Method and system for detecting structural strength of PVC guardrail

Abstract

The invention provides a PVC guardrail structural strength detection method and system, and relates to the technical field of material physical property detection. Comprising a data acquisition unit used for acquiring data, a data processing unit used for processing the data, a management database unit, a system feedback summary unit, a detection work unit used for detecting the strength of the PVC guardrail and a unit used for calibrating and maintaining the system. The detection working unit comprises a detection category selection module, a temperature and humidity regulation and control module and a detection process recording module; and the detection category selection module comprises impact strength detection, compressive strength detection and bending strength detection. Through the arrangement of the detection working unit, impact strength detection, compressive strength detection and bending strength detection can be effectively fused together, specific adjustment is carried out when specific detection items are implemented, and the convenience and efficiency of PVC guardrail structural strength detection are improved.

Description

Technical Field

[0001] The invention relates to the technical field of material physical property detection, and in particular to a PVC guardrail structure strength detection method and system. Background Art

[0002] In the fields of modern construction, road safety protection and garden landscaping, PVC guardrails have been widely used because of their corrosion resistance, beauty and easy installation. The structural strength of PVC guardrails is directly related to their safety and reliability in actual use. If the strength is insufficient, it may be damaged when subjected to external impact, heavy pressure or long-term environmental effects, and thus fail to play the protective role it should, thus leading to safety accidents.

[0003] At present, there are many shortcomings in the detection of the structural strength of PVC guardrails. For example, when detecting the structural strength of different categories of PVC guardrails (impact resistance structural strength detection, compressive strength detection and bending resistance structural strength detection), multiple detection systems need to be used, which makes the detection process more complicated. At the same time, manual comparative analysis is mainly used when analyzing the detection data, which leads to low efficiency in the analysis of the detection data. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a PVC guardrail structural strength detection method and system, which solves many shortcomings in the current detection of the structural strength of PVC guardrails. For example, when detecting the structural strength of different categories of PVC guardrails (impact resistance structural strength detection, compressive strength detection and bending resistance structural strength detection), multiple detection systems need to be used, resulting in a cumbersome detection process. At the same time, manual comparative analysis is mainly used when analyzing the detection data, which leads to the problem of low efficiency in the analysis of the detection data.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a PVC guardrail structure strength detection system, including a system architecture having a data acquisition unit for collecting data, a data processing unit for processing data, a management database unit, a system feedback summary unit, a detection working unit for detecting the strength of the PVC guardrail, and a system calibration and maintenance unit;

[0008] The detection working unit includes a detection category selection module, a temperature and humidity control module for regulating the temperature and humidity when detecting the strength of the PVC guardrail, and a detection process recording module.

[0009] Further, the test category selection module includes impact strength test, compressive strength test and bending strength test, wherein the impact strength test includes pendulum impact test and drop hammer impact test;

[0010] The pendulum impact test can be carried out using a simply supported beam or cantilever beam. The PVC guardrail sample is installed on the impact machine according to the standard requirements, and the pendulum is allowed to fall freely to hit the sample. Then the simply supported beam impact test is to hit the center of the simply supported beam test with the pendulum; the cantilever beam is to hit the free end of the notched cantilever beam with the pendulum, and record the impact energy consumed per unit area or unit width when the sample breaks, so as to determine its impact strength.

[0011] The drop hammer impact test is to drop a ball or hammer of a certain mass freely from a specific height to impact the PVC guardrail sample, and then observe the damage of the guardrail, such as whether cracks or breaks appear, and record the impact energy or height that can cause a specific degree of damage to the guardrail, thereby evaluating its impact strength.

[0012] Furthermore, the compressive strength test is performed using a pressure testing machine. The PVC guardrail sample is placed on the workbench of the testing machine, and uniform axial pressure is applied to the sample through the pressure head of the testing machine. The pressure is gradually increased until the sample is deformed and ruptured, causing the PVC guardrail to fail. The pressure value at this time is then recorded to obtain the compressive strength of the PVC guardrail.

[0013] Furthermore, the bending strength test is to place the PVC guardrail sample on two supporting points, apply a load perpendicular to the sample at the middle position of the sample, thereby causing the sample to bend and deform, and then measure the bending deflection under the specified load or the bending load under the specified deflection, so as to calculate the bending strength of the PVC guardrail.

[0014] Furthermore, the data processing unit includes a data preprocessing module, a data intelligent processing module and a data filtering and conversion module. The data preprocessing module is mainly used to clean the collected data in order to remove interference data. The data intelligent processing module is mainly used to perform intelligent analysis and processing on the collected data. The data filtering and conversion module filters, amplifies, and performs analog-to-digital conversion on the collected data, and then transmits it to the next stage for processing.

[0015] Furthermore, the database management unit includes a historical data comparison module, a data classification storage module and a data monitoring module. The historical data comparison module compares and analyzes the collected data with the historical data stored in the database. The data classification storage module mainly classifies and stores the data to be stored according to categories. The data monitoring module mainly scans and monitors the data stored in the database.

[0016] Furthermore, the calibration and maintenance unit needs to regularly calibrate hardware equipment such as loading devices and sensors to ensure the accuracy of test data. The calibration process records the calibration results in accordance with relevant standards. If it is found that the equipment error exceeds the allowable range, it can be adjusted or repaired in time. At the same time, a daily maintenance plan for the system is formulated, including equipment cleaning, checking whether the connections of various components are loose, and updating the software system. Subsequently, the data storage device is backed up regularly to prevent data loss.

[0017] Furthermore, the system feedback summary unit automatically generates a detailed test report based on the analysis result software system. The report clearly displays the various performance parameters of the guardrail, the conclusion on whether it is qualified, and related analysis charts. At the same time, it evaluates the analyzed data and combines historical data to judge where the structural strength of the PVC guardrail needs to be improved.

[0018] A method for testing the structural strength of a PVC guardrail comprises the following testing steps:

[0019] Step 1: Test preparation. Select the corresponding testing equipment according to the selected test category. For example, when testing the compressive strength of PVC guardrails, you need to prepare a pressure testing machine; when testing the impact strength of PVC guardrails, you need to prepare a pendulum or drop hammer to perform an impact test on PVC guardrails.

[0020] Step 2: Start testing. Test the structural strength of the PVC guardrail according to the selected test category. For example, when conducting a lateral thrust test on the PVC guardrail, apply a thrust of a certain size and direction to the side of the guardrail to observe whether the guardrail will tip over, deform excessively, or experience other unstable phenomena, and evaluate its stability under the action of lateral force.

[0021] Step 3: Collect test data. Collect the data generated by testing the PVC guardrail and record the test steps. For example, use pressure sensors, displacement sensors and acceleration sensors. The pressure sensor is used to measure the magnitude of the force during the loading process; the displacement sensor can monitor the deformation of the guardrail when it is subjected to force; the acceleration sensor can measure the change in acceleration during the impact test, and then analyze the impact force;

[0022] Step 4: Conduct intelligent analysis on the collected data. By conducting intelligent analysis on the collected test data, we can determine whether the structural strength of the PVC guardrail is qualified. The collected data is intelligently analyzed through a convolutional neural network model, and a data threshold red line is set. When the data is lower than the threshold red line, it shows that the structural strength of the PVC guardrail is unqualified.

[0023] Step 5: Summarize and evaluate the results of the analysis, and then determine the direction of improvement of the PVC guardrail by analyzing and comparing the results of the analysis with previous historical data. By continuously testing new PVC guardrail processing technology, the test and analysis data will be compared with the data of PVC guardrails made using mature technology, so that the quality of PVC guardrails can be continuously improved.

[0024] Furthermore, when using the convolutional neural network model to analyze data in step 4, the data must first be prepared, then the data is input into the constructed model for training, and finally the model is used to realize intelligent analysis of the data.

[0025] (III) Beneficial effects

[0026] The present invention provides a method and system for detecting the structural strength of a PVC guardrail, which has the following beneficial effects:

[0027] 1. In this scheme, by setting up a testing work unit, the impact strength test, compressive strength test and bending strength test can be effectively integrated together, and specific adjustments can be made when implementing specific testing items, which is conducive to improving the convenience and efficiency of PVC guardrail structure strength testing.

[0028] 2. In this scheme, by using the convolutional neural network model to perform intelligent analysis on the detected data, it can be intelligently compared with historical data, which can effectively improve the efficiency of data analysis and thereby improve the accuracy of the direction of improving the structural strength of the PVC guardrail. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the flow chart of a PVC guardrail structure strength detection system proposed by the present invention;

[0030] Figure 2 This is a schematic diagram of the data processing unit architecture of a PVC guardrail structure strength detection system proposed by the present invention;

[0031] Figure 3 This is a schematic diagram of the database management unit architecture of a PVC guardrail structure strength detection system proposed by the present invention;

[0032] Figure 4 This is a schematic diagram of the structure of a detection working unit of a PVC guardrail structure strength detection system proposed by the present invention;

[0033] Figure 5 The figure is a schematic flow chart of a method for detecting the structural strength of a PVC guardrail proposed in the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example:

[0036] like Figure 1-5 As shown, an embodiment of the present invention provides a PVC guardrail structure strength detection system, including a system architecture having a data acquisition unit for collecting data, the data acquisition unit including a pressure sensor, a displacement sensor, an acceleration sensor, etc. The pressure sensor is used to measure the magnitude of the force during the loading process; the displacement sensor can monitor the deformation of the guardrail when subjected to force; the acceleration sensor can measure the acceleration change during the impact test and then analyze the impact force and other data;

[0037] A data processing unit for processing data, the data processing unit includes a data preprocessing module, a data intelligent processing module and a data filtering and conversion module. The data preprocessing module mainly cleans the collected data to remove interference data, the data intelligent processing module mainly performs intelligent analysis and processing on the collected data, and the data filtering and conversion module filters, amplifies, and performs analog-to-digital conversion on the collected data, and then transmits it to the next stage for processing;

[0038] The database management unit includes a historical data comparison module, a data classification storage module and a data monitoring module. The historical data comparison module compares and analyzes the collected data with the historical data stored in the database. The data classification storage module mainly stores the data to be stored according to categories. The data monitoring module mainly scans and monitors the data stored in the database.

[0039] The system feedback summary unit automatically generates a detailed test report based on the analysis results. The report clearly displays the performance parameters of the guardrail, the conclusion of whether it is qualified, and related analysis charts. At the same time, it evaluates the analyzed data and judges the aspects that need to be improved in the structural strength of the PVC guardrail in combination with historical data.

[0040] The calibration and maintenance unit for the system needs to calibrate the hardware equipment such as loading devices and sensors regularly to ensure the accuracy of the test data. The calibration process records the calibration results according to relevant standards. If the equipment error is found to be beyond the allowable range, it can be adjusted or repaired in time. At the same time, a daily maintenance plan for the system is formulated, including equipment cleaning, checking whether the connection of each component is loose, and updating the software system. Then, the data storage device is backed up regularly to prevent data loss;

[0041] A testing unit for testing the strength of PVC guardrails, the testing unit comprising a testing category selection module, the testing category selection module comprising impact strength testing, compressive strength testing and bending strength testing, wherein the impact strength testing comprises a pendulum impact test and a drop hammer impact test;

[0042] The pendulum impact test can be carried out using a simply supported beam or cantilever beam. The PVC guardrail sample is installed on the impact machine according to the standard requirements, and the pendulum is allowed to fall freely to hit the sample. Then the simply supported beam impact test is when the pendulum hits the center of the simply supported beam test; the cantilever beam is when the pendulum hits the free end of the notched cantilever beam, and the impact energy consumed per unit area or unit width when the sample breaks is recorded to determine its impact strength.

[0043] The drop hammer impact test is to drop a ball or hammer of a certain mass from a specific height to impact the PVC guardrail sample, and then observe the damage of the guardrail, such as whether there are cracks or breaks, and record the impact energy or height that can cause a specific degree of damage to the guardrail, so as to evaluate its impact strength;

[0044] The compressive strength test is to use a pressure testing machine, place the PVC guardrail sample on the workbench of the testing machine, apply uniform axial pressure to the sample through the pressure head of the testing machine, and gradually increase the pressure until the sample deforms and breaks, causing the PVC guardrail to fail. Then record the pressure value at this time to obtain the compressive strength of the PVC guardrail;

[0045] The bending strength test is to place the PVC guardrail sample on two supporting points, apply a load perpendicular to the sample at the middle position of the sample, so as to make the sample bend and deform, and then measure the bending deflection under the specified load or the bending load under the specified deflection, so as to calculate the bending strength of the PVC guardrail;

[0046] The temperature and humidity control module and the detection process recording module are used to control the temperature and humidity when detecting the strength of the PVC guardrail. The temperature and humidity control module is mainly used to adjust the temperature and humidity during the detection of the structural strength of the PVC guardrail, so as to realize the structural strength of the PVC guardrail under different temperature and humidity conditions, and then judge the influence of temperature and humidity on the structural strength of the PVC guardrail.

[0047] A method for testing the structural strength of a PVC guardrail comprises the following testing steps:

[0048] Step 1: Test preparation. Select the corresponding testing equipment according to the selected test category. For example, when testing the compressive strength of PVC guardrails, you need to prepare a pressure testing machine; when testing the impact strength of PVC guardrails, you need to prepare a pendulum or drop hammer to perform an impact test on PVC guardrails.

[0049] Step 2: Start testing. Test the structural strength of the PVC guardrail according to the selected test category. For example, when conducting a lateral thrust test on the PVC guardrail, apply a thrust of a certain size and direction to the side of the guardrail to observe whether the guardrail will tip over, deform excessively, or experience other unstable phenomena, and evaluate its stability under the action of lateral force.

[0050] Step 3: Collect test data. Collect the data generated by testing the PVC guardrail and record the test steps. For example, use pressure sensors, displacement sensors and acceleration sensors. The pressure sensor is used to measure the magnitude of the force during the loading process; the displacement sensor can monitor the deformation of the guardrail when it is subjected to force; the acceleration sensor can measure the change in acceleration during the impact test, and then analyze the impact force;

[0051] Step 4: Conduct intelligent analysis on the collected data. By conducting intelligent analysis on the collected test data, we can determine whether the structural strength of the PVC guardrail is qualified. The collected data is intelligently analyzed through a convolutional neural network model, and a data threshold red line is set. When the data is lower than the threshold red line, it shows that the structural strength of the PVC guardrail is unqualified.

[0052] Step 5: Summarize and evaluate the results of the analysis, and then determine the direction of improvement of the PVC guardrail by analyzing and comparing the results of the analysis with previous historical data. By continuously testing new PVC guardrail processing technology, the test and analysis data will be compared with the data of PVC guardrails made using mature technology, so that the quality of PVC guardrails can be continuously improved.

[0053] In step 4, when using the convolutional neural network model to analyze data, you first need to prepare the data, then input the data into the constructed model for training, and finally use the model to perform intelligent analysis of the data;

[0054] First, collect and preprocess the data:

[0055] 1. Data collection: During the PVC guardrail structural strength test, various sensor data are collected, such as pressure data collected by force sensors, deformation data recorded by displacement sensors, impact acceleration data captured by acceleration sensors, etc. At the same time, the corresponding test results are recorded, such as whether the guardrail is damaged and the degree of damage;

[0056] 2. Data cleaning: Check whether there are missing values ​​or outliers in the data. For missing values, methods such as mean filling and linear interpolation can be used to process them. For outliers, methods such as the 3σ criterion can be used to identify, correct or eliminate them based on the data distribution characteristics.

[0057] 3. Data normalization: Normalize sensor data of different ranges to the interval [0,1] or [-1,1] to eliminate the dimensional differences between data features and improve the model training effect. For example, for force sensor data , we can use the formula Normalize, where and are the minimum and maximum values ​​of this type of data respectively;

[0058] Data reshaping: Since convolutional neural networks usually process data with specific dimensions, one-dimensional sensor data needs to be reshaped into a multi-dimensional tensor suitable for CNN input. For example, force sensor data collected sequentially over a period of time can be reshaped into a two-dimensional matrix with rows representing time steps and columns representing different features (such as force values ​​at different positions).

[0059] Then build a convolutional neural network model:

[0060] 1. Input layer: Determine the size of the input layer based on the dimension of the preprocessed data. If the data is reshaped into The two-dimensional matrix of the input layer is ;

[0061] 2. Convolutional layer: Set multiple convolutional layers and each convolutional layer contains multiple convolutional kernels. The size (such as 3×3, 5×5) and number (such as 16, 32) of the convolutional kernels are adjusted according to the data characteristics and model complexity requirements. The convolutional layer extracts local features in the data through convolution operations, such as the change pattern of data at different time steps and positions;

[0062] 3. Pooling layer: Add a pooling layer after the convolution layer. Common pooling methods include maximum pooling and average pooling. The pooling layer can reduce the data dimension and thus reduce the amount of calculation while retaining the main features. For example, a 2×2 maximum pooling kernel is used to output the maximum value in each 2×2 area.

[0063] 4. Fully connected layer: The feature map after convolution and pooling is expanded and connected to the fully connected layer. The fully connected layer conducts a comprehensive analysis of the extracted features and outputs the final prediction result. For example, one or more fully connected layers are used, and then the number of neurons in the output layer is determined according to the detection task (such as judging whether the guardrail is qualified, predicting the degree of damage, etc.);

[0064] 5. Activation function: Use activation functions after convolutional layers and fully connected layers, such as ReLU (Rectified Linear Unit) function, to increase the nonlinear expression ability of the model. The formula is .

[0065] The model is then trained and optimized:

[0066] 1. Loss function selection: Select an appropriate loss function according to the type of detection task. For binary classification tasks (such as determining whether a guardrail is qualified), the cross entropy loss function can be used; for regression tasks (such as predicting strength values), the mean square error loss function can be used;

[0067] 2. Optimizer selection: Use optimizers such as stochastic gradient descent (SGD), Adagrad, and Adam to update the model parameters. Taking the Adam optimizer as an example, it combines the advantages of Adagrad and Adadelta, and can adaptively adjust the learning rate to make the model converge faster;

[0068] 3. Training process: The preprocessed data is divided into training set, validation set and test set, generally in the ratio of 70%, 15% and 15%. During the training process, the training set data is input into the model, the prediction result is calculated through forward propagation, and then the error between the predicted value and the true value is calculated according to the loss function. Then, the model parameters are updated through back propagation, and the performance of the model is monitored on the validation set to prevent overfitting.

[0069] Finally, the model is evaluated and applied:

[0070] 1. Evaluation indicators: Use accuracy, recall, F1 value, root mean square error and other indicators to evaluate model performance. For classification tasks, focus on accuracy, recall and F1 value; for regression tasks, focus on root mean square error;

[0071] 2. Model application: After training and evaluation, the model with good performance is applied to the actual PVC guardrail structure strength test data analysis. The real-time sensor data is collected and input into the model after preprocessing. The model can quickly give the test results and realize intelligent analysis.

[0072] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A PVC guardrail structure strength detection system, characterized by: The system architecture includes a data acquisition unit for collecting data, a data processing unit for processing data, a management database unit, a system feedback summary unit, a detection unit for detecting the strength of the PVC guardrail, and a system calibration and maintenance unit; The detection working unit includes a detection category selection module, a temperature and humidity control module for regulating the temperature and humidity when detecting the strength of the PVC guardrail, and a detection process recording module.

2. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The test category selection module includes impact strength test, compressive strength test and bending strength test, wherein the impact strength test includes pendulum impact test and drop hammer impact test; The pendulum impact test can be carried out using a simply supported beam or cantilever beam. The PVC guardrail sample is installed on the impact machine according to the standard requirements, and the pendulum is allowed to fall freely to hit the sample. Then the simply supported beam impact test is to hit the center of the simply supported beam test with the pendulum; the cantilever beam is to hit the free end of the notched cantilever beam with the pendulum, and record the impact energy consumed per unit area or unit width when the sample breaks, so as to determine its impact strength. The drop hammer impact test is to drop a ball or hammer of a certain mass freely from a specific height to impact the PVC guardrail sample, and then observe the damage of the guardrail, such as whether cracks or breaks appear, and record the impact energy or height that can cause a specific degree of damage to the guardrail, thereby evaluating its impact strength.

3. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The compressive strength test is carried out using a pressure testing machine. The PVC guardrail sample is placed on the workbench of the testing machine, and uniform axial pressure is applied to the sample through the pressure head of the testing machine. The pressure is gradually increased until the sample is deformed and ruptured, causing the PVC guardrail to fail. The pressure value at this time is then recorded to obtain the compressive strength of the PVC guardrail.

4. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The bending strength test is to place the PVC guardrail sample on two supporting points, apply a load perpendicular to the sample at the middle position of the sample, thereby causing the sample to bend and deform, and then measure the bending deflection under the specified load or the bending load under the specified deflection, so as to calculate the bending strength of the PVC guardrail.

5. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The data processing unit includes a data preprocessing module, a data intelligent processing module and a data filtering and conversion module. The data preprocessing module is mainly used to clean the collected data in order to remove interference data. The data intelligent processing module is mainly used to perform intelligent analysis and processing on the collected data. The data filtering and conversion module filters, amplifies, and performs analog-to-digital conversion on the collected data, and then transmits it to the next stage for processing.

6. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The database management unit includes a historical data comparison module, a data classification storage module and a data monitoring module. The historical data comparison module compares and analyzes the collected data with the historical data stored in the database. The data classification storage module mainly classifies and stores the data to be stored according to categories. The data monitoring module mainly scans and monitors the data stored in the database.

7. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The calibration and maintenance unit is required to regularly calibrate hardware equipment such as loading devices and sensors to ensure the accuracy of test data. The calibration process records the calibration results in accordance with relevant standards. If it is found that the equipment error exceeds the allowable range, it can be adjusted or repaired in time. At the same time, a daily maintenance plan for the system is formulated, including equipment cleaning, checking whether the connections of various components are loose, and updating the software system. Subsequently, the data storage device is backed up regularly to prevent data loss.

8. A PVC guardrail structure strength detection system according to claim 1, characterized in that: The system feedback summary unit automatically generates a detailed test report based on the analysis result software system. The report clearly displays the performance parameters of the guardrail, the conclusion on whether it is qualified and related analysis charts. At the same time, it evaluates the analyzed data and judges the aspects that need to be improved in the structural strength of the PVC guardrail based on historical data.

9. A method for testing the structural strength of a PVC guardrail, characterized in that: The following detection steps are included: Step 1: Test preparation. Select the corresponding testing equipment according to the selected test category. For example, when testing the compressive strength of PVC guardrails, you need to prepare a pressure testing machine; when testing the impact strength of PVC guardrails, you need to prepare a pendulum or drop hammer to perform an impact test on PVC guardrails. Step 2: Start testing. Test the structural strength of the PVC guardrail according to the selected test category. For example, when conducting a lateral thrust test on the PVC guardrail, apply a thrust of a certain size and direction to the side of the guardrail to observe whether the guardrail will tip over, deform excessively, or experience other unstable phenomena, and evaluate its stability under the action of lateral force. Step 3: Collect test data. Collect the data generated by testing the PVC guardrail and record the test steps, such as using pressure sensors, displacement sensors and acceleration sensors. The pressure sensor is used to measure the force during the loading process. The displacement sensor can monitor the deformation of the guardrail when it is subjected to force; the acceleration sensor can measure the change in acceleration during the impact test and then analyze the impact force; Step 4: Conduct intelligent analysis on the collected data. By conducting intelligent analysis on the collected test data, we can determine whether the structural strength of the PVC guardrail is qualified. The collected data is intelligently analyzed through a convolutional neural network model, and a data threshold red line is set. When the data is lower than the threshold red line, it shows that the structural strength of the PVC guardrail is unqualified. Step 5: Summarize and evaluate the results of the analysis, and then determine the direction of improvement of the PVC guardrail by analyzing and comparing the results of the analysis with previous historical data. By continuously testing new PVC guardrail processing technology, the test and analysis data will be compared with the data of PVC guardrails made using mature technology, so that the quality of PVC guardrails can be continuously improved.

10. A method for detecting the structural strength of a PVC guardrail according to claim 9, characterized in that: When using the convolutional neural network model to analyze data in step 4, the data must first be prepared, then the data is input into the constructed model for training, and finally the model is used to perform intelligent analysis of the data.

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