Multi-source monitoring information fusion slope dangerous rock mass stability intelligent analysis system

Abstract

The invention discloses a slope dangerous rock mass stability intelligent analysis system based on multi-source monitoring information fusion, and the system comprises a slope dangerous rock mass multi-source information monitoring sensor which receives a dangerous rock mass stability intelligent analysis instruction; the multi-source information acquisition sensor acquires slope gradient, form and rock strength information of slopes at different altitudes of each unit time slope and underground water level and surface water level data of a mountain body where each unit time slope is located, and stores and sends the acquired data to the big data intelligent supervision platform; the big data intelligent supervision platform judges whether the gradient, shape and rock strength information of the slopes at different altitudes and the underground water level and surface water level data are within a preset safety threshold range or not; and the information is fed back to the landslide probability analysis model of the mountain multi-source information monitoring point where the slope is located through the 5G network transmission base station. According to the invention, a slope dangerous rock landslide risk prompt signal can be sent in time, and the monitoring efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring of the stability of dangerous rock masses on slopes, and in particular to an intelligent analysis system for the stability of dangerous rock masses on slopes by integrating multi-source monitoring information. Background Art

[0002] Stability analysis of dangerous rock mass on slopes is a key link in geological disaster prevention, especially in complex terrain and changeable climate environments, where landslides, collapses and other disasters seriously threaten the safety of life and property. With the development of 5G, Internet of Things, big data and artificial intelligence technologies, multi-source monitoring information fusion technology has become a cutting-edge means of slope stability analysis. Through comprehensive monitoring of parameters such as different altitudes, slope gradients, morphology, rock strength, groundwater levels, and surface water levels, the stability of slopes can be comprehensively evaluated and early warning can be achieved. Traditional monitoring systems often rely on a single data source, such as slope sensors or groundwater level monitoring equipment, lack the ability to integrate and analyze multiple factors, and are difficult to cope with complex geological environmental changes. The intelligent analysis system achieves more accurate and real-time slope stability assessment through the fusion of multi-source data.

[0003] Existing slope monitoring technologies, such as remote sensing monitoring, ground radar monitoring, and GNSS positioning technology, have high spatial resolution and real-time monitoring capabilities. Remote sensing technology can monitor slope morphology changes over a large area, while GNSS technology can accurately monitor slope displacement. Through these technologies, signs of slope instability can be effectively identified, and early warnings can be provided in combination with historical data and geological models. In addition, with the development of big data and artificial intelligence technologies, existing systems can automatically identify potential risks of landslides and issue alarms through deep learning analysis of historical and real-time data. This intelligent analysis platform has greatly improved the accuracy and timeliness of risk assessment.

[0004] Although existing monitoring technologies have high spatial and temporal accuracy, they still have shortcomings when dealing with the fusion of complex multi-source data. Different monitoring devices obtain different types and formats of data, and there are technical difficulties in the integration and synchronization of information. In addition, existing systems often rely on only a single or a small number of data sources, ignoring the combined effects of multiple environmental factors, such as slope angles, changes in rock strength, and groundwater level fluctuations, which may lead to omissions or false alarms in the early warning system. In addition, the deployment and maintenance costs of existing monitoring equipment and technologies are high, especially in remote or high-altitude areas, making it difficult to carry out comprehensive coverage and long-term monitoring. Therefore, future intelligent analysis systems need to solve the problems of fusion and processing of multi-source data, while improving the reliability and coverage of the system.

[0005] In order to solve the above problems, this application proposes an intelligent analysis system for the stability of dangerous rock masses on slopes by fusing multi-source monitoring information. Summary of the invention

[0006] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides an intelligent analysis system for the stability of dangerous rock masses on slopes by fusing multi-source monitoring information.

[0007] The embodiment of the present invention provides a slope dangerous rock mass stability intelligent analysis system based on multi-source monitoring information fusion, which includes:

[0008] The big data intelligent supervision platform sends dangerous rock mass stability intelligent analysis instructions to the dangerous rock mass multi-source information monitoring sensor on the slope;

[0009] After the multi-source information monitoring sensor for dangerous rock mass on the slope receives the intelligent analysis instruction for the stability of dangerous rock mass, the multi-source information acquisition sensor collects the slope gradient, shape and rock strength information of the slope at different altitudes at each unit time, the groundwater level and surface water level data of the mountain where the slope is located at each unit time, and stores and sends the collected data to the big data intelligent supervision platform;

[0010] The big data intelligent supervision platform determines whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a risk warning signal for landslide of dangerous rock mass of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

[0011] Furthermore, the slope gradient, shape and rock strength information of the slopes at different altitudes include: slope angle, slope height, slope length, slope direction, compressive strength, shear strength and Poisson's ratio.

[0012] Furthermore, the groundwater level and surface water level data include: water level recharge rate and water level change amplitude.

[0013] Furthermore, it also includes: setting up a mountain warning model analysis library in advance in the big data intelligent supervision platform, and storing the slope gradient, morphology and rock strength information of the slopes at different altitudes, the safety threshold range of the groundwater level and surface water level data, and the information of the mountain warning type in the mountain warning model analysis library.

[0014] Furthermore, if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold, a slope dangerous rock mass landslide risk warning signal is generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, including:

[0015] The big data intelligent supervision platform evaluates the received slope gradients, morphology and rock strength information at different altitudes, groundwater level and surface water level data with the mountain warning type in the mountain warning model analysis library;

[0016] The big data intelligent supervision platform sends corresponding slope dangerous rock landslide risk warning signals to the 5G network transmission base station based on the assessment results.

[0017] Furthermore, if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold, a slope dangerous rock mass landslide risk warning signal is generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, and also includes:

[0018] The big data intelligent supervision platform sends mountain maintenance or emergency disease control signals to the emergency management center based on the evaluation results to evacuate personnel.

[0019] Furthermore, the landslide probability analysis model that is fed back to the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station includes: the 5G network transmission base station feeds back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through a special coding method.

[0020] The embodiment of the present invention also provides a slope dangerous rock mass stability intelligent analysis system with multi-source monitoring information fusion, including a slope edge computing gateway, mountain monitoring points where slopes at different altitudes are located, a 5G network transmission base station, and a landslide probability analysis model for multi-source information monitoring points where slopes are located. The slope edge computing gateway is provided with a big data intelligent supervision platform, and the mountain monitoring points where slopes at different altitudes are provided with multi-source information monitoring sensors for dangerous rock masses on slopes;

[0021] The big data intelligent supervision platform is used to send dangerous rock mass stability intelligent analysis instructions to the multi-source information monitoring sensor of the dangerous rock mass on the slope;

[0022] The multi-source information monitoring sensor for dangerous rock mass on the slope is used to collect the slope gradient, shape and rock strength information of the slope at different altitudes of the slope at each unit time, and the groundwater level and surface water level data of the mountain where the slope is located at each unit time after receiving the intelligent analysis instruction of the stability of the dangerous rock mass, and store and send the collected data to the big data intelligent supervision platform;

[0023] The big data intelligent supervision platform is also used to determine whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a risk warning signal for landslide of dangerous rock masses of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

[0024] Furthermore, the 5G network transmission base station is used to feed back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through special coding.

[0025] Furthermore, the big data intelligent supervision platform is used to evaluate the received slope gradients, morphologies and rock strength information, groundwater level and surface water level data at different altitudes with the mountain warning types in the mountain warning model analysis library; and based on the evaluation results, send corresponding slope dangerous rock landslide risk warning signals to the 5G network transmission base station.

[0026] The embodiment of the present invention provides a system for intelligent analysis of the stability of dangerous rock masses on slopes with multi-source monitoring information fusion, which includes: a big data intelligent supervision platform sends an intelligent analysis instruction for the stability of dangerous rock masses to a multi-source information monitoring sensor for dangerous rock masses on slopes; after the multi-source information monitoring sensor for dangerous rock masses on slopes receives the intelligent analysis instruction for the stability of dangerous rock masses, the multi-source information acquisition sensor acquires the slope gradient, morphology and rock strength information of the slopes at different altitudes at each unit time, and the groundwater level and surface water level data of the mountain where the slopes are located at each unit time, and stores and sends the acquired data to the big data intelligent supervision platform; the big data intelligent supervision platform determines whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within a preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a landslide risk warning signal for dangerous rock masses on slopes is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slopes are located through a 5G network transmission base station. The system provided by the embodiment of the present invention can automatically monitor and can promptly issue a landslide risk warning signal for the dangerous rock mass on the slope, thereby improving monitoring efficiency and reducing costs. In addition, the intelligent analysis system for the stability of dangerous rock masses on the slope that integrates multi-source monitoring information provided by the embodiment of the present invention is mainly executed on a big data intelligent supervision platform and a multi-source information monitoring sensor for dangerous rock masses on the slope, and has a fast response speed. The system integrates multi-source monitoring information to monitor the stability of dangerous rock masses on the slope with higher accuracy, thereby protecting the safety of the mountain where residents live. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0028] Figure 1 A schematic diagram of the operation flow of a slope dangerous rock mass stability intelligent analysis system with multi-source monitoring information fusion provided by an embodiment of the present invention;

[0029] Figure 2 A structural diagram of a slope dangerous rock mass stability intelligent analysis system with multi-source monitoring information fusion provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] See also Figure 1 , a schematic diagram of a flow chart of a slope dangerous rock mass stability intelligent analysis system with multi-source monitoring information fusion provided by an embodiment of the present invention, wherein the system operation steps include steps S100 to S300:

[0032] Step S100, the big data intelligent supervision platform sends a dangerous rock mass stability intelligent analysis instruction to the dangerous rock mass multi-source information monitoring sensor on the slope;

[0033] Step S200, after the multi-source information monitoring sensor for dangerous rock mass on the slope receives the intelligent analysis instruction for the stability of dangerous rock mass, the multi-source information acquisition sensor collects the slope gradient, shape and rock strength information of the slope at different altitudes at each unit time, the groundwater level and surface water level data of the mountain where the slope is located at each unit time, and stores and sends the collected data to the big data intelligent supervision platform;

[0034] Step S300, the big data intelligent supervision platform determines whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a landslide risk warning signal of the dangerous rock mass of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

[0035] In an embodiment of the present invention, the big data intelligent supervision platform can send an intelligent analysis instruction for the stability of dangerous rock masses to the multi-source information monitoring sensor of the dangerous rock masses on the slope. The multi-source information monitoring sensor of the dangerous rock masses on the slope can control the collection of slope gradients, morphologies and rock strength information, groundwater levels and surface water level data at different altitudes according to the intelligent analysis instruction for the stability of dangerous rock masses and feed them back to the big data intelligent supervision platform. In this way, the big data intelligent supervision platform can determine whether the slope gradients, morphologies and rock strength information, groundwater levels and surface water level data at different altitudes are within the safety threshold range. If not, a landslide risk warning signal for the dangerous rock masses on the slope is automatically generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station. The management personnel can promptly understand the warning information of the mountain where the slope is located per unit time, and promptly process the warning of the mountain where the slope is located to prevent damage to the slope per unit time. The system provided by the embodiment of the present invention can automatically monitor and can promptly issue a landslide risk warning signal for the dangerous rock mass on the slope, thereby improving monitoring efficiency and reducing costs. In addition, the intelligent analysis system for the stability of dangerous rock masses on the slope with multi-source monitoring information fusion provided by the embodiment of the present invention is mainly executed on a big data intelligent supervision platform and a multi-source information monitoring sensor for dangerous rock masses on the slope, and has a fast response speed.

[0036] Specifically, in step S100, the big data intelligent supervision platform can send dangerous rock mass stability intelligent analysis instructions to the multi-source information monitoring sensor of dangerous rock masses on the slope in different ways, for example, sending dangerous rock mass stability intelligent analysis instructions in a timed manner, that is, sending dangerous rock mass stability intelligent analysis instructions once every same period of time, and the period of time can be the system default period of time or a custom period of time.

[0037] In addition, it is also possible to send dangerous rock mass stability intelligent analysis instructions at irregular intervals, that is, to send dangerous rock mass stability intelligent analysis instructions at different time intervals. In this way, it can be manually started by the management personnel, that is, the management personnel can evaluate whether it is necessary to send dangerous rock mass stability intelligent analysis instructions based on their own experience or the actual mountain where they are located. It is also possible to divide each cycle into several different time periods in advance, and set a time interval for different time periods, so that in a time period, the dangerous rock mass stability intelligent analysis instructions are sent regularly according to the corresponding time intervals. This method is actually also regular monitoring, but the time intervals are different in different time periods. The advantage of setting different time periods is that the detection frequency can be set in a targeted manner.

[0038] In step S200, the multi-source information acquisition sensor is started, and the slope gradient, morphology and rock strength information of the slope at different altitudes at each unit time, and the groundwater level and surface water level data of the mountain where the slope is located at each unit time are collected according to the type of each sensor in the sensor group.

[0039] The slope gradient, shape and rock strength information of the slopes at different altitudes include: slope angle, slope height, slope length, slope direction, compressive strength, shear strength and Poisson's ratio. The groundwater level and surface water level data include: water level recharge rate and water level change amplitude.

[0040] The above data can be collected by corresponding sensors. The combination of sensors can be collectively referred to as a sensor group. A sensor group can be set on each unit time slope to collect the slope gradient, shape and rock strength information of the slope at different altitudes of each unit time, and the groundwater level and surface water level data of the mountain where the slope is located at each unit time, so as to realize the remote early warning function of the slope at each unit time.

[0041] The above-mentioned slope gradient, morphology and rock strength information at different altitudes and the groundwater level and surface water level data have different functions. For example, the slope gradient, morphology and rock strength information at different altitudes can directly reflect the current state of the slope per unit time, and can determine whether the slope has a warning for the mountain per unit time. The groundwater level and surface water level data of the mountain where the slope is located per unit time can directly reflect the current condition of the mountain where the slope is located per unit time, such as whether the mountain where the slope is located has undergone a major change per unit time, or whether the mountain has deteriorated, etc. In this way, it can be expected whether the slope will have a warning for the mountain per unit time, so that timely processing can be carried out.

[0042] The multi-source information monitoring sensor for dangerous rock masses on slopes can store the data collected by the sensor group and eventually send it to the big data intelligent monitoring platform.

[0043] In step S300, after receiving the slope gradient, morphology and rock strength information, groundwater level and surface water level data at different altitudes, the big data intelligent supervision platform can determine whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data at different altitudes are within a preset safety threshold range.

[0044] If the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, that is, the slope gradient, morphology and rock strength information of the slopes at different altitudes are not within the preset safety threshold range, and the groundwater level and surface water level data are not within the preset safety threshold range, then it is necessary to generate a risk warning signal for landslide of the dangerous rock mass of the slope and feed it back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

[0045] The landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located can be the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located of the maintenance personnel, so that the maintenance personnel can promptly deal with the mountain warning that is occurring or is about to occur in the unit time. The landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located can also be the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located of the management personnel, so that the management personnel can arrange personnel to deal with it according to the area to which the slope belongs per unit time.

[0046] In order to better analyze the mountain warning of the slope per unit time, the embodiment of the present invention preferably simultaneously collects the slope gradient, morphology and rock strength information of the slope at different altitudes of the slope per unit time and the groundwater level and surface water level data of the mountain where the slope is located per unit time, and analyzes the mountain warning based on these two types of information. When the slope gradient, morphology and rock strength information of the slope at different altitudes and the groundwater level and surface water level data are all within the preset safety threshold range, no warning signal for the risk of landslide of the dangerous rock mass of the slope is generated. When any of the slope gradient, morphology and rock strength information of the slope at different altitudes and the groundwater level and surface water level data is not within the preset safety threshold range, a warning signal for the risk of landslide of the dangerous rock mass of the slope is generated. In this way, it can ensure that the mountain warning is discovered in time and the mountain warning is processed.

[0047] In one embodiment, the multi-source monitoring information fusion intelligent analysis system for slope dangerous rock mass stability further includes:

[0048] The mountain warning model analysis library is set up in advance in the big data intelligent supervision platform, and the slope gradient, morphology and rock strength information of the slopes at different altitudes, the safety threshold range of the groundwater level and surface water level data, and the information of the mountain warning type are stored in the mountain warning model library.

[0049] In this embodiment, the safety threshold ranges of slope gradients, morphologies and rock strength information, groundwater levels and surface water levels at different altitudes can be stored in advance in the mountain warning model analysis library. In this way, the big data intelligent supervision platform can retrieve the corresponding safety threshold ranges at any time and conduct an evaluation when conducting mountain warning analysis. At the same time, the information on the mountain warning type can also be stored in the mountain warning model analysis library. In this way, the type of mountain warning can be determined, which is convenient for maintenance personnel to handle the mountain warning.

[0050] In one embodiment, if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold, a slope dangerous rock mass landslide risk warning signal is generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, including:

[0051] The big data intelligent supervision platform evaluates the received slope gradients, morphology and rock strength information at different altitudes, groundwater level and surface water level data with the mountain warning type in the mountain warning model analysis library;

[0052] The big data intelligent supervision platform sends corresponding slope dangerous rock landslide risk warning signals to the 5G network transmission base station based on the assessment results.

[0053] Input parameter definition: P(x i ,y i , z i ): Slope information at different altitudes, including: x i : slope, y i : shape (such as concave slope, convex slope), z i : Rock strength, W g : Groundwater level, W s : Surface water level, T x , T y , T z , The preset safety thresholds of each parameter correspond to the safety ranges of slope, morphology, rock strength, groundwater level and surface water level;

[0054] Risk function definition:

[0055] Slope risk function: the greater the slope, the higher the landslide risk. Assuming that the risk increases nonlinearly with the slope, the ratio of the slope to its safety threshold is used to define the risk factor:

[0056]

[0057] If R x >1, indicating that the slope exceeds the safety range;

[0058] Morphological risk function, the morphology of the slope (concave-convex or upright) has an impact on the landslide risk, which can be expressed by the ratio of its value to the safety threshold:

[0059]

[0060] If R y >1, indicating that the morphology increases the risk of landslide;

[0061] Rock strength risk function. The lower the rock strength, the higher the landslide risk. The rock strength risk function is defined as:

[0062]

[0063] If R z >1, indicating that the morphology increases the risk of landslide;

[0064] Groundwater level risk function: the rise of groundwater level will affect the stability of the slope. The water level risk can be expressed as:

[0065]

[0066] like Groundwater levels exceed thresholds, increasing landslide risk;

[0067] Surface water level risk function. Surface water level also has an impact on landslide risk. Its risk function is:

[0068]

[0069] like The surface water level is too high;

[0070] The total landslide risk assessment function combines the above risk functions to form the total landslide risk assessment function. Different weights are assigned to each factor according to the actual situation. The total landslide risk R total It can be expressed as:

[0071]

[0072] Among them, α x , α y , α z , are the weights of each risk respectively;

[0073] The generation of landslide risk warning signal is based on the total landslide risk value R total Compared with the preset landslide risk threshold T R Compare, if R total If the set warning threshold is exceeded, a landslide risk warning signal will be generated;

[0074] Big data intelligent supervision platform analysis, when analyzing these data, the big data intelligent supervision platform will compare the received slope data at different altitudes with the historical data and early warning model of the mountain; suppose: M 历史 represents the historical landslide dataset stored in the big data analysis platform, M 预警 Represents the early warning model library, covering the probability of landslides and early warning types under different geological conditions. The platform will calculate the current total landslide risk value R totalMatch and evaluate with historical data and early warning models:

[0075] Evaluation result = f AI (R total , M 历史 , M 预警 )

[0076] Signal transmission and feedback: If the big data intelligent supervision platform determines that the landslide risk is high, the generated landslide risk warning signal will be transmitted to the base station through the 5G network and fed back to the slope monitoring point for use in the landslide probability analysis model to further evaluate the probability of landslide occurrence;

[0077] The signal transmission delay of the 5G network is low, ensuring that landslide warning signals can be transmitted in real time, ultimately forming a real-time warning system;

[0078] Landslide probability analysis model,The landslide probability analysis model can be based on historical landslide data and the current assessment results provided by the big data platform, and use the probability model to further calculate the probability of landslide occurrence. The landslide probability can be expressed as:

[0079] P(landslide) = f(assessment result, M 历史 , M 预警 )

[0080] When the landslide probability reaches the warning threshold, a landslide warning signal is issued.

[0081] After determining that the slopes at different altitudes, the information on the shape and rock strength, the groundwater level and the surface water level data are not within the preset safety threshold range, the slopes at different altitudes, the information on the shape and rock strength, the groundwater level and the surface water level data can be evaluated with the mountain warning type in the analysis library of the mountain warning model, for example, to determine whether it belongs to the voltage mountain warning, the current mountain warning or other mountain warning. Then, the slope dangerous rock landslide risk warning signal is sent to the 5G network transmission base station, and the 5G network transmission base station is used to feed back the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located. In this way, not only can the information on the occurrence of the mountain warning or the occurrence of the mountain warning per unit time be obtained, but also the information on the occurrence of the mountain warning per unit time can be obtained, so as to facilitate targeted processing.

[0082] In one embodiment, the landslide probability analysis model fed back to the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station includes:

[0083] The 5G network transmission base station feeds back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through a special coding method.

[0084] The 5G network transmission base station can feed back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located by email or signal pop-up window. Of course, other methods can also be used for early warning, such as WeChat, mini-programs, special apps, etc.

[0085] In one embodiment, if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold, a slope dangerous rock mass landslide risk warning signal is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, and also includes:

[0086] The big data intelligent supervision platform sends mountain maintenance or emergency disease control signals to the emergency management center based on the evaluation results to evacuate personnel.

[0087] In this embodiment, when it is determined that the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range and a slope dangerous rock landslide risk warning signal is generated and sent to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located, the maintenance personnel can handle it in time. In the embodiment of the present invention, the big data intelligent supervision platform can also send a mountain maintenance signal or a rescue disease control signal to the emergency management center to cut off or transmit the power supply of the slope per unit time. For example, when it is detected that the rock strength of the mountain where the slope is located per unit time is too high, a mountain maintenance signal can be sent to a relay in the emergency management center to cut off the mountain maintenance source, or a rescue disease control signal can be sent to a relay in the emergency management center to transmit power to the air conditioner.

[0088] In other embodiments, a neural network model may be used to predict the collected data, so as to obtain early warning information about the mountain that is about to occur, which is helpful in avoiding risks such as slope burnout per unit time.

[0089] Generally speaking, if a certain state of a slope continues to change or certain parameters in the mountain body continue to deviate from normal values ​​during use, the risk of damage to the slope per unit time is relatively high. Therefore, the embodiment of the present invention can collect historical mountain body warning data, which includes the type of mountain body warning and the slope gradient, morphology and rock strength information of slopes at different altitudes before the mountain body warning occurs, as well as groundwater level and surface water level data.

[0090] Then, the above historical mountain warning data is preprocessed, and the preprocessing content may include normalization, etc., so that each type of data is classified into the same dimension, and then the historical mountain warning data is divided into a sample set and a test set. Then, a neural network model is used to train the sample set of the historical mountain warning data, and then the trained neural network model is tested using the test set, and finally the preset accuracy requirements are met.

[0091] See also Figure 2 , an embodiment of the present invention further provides a slope dangerous rock mass stability intelligent analysis system with multi-source monitoring information fusion, which includes a slope edge computing gateway, mountain monitoring points where slopes at different altitudes are located, a 5G network transmission base station, and a landslide probability analysis model for multi-source information monitoring points where slopes are located. The slope edge computing gateway is provided with a big data intelligent supervision platform, and the mountain monitoring points where slopes at different altitudes are provided with multi-source information monitoring sensors for dangerous rock masses on slopes;

[0092] The big data intelligent supervision platform is used to send dangerous rock mass stability intelligent analysis instructions to the multi-source information monitoring sensor of the dangerous rock mass on the slope;

[0093] The multi-source information monitoring sensor for dangerous rock mass on the slope is used to collect the slope gradient, shape and rock strength information of the slope at different altitudes of the slope at each unit time, and the groundwater level and surface water level data of the mountain where the slope is located at each unit time after receiving the intelligent analysis instruction of the stability of the dangerous rock mass, and store and send the collected data to the big data intelligent supervision platform;

[0094] The big data intelligent supervision platform is also used to determine whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a risk warning signal for landslide of dangerous rock masses of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

[0095] In one embodiment, the 5G network transmission base station is used to feed back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through a special coding method.

[0096] In one embodiment, the big data intelligent supervision platform is used to evaluate the received slope gradients, morphology and rock strength information, groundwater level and surface water level data at different altitudes with the mountain warning type in the mountain warning model analysis library; and based on the evaluation results, send the corresponding slope dangerous rock landslide risk warning signal to the 5G network transmission base station.

[0097] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. An intelligent analysis system for the stability of dangerous rock mass on slopes based on multi-source monitoring information fusion, characterized in that: The system operation steps include: The big data intelligent supervision platform sends dangerous rock mass stability intelligent analysis instructions to the dangerous rock mass multi-source information monitoring sensor on the slope; After the multi-source information monitoring sensor for dangerous rock mass on the slope receives the intelligent analysis instruction for the stability of dangerous rock mass, the multi-source information acquisition sensor collects the slope gradient, morphology and rock strength information of the slope at different altitudes at each unit time, the groundwater level and surface water level data of the mountain where the slope is located at each unit time, and stores and sends the collected data to the big data intelligent supervision platform; The big data intelligent supervision platform determines whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a landslide risk warning signal of the dangerous rock mass of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

2. According to claim 1, a multi-source monitoring information fusion intelligent analysis system for slope dangerous rock mass stability is characterized by: The slope gradient, shape and rock strength information of the slopes at different altitudes include: slope angle, slope height, slope length, slope direction, compressive strength, shear strength and Poisson's ratio.

3. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 1 is characterized in that: The groundwater level and surface water level data include: water level recharge rate and water level change amplitude.

4. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 1 is characterized in that: Also includes: A mountain warning model analysis library is set up in advance in the big data intelligent supervision platform, and the slope gradient, morphology and rock strength information of the slopes at different altitudes, the safety threshold range of the groundwater level and surface water level data, and the information of the mountain warning type are stored in the mountain warning model analysis library.

5. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 4 is characterized in that: If the slope gradient, morphology and rock strength information, groundwater level and surface water level data at different altitudes are not within the preset safety threshold, a slope dangerous rock mass landslide risk warning signal is generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, including: The big data intelligent supervision platform evaluates the received slope gradients, morphology and rock strength information at different altitudes, groundwater level and surface water level data with the mountain warning type in the mountain warning model analysis library; The big data intelligent supervision platform sends a corresponding slope dangerous rock mass landslide risk warning signal to the 5G network transmission base station according to the evaluation results; Input parameter definition: P(x i ,y i , z i ): Slope information at different altitudes, including: x i :slope,y i : shape (such as concave slope, convex slope), z i : Rock strength, W g :groundwater level, W s :Surface water level, T x , T y , T z , The preset safety thresholds of each parameter correspond to the safety ranges of slope, morphology, rock strength, groundwater level and surface water level; Risk function definition: Slope risk function, the greater the slope, the higher the landslide risk. Assuming that the risk increases nonlinearly with the slope, the ratio of the slope to its safety threshold is used to define the risk factor: If R x >1, indicating that the slope exceeds the safe range; Morphological risk function, the morphology of the slope (concave-convex or upright) has an impact on the landslide risk, which can be expressed by the ratio of its value to the safety threshold: If R y >1, indicating that the morphology increases the risk of landslide; Rock strength risk function. The lower the rock strength, the higher the landslide risk. The rock strength risk function is defined as: If R z >1, indicating that the morphology increases the risk of landslide; Groundwater level risk function: the rise of groundwater level will affect the stability of the slope. The water level risk can be expressed as: like Groundwater levels exceed thresholds, increasing landslide risk; Surface water level risk function. Surface water level also has an impact on landslide risk. Its risk function is: like The surface water level is too high; The total landslide risk assessment function combines the above risk functions to form the total landslide risk assessment function. Different weights are assigned to each factor according to the actual situation. The total landslide risk R total It can be expressed as: Among them, x ,ɑ y ,ɑ z , are the weights of each risk respectively; The generation of landslide risk warning signal is based on the total landslide risk value R total Compared with the preset landslide risk threshold T R Compare, if R total If the set warning threshold is exceeded, a landslide risk warning signal will be generated; Big Data Intelligent Supervision Platform Analysis,When analyzing these data, the big data intelligent supervision platform will compare the received slope data at different altitudes with the historical data and early warning model of the mountain; suppose: M 历史 represents the historical landslide dataset stored in the big data analysis platform, M 预警 Represents the early warning model library, covering the probability of landslides and early warning types under different geological conditions. The platform will calculate the current total landslide risk value R total Match and evaluate with historical data and early warning models: Evaluation result = f AI (R total ,M 历史 , M 预警 ) Signal transmission and feedback: If the big data intelligent supervision platform determines that the landslide risk is high, the generated landslide risk warning signal will be transmitted to the base station through the 5G network and fed back to the slope monitoring point for use in the landslide probability analysis model to further evaluate the probability of landslide occurrence; The signal transmission delay of the 5G network is low, ensuring that landslide warning signals can be transmitted in real time, ultimately forming a real-time warning system; Landslide probability analysis model,The landslide probability analysis model can use the probability model to further calculate the probability of landslide occurrence based on historical landslide data and the current assessment results provided by the big data platform. The landslide probability can be expressed as: P(landslide)=f(assessment result,M 历史 ,M 预警 ) When the landslide probability reaches the warning threshold, a landslide warning signal is issued.

6. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 5 is characterized in that: If the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a slope dangerous rock mass landslide risk warning signal is generated and fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station, and also includes: The big data intelligent supervision platform sends mountain maintenance or emergency disease control signals to the emergency management center based on the evaluation results to evacuate personnel.

7. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 1 is characterized in that: The landslide probability analysis model fed back to the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station includes: The 5G network transmission base station feeds back the landslide risk warning signal of the dangerous rock mass on the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through special coding.

8. The intelligent analysis system for stability of dangerous rock mass on slopes by fusion of multi-source monitoring information according to any one of claims 1 to 7, characterized in that: The system includes a slope edge computing gateway, mountain monitoring points at different altitudes of the slopes, a 5G network transmission base station, and a landslide probability analysis model for the multi-source information monitoring points of the mountain where the slopes are located. The slope edge computing gateway is provided with a big data intelligent supervision platform, and the mountain monitoring points at different altitudes of the slopes are provided with multi-source information monitoring sensors for dangerous rock masses on the slopes; The big data intelligent supervision platform is used to send dangerous rock mass stability intelligent analysis instructions to the multi-source information monitoring sensor of the dangerous rock mass on the slope; The multi-source information monitoring sensor for dangerous rock mass on the slope is used to collect the slope gradient, shape and rock strength information of the slope at different altitudes of the slope at each unit time, and the groundwater level and surface water level data of the mountain where the slope is located at each unit time after receiving the intelligent analysis instruction of the stability of the dangerous rock mass, and store and send the collected data to the big data intelligent supervision platform; The big data intelligent supervision platform is also used to determine whether the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are within the preset safety threshold range; if the slope gradient, morphology and rock strength information, groundwater level and surface water level data of the slopes at different altitudes are not within the preset safety threshold range, a risk warning signal for landslide of dangerous rock masses of the slope is generated, and the signal is fed back to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through the 5G network transmission base station.

9. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 8 is characterized in that: The 5G network transmission base station is used to feed back the landslide risk warning signal of the dangerous rock mass of the slope to the landslide probability analysis model of the multi-source information monitoring point of the mountain where the slope is located through a special coding method.

10. The intelligent analysis system for slope dangerous rock mass stability based on multi-source monitoring information fusion according to claim 8 is characterized in that: The big data intelligent supervision platform is used to evaluate the received slope gradients, morphology and rock strength information, groundwater level and surface water level data at different altitudes with the mountain warning type in the mountain warning model analysis library; and based on the evaluation results, send the corresponding slope dangerous rock landslide risk warning signal to the 5G network transmission base station.

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