A slope safety monitoring method and device based on multi-source heterogeneous sensor data fusion

Abstract

The present invention discloses a slope safety monitoring method and device using multi-source heterogeneous sensor data fusion, comprising the following steps: Step 1: designing the number and type of multi-source heterogeneous sensors and their layout positions according to the pre-monitored slope; Step 2: arranging the multi-source heterogeneous sensors at corresponding positions on the slope, connecting the multi-source heterogeneous sensors to a data transmission module, and wirelessly connecting the data transmission module to a control center module; Step 3: the data transmission module transmits environmental parameters collected by the multi-source heterogeneous sensors to the control center module, and the control center module performs data fusion analysis; Step 4: judging the stability level of the slope, and issuing a warning in a timely manner when the slope is in a relatively unstable or unstable state.

Description

Technical Field

[0001] The present invention belongs to the technical field of slope monitoring, and in particular relates to a slope safety monitoring method and device based on multi-source heterogeneous sensor data fusion. Background Art

[0002] my country has a vast territory and a massive scale of engineering construction. Highway, railway, water conservancy, and urban construction projects generate numerous slopes. Slope stability early warning is a key concern during construction and operation. Excavation and filling of rock and soil during construction can disrupt the original equilibrium of slopes. During operation, slope stability is also subject to dynamic changes due to natural factors such as rainfall and earthquakes. Once a slope becomes unstable, it not only increases the difficulty of construction, but also poses a significant threat to the personal and property safety of construction workers, and can cause significant losses during operation. According to statistics from recent years, direct economic losses from disasters such as slope landslides and collapses in my country amount to tens of billions of yuan annually, and the resulting casualties and losses to families and society are immeasurable.

[0003] Automated slope monitoring uses different sensors to collect physical and mechanical parameters of the slope, such as deformation, stress, vibration, and moisture content. When the slope becomes unstable, the system issues an early warning message according to pre-set early warning conditions and takes emergency measures to avoid or reduce losses.

[0004] At present, in the process of slope monitoring, a single type of sensor is usually used to monitor its deformation process. However, a single type of sensor cannot accurately reflect the slope deformation situation and is prone to misjudgment. In slope monitoring, the use of a single type of sensor to monitor its deformation process can no longer meet the requirements.

[0005] Furthermore, traditional slope monitoring systems use GPRS as a wireless communication method, transmitting sensor data to a cloud platform via GPRS. Experts then analyze the data on the cloud platform and, upon conclusion, send the results to the alarms at the slope monitoring site. Slope hazard warnings typically take around one minute. Using GPRS to transmit environmental parameters to the cloud platform and then for experts to make a decision takes a long time, preventing real-time slope warnings.

[0006] Therefore, the most critical aspects of slope monitoring technology, namely multi-source data fusion and early warning condition setting, are still in the process of accumulating experience. Incorrect early warning information can lead to unnecessary disaster prevention investment, while delayed early warning information can lead to delayed response and unavoidable losses, rendering monitoring and early warning ineffective. Therefore, it is essential to establish an effective integrated monitoring system with multi-source heterogeneous sensors, extract multi-field characteristic information from slope monitoring, and perform multi-field information fusion processing and decision analysis to provide slope monitoring and early warning services. Summary of the Invention

[0007] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art and to provide a slope safety monitoring method and device based on multi-source heterogeneous sensor data fusion.

[0008] The present invention discloses a slope safety monitoring method based on multi-source heterogeneous sensor data fusion, which includes the following steps:

[0009] Step 1: Design the number and type of multi-source heterogeneous sensors and their placement locations based on the slope to be monitored;

[0010] Step 2: Deploy multi-source heterogeneous sensors at corresponding locations on the slope, connect the multi-source heterogeneous sensors to the data transmission module, and wirelessly connect the data transmission module to the control center module;

[0011] Step 3: The data transmission module transmits the environmental parameters collected by the multi-source heterogeneous sensors to the control center module, which performs data fusion analysis;

[0012] Step 4: Determine the stability level of the slope and issue a warning in time when the slope is in a relatively unstable or unstable state.

[0013] Preferably, step 1 is specifically as follows: the number of the multi-source heterogeneous sensors is proportional to the size of the slope, the type of the multi-source heterogeneous sensors is related to the cracks and deformation of the slope, the multi-source heterogeneous sensors include displacement meters, inclinometers and vibration sensors, the displacement meters are arranged on the slope surfaces of the first and second platforms of the slope, the parameters collected by the displacement meters are used to observe the changes in the cracks in the slope, and provide data support for the deformation process of the slope; the inclinometers are suspended or fixed inside or on the surface of the slope, the parameters collected by the inclinometers are used to observe the changes in the angle of the slope sliding body, and provide data support for the deformation direction and deformation size of the slope; the vibration sensor is installed on the pull rope of the slope protection net, and the resonant frequency collected by the vibration sensor is used to calculate the impact force on the pull rope, and judge whether the protection net is damaged according to the magnitude of the impact force, and then infer the deformation state of the slope.

[0014] Preferably, the control center module in step 3 is provided with a data fusion analysis module, which is used to perform data fusion analysis on the environmental parameters collected by multi-source heterogeneous sensors. The data fusion analysis includes the following steps:

[0015] Step 3-1: Determine the stability level standard;

[0016] Step 3-2: Generate basic probability assignment;

[0017] Step 3-3: Evidence fusion and output results.

[0018] Preferably, the stability grade standard determined in step 3-1 is specifically as follows: the slope stability grade is divided into five grades, namely I, II, III, IV, and V. The warning grade corresponding to stability grade I is no warning, the warning grade corresponding to stability grade II is blue warning, the warning grade corresponding to stability grade III is yellow warning, the warning grade corresponding to stability grade IV is orange warning, and the warning grade corresponding to stability grade V is red warning. The expression of the stability grade is:

[0019] A={I,II,III,IV,V}

[0020] A is the slope stability grade.

[0021] Preferably, generating the basic probability assignment in step 3-2 is specifically as follows: determining the monitoring index of the multi-source heterogeneous sensor according to the environment and experience, that is, the upper limit and lower limit of each stability level, using the upper limit and lower limit to obtain the average value of the multi-source heterogeneous sensor in each stability level interval, comparing the upper limit and lower limit with the measured value of the multi-source heterogeneous sensor, forming the probability assignment of the multi-source heterogeneous sensor in each stability level, and forming a probability assignment table, the probability assignment expression is:

[0022]

[0023] c=(a1-a2) / δ

[0024] Where:

[0025] m(A) is the probability assignment of multi-source heterogeneous sensors at each stability level;

[0026] x is the measured value of multi-source heterogeneous sensors;

[0027] is the average value of a certain stability level interval;

[0028] a1 and a2 are the upper and lower limits of a certain stability level range respectively;

[0029] δ is the variance.

[0030] Preferably, the probability assignment expression of each stability level is:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] Where:

[0037] Indicates the upper and lower limits of a certain stability level range;

[0038] is the average value of the ith stability level interval;

[0039] is the variance of the ith stability level interval.

[0040] Preferably, the evidence fusion in step 3-3 is to fuse the probability assignments of multi-source heterogeneous sensors within a certain stability level range, and the fusion expression is:

[0041]

[0042]

[0043] Where:

[0044] M(A) is the probability of multi-source heterogeneous sensors within a certain stability level interval;

[0045] k represents the conflict factor;

[0046] Indicates the degree to which each probability assignment belongs to the same stability level interval;

[0047] A represents slope stability grade I, II, III, IV, V;

[0048] The probabilities of multi-source heterogeneous sensors within multiple stability level intervals are integrated into a stability level vector and the result is output. The expression is:

[0049] M=[M(A I ),M(A II ),M(A III ),M(A IV ),M(A V )]

[0050] Preferably, the step 4 is specifically as follows: when M(A IV ) is greater than 0.5, an orange warning is issued to remind the management department to organize temporary evacuation on site and activate the corresponding level plan; when M(A V ) is greater than 0.5, a red alert is issued, traffic is controlled, residents are evacuated, and other protective measures are taken.

[0051] Preferably, a slope safety monitoring device using multi-source heterogeneous sensor data fusion is used in any of the above-mentioned slope safety monitoring methods using multi-source heterogeneous sensor data fusion, wherein the monitoring device comprises multi-source heterogeneous sensors, a data transmission module, and a control center module, wherein the multi-source heterogeneous sensors are connected to the data transmission module, and the data transmission module is wirelessly connected to the control center module;

[0052] The multi-source heterogeneous sensors include displacement meters, inclinometers, or vibration sensors, which are installed at different locations on the slope. The displacement meters are arranged on the slope's primary and secondary platforms, and the inclinometers are suspended or fixed inside or on the slope's surface. The vibration sensors are installed on the ropes of the slope protection net to monitor the vibration caused by the impact of slope deformation on the ropes of the protection net.

[0053] The displacement meter includes a wire displacement meter, a settlement displacement meter or a crack displacement meter, with a measurement accuracy of ≤0.1mm, and the resolution of the inclinometer is 0.01°;

[0054] The data transmission module is a ZigBee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of multi-source heterogeneous sensors. The multiple terminal nodes are respectively connected to the displacement meter, inclinometer or vibration sensor. The multiple terminal nodes are also respectively connected to the wireless gateway node, and the wireless gateway node is wirelessly connected to the control center module.

[0055] Preferably, it further comprises an early warning information release module, which is connected to the control center module, and the distance between the control center module and the slope is 10m to 3000m.

[0056] Compared with the prior art, the advantages of the present invention are:

[0057] (1) The present invention discloses a slope safety monitoring device that uses multi-source heterogeneous sensor data fusion to perform multi-angle and multi-type measurements on the slope deformation process, and transmits the collected slope data wirelessly to a control center module via a ZigBee wireless communication module. The control center module fuses the data, monitors and warns of the slope stability, and provides accurate and timely warning information, thereby avoiding unnecessary losses.

[0058] (2) The present invention discloses a slope safety monitoring method based on multi-source heterogeneous sensor data fusion, which uses multi-source heterogeneous sensors to perform multi-angle and multi-type measurements on the slope deformation process, and wirelessly transmits the collected slope data to a control center module. The control center module is provided with a data fusion analysis module, which uses DS evidence theory to perform fusion analysis on the environmental data of the multi-source heterogeneous sensors, effectively reducing the probability of misjudgment of the slope status due to the uncertainty of a single sensor, and improving the accuracy of the warning information;

[0059] (3) The distance between the control center module of the present invention and the slope is 10m to 3000m, that is, the control center module is set at the slope monitoring site, and the control center module is connected to the wireless gateway node of the ZigBee wireless communication module. After receiving the environmental parameters, the control center module uses the data fusion analysis module to perform fusion analysis on the parameters. After reaching a conclusion, it can issue early warning information in real time, and the early warning information is released in a timely manner;

[0060] (4) The data fusion analysis module of the present invention includes determining the stability grade standard, generating basic probability assignment and evidence fusion, first determining the slope stability grade, then determining the upper limit and lower limit of the multi-source heterogeneous sensor in each stability grade according to the environment and experience, using the upper limit and lower limit to obtain the average value of the multi-source heterogeneous sensor in each stability grade interval, comparing the upper limit and lower limit with the measured value of the multi-source heterogeneous sensor, forming the probability assignment of the multi-source heterogeneous sensor in each stability grade, fusing the probability assignment of the multi-source heterogeneous sensor in a certain stability grade interval, and integrating the obtained probabilities of the multi-source heterogeneous sensor in multiple stability grade intervals into a stability grade vector. When M(A) in the stability grade vector is IV ) or M(A V ) is greater than 0.5, an early warning needs to be issued in time. The monitoring method is reasonable, the data is highly reliable, and the early warning information is accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a layout diagram of a slope safety monitoring device that integrates multi-source heterogeneous sensor data according to the present invention.

[0062] Figure 2 This is the multi-source heterogeneous sensor data fusion process based on DS evidence theory in the present invention.

[0063] Figure 3 It is a probability distribution curve diagram of each stability level of the present invention.

[0064] Figure 4 This is a layout diagram of a slope safety monitoring device with multi-source heterogeneous sensor data fusion in an actual application scenario of the present invention. DETAILED DESCRIPTION

[0065] The specific implementation of the present invention is described below in conjunction with examples:

[0066] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0067] Example 1

[0068] The present invention discloses a slope safety monitoring method based on multi-source heterogeneous sensor data fusion, which includes the following steps:

[0069] Step 1: Design the number and type of multi-source heterogeneous sensors and their placement locations based on the slope to be monitored;

[0070] Step 2: Deploy multi-source heterogeneous sensors at corresponding locations on the slope, connect the multi-source heterogeneous sensors to the data transmission module, and wirelessly connect the data transmission module to the control center module;

[0071] Step 3: The data transmission module transmits the environmental parameters collected by the multi-source heterogeneous sensors to the control center module, which performs data fusion analysis;

[0072] Step 4: Determine the stability level of the slope and issue a warning in time when the slope is in a relatively unstable or unstable state.

[0073] Example 2

[0074] Preferably, step 1 is specifically as follows: the number of the multi-source heterogeneous sensors is proportional to the size of the slope, the type of the multi-source heterogeneous sensors is related to the cracks and deformation of the slope, the multi-source heterogeneous sensors include displacement meters, inclinometers and vibration sensors, the displacement meters are arranged on the slope surfaces of the first and second platforms of the slope, the parameters collected by the displacement meters are used to observe the changes in the cracks in the slope, and provide data support for the deformation process of the slope; the inclinometers are suspended or fixed inside or on the surface of the slope, the parameters collected by the inclinometers are used to observe the changes in the angle of the slope sliding body, and provide data support for the deformation direction and deformation size of the slope; the vibration sensor is installed on the pull rope of the slope protection net, and the resonant frequency collected by the vibration sensor is used to calculate the impact force on the pull rope, and judge whether the protection net is damaged according to the magnitude of the impact force, and then infer the deformation state of the slope.

[0075] Usually, the number of sensors deployed on a 300m slope is about 15.

[0076] Example 3

[0077] like Figure 2 As shown in Figure 2, the multi-source heterogeneous sensor data fusion process based on DS evidence theory is presented.

[0078] Preferably, the control center module in step 3 is provided with a data fusion analysis module, which is used to perform data fusion analysis on the environmental parameters collected by multi-source heterogeneous sensors. The data fusion analysis includes the following steps:

[0079] Step 3-1: Determine the stability level standard;

[0080] Step 3-2: Generate basic probability assignment;

[0081] Step 3-3: Evidence fusion and output results.

[0082] This method uses multiple heterogeneous sensors from multiple sources to measure slope deformation from multiple angles and types. Multiple sensors of each type are used, and artificial intelligence algorithms are employed to fuse and process the information collected by these sensors, providing technical support for current slope condition assessment and early warning. The D-S evidence theory offers significant advantages in representing both random and subjective uncertainty. By fusing and analyzing data from these sensors for slope monitoring, the probability of misjudgment of slope condition due to the uncertainty of individual sensors is effectively reduced.

[0083] Preferably, the stability grade standard determined in step 3-1 is specifically as follows: the slope stability grade is divided into five grades, namely I, II, III, IV, and V. The warning level corresponding to stability grade I is no warning, the warning level corresponding to stability grade II is blue warning, the warning level corresponding to stability grade III is yellow warning, the warning level corresponding to stability grade IV is orange warning, and the warning level corresponding to stability grade V is red warning. The expression of the stability grade is as follows. The slope stability grade evaluation standard is shown in Table 1:

[0084] A={I,II,III,IV,V}

[0085] A is the slope stability grade.

[0086] Table 1 Slope stability grade evaluation standard

[0087]

[0088] Preferably, generating the basic probability assignment in step 3-2 is specifically as follows: determining the monitoring index of the multi-source heterogeneous sensor according to the environment and experience, that is, the upper limit and lower limit of each stability level, using the upper limit and lower limit to obtain the average value of the multi-source heterogeneous sensor in each stability level interval, comparing the upper limit and lower limit with the measured value of the multi-source heterogeneous sensor, forming the probability assignment of the multi-source heterogeneous sensor in each stability level, and forming a probability assignment table, the probability assignment expression is:

[0089]

[0090] c=(a1-a2) / δ

[0091] Where:

[0092] m(A) is the probability assignment of multi-source heterogeneous sensors at each stability level;

[0093] x is the measured value of multi-source heterogeneous sensors;

[0094] is the average value of a certain stability level interval;

[0095] a1 and a2 are the upper and lower limits of a certain stability level range respectively;

[0096] δ is the variance.

[0097] The specific calculation process is as follows:

[0098] In DS evidence theory, m(A) is called the quality function, which is the set 2 Θ to [0,1], satisfying the following constraints

[0099]

[0100] Where A is a subset of the identification framework Θ. In this paper, A represents the slope stability levels I, II, III, IV, and V respectively. m(A) is the quality function of A, which indicates the degree to which the evidence supports A. In this paper, m(A) represents the probability assignment of multi-source heterogeneous sensors to each stability level.

[0101] The present invention involves three types of sensors: displacement meters, inclinometers, and vibration sensors. The parameters collected by the sensors in different environments will reflect the deformation state of the slope differently. For example, the vibration sensor installed on the slope of a highway is affected by heavy trucks, and its amplitude and frequency are very large, but it cannot reflect the risk of landslide on the slope at this time. The sudden increase in the amplitude and frequency of the vibration sensor installed on the river bank is likely to be a precursor to a landslide. Therefore, the probability assignment of different sensors in different environments requires experts to comprehensively determine professional knowledge and environmental factors. The displacement meter sensor collects the surface displacement of the slope. The larger the surface displacement, the worse the current stability of the slope, and vice versa. The inclinometer collects the change in the slope surface inclination angle. The greater the angle change, the more unstable the slope. The vibration sensor collects the acceleration of the slope. The larger the value, the more unstable the slope.

[0102] After the sensors transmit the collected parameters to the control center module via the data transmission module, the control center preprocesses these parameters and then applies probabilistic assignment based on DS evidence theory to obtain a probability assignment table. The grading in this table is based on design or professional analysis and calculation. In practice, it is established by experts with engineering experience or based on the variance of actual monitoring data at a certain stage, as shown in Table 2. This table generally includes multiple surface displacement sensors, and each sensor should have a corresponding grading standard. Table 2 lists two displacement sensors, two inclination sensors, and one acceleration sensor. Similar types of sensors can also have multiple grading tables, and the assignment table is established in the same manner.

[0103] Table 2 Basic probability assignment table of multi-source heterogeneous sensor data

[0104]

[0105] In the probability assignment process, the expert does not need to assign every value, but only needs to adjust the abnormal values ​​in the assignment table. In the BAP process of the present invention, it is assumed that the parameters collected by the sensor in a fixed period obey the normal distribution, which is recorded as: X~(μ,σ 2 ), where μ and δ are the expectation and variance respectively, and satisfy P(μ-3δ<X<μ+3δ)=0.9974. On this basis, we use the normal function as the probability assignment expression:

[0106]

[0107] c=(a1-a2) / δ

[0108] Example 4

[0109] Preferably, the probability assignment expression of each stability level is:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] Where:

[0116] Indicates the upper and lower limits of a certain stability level range;

[0117] is the average value of the ith stability level interval;

[0118] is the variance of the ith stability level interval.

[0119] The probability distribution of each stability level is as follows Figure 3 As shown, the membership value at each level intersection is 0.5.

[0120] Example 6

[0121] According to Table 2, the data collected by the displacement meter of sensor 1 for monitoring the slope form the first chain of evidence for determining the stability of the slope E1: m1(I) = a1, m1(II) = b1, m1(III) = c1, m1(IV) = d1, m1(V) = e1; similarly, the data collected by sensor 2 form the second chain of evidence for determining the stability of the slope E2 after BPA: m2(I) = a2, m2(II) = b2, m2(III) = c2, m2(IV) = d2, m2(V) = e2; similarly, the data collected by sensor 3 form the third chain of evidence for determining the stability of the slope E3 after BPA: m3(I) = a3, m3(II) = b3, m3(III) = c3, m3(IV) = d3, m3(V) = e3. By analogy, the more sensors there are, the more accurate the monitoring of the slope stability level from multiple angles. According to the Dempster synthesis rule in the DS evidence theory, the multi-source heterogeneous sensor data fusion expression involved in the present invention is obtained.

[0122] Preferably, the evidence fusion in step 3-3 is to fuse the probability assignments of multi-source heterogeneous sensors within a certain stability level range, and the fusion expression is:

[0123]

[0124]

[0125] Where:

[0126] M(A) is the probability of multi-source heterogeneous sensors within a certain stability level interval;

[0127] k represents the conflict factor;

[0128] Indicates the degree to which each probability assignment belongs to the same stability level interval;

[0129] A represents slope stability grade I, II, III, IV, V;

[0130] The probabilities of multi-source heterogeneous sensors within multiple stability level intervals are integrated into a stability level vector and the result is output. The expression is:

[0131] M=[M(A I ),M(A II ),M(A III ),M(A IV ),M(A V )]

[0132] Preferably, the step 4 is specifically as follows: when M(A IV ) is greater than 0.5, an orange warning is issued to remind the management department to organize temporary evacuation on site and activate the corresponding level plan; when M(A V ) is greater than 0.5, a red alert is issued, traffic is controlled, residents are evacuated, and other protective measures are taken.

[0133] Example 7

[0134] Preferably, a slope safety monitoring device with multi-source heterogeneous sensor data fusion is used in the above-mentioned slope safety monitoring method with multi-source heterogeneous sensor data fusion, wherein the monitoring device comprises multi-source heterogeneous sensors, a data transmission module and a control center module, wherein the multi-source heterogeneous sensors are connected to the data transmission module, and the data transmission module is wirelessly connected to the control center module;

[0135] The multi-source heterogeneous sensors include displacement meters, inclinometers, or vibration sensors, which are installed at different locations on the slope. The displacement meters are arranged on the slope's primary and secondary platforms, and the inclinometers are suspended or fixed inside or on the slope's surface. The vibration sensors are installed on the ropes of the slope protection net to monitor the vibration caused by the impact of slope deformation on the ropes of the protection net.

[0136] The displacement meter includes a wire-type displacement meter, a settlement-type displacement meter or a crack-type displacement meter with a measurement accuracy of ≤0.1mm. The displacement meter is arranged at a location where the slope deformation may be large, or arranged in the middle and lower parts along the slope monitoring section. The arrangement data increases or decreases according to the scale of the slope, providing data support for understanding the deformation process of the slope.

[0137] The inclinometer is a type of sensor used to measure the inclination change in one or three directions of a fixed point. Its resolution is 0.01°. The parameters collected by the inclinometer are used to observe the angle change of the slope surface, providing data support for the deformation direction and deformation size of the slope.

[0138] The data transmission module is a ZigBee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of multi-source heterogeneous sensors. The multiple terminal nodes are respectively connected to the displacement meter, inclinometer or vibration sensor. The multiple terminal nodes are also respectively connected to the wireless gateway node, and the wireless gateway node is wirelessly connected to the control center module.

[0139] Preferably, it also includes an early warning information release module, which is connected to the control center module. The distance between the control center module and the slope is 10m to 3000m, and the control center module is arranged on the lower side of the slope or near it.

[0140] Example 8

[0141] Taking the monitoring of a slope in Baoji City, Shaanxi Province as an example, the layout of sensors on the slope is as follows: Figure 4 The DS evidence theory framework constructed in this paper is applied to the slope monitoring and early warning. According to industry standards, the slope environment and expert experience, the conventional monitoring quantities of the four sensors are used as monitoring indicators. The parameter ranges are shown in Table 3:

[0142] Table 3 Classification of monitoring indicators of various sensors on a slope

[0143]

[0144] According to Table 3, the boundary parameters in the probability assignment expression of the stability level can be determined The assignment probability is calculated through the probability assignment expression of each stability level, and the results are shown in Table 4:

[0145] Table 4 Basic probability assignment table for a slope in Baoji

[0146]

[0147] After obtaining the basic probability assignment table, the fusion expression is used to fuse the indicators to obtain the slope stability grade vector (0.012, 0.000, 0.310, 0.528, 0.150).

[0148] According to the stability level vector, M(A IV ) is 0.528, which is greater than 0.5. We can infer that the slope is currently in a relatively unstable state and an orange warning needs to be issued. The relevant departments launched the on-site secondary emergency plan to avoid losses.

[0149] The principles of the present invention are as follows:

[0150] like Figure 1 As shown, the present invention discloses a slope safety monitoring method and device with multi-source heterogeneous sensor data fusion. The sensors used in the device for comprehensive monitoring of the slope include displacement meters, inclinometers, vibration sensors, etc., which collect different state information of the slope. By adopting the Internet of Things technology, the data collected by multiple sensors can be wirelessly transmitted to a control center module located in a safe area. The control center module uses the above algorithm to process, analyze and fuse the received multi-source heterogeneous sensor data, and automatically activates the alarm and sends early warning information to realize intelligent monitoring and early warning of the slope.

[0151] The present invention discloses a slope safety monitoring device that fuses multi-source heterogeneous sensor data. The device uses multi-source heterogeneous sensors to perform multi-angle and multi-type measurements on the slope deformation process, and wirelessly transmits the collected slope data to a control center module through a ZigBee wireless communication module. The control center module fuses the data, monitors and warns of the stability of the slope. The warning information is accurate and released in a timely manner, avoiding unnecessary losses.

[0152] The present invention discloses a slope safety monitoring method based on multi-source heterogeneous sensor data fusion. Multi-source heterogeneous sensors are used to perform multi-angle and multi-type measurements on the slope deformation process, and the collected slope data is wirelessly transmitted to a control center module. The control center module is provided with a data fusion analysis module. The data fusion analysis module uses DS evidence theory to perform fusion analysis on the environmental data of the multi-source heterogeneous sensors, effectively reducing the probability of misjudgment of the slope status due to the uncertainty of a single sensor, and improving the accuracy of the early warning information.

[0153] The control center module of the present invention is located 10m to 3000m away from the slope, that is, the control center module is set up at the slope monitoring site. The control center module is connected to the wireless gateway node of the ZigBee wireless communication module. After receiving the environmental parameters, the control center module uses the data fusion analysis module to perform fusion analysis on the parameters. After reaching a conclusion, it can issue early warning information in real time, and the early warning information is released in a timely manner.

[0154] The data fusion analysis module of the present invention includes determining the stability grade standard, generating basic probability assignment and evidence fusion, first determining the slope stability grade, then determining the upper limit and lower limit of the multi-source heterogeneous sensor in each stability grade according to the environment and experience, using the upper limit and lower limit to obtain the average value of the multi-source heterogeneous sensor in each stability grade interval, comparing the upper limit and lower limit with the measured value of the multi-source heterogeneous sensor to form the probability assignment of the multi-source heterogeneous sensor in each stability grade, fusing the probability assignment of the multi-source heterogeneous sensor in a certain stability grade interval, and integrating the probabilities of the multi-source heterogeneous sensor in multiple stability grade intervals into a stability grade vector. When M(A) in the stability grade vector is IV ) or M(A V ) is greater than 0.5, an early warning needs to be issued in time. The monitoring method is reasonable, the data is highly reliable, and the early warning information is accurate.

[0155] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0156] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A slope safety monitoring method based on multi-source heterogeneous sensor data fusion, characterized in that: The following steps are involved: Step 1: Design the number and type of multi-source heterogeneous sensors and their placement locations based on the slope to be monitored; Step 2: Deploy multi-source heterogeneous sensors at corresponding locations on the slope, connect the multi-source heterogeneous sensors to the data transmission module, and wirelessly connect the data transmission module to the control center module; Step 3: The data transmission module transmits the environmental parameters collected by the multi-source heterogeneous sensors to the control center module, which performs data fusion analysis; The control center module is provided with a data fusion analysis module, which is used to perform data fusion analysis on environmental parameters collected by multi-source heterogeneous sensors. The data fusion analysis includes the following steps: Step 3-1: Determine the stability level standard; Step 3-2: Generate basic probability assignment; Determine the monitoring indicators of multi-source heterogeneous sensors based on the environment and experience, that is, the upper and lower limits of each stability level. Use the upper and lower limits to obtain the average value of the multi-source heterogeneous sensors in each stability level interval. Compare the upper and lower limits with the measured values ​​of the multi-source heterogeneous sensors to form a probability assignment of the multi-source heterogeneous sensors at each stability level, and form a probability assignment table. The probability assignment expression is: c=(a1-a2) / δ Where: m(A) is the probability assignment of multi-source heterogeneous sensors at each stability level; x is the measured value of multi-source heterogeneous sensors; is the average value of a certain stability level interval; a1 and a2 are the upper and lower limits of a certain stability level range respectively; δ is the variance; Step 3-3: Evidence integration and output results; Step 4: Determine the stability level of the slope and issue a warning in time when the slope is in a relatively unstable or unstable state.

2. The slope safety monitoring method based on multi-source heterogeneous sensor data fusion according to claim 1 is characterized in that: The specific steps of step 1 are as follows: the number of the multi-source heterogeneous sensors is proportional to the size of the slope, the type of the multi-source heterogeneous sensors is related to the cracks and deformation of the slope, the multi-source heterogeneous sensors include displacement meters, inclinometers and vibration sensors, the displacement meters are arranged on the slope surfaces of the first and second platforms of the slope, the parameters collected by the displacement meters are used to observe the changes in the cracks in the slope and provide data support for the deformation process of the slope; the inclinometers are suspended or fixed inside or on the surface of the slope, the parameters collected by the inclinometers are used to observe the changes in the angle of the slope sliding body and provide data support for the deformation direction and deformation size of the slope; the vibration sensors are installed on the ropes of the slope protection net, the resonant frequency collected by the vibration sensors is used to calculate the impact force on the ropes, and whether the protection net is damaged is judged according to the magnitude of the impact force, thereby inferring the deformation state of the slope.

3. The slope safety monitoring method based on multi-source heterogeneous sensor data fusion according to claim 1 is characterized in that: The stability grade standard determined in step 3-1 is specifically as follows: the slope stability grade is divided into five grades, namely I, II, III, IV, and V. The warning grade corresponding to stability grade I is no warning, the warning grade corresponding to stability grade II is blue warning, the warning grade corresponding to stability grade III is yellow warning, the warning grade corresponding to stability grade IV is orange warning, and the warning grade corresponding to stability grade V is red warning. The expression of the stability grade is: A={I,II,III,IV,V} A is the slope stability grade.

4. The slope safety monitoring method based on multi-source heterogeneous sensor data fusion according to claim 1 is characterized in that: The probability assignment expressions of the various stability levels are: Where: Indicates the upper and lower limits of a certain stability level range; is the average value of the ith stability level interval; is the variance of the ith stability level interval.

5. The slope safety monitoring method based on multi-source heterogeneous sensor data fusion according to claim 1 is characterized in that: The evidence fusion in step 3-3 is to fuse the probability assignments of multi-source heterogeneous sensors within a certain stability level range. The fusion expression is: Where: M(A) is the probability of multi-source heterogeneous sensors within a certain stability level interval; k represents the conflict factor; Indicates the degree to which each probability assignment belongs to the same stability level interval; A represents slope stability grade I, II, III, IV, V; The probabilities of multi-source heterogeneous sensors within multiple stability level intervals are integrated into a stability level vector and the result is output. The expression is: M=[M(A I ),M(A II ),M(A III ),M(A IV ),M(A V )]。 6. The slope safety monitoring method based on multi-source heterogeneous sensor data fusion according to claim 5 is characterized in that: The step 4 is specifically as follows: when M(A IV ) is greater than 0.5, an orange warning is issued to remind the management department to organize temporary evacuation on site and activate the corresponding level plan; when M(A V ) is greater than 0.5, a red alert is issued, traffic is controlled, residents are evacuated, and other protective measures are taken.

7. A slope safety monitoring device based on multi-source heterogeneous sensor data fusion, characterized by: A slope safety monitoring method for multi-source heterogeneous sensor data fusion according to any one of claims 1 to 6, wherein the monitoring device comprises multi-source heterogeneous sensors, a data transmission module and a control center module, the multi-source heterogeneous sensors are connected to the data transmission module, and the data transmission module is wirelessly connected to the control center module; The multi-source heterogeneous sensors include displacement meters, inclinometers, or vibration sensors, which are installed at different locations on the slope. The displacement meters are arranged on the slope's primary and secondary platforms, and the inclinometers are suspended or fixed inside or on the slope's surface. The vibration sensors are installed on the ropes of the slope protection net to monitor the vibration caused by the impact of slope deformation on the ropes of the protection net. The displacement meter includes a wire displacement meter, a settlement displacement meter or a crack displacement meter, with a measurement accuracy of ≤0.1mm, and the resolution of the inclinometer is 0.01°; The data transmission module is a ZigBee wireless communication module, which includes a wireless gateway node and multiple terminal nodes. The number of the multiple terminal nodes is the same as the number of multi-source heterogeneous sensors. The multiple terminal nodes are respectively connected to the displacement meter, inclinometer or vibration sensor. The multiple terminal nodes are also respectively connected to the wireless gateway node, and the wireless gateway node is wirelessly connected to the control center module.

8. The slope safety monitoring device using multi-source heterogeneous sensor data fusion according to claim 7 is characterized by: It also includes an early warning information release module, which is connected to the control center module. The distance between the control center module and the slope is 10m to 3000m.

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