Pipe jacking convergence and strain monitoring method

Abstract

The invention relates to the technical field of strain monitoring, and provides a jacking pipe convergence and strain monitoring method, which realizes omnibearing monitoring of a jacking pipe structure and environment by reasonably deploying a vibration sensor, a fiber grating strain gauge, a laser displacement meter and a gas and temperature and humidity sensor, and avoids the potential safety hazard of omission caused by incomplete monitoring information. Multi-dimensional data is transmitted to a processing node in real time, it is ensured that the data is accurately transmitted in the data transmission process, meanwhile, comprehensive preprocessing is conducted on the multi-dimensional data, and a solid foundation is provided for subsequent accurate analysis; the convergence strain information of the jacking pipe can be accurately generated by extracting the characteristics of the multi-dimensional data; according to the convergence strain information of the jacking pipe, the operation state of the heat distribution pipeline is scientifically evaluated and accurately predicted, early warning and visual display are carried out in time, safe and stable operation of the heat distribution pipeline is effectively guaranteed, accident risks are reduced, and economic losses and social influences are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of strain monitoring, and in particular to a pipe jacking convergence and strain monitoring method. Background Art

[0002] In urban underground pipeline projects, pipe jacking is widely used as an important construction technology. However, during the construction and use of pipe jacking, it is easily affected by factors such as the complex and changeable geological environment and differences in construction conditions, and convergence and strain problems may occur, threatening the safety of the project.

[0003] Existing pipe jacking monitoring technology has many shortcomings. On the one hand, the monitoring means are single, and most of them only monitor a certain parameter of the pipe jacking, and cannot fully obtain the structural status information of the pipe jacking. For example, only monitoring strain or only monitoring displacement makes it difficult to comprehensively evaluate the safety of the pipe jacking. Missing changes in other key indicators will lead to the inability to timely discover potential risks; on the other hand, there are also defects in the data processing and transmission process. Data transmission may have problems such as delays and packet loss, which cannot achieve real-time monitoring and it is difficult to detect abnormal changes in the pipe jacking in time; the data preprocessing link lacks effective methods for handling outliers, duplicate values and missing values, which affects data quality and reduces the accuracy of subsequent analysis and prediction. In addition, the existing technology does not monitor the correlation between the pipe jacking and the thermal pipeline sufficiently, and cannot accurately evaluate the operating status of the thermal pipeline based on the convergent strain information of the pipe jacking, making it difficult to provide early warning and take effective measures to ensure the safe operation of the thermal pipeline. Summary of the Invention

[0004] To address the shortcomings of the existing technology, the present application provides a method for monitoring pipe convergence and strain, which includes: deploying a vibration sensor, a fiber Bragg grating strain gauge, and a laser displacement meter on the pipe to obtain vibration data, strain data, and deformation data of the pipe, respectively; and deploying a gas and temperature and humidity sensor at the bottom of the thermal pipeline to obtain gas and temperature and humidity data of the internal environment of the pipe, and integrating the data into multi-dimensional data;

[0005] Transmit multi-dimensional data to processing nodes in real time;

[0006] Preprocess the received multi-dimensional data, analyze the multi-dimensional data, and generate the convergence strain information of the jacking pipe;

[0007] Based on the convergence strain information of the jacking pipe, the operating status of the thermal pipeline is evaluated and predicted to provide early warning and visual display.

[0008] As an optional implementation, the deployment logic includes:

[0009] With the help of a clamp, the vibration sensor is deployed above the side of the top pipe, the fiber grating displacement meter is deployed above the left and right sides of the inner wall of the top pipe by gluing, and the laser displacement meter is deployed on the inner wall of the top pipe and the top side of the thermal pipeline.

[0010] As an optional implementation, the deformation data acquisition logic includes:

[0011] The laser displacement meter emits a laser beam onto the reflector, measures the time from laser emission to laser reception, and calculates the initial distance between the laser displacement meter and the reflector based on the speed of light and time;

[0012] During the use of the jacking pipe, the distance data between the laser displacement meter and the reflector is measured in real time, and the distance data is acquired and stored at the set time interval;

[0013] The distance data obtained at different time points are compared with the initial distance, and the distance change is calculated. According to the size and change trend of the distance change, the deformation degree and deformation direction of the jacking pipe are judged to obtain the deformation data of the jacking pipe.

[0014] As an optional implementation, the multi-dimensional data integration logic includes:

[0015] Perform time synchronization processing on the data acquired by each sensor. Using the time base as a reference, calibrate the acquisition time of vibration data, strain data, deformation data, and gas, temperature and humidity data to the time base;

[0016] Standardize the formats of vibration data, strain data, deformation data, gas data, temperature and humidity data;

[0017] The data that has been time synchronized and format standardized is integrated and stored according to the storage structure.

[0018] As an optional implementation, the multi-dimensional data transmission logic includes:

[0019] Divide the multi-dimensional data into data packets according to time, and the data packets contain the acquisition time, multi-dimensional data and a check code;

[0020] Evaluate and select the transmission network based on the pipe jacking environment conditions and data transmission requirements, and establish connections between sensors and processing nodes;

[0021] Send data packets to processing nodes according to the transmission protocol and perform flow control based on the data volume of multi-dimensional data;

[0022] The data packet is temporarily stored in the buffer of the processing node, and the check code in the data packet is checked to trigger the data retransmission mechanism.

[0023] As an optional implementation, the preprocessing logic of the multi-dimensional data includes:

[0024] Identify outliers, handle duplicate values, and fill in missing values for the received multi-dimensional data;

[0025] Normalize multi-dimensional data;

[0026] Perform filtering and wavelet denoising on multi-dimensional data.

[0027] As an optional implementation, the logic for generating the convergence strain information of the jacking pipe includes:

[0028] Extract features of multi-dimensional data, including vibration intensity, frequency components, and time domain characteristics of vibration data, growth rate, strain change rate, and distribution characteristics of strain data, and the magnitude and trend of distance change of deformation data;

[0029] Establishing a correlation between the characteristics of the multi-dimensional data and the convergence strain, the correlation including a threshold range of the characteristics of the multi-dimensional data and a correlation between the characteristics of the multi-dimensional data and the convergence strain;

[0030] Use a multi-layer perceptron to learn the relationship between the characteristics of multi-dimensional data and convergence strain, and verify the prediction accuracy of the multi-layer perceptron;

[0031] The features of the multi-dimensional data obtained through real-time monitoring are input into a multi-layer perceptron to output the convergence strain information of the pipe jacking, including the strain magnitude at different positions of the pipe jacking, the strain change trend, and the estimated value of the convergence displacement.

[0032] As an optional implementation, the sub-logic for extracting features of the multi-dimensional data includes:

[0033] The vibration intensity of the vibration data is extracted by calculating the amplitude of the vibration signal, the frequency components of the vibration data are obtained through spectrum analysis, and the time domain characteristics of the vibration data are analyzed;

[0034] Calculate the increments of strain data at different time points to extract the growth rate of strain data, perform derivative operations on continuous strain data to obtain the strain change rate, and analyze the distribution characteristics of strain data at different locations of the jacking pipe;

[0035] Extract the magnitude and trend of distance change from deformation data.

[0036] Compared with the existing technology, the beneficial effects of the present application are: by rationally deploying vibration sensors, fiber grating strain gauges, laser displacement meters, and gas and temperature and humidity sensors, it is possible to obtain multi-dimensional data on the vibration, strain, deformation, and internal environment of the jacking pipe, thereby realizing all-round monitoring of the jacking pipe structure and environment, comprehensively grasping the operating status of the jacking pipe, and avoiding missing safety hazards due to incomplete monitoring information; transmitting multi-dimensional data to the processing node in real time, ensuring the timeliness of the data, and ensuring the accurate transmission of the data during the data transmission process, and at the same time performing comprehensive pre-processing of the multi-dimensional data, which greatly improves the data quality and provides a solid foundation for subsequent accurate analysis.

[0037] By extracting the features of multi-dimensional data, the convergence strain information of the jacking pipe can be accurately generated, providing a key basis for accurately evaluating the stability of the jacking pipe structure. Based on the convergence strain information of the jacking pipe, the operating status of the thermal pipeline can be scientifically evaluated and accurately predicted, and timely warnings and visual displays can be provided. This enables managers to promptly discover potential safety hazards of the thermal pipeline and take measures in advance to effectively ensure the safe and stable operation of the thermal pipeline, reduce accident risks, and minimize economic losses and social impacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be derived from these drawings without inventive work. Among them:

[0039] Figure 1 A flow chart of a method for monitoring pipe jacking convergence and strain provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of the deployment of pipe jacking equipment for a pipe jacking convergence and strain monitoring method provided in an embodiment of the present application;

[0041] Figure 3 A deformation data acquisition logic diagram of a pipe jacking convergence and strain monitoring method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the objectives, technical solutions and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the drawings in the specification. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0043] Example

[0044] like Figure 1FIG. 1 is a flowchart of a method for monitoring pipe jacking convergence and strain according to an embodiment of the present application, the method comprising:

[0045] S1. Deploy vibration sensors, fiber Bragg grating strain gauges, and laser displacement meters to the top pipe to obtain vibration data, strain data, and deformation data of the top pipe respectively. Deploy gas and temperature and humidity sensors at the bottom of the thermal pipeline to obtain gas and temperature and humidity data of the internal environment of the top pipe and integrate them into multi-dimensional data.

[0046] like Figure 2 As shown, the vibration sensor is deployed above the side of the top pipe with the help of a clamp, the fiber grating displacement meter is deployed above the left and right sides of the inner wall of the top pipe by gluing, and the laser displacement meter is deployed to the inner wall of the top pipe and the top side of the thermal pipeline.

[0047] The jacking pipe will generate vibration during operation, and the vibration situation can reflect the stress state and structural stability of the jacking pipe. The vibration sensor is deployed above the side of the jacking pipe because this position can more comprehensively capture the vibration information of the jacking pipe in the horizontal and vertical directions. The upper side is relatively less affected by external interference, and the vibration characteristics of the jacking pipe itself can be obtained more accurately. The vibration sensor is fixed above the side of the jacking pipe with the help of a clamp. The clamp should have good rigidity and stability to ensure that the vibration sensor is tightly connected to the jacking pipe and reduce measurement errors caused by loose connections. The material of the clamp can be selected from high-strength metals, such as stainless steel, to adapt to the complex use environment of the jacking pipe. During the installation process, it is necessary to ensure that the mounting surface of the sensor is fully fitted with the surface of the jacking pipe. Gaskets can be used for fine-tuning to ensure that the measurement direction of the vibration sensor is consistent with the vibration direction to be monitored. By accurately deploying the vibration sensor, the vibration data of the jacking pipe can be obtained in real time and accurately, providing a reliable basis for subsequent analysis of the operating status of the jacking pipe.

[0048] Fiber Bragg grating strain gauges can accurately measure the strain of the inner wall of the top pipe. They are deployed on the upper left and right sides of the inner wall of the top pipe because these positions are usually areas where the force on the top pipe is more concentrated. They can more sensitively reflect the strain changes of the top pipe when it is under force, which helps to timely discover potential problems of the top pipe structure. The fiber Bragg grating strain gauges are fixed on the upper left and right sides of the inner wall of the top pipe by gluing. Before gluing, the surface of the inner wall of the top pipe needs to be cleaned and treated to remove impurities such as oil and dust to improve the firmness of the glue. Adhesives with good temperature and moisture resistance are selected to ensure that the strain gauge can work stably for a long time in the use environment of the top pipe. During the gluing process, attention should be paid to the direction and position of the strain gauge to ensure that it can accurately measure the strain in the required direction. Through the reasonable deployment of fiber Bragg grating strain gauges, the strain data of the inner wall of the top pipe can be accurately obtained, providing key information for evaluating the structural safety of the top pipe.

[0049] Laser displacement meters are used to measure the deformation of pipe jacking. Deploying them on the inner wall of the pipe jacking and the top side of the thermal pipeline can comprehensively monitor the relative position changes and deformation of the pipe jacking and the thermal pipeline. The laser displacement meter on the inner wall of the pipe jacking can directly measure the deformation of the pipe jacking itself, while the laser displacement meter on the top side of the thermal pipeline can monitor the relative displacement between the thermal pipeline and the pipe jacking, providing an important basis for judging the operating status of the thermal pipeline. When deploying the laser displacement meter, it is necessary to ensure that its installation position is firm to avoid changes in the position of the displacement meter due to vibration or other factors. During the installation process, it is necessary to ensure that the emission direction of the laser displacement meter is accurately pointed at the reflector, and the focal length and measurement range of the laser displacement meter are adjusted to ensure that the distance can be accurately measured. At the same time, the laser displacement meter must be calibrated regularly to ensure measurement accuracy. Through the reasonable deployment of laser displacement meters, the deformation data of pipe jacking and thermal pipelines can be comprehensively and accurately obtained, providing strong support for subsequent analysis and evaluation.

[0050] The gas, temperature and humidity conditions in the internal environment of the jacking pipe will affect the material properties and service life of the jacking pipe and the thermal pipeline. The gas and temperature and humidity sensors are deployed at the bottom of the thermal pipeline because this location can better reflect the overall situation of the internal environment of the jacking pipe. At the same time, it can also monitor environmental changes around the thermal pipeline, which helps to timely discover potential safety hazards. When installing the gas and temperature and humidity sensors at the bottom of the thermal pipeline, it is necessary to ensure that the installation location of the sensor is well ventilated so that it can accurately measure the gas composition and temperature and humidity of the surrounding environment. The sensor should be installed at a moderate height to avoid being affected by accumulated water or other obstacles. During the installation process, the sensor should be sealed to prevent external factors from interfering with the sensor's measurement results. By deploying gas and temperature and humidity sensors, the gas, temperature and humidity conditions in the internal environment of the jacking pipe can be monitored in real time, providing important information to ensure the safe operation of the jacking pipe and thermal pipeline.

[0051] Specifically, if Figure 3 As shown, the logic for obtaining deformation data includes:

[0052] The laser displacement meter emits a laser beam onto the reflector, measures the time from laser emission to laser reception, and calculates the initial distance between the laser displacement meter and the reflector based on the speed of light and time;

[0053] During the use of the jacking pipe, the distance data between the laser displacement meter and the reflector is measured in real time, and the distance data is acquired and stored at the set time interval;

[0054] The distance data obtained at different time points are compared with the initial distance, and the distance change is calculated. According to the size and change trend of the distance change, the deformation degree and deformation direction of the jacking pipe are judged to obtain the deformation data of the jacking pipe.

[0055] The initial distance is the basis for the subsequent calculation of the deformation of the jacking pipe. By measuring the initial distance between the laser displacement meter and the reflector, a benchmark value can be provided for the subsequent deformation monitoring, so as to accurately judge the deformation of the jacking pipe during use. After the jacking pipe is installed, the laser displacement meter emits a laser beam to the reflector, and the time from laser emission to reception is measured. In order to improve the accuracy of the measurement, multiple measurements can be taken to obtain the average value. At the same time, it is necessary to ensure that the installation position of the laser displacement meter and the reflector is accurate to avoid inaccurate initial distance measurement due to installation errors. According to the speed of light and time, the initial distance between the laser displacement meter and the reflector is calculated by the formula d=1 / 2×c×t, where d is the initial distance, c is the speed of light, and d is the time from laser emission to reception. Accurate initial distance measurement provides a reliable benchmark for subsequent deformation monitoring and helps to improve the accuracy of deformation data.

[0056] The jacking pipe will deform during use. The distance data between the laser displacement meter and the reflector is measured in real time and stored at the set time interval. This can record the dynamic process of the jacking pipe deformation and provide data support for the subsequent analysis of the deformation trend of the jacking pipe. The measurement time interval of the laser displacement meter is set. According to the use of the jacking pipe and the monitoring requirements, the time interval can be reasonably determined. Generally, it can be set to a few minutes to a few hours. During the measurement process, the laser displacement meter will automatically monitor and obtain distance data and transfer the data to the storage device for storage. By measuring and storing the distance data in real time, the deformation process of the jacking pipe can be fully recorded, providing rich data resources for the subsequent analysis of the deformation trend of the jacking pipe and the prediction of the future state of the jacking pipe. The stored distance data is the basis for calculating the distance change. Accurate distance data can ensure that the calculated distance change is accurate and reliable, and provide accurate data support for judging the deformation degree and deformation direction of the jacking pipe.

[0057] By comparing the distance data at different time points with the initial distance and calculating the distance change, the degree of deformation of the jacking pipe during use can be intuitively reflected. At the same time, by analyzing the changing trend of the distance change, the deformation direction of the jacking pipe can be judged, providing important information for timely discovery of safety hazards of the jacking pipe; the distance data obtained at different time points are subtracted from the initial distance to obtain the distance change, and the distance change is statistically analyzed to observe its changing trend, such as whether it shows a trend of gradual increase or decrease, and the rate of change, etc. According to the size and trend of the distance change, combined with the design standards and safety requirements of the jacking pipe, the deformation degree and deformation direction of the jacking pipe can be judged; by calculating the distance change and judging the deformation situation, the deformation problem of the jacking pipe can be discovered in time, providing a basis for taking corresponding measures to ensure the safe operation of the jacking pipe. Accurate deformation data is an important part of multi-dimensional data, providing key information for subsequent multi-dimensional data integration and analysis, and helping to improve the accuracy of judgment on the convergence and strain of the jacking pipe.

[0058] Specifically, the integration logic of multi-dimensional data includes:

[0059] Perform time synchronization processing on the data acquired by each sensor. Using the time base as a reference, calibrate the acquisition time of vibration data, strain data, deformation data, and gas, temperature and humidity data to the time base;

[0060] Standardize the formats of vibration data, strain data, deformation data, gas data, temperature and humidity data;

[0061] The data that has been time synchronized and format standardized is integrated and stored according to the storage structure.

[0062] The data acquisition time of different sensors may vary. In order to ensure the consistency and comparability of the data from each sensor, time synchronization processing is required. By calibrating the acquisition time of each sensor data to the time base, different types of data can be aligned in the time dimension, which is convenient for subsequent data analysis and association. Select a high-precision time base, such as the time signal provided by the global positioning system. Each sensor device communicates with the time base to obtain accurate time information and calibrates the timestamp of its own collected data to the time base. During the data collection process, ensure that the clocks of each sensor device are synchronized to avoid time asynchrony due to clock errors. Time synchronization processing can ensure the temporal consistency of multi-dimensional data, improve data quality and analysis accuracy, and provide a reliable foundation for subsequent data analysis and association.

[0063] The data formats acquired by different sensors are different. In order to facilitate subsequent data processing and analysis, the data of each sensor needs to be standardized. By unifying the data format, different types of data can be kept consistent in structure, improving the readability and processability of the data; according to the type and characteristics of the data, a unified data format standard is formulated. For example, the analog signal data collected by the vibration sensor is converted into a digital form and stored in a unified format, and the gas concentration data collected by the gas sensor is standardized with a specific numerical representation and unit. During the data acquisition process, each sensor device converts and stores the acquired data according to the unified data format standard; format standardization can keep multi-dimensional data consistent in structure, improve the readability and processability of the data, and facilitate subsequent data analysis and association.

[0064] The data that has been time synchronized and format standardized is fused and stored, which can integrate different types of sensor data to form a complete multi-dimensional data set. Through fused storage, it is convenient to conduct subsequent comprehensive analysis and mining of multi-dimensional data, and discover the associations and patterns between data. Through the database management system, a table containing fields of different data types is established, and various types of data are stored in the corresponding fields in chronological order. For example, in a table called "pipe jacking monitoring data", fields such as "time", "vibration data", "strain data", "deformation data", "gas data" and "temperature and humidity data" are set, and the processed data is entered into the table line by line to form complete multi-dimensional data. Fusion storage can integrate multi-dimensional data to form a complete data set, which is convenient for subsequent comprehensive analysis and mining of data and improves the utilization value of data. The fused stored multi-dimensional data provides a complete data foundation for subsequent data transmission, preprocessing and analysis, which helps to improve the efficiency and accuracy of the entire monitoring system.

[0065] S2. Transmit multi-dimensional data to the processing node in real time.

[0066] Specifically, the transmission logic of multi-dimensional data includes:

[0067] Divide the multi-dimensional data into data packets according to time, and the data packets contain the acquisition time, multi-dimensional data and a check code;

[0068] Evaluate and select the transmission network based on the pipe jacking environment conditions and data transmission requirements, and establish connections between sensors and processing nodes;

[0069] Send data packets to processing nodes according to the transmission protocol and perform flow control based on the data volume of multi-dimensional data;

[0070] The data packet is temporarily stored in the buffer of the processing node, and the check code in the data packet is checked to trigger the data retransmission mechanism.

[0071] Multidimensional data is large in size, and direct transmission can easily lead to data confusion and difficulty in management. Dividing data packets by time can keep multidimensional data in order during transmission, making it easier for processing nodes to receive and parse it. Each data packet also contains the acquisition time, facilitating subsequent time series analysis and integration of data. An appropriate time interval, such as 1 minute, is set. Vibration data, strain data, deformation data, and gas, temperature, and humidity data acquired within that time period are packaged according to this time interval. The acquisition time information is added to the header of each data packet, using a timestamp accurate to the millisecond level to ensure the accuracy of time recording. To ensure data integrity and accuracy, a checksum is added to the end of the data packet. The checksum can be generated using the CRC algorithm to verify the multidimensional data in the data packet. After data is divided into data packets by time, the transmission process is more orderly, reducing the risk of data loss and confusion. The included time information and checksum can improve the reliability and traceability of data transmission. The ordered data packets facilitate subsequent targeted processing based on the characteristics of the transmission network, such as selecting an appropriate transmission protocol and performing flow control. It also makes it easier for processing nodes to receive and process data in chronological order, facilitating data verification and storage.

[0072] The use environment of pipe jacking is complex and diverse, and the data transmission requirements are different in different environments. Selecting a suitable transmission network and establishing a stable connection are the key to ensuring accurate and real-time data transmission. An inappropriate transmission network will lead to data loss and excessive delay, which cannot meet the monitoring needs. Evaluate the use environment of the pipe jacking. If the pipe jacking is in an area with stable wired network coverage, give priority to using a wired network, such as Ethernet connection, connect the sensor equipment to the processing node by laying a dedicated network cable, and configure the corresponding network parameters, such as IP address, subnet mask, gateway, etc., to ensure stable data transmission. If the pipe jacking is in an area where wiring is difficult, such as in the wild or complex underground environment, evaluate the wireless transmission network. Based on the data transmission volume and real-time requirements, choose 4G / 5G network or low-power wide area network technology; if the data volume is large and the real-time requirements are high, use 4G / 5G network, equip the sensor equipment with a communication module that supports the corresponding network, set parameters such as APN and authentication information, and connect to the operator's base station; if the data volume is small and the real-time requirements are relatively low, choose LoRa network, deploy LoRa gateway, configure LoRa module on the sensor equipment, set parameters such as working frequency band and spreading factor, and establish a connection with the gateway; choosing a suitable transmission network and establishing a stable connection can ensure that data can be transmitted efficiently and reliably in different environments, thereby improving the success rate and stability of data transmission.

[0073] The transmission protocol can ensure that data packets are correctly transmitted in the network. However, the amount of multi-dimensional data is large. If flow control is not performed, it is easy to cause network congestion, affecting the timeliness and accuracy of data transmission. If wired network transmission is adopted, the TCP protocol in the TCP / IP protocol stack is usually used. The data packet is encapsulated according to the specifications of the TCP protocol, and information such as the source IP address, destination IP address and port number are added. After a reliable connection is established using the three-way handshake mechanism of the TCP protocol, the data packet is sent. According to the data volume and network bandwidth, the sliding window mechanism is used for flow control. The sender dynamically adjusts the sending data packet based on the window size information returned by the receiver. To avoid network congestion caused by sending too quickly, if wireless network transmission is adopted, such as 4G / 5G network, transmission is optimized based on UDP protocol. Since UDP protocol itself does not have a flow control mechanism, flow control is implemented through a custom algorithm at the application layer. The sending frequency of data packets is dynamically adjusted according to the network signal strength and historical data transmission rate. When the network signal is weak or packet loss occurs during data transmission, the sending frequency is reduced. When the network condition is good, the sending frequency is appropriately increased. Sending data packets in accordance with the transmission protocol ensures the standardization and accuracy of data transmission, and flow control effectively avoids network congestion and improves the efficiency and stability of data transmission.

[0074] Before processing a data packet, the processing node needs to temporarily store the data packet for verification and subsequent processing. The check code is used to detect whether an error occurs during the transmission of the data packet. If an error occurs, a retransmission mechanism is triggered to ensure data accuracy. A buffer of appropriate size is set at the processing node to temporarily store the received data packet. The size of the buffer is reasonably configured according to the data transmission rate and processing capacity to avoid buffer overflow and data loss. When the data packet arrives at the processing node, it is first stored in the buffer, and then the check code in the data packet is verified. According to the CRC algorithm used when generating the check code, the processing node recalculates the check code for the received data packet and compares it with the check code carried in the data packet. If the two are inconsistent, it means that an error occurred during the transmission of the data packet. The processing node sends a retransmission request to the sensor, and the sensor resends the data packet according to the request. The data packet temporary storage and verification mechanism ensures data accuracy. The data retransmission mechanism effectively corrects transmission errors and improves data reliability. Accurate data packets provide a reliable data basis for subsequent multi-dimensional data preprocessing and analysis, avoiding deviations in analysis results due to erroneous data.

[0075] S3. Preprocessing the received multi-dimensional data, analyzing the multi-dimensional data, and generating convergence strain information of the pipe jacking;

[0076] Specifically, the preprocessing logic of multi-dimensional data includes:

[0077] Identify outliers, handle duplicate values, and fill in missing values for the received multi-dimensional data;

[0078] Normalize multi-dimensional data;

[0079] Perform filtering and wavelet denoising on multi-dimensional data.

[0080] Abnormal values are caused by factors such as sensor failure and external interference. These abnormal data will cause large deviations in subsequent analysis and modeling, affecting the accuracy of converged strain information. The existence of duplicate values will increase data redundancy, waste computing resources, and cause the model to overfit certain data, reducing the generalization ability of the model. Missing values are caused by sensor failure and data transmission interruption. Missing values will lead to incomplete data, affecting the effect of subsequent analysis and modeling. The multi-dimensional data obtained by vibration sensors, fiber grating strain gauges, laser displacement meters, and gas and temperature and humidity sensors are checked for abnormal values, and reasonable thresholds are set based on the historical distribution laws, physical limits and engineering experience of each sensor data. For example, if the distance data measured by the laser displacement meter mutates and differs too much from the surrounding data, exceeding the normal fluctuation range, it is judged as an abnormal value, and the vibration If the vibration amplitude obtained by the sensor far exceeds the vibration amplitude during normal operation of the jacking pipe, it will also be marked as an abnormality; by comparing each record in the data set one by one, data structures such as hash tables are used to quickly determine whether there are duplicate records. For duplicate records, one can choose to retain one and delete the remaining duplicate records; for numerical data, the mean filling method can be used to calculate the mean of the data in that dimension and use the mean to fill the missing values; interpolation methods, such as linear interpolation, can also be used to fill the missing values by performing linear calculations based on the values of adjacent data points; removing outliers can improve data quality, reduce the impact of noise on subsequent analysis, and make the model more stable and accurate. Processing duplicate values can reduce data redundancy, improve computing efficiency, and avoid overfitting of the model. Filling missing values can ensure data integrity, so that subsequent analysis and modeling can be based on complete data, thereby improving the reliability of the model.

[0081] The data obtained by different sensors have different dimensions and value ranges, which will cause some features to have too much influence on the results during data analysis and modeling, while the influence of other features is ignored. Normalization processing can unify data with different features to the same scale, so that each feature has the same weight in the model, improving the stability and accuracy of the model; through the minimum-maximum normalization method, for each dimension of data, its minimum and maximum values are calculated, and then the data is mapped to the [0,1] interval; normalization processing makes data with different features comparable, avoids the problem of unbalanced feature weights caused by different dimensions and value ranges, improves the training effect and prediction accuracy of the model, and accelerates the convergence speed of the model. Especially when using optimization algorithms such as gradient descent, the normalized data can enable the algorithm to find the optimal solution more quickly.

[0082] The data acquired by the sensor may contain various noises, such as high-frequency noise and random noise. These noises will interfere with the feature extraction and analysis of the data and affect the accuracy of the convergence strain information. Filtering and wavelet denoising can remove noise from the data and retain useful signal features. For time series data, a low-pass filter, such as a moving average filter, is used. The average value of the data within a certain window size is taken as the filtered value at that point. An appropriate wavelet basis function, such as the Daubechies wavelet, is selected to perform wavelet decomposition on the data, decomposing the data into wavelet coefficients of different scales. The wavelet coefficients are then thresholded to remove the wavelet coefficients corresponding to the noise. Finally, wavelet reconstruction is performed to obtain the denoised data. Filtering and wavelet denoising can effectively remove noise from the data, improve data quality and signal-to-noise ratio, make the data features more distinct, and facilitate subsequent feature extraction and analysis. The data after denoising can reduce interference with the model and improve the model's prediction accuracy and stability. Moreover, the features of the data after filtering and wavelet denoising are clearer and more prominent, which is conducive to the subsequent extraction of multi-dimensional data features, can improve the accuracy and reliability of feature extraction, and lay the foundation for generating accurate convergence strain information.

[0083] Specifically, the generation logic of the convergent strain information of the pipe jacking includes:

[0084] Extract features of multi-dimensional data, including vibration intensity, frequency components, and time domain characteristics of vibration data, growth rate, strain change rate, and distribution characteristics of strain data, and the magnitude and trend of distance change of deformation data;

[0085] Establishing a correlation between the characteristics of the multi-dimensional data and the convergence strain, the correlation including a threshold range of the characteristics of the multi-dimensional data and a correlation between the characteristics of the multi-dimensional data and the convergence strain;

[0086] Use a multi-layer perceptron to learn the relationship between the characteristics of multi-dimensional data and convergence strain, and verify the prediction accuracy of the multi-layer perceptron;

[0087] The features of the multi-dimensional data obtained through real-time monitoring are input into a multi-layer perceptron to output the convergence strain information of the pipe jacking, including the strain magnitude at different positions of the pipe jacking, the strain change trend, and the estimated value of the convergence displacement.

[0088] Furthermore, the sub-logic for extracting features from multi-dimensional data includes:

[0089] The vibration intensity of the vibration data is extracted by calculating the amplitude of the vibration signal, the frequency components of the vibration data are obtained through spectrum analysis, and the time domain characteristics of the vibration data are analyzed;

[0090] Calculate the increments of strain data at different time points to extract the growth rate of strain data, perform derivative operations on continuous strain data to obtain the strain change rate, and analyze the distribution characteristics of strain data at different locations of the jacking pipe;

[0091] Extract the magnitude and trend of distance change from deformation data.

[0092] The original multidimensional data contains a large amount of information, but directly using the original multidimensional data for analysis and modeling will make the model complex and inefficient. By extracting the features of the multidimensional data, the useful information in the original multidimensional data can be condensed and refined, highlighting the information related to the convergence strain of the jacking pipe, which is convenient for the subsequent establishment of correlation relationships and modeling analysis; the amplitude of the vibration signal is calculated by the root mean square amplitude to extract the vibration intensity. The amplitude reflects the intensity of the vibration. A larger amplitude indicates that the jacking pipe is subjected to a larger external force, which in turn affects its convergence and strain state; and spectrum analysis is performed through fast Fourier transform to convert the time domain signal into a frequency domain signal to obtain the frequency component of the vibration data. Vibration of a specific frequency is related to the structural resonance of the jacking pipe or external interference sources, which affects the stability of the jacking pipe; the time domain characteristics of the vibration data are analyzed, such as the peak factor and kurtosis. These characteristics can reflect the impact characteristics of the vibration signal, which helps to determine whether the jacking pipe is subjected to sudden impact loads and indirectly provides a basis for convergence and strain analysis.

[0093] Calculate the increment of strain data at different time points, and divide the difference of strain values at adjacent time points by the time interval to obtain the strain growth rate; use numerical differentiation methods to perform derivative operations on continuous strain data, such as the central difference method, to obtain the strain change rate. A sharp change in the strain change rate indicates a rapid adjustment of the stress distribution inside the jacking structure, which is closely related to the convergence trend of the jacking pipe; through statistical analysis of strain data at different locations, such as calculating the mean and standard deviation, analyze the distribution characteristics of strain data at different locations of the jacking pipe. If the strain in some areas is significantly higher than that in other areas, it indicates that stress concentration exists in these areas, and they are sensitive areas for convergence and deformation of the jacking pipe.

[0094] The size of the distance change, that is, the difference between the distance data at different time points and the initial distance, is extracted from the deformation data. Through the comprehensive analysis of the displacement data of multiple measurement points, the overall deformation trend of the jacking pipe, such as whether there is local depression or expansion, is obtained; by fitting and analyzing the curve of the distance change over time, such as using polynomial fitting, the trend of the distance change is obtained. If the curve shows a continuous upward or downward trend, it means that the jacking pipe is in a state of continuous deformation, which is directly related to the convergence and strain of the jacking pipe; extracting the features of multi-dimensional data can reduce the dimension of the data, reduce the amount of calculation, improve the training efficiency and prediction speed of the model, highlight the key information related to the convergence strain of the jacking pipe, make the subsequent established correlation relationship more accurate and effective, and improve the prediction accuracy of the model.

[0095] There is an inherent connection between the features of multidimensional data and the convergence strain of pipe jacking, but this connection needs to be clarified and quantified by establishing an association relationship. By establishing an association relationship, we can better understand the impact of each feature on the convergence strain, providing a basis for subsequent modeling and prediction. Based on historical data and engineering experience, a threshold range is set for each multidimensional data feature. For example, for vibration intensity, a safety threshold is determined. When the vibration intensity exceeds this safety threshold, it indicates that it will have a significant impact on the convergence strain of the pipe jacking. Correlation analysis methods, such as the Pearson correlation coefficient, are used to calculate the correlation coefficient between each multidimensional data feature and the convergence strain. The closer the absolute value of the correlation coefficient is to 1, the stronger the correlation between the feature and the convergence strain. This clarifies the relationship between the features of each multidimensional data and the convergence strain, helping to determine which multidimensional data features have a greater impact on the convergence strain and which multidimensional data features can be ignored, thereby optimizing the model input and providing prior knowledge for subsequent multilayer perceptron learning. This enables the model to learn the mapping relationship between features and convergence strain in a more targeted manner, improving the model's learning efficiency and accuracy.

[0096] The relationship between the features of multi-dimensional data and the convergence strain is nonlinear. The multi-layer perceptron has powerful nonlinear mapping capabilities and can learn this complex relationship. By training the multi-layer perceptron, an accurate prediction model can be established to predict the convergence strain information of the jacking pipe; a multi-layer perceptron model is constructed, including an input layer, a hidden layer and an output layer. The number of neurons in the input layer is equal to the number of features extracted from the multi-dimensional data. The number of neurons in the output layer is the dimension of the convergence strain information to be predicted, such as the strain size, the strain change trend and the estimated value of the convergence displacement. The number of hidden layers and the number of neurons can be optimized through methods such as cross-validation.

[0097] Using historical data as a training set, the extracted features as input, and the corresponding convergence strain information as output, the multi-layer perceptron is trained, and the back-propagation algorithm is used to adjust the weights and biases of the model to minimize the error between the predicted value and the true value, such as using the mean square error loss function; the trained model is verified using a validation set, and the error indicators between the predicted value and the true value, such as the root mean square error and the mean absolute error, are calculated. If the error indicator is greater than a certain error threshold, the parameters or structure of the multi-layer perceptron model are adjusted, and retraining and verification are performed; the multi-layer perceptron can learn the complex nonlinear relationship between multi-dimensional data features and convergence strain, thereby improving the accuracy and reliability of convergence strain information prediction. Through model verification, the performance of the multi-layer perceptron model can be evaluated, problems in the multi-layer perceptron model can be discovered in a timely manner and adjusted to ensure the generalization ability of the model.

[0098] After the processing and modeling in the previous steps, the multilayer perceptron has learned the relationship between multidimensional data features and convergence strain. By inputting the features obtained from real-time monitoring into the model, it can predict the convergence strain information of pipe jacking in real time, providing timely decision-making basis for the safety monitoring and management of pipe jacking and thermal pipelines. The multidimensional data obtained from real-time monitoring is preprocessed and feature extracted to ensure that the features input into the multilayer perceptron are consistent with the feature format and dimension during training. The extracted multidimensional data features are input into the trained multilayer perceptron model. The output of the multilayer perceptron model is obtained through forward propagation calculation, namely the strain magnitude, strain change trend, and estimated convergence displacement at different locations of the pipe jacking. This enables real-time acquisition of convergence strain information of pipe jacking, timely detection of pipe jacking anomalies, and timely implementation of appropriate measures. This improves the safety and reliability of pipe jacking and thermal pipelines, and provides key input information for subsequent evaluation and prediction of the operating status of thermal pipelines, making the evaluation and prediction more accurate and effective.

[0099] S4. Based on the convergence strain information of the jacking pipe, the operating status of the thermal pipeline is evaluated and predicted to provide early warning and visual display.

[0100] The convergence strain information of a pipe jacking directly reflects its structural stability. Changes in the pipe jacking structure can affect the operation of the internal thermal pipeline. By evaluating the operating status of the thermal pipeline, potential risks can be promptly identified, ensuring the safety and stability of heat transmission. An evaluation index system is established, combining the strain magnitude, strain change trend, and convergence displacement estimate of the pipe jacking with the operating parameters of the thermal pipeline (such as temperature, pressure, and flow). For example, when the pipe jacking strain exceeds a certain strain threshold, the additional stress generated on the thermal pipeline is calculated using a mechanical model. Based on historical data and industry standards, a normal range, warning threshold, and danger threshold are set for each indicator. The weight of each indicator is determined using the hierarchical analysis method, and the operating status of the thermal pipeline is comprehensively evaluated. If the strain change rate of a certain section of the pipe jacking is large, causing the local stress of the thermal pipeline to approach the danger threshold, and the pressure fluctuations within the pipeline are large, the comprehensive calculation determines that the operating status of this section of the thermal pipeline is poor. This comprehensive understanding of the operating status of the thermal pipeline can be achieved, and potential safety hazards can be detected in advance, providing a basis for subsequent maintenance and management. Accurate evaluation results provide a realistic basis for predicting the future operating trends of the thermal pipeline, making the prediction more targeted and accurate.

[0101] Evaluating the current status alone is not enough to respond to potential risks in advance. Predicting future operating trends can help managers formulate response strategies in advance and prevent accidents. Based on the evaluation results and historical data, a suitable prediction model is selected, such as a long-short-term memory network. The convergence strain information of the jacking pipe, the operating parameters of the thermal pipeline, and time series data are used as input to train the long-short-term memory network model. During the training process, the parameters of the long-short-term memory network model are adjusted to optimize the prediction accuracy. The trained long-short-term memory network model is used to predict the operating status of the thermal pipeline in the future (such as the next week), including the changing trends of various evaluation indicators. For example, it is predicted that the internal pressure of a certain section of thermal pipeline will gradually increase and approach the danger threshold in the next few days due to the continuous convergence of the jacking pipe. This can predict the changes in the operating status of the thermal pipeline in advance, buy time for taking preventive measures, and reduce the possibility of accidents. The prediction results are the key basis for triggering early warnings and visualization displays, and determine the content of the early warning information and the focus of the visualization display.

[0102] When assessment and prediction find that there is a risk in the operation status of the thermal pipeline, an early warning will be issued in time to remind managers to take measures to avoid accidents; when the assessment or prediction results reach the early warning threshold, the system automatically triggers the early warning mechanism and issues early warnings in various ways, such as SMS notifications to managers, and displaying early warning information in the form of flashing red lights and pop-up windows on the monitoring center interface. The early warning content includes the specific location of the thermal pipeline, the type of risk (such as excessive pressure and excessive pipe jacking strain) and the consequences, etc. At the same time, the early warning level is divided according to the severity of the risk. For example, a level one warning indicates high risk and requires immediate processing; a level two warning indicates medium risk and requires close attention and preparation of response measures; thereby promptly reminding managers to take action, reduce accident losses, and ensure the safe operation of the thermal pipeline. The early warning information prompts managers to view the visual display content and gain an in-depth understanding of the abnormal situation of the thermal pipeline in order to make decisions.

[0103] The operating status of thermal pipelines is presented in an intuitive manner, making it convenient for managers to quickly understand the overall situation and make accurate decisions. Geographic information system technology is used to mark the location and direction of thermal pipelines on the map. Different colors represent different operating statuses, such as green for normal, yellow for warning, and red for danger. Charts (such as line charts showing the changes in pipe jacking strain over time and bar charts comparing the pressure of thermal pipelines at different locations) are used to show the changing trends of key indicators. Interactive functions are set up, and managers can click on elements on the map or chart to view detailed data and analysis reports. For example, clicking on a section of thermal pipeline can display its current operating parameters, historical data, and predicted trends. This improves the efficiency of information acquisition, enhances the scientific nature and accuracy of decision-making, and facilitates managers to promptly identify problems and develop solutions.

Claims

1. A method for monitoring pipe jacking convergence and strain, characterized in that: include: Vibration sensors, fiber Bragg grating strain gauges, and laser displacement meters are deployed on the pipe jacking to obtain vibration data, strain data, and deformation data of the pipe jacking, respectively. Gas and temperature and humidity sensors are also deployed at the bottom of the thermal pipeline to obtain gas and temperature and humidity data of the pipe jacking environment, and the data are integrated into multi-dimensional data. Transmit multi-dimensional data to processing nodes in real time; Preprocess the received multi-dimensional data, analyze the multi-dimensional data, and generate the convergence strain information of the pipe jacking; Based on the convergence strain information of the jacking pipe, the operating status of the thermal pipeline is evaluated and predicted to provide early warning and visual display.

2. A pipe jacking convergence and strain monitoring method according to claim 1, characterized in that: The deployment logic includes: With the help of a clamp, the vibration sensor is deployed above the side of the top pipe, the fiber grating displacement meter is deployed above the left and right sides of the inner wall of the top pipe by gluing, and the laser displacement meter is deployed on the inner wall of the top pipe and the top side of the thermal pipeline.

3. A pipe jacking convergence and strain monitoring method according to claim 2, characterized in that: The acquisition logic of the deformation data includes: The laser displacement meter emits a laser beam onto the reflector, measures the time from laser emission to laser reception, and calculates the initial distance between the laser displacement meter and the reflector based on the speed of light and time; During the use of the jacking pipe, the distance data between the laser displacement meter and the reflector is measured in real time, and the distance data is acquired and stored at the set time interval; The distance data obtained at different time points are compared with the initial distance, and the distance change is calculated. According to the size and change trend of the distance change, the deformation degree and deformation direction of the jacking pipe are judged to obtain the deformation data of the jacking pipe.

4. A pipe jacking convergence and strain monitoring method according to claim 3, characterized in that: The integration logic of the multi-dimensional data includes: Perform time synchronization processing on the data acquired by each sensor. Using the time base as a reference, calibrate the acquisition time of vibration data, strain data, deformation data, and gas, temperature and humidity data to the time base; Standardize the formats of vibration data, strain data, deformation data, gas data, temperature and humidity data; The data that has been time synchronized and format standardized is integrated and stored according to the storage structure.

5. A pipe jacking convergence and strain monitoring method according to claim 4, characterized in that: The transmission logic of the multi-dimensional data includes: Divide the multi-dimensional data into data packets according to time, and the data packets contain the acquisition time, multi-dimensional data and a check code; Evaluate and select the transmission network based on the pipe jacking environment conditions and data transmission requirements, and establish connections between sensors and processing nodes; Send data packets to processing nodes according to the transmission protocol and perform flow control based on the data volume of multi-dimensional data; The data packet is temporarily stored in the buffer of the processing node, and the check code in the data packet is checked to trigger the data retransmission mechanism.

6. A pipe jacking convergence and strain monitoring method according to claim 5, characterized in that: The preprocessing logic of the multi-dimensional data includes: Identify outliers, handle duplicate values, and fill in missing values for the received multi-dimensional data; Normalize multi-dimensional data; Perform filtering and wavelet denoising on multi-dimensional data.

7. A pipe jacking convergence and strain monitoring method according to claim 6, characterized in that: The generation logic of the convergence strain information of the jacking pipe includes: Extract features of multi-dimensional data, including vibration intensity, frequency components, and time domain characteristics of vibration data, growth rate, strain change rate, and distribution characteristics of strain data, and the magnitude and trend of distance change of deformation data; Establishing a correlation between the characteristics of the multi-dimensional data and the convergence strain, the correlation including a threshold range of the characteristics of the multi-dimensional data and a correlation between the characteristics of the multi-dimensional data and the convergence strain; Use a multi-layer perceptron to learn the relationship between the characteristics of multi-dimensional data and convergence strain, and verify the prediction accuracy of the multi-layer perceptron; The features of the multi-dimensional data obtained through real-time monitoring are input into a multi-layer perceptron to output the convergence strain information of the pipe jacking, including the strain magnitude at different positions of the pipe jacking, the strain change trend, and the estimated value of the convergence displacement.

8. A pipe jacking convergence and strain monitoring method according to claim 7, characterized in that: The sub-logic of extracting the features of the multi-dimensional data includes: The vibration intensity of the vibration data is extracted by calculating the amplitude of the vibration signal, the frequency components of the vibration data are obtained through spectrum analysis, and the time domain characteristics of the vibration data are analyzed; Calculate the increments of strain data at different time points to extract the growth rate of strain data, perform derivative operations on continuous strain data to obtain the strain change rate, and analyze the distribution characteristics of strain data at different locations of the jacking pipe; Extract the magnitude and trend of distance change from deformation data.

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