Roadbed settlement data processing device based on BP neural network algorithm
By applying the data processing device of BP neural network algorithm in the subgrade settlement monitoring system, the problems of low accuracy and poor real-time performance of traditional monitoring methods are solved, and high-precision and real-time subgrade settlement data processing are achieved, ensuring the safety of subgrade construction and operation.
Patent Information
- Application Number
- CN202510015952.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-06
- Publication Date
- 2025-05-13
AI Technical Summary
Traditional roadbed settlement monitoring methods rely on manual measurement and data analysis, resulting in low accuracy and low efficiency, and the inability to receive and process roadbed settlement data in real time.
The roadbed settlement data processing device based on the BP neural network algorithm is adopted to receive the original data through the data acquisition module, and a BP neural network model is built in the data processing module to process the monitoring data in real time and provide high-precision settlement prediction results.
It improves the accuracy and real-time performance of roadbed settlement data processing, can promptly detect abnormal settlement values, and ensures the safety and stability of roadbed construction and operation.
Smart Images
Figure CN119987494A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of civil engineering monitoring, and in particular to a roadbed settlement data processing device based on a BP neural network algorithm. Background Art
[0002] Roadbed settlement refers to the long-term effects of various loads (such as vehicle loads, etc.) and natural factors (such as groundwater changes, etc.) on the road surface and its underlying infrastructure. Roadbed settlement usually begins to occur within a period of time after roadbed construction, and the amount of settlement may also gradually increase during long-term operation. Roadbed settlement is a crucial issue in highway construction, and it is particularly evident in high-fill roadbeds. High-fill roadbeds generally refer to roadbed projects with high fill and wide fill areas. High-fill roadbeds require a large number of filling materials of various types, so there are many problems in the filling construction, and it is difficult to effectively control the filling quality of high-fill roadbeds. The problem of roadbed settlement is particularly prominent.
[0003] Traditional roadbed settlement monitoring methods usually rely on manual measurement and data analysis. The settlement monitoring products produced based on this have problems such as low accuracy and low efficiency, and cannot receive and process roadbed settlement data in real time. Summary of the invention
[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention intends to provide a roadbed settlement data processing device based on the BP neural network algorithm; it aims to improve the accuracy and real-time performance of roadbed settlement data processing, receive the original data of roadbed settlement through the data acquisition module, build a BP neural network model in the data processing module, process the monitoring data in real time and provide high-precision settlement prediction results. The data processing results will be transmitted to the remote data center in real time for storage and management.
[0005] A roadbed settlement data processing device based on BP neural network algorithm includes a device body, an Internet of Things module, a power supply module, a data processing module, and a data acquisition module. The device body can be connected to different support members by bolts, and the Internet of Things module, the power supply module, the data processing module, and the data acquisition module are all connected to the device body by bolts;
[0006] The Internet of Things module needs to be inserted with an Internet of Things card to connect to the network; the power supply module needs to be connected to an external photovoltaic panel for power supply; the data processing module includes a BP neural network algorithm to process data; the data acquisition module needs to be connected to on-site sensors to collect data.
[0007] Preferably, the device body can be connected to different support members by bolts, and has strong adaptability to different engineering environments.
[0008] Preferably, the device requires an Internet of Things card to be inserted into an Internet of Things card interface to transmit data with a remote data center through an Internet of Things module.
[0009] Preferably, the power supply module connects the external photovoltaic panel wires to the photovoltaic panel interface, and then the power supply module uses the electric energy transmitted by the external photovoltaic panel through the power supply line to power the entire device, and controls the overall power supply status of the device through the circuit control module and the main circuit switch.
[0010] Preferably, the data acquisition module is connected to an on-site settlement monitoring meter via a settlement data line receiving interface to collect roadbed settlement data, and transmits the collected settlement data to the data processing module via the data line.
[0011] Preferably, the data processing module is connected to the data acquisition module via a data line, receives the roadbed settlement data from the data processing module, processes the collected settlement data through the data processing element, transmits it to the remote data center by the data transmission element, and is equipped with a GPS positioning element.
[0012] Preferably, the data processing module selects 3 neurons in the input layer of the neural network, which are "fill height", "temperature" and "time" respectively, and 1 neuron in the output layer, which is "roadbed settlement value".
[0013] Preferably, the data processing module (4) implements the following process for processing roadbed settlement: initializing the network, selecting input layer neurons and output layer neurons of the neural network; forward propagation, transmitting input data to neurons in the input layer; normalizing the data and performing error calculation; iterative training, followed by denormalizing the data; outputting the prediction results, and evaluating the results.
[0014] Preferably, the normalization formula used by the data processing module is:
[0015] Preferably, the denormalization formula used by the data processing module is: i =y(x max -x min )+x min The predicted value output by the BP neural network is restored to the actual required roadbed settlement predicted value.
[0016] Preferably, the hidden layer selection empirical formula adopted by the data processing module is:
[0017] The beneficial effects of the present invention include: the present invention discloses a roadbed settlement data processing device based on BP neural network algorithm, which aims to improve the accuracy and real-time performance of roadbed settlement data processing, including a device body, an Internet of Things module, a power supply module, a data processing module, and a data acquisition module. The device is powered by photovoltaic equipment, receives the original data of roadbed settlement through the data acquisition module, builds a BP neural network model in the data processing module, processes the monitoring data in real time, and provides high-precision settlement prediction results. The data processing results will be transmitted to a remote data center in real time for storage and management. The present invention has the advantages of high precision and strong real-time performance, is suitable for roadbed settlement monitoring projects of different scales and types, and provides a new solution for the field of civil engineering monitoring technology. Specifically include: 1. The present invention adopts photovoltaic power generation, does not require an external power supply, the device is simple and convenient to install, and has strong adaptability to different engineering environments. 2. The present invention receives and processes the settlement value of the roadbed in real time, and can be viewed remotely through the data center, can timely discover abnormal settlement values, timely eliminate safety hazards, and ensure the safety and stability of roadbed construction and highway operation. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention, wherein:
[0019] Figure 1 The overall schematic diagram of the device.
[0020] Figure 2 This is a schematic diagram of the location of each module inside the device.
[0021] Figure 3 Schematic diagram of the bolt hole locations on the back of the device.
[0022] Figure 4 This is the structure diagram of the BP neural network.
[0023] Among them: 1-device body, 2-Internet of Things module, 3-power supply module, 4-data processing module, 5-data acquisition module, 6-bolt hole, 7-Internet of Things card interface, 8-photovoltaic panel wiring port, 9-GPS positioning element, 10-data transmission element, 11-data line, 12-circuit control module, 13-circuit main switch, 14-data processing element, 15-settlement data line interface, 16-back bolt hole. DETAILED DESCRIPTION
[0024] The following will be combined with the attached Figure 1-Figure 4The present invention is described in detail, and the technical solutions in the embodiments of the invention are clearly and completely described. 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.
[0025] Example 1
[0026] like Figure 1 As shown, a roadbed settlement data processing device based on BP neural network algorithm in this embodiment includes a device body 1, which is made of steel, and the body includes an Internet of Things module 2, a power supply module 3, a data processing module 4, and a data acquisition module 5. A bolt hole 6 is opened on the back, and the device body 1 is connected to a support through a bolt connected to the bolt hole 6. The Internet of Things module 2, the power supply module 3, the data processing module 4, and the data acquisition module 5 are connected to the device body 1 by bolts.
[0027] The Internet of Things module 2 can be inserted into an Internet of Things card to transmit data with a remote data center so that the data collected by the device can be saved, processed and uploaded to the remote data center in a timely manner.
[0028] The power supply module 3 can be connected to an external photovoltaic panel and use the electric energy transmitted by the photovoltaic panel to power other modules. Therefore, the device does not need additional power supply, which saves energy and has a stronger ability to adapt to different engineering environments and a wider range of applications.
[0029] The data acquisition module 5 can be connected to an on-site settlement monitoring meter to collect the roadbed settlement data recorded by the monitoring meter.
[0030] The data processing module 4 is provided with a data storage device and a wireless transmitter to process the settlement data transmitted by the data acquisition module 5. The normalized data is shown in Table 1 below.
[0031] Table 1 Normalized data of a roadbed settlement
[0032]
[0033]
[0034] The network model can be used for prediction only after training. The settings of training parameters are shown in Table 2 below.
[0035] Table 2 Training parameters
[0036] Number of training sessions Training Goals Learning Rate 1000 1e-6 0.1
[0037] The prediction results performance evaluation is shown in Table 3 below.
[0038] Table 3 Performance parameters of the evaluation prediction model
[0039] <![CDATA[R 2 ]]> MSE MAPE Training time(s) 0.99741 0.025％ 0.617％ 8.35
[0040] Note: R 2 is the goodness of fit, MSE is the mean square error, and MAPE is the mean relative error.
[0041] The results show that the model converges quickly, has an excellent network structure, and the prediction results are within an acceptable range, which can be used as a reference for actual roadbed construction.
[0042] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A roadbed settlement data processing device based on a BP neural network algorithm, comprising a device body (1), wherein the body comprises an Internet of Things module (2), a power supply module (3), a data processing module (4), and a data acquisition module (5), and a bolt hole (16) is opened on the back. The device body (1) is connected to a support through bolts connected to the bolt hole (16), and has strong adaptability to different engineering environments.
2. A roadbed settlement data processing device based on BP neural network algorithm according to claim 1, characterized in that: The device body can be connected to different support members by means of bolts, and the Internet of Things module (2), the power supply module (3), the data processing module (4), and the data acquisition module (5) are all connected to the device body (1) by means of back bolts.
3. A roadbed settlement data processing device based on BP neural network algorithm according to claim 1, characterized in that: Inserting the Internet of Things card into the Internet of Things card interface (7) transmits data to the remote data center through the Internet of Things module (2); so that the data collected by the device can be timely stored, processed and uploaded to the remote data center; The Internet of Things module (2) needs to be inserted with an Internet of Things card to connect to the network; the power supply module needs to be externally connected to a photovoltaic panel to supply power; the data processing module includes a BP neural network algorithm to process data; and the data acquisition module needs to be connected to an on-site sensor to collect data.
4. A roadbed settlement data processing device based on BP neural network algorithm according to claim 1, characterized in that: The external photovoltaic panel wires are connected to the photovoltaic panel interface (8), and then the power supply module (3) uses the power transmitted by the photovoltaic panel through the power supply line (6) to power the entire device, and controls the overall power supply status of the device through the circuit control module (12) and the main circuit switch (13).
5. The roadbed settlement data processing device based on BP neural network algorithm according to claim 1 is characterized in that: The data acquisition module (5) is connected to an on-site settlement monitoring meter via a settlement data line receiving interface (15) to collect roadbed settlement data, and transmits the collected settlement data to the data processing module (4) via a data line (11).
6. A roadbed settlement data processing device based on BP neural network algorithm according to claim 1, characterized in that: The data processing module (4) receives the roadbed settlement data from the data processing module (5) via a data line (11), processes the collected settlement data via a data processing element (14), transmits the collected settlement data to a remote data center via a data transmission element (10), and is equipped with a GPS positioning element (9); the data processing module (4) is provided with a data storage device and a wireless transmission device to process the settlement data transmitted by the data collection module 5.
7. The roadbed settlement data processing device based on BP neural network algorithm according to claim 1 is characterized in that: The data processing module (4) selects 3 neurons in the input layer of the neural network, which are "fill height", "temperature" and "time" respectively; and 1 neuron in the output layer, which is "roadbed settlement value".
8. The roadbed settlement data processing device based on BP neural network algorithm according to claim 1 is characterized in that: The data processing module (4) implements the following process for processing the roadbed settlement: initializing the network, selecting the input layer neurons and output layer neurons of the neural network; forward propagation, transferring the input data to the neurons of the input layer; normalizing the data and performing error calculation; Iterate the training and then denormalize the data; Output the prediction results and evaluate the results.
9. A roadbed settlement data processing device based on BP neural network algorithm according to claim 8, characterized in that: The normalization formula adopted by the data processing module (4) is: In the formula, x i represents the input sample data, x min Represents the minimum value in the input sample data, x max represents the maximum value of the input sample data, and y represents the normalized data; The denormalization formula adopted by the data processing module (4) is: i =y(x max -x min )+x min The predicted value output by the BP neural network is restored to the actual required roadbed settlement predicted value.
10. The roadbed settlement data processing device based on BP neural network algorithm according to claim 1, characterized in that: The number of neurons in the hidden layer is selected by using an empirical formula through the data processing module (4). The empirical formula for selecting the hidden layer is: Where a is the number of neurons in the input layer, b is the number of neurons in the output layer, and c is a positive integer less than or equal to 10.