In-situ injection method and device for barrier material

Abstract

The present invention relates to the field of barrier construction technology, and in particular to a method and apparatus for in-situ injection of barrier materials. By optimizing the injection scheme and obtaining standard data through a computer simulation system, the Internet of Things (IoT) is used to obtain actual data during the actual injection process and compare and analyze the working status with the standard data. Unexpected conditions are evaluated and regulated using a gradient boosting tree model constructed based on historical feature data. This method can improve the accuracy and effectiveness of barrier material injection, optimize the injection scheme in advance through simulation, monitor and adjust unexpected conditions in real time, reduce errors and failures during the injection process, and ensure the injection effect of the barrier material in the target area.

Description

Technical Field

[0001] The present invention relates to the technical field of barrier construction, in particular to an in-situ injection method and device of a barrier material. Background Art

[0002] With the development of industries, agriculture, and environmental protection, many engineering projects involve tasks such as groundwater pollution prevention and control and soil remediation. Demand for the isolation and quarantine of hazardous substances is increasing, particularly for these applications. While traditional barrier material injection techniques have been applied in some projects, they still face challenges such as difficulty in uniformly controlling the injection effect, material waste, and long construction cycles. Therefore, developing a more precise, stable, and efficient in-situ injection technology has become a research hotspot in the industry.

[0003] Currently, common barrier materials include alkali-activated gels and modified geomembranes. These materials typically possess excellent physical and chemical properties, effectively forming a barrier to prevent the spread of pollutants. However, traditional barrier material injection methods rely primarily on manual operation, making it difficult to achieve high-precision and efficient injection control. Therefore, achieving precise in-situ injection to ensure that these materials form a uniform and lasting barrier effect in the target area remains a technical challenge. Furthermore, traditional injection devices are complex in structure and cumbersome to operate, making them difficult to meet the demands of modern engineering for high efficiency and intelligence. Summary of the Invention

[0004] The present invention overcomes the deficiencies of the prior art and provides an in-situ injection method and device for a barrier material.

[0005] In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0006] The present invention discloses an in-situ injection method of a barrier material, comprising the following steps:

[0007] Obtaining a preset injection plan for the barrier material, importing the preset injection plan into a computer simulation system for simulated injection evaluation, and outputting the final injection plan of the in-situ injection device and its standard dynamic injection data pool during the working process after the simulation results meet the preset requirements;

[0008] Transmitting the final injection plan to a controller of an in-situ injection device, and controlling the in-situ injection device to inject the barrier material into the target area according to the final injection plan;

[0009] During the injection process, actual injection data of the in-situ injection device at each preset injection time point is obtained based on the Internet of Things; and standard injection data of the in-situ injection device at each preset injection time point is obtained from the standard dynamic injection data pool;

[0010] Analyze the working status of the in-situ injection device based on the actual injection data and the standard injection data at each preset injection time point; if the working status of the in-situ injection device is the expected state, there is no need to correct the injection data of the in-situ injection device;

[0011] Acquire historical feature data corresponding to various abnormal operation events of the in-situ injection device, and construct a gradient boosting tree model based on the historical feature data;

[0012] If the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results; wherein, the barrier material includes alkali-activated gel and modified geomembrane barrier material.

[0013] Furthermore, a preset injection plan for the barrier material is obtained, and the preset injection plan is imported into a computer simulation system for simulated injection evaluation. After the simulation results meet the preset requirements, the final injection plan of the in-situ injection device and its standard dynamic injection data pool during the working process are output, specifically:

[0014] Obtaining a three-dimensional structural model diagram of the in-situ injection device and a regional characteristic model diagram of the target area, and constructing a computer simulation system for injecting barrier material into the target area based on the three-dimensional structural model diagram of the in-situ injection device and the regional characteristic model diagram of the target area in combination with computer simulation software; wherein the target area is the area to be blocked;

[0015] Obtaining a preset injection plan for the barrier material, importing the preset injection plan into the computer simulation system to perform injection simulation analysis, and outputting the simulation results after the simulation is completed;

[0016] Obtaining, from the simulation results, a predicted permeability coefficient of the barrier material after the barrier material is simulated and injected into the target area according to the preset injection scheme; and comparing the predicted permeability coefficient with a preset threshold;

[0017] If the predicted permeability coefficient is greater than a preset threshold, there is no need to adjust or optimize the preset injection plan, and the preset injection plan is output as the final injection plan;

[0018] If the predicted permeability coefficient is not greater than a preset threshold, a genetic algorithm is introduced to iteratively adjust and optimize the preset injection scheme. After each iterative optimization cycle, an optimized injection scheme is obtained, and the predicted permeability coefficient of the optimized injection scheme is evaluated through a simulation system. When the predicted permeability coefficient of the optimized injection scheme is greater than the preset threshold, the iteration is stopped and the optimized injection scheme is output as the final injection scheme.

[0019] After the final injection plan is output, the standard injection data of the in-situ injection device based on the time series when the in-situ injection device is controlled by the final injection plan to perform simulation work is obtained in the computer simulation system, and the various standard injection data are aggregated to obtain a standard dynamic injection data pool of the in-situ injection device during the injection work.

[0020] Furthermore, the working status of the in-situ injection device is analyzed based on the actual injection data and the standard injection data at each preset injection time point, specifically:

[0021] Constructing a dynamic transformation curve graph of actual injection data based on actual injection data of the in-situ injection device at each preset injection time point; and constructing a dynamic transformation curve graph of standard injection data based on standard injection data of the in-situ injection device at each preset injection time point;

[0022] Performing a coincidence analysis on the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph to obtain a curve coincidence degree between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph;

[0023] Comparing the curve coincidence between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph with a preset coincidence;

[0024] If the curve overlap between the actual injection data dynamic transformation curve diagram and the standard injection data dynamic transformation curve diagram is greater than the preset overlap, it means that the actual working state of the in-situ injection device is consistent with the expected working state and the injection effect meets expectations, then the working state of the in-situ injection device is calibrated as the expected state;

[0025] If the curve overlap between the actual injection data dynamic transformation curve and the standard injection data dynamic transformation curve is not greater than the preset overlap, it means that the actual working state of the in-situ injection device deviates from the expected working state, and the injection effect does not meet expectations, then the working state of the in-situ injection device is calibrated as an unexpected state.

[0026] Furthermore, historical feature data corresponding to various abnormal operation events of the in-situ injection device are obtained, and a gradient boosting tree model is constructed based on the historical feature data, specifically:

[0027] Obtain historical characteristic data corresponding to various abnormal operation events of the in-situ injection device through the big data network;

[0028] The collected historical characteristic data of abnormal events in the operation of the in-situ injection device are organized into a structured table format, where each record corresponds to an abnormal event name and a characteristic value of the corresponding historical characteristic data;

[0029] Extract the eigenvalues ​​from the table to form a feature matrix X, and the abnormal results to form a label vector y; where X contains the eigenvalues ​​used to train the model, and y contains the corresponding abnormal labels;

[0030] Introducing the gradient boosting tree algorithm, initializing the gradient boosting tree model, and setting the number of iterations; using the gradient boosting framework and combining it with the feature matrix X to iteratively construct a decision tree, and correcting the residual of the previous decision tree according to the corresponding label vector y to form a strong learner;

[0031] At the same time, in each iteration, the gradient and second-order derivative of the loss function are calculated and used as the input of the new weak learner to update the model to minimize the loss function;

[0032] After reaching the number of iterations, a gradient boosting tree model consisting of multiple decision trees is constructed.

[0033] Furthermore, if the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, specifically:

[0034] If the working state of the in-situ injection device is an unexpected state, obtaining actual injection data of the in-situ injection device at each preset injection time point;

[0035] Importing actual injection data of the in-situ injection device at each preset injection time point into the gradient boosting tree model to diagnose the in-situ injection device in an unexpected state, and obtaining the name of the abnormal event currently occurring in the in-situ injection device in the unexpected state;

[0036] Determine the characteristic type of the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state; wherein the characteristic type of the abnormal event name includes an uncontrollable abnormal operation event and a controllable abnormal operation event;

[0037] If the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state is an uncontrollable abnormal operation event, the in-situ injection device is controlled to suspend the injection work, and the name of the abnormal event currently occurring in the in-situ injection device is sent to the preset terminal for display.

[0038] Furthermore, if the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, further comprising the following steps:

[0039] Obtaining control log information of the in-situ injection device, obtaining adjustment measures corresponding to various deviations of various injection data in the in-situ injection device based on the control log information, and obtaining adjustment accuracy after adjusting the deviated injection data through the various adjustment measures;

[0040] Obtaining the adjustment measure corresponding to the maximum adjustment accuracy, and taking the adjustment measure corresponding to the maximum adjustment accuracy as the optimal adjustment measure for the corresponding deviation of the corresponding injection data, and so on, obtaining the optimal adjustment measures corresponding to various deviations of the injection data in the in-situ injection device;

[0041] Constructing a knowledge matching library, and importing the optimal adjustment measures corresponding to various deviations of various injection data in the in-situ injection device into the knowledge matching library;

[0042] If the abnormal event currently occurring in the in-situ injection device in an unexpected state is named as a controllable abnormal operation event, then obtaining various real-time injection data of the in-situ injection device at the current injection time point; and obtaining various standard injection data of the in-situ injection device at the current injection time point from the standard dynamic injection data pool;

[0043] Calculate the data deviation between various real-time injection data of the in-situ injection device at the current injection time point and the corresponding standard injection data;

[0044] Marking the real-time injection data whose data deviation value is greater than the preset difference value as running deviation injection data, and obtaining the data deviation value between the running deviation injection data and the corresponding standard injection data;

[0045] The data deviation value between the operation deviation injection data and the corresponding standard injection data is imported into the knowledge matching library for matching, and the best adjustment measure is obtained by matching. The best adjustment measure obtained by matching is sent to the controller to correct the operation deviation injection data.

[0046] Another aspect of the present invention discloses an in-situ injection device for a barrier material, which is applicable to any one of the in-situ injection methods for a barrier material. The in-situ injection device comprises a material tank and a syringe, and the material tank and the syringe are connected by a feeding pipe.

[0047] The injector includes a base, hydraulic cylinders are provided on both sides of the base, the output end of the hydraulic cylinder is cooperatively connected to a pull rod, the top end of the pull rod is cooperatively connected to a mounting platform, a material pump is provided on the mounting platform, the input end of the material pump is connected to the feeding pipe, the output end of the material pump is cooperatively connected to a steel pipe, and the bottom end of the steel pipe is cooperatively connected to an injection head;

[0048] The syringe is also provided with a pressure gauge and a controller.

[0049] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: the in-situ injection method of the barrier material optimizes the injection scheme and obtains standard data through a computer simulation system, obtains actual data with the help of the Internet of Things during the actual injection process, and compares and analyzes the working status with the standard data. For unexpected conditions, a gradient boosting tree model constructed based on historical feature data is used to evaluate and adjust the control, which can improve the accuracy and effectiveness of the injection of the barrier material, optimize the injection scheme in advance through simulation, monitor and adjust the unexpected conditions in real time, reduce errors and faults in the injection process, and ensure the injection effect of the barrier material in the target area. It is particularly suitable for the in-situ injection of alkali-activated gel and modified geomembrane barrier materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[0051] Figure 1 A first method flow chart of a method for in-situ injection of a barrier material;

[0052] Figure 2 The second method flow chart is a method for in-situ injection of barrier materials;

[0053] Figure 3 This is a structural schematic diagram of an in-situ injection device for barrier materials. DETAILED DESCRIPTION

[0054] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0056] like Figure 1 As shown, the present invention discloses an in-situ injection method of a barrier material, comprising the following steps:

[0057] S102: Obtain a preset injection plan for the barrier material, import the preset injection plan into a computer simulation system for simulated injection evaluation, and output a final injection plan for the in-situ injection device and a standard dynamic injection data pool during the working process after the simulation results meet the preset requirements;

[0058] S104, transmitting the final injection plan to a controller of an in-situ injection device, and controlling the in-situ injection device to inject the barrier material into the target area according to the final injection plan;

[0059] S106. During the injection process, actual injection data of the in-situ injection device at each preset injection time point is obtained based on the Internet of Things; and standard injection data of the in-situ injection device at each preset injection time point is obtained from the standard dynamic injection data pool;

[0060] S108, analyzing the working state of the in-situ injection device based on the actual injection data and the standard injection data at each preset injection time point; if the working state of the in-situ injection device is the expected state, there is no need to correct the injection data of the in-situ injection device;

[0061] S110, acquiring historical feature data corresponding to various abnormal operation events occurring in the in-situ injection device, and constructing a gradient boosting tree model based on the historical feature data;

[0062] S112. If the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results; wherein the barrier material includes alkali-activated gel and modified geomembrane barrier material.

[0063] Among them, the final injection plan is a set of static parameter configurations to guide actual operations, aiming to ensure that the barrier effect meets the standards; and the standard dynamic working data pool is a set of dynamic data generated when simulating the operation of the device in a simulation environment based on the plan, reflecting the expected performance of the device in the process of executing the plan, providing a reference and monitoring basis for actual operations.

[0064] It should be noted that a preset injection plan for the barrier material is first obtained and imported into a computer simulation system for simulated injection evaluation. During this process, the computer simulation system simulates the preset injection plan based on the characteristics of the in-situ injection device and relevant parameters of the target area. The preset injection plan includes parameters such as injection rate, injection pressure, and injection volume over time. Only when the simulation results meet the preset requirements will the final injection plan and the corresponding standard dynamic injection data pool be output for the in-situ injection device. The standard dynamic injection data pool is a collection of standard injection data recorded in a time series during the simulation of the final injection plan. This data will serve as a reference for the subsequent actual injection process. The resulting final injection plan is transmitted to the controller of the in-situ injection device. The controller controls the in-situ injection device according to this plan to inject the barrier material into the target area. During this process, the in-situ injection device operates according to the parameters set in the final injection plan, injecting the barrier material (such as alkali-activated gel and modified geomembrane barrier material) into the target area. During the injection process, the actual injection data of the in-situ injection device at each preset injection time point is obtained using the Internet of Things. At the same time, the standard injection data of the same preset injection time point is obtained from the standard dynamic injection data pool obtained previously. Then, the working status of the in-situ injection device is analyzed based on these two sets of data. If the actual injection data is consistent with the standard injection data, that is, the working status of the in-situ injection device is the expected state, it means that the injection work is proceeding normally and there is no need to correct the injection data. When the working state of the in-situ injection device is unexpected, the actual injection data of each preset injection time point is imported into this gradient boosting tree model for evaluation. The model will determine the possible abnormal conditions of the device based on the input data, and then adjust and control the in-situ injection device based on the evaluation results, so that the working state of the device can be restored to the expected state as much as possible.

[0065] This in-situ injection method of barrier materials optimizes the injection scheme and obtains standard data through a computer simulation system. During the actual injection process, the Internet of Things is used to obtain actual data and compare and analyze the working status with the standard data. For unexpected conditions, a gradient boosting tree model constructed based on historical feature data is used for evaluation and regulation control. This can improve the accuracy and effectiveness of barrier material injection, optimize the injection scheme in advance through simulation, monitor and adjust unexpected conditions in real time, reduce errors and failures during the injection process, and ensure the injection effect of the barrier material in the target area. It is particularly suitable for the in-situ injection of alkali-activated gel and modified geomembrane barrier materials.

[0066] Furthermore, a preset injection scheme of the barrier material is obtained, and the preset injection scheme is imported into the computer simulation system for simulated injection evaluation. After the simulation results meet the preset requirements, the final injection scheme of the in-situ injection device and its standard dynamic injection data pool during the working process are output, such as Figure 2 As shown, specifically:

[0067] S202: Obtain a three-dimensional structural model diagram of the in-situ injection device and a regional characteristic model diagram of the target area, and construct a computer simulation system for injecting the barrier material into the target area based on the three-dimensional structural model diagram of the in-situ injection device and the regional characteristic model diagram of the target area in combination with computer simulation software; wherein the target area is the area to be blocked;

[0068] S204, obtaining a preset injection plan for the barrier material, importing the preset injection plan into the computer simulation system to perform injection simulation analysis, and outputting the simulation results after the simulation is completed;

[0069] S206, obtaining, from the simulation results, a predicted permeability coefficient of the barrier material after the barrier material is simulated and injected into the target area according to the preset injection scheme; and comparing the predicted permeability coefficient with a preset threshold;

[0070] S208: If the predicted permeability coefficient is greater than a preset threshold, there is no need to adjust or optimize the preset injection plan, and the preset injection plan is output as the final injection plan;

[0071] S210: If the predicted permeability coefficient is not greater than a preset threshold, a genetic algorithm is introduced to iteratively adjust and optimize the preset injection scheme. After each iterative optimization cycle, an optimized injection scheme is obtained. The predicted permeability coefficient of the optimized injection scheme is evaluated through a simulation system. The iteration is stopped until the predicted permeability coefficient of the optimized injection scheme is greater than the preset threshold. The optimized injection scheme is output as the final injection scheme.

[0072] S212. After the final injection plan is output, the standard injection data of the in-situ injection device based on the time series when the in-situ injection device is controlled by the final injection plan to perform simulation work is obtained in the computer simulation system, and each standard injection data is aggregated to obtain a standard dynamic injection data pool of the in-situ injection device during the injection work.

[0073] Among them, the preset injection plan is designed by relevant technical personnel based on their experience; the regional characteristic model diagram is a graphical model representation that can reflect the geological structure (such as soil type, stratum distribution, etc.), spatial form (shape, size, slope, etc.), physical properties (such as porosity, permeability, etc.) of the target area (area to be blocked) and other related characteristics that may affect the injection effect of the barrier material.

[0074] Specifically, a three-dimensional structural model of the in-situ injection device and a regional characteristic model of the target area (the area to be blocked) are obtained. Then, using computer simulation software (such as ANSYS or COMSOL Multiphysics), a computer simulation system specifically designed to simulate the barrier material injection process is constructed. This serves as the foundation for the entire simulation evaluation process. By integrating relevant models of the device and the target area, the injection process can be accurately simulated in a virtual environment. The preset barrier material injection plan is then input into the constructed computer simulation system for injection simulation analysis. After the simulation is complete, the simulation results are obtained. The predicted permeability coefficient after the barrier material is injected into the target area using the preset injection plan is of particular interest. This predicted permeability coefficient is a key indicator of the injection plan's effectiveness. It is then compared with a preset threshold. If the predicted permeability coefficient exceeds the threshold, the preset injection plan meets the requirements and no adjustments are required. The final injection plan is then output directly. If the predicted permeability coefficient does not exceed the threshold, a genetic algorithm is used to iteratively adjust and optimize the preset injection plan. A genetic algorithm is an optimization algorithm that mimics the biological evolution process to find the optimal solution. After each iterative optimization cycle, an optimized injection plan is obtained. The predicted permeability coefficient of this optimized injection plan is then evaluated by the simulation system. This process is repeated until the predicted permeability coefficient of the optimized injection plan exceeds a preset threshold. At this point, iterations are stopped and this optimized injection plan is output as the final injection plan. Once the final injection plan is determined, the computer simulation system obtains standard injection data based on a time series when the in-situ injection device is controlled by this final injection plan to perform simulation operations. By combining these standard injection data, a standard dynamic injection data pool for the in-situ injection device during the injection process is obtained. This data pool provides a standard reference for subsequent data comparison and analysis during the actual injection process.

[0075] Through the aforementioned series of steps, a computer simulation system can be used to comprehensively and meticulously evaluate and optimize the pre-set injection plan before the actual barrier material injection. The construction of a computer simulation system, combining models of the target area and the injection device, ensures that the simulation is more realistic. Through analysis of the predicted permeability coefficient and optimization using a genetic algorithm, the resulting injection plan is guaranteed to meet the barrier effect requirements, ensuring that the permeability coefficient of the barrier material in the target area meets the expected standard. The establishment of a standard dynamic injection data pool provides an accurate reference for subsequent monitoring and adjustments during the actual injection process, helping to improve the accuracy, effectiveness, and reliability of in-situ injection, thereby achieving efficient and precise in-situ injection of barrier materials.

[0076] Furthermore, the working status of the in-situ injection device is analyzed based on the actual injection data and the standard injection data at each preset injection time point, specifically:

[0077] Constructing a dynamic transformation curve graph of actual injection data based on actual injection data of the in-situ injection device at each preset injection time point; and constructing a dynamic transformation curve graph of standard injection data based on standard injection data of the in-situ injection device at each preset injection time point;

[0078] Performing a coincidence analysis on the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph to obtain a curve coincidence degree between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph;

[0079] Comparing the curve coincidence between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph with a preset coincidence;

[0080] If the curve overlap between the actual injection data dynamic transformation curve diagram and the standard injection data dynamic transformation curve diagram is greater than the preset overlap, it means that the actual working state of the in-situ injection device is consistent with the expected working state and the injection effect meets expectations, then the working state of the in-situ injection device is calibrated as the expected state;

[0081] If the curve overlap between the actual injection data dynamic transformation curve and the standard injection data dynamic transformation curve is not greater than the preset overlap, it means that the actual working state of the in-situ injection device deviates from the expected working state, and the injection effect does not meet expectations, then the working state of the in-situ injection device is calibrated as an unexpected state.

[0082] Specifically, first, a dynamic transformation curve diagram of actual injection data is constructed for the actual injection data of the in-situ injection device at each preset injection time point. This curve can intuitively show the dynamic change trend of the actual injection data (such as the change of injection volume, injection pressure, etc. over time) during the entire injection process. At the same time, a dynamic transformation curve diagram of standard injection data is constructed based on the standard injection data of the same preset injection time point. This curve represents the change of injection data over time that the injection device should achieve under ideal circumstances. The constructed dynamic transformation curve diagram of actual injection data and the dynamic transformation curve diagram of standard injection data are analyzed for overlap, with the aim of calculating the curve overlap between the two. This curve overlap is a quantitative indicator used to measure the degree of similarity between the actual injection process and the ideal injection process. If the curve overlap is greater than the preset overlap, this means that the actual injection process is closer to the ideal situation, and the actual working state of the in-situ injection device is consistent with the expected working state, indicating that the injection effect is in line with expectations. At this time, the working state of the in-situ injection device is calibrated as the expected state. On the contrary, if the curve overlap is not greater than the preset overlap, it means that there is a large deviation between the actual injection process and the ideal situation, the actual working state of the in-situ injection device deviates from the expected working state, and the injection effect does not meet expectations, so the working state of the in-situ injection device is calibrated as an unexpected state.

[0083] By constructing a dynamic transformation curve chart of actual and standard injection data, calculating the degree of overlap between the two curves, and comparing it with the preset overlap, the operating status of the in-situ injection device can be determined. This allows for intuitive and quantitative evaluation of the in-situ injection device's operating status, promptly identifying discrepancies between the actual injection process and the ideal state. This provides a basis for subsequent adjustments or optimizations to the injection device, ensuring that the injection process proceeds as expected and improving the accuracy and effectiveness of the in-situ injection of barrier materials.

[0084] Furthermore, historical feature data corresponding to various abnormal operation events of the in-situ injection device are obtained, and a gradient boosting tree model is constructed based on the historical feature data, specifically:

[0085] Obtain historical characteristic data corresponding to various abnormal operation events of the in-situ injection device through the big data network;

[0086] The collected historical characteristic data of abnormal events in the operation of the in-situ injection device are organized into a structured table format, where each record corresponds to an abnormal event name and a characteristic value of the corresponding historical characteristic data;

[0087] Extract the eigenvalues ​​from the table to form a feature matrix X, and the abnormal results to form a label vector y; where X contains the eigenvalues ​​used to train the model, and y contains the corresponding abnormal labels;

[0088] Introducing the gradient boosting tree algorithm, initializing the gradient boosting tree model, and setting the number of iterations; using the gradient boosting framework and combining it with the feature matrix X to iteratively construct a decision tree, and correcting the residual of the previous decision tree according to the corresponding label vector y to form a strong learner;

[0089] At the same time, in each iteration, the gradient and second-order derivative of the loss function are calculated and used as the input of the new weak learner to update the model to minimize the loss function;

[0090] After reaching the number of iterations, a gradient boosting tree model consisting of multiple decision trees is constructed.

[0091] Specifically, historical feature data from various operational anomalies occurring in the in-situ injection system is first collected through a big data network. This data, sourced from a wide range of sources, contains a wide range of relevant information from past anomalies encountered during the system's operation. This data is then organized into a structured table, where each record clearly corresponds to the name of the anomaly and the eigenvalues ​​of the associated historical feature data. This organization creates a clear data structure, facilitating subsequent model construction. Eigenvalues ​​are extracted from the organized table to form a feature matrix X, and the anomaly results are converted into a label vector y. The feature matrix X contains the eigenvalues ​​used to train the model, such as the values ​​of relevant parameters such as injection pressure, injection rate, and equipment temperature at the time of the anomaly, while the label vector y contains the corresponding anomaly labels, such as blockage, leakage, and other anomaly types. The gradient boosting tree algorithm is then introduced, the model is initialized, and the number of iterations is set. The gradient boosting tree algorithm is a decision tree-based ensemble learning algorithm that iteratively constructs decision trees to improve model accuracy. During model construction, the gradient boosting framework is combined with the feature matrix X to iteratively construct the decision tree. After each decision tree is constructed, the residual of the previous decision tree is corrected based on the corresponding label vector y, gradually building a strong learner. At the same time, during each iteration, the gradient and second-order derivative of the loss function are calculated and used as input to a new weak learner (i.e., a new decision tree). The loss function is minimized by continuously updating the model. This process is repeated until the preset number of iterations is reached, ultimately constructing a gradient boosting tree model composed of multiple decision trees. This model can comprehensively consider the relationships between multiple eigenvalues ​​and accurately predict and classify abnormal operating conditions of the in-situ injection device.

[0092] By acquiring historical characteristic data of abnormal operation events of the in-situ injection device, a gradient boosting tree model was constructed. This approach fully utilizes the information in the historical data and integrates and analyzes various characteristic values ​​that may affect the device's operation. The constructed gradient boosting tree model can accurately classify and predict abnormal operation of the in-situ injection device, providing an effective tool for timely identification and resolution of operational issues, helping to improve the stability and reliability of the in-situ injection device and ensure the smooth operation of the barrier material injection process.

[0093] Furthermore, if the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, specifically:

[0094] If the working state of the in-situ injection device is an unexpected state, obtaining actual injection data of the in-situ injection device at each preset injection time point;

[0095] Importing actual injection data of the in-situ injection device at each preset injection time point into the gradient boosting tree model to diagnose the in-situ injection device in an unexpected state, and obtaining the name of the abnormal event currently occurring in the in-situ injection device in the unexpected state;

[0096] Determine the characteristic type of the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state; wherein the characteristic type of the abnormal event name includes an uncontrollable abnormal operation event and a controllable abnormal operation event;

[0097] If the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state is an uncontrollable abnormal operation event, the in-situ injection device is controlled to suspend the injection work, and the name of the abnormal event currently occurring in the in-situ injection device is sent to the preset terminal for display.

[0098] Uncontrollable abnormal operating events refer to abnormal conditions that occur during the operation of an in-situ injection device and cannot be corrected by adjusting the device's own operations or parameters. For example, irreparable mechanical damage to a key component of the device, such as a ruptured syringe tube that cannot be repaired on-site, or severe damage to the device caused by external force majeure factors such as earthquakes and floods, resulting in the device being unable to operate normally, are beyond the device's ability to regulate itself.

[0099] Adjustable abnormal operating events are abnormal conditions that occur during the operation of the in-situ injection device and can be restored to normal operation by adjusting the device's internal parameters, operating mode, or operating procedures. For example, if the injection pressure deviates from the standard value, it may be due to an improper valve opening. Adjusting the valve opening can restore the injection pressure to normal. Or, if the injection speed is abnormal, it may be due to an unreasonable frequency setting of the material pump. Resetting the pump frequency can solve the injection speed problem.

[0100] It should be noted that when it is determined that the working state of the in-situ injection device is an unexpected state, the actual injection data of the device at each preset injection time point is first obtained. These actual injection data contain various information of the device during the actual operation process, such as injection volume, injection pressure, etc. These actual injection data are then imported into the previously constructed gradient boosting tree model. The gradient boosting tree model is constructed based on a large amount of historical feature data of abnormal operation events of the in-situ injection device, and has the ability to diagnose abnormal situations. By importing the actual injection data into the model, the in-situ injection device in an unexpected state can be diagnosed, so as to obtain the name of the abnormal event currently occurring in the device. After obtaining the name of the abnormal event, it is necessary to determine its feature type. Here, the feature types of the abnormal event names are divided into uncontrollable abnormal operation events and controllable abnormal operation events. This classification is to take different countermeasures according to different abnormal types. If the in-situ injection device is determined to be in an unexpected state and the current abnormal event is an uncontrollable abnormal operation event, since this type of abnormality cannot be resolved by adjusting the device itself, the measure taken is to control the in-situ injection device to suspend injection operations to avoid potentially more serious consequences, such as equipment damage or severe deterioration of injection results. At the same time, the name of the abnormal event currently occurring in the in-situ injection device is sent to a preset terminal for display, so that relevant personnel can be promptly informed of the abnormal situation and perform subsequent processing, such as repairing or replacing the equipment.

[0101] By importing actual injection data into a gradient boosting tree model for diagnosis when the in-situ injection device is operating in an unexpected state, the system can accurately determine the name and characteristic type of the device's current abnormal event. For uncontrollable abnormal operating events, the system suspends injection operations and notifies relevant personnel, helping to prevent further damage to the equipment and ensure the safety of the injection process. This approach enables intelligent diagnosis and targeted handling of in-situ injection device anomalies, improving the reliability of the device's operation and the controllability of the injection process.

[0102] Furthermore, if the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, further comprising the following steps:

[0103] Obtaining control log information of the in-situ injection device, obtaining adjustment measures corresponding to various deviations of various injection data in the in-situ injection device based on the control log information, and obtaining adjustment accuracy after adjusting the deviated injection data through the various adjustment measures;

[0104] Obtaining the adjustment measure corresponding to the maximum adjustment accuracy, and taking the adjustment measure corresponding to the maximum adjustment accuracy as the optimal adjustment measure for the corresponding deviation of the corresponding injection data, and so on, obtaining the optimal adjustment measures corresponding to various deviations of the injection data in the in-situ injection device;

[0105] Constructing a knowledge matching library, and importing the optimal adjustment measures corresponding to various deviations of various injection data in the in-situ injection device into the knowledge matching library;

[0106] If the abnormal event currently occurring in the in-situ injection device in an unexpected state is named as a controllable abnormal operation event, then obtaining various real-time injection data of the in-situ injection device at the current injection time point; and obtaining various standard injection data of the in-situ injection device at the current injection time point from the standard dynamic injection data pool;

[0107] Calculate the data deviation between various real-time injection data of the in-situ injection device at the current injection time point and the corresponding standard injection data;

[0108] Marking the real-time injection data whose data deviation value is greater than the preset difference value as running deviation injection data, and obtaining the data deviation value between the running deviation injection data and the corresponding standard injection data;

[0109] The data deviation value between the operation deviation injection data and the corresponding standard injection data is imported into the knowledge matching library for matching, and the best adjustment measure is obtained by matching. The best adjustment measure obtained by matching is sent to the controller to correct the operation deviation injection data.

[0110] Specifically, the control log information of the in-situ injection device is first obtained. From this information, the corresponding adjustment measures for each injection data deviation are identified, as well as the adjustment accuracy after these adjustment measures are implemented. By comparing the adjustment accuracy of different adjustment measures, the adjustment measure with the highest adjustment accuracy is found and determined as the optimal adjustment measure for the corresponding deviation of each injection data. For example, for injection pressure deviation, there may be multiple adjustment measures such as adjusting the pump power and changing the valve opening. After analyzing the control log, the measure that has achieved the highest adjustment accuracy for injection pressure deviation in the past is determined, and this measure is used as the optimal adjustment measure for injection pressure deviation. The optimal adjustment measures for each injection data are then constructed into a knowledge matching library.

[0111] When the in-situ injection device is in an unexpected state and the current abnormal event is a controllable abnormal operation event, it is necessary to obtain the real-time injection data and the corresponding standard injection data at the current injection time point. Calculate the data deviation value between them, calibrate the real-time injection data with a deviation value greater than the preset difference as the operation deviation injection data, and obtain a more accurate data deviation value between it and the standard injection data again. Finally, import this data deviation value into the knowledge matching library for matching, find the corresponding optimal adjustment measure, and send the measure to the controller to correct the operation deviation injection data. For example, if it is found that the real-time data of the injection volume deviates too much from the standard data, find the optimal adjustment measure for the injection volume deviation through the knowledge matching library (such as adjusting the pumping speed of the pumping pump, etc.), and then the controller corrects the injection volume according to this measure.

[0112] By analyzing control logs to determine the optimal adjustment measures for various injection data deviations and building a knowledge matching library, when a controllable abnormal operation event occurs, the deviation between the real-time injection data and the standard injection data can be quickly calculated, the optimal adjustment measures are matched in the knowledge matching library, and sent to the controller for correction. This approach enables rapid and accurate handling of controllable anomalies, improves the in-situ injection device's ability to respond to abnormal situations, ensures the accuracy and stability of injection operations, and reduces the risk of poor injection results due to abnormal conditions.

[0113] In practical application, the method may further include the following steps:

[0114] Obtaining operation log information of the in-situ injection device, obtaining historical blockage times of the in-situ injection device based on the operation log information, and obtaining characteristic working conditions of the in-situ injection device when various historical blockage times occurred;

[0115] Conduct statistical analysis on the operating characteristic conditions of the in-situ injection device at various historical silting times to obtain the silting risk coefficient of the in-situ injection device under various operating characteristic conditions;

[0116] Constructing a knowledge base and dividing the knowledge base into a plurality of sub-base spaces, presetting encoding rules, and performing encoding operations on various working characteristic conditions based on the encoding rules to encode the various working characteristic conditions into binary digits;

[0117] Compress and bundle the corresponding binary digits and the corresponding siltation risk coefficients to obtain several knowledge data packets, and map each knowledge data packet into the corresponding sub-library space;

[0118] Acquiring a real-time characteristic working condition of the in-situ injection device, encoding the real-time characteristic working condition according to the encoding rule, and obtaining a string of binary digits corresponding to the real-time characteristic working condition;

[0119] Importing a string of binary digits corresponding to the real-time working characteristic condition into the knowledge base and matching it with the binary digits of each knowledge data packet to determine whether there is an identical binary digit;

[0120] If the same binary digit exists, the siltation risk coefficient of the knowledge data packet to which the same binary digit belongs is obtained, and the siltation risk coefficient of the in-situ injection device operating under the real-time working characteristic conditions is obtained;

[0121] If the clogging risk coefficient of the in-situ injection device operating under real-time working characteristic conditions is greater than a preset coefficient threshold, an early warning message is generated.

[0122] Specifically, the system first obtains the operating logs of the in-situ injection device to identify historical blockage events and the operating conditions under which the device operated each time a blockage occurred. These operating conditions include, but are not limited to, injection pressure, injection rate, material viscosity, ambient temperature, and the total operating time of the device. Statistical analysis is then performed on these operating conditions to determine the blockage risk coefficient for the in-situ injection device under different operating conditions. This step uses extensive historical data to mine the relationship between device blockage and operating conditions, providing a data foundation for subsequent early warning. A knowledge base is constructed and divided into several sub-database spaces. To facilitate data storage and retrieval, pre-set encoding rules are used to encode various operating conditions into binary digits. The corresponding binary digits are then compressed and bundled with the corresponding blockage risk coefficients to form knowledge data packages, which are then mapped to corresponding sub-database spaces. This approach stores complex operating conditions and blockage risk coefficients in a compact and organized manner within the knowledge base, improving data management efficiency. The real-time operating conditions of the in-situ injection device are obtained and encoded into binary digits according to the previously described encoding rules. This binary digit is then imported into the knowledge base and matched with the binary digits in each knowledge data package. If the same binary digits exist, the siltation risk coefficient corresponding to the knowledge data packet is obtained. This coefficient is the siltation risk coefficient of the device under the current real-time working characteristic conditions. If this siltation risk coefficient is greater than the preset coefficient threshold, an early warning message is generated. This process realizes the rapid assessment and early warning of the siltation risk of the in-situ injection device under the real-time working state. By analyzing the historical siltation data of the in-situ injection device, a knowledge base containing the working characteristic condition coding and siltation risk coefficient is constructed, and then the real-time working characteristic condition coding is matched with the knowledge base, the siltation risk coefficient of the device under the current working state can be quickly obtained and an early warning message can be generated when the risk is high. The siltation risk of the in-situ injection device can be predicted in advance, which helps to take timely measures to prevent the occurrence of siltation, ensure the normal operation of the device, and improve the continuity and reliability of the in-situ injection of barrier materials.

[0123] In practical application, the method may further include the following steps:

[0124] Obtaining an operation log of the in-situ injection device, and obtaining electrical characteristic data corresponding to various operating states of the in-situ injection device based on the operation log; wherein the operating states include a normal state, a state requiring regulation, and an unregulatable state (i.e., a fault state);

[0125] Introducing a factor analysis algorithm, and analyzing the correlation factor feature texts between various electrical characteristic data under various operating conditions based on the factor analysis algorithm;

[0126] generating a feature text topology structure diagram according to the correlation factor feature text, and analyzing the state transition probability between various operating states under various electrical feature data conditions according to the feature text topology structure diagram in combination with a Markov chain;

[0127] Constructing a state transition probability matrix according to the state transition probability between various operating states under various electrical characteristic data conditions;

[0128] Constructing a Markov model based on a deep learning network, embedding the state transition probability matrix into the Markov model for coding learning, and outputting the learned Markov model after the model parameters meet the preset requirements;

[0129] Acquire real-time electrical characteristic data of the in-situ injection device at a preset time node, import the real-time electrical characteristic data into the learned Markov model for deduction, and obtain the state transition probability value of the in-situ injection device in the future time;

[0130] If the state transition probability value of the in-situ injection device in the future time is greater than the preset probability value, an early warning message is generated.

[0131] It should be noted that the electrical characteristic data corresponding to different operating states (normal state, state requiring regulation, and unregulatory state, i.e., fault state) are first obtained from the operation log of the in-situ injection device. These electrical characteristic data include but are not limited to parameters such as voltage, current, and power factor, which can reflect the electrical characteristics of the device under different operating states. The factor analysis algorithm can find a few common factors hidden behind many variables (electrical characteristic data). These common factors can explain the correlation between variables, thereby generating a correlation factor characteristic text. This text describes the intrinsic connection between the electrical characteristic data.

[0132] Based on the correlation factor feature text, a feature text topology diagram is constructed. This diagram graphically illustrates the relationships between the electrical feature data. A Markov chain is then used to analyze the state transition probabilities between various operating states under different electrical feature data conditions. Based on these state transition probabilities, a state transition probability matrix is ​​constructed. This matrix is ​​a two-dimensional matrix, with rows and columns representing different operating states, and the elements in the matrix represent the probability of transitioning from one state to another.

[0133] A Markov model is constructed based on a deep learning network, and the state transition probability matrix is ​​embedded within it for encoding and learning. The deep learning network can learn and optimize complex probabilistic relationships, continuously adjusting model parameters until preset requirements are met. This process enables the Markov model to more accurately describe the operating state transition patterns of the in-situ injection device.

[0134] At preset time points, the in-situ injection device acquires real-time electrical characteristic data and applies it to the learned Markov model for deduction. Based on the real-time data and previously learned state transition patterns, the model calculates the probability of the in-situ injection device's state transitions in the future. If this probability is greater than the preset value, it indicates a high probability of the device undergoing a state transition (for example, from a normal state to a faulty state) in the future, and a warning message is generated.

[0135] By acquiring electrical characteristic data from the in-situ injection device under different operating conditions, a Markov model is constructed and trained using factor analysis algorithms, Markov chain analysis, and deep learning networks. Real-time electrical characteristic data is then imported into the model for deduction. This method can predict the probability of state transitions of the in-situ injection device in the future. When the probability value exceeds a preset value, a warning message is generated in a timely manner, which helps to take measures to prevent device failure or other adverse conditions in advance, improve the reliability and stability of the device's operation, and ensure the smooth progress of the in-situ injection of barrier materials.

[0136] Another aspect of the present invention discloses an in-situ injection device for a barrier material, which is applicable to any of the in-situ injection methods for a barrier material, such as Figure 3 As shown, the in-situ injection device includes a material tank 2011 and a syringe, and the material tank and the syringe are connected through a feeding pipe 2012; wherein, the material tank may be provided with a stirring and mixing mechanism, such as a spiral blade stirring mechanism;

[0137] The injector includes a base 2013, with hydraulic cylinders 2014 provided on both sides of the base. The output end of the hydraulic cylinder is cooperatively connected to a pull rod 2015, the top end of the pull rod is cooperatively connected to a mounting platform 2016, and a material pump 2017 is provided on the mounting platform. The input end of the material pump is connected to the feeding pipe, and the output end of the material pump is cooperatively connected to a steel pipe 2018, and the bottom end of the steel pipe is cooperatively connected to an injection head 2019.

[0138] The syringe is also provided with a pressure gauge 2020 and a controller 2021 .

[0139] It should be noted that before the barrier material injection work begins, the material tank 2011 is used to store the barrier material. The material tank can be provided with a stirring and mixing mechanism (such as a spiral blade stirring mechanism) to stir and mix the barrier material in the material tank. This helps to ensure that the composition of the barrier material is uniform, especially for some barrier materials that may have precipitation or need to mix multiple components (such as alkali-activated gel, etc.). The stirring operation can ensure the consistency of the material performance during injection. The hydraulic cylinders 2014 on both sides of the base 2013 of the syringe are in the initial state, and the pull rod 2015 is in the appropriate starting position under the action of the hydraulic cylinder. The extraction pump 2017 on the mounting platform 2016 is also in standby mode, the pressure gauge 220 displays the initial pressure value (usually zero or a value balanced with the ambient pressure), and the controller 221 is in a state ready to receive instructions. When the controller 221 receives the instruction to start injection, the extraction pump 2017 starts. Because the input end of the pump is connected to the material tank 2011 via the feed pipe 2012, the barrier material is drawn from the material tank into the pump through the feed pipe. The barrier material drawn into the pump 2017 flows through the steel pipe 2018 at its output end to the injection head 2019. During this process, the hydraulic cylinders 2014 on both sides of the base 2013 can operate according to the instructions of the controller 2021. For example, if the depth of the injection head within the soil needs to be adjusted, the hydraulic cylinder can push the pull rod 2015, which drives the mounting platform 2016 and the pump 2017 on it to move up and down as a whole, thereby adjusting the position of the injection head. When the injection head reaches the appropriate position, the barrier material is injected into the target area through the injection head. During the injection process, the pressure gauge 2020 continuously monitors the pressure, and the controller 2021 controls the entire injection process based on the pressure value and other preset injection parameters (such as injection speed and injection volume). If the injection speed needs to be changed, the controller can adjust the speed of the pump; if the injection pressure needs to be changed, in addition to adjusting the power of the pump, it can also be achieved by adjusting the insertion depth of the injection head through the hydraulic cylinder.

[0140] The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for in-situ injection of a barrier material, characterized in that: The following steps are involved: Obtaining a preset injection plan for the barrier material, importing the preset injection plan into a computer simulation system for simulated injection evaluation, and outputting the final injection plan of the in-situ injection device and its standard dynamic injection data pool during the working process after the simulation results meet the preset requirements; Transmitting the final injection plan to a controller of an in-situ injection device, and controlling the in-situ injection device to inject the barrier material into the target area according to the final injection plan; During the injection process, the actual injection data of the in-situ injection device at each preset injection time point is obtained based on the Internet of Things; and obtaining standard injection data of the in-situ injection device at each preset injection time point in the standard dynamic injection data pool; Analyze the working status of the in-situ injection device based on the actual injection data and the standard injection data at each preset injection time point; if the working status of the in-situ injection device is the expected state, there is no need to correct the injection data of the in-situ injection device; Acquire historical feature data corresponding to various abnormal operation events of the in-situ injection device, and construct a gradient boosting tree model based on the historical feature data; If the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results; wherein, the barrier material includes alkali-activated gel and modified geomembrane barrier material.

2. The in-situ injection method of a barrier material according to claim 1, characterized in that: Obtain a preset injection plan for the barrier material, import the preset injection plan into the computer simulation system for simulated injection evaluation, and output the final injection plan of the in-situ injection device and its standard dynamic injection data pool during the working process after the simulation results meet the preset requirements, specifically: Obtaining a three-dimensional structural model diagram of the in-situ injection device and a regional characteristic model diagram of the target area, and constructing a computer simulation system for injecting barrier material into the target area based on the three-dimensional structural model diagram of the in-situ injection device and the regional characteristic model diagram of the target area in combination with computer simulation software; wherein the target area is the area to be blocked; Obtaining a preset injection plan for the barrier material, importing the preset injection plan into the computer simulation system to perform injection simulation analysis, and outputting the simulation results after the simulation is completed; Obtaining, from the simulation results, a predicted permeability coefficient of the barrier material after the barrier material is simulated and injected into the target area according to the preset injection scheme; and comparing the predicted permeability coefficient with a preset threshold; If the predicted permeability coefficient is greater than a preset threshold, there is no need to adjust or optimize the preset injection plan, and the preset injection plan is output as the final injection plan; If the predicted permeability coefficient is not greater than a preset threshold, a genetic algorithm is introduced to iteratively adjust and optimize the preset injection scheme. After each iterative optimization cycle, an optimized injection scheme is obtained, and the predicted permeability coefficient of the optimized injection scheme is evaluated through a simulation system. When the predicted permeability coefficient of the optimized injection scheme is greater than the preset threshold, the iteration is stopped and the optimized injection scheme is output as the final injection scheme. After the final injection plan is output, the standard injection data of the in-situ injection device based on the time series when the in-situ injection device is controlled by the final injection plan to perform simulation work is obtained in the computer simulation system, and the various standard injection data are aggregated to obtain a standard dynamic injection data pool of the in-situ injection device during the injection work.

3. The in-situ injection method of a barrier material according to claim 1, characterized in that: The working status of the in-situ injection device is analyzed based on the actual injection data and the standard injection data at each preset injection time point, specifically: Constructing a dynamic transformation curve graph of actual injection data based on actual injection data of the in-situ injection device at each preset injection time point; and constructing a standard injection data dynamic transformation curve diagram based on the standard injection data of the in-situ injection device at each preset injection time point; Performing a coincidence analysis on the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph to obtain a curve coincidence degree between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph; Comparing the curve coincidence between the actual injection data dynamic transformation curve graph and the standard injection data dynamic transformation curve graph with a preset coincidence; If the curve overlap between the actual injection data dynamic transformation curve diagram and the standard injection data dynamic transformation curve diagram is greater than the preset overlap, it means that the actual working state of the in-situ injection device is consistent with the expected working state and the injection effect meets expectations, then the working state of the in-situ injection device is calibrated as the expected state; If the curve overlap between the actual injection data dynamic transformation curve and the standard injection data dynamic transformation curve is not greater than the preset overlap, it means that the actual working state of the in-situ injection device deviates from the expected working state, and the injection effect does not meet expectations, then the working state of the in-situ injection device is calibrated as an unexpected state.

4. The in-situ injection method of a barrier material according to claim 1, characterized in that: Obtain historical feature data corresponding to various abnormal operation events of the in-situ injection device, and construct a gradient boosting tree model based on the historical feature data, specifically: Obtain historical characteristic data corresponding to various abnormal operation events of the in-situ injection device through the big data network; The collected historical characteristic data of abnormal events in the operation of the in-situ injection device are organized into a structured table format, where each record corresponds to an abnormal event name and a characteristic value of the corresponding historical characteristic data; Extract the eigenvalues ​​from the table to form a feature matrix X, and the abnormal results to form a label vector y; where X contains the eigenvalues ​​used to train the model, and y contains the corresponding abnormal labels; Introducing the gradient boosting tree algorithm, initializing the gradient boosting tree model, and setting the number of iterations; using the gradient boosting framework and combining it with the feature matrix X to iteratively construct a decision tree, and correcting the residual of the previous decision tree according to the corresponding label vector y to form a strong learner; At the same time, in each iteration, the gradient and second-order derivative of the loss function are calculated and used as the input of the new weak learner to update the model to minimize the loss function; After reaching the number of iterations, a gradient boosting tree model consisting of multiple decision trees is constructed.

5. The in-situ injection method of a barrier material according to claim 1, characterized in that: If the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, specifically: If the working state of the in-situ injection device is an unexpected state, obtaining actual injection data of the in-situ injection device at each preset injection time point; Importing actual injection data of the in-situ injection device at each preset injection time point into the gradient boosting tree model to diagnose the in-situ injection device in an unexpected state, and obtaining the name of the abnormal event currently occurring in the in-situ injection device in the unexpected state; Determine the characteristic type of the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state; wherein the characteristic type of the abnormal event name includes an uncontrollable abnormal operation event and a controllable abnormal operation event; If the name of the abnormal event currently occurring in the in-situ injection device in an unexpected state is an uncontrollable abnormal operation event, the in-situ injection device is controlled to suspend the injection work, and the name of the abnormal event currently occurring in the in-situ injection device is sent to the preset terminal for display.

6. The in-situ injection method of a barrier material according to claim 5, characterized in that: If the working state of the in-situ injection device is unexpected, the actual injection data of the in-situ injection device at each preset injection time point is imported into the gradient boosting tree model for evaluation, and the in-situ injection device is adjusted and controlled according to the evaluation results, further comprising the following steps: Obtaining control log information of the in-situ injection device, obtaining adjustment measures corresponding to various deviations of various injection data in the in-situ injection device based on the control log information, and obtaining adjustment accuracy after adjusting the deviated injection data through the various adjustment measures; Obtaining the adjustment measure corresponding to the maximum adjustment accuracy, and taking the adjustment measure corresponding to the maximum adjustment accuracy as the optimal adjustment measure for the corresponding deviation of the corresponding injection data, and so on, obtaining the optimal adjustment measures corresponding to various deviations of the injection data in the in-situ injection device; Constructing a knowledge matching library, and importing the optimal adjustment measures corresponding to various deviations of various injection data in the in-situ injection device into the knowledge matching library; If the abnormal event currently occurring in the in-situ injection device in an unexpected state is named as a controllable abnormal operation event, then obtaining various real-time injection data of the in-situ injection device at the current injection time point; and obtaining various standard injection data of the in-situ injection device at the current injection time point from the standard dynamic injection data pool; Calculate the data deviation between various real-time injection data of the in-situ injection device at the current injection time point and the corresponding standard injection data; Marking the real-time injection data whose data deviation value is greater than the preset difference value as running deviation injection data, and obtaining the data deviation value between the running deviation injection data and the corresponding standard injection data; The data deviation value between the operation deviation injection data and the corresponding standard injection data is imported into the knowledge matching library for matching, and the best adjustment measure is obtained by matching. The best adjustment measure obtained by matching is sent to the controller to correct the operation deviation injection data.

7. An in-situ injection device for a barrier material, applied to the in-situ injection method for a barrier material according to any one of claims 1 to 6, characterized in that: The in-situ injection device includes a material tank and a syringe, and the material tank and the syringe are connected through a feeding pipe; The injector includes a base, hydraulic cylinders are provided on both sides of the base, the output end of the hydraulic cylinder is cooperatively connected to a pull rod, the top end of the pull rod is cooperatively connected to a mounting platform, a material pump is provided on the mounting platform, the input end of the material pump is connected to the feeding pipe, the output end of the material pump is cooperatively connected to a steel pipe, and the bottom end of the steel pipe is cooperatively connected to an injection head; The syringe is also provided with a pressure gauge and a controller.

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