A multi-dimensional protection and monitoring collaborative optimization system for reservoir banks

Abstract

The present invention relates to the field of reservoir bank slope protection, and in particular to a multi-dimensional reservoir bank protection and monitoring collaborative optimization system, comprising: a three-dimensional collaborative protection network: composed of anchoring engineering, anti-sliding pile groups, and stone-filled counter-pressure structures; a phased anti-corrosion strategy: including a pre-water storage protection stage and a post-water storage protection stage. In the pre-water storage protection stage, epoxy resin asphalt coating is used to effectively protect the surface of the structure to block corrosive media; in the post-water storage protection stage, glass fiber sleeve physical isolation technology is applied to achieve durable protection of the structure in an underwater environment; a dynamic closed-loop monitoring system: capable of achieving real-time and efficient monitoring of reservoir bank slopes or bridge piers, and taking timely countermeasures when potential risks are discovered to ensure the safety and stability of the structure. This system solves the problems of a single protection mechanism and poor anti-corrosion durability in traditional reservoir bank slope and bridge pier protection technologies.

Description

Technical Field

[0001] The present invention relates to the field of reservoir bank slope protection, and in particular to a reservoir bank multi-dimensional protection and monitoring collaborative optimization system. Background Art

[0002] Currently, with the continuous advancement of infrastructure construction and the complex changes in the reservoir environment, traditional protection technologies for the safety and protection of geological structures such as reservoir bank slopes and bridge piers face numerous challenges. Currently, due to natural factors such as reservoir water storage, rainfall, and geological activity, these structures often experience geological disasters such as slippage and instability, seriously threatening the safety and operational life of the engineering structures. Traditional protection measures often focus on optimizing a single structure or mechanism, which presents the following problems:

[0003] Single protection mechanism: Traditional methods make it difficult to achieve coordinated protection at multiple points and mechanisms, resulting in limited overall protection effect; Poor corrosion resistance and durability: Structures in underwater or humid environments are susceptible to corrosion by corrosive media, and traditional anti-corrosion measures are difficult to maintain long-term protection effects. Summary of the Invention

[0004] Therefore, in response to the above problems, the present invention proposes a multi-dimensional reservoir bank protection and monitoring collaborative optimization system, which solves the problems of single protection mechanism and poor corrosion resistance and durability in traditional reservoir bank slope and bridge pier protection technologies.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A multi-dimensional reservoir bank protection and monitoring collaborative optimization system includes the following components:

[0007] Three-dimensional coordinated protection network: composed of anchoring engineering, anti-slide pile groups and rock-fill counter-pressure structures, it is used to provide multi-point, multi-mechanism coordinated protection at different depths and locations for structures in the reservoir bank geological environment;

[0008] The anchoring project uses prestressed anchor cables to actively reinforce and reconstruct the stress field, forming a stable support system, thereby enhancing the anti-slip ability of the slope or pier structure. The anti-slip pile group uses rigid anti-slip pile technology to deeply suppress the expansion of the sliding surface and improve the anti-slip ability of the slope or pier. The stone fill counter-pressure structure uses the stone fill to widen the slope foot and generate counter-pressure, enhancing the stability of the slope foot and optimizing drainage performance.

[0009] Phased anti-corrosion strategy: This includes the pre-water storage protection stage and the post-water storage protection stage. In the pre-water storage protection stage, epoxy resin asphalt coating is used to effectively protect the surface of the structure to block the corrosive medium. In the post-water storage protection stage, glass fiber sleeve physical isolation technology is applied to achieve durable protection of the structure in the underwater environment.

[0010] Dynamic closed-loop monitoring system: It can realize real-time and efficient monitoring of reservoir bank slopes or bridge piers, and take timely response measures when potential risks are discovered to ensure the safety and stability of the structure.

[0011] Furthermore, the dynamic closed-loop monitoring system includes a real-time data acquisition module, a data processing and analysis module, an early warning and decision support module, and a dynamic response strategy execution module;

[0012] Real-time data acquisition module, including inclinometer holes and comprehensive monitoring points arranged in key monitoring areas, is used to continuously collect displacement data and support parameters, and send them to the data processing and analysis unit in real time through the data transmission system;

[0013] The data processing and analysis module processes and analyzes the collected displacement data and support parameters in real time, including calculating one or more of the displacement rate, trend prediction, and stability assessment to generate quantitative results of the structural stability status;

[0014] The early warning and decision support module automatically issues an early warning signal and provides decision support information when the structural stability status falls below a preset threshold based on the results of the data processing and analysis module;

[0015] The dynamic response strategy execution module automatically triggers or assists manual execution of corresponding response measures according to the instructions of the early warning and decision support module, and records the execution process and results for subsequent analysis.

[0016] Furthermore, in the data processing and analysis module, the time series anomaly detection algorithm is used to perform pattern recognition and anomaly detection on the collected data;

[0017] Time series anomaly detection algorithms include: LSTM, autoencoder, and anomaly detection;

[0018] LSTM is used to process time series data and capture the long-term dependencies between displacement data and support parameters. The update formula for each LSTM unit is as follows:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] in, The output of the forget gate is a value between 0 and 1, activated by the Sigmoid function. Generate and control the cell state at the previous moment What information needs to be forgotten?

[0026] Sigmoid activation function is used to compress the input value to between 0 and 1 and control the strength of the gating signal;

[0027] is the weight matrix of the forget gate, which is used to convert the hidden state of the previous moment and the current input Mapped to the output of the forget gate;

[0028] Indicates that the hidden state of the previous moment and the current input Splice into a vector;

[0029] is the bias term of the forget gate, which is used to adjust the output of the forget gate;

[0030] Is the input gate output, controlling the current input Hidden state at the previous moment Which of the combined information needs to be updated to the cell state?

[0031] is the weight matrix of the input gate, which has the same effect as Same, but for input gate;

[0032] is the bias term of the input gate, which is used to adjust the output of the input gate;

[0033] is the candidate cell state at the current moment, Activation function generation, representing possible new information at the current moment;

[0034] The weight matrix updated for the cell state is used to map the concatenated vector to the candidate cell state;

[0035] The bias term for updating the cell state is used to adjust the candidate cell state;

[0036] It is a hyperbolic tangent activation function, which is used to compress the input value to between -1 and 1 to generate candidate cell states or hidden states;

[0037] is the cell state at the current moment, which is determined by the forget gate, input gate, and candidate cell state, and represents the memory at the current moment;

[0038] is the cell state at the previous moment, indicating the memory of the previous moment;

[0039] Output gate output, controlling the current cell state What information needs to be output to the hidden state? ;

[0040] is the weight matrix of the output gate, which is used to map the concatenated vector to the output of the output gate;

[0041] is the bias term of the output gate, which is used to adjust the output of the output gate;

[0042] is the hidden state at the current moment, which is determined by the output gate and the cell state at the current moment, and represents the feature representation at the current moment;

[0043] The autoencoder is used to compress the output of LSTM, learn the low-dimensional representation of the data, and reconstruct the input data during the decoding process. The formulas of the encoder and decoder are as follows:

[0044] encoder, ;

[0045] decoder, ;

[0046] is a low-dimensional representation, that is, the compressed feature representation output by the encoder;

[0047] is the encoding function, which is a fully connected layer or convolutional layer used to compress high-dimensional input into a low-dimensional representation;

[0048] is the reconstructed hidden state, that is, the reconstructed feature representation output by the decoder;

[0049] The decoding function is a fully connected layer or deconvolution layer used to reconstruct low-dimensional representations into high-dimensional features;

[0050] Anomaly detection, detect anomalies by calculating the reconstruction error:

[0051] ;

[0052] in, is the mean square error, which is used to measure the size of the reconstruction error and serves as the basis for anomaly detection;

[0053] is the length of the time series data, that is, the number of data points involved in the calculation;

[0054] Represents the summation operation from t=1 to t=N.

[0055] Furthermore, in the warning and decision support module, the warning signal is conveyed to the operators in one or more ways of sound, light flashing, and remote communication to ensure that they can receive the warning information in a timely manner.

[0056] Furthermore, in the early warning and decision support module, the decision support information includes a detailed description of potential risks, possible scope of impact, recommended response measures and their expected effects, providing operators with a comprehensive basis for decision-making.

[0057] Furthermore, in the dynamic response strategy execution module, response measures are dynamically adjusted or monitoring strategies are optimized based on the comparison between the execution results and the expected effects, so as to achieve continuous optimization and improvement of the system.

[0058] Furthermore, a comprehensive dynamic response strategy library is built in, covering response measures for different risk levels and anomaly types. Based on the anomaly detection results and warning levels, the appropriate response measures are automatically matched and selected from the strategy library;

[0059] For results that are not in the dynamic response strategy library, a request message is sent to notify relevant personnel, and abnormal information and recommended response measures are provided for manual decision-making and execution.

[0060] Furthermore, after the response measures are executed, the module continues to monitor the changes in relevant parameters in real time, evaluates the effectiveness of the response measures, and automatically adjusts or updates the relevant strategies in the response strategy library based on the feedback of the execution effect to adapt to the ever-changing engineering conditions and risk conditions.

[0061] By adopting the above technical solution, the beneficial effects of the present invention are:

[0062] 1. This solution integrates anchoring, anti-slide piles, and rockfill counterpressure structures to achieve multi-point, multi-mechanism coordinated protection, significantly improving the overall stability of the reservoir bank slopes and bridge piers, and reducing the risk of slippage and instability. A phased anti-corrosion strategy: Differentiated anti-corrosion technologies are employed to address environmental changes before and after water storage, effectively blocking corrosive media, extending the service life of the structure, and reducing maintenance costs. A dynamic closed-loop monitoring system: This system enables real-time data collection, analysis, and early warning, enabling timely identification of potential risks and the implementation of countermeasures to ensure structural safety and stability and enhance accident prevention capabilities.

[0063] 2. The dynamic closed-loop monitoring system adopts real-time data collection: displacement and support parameters are continuously collected through incline holes and comprehensive monitoring points to ensure the accuracy and timeliness of the data; data processing and analysis: time series anomaly detection algorithms (such as LSTM, autoencoders) are used to perform pattern recognition and anomaly detection on data to achieve dynamic risk identification and improve early warning accuracy; early warning and decision support: automatic issuance of early warning signals and provision of quantitative decision-making basis to assist operators in making decisions quickly and reduce accident response time; dynamic response strategy execution: automatic matching and execution of response measures according to the warning level, recording the execution process and results, achieving closed-loop management, and continuously optimizing system performance.

[0064] 3. Application of time series anomaly detection algorithms, including the LSTM algorithm: This algorithm improves anomaly detection accuracy and reduces false positives and negatives by capturing long-term dependencies between displacement data and support parameters. The autoencoder compresses the LSTM output, learns a low-dimensional representation of the data, and reconstructs the input data during the decoding process. Anomalies are detected by calculating the reconstruction error, improving anomaly detection efficiency. The anomaly detection mechanism combines the outputs of the LSTM and autoencoder to achieve more accurate anomaly identification, providing a reliable basis for the early warning and decision support modules.

[0065] 4. Early warning signals are transmitted through various means, including sound, flashing lights, and remote communication, ensuring that operators receive early warning information in a timely manner in different environments, improving the coverage and effectiveness of early warnings. This reduces the delay in early warning signal transmission, allowing operators to respond quickly and take countermeasures to reduce the risk of accidents.

[0066] 5. Provide a detailed description of potential risks, the scope of impact, and recommended countermeasures to provide operators with a comprehensive basis for decision-making, reducing subjectivity and blindness in decision-making. Clarify the expected effects of countermeasures to help operators evaluate the feasibility and effectiveness of measures and optimize decision-making plans.

[0067] 6. By comparing implementation results with expected outcomes, dynamically adjust response measures or optimize monitoring strategies to achieve continuous system optimization and improvement, thereby enhancing protection effectiveness. Adapting to ever-changing engineering conditions and risk profiles ensures the system is always in optimal operating condition, improving the safety and stability of engineering structures.

[0068] 7. Through the built-in dynamic response strategy library, you can quickly match and select appropriate response measures, reduce decision-making time, and improve response efficiency. Ensure the standardization and regularization of response measures, reduce response errors caused by human factors, and improve the accuracy and effectiveness of responses.

[0069] 8. By monitoring changes in relevant parameters in real time, the effectiveness of response measures is evaluated, providing a basis for subsequent strategy adjustments. Based on feedback from implementation results, relevant strategies in the response strategy library are automatically adjusted or updated to ensure that the strategy library always matches project conditions and risk profiles, improving the adaptability and reliability of the system. DETAILED DESCRIPTION

[0070] The present invention will now be further described with reference to specific embodiments.

[0071] This embodiment provides a multi-dimensional reservoir bank protection and monitoring collaborative optimization system, including the following components:

[0072] Three-dimensional coordinated protection network: composed of anchoring engineering, anti-slide pile groups and rock-fill counter-pressure structures, it is used to provide multi-point, multi-mechanism coordinated protection at different depths and locations for structures in the reservoir bank geological environment;

[0073] The anchoring project uses prestressed anchor cables to actively reinforce and reconstruct the stress field, forming a stable support system, thereby enhancing the anti-slip ability of the slope or pier structure. The anti-slip pile group uses rigid anti-slip pile technology to deeply suppress the expansion of the sliding surface and improve the anti-slip ability of the slope or pier. The stone fill counter-pressure structure uses the stone fill to widen the slope foot and generate counter-pressure, enhancing the stability of the slope foot and optimizing drainage performance.

[0074] Phased anti-corrosion strategy: This includes the pre-water storage protection stage and the post-water storage protection stage. In the pre-water storage protection stage, epoxy resin asphalt coating is used to effectively protect the surface of the structure to block the corrosive medium. In the post-water storage protection stage, glass fiber sleeve physical isolation technology is applied to achieve durable protection of the structure in the underwater environment.

[0075] Dynamic closed-loop monitoring system: It can realize real-time and efficient monitoring of reservoir bank slopes or bridge piers, and take timely response measures when potential risks are discovered to ensure the safety and stability of the structure.

[0076] The design of anchoring engineering needs to comprehensively consider geological conditions (such as rock and soil type, degree of joint and fissure development), load distribution and safety factor requirements. The implementation steps include:

[0077] S11. Geological Survey and Anchorage Parameter Determination: Conduct a detailed geological survey to obtain geotechnical parameters (such as internal friction angle, cohesion, and elastic modulus). Based on the stability analysis of the slope or pier, calculate the required anchoring force and determine the anchor cable length, diameter, inclination, and prestressing force. For example, for high and steep slopes, the anchor cable length is typically 15m-30m, the diameter is 150mm-200mm, the inclination is 15°-30°, and the design prestressing force is 500kN-1000kN.

[0078] S12. Drilling and anchor cable installation: Use a down-the-hole drill or geological drill. The hole diameter should be 20mm-30mm larger than the anchor cable diameter. Ensure the hole wall is smooth. After drilling, use high-pressure air or water to clean the hole to remove rock debris and mud. Slowly lower the prestressed anchor cable (consisting of steel strands, anchors, and grouting pipes) to the bottom of the hole, ensuring that the anchor cable is centered.

[0079] S13. Grouting and anchoring: Use cement mortar or chemical grout for grouting at a pressure of 0.5MPa-2.0MPa to ensure the grout fills the pores and wraps around the anchor cable. After grouting, cure for 7-14 days. Once the grout strength reaches the designed value (usually ≥30MPa), tension and lock the anchor cable.

[0080] S14. Tensioning and Locking: Use a jack to tension the anchor cable in stages, with each stage applying 20%-30% of the design value. Hold the load for 5-10 minutes and observe the displacement changes. After tensioning to the design value, lock the anchor and install a protective cover to prevent rust on the anchor cable.

[0081] The anchor cable material must comply with national standards (e.g., GB / T 5224) and have a tensile strength of ≥1860 MPa. Grout strength testing should be performed using test block testing or non-destructive testing (e.g., ultrasonic testing) to ensure it meets design requirements.

[0082] The design of anti-slide pile groups requires determining the pile type (e.g., circular, rectangular) and size based on the location of the sliding surface, the distance between piles, and the embedment depth. Implementation steps include:

[0083] S21. Pile placement and excavation: Based on slope stability analysis, determine the location and spacing of anti-slide piles (usually 3 to 5 times the pile diameter). Use manual excavation or mechanical drilling (such as a rotary drilling rig) with a hole diameter of 1.0 to 2.5 meters and a depth of ≥ 3 meters embedded in stable rock formations.

[0084] S22. Fabricate and install the rebar cage. Tie the rebar cage according to the design requirements. Main rebar diameter ≥ 25mm, stirrup spacing 150mm-200mm, and reinforcing stirrup spacing 2m-3m. After hoisting the rebar cage into the hole, secure its position to prevent it from floating or shifting.

[0085] For S23 concrete pouring and curing, use commercial concrete (strength grade ≥ C30) and compact it layer by layer, with each layer ≤ 500mm thick. After pouring, cover and maintain moisture retention for ≥ 14 days to prevent cracks.

[0086] S24. Pile top connection and reinforcement: A cap beam is installed on the pile top to connect the individual piles into a whole and improve the collaborative working ability of the pile group. The size of the cap beam is determined by the pile diameter and spacing. Generally, the width is ≥ the pile diameter and the height is ≥ 0.8m.

[0087] The low-strain method or acoustic transmission method is used to test the integrity of the pile body to ensure that there are no defects such as broken piles and reduced diameter. The rebound method or core drilling method is used to test the concrete strength to ensure that it meets the design requirements.

[0088] The design of the rockfill counterpressure structure requires determining the size and slope of the rockfill body based on the slope height, slope rate, and rockfill material properties. The implementation steps include:

[0089] S31. Selection and treatment of rock fill materials: Use well-graded, high-strength rock (particle size ≥ 300mm) with a mud content ≤ 5%. Before rock filling, remove the soft soil at the foot of the slope and compact the base.

[0090] S32. Layered filling and compaction: Use the layered filling method, with each layer ≤1.0m thick, and compact with a vibrating roller or impact rammer. The compaction degree should be ≥93% (heavy compaction standard) to ensure the stone fill is dense.

[0091] S33. Slope protection and drainage: Geogrids or three-dimensional vegetation nets should be laid on rock-filled slopes to prevent rocks from rolling down. Blind drainage ditches or seepage pipes should be installed to drain accumulated water from the slope and reduce pore water pressure.

[0092] S34. Widen and monitor the counterpressure body. Based on displacement monitoring data, if the slope foot is unstable, the counterpressure body can be dynamically widened (by 1-2 meters at a time). After widening, recompact the body and monitor displacement changes to ensure the counterpressure effect.

[0093] The compaction of the rockfill is tested using the sand filling method or a nuclear density meter to ensure that it meets the design requirements. The drainage system's smoothness is checked using a water injection test to ensure drainage efficiency.

[0094] During the protection phase before water storage, measures to be implemented include:

[0095] S41. Material selection: Epoxy resin asphalt coating is used as the protective material. Epoxy resin asphalt coating has good adhesion, water resistance, chemical corrosion resistance and mechanical strength, and can effectively resist erosion by the atmosphere, rainwater and mild chemical corrosive media.

[0096] S42. Surface treatment: Before painting, thoroughly clean the surface of the structure to remove impurities such as oil, rust, and old coatings, ensuring that the surface is dry, clean, and free of loose particles. (Note: Although this step is not directly anti-corrosion, it provides a good foundation for coating adhesion and can be classified as protective preparation work)

[0097] S43. Apply the epoxy resin asphalt coating evenly to the surface of the structure by brushing, rolling, or spraying. The coating thickness must be controlled according to the design requirements. Typically, multiple layers are applied to achieve the specified corrosion protection effect.

[0098] S44. Curing and Inspection: After the coating is applied, it needs to be cured for a certain period of time to achieve optimal performance. After curing, the coating is inspected for appearance, thickness, and adhesion to ensure that the coating meets the quality requirements.

[0099] After the coating is cured, the integrity, thickness and adhesion of the coating are evaluated through visual inspection, coating thickness gauge and adhesion test to ensure that the coating can effectively block the intrusion of corrosive media.

[0100] During the protection phase after water storage, measures to be implemented include:

[0101] S51. Material selection: Fiberglass sleeves are used as physical isolation materials. Fiberglass sleeves have high strength, corrosion resistance, water resistance, and good flexibility, and can effectively wrap and protect the structure from erosion by water and corrosive media.

[0102] S52, sleeve customization, according to the size and shape of the structure, we can customize the appropriate fiberglass sleeve. The sleeve must have sufficient strength and flexibility to adapt to the deformation of the structure.

[0103] S53. Sleeve installation: When installing underwater or in a humid environment, wrap the fiberglass sleeve around the surface of the structure to ensure a tight fit between the sleeve and the structure without any gaps. During installation, use special glue or sealing material to seal the sleeve joints to prevent the intrusion of water and corrosive media.

[0104] S54, Sleeve fixation, use mechanical fixation or chemical bonding to firmly fix the fiberglass sleeve to the structure to ensure its long-term stability.

[0105] After the sleeve is installed, the integrity, tightness and sealing of the sleeve are evaluated through visual inspection, sleeve tightness test and sealing test to ensure that the sleeve can effectively isolate water and corrosive media and protect the structure from erosion.

[0106] The dynamic closed-loop monitoring system includes a real-time data acquisition module, a data processing and analysis module, an early warning and decision support module, and a dynamic response strategy execution module;

[0107] Real-time data acquisition module, including inclinometer holes and comprehensive monitoring points arranged in key monitoring areas, is used to continuously collect displacement data and support parameters, and send them to the data processing and analysis unit in real time through the data transmission system;

[0108] The data processing and analysis module processes and analyzes the collected displacement data and support parameters in real time, including calculating one or more of the displacement rate, trend prediction, and stability assessment to generate quantitative results of the structural stability status;

[0109] The early warning and decision support module automatically issues an early warning signal and provides decision support information when the structural stability status falls below a preset threshold based on the results of the data processing and analysis module;

[0110] The dynamic response strategy execution module automatically triggers or assists manual execution of corresponding response measures according to the instructions of the early warning and decision support module, and records the execution process and results for subsequent analysis.

[0111] In the data processing and analysis module, the time series anomaly detection algorithm is used to perform pattern recognition and anomaly detection on the collected data;

[0112] Time series anomaly detection algorithms include: LSTM, autoencoder, and anomaly detection;

[0113] LSTM is used to process time series data and capture the long-term dependencies between displacement data and support parameters. The update formula for each LSTM unit is as follows:

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] ;

[0119] ;

[0120] in, The output of the forget gate is a value between 0 and 1, activated by the Sigmoid function. Generate and control the cell state at the previous moment What information needs to be forgotten?

[0121] Sigmoid activation function is used to compress the input value to between 0 and 1 and control the strength of the gating signal;

[0122] is the weight matrix of the forget gate, which is used to convert the hidden state of the previous moment and the current input Mapped to the output of the forget gate;

[0123] Indicates that the hidden state of the previous moment and the current input Splice into a vector;

[0124] is the bias term of the forget gate, which is used to adjust the output of the forget gate;

[0125] Is the input gate output, controlling the current input Hidden state at the previous moment Which of the combined information needs to be updated to the cell state?

[0126] is the weight matrix of the input gate, which has the same effect as Same, but for input gate;

[0127] is the bias term of the input gate, which is used to adjust the output of the input gate;

[0128] is the candidate cell state at the current moment, Activation function generation, representing possible new information at the current moment;

[0129] The weight matrix updated for the cell state is used to map the concatenated vector to the candidate cell state;

[0130] The bias term for updating the cell state is used to adjust the candidate cell state;

[0131] It is a hyperbolic tangent activation function, which is used to compress the input value to between -1 and 1 to generate candidate cell states or hidden states;

[0132] is the cell state at the current moment, which is determined by the forget gate, input gate, and candidate cell state, and represents the memory at the current moment;

[0133] is the cell state at the previous moment, indicating the memory of the previous moment;

[0134] Output gate output, controlling the current cell state What information needs to be output to the hidden state? ;

[0135] is the weight matrix of the output gate, which is used to map the concatenated vector to the output of the output gate;

[0136] is the bias term of the output gate, which is used to adjust the output of the output gate;

[0137] is the hidden state at the current moment, which is determined by the output gate and the cell state at the current moment, and represents the feature representation at the current moment;

[0138] The autoencoder is used to compress the output of LSTM, learn the low-dimensional representation of the data, and reconstruct the input data during the decoding process. The formulas of the encoder and decoder are as follows:

[0139] encoder, ;

[0140] decoder, ;

[0141] is a low-dimensional representation, that is, the compressed feature representation output by the encoder;

[0142] is the encoding function, which is a fully connected layer or convolutional layer used to compress high-dimensional input into a low-dimensional representation;

[0143] is the reconstructed hidden state, that is, the reconstructed feature representation output by the decoder;

[0144] The decoding function is a fully connected layer or deconvolution layer used to reconstruct low-dimensional representations into high-dimensional features;

[0145] Anomaly detection, detect anomalies by calculating the reconstruction error:

[0146] ;

[0147] in, is the mean square error, which is used to measure the size of the reconstruction error and serves as the basis for anomaly detection;

[0148] is the length of the time series data, that is, the number of data points involved in the calculation;

[0149] Represents the summation operation from t=1 to t=N.

[0150] In the early warning and decision support module, warning signals are transmitted to operators through one or more methods: sound, flashing lights, or remote communication, ensuring they receive warning information in a timely manner. Decision support information in this module includes a detailed description of potential risks, the possible scope of impact, recommended response measures, and their expected effects, providing operators with a comprehensive basis for decision-making.

[0151] The dynamic response strategy execution module dynamically adjusts response measures or optimizes monitoring strategies based on the comparison of execution results with expected outcomes, enabling continuous system optimization and improvement. A comprehensive built-in dynamic response strategy library encompasses response measures for different risk levels and anomaly types. Based on anomaly detection results and warning levels, the module automatically matches and selects appropriate response measures from the library. For results not in the dynamic response strategy library, a request is sent to notify relevant personnel, providing anomaly information and recommended response measures for manual decision-making and execution.

[0152] After the response measures are executed, the module continues to monitor the changes in relevant parameters in real time, evaluates the effectiveness of the response measures, and automatically adjusts or updates the relevant strategies in the response strategy library based on the feedback from the execution effect to adapt to the changing engineering conditions and risk conditions.

[0153] Although the present invention has been particularly shown and described in conjunction with preferred embodiments, it will be understood by those skilled in the art that various changes in form and details may be made to the present invention without departing from the spirit and scope of the invention as defined in the appended claims, and all such changes are within the scope of protection of the present invention.

Claims

1. A multi-dimensional protection and monitoring collaborative optimization system for reservoir banks, characterized by: Includes the following components: Three-dimensional coordinated protection network: composed of anchoring engineering, anti-slide pile groups and rock-fill counter-pressure structures, it is used to provide multi-point, multi-mechanism coordinated protection at different depths and locations for structures in the reservoir bank geological environment; The anchoring project uses prestressed anchor cables to actively reinforce and reconstruct the stress field, forming a stable support system, thereby enhancing the anti-slip ability of the slope or pier structure. The anti-slip pile group uses rigid anti-slip pile technology to deeply suppress the expansion of the sliding surface and improve the anti-slip ability of the slope or pier. The stone fill counter-pressure structure uses the stone fill to widen the slope foot and generate counter-pressure, enhancing the stability of the slope foot and optimizing drainage performance. Phased anti-corrosion strategy: This includes the pre-water storage protection stage and the post-water storage protection stage. In the pre-water storage protection stage, epoxy resin asphalt coating is used to effectively protect the surface of the structure to block the corrosive medium. In the post-water storage protection stage, glass fiber sleeve physical isolation technology is applied to achieve durable protection of the structure in the underwater environment. Dynamic closed-loop monitoring system: It can realize real-time and efficient monitoring of reservoir bank slopes or bridge piers, and take timely response measures when potential risks are discovered to ensure the safety and stability of the structure.

2. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 1 is characterized in that: The dynamic closed-loop monitoring system includes a real-time data acquisition module, a data processing and analysis module, an early warning and decision support module, and a dynamic response strategy execution module; Real-time data acquisition module, including inclinometer holes and comprehensive monitoring points arranged in key monitoring areas, is used to continuously collect displacement data and support parameters, and send them to the data processing and analysis unit in real time through the data transmission system; The data processing and analysis module processes and analyzes the collected displacement data and support parameters in real time, including calculating one or more of the displacement rate, trend prediction, and stability assessment to generate quantitative results of the structural stability status; The early warning and decision support module automatically issues an early warning signal and provides decision support information when the structural stability status falls below a preset threshold based on the results of the data processing and analysis module; The dynamic response strategy execution module automatically triggers or assists manual execution of corresponding response measures according to the instructions of the early warning and decision support module, and records the execution process and results for subsequent analysis.

3. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 2 is characterized in that: In the data processing and analysis module, the time series anomaly detection algorithm is used to perform pattern recognition and anomaly detection on the collected data; the time series anomaly detection algorithm includes: LSTM, autoencoder and anomaly detection; LSTM is used to process time series data and capture the long-term dependencies between displacement data and support parameters. The update formula for each LSTM unit is as follows: ； ； ； ； ； ； in, The output of the forget gate is a value between 0 and 1, activated by the Sigmoid function. Generate and control the cell state at the previous moment What information needs to be forgotten? Sigmoid activation function is used to compress the input value to between 0 and 1 and control the strength of the gating signal; is the weight matrix of the forget gate, which is used to convert the hidden state of the previous moment and the current input Mapped to the output of the forget gate; Indicates that the hidden state of the previous moment and the current input Splice into a vector; is the bias term of the forget gate, which is used to adjust the output of the forget gate; Is the input gate output, controlling the current input Hidden state at the previous moment Which of the combined information needs to be updated to the cell state? is the weight matrix of the input gate, which has the same effect as Same, but for input gate; is the bias term of the input gate, which is used to adjust the output of the input gate; is the candidate cell state at the current moment, Activation function generation, representing possible new information at the current moment; The weight matrix updated for the cell state is used to map the concatenated vector to the candidate cell state; The bias term for updating the cell state is used to adjust the candidate cell state; It is a hyperbolic tangent activation function, which is used to compress the input value to between -1 and 1 to generate candidate cell states or hidden states; is the cell state at the current moment, which is determined by the forget gate, input gate, and candidate cell state, and represents the memory at the current moment; is the cell state at the previous moment, indicating the memory of the previous moment; Output gate output, controlling the current cell state What information needs to be output to the hidden state? ; is the weight matrix of the output gate, which is used to map the concatenated vector to the output of the output gate; is the bias term of the output gate, which is used to adjust the output of the output gate; is the hidden state at the current moment, which is determined by the output gate and the cell state at the current moment, and represents the feature representation at the current moment; The autoencoder is used to compress the output of LSTM, learn the low-dimensional representation of the data, and reconstruct the input data during the decoding process. The formulas of the encoder and decoder are as follows: encoder, ; decoder, ; is a low-dimensional representation, that is, the compressed feature representation output by the encoder; is the encoding function, which is a fully connected layer or convolutional layer used to compress high-dimensional input into a low-dimensional representation; is the reconstructed hidden state, that is, the reconstructed feature representation output by the decoder; The decoding function is a fully connected layer or deconvolution layer used to reconstruct low-dimensional representations into high-dimensional features; Anomaly detection, detect anomalies by calculating the reconstruction error: ； in, is the mean square error, which is used to measure the size of the reconstruction error and serves as the basis for anomaly detection; is the length of the time series data, that is, the number of data points involved in the calculation; Represents the summation operation from t=1 to t=N.

4. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 2 is characterized in that: In the early warning and decision support module, the early warning signal is conveyed to the operators in one or more ways of sound, light flashing, and remote communication to ensure that they can receive the early warning information in a timely manner.

5. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 2 is characterized in that: In the early warning and decision support module, the decision support information includes a detailed description of potential risks, possible impact ranges, recommended response measures and their expected effects, providing operators with a comprehensive basis for decision-making.

6. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 2 is characterized in that: In the dynamic response strategy execution module, response measures are dynamically adjusted or monitoring strategies are optimized based on the comparison between the execution results and the expected effects, so as to achieve continuous optimization and improvement of the system.

7. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 6 is characterized in that: A comprehensive dynamic response strategy library is built in, covering response measures for different risk levels and anomaly types. Based on anomaly detection results and warning levels, appropriate response measures are automatically matched and selected from the strategy library. For results that are not in the dynamic response strategy library, a request message is sent to notify relevant personnel, and abnormal information and recommended response measures are provided for manual decision-making and execution.

8. The multi-dimensional protection and monitoring collaborative optimization system for reservoir banks according to claim 6 is characterized in that: After the response measures are executed, the module continues to monitor the changes in relevant parameters in real time, evaluates the effectiveness of the response measures, and automatically adjusts or updates the relevant strategies in the response strategy library based on the feedback from the execution effect to adapt to the changing engineering conditions and risk conditions.

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