Method and system for reinforcing concrete structure of power plant based on bar planting technology

Abstract

The invention discloses a power plant concrete structure reinforcing method and system based on a steel bar planting technology. According to the power plant concrete structure reinforcing method, the random forest algorithm is introduced to optimize the embedded steel bar layout, and the whole power plant concrete structure reinforcing method achieves precision and high efficiency of the embedded steel bar process. Traditional embedded steel bar layout often depends on experience and intuition of engineers, and certain uncertainty and errors exist. The random forest algorithm can accurately identify key features, such as the coordinates, depth and angle of the embedded steel bar position, influencing the embedded steel bar layout through the collection and preprocessing of a large amount of sensor data. In a model training process, a random forest model with strong generalization ability is constructed by dividing a data set and setting model parameters. Through cross validation and performance index calculation in the model evaluation link, the accuracy and reliability of the model are further ensured, the accuracy of the embedded steel bar layout is greatly improved, personal errors are reduced, meanwhile, the layout design speed is increased, and the construction efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of power plant concrete reinforcement, and specifically relates to a power plant concrete structure reinforcement method and system based on the reinforcement technology. Background Art

[0002] The concrete structure of a power plant refers to the load-bearing and non-load-bearing components such as frames, foundations, walls, and floor slabs built with concrete materials in power plant buildings and facilities. These structures have high strength, good durability, fire resistance, and earthquake resistance, and can withstand various loads during the operation of equipment inside the power plant and the erosion of the external environment. The reinforcement of the concrete structure of a power plant refers to a series of strengthening measures taken to address the problem of structural performance degradation in existing power plants due to improper design, material aging, environmental erosion, or changes in usage functions. These reinforcement methods include but are not limited to increasing the cross-section, wrapping steel, pasting carbon fiber cloth, applying prestress, injecting chemical grouting, etc., aiming to improve the bearing capacity, stiffness, and stability of the structure. During the reinforcement process, it is necessary to comprehensively consider the current status of the structure, the performance of the reinforcement materials, the feasibility of the construction process, and the use requirements after reinforcement to ensure the economy, effectiveness, and safety of the reinforcement plan. Through reasonable structural reinforcement, the service life of the concrete structure of the power plant can be extended, the safety and stability of power production can be guaranteed, and the downtime loss and maintenance costs caused by structural damage can be reduced.

[0003] However, the traditional method of reinforcing steel bars mainly relies on the experience and intuition of engineers to design the layout of reinforcing bars, lacking scientific data analysis and optimization methods. This subjective layout design is difficult to meet the needs of large-scale and rapid construction. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for reinforcing a concrete structure of a power plant based on a reinforcing bar embedding technology in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for reinforcing a concrete structure of a power plant based on a reinforcing bar implantation technology, characterized in that the method comprises the following steps:

[0006] S1: Conduct a comprehensive survey of the power plant concrete structure to identify structural damage, cracks and areas of insufficient strength; use professional equipment to perform concrete strength tests and non-destructive testing to assess the current status of the structure;

[0007] S2: Based on the survey results, design a reinforcement plan for planting steel bars, including the location, depth, diameter and number of the planting steel bars; consider the structural stress characteristics and usage requirements to ensure the safety and feasibility of the reinforcement plan;

[0008] S3: Use drilling equipment to prefabricate the rebar holes at the designed locations; ensure that the hole diameter matches the rebar diameter and the hole depth meets the design requirements;

[0009] S4: Use compressed air and a brush to clean the hole to remove dust and debris; chemically treat the inner wall of the hole to improve the bond between the anchor bar and the concrete;

[0010] S5: Select high-precision sensors, such as displacement sensors, angle sensors, and depth sensors; integrate sensors into rebar implantation equipment to ensure real-time monitoring of key parameters in the rebar implantation process; use big data analysis technology to perform statistical analysis on rebar implantation data; based on the data analysis results, use machine learning algorithms to optimize the rebar implantation layout;

[0011] S6: Insert the rebar material into the prefabricated holes; use a special rebar glue or anchoring agent to ensure a firm bond between the rebar and the concrete;

[0012] S7: Allow the rebar glue or anchoring agent to cure within the specified time, and avoid disturbance during the period; maintain the reinforced area to ensure that the material is fully cured and reaches the designed strength;

[0013] S8: Carry out quality inspection on the reinforced concrete structure, including anchor bar pull-out test and concrete strength test; ensure that the reinforcement effect meets the design requirements and specifications, and conduct acceptance.

[0014] In a preferred embodiment, in step S1, visual inspection and tapping method are used to preliminarily identify surface damage, cracks and potential areas of insufficient strength of the concrete structure; the visual inspection covers all visible structural surfaces, and the tapping method uses a metal hammer to tap the structural surface to determine internal cavities or cracks by sound changes; then, a rebound hammer is used to test the concrete strength, and the strength level of the concrete is estimated based on the relationship between the rebound value and the strength; the test angle of the rebound hammer is kept perpendicular to the concrete surface, the distance between the test points is not greater than 20 cm, and at least 10 rebound tests are performed on each test area.

[0015] In a preferred embodiment, in step S2, no less than 4 embedded bars are arranged per square meter of reinforcement area; during the design process, structural analysis software is used to perform simulation calculations to verify the effectiveness of the reinforcement scheme, and the calculation results include embedded bar force, structural deformation and bearing capacity parameters.

[0016] In a preferred embodiment, in step S3, the drilling equipment selects a diamond drill bit suitable for concrete material to ensure the accuracy of the hole diameter and the flatness of the hole wall; the hole diameter is 4-6mm larger than the diameter of the embedded reinforcement. For a steel bar with a diameter of 16mm, the hole diameter is selected to be 20-22mm; the hole depth is 20-30mm deeper than the designed embedded reinforcement depth. When the designed embedded reinforcement depth is 240mm, the hole depth reaches 260-270mm; during the drilling process, the drilling rig is kept stable to avoid hole deviation, the drilling speed is controlled at 300-500 rpm, and the feed speed is controlled at 1-2mm / second; after drilling is completed, compressed air is used to blow away dust and debris in the hole to ensure that the hole is clean, and the compressed air pressure is not less than 0.6MPa.

[0017] In a preferred embodiment, in step S4, compressed air is used to blow away dust and debris in the hole to ensure that the inner wall of the hole is free of impurities; the compressed air pressure is controlled at 0.6-0.8 MPa, and the blowing time is not less than 30 seconds.

[0018] In a preferred embodiment, in step S5, the random forest algorithm is used to optimize the rebar planting layout, and the specific steps include:

[0019] Step 1: Data preparation:

[0020] Data collection: Collect sensor data during the rebar implantation process, including the rebar implantation position coordinates X, Y, Z, depth D and angle θ; collect structural response data, such as force and deformation;

[0021] Data preprocessing: clean the data to remove outliers and noise; normalize the data to make the scales of different features consistent;

[0022] Step 2: Feature selection:

[0023] Feature importance evaluation: Use the feature importance evaluation function of random forest to determine the key features that affect the rebar layout;

[0024] Select important features: According to the feature importance score, select the features that have the greatest impact on the rebar layout as input features;

[0025] Step 3: Model training:

[0026] Divide the dataset: Divide the dataset into training set and test set, with a ratio of 70% training set and 30% test set;

[0027] Build a random forest model: Use the training set data to build a random forest model; set model parameters, including the number of trees (n_estimators) and the depth of the tree (max_depth);

[0028] Step 4: Model evaluation:

[0029] Cross-validation: Use cross-validation method to evaluate the generalization ability of the model;

[0030] Performance indicators: Calculate the performance indicators of the model, such as accuracy, recall, and F1 score;

[0031] Step 5: Optimize the layout of rebar planting:

[0032] Predictive analysis: Use the trained random forest model to perform predictive analysis on new rebar implant data;

[0033] Layout optimization: According to the prediction results, adjust the location, depth and angle of the implanted bars to optimize the implanted bar layout;

[0034] The feature importance evaluation is based on Gini impurity or information gain calculation;

[0035] The formula is: Importance(f) = ΣΔi / n;

[0036] Where Δi is the reduction in impurity of feature f in tree i, and n is the number of trees;

[0037] The model performance evaluation formula includes:

[0038] Accuracy = (TP + TN) / (P + N)

[0039] Recall = TP / P

[0040] F1 Score=2*(Precision*Recall) / (Precision+Recall);

[0041] Among them: TP is the true positive example, TN is the true negative example, P is the total number of positive examples, and N is the total number of negative examples.

[0042] In a preferred embodiment, in step S6, the rebar embedding material is derusted in advance to ensure that the surface is clean and the rust removal level reaches Sa2.5; when inserting, ensure that the rebar embedding material is aligned with the center of the hole and the deviation angle does not exceed 2 degrees; then, use a special rebar embedding glue or anchoring agent to fill the gap between the hole and the rebar embedding material; the bonding strength of the rebar embedding glue or anchoring agent is not less than 30MPa.

[0043] In a preferred embodiment, in step S7, the rebar embedding material is derusted in advance to ensure that the surface is clean and the rust removal level reaches Sa2.5; when inserting, ensure that the rebar embedding material is aligned with the center of the hole and the deviation angle does not exceed 2 degrees; then, use a special rebar embedding glue or anchoring agent to fill the gap between the hole and the rebar embedding material; the rebar embedding glue or anchoring agent should be a product that meets national standards, such as epoxy resin glue, with a bonding strength of not less than 30MPa; during the filling process, use a syringe or special tool to evenly inject the colloid into the hole, and control the injection pressure at 0.2-0.3MPa to ensure that there is no air residue.

[0044] In a preferred embodiment, in step S8, during ultrasonic testing, the sound wave propagation speed is not less than 3500m / s, and the rebound value meets the strength level required by the design; in addition, a rebar pull-out test is carried out, and the test frequency is at least 1 rebar for every 100 rebars, the pull-out force meets the design requirements, and the displacement does not exceed 2mm; all test data are recorded and a test report is formed.

[0045] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0046] 1. In the present invention, the random forest algorithm is introduced to optimize the rebar layout, and the entire power plant concrete structure reinforcement method realizes the precision and efficiency of the rebar embedding process. Traditional rebar embedding layout often relies on the experience and intuition of engineers, and there are certain uncertainties and errors. The random forest algorithm can accurately identify the key features that affect the rebar embedding layout, such as the rebar embedding position coordinates, depth and angle, etc., through the collection and preprocessing of a large amount of sensor data. The feature importance evaluation is based on Gini impurity or information gain calculation to ensure the representativeness and influence of the selected features. During the model training process, a random forest model with strong generalization ability is constructed by dividing the data set and setting the model parameters. The cross-validation and performance index calculation of the model evaluation link further ensure the accuracy and reliability of the model. Finally, the predictive analysis and layout optimization steps can accurately adjust the rebar embedding position, depth and angle according to the model output to achieve the optimization of the rebar embedding layout. This data-driven method greatly improves the accuracy of the rebar embedding layout, reduces human errors, and speeds up the layout design speed and improves the construction efficiency.

[0047] 2. In the present invention, the application of random forest algorithm in optimizing the layout of rebar planting not only improves the accuracy and efficiency of the rebar planting process, but more importantly, significantly enhances the reinforcement effect and long-term stability of the concrete structure of the power plant. In traditional reinforcement methods, due to the unreasonable layout of rebar planting, uneven distribution of structural stress may occur, which in turn affects the reinforcement effect and structural safety. The rebar planting layout optimized by the random forest algorithm can distribute stress more reasonably and avoid stress concentration, thereby improving the overall bearing capacity and seismic performance of the structure. In addition, the random forest model fully considers the structural response data, such as stress and strain, during the feature selection and model training process, to ensure the matching of the rebar planting layout with the actual needs of the structure. Indicators such as accuracy, recall rate and F1 score in the model performance evaluation link ensure the accuracy and comprehensiveness of the model in predicting the rebar planting layout. Therefore, the optimized rebar planting layout can not only meet the reinforcement needs in the short term, but also ensure the stability and safety of the structure during long-term use, extend the service life of the structure, and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the process principle of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0050] Example:

[0051] Reference Figure 1 A method for reinforcing a concrete structure of a power plant based on a reinforcing bar embedding technology comprises the following steps:

[0052] S1: Conduct a comprehensive survey of the power plant concrete structure to identify structural damage, cracks and areas of insufficient strength. Use professional equipment to perform concrete strength tests and non-destructive testing to assess the current status of the structure;

[0053] S2: Based on the survey results, design a reinforcement plan, including the location, depth, diameter and number of reinforcements. Consider the structural stress characteristics and usage requirements to ensure the safety and feasibility of the reinforcement plan.

[0054] S3: Use drilling equipment to prefabricate the rebar holes at the designed locations. Ensure that the hole diameter matches the rebar diameter and the hole depth meets the design requirements;

[0055] S4: Use compressed air and a brush to clean the hole to remove dust and debris. Chemically treat the inner wall of the hole to improve the bond between the anchor bar and the concrete;

[0056] S5: Select high-precision sensors, such as displacement sensors, angle sensors, and depth sensors. Integrate sensors into rebar implantation equipment to ensure real-time monitoring of key parameters in the rebar implantation process; use big data analysis technology to perform statistical analysis on rebar implantation data. Based on the data analysis results, use machine learning algorithms to optimize the rebar implantation layout;

[0057] S6: Insert the rebar material into the prefabricated holes. Use special rebar glue or anchoring agent to ensure a firm bond between the rebar and the concrete.

[0058] S7: Allow the rebar glue or anchoring agent to cure within the specified time and avoid disturbance during the period. Maintain the reinforced area to ensure that the material is fully cured and reaches the designed strength.

[0059] S8: Carry out quality inspection on the reinforced concrete structure, including anchor bar pull-out test, concrete strength test, etc. Ensure that the reinforcement effect meets the design requirements and specification standards, and conduct acceptance.

[0060] In step S1, visual inspection and tapping methods are used to preliminarily identify surface damage, cracks and potential areas of insufficient strength of the concrete structure. The visual inspection covers all visible structural surfaces. The tapping method uses a metal hammer to tap the surface of the structure to determine internal cavities or cracks by sound changes. Subsequently, a rebound hammer is used to test the concrete strength, and the strength grade of the concrete is estimated based on the corresponding relationship between the rebound value and the strength. The test angle of the rebound hammer should be kept perpendicular to the concrete surface, the spacing between the test points should not be greater than 20 cm, and at least 10 rebound tests should be performed in each test area, and the average value is taken as the representative strength value of the area. In terms of non-destructive testing, the ultrasonic pulse method is used to detect cracks and cavities inside the concrete, and the density and uniformity of the concrete are evaluated by measuring the propagation speed and attenuation of sound waves in the concrete. During ultrasonic testing, the spacing between the transmitting and receiving probes is set according to the thickness of the concrete, usually between 20 and 50 cm, the detection frequency is set to 100 kHz, and the sound time and amplitude values ​​are recorded at each measuring point. The evaluation results should be compiled into a detailed survey report, including a structural damage distribution map, strength test results and ultrasonic testing data, which will serve as the basis for subsequent reinforcement design.

[0061] In step S2, no less than 4 embedded bars are arranged per square meter of reinforcement area. During the design process, structural analysis software should be used for simulation calculation to verify the effectiveness of the reinforcement scheme. The calculation results should include parameters such as embedded bar stress, structural deformation and bearing capacity.

[0062] In step S3, the drilling equipment should select a diamond drill bit suitable for concrete material to ensure the accuracy of the hole diameter and the flatness of the hole wall. The hole diameter should be 4-6mm larger than the diameter of the embedded reinforcement. For a steel bar with a diameter of 16mm, the hole diameter should be 20-22mm. The hole depth should be 20-30mm deeper than the designed embedded reinforcement depth. When the designed embedded reinforcement depth is 240mm, the hole depth should reach 260-270mm. During the drilling process, the drilling rig should be kept stable to avoid hole deviation. The drilling speed should be controlled at 300-500 rpm and the feed speed should be controlled at 1-2mm / second. After drilling is completed, compressed air should be used to blow away dust and debris in the hole to ensure that the hole is clean. The compressed air pressure should not be lower than 0.6MPa.

[0063] In step S4, use compressed air to blow away the dust and debris in the hole to ensure that there are no impurities on the inner wall of the hole. The compressed air pressure should be controlled at 0.6-0.8MPa, and the blowing time should be no less than 30 seconds. Subsequently, use a brush to further clean the inner wall of the hole, especially the bottom and edge of the hole. The brush should be a hard nylon brush with a diameter slightly smaller than the hole diameter. After cleaning, the inner wall of the hole is chemically treated to improve the bonding strength between the anchor bar and the concrete. Chemical treatment agents are usually acidic or alkaline solutions, such as phosphoric acid or sodium hydroxide solutions, with a concentration controlled between 5% and 10%. The treatment agent should be evenly applied to the inner wall of the hole, left to stand for 10 to 15 minutes, then rinsed with clean water and dried. The holes after chemical treatment should be rebar-planted as soon as possible to avoid re-contamination, and the treated holes should be rebar-planted within 24 hours.

[0064] In step S5, the random forest algorithm is used to optimize the rebar planting layout, and the specific steps include:

[0065] Step 1: Data preparation:

[0066] Data collection: Collect sensor data during the rebar implantation process, including the rebar implantation position coordinates X, Y, Z, depth D and angle θ. Collect structural response data, such as stress, strain, etc.

[0067] Data preprocessing: Clean the data to remove outliers and noise. Normalize the data to make the scales of different features consistent.

[0068] Step 2: Feature selection:

[0069] Feature Importance Assessment: Use the feature importance assessment function of random forest to determine the key features that affect the rebar layout.

[0070] Select important features: Based on the feature importance score, select the features that have the greatest impact on the rebar layout as input features.

[0071] Step 3: Model training:

[0072] Divide the dataset: Divide the dataset into training and test sets, usually with a ratio of 70% training set and 30% test set.

[0073] Build a random forest model: Use the training set data to build a random forest model. Set the model parameters, including the number of trees (n_estimators) and the depth of the tree (max_depth);

[0074] Step 4: Model evaluation:

[0075] Cross-validation: Use cross-validation method to evaluate the generalization ability of the model.

[0076] Performance indicators: Calculate the performance indicators of the model, such as accuracy, recall, F1 score, etc.

[0077] Step 5: Optimize the layout of rebar planting:

[0078] Predictive analysis: Use the trained random forest model to perform predictive analysis on new rebar implant data.

[0079] Layout optimization: According to the prediction results, adjust the location, depth and angle of the implanted bars to optimize the implanted bar layout;

[0080] The feature importance evaluation is based on Gini impurity or information gain calculation.

[0081] The formula is: Importance(f) = ΣΔi / n;

[0082] Where Δi is the reduction in impurity of feature f in tree i, and n is the number of trees;

[0083] The model performance evaluation formula includes:

[0084] Accuracy = (TP + TN) / (P + N)

[0085] Recall = TP / P

[0086] F1 Score=2*(Precision*Recall) / (Precision+Recall);

[0087] Among them: TP is the true positive example, TN is the true negative example, P is the total number of positive examples, and N is the total number of negative examples.

[0088] In step S6, the rebar embedding material should be derusted in advance to ensure that the surface is clean, and the rust removal level should reach Sa2.5. During insertion, ensure that the rebar embedding material is aligned with the center of the hole, and the deflection angle should not exceed 2 degrees. Subsequently, a special rebar embedding glue or anchoring agent is used to fill the gap between the hole and the rebar embedding material. The rebar embedding glue or anchoring agent should be a product that meets national standards, such as epoxy resin glue, and the bonding strength should not be less than 30MPa. During the filling process, a syringe or special tool should be used to evenly inject the colloid into the hole, and the injection pressure should be controlled at 0.2-0.3MPa to ensure that no air remains.

[0089] In step S7, the rebar embedding material should be derusted in advance to ensure that the surface is clean, and the rust removal level should reach Sa2.5. During insertion, ensure that the rebar embedding material is aligned with the center of the hole, and the deflection angle should not exceed 2 degrees. Subsequently, a special rebar embedding glue or anchoring agent is used to fill the gap between the hole and the rebar embedding material. The rebar embedding glue or anchoring agent should be a product that meets national standards, such as epoxy resin glue, and the bonding strength should not be less than 30MPa. During the filling process, a syringe or special tool should be used to evenly inject the colloid into the hole, and the injection pressure should be controlled at 0.2-0.3MPa to ensure that no air remains.

[0090] In step S8, during ultrasonic testing, the sound wave propagation speed should not be less than 3500m / s, and the rebound value should meet the strength grade required by the design. In addition, a pull-out test of the implanted steel bars is conducted, and the test frequency is at least 1 bar for every 100 implanted steel bars. The pull-out force should meet the design requirements, and the displacement should not exceed 2mm. All test data should be recorded and a test report should be formed.

[0091] A power plant concrete structure reinforcement system based on the rebar embedding technology, the system uses the power plant concrete structure reinforcement method based on the rebar embedding technology to reinforce the power plant concrete structure.

[0092] In the present invention, by introducing the random forest algorithm to optimize the rebar planting layout, the entire power plant concrete structure reinforcement method realizes the precision and efficiency of the rebar planting process. Traditional rebar planting layout often relies on the experience and intuition of engineers, and there are certain uncertainties and errors. The random forest algorithm can accurately identify the key features that affect the rebar planting layout, such as the rebar planting position coordinates, depth and angle, etc., through the collection and preprocessing of a large amount of sensor data. The feature importance evaluation is based on Gini impurity or information gain calculation to ensure the representativeness and influence of the selected features. During the model training process, a random forest model with strong generalization ability is constructed by dividing the data set and setting the model parameters. The cross-validation and performance index calculation of the model evaluation link further ensure the accuracy and reliability of the model. Finally, the predictive analysis and layout optimization steps can accurately adjust the rebar planting position, depth and angle according to the model output to achieve the optimization of the rebar planting layout. This data-driven method greatly improves the accuracy of the rebar planting layout, reduces human errors, and speeds up the layout design speed and improves the construction efficiency.

[0093] In the present invention, the application of random forest algorithm in optimizing the layout of rebar planting not only improves the accuracy and efficiency of the rebar planting process, but more importantly, significantly enhances the reinforcement effect and long-term stability of the concrete structure of the power plant. In traditional reinforcement methods, due to the unreasonable layout of rebar planting, uneven distribution of structural stress may occur, which in turn affects the reinforcement effect and structural safety. The rebar planting layout optimized by the random forest algorithm can distribute stress more reasonably and avoid stress concentration, thereby improving the overall bearing capacity and seismic performance of the structure. In addition, the random forest model fully considers the structural response data, such as stress and strain, during the feature selection and model training process, to ensure the matching of the rebar planting layout with the actual needs of the structure. Indicators such as accuracy, recall rate and F1 score in the model performance evaluation link ensure the accuracy and comprehensiveness of the model in predicting the rebar planting layout. Therefore, the optimized rebar planting layout can not only meet the reinforcement needs in the short term, but also ensure the stability and safety of the structure during long-term use, extend the service life of the structure, and reduce maintenance costs.

[0094] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reinforcing a power plant concrete structure based on the reinforcement technology, characterized in that: The method comprises the following steps: S1: Conduct a comprehensive survey of the power plant concrete structure to identify structural damage, cracks and areas of insufficient strength; use professional equipment to perform concrete strength tests and non-destructive testing to assess the current status of the structure; S2: Based on the survey results, design a reinforcement plan for planting steel bars, including the location, depth, diameter and number of the planting steel bars; consider the structural stress characteristics and usage requirements to ensure the safety and feasibility of the reinforcement plan; S3: Use drilling equipment to prefabricate the rebar holes at the designed locations; ensure that the hole diameter matches the rebar diameter and the hole depth meets the design requirements; S4: Use compressed air and a brush to clean the hole to remove dust and debris; chemically treat the inner wall of the hole to improve the bond between the anchor bar and the concrete; S5: Select high-precision sensors, such as displacement sensors, angle sensors, and depth sensors; integrate sensors into rebar implantation equipment to ensure real-time monitoring of key parameters in the rebar implantation process; use big data analysis technology to perform statistical analysis on rebar implantation data; based on the data analysis results, use machine learning algorithms to optimize the rebar implantation layout; S6: Insert the rebar material into the prefabricated holes; use a special rebar glue or anchoring agent to ensure a firm bond between the rebar and the concrete; S7: Allow the rebar glue or anchoring agent to cure within the specified time, and avoid disturbance during this period; maintain the reinforced area to ensure that the material is fully cured and reaches the designed strength; S8: Carry out quality inspection on the reinforced concrete structure, including anchor bar pull-out test and concrete strength test; ensure that the reinforcement effect meets the design requirements and specifications, and conduct acceptance.

2. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In step S1, visual inspection and tapping method are used to preliminarily identify surface damage, cracks and potential strength deficiency areas of the concrete structure; the visual inspection covers all visible structural surfaces, and the tapping method uses a metal hammer to tap the structural surface to determine internal cavities or cracks by sound changes; then, a rebound hammer is used to test the concrete strength, and the strength level of the concrete is estimated based on the relationship between the rebound value and the strength; the test angle of the rebound hammer is kept perpendicular to the concrete surface, the distance between the test points is not greater than 20 cm, and at least 10 rebound tests are performed on each test area.

3. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In the step S2, no less than 4 embedded bars are arranged per square meter of the reinforcement area; During the design process, structural analysis software was used to perform simulation calculations to verify the effectiveness of the reinforcement scheme. The calculation results included reinforcement force, structural deformation and bearing capacity parameters.

4. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In the step S3, the drilling equipment selects a diamond drill bit suitable for concrete material to ensure the accuracy of the hole diameter and the flatness of the hole wall; the hole diameter is 4-6mm larger than the diameter of the embedded steel bar, and for a steel bar with a diameter of 16mm, the hole diameter is selected to be 20-22mm; the hole depth is 20-30mm deeper than the designed embedded steel bar depth, and when the designed embedded steel bar depth is 240mm, the hole depth reaches 260-270mm; during the drilling process, the drilling rig is kept stable to avoid hole diameter deviation, the drilling speed is controlled at 300-500 rpm, and the feed speed is controlled at 1-2mm / sec; After drilling is completed, use compressed air to blow away dust and debris in the hole to ensure that the hole is clean and the compressed air pressure is not less than 0.6MPa.

5. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In step S4, compressed air is used to blow away dust and debris in the hole to ensure that the inner wall of the hole is free of impurities; the compressed air pressure is controlled at 0.6-0.8 MPa, and the blowing time is not less than 30 seconds.

6. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1, characterized in that: In step S5, the random forest algorithm is used to optimize the rebar planting layout, and the specific steps include: Step 1: Data preparation: Data collection: Collect sensor data during the rebar implantation process, including the rebar implantation position coordinates X, Y, Z, depth D and angle θ; collect structural response data, such as force and deformation; Data preprocessing: clean the data to remove outliers and noise; normalize the data to make the scales of different features consistent; Step 2: Feature selection: Feature importance evaluation: Use the feature importance evaluation function of random forest to determine the key features that affect the rebar layout; Select important features: According to the feature importance score, select the features that have the greatest impact on the rebar layout as input features; Step 3: Model training: Divide the dataset: Divide the dataset into training set and test set, with a ratio of 70% training set and 30% test set; Build a random forest model: Use the training set data to build a random forest model; set model parameters, including the number of trees (n_estimators) and the depth of the tree (max_depth); Step 4: Model evaluation: Cross-validation: Use cross-validation method to evaluate the generalization ability of the model; Performance indicators: Calculate the performance indicators of the model, such as accuracy, recall, and F1 score; Step 5: Optimize the layout of rebar planting: Predictive analysis: Use the trained random forest model to perform predictive analysis on new rebar implant data; Layout optimization: According to the prediction results, adjust the location, depth and angle of the implanted bars to optimize the implanted bar layout; The feature importance evaluation is based on Gini impurity or information gain calculation; The formula is: Importance(f) = ΣΔi / n; Where Δi is the reduction in impurity of feature f in tree i, and n is the number of trees; The model performance evaluation formula includes: Accuracy = (TP + TN) / (P + N) Recall = TP / P F1 score (F1Score)=2*(Precision*Recall) / (Precision+Recall); Among them: TP is the true positive example, TN is the true negative example, P is the total number of positive examples, and N is the total number of negative examples.

7. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In step S6, the rebar embedding material is rust-removed in advance to ensure that the surface is clean and the rust removal level reaches Sa2.5; when inserting, ensure that the rebar embedding material is aligned with the center of the hole and the deflection angle does not exceed 2 degrees; Subsequently, a special anchoring glue or anchoring agent is used to fill the gap between the hole and the anchoring material; the bonding strength of the anchoring glue or anchoring agent shall not be less than 30MPa.

8. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1 is characterized in that: In step S7, the rebar embedding material is rust-removed in advance to ensure that the surface is clean and the rust removal level reaches Sa2.5; when inserting, ensure that the rebar embedding material is aligned with the center of the hole and the deflection angle does not exceed 2 degrees; Subsequently, a special anchoring glue or anchoring agent is used to fill the gap between the hole and the anchoring material. The anchoring glue or anchoring agent should be a product that meets national standards, such as epoxy resin glue, with a bonding strength of not less than 30MPa. During the filling process, a syringe or special tool is used to evenly inject the colloid into the hole, and the injection pressure is controlled at 0.2-0.3MPa to ensure that no air remains.

9. The method for reinforcing the concrete structure of a power plant based on the reinforcing bar implantation technology according to claim 1, characterized in that: In step S8, during ultrasonic testing, the sound wave propagation speed is not less than 3500m / s, and the rebound value meets the strength level required by the design; in addition, a rebar pull-out test is carried out, and the test frequency is at least 1 rebar for every 100 rebars. The pull-out force meets the design requirements and the displacement does not exceed 2mm; all test data are recorded and a test report is formed.

10. A power plant concrete structure reinforcement system based on the reinforcement technology, characterized in that: The system uses the power plant concrete structure reinforcement method based on the anchor bar technology as described in any one of claims 1 to 9 to reinforce the power plant concrete structure.

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