Permafrost region power transmission tower footing settlement prediction system based on Beidou static multi-frequency double-difference technology

Abstract

The invention discloses a permafrost region power transmission tower footing settlement prediction system based on a Beidou static multi-frequency double-difference technology, the system comprises a data acquisition module, a data processing module and a data display module, the data acquisition module is used for acquiring real-time positioning monitoring data according to a Beidou navigation positioning system; the data processing module is used for processing the original measurement value of the satellite signal so as to realize differential positioning and obtain a three-dimensional coordinate accurate measurement result of each monitoring station; and the data display module is used for displaying the settlement monitoring result and the settlement prediction result of the tower footing. According to the invention, the dynamic monitoring of the real-time deformation of the tower footing of the power transmission tower is realized, the data processing rate and the real-time performance of deformation monitoring are effectively improved, the average relative error of prediction is reduced, problems can be found as early as possible, necessary maintenance and repair measures can be taken, the automation, intelligence and digitization levels of the monitoring of the power transmission tower can be improved, and the working efficiency is improved. Therefore, the reliability and safety of a power transmission system are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of tower foundation settlement prediction, and relates to a transmission tower foundation settlement prediction system in permafrost areas, and specifically relates to a transmission tower foundation settlement prediction system in permafrost areas based on Beidou static multi-frequency double-difference technology. Background Art

[0002] The permafrost area in my country accounts for about 22.4% of the country's land area. Permafrost has the disadvantages of poor thermal stability and is very sensitive to external temperature changes. During operation, changes in ground temperature will cause thawing and frost heaving of the permafrost, resulting in uneven settlement and even cracking of the foundation. With the development of my country's economy, the number of power transmission and transformation lines built in permafrost areas has gradually increased, making the frost heaving problem in permafrost areas extremely prominent, which has a great impact on the stability of the tower foundation of the entire power transmission and transformation line and the safety of the power grid in the region.

[0003] At present, the uneven settlement monitoring of the tower foundation is usually measured by Beidou carrier phase difference method or leveling method, and Beidou carrier phase difference method can solve the problems of complicated steps and inability to monitor in real time in traditional field observation and measurement. Beidou carrier phase difference positioning technology is a relative positioning technology of the global satellite navigation system. It can provide real-time three-dimensional positioning results of the measuring station in a specified coordinate system. Carrier refers to a high-frequency electromagnetic wave that can be modulated by a modulation signal, and phase is a measure that describes the change of the signal waveform.

[0004] In the related research on frozen soil, the main focus is on the use of numerical simulation methods. There is a lack of theoretical research and engineering application technology for predicting roadbed settlement based on field data as the analysis source, and the existing prediction models mostly use relatively simple artificial neural networks for prediction.

[0005] Therefore, it is urgent to propose a high-precision positioning and monitoring system based on Beidou static multi-frequency double-difference technology for permafrost areas, and use an improved artificial neural network combined model to predict tower foundation settlement. Summary of the invention

[0006] In view of the above shortcomings in the prior art, the present invention provides a permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology. The system can realize dynamic monitoring of the real-time deformation of the transmission tower foundation and high-precision tower foundation settlement prediction, effectively improving the automation, intelligence and digitalization level of tower foundation monitoring and subsequent maintenance.

[0007] The objective of the present invention is achieved through the following technical solutions:

[0008] A permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology, comprising a data acquisition module, a data processing module and a data display module, wherein the data acquisition module is used to obtain real-time positioning monitoring data according to the Beidou navigation and positioning system; the data processing module is used to process the original measurement values ​​of the satellite signal to achieve differential positioning and obtain the three-dimensional coordinate accurate measurement results of each monitoring station; the data display module is used to display the settlement monitoring results and settlement prediction results of the tower foundation;

[0009] The data acquisition module is composed of a reference station, a mobile station and a user receiver. The user receiver uses the Beidou navigation and positioning system to obtain real-time navigation positioning monitoring data, including a Beidou receiver and a geosynchronous satellite communication receiver. The Beidou receiver is used to track all visible Beidou satellites and obtain the measurement values ​​of Beidou satellites, and the geosynchronous satellite communication receiver is used to receive differential correction data; the reference station uses the Beidou receiver to monitor Beidou satellite signals all day and all weather, and sends Beidou satellite signals to the data processing center in real time; the mobile station fixes the user receiver at the monitoring point, obtains positioning monitoring data all day and all weather, and transmits the obtained monitoring data to the data center;

[0010] The data processing module is deployed in the operation and maintenance center and is composed of a server and a UPS power supply. The server is composed of a deformation monitoring system and a database. The deformation monitoring system processes the original measurement values ​​of the satellite signal to achieve differential positioning and obtain the three-dimensional coordinate calculation results of each monitoring station with high accuracy.

[0011] The data display module consists of a settlement monitoring module and a settlement prediction module. The settlement monitoring module is used to display the settlement monitoring results of the tower foundation in real time, generate and evaluate a prediction function related to the real-time monitoring results of the tower foundation settlement, and use the prediction function to predict the development trend of future settlement; the settlement prediction module is used to display the settlement prediction data calculated by the settlement prediction model in real time and evaluate and determine the prediction error.

[0012] Compared with the prior art, the present invention has the following advantages:

[0013] 1. The present invention constructs a transmission tower foundation settlement prediction system in permafrost areas based on Beidou static multi-frequency double-difference technology, realizes the settlement calculation of the Beidou real-time dynamic differential positioning and monitoring model for permafrost areas, uses a gray BP neural network combination model to predict tower foundation settlement, and completes the establishment of tower foundation settlement prediction function and automatic early warning.

[0014] 2. The transmission tower foundation settlement prediction system in permafrost areas of the present invention realizes dynamic monitoring of the real-time deformation of the transmission tower foundation, effectively improves the data processing rate and the real-time performance of deformation monitoring, and the tower foundation settlement prediction model adopted has a high overall prediction accuracy, reduces the average relative error of the prediction, and helps to detect problems early and take necessary maintenance and repair measures. It can improve the automation, intelligence and digitization level of transmission tower monitoring to ensure the reliability and safety of the transmission system.

[0015] 3. The permafrost area transmission tower foundation settlement prediction system of the present invention can also be used for other large buildings, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is the block diagram of the transmission tower foundation settlement prediction system in permafrost areas based on Beidou static multi-frequency double-difference technology;

[0017] Figure 2 It is a relative positioning diagram;

[0018] Figure 3 This is the flow chart of the prediction function. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0020] The present invention provides a transmission tower foundation settlement prediction system in permafrost areas based on Beidou static multi-frequency double-difference technology. Figure 1 As shown, the system includes a data acquisition module 1, a data processing module 2 and a data display module 3. The data acquisition module 1 is used to obtain real-time positioning and collect monitoring data according to the Beidou navigation and positioning system; the data processing module 2 is used to process the original measurement values ​​of satellite signals to achieve differential positioning and obtain accurate measurement results of the three-dimensional coordinates of each monitoring station; the data display module 3 is used to display the settlement monitoring results of the tower base of the data processing module and the settlement prediction results calculated by the settlement prediction model.

[0021] In the present invention, the data acquisition module is composed of a reference station 11, a mobile station 12 and a user receiver 13. The user receiver 13 uses the Beidou navigation and positioning system to obtain real-time navigation positioning, including a Beidou receiver and a geosynchronous satellite communication receiver. The Beidou receiver is used to track all visible Beidou satellites and obtain the measurement values ​​of Beidou satellites, and the geosynchronous satellite communication receiver is used to receive differential correction data; the reference station 11 uses the Beidou receiver to monitor Beidou satellite signals all day and all weather, and sends the Beidou satellite signals to a data processing center in real time; the mobile station 12 fixes the user receiver 13 at the monitoring point, obtains monitoring data all day and all weather, and transmits the obtained monitoring data to the data center.

[0022] In the present invention, it is necessary to complete navigation positioning according to Beidou real-time dynamic differential technology, which is mainly completed by the aforementioned reference station 11, injection station, geosynchronous satellite and user receiver 13, and the reference station 11 mainly collects carrier phase and observation; its observation value and station coordinate information are transmitted to the data processing center together through the data link, and the data center receives the data from the reference station 11 through the data link, analyzes and calculates to obtain Beidou satellite orbit correction number and clock correction number, and then transmits it to the geosynchronous satellite through the injection station (i.e. satellite signal upload system), thereby completing the information exchange between the ground and the satellite, and the user receiver 13 can obtain the position coordinates of the user carrier according to the measurement value of the Beidou satellite and the differential correction data comparison calculation, thereby completing the accurate positioning of the user carrier. This process only requires the reference station 11, the injection station, the geosynchronous satellite and the user receiver 13 to complete high-precision positioning, and meets the global monitoring of all day and all weather, saving a large amount of hardware and manpower maintenance costs.

[0023] In the present invention, after completing navigation positioning according to Beidou real-time dynamic differential technology, it is necessary to complete the fixation of the mobile station 12 at the monitoring point. The mobile station 12 needs to carry the above-mentioned user receiver 13 and fix it on a tripod with adjustable balance. The tripod needs to be installed on the base of the transmission tower in the permafrost area that needs to be monitored. After breaking the hard frozen soil with a sawtooth cutter, it is quickly fixed to the tower base with expansion screws, and then the level bubble on the tripod is adjusted to make the instrument parallel to the surface tangent, thereby completing the establishment of the ground mobile station.

[0024] In the present invention, the data acquisition module uses an optical fiber communication network to transmit the original data collected by the user receiver 13 to the data processing module 2 in real time.

[0025] In the present invention, the data processing module 2 is deployed in the operation and maintenance center, and is composed of a server 21 and a UPS power supply 22, wherein: the server 21 is composed of a deformation monitoring system and a database, and the data processing module 2 uses the deformation monitoring system to process the original measurement value of the satellite signal to achieve differential positioning, and obtain the three-dimensional coordinate calculation results of each monitoring station with high precision. This system adopts a static multi-frequency double difference algorithm to achieve this. The specific steps of the multi-frequency double difference algorithm are as follows:

[0026] First, establish the double difference monitoring equation, such as Figure 2 As shown in the figure, taking the synchronous monitoring of the base station and the mobile station by four satellites P, Q, M, and N as an example, after epoch t1 and epoch t2, 8 independent carrier phase monitoring values ​​can be obtained, corresponding to 8 carrier monitoring equations. Then, the double difference monitoring equation of the carrier phase can be obtained by making a difference twice. Next, the floating point solution of the ambiguity is solved. The floating point means that after assuming that the random error of the carrier monitoring value obeys the normal distribution, the optimal estimate of each unknown in the real number domain can be obtained by the least squares method. These errors may come from factors such as baseline vector, ambiguity or position. Finally, the double difference integer ambiguity is fixed, and the integer least squares method combined with LAMBDA is used to find the candidate value of the integer solution of the ambiguity. The candidate value of the integer solution of the ambiguity is tested to obtain the integer solution of the positioning unknown. Since the transmission tower is in a static state, its coordinates will only change slightly in a long period of time, so static positioning solution is required. The Kalman filtering method is used in this system to realize static observation. After the Kalman filter is used for data processing, the monitoring data can obtain higher plane accuracy and elevation accuracy in long-term static observation.

[0027] In the present invention, after completing the data processing, the monitoring data needs to be transmitted to the data display module 3 using the local area network, and displayed on a client such as a mobile phone or a computer.

[0028] In the present invention, the data display module 3 is composed of a settlement monitoring module 31 and a settlement prediction module 32, wherein: the settlement monitoring module 31 is used to display the tower foundation settlement monitoring data obtained by the data processing module in real time, and to generate and evaluate the prediction function related to the real-time monitoring results of the tower foundation settlement. Figure 3As shown, since the change of foundation settlement basically conforms to the logarithmic function relationship over time, the settlement prediction data can be substituted into the logarithmic model to solve for the unknown coefficients, and then the measured data is used to use the self-learning method to establish a relationship model between settlement and wind speed, ground temperature, transmission tower inclination angle and time, thereby obtaining the settlement prediction function of the monitoring point, and using this prediction function to predict the development trend of future settlement. The prediction effect of this prediction function needs to be evaluated by evaluation indicators. If the fit to the measured data is not enough, the prediction function needs to be corrected to make it have a higher fit. The evaluation index selects the determination coefficient R 2 As an evaluation index of the goodness of fit of the prediction function, the calculation formula is:

[0029]

[0030] Where, X (0) (t) is the measured settlement value at time t of the measured data of the i-th period, X ′(0) (t) is the fitted settlement value at time t in period i, R 2 The closer it is to 1, the better the function fit is.

[0031] The settlement prediction module 32 is used to display the settlement prediction data calculated by the settlement prediction model in real time and evaluate the prediction error. The settlement prediction model is implemented by a gray BP neural network combination model, which is a combination of a gray system theoretical model and a BP neural network model. The gray model has strong universality and effectiveness, while the BP neural network has good self-learning and adaptability, so it can effectively process known nonlinear settlement monitoring data.

[0032] According to the existing monitoring results of tower foundation settlement, the original series X of the tower foundation cumulative settlement can be obtained: (0) =[X (0) (1),X (0) (2),…,X (0) (n)], for X (0) Accumulate (1-AGO) to get the new sequence X (1) , then through X (1) Generate sequence Z next to the mean (1) , we can get the grey model:

[0033] x (0) (k)+az (1) (k) = b

[0034] The time response is:

[0035]

[0036] A set of fitting data can be obtained from the grey model:

[0037]

[0038] Then the residual sequence is calculated:

[0039]

[0040] Then we need to establish a grey BP neural network model and train the BP neural network. As the prediction sample of BP neural network, ε (0) As the predicted expected value of the BP neural network, the error sequence ε can be obtained ′(0) , and finally By making corrections, a new accurate and effective tower foundation settlement prediction value can be obtained:

[0041]

[0042] The prediction error evaluation method adopts mean absolute relative error (MAPE) and mean prediction error (MPF) indicators to determine the prediction effect of the prediction function extrapolation.

[0043] The MAPE value can better measure the prediction accuracy of the model. The smaller the calculated value, the higher the accuracy. The calculation formula is:

[0044]

[0045] In the formula, is the predicted settlement value at time t in period i.

[0046] The MFE value can better reflect the unbiasedness of the model, and the calculation formula is:

[0047]

[0048] By utilizing the prediction error evaluation method, as the monitoring data accumulates, the prediction model can be continuously optimized and improved to enhance the prediction precision and accuracy.

[0049] In the present invention, the data display module 3 needs to provide the user with selection and screening conditions for the monitoring start and end dates of different monitoring points, so that the user can filter the required data by himself, and support the selection of chart types and the export function of charts.

[0050] In the present invention, the data display module 3 of the permafrost area transmission tower foundation settlement prediction system is also provided with a settlement monitoring and early warning terminal. The settlement monitoring and early warning terminal sets a threshold early warning value of the cumulative settlement amount according to the monitoring data. When the cumulative settlement amount of the settlement prediction result reaches the threshold early warning value, the system will issue an early warning to remind relevant personnel to make relevant plans. When the cumulative settlement amount of the settlement real-time monitoring result reaches the threshold early warning value, the system will issue an alarm and send it to the designated personnel in the form of text messages, emails, daily reports, etc., and then perform maintenance processing according to the relevant plans.

[0051] In the present invention, the transmission tower foundation settlement prediction system in permafrost areas also needs to set up an auxiliary security system, which mainly includes subsystems such as power supply, lightning protection, integrated wiring and field cabinets, which can assist the Beidou static multi-frequency double-difference technology in automatically monitoring the transmission tower foundation settlement.

Claims

1. A transmission tower foundation settlement prediction system in permafrost areas based on Beidou static multi-frequency double-difference technology, characterized in that The system includes a data acquisition module, a data processing module and a data display module. The data acquisition module is used to obtain real-time positioning monitoring data according to the Beidou navigation and positioning system; the data processing module is used to process the original measurement values ​​of the satellite signals to realize differential positioning and obtain the accurate measurement results of the three-dimensional coordinates of each monitoring station; the data display module is used to display the settlement monitoring results and settlement prediction results of the tower base.

2. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 1 is characterized in that The data acquisition module is composed of a base station, a mobile station and a user receiver. The user receiver uses the Beidou navigation and positioning system to obtain real-time navigation positioning monitoring data, including a Beidou receiver and a geosynchronous satellite communication receiver. The Beidou receiver is used to track all visible Beidou satellites and obtain the measurement values ​​of Beidou satellites, and the geosynchronous satellite communication receiver is used to receive differential correction data; the base station uses the Beidou receiver to monitor Beidou satellite signals around the clock and all weather, and sends the Beidou satellite signals to the data processing center in real time; the mobile station fixes the user receiver at the monitoring point, obtains positioning monitoring data around the clock and all weather, and transmits the obtained monitoring data to the data center.

3. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 2 is characterized in that The navigation positioning is completed according to the Beidou real-time dynamic differential technology, which is mainly completed by a base station, an injection station, a geosynchronous satellite and a user receiver. The base station collects carrier phase and observation values, and transmits the observation values ​​and the station coordinate information to a data processing center through a data link. The data center receives data from the base station through a data link, analyzes and calculates the Beidou satellite orbit correction number and the clock correction number, and then transmits them to the geosynchronous satellite through the injection station, thereby completing the information exchange between the ground and the satellite. The user receiver compares and calculates the position coordinates of the user carrier based on the Beidou satellite's measurement value and the differential correction data, thereby completing the accurate positioning of the user carrier.

4. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 1 is characterized in that The data processing module is deployed in the operation and maintenance center and is composed of a server and a UPS power supply. The server is composed of a deformation monitoring system and a database. The deformation monitoring system processes the original measurement values ​​of the satellite signal to achieve differential positioning and obtain the three-dimensional coordinate calculation results of each monitoring station.

5. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 4 is characterized in that The three-dimensional coordinate calculation is implemented by using a static multi-frequency double difference algorithm.

6. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 1 is characterized in that The data display module is composed of a settlement monitoring module and a settlement prediction module. The settlement monitoring module is used to display the settlement monitoring results of the tower foundation in real time, generate and evaluate the prediction function related to the real-time monitoring results of the tower foundation settlement, and use the prediction function to predict the development trend of future settlement; The settlement prediction module is used to display the settlement prediction data calculated by the settlement prediction model in real time and to evaluate and determine the prediction error.

7. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 6 is characterized in that The prediction effect of the prediction function is evaluated by an evaluation index, and the evaluation index is selected as the determination coefficient R 2 As an evaluation index of the goodness of fit of the prediction function, the calculation formula is: Where, X (0) (t) is the measured settlement value at time t of the measured data of the i-th period, X ′(0) (t) is the fitted settlement value at time t in period i, R 2 The closer it is to 1, the better the function fit is.

8. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 6 is characterized in that The settlement prediction model is implemented by a grey BP neural network combination model, and the settlement prediction data is obtained according to the following steps: According to the existing monitoring results of tower foundation settlement, the original series X of the tower foundation cumulative settlement is obtained. (0) =[X (0) (1),X (0) (2),…,X (0) (n)], for X (0) Accumulate (1-AGO) to get the new sequence X (1) , then through X (1) Generate sequence Z next to the mean (1) , and the grey model is obtained: x (0) (k)+az (1) (k)=b The time response is: A set of fitting data is obtained from the grey model: Then the residual sequence is calculated: Then the grey BP neural network model is established and the BP neural network is trained. As the prediction sample of BP neural network, ε (0) As the predicted expected value of the BP neural network, the error sequence ε is obtained ′(0) , and finally The correction is made to obtain a new accurate and effective tower foundation settlement prediction value:

9. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 6 is characterized in that The data display module is also provided with a settlement monitoring and early warning terminal. The settlement monitoring and early warning terminal sets a threshold early warning value of the cumulative settlement according to the monitoring data. When the cumulative settlement of the settlement prediction result reaches the threshold early warning value, the system will issue an early warning to remind relevant personnel to make relevant plans. When the cumulative settlement of the real-time settlement monitoring result reaches the threshold early warning value, the system will issue an alarm and send it to the designated personnel in the form of text messages, emails, and daily reports, and then perform maintenance processing according to the relevant plans.

10. The permafrost area transmission tower foundation settlement prediction system based on Beidou static multi-frequency double-difference technology according to claim 1 is characterized in that The system is also equipped with an auxiliary security system to assist the Beidou static multi-frequency double-difference technology in automatically monitoring the settlement of the transmission tower foundation.

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