Underground engineering water permeation accident monitoring device and early warning method based on 5G communication technology

Abstract

The invention provides an underground engineering water permeation accident monitoring device and early warning method based on a 5G communication technology, and relates to the technical field of underground engineering water permeation accident monitoring and early warning. The device comprises an alarm, a multi-source information acquisition box, a water temperature and water pressure sensor, a flow meter, a 5G signal transmitter, a power distribution system, a water quality sensor and a power supply cable, by means of the method, effective transmission of on-site water temperature, water pressure, water inflow and ion concentration monitoring signals is achieved, and the problems that traditional wired transmission wiring is complex and prone to damage are solved. According to the method, an analytic hierarchy process is adopted to construct a water permeation accident risk assessment model, and meanwhile, the risk assessment model is embedded into a water permeation accident potential monitoring and intelligent analysis system, so that underground engineering water permeation accident potential information can be analyzed in real time, and real-time display of monitoring index abnormal levels and real-time alarm of water permeation accident risk levels can be realized.

Description

Technical Field

[0001] The present invention relates to the field of underground engineering water seepage accident monitoring and early warning technology, and in particular to an underground engineering water seepage accident monitoring device and early warning method based on 5G communication technology. Background Art

[0002] Under the current conditions of the continuous expansion of the construction scale of underground projects (metal mines, coal mines, tunnel projects, etc.) in my country and the continuous increase in mining depth, the frequency of various safety accidents is also increasing. Among them, water seepage accidents are one of the major engineering risk accidents. The occurrence of water seepage accidents is sudden and uncertain. At the same time, the precursor information of water seepage accidents changes during the process of incubation. The site often adopts the measure of "observing and controlling while constructing", which is very passive. Therefore, it is urgent to carry out real-time monitoring of multi-source precursor information of water seepage accidents and disaster warning to provide guarantee for safe construction.

[0003] There are also some patents in the prior art that monitor and warn of water seepage accidents in underground projects. For example, patent number CN115182784A is a combined sensor for warning of mine water inrush disasters, which realizes the monitoring of parameter information such as fracture parameters, temperature, and humidity in the detection hole; patent number CN105179014A is a method and device for warning of coal mine water inrush disasters, which realizes the monitoring of water temperature, water pressure and water quality of the mining working face and warning of water inrush.

[0004] Most of the existing monitoring and early warning devices above adopt wired signal transmission. The large amount of wiring is easily damaged by on-site construction, resulting in discontinuity of information monitoring, which in turn affects the real-time detection of multi-source precursor information of water seepage accidents and the early warning of multi-source disasters. Therefore, based on the advantages of 5G communication technology, which has large data transmission volume, low latency and high reliability, the regional Wifi module is embedded in the sensor to achieve effective transmission of multi-source monitoring signals on site, as well as synchronous early warning of water disasters on site and well terminals. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the above-mentioned prior art and to provide an underground engineering water seepage accident monitoring device and early warning method based on 5G communication technology, to conduct real-time monitoring of the water pressure, water temperature, water ion concentration and water inflow in rock fissures, and to combine 5G communication technology to realize synchronous early warning of water seepage disasters at on-site and well terminals, so as to solve the problem of discontinuous wired signal transmission used in existing monitoring devices.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] On the one hand, the present invention provides an underground engineering water seepage accident monitoring device based on 5G communication technology, including an alarm, a multi-source information acquisition box, a water temperature and water pressure sensor, a flow meter, a 5G signal transmitter, a power distribution system, a water quality sensor and a power supply cable; the multi-source information acquisition box is composed of an equipment protection box, an industrial touch-control integrated machine and a collector, and the industrial touch-control integrated machine and the collector are arranged in the equipment protection box; the industrial touch-control integrated machine integrates a water seepage accident hidden danger monitoring and intelligent analysis system for performing water seepage accident hidden danger monitoring and intelligent analysis; the alarm is arranged on the equipment protection box, connected to the industrial touch-control integrated machine in the equipment protection box, and used for water seepage disaster alarm;

[0008] The water temperature and water pressure sensors are used to collect water temperature and water pressure, the flow meter is used to collect water inflow, and the water quality sensor is used to collect ion concentration;

[0009] The water temperature and water pressure sensors, flow meters and water quality sensors transmit the collected monitoring values ​​to the collector in the multi-source information collection box through a wireless network. The collector converts the collected analog signals into current signals and transmits them to the water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-control integrated machine for water seepage accident hidden danger monitoring and intelligent analysis. When the monitoring indicators are abnormal, the alarm will sound a water seepage disaster alarm.

[0010] The power distribution system is connected to the AC power supply through the power supply cable to convert the power supply voltage at the underground engineering site into 12V DC power to power the multi-source information acquisition box;

[0011] The 5G signal generator transmits the monitoring data acquired by the multi-source information acquisition box to the well terminal system in real time, realizing synchronous water seepage disaster warning at the underground engineering site and the well terminal.

[0012] On the other hand, the present invention also provides an underground engineering water seepage accident early warning method based on 5G communication technology, comprising the following steps:

[0013] Step S1: drilling holes on the side wall of the tunnel to install water temperature and water pressure sensors;

[0014] Step S2: drilling holes at water inflow points on the side walls of the tunnel and installing flow meters;

[0015] Step S3: installing a water quality sensor at the downstream end of the water pipe of the flow meter;

[0016] Step S4: installing a multi-source information acquisition box, a power distribution system and an alarm on the side wall of the lane according to the installation positions of the water temperature and water pressure sensors, the flow meter and the water quality sensor;

[0017] Step S5: The water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-screen integrated machine performs risk assessment based on the water seepage accident risk assessment model. The water seepage accident hidden danger monitoring and intelligent analysis system sets a warning threshold of the risk assessment index. If a certain index value exceeds the warning threshold, the industrial touch-screen integrated machine triggers an alarm to immediately issue a water seepage disaster warning;

[0018] Step S6: The 5G signal generator transmits the monitoring data acquired by the water seepage accident hazard monitoring and intelligent analysis system to the well terminal system in real time, thereby realizing synchronous water seepage disaster warning at the site and the well terminal.

[0019] The establishment of the water seepage accident risk assessment model in step S5 includes the following steps:

[0020] Step S51: Establishing a water seepage accident risk assessment index system: selecting water inflow, ion concentration, water pressure and water temperature as monitoring indicators in the water seepage accident risk assessment index system, and considering the influence of engineering geological conditions on water seepage accidents, selecting permeability coefficient and water richness as geological condition indicators in the risk assessment index system; at the same time, dividing the evaluation index abnormality levels into: level IV, level III, level II and level I, among which level IV is no danger, level III is low danger, level II is medium danger, and level I is high danger;

[0021] Step S52: Establishing a water inflow abnormality discrimination model based on BP neural network to determine the water inflow abnormality criterion; dividing the water inflow accident into three levels according to the consequences caused by the water inflow accident: level three, level two and level one; establishing a water inflow abnormality discrimination model based on BP neural network; the initial model parameters of the BP neural network model are: the input layer nodes are the annual average rainfall, water inflow accident level value, water richness and burial depth; the number of hidden layer nodes is five, there are two hidden layers, and the activation function is the Sigmoid function; the output node is the instantaneous growth rate k of the water inflow, and the output layer transfer function uses the Purelin function; after multiple trainings, the prediction results obtained by the training are compared with the actual k value in the prediction set, and finally the optimal water inflow abnormality discrimination model is determined; the annual average rainfall, water inflow accident level value, water richness and burial depth of the underground engineering site are input into the optimal water inflow abnormality discrimination model, and the model outputs three water inflow instantaneous growth rates k a , k b , k c , k a <k b <k c ; If the instantaneous growth rate of water inflow at the current moment k1<k a , the abnormal water inflow index is level IV; k a ≤k1<k b , the abnormal index of water inflow is level Ⅲ; k b ≤k1<k c, the abnormal index of water inflow is level II; k1≥c, the abnormal index of water inflow is level I;

[0022] Step S53: Determine the water temperature abnormality criterion based on the real-time monitored water temperature and the abnormal threshold; select the average water temperature T within the set time period under normal water inflow conditions s As the threshold value of abnormal water temperature, the real-time monitored water temperature T n With abnormal threshold T s By comparison, we can get the water temperature abnormality index value TI, TI=|T n -T s |; When TI≤0.3, the water temperature abnormality index is level IV; 0.3<TI≤0.5, the water temperature abnormality index is level III; 0.5<TI≤1.0, the water temperature abnormality index is level II; TI>1.0, the water temperature abnormality index is level I;

[0023] Step S54: Determine the water pressure anomaly criterion based on the current water pressure value and the critical water pressure that the surrounding rock aquitard can withstand; set the critical water pressure P that the surrounding rock aquitard can withstand cr = Pl, where P is the critical water pressure that the rock mass per unit thickness can withstand, l is the thickness of the aquiclude; when the water pressure value P at the current moment 测 <0.85P cr , and the current water pressure value P 测 The difference between the water pressure at the previous moment and ΔP is less than 0, and the water pressure abnormality index is level IV; P 测 <0.85P cr , and ΔP ≥ 0, the water pressure abnormality index is level III; 0.85P cr ≤P 测 <P cr , and ΔP<0, the water pressure abnormality index is level II; 0.85P cr ≤P 测 <P cr , and ΔP ≥ 0, the water pressure abnormality index is level Ⅰ;

[0024] Step S55: Determine the abnormal criterion of chloride ion concentration in water based on the average chloride ion concentration in the mine and the chloride ion concentration at the current moment; let σ be the standard deviation of ion concentration, select the average chloride ion concentration μ in the mine under stable conditions within a set time period, let CI be the absolute value of the change in ion concentration, that is, CI = |C 测 -μ|, where C 测 is the ion concentration at the current moment; when CI<σ, the ion concentration abnormality index is level IV; σ≤CI<2σ, the ion concentration abnormality index is level III; 2σ≤CI<3σ, the ion concentration abnormality index is level II; CI≥3σ, the ion concentration abnormality index is level I;

[0025] Step S56: Determine the abnormal criterion of permeability coefficient; when the permeability coefficient K is less than 10 -5 , the permeability coefficient abnormal index is level IV; 10 -5 ≤K<10 -4 , the permeability coefficient abnormal index is level III; 10 -4 ≤K<10 -2 , the permeability coefficient abnormal index is level II; K≥10 -2 , the permeability coefficient abnormal index is level I.

[0026] Step S57: Determine the water-richness abnormality criterion; when the water-richness q is less than 0.1, the water-richness index is level IV; 0.1≤q<1.0, the water-richness index is level III; 1.0≤q<5.0, the water-richness index is level II; q≥5.0, the water-richness index is level I.

[0027] Step S58: constructing a water seepage accident risk assessment model using the analytic hierarchy process to conduct water seepage accident risk assessment;

[0028] The risk assessment index system for water seepage accidents is divided into two categories: monitoring indicators and geological condition indicators. The monitoring indicators include water inflow, water temperature, water pressure and ion concentration, and the geological condition indicators include permeability coefficient and water richness. After comprehensively analyzing the influencing factors of water seepage accidents, according to the sensitivity of each indicator and the range of change in the process of water seepage disasters, water pressure and water inflow are divided into the first-level indicators, ion concentration and water temperature are divided into the second-level indicators, permeability coefficient and water richness are divided into the third-level indicators, and the levels are gradually reduced. Then, the importance of indicators is compared, a discriminant matrix is ​​established, a consistency test is performed, and the final weight is determined. At the same time, the weight of the lowest-level indicator relative to the highest-level indicator is calculated, and a weight table of the risk assessment system is established to conduct risk assessment of water seepage accidents.

[0029] The beneficial effects of adopting the above technical solution are: the present invention provides an underground engineering water seepage accident monitoring device and method based on 5G communication technology, which realizes the effective transmission of on-site water temperature, water pressure, water inflow and ion concentration monitoring signals, and solves the problem of complex wiring and susceptibility to damage of traditional wired transmission. A water seepage accident risk assessment model is constructed using the analytic hierarchy process (AHP), and the risk assessment model is embedded in the water seepage accident hidden danger monitoring and intelligent analysis system, which can perform real-time analysis of underground engineering water seepage accident hidden danger information, and realize real-time display of abnormal level of monitoring indicators and real-time alarm of water seepage accident risk level. Based on the advantages of large data transmission volume, low latency and high reliability of 5G communication technology, synchronous early warning of water seepage disasters on site and on-site terminals is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of the installation of an underground engineering water seepage accident monitoring device based on 5G communication technology along the direction of the tunnel provided by an embodiment of the present invention;

[0031] Figure 2 A schematic diagram of the installation of an underground engineering water seepage accident monitoring device based on 5G communication technology along the tunnel cross-section direction provided by an embodiment of the present invention;

[0032] Figure 3 A structural block diagram of a water seepage accident risk assessment model provided by an embodiment of the present invention;

[0033] Figure 4 A functional module design block diagram of a water seepage accident hazard monitoring and intelligent analysis system provided by an embodiment of the present invention;

[0034] Figure 5 This is a diagram of the interface of a real-time monitoring module of a water seepage accident hazard monitoring and intelligent analysis system provided by an embodiment of the present invention;

[0035] Figure 6 This is an interface diagram of a risk assessment module of a water seepage accident hazard monitoring and intelligent analysis system provided in an embodiment of the present invention.

[0036] In the figure: 1. Alarm; 2. Multi-source information acquisition box; 3. Water temperature and water pressure sensors; 4. Flow meter; 5. 5G signal transmitter; 6. Power distribution system; 7. Water pipe; 8. Water quality sensor; 9. Power supply cable; A. Well terminal; B. Water gushing point. DETAILED DESCRIPTION

[0037] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0038] In this embodiment, a device for monitoring water seepage accidents in underground projects based on 5G communication technology is provided. Figure 1 , 2 As shown, it includes an alarm 1, a multi-source information acquisition box 2, a water temperature and water pressure sensor 3, a flow meter 4, a 5G signal transmitter 5, a power distribution system 6, a water quality sensor 8 and a power supply cable 9; the multi-source information acquisition box 2 is composed of an equipment protection box, an industrial touch-control integrated machine and a collector, and the industrial touch-control integrated machine and the collector are arranged in the equipment protection box; the industrial touch-control integrated machine integrates a water seepage accident hidden danger monitoring and intelligent analysis system for water seepage accident hidden danger monitoring and intelligent analysis; the alarm 1 is arranged on the equipment protection box, connected to the industrial touch-control integrated machine in the equipment protection box, and is used for water seepage disaster alarm;

[0039] The water temperature and water pressure sensor 3 is used to collect water temperature and water pressure, the flow meter 4 is used to collect water inflow, and the water quality sensor 8 is used to collect ion concentration;

[0040] The water temperature and water pressure sensor 3, flow meter 4 and water quality sensor 8 transmit the collected monitoring values ​​to the collector in the multi-source information collection box 2 through a wireless network. The collector converts the collected analog signals into current signals and transmits them to the water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-control integrated machine for water seepage accident hidden danger monitoring and intelligent analysis. When the monitoring indicators are abnormal, the alarm 1 issues a water seepage disaster alarm.

[0041] The power distribution system 6 is connected to the AC power supply through the power supply cable 9 to convert the power supply voltage at the underground engineering site into 12V DC power to supply power to the multi-source information acquisition box 2;

[0042] The 5G signal generator 5 transmits the monitoring data acquired by the multi-source information acquisition box 2 to the above-ground terminal system A in real time, thereby realizing synchronous water seepage disaster warning at the underground engineering site and the above-ground terminal.

[0043] In this embodiment, a method for warning of underground engineering water seepage accidents based on 5G communication technology includes the following steps:

[0044] Step S1: Drill a hole on the side wall of the tunnel to install the water temperature and water pressure sensor; first, use an impact drill to drill a hole on the side wall of the tunnel, the diameter of the hole is larger than the diameter of the pressure pipe of the water temperature and water pressure sensor, preferably about 1 cm larger than the diameter of the pressure pipe, the drilling direction is horizontal, and the hole depth can be determined according to the occurrence of water gushing during the drilling process (generally not more than 2m); then, clean the inside of the borehole with water to ensure that the inside of the borehole is clean; secondly, put the pressure pipe of the water temperature and water pressure sensor into the borehole, and the test joint of the sensor is close to the sand-proof net at the bottom of the borehole; again, inject waterproof mortar into the gap between the pressure pipe and the borehole; finally, fix the water temperature and water pressure sensor to the side wall of the tunnel through the mounting plate on the housing of the water temperature and water pressure sensor and the fixing bolts.

[0045] Step S2: Drill a hole at the water gushing point on the side wall of the tunnel to install the flow meter 4; first, use an impact drill to drill a hole at the water gushing point B on the side wall of the tunnel. The diameter of the hole needs to be determined according to the diameter of the water pipe 7 provided with the flow meter 4, and the drilling direction is horizontal; then, clean the inside of the hole with water to ensure that the inside of the hole is clean; again, put one end of the water pipe 7 into the hole, and connect the other end to the flow meter 4; finally, fix the flow meter 4 and the water pipe 7 to the rock wall by fixing the clips and expansion bolts.

[0046] Step S3: Install a water quality sensor 8 at the downstream end of the water pipe 7 of the flow meter 4; install the water quality sensor 8 at the downstream end of the water pipe 7, immerse the probe of the water quality sensor 8 in the flowing water, and fix it by a fixing bracket.

[0047] Step S4: According to the installation positions of the water temperature and water pressure sensors 3, the flow meter 4 and the water quality sensor 8, the multi-source information acquisition box 2, the power distribution system 6 and the alarm 1 are installed on the side wall of the tunnel; first, according to the installation positions of the water temperature and water pressure sensors 3, the flow meter 4 and the water quality sensor 8, the installation positions of the multi-source information acquisition box 2, the power distribution system 6 and the alarm 1 are determined; then, the multi-source information acquisition box 2, the power distribution system 6 and the alarm 1 are fixed to the side wall of the tunnel by expansion bolts; finally, the on-site construction personnel connect the AC power to the power distribution system 6 through the power supply cable 9.

[0048] Step S5: The water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-screen integrated machine performs risk assessment based on the water seepage accident risk assessment model. The water seepage accident hidden danger monitoring and intelligent analysis system sets a warning threshold of the risk assessment index. If a certain index value exceeds the warning threshold, the industrial touch-screen integrated machine triggers the alarm 1 to immediately issue a water seepage disaster warning;

[0049] Step S6: The 5G signal generator 5 transmits the monitoring data acquired by the water seepage accident hazard monitoring and intelligent analysis system to the well terminal system A in real time, so as to realize synchronous water seepage disaster warning on site and at the well terminal A.

[0050] In this embodiment, the establishment of the water seepage accident risk assessment model in step S5 includes the following steps:

[0051] Step S51: Establish a water seepage accident risk assessment index system: The geological conditions of underground projects are complex, and different geologies have different effects on water seepage accidents. If the groundwater in the project is abundant and the surrounding rock stability is poor, the probability of water seepage accidents will increase accordingly. Water inflow, ion concentration, water pressure and water temperature are selected as monitoring indicators in the water seepage accident risk assessment index system. Considering the impact of engineering geological conditions on water seepage accidents, permeability coefficient and water richness are selected as geological condition indicators in the risk assessment index system; at the same time, the abnormal levels of evaluation indicators are divided into: Level IV, Level III, Level II and Level I, among which Level IV is no danger, Level III is low danger, Level II is medium danger, and Level I is high danger.

[0052] Step S52: Determine the abnormal criterion of water inflow: Statistically analyze water seepage cases at home and abroad, and divide water seepage accidents into three levels according to the consequences of water seepage accidents: level 3, level 2 and level 1; establish a water inflow abnormality discrimination model based on BP neural network; the initial model parameters of the BP neural network model are: the input layer nodes are the annual average rainfall, water seepage accident level value, water richness and burial depth; the number of hidden layer nodes is five, there are two hidden layers, and the activation function is the Sigmoid function; the output node is the instantaneous growth rate k of water inflow, and the output layer transfer function uses the Purelin function; in order to ensure the accuracy and scientificity of the neural network model, the case samples are divided into training set, verification set and test set, and the prediction results are compared with the actual k value through training to finally determine a reasonable prediction model;

[0053] Step S53: Determine the water temperature abnormality criterion: Under normal circumstances, the water temperature in the normal water inflow is relatively stable and is unlikely to rise or fall suddenly. Select the average water temperature T for one month under normal water inflow. s As the threshold value of abnormal water temperature, the real-time monitored water temperature T n With abnormal threshold T s By comparison, we can get the water temperature abnormality index value TI, TI=|T n -T s |; When TI≤0.3, the water temperature abnormality index is level IV; 0.3<TI≤0.5, the water temperature abnormality index is level III; 0.5<TI≤1.0, the water temperature abnormality index is level II; TI>1.0, the water temperature abnormality index is level I;

[0054] Step S54: Determine the abnormal water pressure criterion: During the construction of underground projects, as the development progresses, water in the surrounding rock cracks will gush out from the rock wall. Generally speaking, the greater the water pressure, the more significant the deformation and damage of the surrounding rock. As the water pressure increases, the possibility of water seepage accidents increases. The critical water pressure that the aquitard can withstand varies with different strata and lithologies. Set the critical water pressure P that the surrounding rock aquitard can withstand cr = Pl, where P is the critical water pressure that the rock mass per unit thickness can withstand, l is the thickness of the aquiclude; when the water pressure value P at the current moment 测 <0.85P cr , and the current water pressure value P 测 The difference between the water pressure at the previous moment and ΔP is less than 0, and the water pressure abnormality index is level IV; P 测 <0.85P cr , and ΔP ≥ 0, the water pressure abnormality index is level III; 0.85P cr ≤P 测 <P cr , and ΔP<0, the water pressure abnormality index is level II; 0.85P cr ≤P 测 <Pcr , and ΔP ≥ 0, the water pressure abnormality index is level Ⅰ;

[0055] Step S55: Determine the abnormal criterion for chloride ion concentration in water: When a water seepage accident occurs in an underground project, the water output at the water outlet will increase, and the water from other aquifers will gush out along the water gushing channel. The water chemical properties of different aquifers are different, so the ion concentration of water will also change greatly. Under normal water gushing conditions in the mine, the change trend of the chloride ion concentration monitoring curve in the water tends to be stable, and the distribution law of the ion concentration value is similar to the normal distribution curve. In mathematical statistics, if the random variable obeys the normal distribution, the probability of occurrence within the range of ±σ is 68.3%, and the probability of occurrence within the range of ±3σ is close to 1. Let σ be the standard deviation of the ion concentration, select the average ion concentration μ of the mine under stable conditions for one month, and let CI be the absolute value of the change in ion concentration, that is, CI = |C 测 -μ|, where C 测 is the ion concentration at the current moment; when CI<σ, the ion concentration abnormality index is level IV; σ≤CI<2σ, the ion concentration abnormality index is level III; 2σ≤CI<3σ, the ion concentration abnormality index is level II; CI≥3σ, the ion concentration abnormality index is level I;

[0056] Step S56: Determine the abnormal criterion of permeability coefficient: when the permeability coefficient K is less than 10 -5 , the permeability coefficient abnormal index is level IV; 10 -5 ≤K<10 -4 , the permeability coefficient abnormal index is level III; 10 -4 ≤K<10 -2 , the permeability coefficient abnormal index is level II; K≥10 -2 , the permeability coefficient abnormal index is level I.

[0057] Step S57: Determine the water-richness abnormality criterion: when the water-richness q is less than 0.1, the water-richness index is level IV; 0.1≤q<1.0, the water-richness index is level III; 1.0≤q<5.0, the water-richness index is level II; q≥5.0, the water-richness index is level I.

[0058] Step S58: Construct a water seepage accident risk assessment model, such as Figure 3As shown: The core concept of the analytic hierarchy process (AHP) is to decompose a complex problem into multiple factors, sort out other related factors based on the dominant factor, and then subdivide these factors step by step to build a complete hierarchical structure. When evaluating the importance of each factor, it is mainly through comparing the factors in the same level, determining their relative importance, sorting the weights of these factors, and then passing them up step by step to finally determine the weight of each factor relative to the target. The main process includes building a hierarchical model, comparing the importance of indicators, establishing a discriminant matrix, conducting consistency tests, and determining the final weights.

[0059] The water seepage accident risk assessment index system is divided into two categories: monitoring indicators and geological condition indicators. The monitoring indicators include water inflow, water temperature, water pressure and ion concentration, and the geological condition indicators include permeability coefficient and water richness. After comprehensively analyzing the factors affecting water seepage accidents, according to the sensitivity of each indicator and the range of change in the process of water seepage disaster, water pressure and water inflow are divided into the first-level indicators, ion concentration and water temperature are divided into the second-level indicators, and permeability coefficient and water richness are divided into the third-level indicators, and the levels are gradually reduced. Then, the importance of indicators is compared, the discriminant matrix is ​​established, the consistency test is carried out, and the final weight is determined.

[0060] In this embodiment, after weight calculation and passing the random consistency test requirements, it is determined that the established matrix and the determined weights are reasonable. At the same time, the weights of the bottom-level indicators relative to the highest level are counted to establish a risk assessment system weight table, as shown in Table 1;

[0061] Table 1 Risk assessment index system weight table

[0062]

[0063]

[0064] In this embodiment, based on the constructed water seepage accident risk assessment model, a water seepage accident hidden danger monitoring and intelligent analysis system software is developed. Figure 4-6 The main functional modules include: user login, real-time monitoring, equipment management, historical curve display, historical data query and risk assessment. The software realizes real-time monitoring of water seepage accident precursor information such as water pressure, water temperature, water quality and water inflow, and real-time alarm of risk level.

[0065] (1) Login module

[0066] After starting the software, it will automatically enter the login module. After entering the user name and password, click "Login" to enter the main interface of the software. The purpose of setting this module is to prevent unauthorized login, which may cause system damage and loss of monitoring data.

[0067] (2) Real-time monitoring module

[0068] As the main interface of the software, this module can display the precursor information and water seepage accident risk level collected by each sensor in real time. When the value collected by the sensor reaches the alarm threshold, the data panel will display the corresponding abnormal level color. When the user needs to view the real-time monitoring curve, he can click the box in the upper left corner of the data panel, and then the real-time monitoring curve will be displayed on the right side of the interface.

[0069] (3) Historical curve module

[0070] Users can filter by channel number, sensor type, alarm level, start and end time. After selecting, click "Query" and the interface will display the historical curves that meet the query conditions. Click "Export" to export the historical curves.

[0071] (4) Historical data module

[0072] Select the start and end time of the historical data to be queried, click "Query", and the interface will display the historical data list. The data list includes time, monitoring indicators and geological condition indicators and the corresponding abnormal level color, water seepage accident risk value and the corresponding risk level color, etc. When the user needs to export historical data, click "Export" to export the historical data file stored in ".CSV" format.

[0073] (5) Equipment Management Module

[0074] The interface displays a list including the sensor type, location, abnormal threshold, discrimination parameter and conversion coefficient of all channels. When the user needs to change the sensor information, double-click the "channel number" where the sensor is located, and a change list will pop up on the right side of the interface. The user can modify the sensor type, location, abnormal threshold, discrimination parameter and conversion coefficient in the table.

[0075] (6) Risk Assessment Module

[0076] The left side of the interface is for the setting of abnormal classification values ​​of geological condition indicators for water seepage accident evaluation. Enter the corresponding classification values ​​in the three text boxes to the right of the permeability coefficient K and water richness q. The right side of the interface is for the setting of weights of water seepage accident risk assessment indicators. Enter the weight values ​​corresponding to different indicators in the text boxes below the weights, and click "Modify Weight" to complete the weight setting of risk assessment indicators.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. An underground engineering water seepage accident monitoring device based on 5G communication technology, characterized in that: It includes an alarm, a multi-source information acquisition box, a water temperature and water pressure sensor, a flow meter, a 5G signal transmitter, a power distribution system, a water quality sensor and a power supply cable; the multi-source information acquisition box is composed of an equipment protection box, an industrial touch-control integrated machine and a collector, and the industrial touch-control integrated machine and the collector are arranged in the equipment protection box; the industrial touch-control integrated machine integrates a water seepage accident hidden danger monitoring and intelligent analysis system for water seepage accident hidden danger monitoring and intelligent analysis; the alarm is arranged on the equipment protection box, connected to the industrial touch-control integrated machine in the equipment protection box, and used for water seepage disaster alarm; The water temperature and water pressure sensors are used to collect water temperature and water pressure, the flow meter is used to collect water inflow, and the water quality sensor is used to collect ion concentration; The water temperature and water pressure sensors, flow meters and water quality sensors transmit the collected monitoring values ​​to the collector in the multi-source information collection box through a wireless network. The collector converts the collected analog signals into current signals and transmits them to the water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-control integrated machine for water seepage accident hidden danger monitoring and intelligent analysis. When the monitoring indicators are abnormal, the alarm will sound a water seepage disaster alarm. The power distribution system is connected to the AC power supply through the power supply cable to convert the power supply voltage at the underground engineering site into 12V DC power to power the multi-source information acquisition box; The 5G signal generator transmits the monitoring data acquired by the multi-source information acquisition box to the well terminal system in real time, realizing synchronous water seepage disaster warning at the underground engineering site and the well terminal.

2. A method for warning of underground engineering water seepage accidents based on 5G communication technology, implemented based on the device described in claim 1, characterized in that: The following steps are involved: Step S1: drilling holes on the side wall of the tunnel to install water temperature and water pressure sensors; Step S2: drilling holes at water inflow points on the side walls of the tunnel and installing flow meters; Step S3: installing a water quality sensor at the downstream end of the water pipe of the flow meter; Step S4: installing a multi-source information acquisition box, a power distribution system and an alarm on the side wall of the lane according to the installation positions of the water temperature and water pressure sensors, the flow meter and the water quality sensor; Step S5: The water seepage accident hidden danger monitoring and intelligent analysis system integrated in the industrial touch-screen integrated machine performs risk assessment based on the water seepage accident risk assessment model. The water seepage accident hidden danger monitoring and intelligent analysis system sets a warning threshold of the risk assessment index. If a certain index value exceeds the warning threshold, the industrial touch-screen integrated machine triggers an alarm to immediately issue a water seepage disaster warning; Step S6: The 5G signal generator transmits the monitoring data acquired by the water seepage accident hazard monitoring and intelligent analysis system to the well terminal system in real time, thereby realizing synchronous water seepage disaster warning at the site and the well terminal.

3. According to claim 2, a method for warning of underground engineering water seepage accidents based on 5G communication technology is characterized in that: The water seepage accident risk assessment model described in step S5 is established based on the following steps: Step S51: Establishing a water seepage accident risk assessment index system: selecting water inflow, ion concentration, water pressure and water temperature as monitoring indicators in the water seepage accident risk assessment index system, and considering the influence of engineering geological conditions on water seepage accidents, selecting permeability coefficient and water richness as geological condition indicators in the risk assessment index system; at the same time, dividing the evaluation index abnormality levels into: level IV, level III, level II and level I, among which level IV is no danger, level III is low danger, level II is medium danger, and level I is high danger; Step S52: establishing a water inflow abnormality discrimination model based on the BP neural network to determine the water inflow abnormality criterion; Step S53: determining a water temperature abnormality criterion according to the real-time monitored water temperature and an abnormal threshold; Step S54: determining a water pressure anomaly criterion according to the current water pressure value and the critical water pressure that the surrounding rock aquiclude can withstand; Step S55: determining the abnormal chloride ion concentration criterion in the water based on the average chloride ion concentration in the water in the mine and the chloride ion concentration at the current moment; Step S56: determining the permeability coefficient abnormality criterion; Step S57: Determine the water-richness abnormality criterion; Step S58: constructing a water seepage accident risk assessment model using the analytic hierarchy process to conduct water seepage accident risk assessment.

4. According to claim 3, a method for warning of underground engineering water seepage accidents based on 5G communication technology is characterized in that: The specific method of step S52 to establish a water inflow abnormality discrimination model based on the BP neural network and determine the water inflow abnormality criterion is: According to the consequences of water seepage accidents, water seepage accidents are divided into three levels: level 3, level 2 and level 1. A water inflow abnormality discrimination model is established based on BP neural network. The initial model parameters of the BP neural network model are as follows: the input layer nodes are the annual average rainfall, water seepage accident level, water-richness and burial depth; the number of hidden layer nodes is five, with a total of two hidden layers, and the activation function is the Sigmoid function; the output node is the instantaneous growth rate k of the water inflow, and the output layer transfer function uses the Purelin function; after multiple trainings, the prediction results obtained by the training are compared with the actual k value in the prediction set, and finally the optimal water inflow abnormality discrimination model is determined; the annual average rainfall, water seepage accident level, water-richness and burial depth of the underground engineering site are input into the optimal water inflow abnormality discrimination model, and the model outputs three water inflow instantaneous growth rate k a , k b , k c , k a <k b <k c ; If the instantaneous growth rate of water inflow at the current moment k1<k a , the abnormal water inflow index is level IV; k a ≤k1<k b , the abnormal index of water inflow is level Ⅲ; k b ≤k1<k c , the abnormal water inflow index is level II; k1≥c, the abnormal water inflow index is level I.

5. According to claim 3, a method for warning of underground engineering water seepage accidents based on 5G communication technology is characterized in that: The specific method of determining the water temperature abnormality criterion according to the real-time monitored water temperature and the abnormal threshold value in step S53 is: Select the average water temperature T within the set time period under normal water inflow conditions s As the threshold value of abnormal water temperature, the water temperature T n With abnormal threshold T s By comparison, we can get the water temperature abnormality index value TI, TI=|T n -T s |; When TI≤0.3, the water temperature abnormality index is level IV; 0.3<TI≤0.5, the water temperature abnormality index is level III; 0.5<TI≤1.0, the water temperature abnormality index is Level II; TI>1.0, the water temperature abnormality index is Level I.

6. According to claim 3, a method for warning of underground engineering water seepage accidents based on 5G communication technology is characterized in that: The specific method of determining the water pressure anomaly criterion according to the water pressure value at the current moment and the critical water pressure that the surrounding rock aquiclude can withstand in step S54 is: Set the critical water pressure P that the surrounding rock aquiclude can withstand cr = Pl, where P is the critical water pressure that the rock mass per unit thickness can withstand, l is the thickness of the aquiclude; when the water pressure value P at the current moment 测 <0.85P cr , and the current water pressure value P 测 The difference between the water pressure at the previous moment and ΔP is less than 0, and the water pressure abnormality index is level IV; P 测 <0.85P cr , and ΔP ≥ 0, the water pressure abnormality index is level III; 0.85P cr ≤P 测 <P cr , and ΔP<0, the water pressure abnormality index is level II; 0.85P cr ≤P 测 <P cr , and ΔP ≥ 0, the water pressure abnormality index is level Ⅰ.

7. The underground engineering water seepage accident early warning method based on 5G communication technology according to claim 3 is characterized by: The specific method of determining the abnormal criterion of chloride ion concentration in water based on the average chloride ion concentration in the mine water and the chloride ion concentration at the current moment in step S55 is: Let σ be the standard deviation of ion concentration, select the average value μ of chloride ion concentration in a set time period under stable conditions, and let CI be the absolute value of the change in ion concentration, that is, CI = |C 测 -μ|, where C 测 is the ion concentration at the current moment; when CI<σ, the ion concentration abnormality index is level IV; σ≤CI<2σ, the ion concentration abnormality index is level III; 2σ≤CI<3σ, the ion concentration abnormality index is level II; CI≥3σ, the ion concentration abnormality index is level I.

8. The underground engineering water seepage accident early warning method based on 5G communication technology according to claim 3 is characterized by: The permeability anomaly criterion determined in step S56 is: When the permeability coefficient K is less than 10 -5 , the permeability coefficient abnormal index is level IV; 10 -5 ≤K<10 -4 , the permeability coefficient abnormal index is level III; 10 -4 ≤K<10 -2 , the permeability coefficient abnormal index is level II; K≥10 -2 , the permeability coefficient abnormal index is level I.

9. The underground engineering water seepage accident early warning method based on 5G communication technology according to claim 3 is characterized by: The water-richness abnormality criterion determined in step S57 is: When the water-richness q is less than 0.1, the water-richness index is level IV; 0.1≤q<1.0, the water-richness index is level III; 1.0≤q<5.0, the water-richness index is level II; q≥5.0, the water-richness index is level I.

10. The underground engineering water seepage accident early warning method based on 5G communication technology according to claim 3, characterized in that: The specific method of step S58 is: The water seepage accident risk assessment index system is divided into two categories: monitoring indicators and geological condition indicators. The monitoring indicators include water inflow, water temperature, water pressure and ion concentration, and the geological condition indicators include permeability coefficient and water richness. After a comprehensive analysis of the factors affecting water seepage accidents, according to the sensitivity of each indicator and the range of change during the water seepage disaster process, water pressure and water inflow are divided into the first-level indicators, ion concentration and water temperature are divided into the second-level indicators, and permeability coefficient and water richness are divided into the third-level indicators, and the levels are gradually reduced. Then the importance of indicators is compared, a discriminant matrix is ​​established, consistency tests are performed, and the final weights are determined; At the same time, the weight of the lowest level indicators relative to the highest level was calculated, and a risk assessment system weight table was established to conduct risk assessment of water seepage accidents.

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