaining the active area assessment value of the active zone and the idle area assessment value of the idle zone; specifically:

[0052] The dust concentration, harmful gases, temperature, and humidity within the tunnel's active zone are comprehensively analyzed to obtain the corresponding active zone assessment value, specifically:

[0053] S1: Perform steps S1-1 to S1-3 to conduct a safety assessment of dust concentration in the tunnel activity area;

[0054] S1-1: The tunnel activity area is divided into various height zones according to the preset height division ratio; each height zone includes a high-altitude zone, a mid-altitude zone, and a low-altitude zone.

[0055] Obtain dust concentration data for each height area in the tunnel activity zone within a set time window;

[0056] For the dust concentration data of each altitude area at each time point within the set time window, the average value is calculated to obtain the dust concentration performance values ​​corresponding to the high-altitude area, the middle-altitude area and the low-altitude area in the tunnel activity area, respectively, which are denoted as f1, f2 and f3.

[0057] The standard value corresponding to the preset dust concentration is denoted by Ka, and is obtained through the formula... The dust concentration assessment index of the tunnel activity area was calculated. ,in , , These are the weighting factors corresponding to the high-altitude, mid-altitude, and low-altitude regions within the tunnel's activity area, respectively.

[0058] The above-mentioned dust concentration assessment index for the tunnel activity area is calculated. By using stratified monitoring and weighted evaluation, the dimensions of dust concentration monitoring have been refined, which can more accurately reflect the dust situation in the tunnel activity area;

[0059] S1-2: Let Gi, Zi, and Di represent the dust concentration data of the high-altitude region, the mid-altitude region, and the low-altitude region at each time point within the set time window, respectively. Here, i represents the number of each time point, and i = 1, 2, 3, ..., n, where n is the total number of time points within the set time window.

[0060] For high-altitude areas, use formula (1) Calculate the average rate of change of dust concentration in the high-altitude zone of the tunnel; for the mid-altitude and low-altitude zones, calculate the corresponding average rate of change according to formula (1), and mark them as follows. and ;

[0061] Weighting coefficients are preset for the average rate of change corresponding to the high-altitude, mid-altitude, and low-altitude regions. The average rate of change corresponding to each of the three regions is multiplied by the corresponding preset weighting coefficient, and then summed to obtain the powder concentration change value. ;

[0062] The above content calculates the average rate of change of dust concentration in each height zone and combines it with a weighting coefficient to obtain the dust concentration change value. It can dynamically reflect the changing trend of dust concentration in the tunnel activity area;

[0063] S1-3: Set the dust concentration assessment value for the tunnel activity zone Changes in powder concentration The corresponding weighting coefficients for the powder concentration evaluation value Changes in powder concentration After normalization, the dust concentration is multiplied by the corresponding set weight coefficients, and then summed to obtain the dust assessment index P1.

[0064] The dust assessment index P1 obtained above covers both the static assessment of the current status of the tunnel activity zone and the dynamic prediction of the future trend of the tunnel activity zone, forming a dual guarantee of status monitoring and trend early warning.

[0065] S2: Perform steps S2-1 to S2-2 to conduct a safety assessment of the concentration of harmful gases, specifically:

[0066] S2-1: Obtain the concentration of harmful gases in each height area of ​​the tunnel activity area within a set time window, with j representing the type number of the harmful gas, j=1,2,......,u, where u represents the total number of types of harmful gases;

[0067] The types of harmful gases include, but are not limited to, carbon monoxide concentration, nitrogen dioxide concentration, and methane concentration. The specific total number of types will be collected and set according to the tunnel construction requirements.

[0068] S2-2: Take the highest value from the average values ​​of different harmful gas concentrations in each height area as the harmfulness value of different harmful gases in the tunnel activity area, denoted as Mj;

[0069] Set the maximum allowable value for different harmful gases in the tunnel, denoted by Hj;

[0070] According to the formula A comprehensive calculation was performed to obtain the gas assessment index P2 for the active zone of the tunnel; whereby... This represents the weighting coefficients corresponding to different harmful gases;

[0071] The settings are based on the degree of harm caused to people by different harmful gases;

[0072] The gas assessment index P2 obtained above, combined with weighting coefficients set for different gas hazard levels, can be comprehensively calculated to transform the spatial distribution risk of multiple types of harmful gases into a unified quantitative indicator, accurately reflecting the overall threat level of harmful gases in the tunnel activity area.

[0073] S3: Perform steps S3-1 to S3-2 to conduct a safety assessment of the temperature in the tunnel's active area, specifically:

[0074] S3-1: Divide the tunnel activity area into equipment area and construction area according to its function, and extract the temperature parameters of equipment area and construction area at each time point within the set time window;

[0075] S3-2: Take the highest value from the temperature parameters of each area as the temperature threat value of that area, and record the temperature threat values ​​of the equipment area and the construction area as N1 and N2, respectively.

[0076] Set the maximum allowable temperatures for the equipment area and construction area to T1 and T2 respectively, and use the formula... The temperature assessment index P3 of the tunnel activity zone was calculated, where , These are the weighting coefficients for the equipment area and the construction area, respectively.

[0077] The temperature assessment index P3 obtained above, combined with the tolerance thresholds and influence weights of different functional areas, is used to calculate the temperature risk of the tunnel activity area into a quantitative indicator, which accurately reflects the temperature safety status of the equipment operation and personnel working environment.

[0078] S4: Perform steps S4-1 to S4-3 to conduct a safety assessment of humidity in the tunnel activity area, specifically:

[0079] S4-1: Divide the tunnel activity area into equipment area and construction area according to its function, and extract the humidity parameters of equipment area and construction area at each time point within the set time window;

[0080] S4-2: Extract the maximum and minimum values ​​of humidity parameters in the equipment area and construction area, and calculate the difference to obtain the equipment humidity difference and construction humidity difference. Preset the allowable humidity deviations corresponding to the equipment area and construction area respectively. Use the equipment humidity difference and construction humidity difference as the numerators and the corresponding allowable humidity deviations as the denominators respectively, and calculate the ratio to obtain the equipment deviation index and construction deviation index corresponding to the equipment area and construction area respectively.

[0081] S4-3: Preset the weight coefficients corresponding to the equipment area and construction area. Multiply the humidity deviation index of the corresponding area by the weight coefficient of each area and sum them to obtain the humidity assessment index P4 of the tunnel operation area.

[0082] The humidity assessment index P4 obtained above, combined with the weighting coefficients set according to the differences in tolerance to humidity fluctuations in different regions, can accurately quantify the impact of humidity fluctuations in the tunnel activity area on the stability of equipment operation (such as the risk of circuit moisture) and the comfort of personnel operation (such as the risk of slipping due to dampness), and provide dynamic data support for dehumidification equipment start-up and shutdown strategies, moisture-proof maintenance plans and environmental control schemes.

[0083] Set the threshold values ​​corresponding to P1, P2, P3, and P4;

[0084] Using P1, P2, P3, and P4 as numerators and the corresponding thresholds as denominators, the ratios between each pair are calculated to obtain four sets of ratios Q1, Q2, Q3, and Q4. The sum of Q1 and Q2 is recorded as the constructing one value, and the sum of Q3 and Q4 is recorded as the constructing two value.

[0085] Using the constructed single-value and constructed double-value as the two legs of a right triangle, the constructed right triangle is used as the environmental assessment model of the tunnel activity area; the stored standard assessment model is retrieved from the database, and the area of ​​the environmental assessment model is subtracted from the area of ​​the standard assessment model to obtain the activity area assessment value;

[0086] It should be noted that the values ​​of both right-angled sides of the standard evaluation model must be greater than the values ​​of the first and second construction values;

[0087] Similarly, steps S1 to S2 are used to conduct a safety assessment of the idle area of ​​the tunnel, and obtain the dust concentration assessment index and gas concentration assessment index corresponding to the dust concentration and harmful gas concentration in the idle area of ​​the tunnel, respectively. The weighting factors of the dust concentration assessment index and the gas concentration assessment index are preset. The dust concentration assessment index and the gas concentration assessment index are multiplied by the corresponding weighting factors, added together and divided by two to calculate the idle area rating of the tunnel.

[0088] The monitoring and processing module is used to receive the active zone assessment value of the active zone and the idle zone assessment value of the idle zone in the tunnel, selectively trigger alarm signals and execute corresponding steps;

[0089] Specifically:

[0090] The active zone value of the tunnel is compared with the preset active zone threshold. If it is less than the preset threshold, an alarm signal for the active zone is triggered. Similarly, the idle zone value of the tunnel is compared with the preset idle zone threshold. If it is less than the preset threshold, an alarm signal for the idle zone is triggered. Alarms are simultaneously issued via audible and visual alarms (distance ≤ 50 meters), the tunnel broadcast system, and the management center's large screen, along with real-time environmental parameters (specific values ​​and risk levels for P1-P4). Evacuation instructions are simultaneously sent to all personnel positioning terminals within the tunnel. Evacuation routes are dynamically planned using the BIM model (avoiding high-risk areas, such as gas accumulation areas with high P2 values).

[0091] Retrieve and analyze historical cases:

[0092] After an alarm signal is triggered, historical alarm cases corresponding to the active or idle areas of the tunnel are retrieved from the database. Each set of historical alarm cases includes the alarm trigger time, subsequent processing method, and processing time. The case fit index of each set of historical alarm cases is analyzed, and the historical alarm case with the highest case fit index is selected as a reference case and sent to the management personnel. The processing time is calculated from the alarm trigger time and is stopped by the technicians after processing is completed.

[0093] Based on the specific regions where the alarm signal is triggered, the Euclidean distance algorithm is used to calculate the similarity between the current alarm signal and each group of historical alarm cases. M1, M2, M3, and M4 represent the dust assessment index, gas assessment index, temperature assessment index, and humidity assessment index, respectively, when the alarm signal was triggered for each group of historical alarm cases.

[0094] The historical alarm cases in each group are sorted from smallest to largest according to their similarity; the top five historical alarm cases from left to right are selected as candidate cases.

[0095] Extract the similarity and processing time of each group of candidate cases, normalize them, and then input them into the formula. The case fit index of each group of candidate cases is calculated; where a1 and a2 are the weight coefficients of similarity and processing time in each group of candidate cases, respectively.

[0096] Select Case Fit Index The largest candidate case was selected as a reference case and sent to management.

[0097] Example of subsequent processing methods:

[0098] If P2 > 0.7, the tunnel jet fan (wind speed increased to 25m / s) and gas extraction pump (power adjusted to 110% of rated value) will be automatically started, and a request to start the sprinkler dust suppression will be sent to the fire control center (to address the risk of dust and gas mixing).

[0099] When P3 > 0.8 or P4 > 1.0, the air conditioning system in the equipment area is turned on (cooling down to 26℃±2℃), the dehumidifier in the construction area is started (target humidity ≤ 65%RH), and the condensate generation is monitored by infrared sensors embedded in the tunnel arch.

[0100] The nearest emergency response team (equipped with gas detectors, respirators, etc.) can be located through the IoT platform, and an AR work order containing the risk location and handling procedures can be sent (which can be viewed on the smart helmet display).

[0101] The system automatically retrieves the corresponding equipment from the emergency supplies warehouse (e.g., 3 dust suppression cannons when dust levels exceed the standard) and transports it to the designated area via a track-mounted logistics robot (speed ≤ 1.5m / s).

[0102] Real-time evaluation of processing results, closed-loop handling, and data archiving:

[0103] New P1-P4 data are collected every 10 minutes, and the deviation from the standard evaluation model is calculated. If a certain indicator (such as P1) decreases by less than 15% within 30 minutes, the system automatically upgrades the treatment plan (such as increasing the number of dust suppression cannons to 5).

[0104] By deploying lidar at the top of the tunnel, the distribution of dust clouds is scanned in real time. If a local concentration of >500mg / m³ is detected, a fixed-point spray dust suppression device is triggered (atomized particles ≤50μm).

[0105] When the evaluation value is less than 0.5 for two consecutive tests and P1-P4 are all below the threshold, the alarm is lifted and a handling report (including handling time, equipment energy consumption, and environmental recovery curve) is generated.

[0106] The data from this disposal (including real-time parameters and the execution process of the plan) will be encrypted and archived on the blockchain platform to provide data support for subsequent AI model training (such as optimizing the setting of weight coefficients).

[0107] The above formulas are all dimensionless calculations. Dimensionless calculations can be performed using various methods such as standardization, which will not be elaborated here. The formulas are derived from software simulations based on a large amount of collected data, and the preset parameters in the formulas can be set by those skilled in the art according to the actual situation.

[0108] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, ATA hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state ATA hard disk.

[0109] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0110] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0113] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0114] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable ATA hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0115] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A tunnel safety monitoring system based on big data, characterized in that, include: Data acquisition module: Collects environmental parameters for each zone of the tunnel, including dust concentration, harmful gases, temperature and humidity; The area is divided into a tunnel activity zone and a tunnel idle zone; Data processing module: Conducts safety assessments on environmental parameters of each divided area within the tunnel to obtain the active zone rating and the idle zone rating of the tunnel. Monitoring and processing module: Receives the active zone assessment value of the active area and the idle zone assessment value of the idle area of ​​the tunnel, selectively triggers alarm signals and executes corresponding steps.

2. The tunnel safety monitoring system based on big data according to claim 1, characterized in that, The process of conducting a safety assessment of dust concentration in the tunnel activity area is as follows: The tunnel activity area is divided into various height zones according to a preset height division ratio; each height zone includes a high-altitude zone, a mid-altitude zone, and a low-altitude zone. Dust concentration data for each height zone within a set time window is acquired. For the dust concentration data of each height zone at each time point within the set time window, the average value is calculated to obtain the dust concentration performance values ​​corresponding to the high-altitude, mid-altitude, and low-altitude zones within the tunnel activity area, denoted as f1, f2, and f3, respectively. The preset standard value corresponding to the dust concentration is represented by Ka, and is obtained through the formula... The dust concentration assessment index of the tunnel activity area was calculated. ,in , , These are the weighting factors corresponding to the high-altitude, mid-altitude, and low-altitude regions within the tunnel's activity area, respectively. Let Gi, Zi, and Di represent the dust concentration data at each time point within the set time window for the high-altitude, mid-altitude, and low-altitude regions, respectively, where i represents the number of each time point, and i = 1, 2, 3, ..., n, where n is the total number of time points within the set time window; for the high-altitude region, use formula (1). Calculate the average rate of change of dust concentration in the high-altitude zone of the tunnel; for the mid-altitude and low-altitude zones, calculate the corresponding average rate of change according to formula (1), and mark them as follows. and ; Weighting coefficients are preset for the average rate of change corresponding to the high-altitude, mid-altitude, and low-altitude regions. The average rate of change corresponding to each of the three regions is multiplied by the corresponding preset weighting coefficient, and then summed to obtain the powder concentration change value. ; Setting the dust concentration assessment value for the tunnel activity zone Changes in powder concentration The corresponding weighting coefficients, for the powder concentration evaluation value Changes in powder concentration After normalization, the dust concentration is multiplied by the corresponding set weight coefficients, and then summed to obtain the dust assessment index P1.

3. The tunnel safety monitoring system based on big data according to claim 2, characterized in that, The process of conducting a safety assessment of the concentration of harmful gases in the tunnel activity area is as follows: Obtain the concentration of harmful gases in each height area of ​​the tunnel activity zone within a set time window. Let j represent the type number of the harmful gas, j=1,2,...,u, where u represents the total number of types of harmful gases. Take the highest value from the gas average of different harmful gas concentrations in each height area as the degree of harm of the corresponding harmful gas in the tunnel activity zone, denoted as Mj. Set the maximum allowable value for different harmful gases in the tunnel, denoted by Hj; according to the formula... A comprehensive calculation was performed to obtain the gas assessment index P2 for the active zone of the tunnel; whereby... This represents the weighting coefficients corresponding to different harmful gases.

4. The tunnel safety monitoring system based on big data according to claim 3, characterized in that, The process of conducting a temperature safety assessment of the tunnel activity area is as follows: The tunnel activity area is divided into an equipment area and a construction area according to its function. Temperature parameters of the equipment area and the construction area are extracted at each time point within a set time window. The highest value among the temperature parameters of each area is taken as the temperature threat value of that area. The temperature threat values ​​of the equipment area and the construction area are N1 and N2, respectively. Set the maximum allowable temperatures for the equipment area and construction area to T1 and T2 respectively, and use the formula... The temperature assessment index P3 of the tunnel activity zone was calculated, where , These are the weighting coefficients for the equipment area and the construction area, respectively.

5. A tunnel safety monitoring system based on big data according to claim 4, characterized in that, The process of conducting a humidity safety assessment of the tunnel activity area is as follows: The tunnel activity area is divided into an equipment area and a construction area according to its function. The humidity parameters of the equipment area and the construction area are extracted at each time point within a set time window. The maximum and minimum values ​​of the humidity parameters in the equipment area and the construction area are extracted and the difference is calculated to obtain the equipment humidity difference and the construction humidity difference. The allowable humidity deviations corresponding to the equipment area and the construction area are preset. The equipment humidity difference and the construction humidity difference are used as the numerators and the corresponding allowable humidity deviations are used as the denominators to calculate the ratio to obtain the equipment deviation index and the construction deviation index corresponding to the equipment area and the construction area, respectively. The humidity assessment index P4 of the tunnel operation area is obtained by multiplying the humidity deviation index of the corresponding area by the weight coefficient of each area and summing them.

6. A tunnel safety monitoring system based on big data according to claim 5, characterized in that, The active zone evaluation of the tunnel is processed as follows: Set thresholds corresponding to P1, P2, P3, and P4; calculate the ratios between each pair of P1, P2, P3, and P4 as numerators and the corresponding thresholds as denominators to obtain four sets of ratios Q1, Q2, Q3, and Q4. The sum of Q1 and Q2 is recorded as the first value, and the sum of Q3 and Q4 is recorded as the second value. Using the constructed single-value and constructed double-value as the two legs of a right triangle, the constructed right triangle is used as the environmental assessment model of the tunnel activity area. The stored standard assessment model is retrieved from the database, and the area of ​​the environmental assessment model is subtracted from the area of ​​the standard assessment model to obtain the activity area assessment value.

7. A tunnel safety monitoring system based on big data according to claim 6, characterized in that, The idle area assessment of the tunnel's unused zone is handled as follows: Similarly, the safety assessment process for dust and harmful gas concentrations in the tunnel activity area is carried out by assessing the dust and harmful gas concentrations in the tunnel idle area, and obtaining the dust concentration assessment index and gas concentration assessment index corresponding to the dust concentration and harmful gas concentration in the tunnel idle area, respectively. The weighting factors for the dust concentration assessment index and the gas concentration assessment index are preset. The dust concentration assessment index and the gas concentration assessment index are multiplied by their respective weighting factors, and the sums are divided by two to calculate the idle area assessment value of the tunnel idle area.

8. A tunnel safety monitoring system based on big data according to claim 7, characterized in that, The selective triggering of the alarm signal and the execution of the corresponding steps are as follows: The active zone value of the tunnel is compared with the preset active zone threshold. If it is less than the preset threshold, an alarm signal for the active zone is triggered. The idle zone value of the tunnel is compared with the preset idle zone threshold. If it is less than the preset threshold, an alarm signal for the idle zone is triggered. After an alarm signal is triggered, historical alarm cases corresponding to the active or idle areas of the tunnel are retrieved from the database. Each set of historical alarm cases includes the alarm trigger time, subsequent processing method, and processing time. The case fit index of each set of historical alarm cases is analyzed, and the historical alarm case with the highest case fit index is selected as a reference case and sent to the management personnel.

9. A tunnel safety monitoring system based on big data according to claim 8, characterized in that, The matching index of the selected cases The highest historical alarm case is used as a reference case, specifically: Based on the specific regions where the alarm signal is triggered, the Euclidean distance algorithm is used to calculate the similarity between the current alarm signal and each group of historical alarm cases. M1, M2, M3, and M4 represent the dust assessment index, gas assessment index, temperature assessment index, and humidity assessment index, respectively, at the time the alarm signal was triggered for each group of historical alarm cases. The historical alarm cases are sorted from smallest to largest according to their similarity; the top five groups of historical alarm cases are selected from left to right as candidate cases. Extract the similarity and processing time of each group of candidate cases, normalize them, and then input them into the formula. The case fit index of each group of candidate cases was calculated. ; where a1 and a2 are the weight coefficients of similarity and processing time in each group of candidate cases, respectively.

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