Earth and rockfill dam leakage dangerous case emergency rescue method based on multi-model cooperation

Abstract

The earth-rockfill dam leakage dangerous case emergency rescue method based on multi-model cooperation comprises the steps that earth-rockfill dam leakage points are obtained; collecting stress data of leakage points of the earth and rockfill dam, inputting the stress data into the early warning model, and outputting early warning values of the leakage points; obtaining leakage point water flow velocity data and impact force data of a water flow carrying object, inputting the leakage point early warning value into a leakage point danger early warning model, and outputting a leakage point danger value; performing real-time priority blocking sequencing through a leakage point priority model; the method comprises the following steps: acquiring stress data of a leakage point of a blocked earth-rockfill dam, acquiring the real-time flow velocity of the blocked leakage point and the impact force of a water flow carrying object, inputting the data into a pre-constructed blocking model, outputting a blocking stress value, and determining the separation early warning of the blocked leakage point through a preset threshold value library. According to the method, the priority sequence of emergency rescue resources is reasonably arranged according to the emergency degrees of different conditions of earth-rock dam leakage points, and efficient and rapid emergency rescue is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of rapid emergency rescue of earth-rock dams, and in particular to an emergency rescue method for leakage danger of earth-rock dams based on multi-model collaboration. Background Art

[0002] In the field of water conservancy engineering, earth-rock dams are important flood control facilities, and their safety and stability are of vital importance. However, in the long-term operation of earth-rock dams, leakage hazards often occur due to various factors such as water erosion, changes in geological conditions, and construction quality. Once leakage occurs, if it is not handled in a timely and effective manner, it will cause serious disasters such as dam collapse, posing a huge threat to the safety of life and property of people in the surrounding areas. Therefore, it is urgent to develop an efficient and accurate method for rapid rescue of earth-rock dam leakage hazards.

[0003] Traditional earth-rock dam leakage rescue technology mainly includes manual inspection to find leakage points, and then judge the severity of leakage based on experience and adopt a single plugging method, such as simple sandbag stacking or simple grouting. This method has many disadvantages. In terms of leakage point search, manual inspection is inefficient and it is difficult to quickly and accurately locate all leakage points, especially when the dam area is large or the leakage situation is complicated, it is easy to miss. In rescue decision-making, relying solely on experience to judge lacks scientific basis, and it is impossible to accurately assess the actual danger level of leakage, which may lead to unreasonable allocation of rescue resources. Moreover, traditional plugging methods lack pertinence. The same or similar methods are used for leakage points of different danger levels, and the plugging effect is poor. When facing complex water flow and impact of carrying objects, the plugging structure is easily damaged and cannot effectively prevent the leakage from worsening.

[0004] The Chinese patent application with publication number CN115187876A (patent name: Method and device for automatic inspection and early warning of leakage risk of earth-rock dam by unmanned aerial platform) discloses the use of unmanned aerial platform to continuously and with a certain degree of overlap to take infrared images of the earth-rock dam site; the infrared images are input into the trained deep convolutional neural network model, and the deep convolutional neural network model predicts and outputs the leakage type of the dam. There is a focus on using unmanned aerial platform to take infrared images and predict the leakage type of the dam and issue early warning through the deep convolutional neural network model, which is unable to accurately collect and analyze the force data of the leakage point of the earth-rock dam, and it is difficult to accurately assess the degree of leakage risk. In terms of water flow, the influence of water flow velocity data and the impact force data of water flow carrying objects on the leakage risk is not considered, and multiple leakage points cannot be scientifically evaluated for danger value and emergency rescue priority ranking. In the face of complex leakage situations, it is impossible to reasonably allocate emergency rescue resources and determine the order of emergency rescue.

[0005] At the same time, in the existing technology, some simple monitoring equipment is used to assist in finding leakage points, but the overall rescue process is still imperfect. For example, it can only monitor the approximate location of the leakage point, and cannot accurately obtain detailed information on the force on the leakage point and the surrounding water flow. The means of detecting objects carried by the water flow are also relatively rough, and it is impossible to accurately judge the magnitude of its impact force. In terms of hazard warning and priority determination, there is a lack of systematic model construction, and it is difficult to scientifically rank the hazards of multiple leakage points, and it is impossible to provide a reasonable decision-making sequence for rescue work. In terms of plugging effect evaluation, there is no effective model to judge whether the plugging is stable and reliable, and potential risks cannot be discovered in time.

[0006] Therefore, in response to the above problems, this technical solution is based on a multi-model collaborative earth-rock dam leakage rescue method. By constructing an early warning model, it solves the problem of unscientific judgment of the degree of leakage risk in traditional and existing technologies. With the help of the leakage point priority model, the risk value and location information are integrated to achieve a reasonable sorting of leakage point rescue, which changes the previous situation of waste of resources or untimely rescue due to lack of scientific sorting. The plugging effect is evaluated through the plugging model, which improves the success rate and stability of the plugging, overcomes the defects of the traditional plugging method of singleness and poor effect, greatly improves the efficiency and reliability of rescue of earth-rock dam leakage risks, and ensures the safety of the dam. Summary of the invention

[0007] In view of the above content, the present invention proposes a rescue method for leakage of earth-rock dams based on multi-model collaboration, including:

[0008] Obtain at least one or more leakage points of the earth-rock dam;

[0009] Collect the stress data of the leakage point of the earth-rock dam, input it into the pre-built early warning model, and output the early warning value of the leakage point;

[0010] Obtain the current water velocity data and the impact force data of the objects carried by the water flow in the watershed area of ​​the leakage point of the earth-rock dam, combine the leakage point warning value with the input of the leakage point danger warning model, and output the leakage point danger value;

[0011] The leakage point priority model is used to obtain the leakage point hazard value and perform real-time priority plugging sorting;

[0012] Obtain the force data of the leakage point of the earth-rock dam after plugging, collect the real-time flow velocity and impact force of the water flow at the plugged leakage point, input them into the pre-built plugging model, output the plugging force value, and determine the plugging leakage point out of warning through the preset threshold library;

[0013] Outputting the leakage point danger value of at least one leakage point of the earth-rock dam in real time, adjusting the priority order of the leakage points of the earth-rock dam, and carrying out emergency rescue of the leakage points of the earth-rock dam with priority;

[0014] Among them, the leakage point hazard value is obtained to set three or more level threshold intervals, and different categories are realized through corresponding level threshold intervals; the plugging types include but are not limited to grouting, geomembrane sandbags and expansion balls.

[0015] Preferably, outputting the leakage point warning value through the warning model specifically includes:

[0016] Obtain the stress data of the earth-rock dam under normal conditions and leakage conditions, perform preprocessing, and extract characteristic parameters; the preprocessing includes removing outliers and filling missing values;

[0017] Through the characteristic parameters, the initial warning model is constructed using the logistic regression algorithm. The formula is: ,in is the selected characteristic parameter, is the selected characteristic parameter coefficient, is the intercept, is the probability of leakage risk in earth-rock dam;

[0018] The characteristic parameters of the dam stress data under normal and leakage conditions are used for training, and the characteristic parameter coefficients in the logistic regression algorithm are determined by the maximum likelihood estimation method. , iterative training meets the verification requirements, forms an early warning model, and outputs the leakage point warning value.

[0019] Preferably, the objects carried by the water flow include but are not limited to stones, gravel and mud;

[0020] The objects carried by the water flow are obtained through magnetic imaging technology. The real-time cross-section of the water flow is scanned by a magnetic imaging sensor to obtain the contour information of the real-time cross-section of the watershed area of ​​the leakage point of the earth-rock dam, and the objects carried by the water flow are determined based on the contour information.

[0021] Preferably, the acquisition of the impact force data of the object carried by the water flow specifically includes:

[0022] Predict the volume of rocks, gravel and silt per unit volume of water flow using magnetic imaging technology, and determine the mass of rocks, gravel and silt by volume , the formula is: ;in , and is the unit volume ratio of stone, gravel and mud and sand sections, , and is the density of rocks, gravel and sediment, is the unit volume;

[0023] Determine the speed of the water flow by using a velocimeter ;

[0024] Calculate the water flow impact force, the formula is: ,in Unit water flow impact force, is the unit area, is the water flow impact force, where ,in is the speed correction factor, For time.

[0025] Preferably, the construction of the leakage point hazard warning model specifically includes:

[0026] Obtain water flow velocity data and impact force data of objects carried by water flow, perform preprocessing, and extract characteristic parameters;

[0027] Divide the feature parameters into training set, validation set and test set;

[0028] The training set data is input into the random forest model for training, samples are randomly extracted with replacement from the training set, and the training subset of the decision tree is constructed. This process is repeated until the preset maximum depth is reached or the number of node samples is less than the minimum number of samples.

[0029] Add decision trees to the random forest model and conduct multiple rounds of training to form an initial leakage point hazard warning model;

[0030] The validation set and the test set are input into the initial leakage point hazard warning model to conduct performance evaluation, adjust the parameters of the initial leakage point hazard warning model, and form a leakage point hazard warning model.

[0031] Preferably, the output of the leakage point danger value is to comprehensively output the prediction results of the decision tree through a random forest model, and the leakage point danger value is output using the mean method, and the formula is: ,in For decision tree The prediction results, It is the danger value of leakage point.

[0032] Preferably, the leakage point priority model obtains the leakage point hazard values ​​output by the leakage point hazard warning model, and sorts the hazard values ​​from high to low to form a first hazard value sequence; the leakage point priority model obtains the location information of the leakage points, and sorts them according to the key points of the dam structure to form a second hazard value sequence; constructs a leakage point association framework, uses the leakage point hazard values ​​in the first hazard value sequence as nodes, and assigns important information to the location information of the key points of the leakage points in the second hazard value sequence, traverses the leakage point association framework through the ant colony algorithm, selects the next node according to the pheromone concentration on the path of the leakage point association framework, and iterates multiple rounds to form a priority order of the target leakage points.

[0033] Preferably, the priority order of the target leakage points is obtained, the leakage point risk value of the first leakage point is extracted, and the preset threshold interval to which it belongs is determined; the preset threshold interval includes but is not limited to a high-risk interval, a medium-risk interval and a low-risk interval.

[0034] Preferably, different types of plugging are adopted in the preset threshold intervals; the high-risk interval of the preset threshold interval adopts the concrete rapid-setting grouting method, in which the concrete rapid-setting slurry is injected into the leakage channel through high-pressure equipment, and quickly solidifies to form a high-strength plugging structure; the medium-risk interval adopts the method of geomembrane laying combined with sandbag stacking; the low-risk interval is expanded and plugged by drones carrying expansion balls.

[0035] Preferably, the plugging model is constructed by a multi-layer perceptron neural network model, the plugging force value is output by the multi-layer perceptron neural network model, and whether the plugging leakage point is out of warning is determined according to a preset threshold library, specifically including:

[0036] Obtain the force data of the leakage point of the earth-rock dam after plugging, the real-time flow velocity of the plugged leakage point and the impact force of the water flow carrying objects, perform standardization processing, and form standardized data;

[0037] The standardized data is input into the input layer of the multi-layer perceptron neural network model, weighted sum is performed through the hidden layer of the multi-layer perceptron neural network model, and the blocking force value is output through the output layer;

[0038] The plugging force value is compared with the escape warning threshold in the preset threshold library. If the plugging force value is less than the escape warning threshold, the plugging part is in a safe range and the plugging leakage point is determined to be out of warning. If the plugging force value is greater than or equal to the escape warning threshold, there is a risk and it is elevated to the high-risk range of the preset threshold range for processing.

[0039] Compared with the prior art, the technical solution of this application has the following technical effects:

[0040] The present invention constructs an early warning model, utilizes the dam stress data under normal conditions and leakage conditions, and determines the characteristic parameter coefficients through preprocessing and logistic regression algorithm training to form an early warning model to output leakage point warning values. This solves the problem that traditional methods are difficult to scientifically judge the leakage risk of earth-rock dams, can accurately provide early warning of leakage situations, provide key risk assessment basis for subsequent rescue work, and improve the pertinence and timeliness of rescue work.

[0041] The present invention takes into account the problem of water conservancy carrying objects that traditional technologies have not involved, obtains water flow carrying object information by adopting magnetic imaging technology, and determines the speed in combination with a velocimeter, calculates the impact force data of water flow carrying objects, and inputs the water flow velocity data, the impact force data of water flow carrying objects and the leakage point warning value into the leakage point danger warning model, thereby solving the problem that it was impossible to comprehensively consider the impact of water flow factors on leakage risks, and comprehensively and accurately assesses the danger level of the leakage point, so that rescue personnel can clearly understand the urgency of each leakage point and reasonably arrange rescue resources.

[0042] This application uses a leakage point priority model to sort the danger values ​​output by the leakage point danger warning model, and combines the leakage point location information to traverse the leakage point association framework through an ant colony algorithm to determine the target priority order, thereby overcoming the drawback of lack of scientific planning for the leakage point processing sequence in traditional rescue work, achieving orderly and efficient rescue of leakage points, ensuring priority treatment of leakage points with high danger levels and in key locations, effectively improving rescue efficiency, and reducing the risk of dam breach.

[0043] This application builds a plugging model based on a multi-layer perceptron neural network model, inputs the force data of the earth-rock dam leakage point after plugging, the real-time flow rate of the plugged leakage point, and the impact force data of the water flow, processes and outputs the plugging force value, and compares it with the preset threshold library. It solves the problem of lack of effective evaluation mechanism after traditional plugging, can timely and accurately judge whether the plugging part is safe and reliable, and take further measures in time for the plugging points with risks, ensuring the durability of the plugging effect and enhancing the safety of the earth-rock dam.

[0044] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application so that it can be implemented in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings as follows.

[0045] Based on the detailed description of the specific embodiments of the present application in combination with the accompanying drawings below, those skilled in the art will become more aware of the above and other objects, advantages and features of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings without creative work. In all drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, each element or part is not necessarily drawn according to the actual scale.

[0047] Figure 1 It is a schematic diagram of the process of the emergency rescue method for earth-rock dam leakage based on multi-model collaboration of the present invention;

[0048] Figure 2 The present invention is a priority sorting flow chart of the emergency rescue method for leakage of earth-rock dams based on multi-model collaboration. DETAILED DESCRIPTION

[0049] To make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, rather than all of the embodiments. In the following description, specific details such as specific configuration and components are provided only to help fully understand the embodiments of the present application. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. In addition, for clarity and brevity, the description of known functions and structures is omitted in the embodiment.

[0050] It should be understood that the references to "one embodiment" or "this embodiment" throughout the specification mean that the specific features, structures, or characteristics associated with the embodiment are included in at least one embodiment of the present application. Therefore, the references to "one embodiment" or "this embodiment" appearing throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] In addition, the present application may repeat reference numerals and / or letters in different examples. This repetition is for the purpose of simplicity and clarity, and does not in itself indicate the relationship between the various embodiments and / or settings discussed.

[0052] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist at the same time. The term " / and" in this article describes another type of association object relationship, indicating that there can be two relationships. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " in this article generally indicates that the previous and next associated objects are in an "or" relationship.

[0053] The term "at least one" in this article is merely a description of the association relationship of associated objects, indicating that there may be three relationships. For example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0054] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusions.

[0055] Example 1

[0056] This embodiment mainly describes a method for rescuing earth-rock dam leakage based on multi-model collaboration. Figure 1 As shown, including:

[0057] Obtain at least one or more leakage points of the earth-rock dam;

[0058] Collect the stress data of the leakage point of the earth-rock dam, input it into the pre-built early warning model, and output the early warning value of the leakage point;

[0059] Obtain the current water velocity data and the impact force data of the objects carried by the water flow in the watershed area of ​​the leakage point of the earth-rock dam, combine the leakage point warning value with the input of the leakage point danger warning model, and output the leakage point danger value;

[0060] The leakage point priority model is used to obtain the leakage point hazard value and perform real-time priority plugging sorting;

[0061] Obtain the force data of the leakage point of the earth-rock dam after plugging, collect the real-time flow velocity and impact force of the water flow at the plugged leakage point, input them into the pre-built plugging model, output the plugging force value, and determine the plugging leakage point out of warning through the preset threshold library;

[0062] Outputting the leakage point danger value of at least one leakage point of the earth-rock dam in real time, adjusting the priority order of the leakage points of the earth-rock dam, and carrying out emergency rescue of the leakage points of the earth-rock dam with priority;

[0063] Among them, the leakage point hazard value is obtained to set three or more level threshold intervals, and different categories are realized through corresponding level threshold intervals. The plugging types include but are not limited to grouting, geomembrane sandbags and expansion balls.

[0064] Further, the outputting of the leakage point warning value through the warning model specifically includes:

[0065] Obtain the stress data of the earth-rock dam under normal conditions and leakage conditions, perform preprocessing, and extract characteristic parameters; the preprocessing includes removing outliers and filling missing values;

[0066] Through the characteristic parameters, the initial warning model is constructed using the logistic regression algorithm. The formula is: ,in is the selected characteristic parameter, is the selected characteristic parameter coefficient, is the intercept, is the probability of leakage risk in earth-rock dam;

[0067] The characteristic parameters of the dam stress data under normal and leakage conditions are used for training, and the characteristic parameter coefficients in the logistic regression algorithm are determined by the maximum likelihood estimation method. , iterative training meets the verification requirements, forms an early warning model, and outputs the leakage point warning value.

[0068] Furthermore, the objects carried by the water flow include but are not limited to stones, gravel and mud;

[0069] The objects carried by the water flow are obtained through magnetic imaging technology. The real-time cross-section of the water flow is scanned by a magnetic imaging sensor to obtain the contour information of the real-time cross-section of the watershed area of ​​the leakage point of the earth-rock dam, and the objects carried by the water flow are determined based on the contour information.

[0070] Furthermore, the acquisition of the impact force data of the object carried by the water flow specifically includes:

[0071] Predict the volume of rocks, gravel and silt per unit volume of water flow using magnetic imaging technology, and determine the mass of rocks, gravel and silt by volume , the formula is: ;in , and is the unit volume ratio of stone, gravel and mud and sand sections, , and is the density of rocks, gravel and sediment, is the unit volume;

[0072] Determine the speed of the water flow by using a velocimeter ;

[0073] Calculate the water flow impact force, the formula is: ,in Unit water flow impact force, is the unit area, is the water flow impact force, where ,in is the speed correction factor, For time.

[0074] Furthermore, the construction of the leakage point hazard warning model specifically includes:

[0075] Obtain water flow velocity data and impact force data of objects carried by water flow, perform preprocessing, and extract characteristic parameters;

[0076] The characteristic parameters are divided into a training set, a validation set and a test set;

[0077] The training set data is input into the random forest model for training, samples are randomly extracted with replacement from the training set, and the training subset of the decision tree is constructed. This process is repeated until the preset maximum depth is reached or the number of node samples is less than the minimum number of samples.

[0078] Add decision trees to the random forest model and conduct multiple rounds of training to form an initial leakage point hazard warning model;

[0079] The validation set and the test set are input into the initial leakage point hazard warning model to conduct performance evaluation, adjust the parameters of the initial leakage point hazard warning model, and form a leakage point hazard warning model.

[0080] Furthermore, the output of the leakage point danger value is to comprehensively output the prediction results of the decision tree through the random forest model, and the leakage point danger value is output using the mean method, and the formula is: ,in For decision tree The prediction results, It is the danger value of leakage point.

[0081] Furthermore, the leakage point priority model obtains the leakage point hazard values ​​output by the leakage point hazard warning model, and sorts the hazard values ​​from high to low to form a first hazard value sequence; the leakage point priority model obtains the location information of the leakage points, sorts them according to the key points of the dam structure, and forms a second hazard value sequence; constructs a leakage point association framework, takes the leakage point hazard values ​​in the first hazard value sequence as nodes, and assigns important information to the location information of the key points of the leakage points in the second hazard value sequence, traverses the leakage point association framework through the ant colony algorithm, selects the next node according to the pheromone concentration on the path of the leakage point association framework, and iterates multiple rounds to form a priority order of the target leakage points.

[0082] Furthermore, the priority order of the target leakage points is obtained, the leakage point risk value of the first leakage point is extracted, and the preset threshold interval to which it belongs is determined; the preset threshold interval includes but is not limited to a high-risk interval, a medium-risk interval and a low-risk interval.

[0083] Furthermore, different types of plugging are adopted in the preset threshold intervals; the high-risk interval of the preset threshold interval adopts the concrete rapid-setting grouting method, in which the concrete rapid-setting slurry is injected into the leakage channel through high-pressure equipment, and quickly solidifies to form a high-strength plugging structure; the medium-risk interval adopts the method of geomembrane laying combined with sandbag stacking; the low-risk interval is expanded and sealed by using an expansion ball carried by a drone.

[0084] Furthermore, the plugging model is constructed by a multi-layer perceptron neural network model, and the plugging force value is output by the multi-layer perceptron neural network model, and whether the plugging leakage point is out of warning is determined according to a preset threshold library, specifically including:

[0085] Obtain the force data of the leakage point of the earth-rock dam after plugging, the real-time flow velocity of the plugged leakage point and the impact force of the water flow carrying objects, perform standardization processing, and form standardized data;

[0086] The standardized data is input into the input layer of the multi-layer perceptron neural network model, weighted sum is performed through the hidden layer of the multi-layer perceptron neural network model, and the blocking force value is output through the output layer;

[0087] The plugging force value is compared with the escape warning threshold in the preset threshold library. If the plugging force value is less than the escape warning threshold, the plugging part is in a safe range and the plugging leakage point is determined to be out of warning. If the plugging force value is greater than or equal to the escape warning threshold, there is a risk and it is elevated to the high-risk range of the preset threshold range for processing.

[0088] This embodiment describes in detail that by constructing an early warning model, the force data of the earth-rock dam is used to accurately output the early warning value of the leakage point, and the water flow velocity and the impact force data of the water flow are combined to construct a danger warning model to output the danger value, which can accurately evaluate the leakage situation and solve the problem of unscientific judgment of the degree of leakage danger in traditional and existing technologies. With the help of the leakage point priority model, the danger value and location information are integrated to achieve a reasonable sorting of the rescue of the leakage points, which changes the previous situation of waste of resources or untimely rescue due to the lack of scientific sorting. For leakage points of different danger levels, various and adaptive plugging methods such as concrete quick-setting grouting, geomembrane sandbag combination, and expansion ball plugging are adopted, and the plugging effect is evaluated through the plugging model, which effectively improves the success rate and stability of the plugging, overcomes the defects of the traditional plugging method of singleness and poor effect, greatly improves the efficiency and reliability of the rescue of the earth-rock dam leakage danger, and ensures the safety of the dam.

[0089] Based on Example 1, the force data of the leakage point of the earth-rock dam and the force data of the leakage point of the earth-rock dam after plugging are obtained through a distributed sensor group. The distributed sensor group includes a pressure sensor and a strain sensor. The pressure sensor usually adopts a piezoresistive structure. Its core component is a sensitive element made of semiconductor material. When subjected to external force, its resistance value will change, and the resistance change is converted into a voltage signal output through a Wheatstone bridge. The strain sensor is based on the principle of a resistance strain gauge. The strain gauge is pasted on the elastic element. When the elastic element is strained with the soil, the resistance value of the strain gauge changes, thereby generating an electrical signal proportional to the strain.

[0090] Regarding the layout of earth-rock dams, according to the structural characteristics of earth-rock dams and historical leakage data, sensors are arranged in layers at certain intervals and depths in the dam body, dam foundation, and weak parts of the dam where leakage is predicted, such as dam shoulders and around culverts. For example, in the vertical direction of the dam body, a layer of sensors is set every 1-2 meters, and each layer is arranged at intervals of 3-5 meters in the horizontal direction; when installing the sensors, it is necessary to ensure that they are in close contact with the soil to accurately sense the stress conditions. After the arrangement is completed, the sensor transmits the real-time monitored pressure and strain signals to the data acquisition box through the data line, and the data acquisition box amplifies, filters, and performs analog-to-digital conversion on the signals, and then transmits the processed digital signals to the computer control system. In the computer, the data processing software is used to analyze and calculate the signals, and the electrical signals collected by the sensors are converted into actual force values ​​according to the pre-set mechanical model algorithm to obtain the force data of the leakage points of the earth-rock dam.

[0091] In acquiring stress data after plugging, the plugging operation will change the stress state of the soil around the leakage point. The previously arranged distributed sensor group continues to work, and the technicians pay close attention to the data change trend transmitted by the sensor group. If the pressure or strain value gradually stabilizes after plugging and fluctuates within the preset safety range, it indicates that the plugging effect is good and the soil stress is under control. On the contrary, if the data fluctuates abnormally or continues to increase, exceeding the safety threshold, it indicates that there is a problem with the plugging and further inspection and processing are required. In this way, the stress condition of the earth-rock dam after plugging can be evaluated in a timely and accurate manner to ensure the safe and stable operation of the dam.

[0092] This embodiment adopts pressure and strain sensor technology. Its unique structure and careful arrangement ensure that it can accurately sense the force on the earth-rock dam. The sensor converts the force into an electrical signal, which is collected, transmitted and processed to provide key data for emergency rescue. It also continuously monitors after blocking and effectively judges the blocking effect, thus effectively ensuring the safety and stability of the dam.

[0093] Based on Example 1, the current water flow velocity data of the leakage point of the earth-rock dam is obtained by a flow meter. For the flow meter, the Doppler effect of sound waves propagating in water is used to measure the water flow velocity; for the layout of the leakage area in the earth-rock dam, according to the distribution of the leakage points of the dam and the flow path of the water flow, it is installed near the leakage point and at key positions upstream. For example, at the opening of the suspected leakage area.

[0094] The flow meter continuously emits sound wave signals and receives sound waves reflected by suspended particles in the water. By analyzing the frequency changes of the reflected waves, the speed of the water flow relative to the flow meter is calculated according to the Doppler formula. The processor inside the instrument processes and stores these data, and sends the real-time water flow velocity data to the data receiving terminal through wired or wireless transmission to obtain the current water flow velocity data at the leakage point of the earth-rock dam.

[0095] This embodiment obtains the water flow velocity at the leakage point to provide a more accurate risk value, making it more accurate in priority sorting.

[0096] Based on Example 1, Figure 2 As shown, for obtaining the leakage point danger value through the leakage point priority model, real-time priority plugging sorting is performed. For the priority model, after receiving the danger value output by the leakage point danger warning model, the sorting program will be quickly started to arrange the leakage point danger value from high to low, so as to accurately form the first danger value sequence. In this process, each danger value is clearly marked and distinguished;

[0097] For the location information collection of leakage points, the specific location of the leakage points in the dam structure is determined through monitoring equipment, and the leakage points are sorted according to the importance of their locations based on the design drawings of the dam and the classification standards of key structural parts, thereby forming a second hazard value sequence. For example, leakage points located in the core bearing part of the dam body or near the connection of important water conservancy facilities will be given a higher location importance score;

[0098] For the leakage point association framework, the danger value in the first danger value sequence is used as the basic node, and the location information in the second danger value sequence is used as the important attribute of the node for association; in this framework, each node represents a leakage point, which contains key data such as danger value and location information; through the ant colony algorithm, the path selection behavior of ants in the process of finding food is simulated, and virtual "ants" are allowed to traverse in the leakage point association framework. During the movement, the "ants" will choose the next node according to the pheromone concentration on the path. The higher the pheromone concentration, the greater the probability of being selected. After multiple rounds of iterations, the path selection is continuously optimized, and finally a scientific and reasonable priority order of target leakage points is formed.

[0099] During the rescue process, for the leakage points that have been successfully repaired, the system will automatically downgrade their danger values ​​and reduce their weight in the subsequent priority sorting; when it is found that the water flow velocity has changed, the danger level of the leakage point will be re-evaluated; for the leakage points in the area where the flow velocity increases significantly, if the flow velocity increases significantly, its danger value will be upgraded accordingly and adjusted forward in the priority order; conversely, the leakage points in the area where the flow velocity increases less or decreases will be downgraded to ensure that rescue resources are always allocated first to the leakage points that need to be treated most, thereby maximizing the safety of the earth-rock embankment.

[0100] The leakage point priority model of this embodiment integrates the hazard value and location information, and forms an accurate priority order through scientific sorting and ant colony algorithm iteration. It can downgrade the repaired points and dynamically adjust them according to the flow rate, ensure the rational allocation of emergency resources, efficiently deal with dangerous leakage points, and effectively enhance the timeliness and effectiveness of the safety protection of earth-rock dams.

[0101] Based on Example 1, the leakage point hazard value of the first leakage point in the extraction priority ranking is determined to belong to the preset threshold interval; the setting of the preset threshold interval is based on an in-depth analysis of historical data of a large number of earth-rock dam leakage cases, and is obtained in combination with a professional water conservancy project risk assessment model, taking into account multiple factors such as the force on the leakage point, the speed of the water flow, the impact force of the objects carried by the water flow, and the criticality of the leakage point in the dam structure, and the boundary values ​​of different intervals are determined using statistical methods.

[0102] For the preset threshold interval, if a high-risk interval, a medium-risk interval and a low-risk interval are set, the high-risk interval is usually characterized by the force at the leakage point approaching or exceeding the bearing limit of the dam material, the water flow rate is extremely fast and carries a large number of large-sized stones and other heavy objects, the impact force is enough to quickly destroy the dam structure, and the location is in the core of the dam body or a key weak point; for the medium-risk interval, the force and flow rate are at an intermediate level, and the objects carried by the water flow pose a certain threat; for the low-risk interval, the force and flow rate are relatively small, the impact force of the objects carried is weak, and the overall stability of the dam is less affected.

[0103] Different plugging types are adopted in preset threshold intervals; the high-risk interval of the preset threshold interval adopts the concrete quick-setting grouting method, in which the concrete quick-setting slurry is injected into the leakage channel through high-pressure equipment, and quickly solidifies to form a high-strength plugging structure; the medium-risk interval adopts the method of geomembrane laying combined with sandbag stacking; the low-risk interval is expanded and plugged by drones carrying expansion balls;

[0104] For high-risk areas, the rapid-setting concrete grouting method determines that a leakage point is in a high-risk area. The construction team quickly dispatches high-pressure equipment to the site. The pressure control system of the high-pressure equipment can ensure that the rapid-setting concrete slurry is injected into the leakage channel at a suitable pressure and flow rate, ensuring rapid solidification while having extremely high strength and adhesion. When the slurry enters the leakage channel, it quickly fills the gap and solidifies in a very short time, tightly combining with the surrounding soil to form a solid whole, effectively resisting the erosion of high-speed water flow and strong impact force, fundamentally stabilizing the dam structure and preventing further deterioration of leakage.

[0105] For the treatment of medium-risk areas, geomembrane laying combined with sandbag stacking is adopted. High-strength, corrosion-resistant geomembrane is selected, and its material has good flexibility and waterproof performance. Construction workers accurately cut the geomembrane according to the specific location and range of the leakage point, and lay it tightly in the leakage area. During the laying process, ensure that the geomembrane is wrinkle-free and undamaged, and use special sealing measures on the edges to prevent water from seeping in from the gaps. Subsequently, sandbags are stacked on the geomembrane. There are strict requirements for the filling amount and stacking method of sandbags. It is necessary to ensure the stability and compactness of the sandbag stacking to form a solid protective layer, which can not only enhance the impact resistance of the geomembrane, but also further block the water flow, providing reliable protection for the dam.

[0106] The method for plugging low-risk areas is to use drones carrying expansion balls for operations. The drone is equipped with a positioning system and a delivery device, which can quickly and accurately reach the top of the leakage point. The expansion ball is made of a special water-absorbing and expanding material. When it is not in contact with water, it is small and light, which is convenient for drones to carry. When the drone arrives at the designated location, the expansion ball is accurately delivered to the leakage point according to the preset program. The expansion ball expands rapidly after contacting the water flow and fills the leakage channel. Its expanded shape and size can perfectly fit the channel and effectively prevent water from passing through. In addition, its material has a certain elasticity and durability, which can maintain the plugging effect for a long time, greatly improving the treatment efficiency and convenience of low-risk leakage points.

[0107] The sealing ball can quickly seal the leakage channel, including:

[0108] Load the blocking balls onto the special delivery device carried by the transport drone;

[0109] The optimal path is planned based on the coordinates of the leakage point, the target point is locked using visual recognition technology, the drop height is adjusted, and the coordinates and height of the location point are fed back to the ground control center;

[0110] Send a command to activate the drone's delivery device and release the plugging ball near the leakage point;

[0111] The sealing ball hits the leakage point through the internal propulsion device, expands rapidly and forms a water-blocking barrier.

[0112] For point-to-point delivery of drones, the delivery location and location height are determined by detecting the current wind speed and wind direction to achieve precise delivery; the sealing ball includes a large number of blocking point balls, which quickly spread out when hitting the leakage point to form a blocking net, and quickly expand to fill the tiny leakage and edge area; the sealing point ball stores expansion fillers, which include but are not limited to polysorbate absorbent resin, expansion rubber or polyurethane gel.

[0113] This embodiment uses a differentiated blocking strategy for different threshold intervals to make the rescue work of earth-rock dam leakage more scientific and efficient. Whether it is emergency rescue in high-risk intervals or effective treatment in medium-risk and low-risk intervals, it can maximize the safety and stability of the dam, reduce the potential threat of leakage to the surrounding environment and people's lives and property, and provide strong technical support and guarantee for the safe operation of water conservancy projects.

[0114] The above are only preferred embodiments of the present invention, which do not limit the scope of protection of the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any changes, modifications, replacements, integrations and parameter changes to these embodiments within the spirit and principles of the present invention through conventional substitutions or without departing from the principles and spirit of the present invention fall within the scope of protection of the present invention.

Claims

1. A multi-model collaborative earth-rock dam leakage emergency rescue method, characterized in that: include: Obtain at least one or more leakage points of the earth-rock dam; Collect the stress data of the leakage point of the earth-rock dam, input it into the pre-built early warning model, and output the early warning value of the leakage point; Obtain the current water velocity data and the impact force data of the objects carried by the water flow in the watershed area of ​​the leakage point of the earth-rock dam, combine the leakage point warning value with the input of the leakage point danger warning model, and output the leakage point danger value; The leakage point priority model is used to obtain the leakage point hazard value and perform real-time priority plugging sorting; Obtain the force data of the leakage point of the earth-rock dam after plugging, collect the real-time flow velocity and impact force of the water flow at the plugged leakage point, input them into the pre-built plugging model, output the plugging force value, and determine the plugging leakage point out of warning through the preset threshold library; Outputting the leakage point danger value of at least one leakage point of the earth-rock dam in real time, adjusting the priority order of the leakage points of the earth-rock dam, and carrying out emergency rescue of the leakage points of the earth-rock dam with priority; Among them, the leakage point hazard value is obtained to set three or more level threshold intervals, and different categories are realized through corresponding level threshold intervals; the plugging types include but are not limited to grouting, geomembrane sandbags and expansion balls.

2. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The early warning model outputs the leakage point early warning value, including: Obtain the stress data of the earth-rock dam under normal conditions and leakage conditions, perform preprocessing, and extract characteristic parameters; the preprocessing includes removing outliers and filling missing values; Through the characteristic parameters, the initial warning model is constructed using the logistic regression algorithm. The formula is: ,in is the selected characteristic parameter, is the selected characteristic parameter coefficient, is the intercept, is the probability of leakage risk in earth-rock dam; The characteristic parameters of the dam stress data under normal and leakage conditions are used for training, and the characteristic parameter coefficients in the logistic regression algorithm are determined by the maximum likelihood estimation method. , iterative training meets the verification requirements, forms an early warning model, and outputs the leakage point warning value.

3. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The water flow carries but is not limited to stones, gravel and mud; The objects carried by the water flow are obtained through magnetic imaging technology. The real-time cross-section of the water flow is scanned by a magnetic imaging sensor to obtain the contour information of the real-time cross-section of the watershed area of ​​the leakage point of the earth-rock dam, and the objects carried by the water flow are determined based on the contour information.

4. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The acquisition of the impact force data of the object carried by the water flow specifically includes: Predict the volume of rocks, gravel and silt per unit volume of water flow using magnetic imaging technology, and determine the mass of rocks, gravel and silt by volume , the formula is: ;in , and is the unit volume ratio of stone, gravel and mud and sand sections, , and is the density of rocks, gravel and sediment, is the unit volume; Determine the speed of the water flow by using a velocimeter ; Calculate the water flow impact force, the formula is: ,in Unit water flow impact force, is the unit area, is the water flow impact force, ,in is the speed correction factor, For time.

5. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The construction of the leakage point hazard early warning model specifically includes: Obtain water flow velocity data and impact force data of objects carried by water flow, perform preprocessing, and extract characteristic parameters; Divide the feature parameters into training set, validation set and test set; The training set data is input into the random forest model for training, samples are randomly extracted with replacement from the training set, and the training subset of the decision tree is constructed. This process is repeated until the preset maximum depth is reached or the number of node samples is less than the minimum number of samples. Add decision trees to the random forest model and conduct multiple rounds of training to form an initial leakage point hazard warning model; The validation set and the test set are input into the initial leakage point hazard warning model to conduct performance evaluation, adjust the parameters of the initial leakage point hazard warning model, and form a leakage point hazard warning model.

6. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The output of the leakage point danger value is to comprehensively output the prediction results of the decision tree through the random forest model, and the leakage point danger value is output using the mean method, and the formula is: ,in For decision tree The prediction results, It is the danger value of leakage point.

7. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: The leakage point priority model obtains the leakage point hazard value output by the leakage point hazard warning model, sorts the hazard value from high to low, and forms a first hazard value sequence; the leakage point priority model obtains the location information of the leakage point, sorts it according to the key points of the dam structure, and forms a second hazard value sequence; constructs a leakage point association framework, takes the leakage point hazard value in the first hazard value sequence as a node, and assigns important information to the location information of the key points of the leakage point in the second hazard value sequence, traverses the leakage point association framework through the ant colony algorithm, selects the next node according to the pheromone concentration on the path of the leakage point association framework, and iterates multiple rounds to form a priority order of the target leakage points.

8. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 7 is characterized in that: The priority order of the target leakage points is obtained, the leakage point risk value of the first leakage point is extracted, and the preset threshold interval to which it belongs is determined; the preset threshold interval includes but is not limited to a high-risk interval, a medium-risk interval and a low-risk interval.

9. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1 is characterized in that: Different types of plugging are adopted in preset threshold intervals; the high-risk area within the preset threshold interval is filled with concrete quick-setting grouting method, in which concrete quick-setting slurry is injected into the leakage channel through high-pressure equipment, which quickly solidifies to form a high-strength plugging structure; the medium-risk area adopts geomembrane laying combined with sandbag stacking; the low-risk area is sealed with expansion balls carried by drones.

10. The method for emergency rescue of earth-rock dam leakage based on multi-model collaboration according to claim 1, characterized in that: The plugging model is constructed by a multi-layer perceptron neural network model, and the plugging force value is output by the multi-layer perceptron neural network model. It is determined whether the plugging leakage point is out of warning according to the preset threshold library, which specifically includes: Obtain the force data of the leakage point of the earth-rock dam after plugging, the real-time flow velocity of the plugged leakage point and the impact force of the water flow carrying objects, perform standardization processing, and form standardized data; The standardized data is input into the input layer of the multi-layer perceptron neural network model, weighted sum is performed through the hidden layer of the multi-layer perceptron neural network model, and the blocking force value is output through the output layer; The plugging force value is compared with the escape warning threshold in the preset threshold library. If the plugging force value is less than the escape warning threshold, the plugging part is in a safe range and the plugging leakage point is determined to be out of warning. If the plugging force value is greater than or equal to the escape warning threshold, there is a risk and it is elevated to the high-risk range of the preset threshold range for processing.

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