Tower foundation settlement deformation prediction method and system based on sensor network

Abstract

The invention discloses a method and system for predicting settlement deformation of a tower foundation based on a sensor network, and belongs to the field of settlement deformation predication.According to the method and system, risk assessment of the tower in future time is carried out based on a foundation change anomaly analysis result, a tower operation anomaly analysis result and weather data in the future time; tower reinforcement early warning is carried out based on a tower risk assessment result, double dimensions of'static stability 'and'dynamic operation health' of a tower are covered by combining basic structure change and operation state abnormity, future weather data are introduced, dynamic influence of an external environment on risks is quantified, misjudgment caused by sudden weather change is avoided, and the safety of the tower reinforcement early warning is improved. According to the technical scheme, through multi-dimensional data fusion, dynamic risk assessment and predictive maintenance, the accuracy, initiative and economical efficiency of tower safety management are remarkably improved, and the problems of data isolation, response lag, high cost and the like in a traditional method are solved.

Description

Technical Field

[0001] The present invention belongs to the field of settlement deformation prediction, and specifically relates to a method and system for predicting the settlement deformation of a tower foundation based on a sensor network. Background Technique

[0002] With the continuous development of power grid construction, the line inevitably needs to pass through coal gob areas. Geological disasters are likely to occur in gob areas, resulting in the deformation of tower foundations, damage to transmission towers, and significant economic losses. The main forms of ground deformation in gob areas are settlement, inclination, curvature, and horizontal slip deformation. Transmission towers are very sensitive to uneven settlement deformation of the foundation. A small foundation settlement can cause a large additional stress on the transmission tower, resulting in internal force redistribution of the transmission tower, reducing the bearing capacity of the transmission tower, and even causing local damage or collapse in severe cases. At present, the safety status evaluation of transmission towers mainly focuses on line inspection and tower material corrosion. The results of this safety status evaluation are inaccurate. In the Chinese patent with the publication number CN 116930964A in the prior art, a safety evaluation method and system for transmission towers in mined areas based on radar remote sensing are provided, including obtaining the deformation amount of the transmission tower foundation settlement by using spaceborne synthetic aperture radar interferometry based on the geometric information of the centroid of the tower leg of the transmission tower; performing finite element analysis on the transmission tower structure finite element model constructed in advance under various working conditions based on the deformation amount of the transmission tower foundation settlement to obtain the axial force of the tower leg members in the transmission tower; and evaluating the overall safety of the transmission tower based on the axial force of the tower leg members. This invention accurately obtains the deformation amount of the transmission tower foundation settlement by using spaceborne synthetic aperture radar interferometry, and on this basis, combines the finite element analysis of the transmission tower structure finite element model under various working conditions, improving the accuracy of the safety evaluation of the transmission tower; however, it does not combine the change of the foundation structure with the abnormal operation state, covering the double dimensions of the "static stability" and "dynamic operation health" of the tower, and at the same time does not introduce future weather data to quantify the dynamic impact of the external environment on the risk, resulting in low precision in the safety management of the tower. Most of the prior arts have the above problems;

[0003] To solve the problems raised in this background technique, the present application designs a method and system for predicting the settlement deformation of a tower foundation based on a sensor network. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention proposes a method and system for predicting the settlement and deformation of the tower foundation based on a sensor network. The present invention obtains the monitoring data of the tower foundation, conducts an analysis of abnormal foundation changes based on the monitoring data of the tower foundation, imports the tower operation data into the tower operation abnormal assessment model for tower operation abnormal analysis, conducts a risk assessment of the tower at a future time based on the results of the abnormal foundation change analysis, the results of the tower operation abnormal analysis, and the weather data at the future time, and issues a warning for tower reinforcement based on the tower risk assessment results. By combining the changes in the foundation structure and the abnormal operation status, covering the two dimensions of the "static stability" and "dynamic operation health" of the tower, introducing future weather data, and quantifying the dynamic impact of the external environment on the risk, it avoids misjudgment caused by sudden meteorological changes. This technical solution significantly improves the accuracy, initiative, and economy of tower safety management through multi-dimensional data fusion, dynamic risk assessment, and predictive maintenance, and solves the problems of data isolation, lagging response, and high cost in traditional methods.

[0005] To achieve the above object, the present invention provides the following technical solution: A method for predicting the settlement and deformation of a tower foundation based on a sensor network, which includes the following specific steps:

[0006] Obtain the operation data of the tower, the monitoring data of the tower foundation, and the weather data at each time, and store them separately;

[0007] Obtain the monitoring data of the tower foundation, and conduct an analysis of abnormal foundation changes based on the monitoring data of the tower foundation;

[0008] Import the tower operation data into the tower operation abnormal assessment model for tower operation abnormal analysis;

[0009] Conduct a risk assessment of the tower at a future time based on the results of the abnormal foundation change analysis, the results of the tower operation abnormal analysis, and the weather data at the future time;

[0010] Issue a warning for tower reinforcement based on the tower risk assessment results.

[0011] It should be noted here that, as a preferred technical solution of the method for predicting the settlement and deformation of a tower foundation based on a sensor network, the operation data of the tower includes the swing data of the tower line connection part and the line tension data of the circuit transmission, etc., which reflect the operation quality of the tower. The monitoring data of the tower foundation includes the surface defect data of the tower foundation and the settlement data of each position of the tower foundation. Among them, the surface defect data of the tower foundation includes the size and depth data, and also includes the average distance data from each position of the defect to the connection between the tower bottom and the foundation.

[0012] It should be noted here that, as a preferred technical solution of the tower foundation settlement and deformation prediction method based on the sensor network, the analysis of abnormal foundation changes based on the monitoring data of the tower foundation includes the following specific steps:

[0013] S21. Obtain the surface defect data of the corresponding tower foundation and the settlement data of each position of the tower foundation, and conduct a foundation anomaly assessment based on the surface defect data of the corresponding tower foundation and the settlement data of each position of the tower foundation;

[0014] Among them, the foundation anomaly assessment includes the following specific steps:

[0015] S211. Obtain the size and depth data of the surface defects of the corresponding tower foundation, and at the same time obtain the average distance data from each position of the defect to the connection between the tower bottom and the foundation. Since the closer the defect is to the connection between the tower bottom and the foundation, the greater the impact on the tower stability, it is necessary to analyze the danger degree of the surface defect by weighting with the reciprocal of the average distance data. Among them, the calculation formula for the danger degree of the surface defect can be: Among them, G is the number of surface defects of the foundation, Lm is the perimeter of the upper surface of the tower foundation, Lc is the average distance from each position of the c-th surface defect of the foundation to the connection between the tower bottom and the foundation, dc is the length of the c-th defect, hc is the width of the widest position of the c-th defect, lc is the depth of the c-th defect position, and Vm is the safety volume of the defect;

[0016] S212. Obtain the settlement data of each position of the tower foundation, and conduct a settlement anomaly analysis based on the settlement size and settlement uniformity of each position. Among them, the calculation formula for the settlement anomaly analysis is: Among them, ci is the settlement data of the i-th position of the foundation, n is the number of detection positions, cm is the maximum value of the safe settlement range, cv is the average settlement of the foundation detection positions, and λ is the weight ratio of settlement uniformity;

[0017] S213. Perform a weighted sum based on the surface defect danger degree and the settlement anomaly analysis result to obtain the foundation anomaly assessment result;

[0018] S22. Obtain the change rate of the foundation anomaly assessment result over time during the monitoring period, and at the same time obtain the real-time foundation anomaly assessment result;

[0019] It should be noted here that, as a preferred technical solution of the tower foundation settlement and deformation prediction method based on the sensor network, the analysis of abnormal tower operation by importing the tower operation data into the tower operation anomaly assessment model includes the following specific steps:

[0020] S31. Obtain the swing data of the connection part of the pole tower line and the line tension data of the circuit transmission. Conduct the volatility assessment of the pole tower under environmental influence based on the swing data of the connection part of the pole tower line. The pole tower volatility assessment formula is as follows: Where, w is the number of swings of the connection part of the pole tower line within the monitoring period, Yz is the swing amplitude of the z-th swing, Ym is the safety value of the swing amplitude, and F is the wind force level.

[0021] S32. Conduct the volatility assessment of the circuit transmission under environmental influence based on the line tension data of the circuit transmission. The circuit transmission volatility assessment formula is as follows: Where, T is the duration of the monitoring period, ft is the line tension of the corresponding line of the pole tower at time t, fm is the maximum value of the line tension that the line can withstand, and dt is the time integral constant.

[0022] S33. Obtain the pole tower volatility assessment result and the circuit transmission volatility assessment result, and perform weighted summation to obtain the abnormal operation analysis result of the pole tower.

[0023] Here, it should be noted that as the preferred technical solution of the pole tower foundation settlement and deformation prediction method based on the sensor network, the future-time pole tower hazard assessment based on the foundation change abnormal analysis result, the pole tower operation abnormal analysis result, and the weather data of the future time includes the following specific contents:

[0024] Obtain the foundation change abnormal analysis result, the pole tower operation abnormal analysis result, and the weather data of the future time. Evaluate the abnormal operation of the pole tower under the future environmental influence through the pole tower operation abnormal analysis result and the weather data of the future time. The calculation formula for the abnormal operation of the pole tower under the future environmental influence is preferably: Ht＝(Wd+δXl)×Fh , where Fh is the average wind force data of the future monitoring period, δ is the weight ratio of the circuit transmission volatility;

[0025] Perform weighted summation on the obtained abnormal operation of the pole tower under the future environmental influence and the foundation change abnormal analysis result to obtain the future-period pole tower hazard assessment result.

[0026] Here, it should be noted that as the preferred technical solution of the pole tower foundation settlement and deformation prediction method based on the sensor network, the pole tower reinforcement warning based on the pole tower hazard assessment result includes the following specific contents: If the obtained future-period pole tower hazard assessment result is greater than or equal to the set hazard threshold, issue a pole tower reinforcement warning to remind the staff to reinforce the pole tower. If the obtained future-period pole tower hazard assessment result is less than the set hazard threshold, do not issue a pole tower reinforcement warning.

[0027] The tower foundation settlement and deformation prediction system based on the sensor network is implemented based on the above-mentioned tower foundation settlement and deformation prediction method based on the sensor network, and specifically includes the following modules:

[0028] The acquisition module: used to obtain the operation data of the tower, the monitoring data of the tower foundation, and the weather data at each time, and store them separately;

[0029] The foundation change anomaly analysis module: used to obtain the monitoring data of the tower foundation and conduct foundation change anomaly analysis based on the monitoring data of the tower foundation;

[0030] The tower operation anomaly analysis module: used to import the tower operation data into the tower operation anomaly evaluation model for tower operation anomaly analysis;

[0031] The tower hazard assessment module: conduct future tower hazard assessment based on the foundation change anomaly analysis result, the tower operation anomaly analysis result, and the weather data at future times;

[0032] The reinforcement warning module: conduct tower reinforcement warning based on the tower hazard assessment result.

[0033] An electronic device includes: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory;

[0034] The processor executes the above-mentioned tower foundation settlement and deformation prediction method based on the sensor network by calling the computer program stored in the memory.

[0035] A computer-readable storage medium stores instructions, and when the instructions run on a computer, the computer is made to execute the tower foundation settlement and deformation prediction method based on the sensor network as described above.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The present invention obtains the monitoring data of the tower foundation, conducts foundation change anomaly analysis based on the monitoring data of the tower foundation, imports the tower operation data into the tower operation anomaly evaluation model for tower operation anomaly analysis, conducts future tower hazard assessment based on the foundation change anomaly analysis result, the tower operation anomaly analysis result, and the weather data at future times, and conducts tower reinforcement warning based on the tower hazard assessment result. By combining the foundation structure change and the operation state anomaly, covering the two dimensions of the "static stability" and "dynamic operation health" of the tower, introducing future weather data, quantifying the dynamic impact of the external environment on the risk, and avoiding misjudgment caused by sudden meteorological changes, this technical solution significantly improves the accuracy, initiative, and economy of tower safety management through multi-dimensional data fusion, dynamic risk assessment, and predictive maintenance. Description of the Drawings

[0038] Figure 1 This is a schematic diagram of the overall process of the pole tower foundation settlement and deformation prediction method based on the sensor network of the present invention;

[0039] Figure 2 This is a schematic diagram of the process of step S21 of the pole tower foundation settlement and deformation prediction method based on the sensor network of the present invention;

[0040] Figure 3 This is a schematic diagram of the process of step S3 of the pole tower foundation settlement and deformation prediction method based on the sensor network of the present invention;

[0041] Figure 4 This is a schematic diagram of the overall framework of the pole tower foundation settlement and deformation prediction system based on the sensor network of the present invention. Specific embodiments

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0043] It should also be noted that in this specification, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0044] Embodiment 1

[0045] To solve the technical problems raised in the background art: The prior art does not combine the changes in the basic structure with the abnormal operating state, covering the dual dimensions of the "static stability" and "dynamic operation health" of the pole tower, and at the same time does not introduce future weather data to quantify the dynamic impact of the external environment on the risk, resulting in low accuracy of the pole tower safety management;

[0046] The present invention provides a preferred embodiment: As Figures 1 - 3As shown, a method for predicting the settlement and deformation of the tower foundation based on a sensor network includes the following specific steps:

[0047] S1. Obtain the operation data of the tower, the monitoring data of the tower foundation, and the weather data at each time, and store them separately. Here, the data collection process is carried out through various data collection terminals and stored in the corresponding storage components;

[0048] In this embodiment, the operation data of the tower includes data reflecting the operation quality of the tower such as the swing data of the connection part of the tower line and the line tension data of the circuit transmission. The monitoring data of the tower foundation includes the surface defect data of the tower foundation and the settlement data of each position of the tower foundation. Among them, the surface defect data of the tower foundation includes the size and depth data, and also includes the average distance data from each position of the defect to the connection between the tower bottom and the foundation. The weather data at each time includes the historical and future wind force data of the weather forecast, and here the wind force data is obtained through the weather forecast;

[0049] S2. Obtain the monitoring data of the tower foundation and perform an abnormal analysis of the foundation change based on the monitoring data of the tower foundation;

[0050] In this embodiment, the abnormal analysis of the foundation change based on the monitoring data of the tower foundation includes the following specific steps:

[0051] S21. Obtain the surface defect data of the corresponding tower foundation and the settlement data of each position of the tower foundation, and perform a foundation abnormality assessment based on the surface defect data of the corresponding tower foundation and the settlement data of each position of the tower foundation;

[0052] Among them, the foundation abnormality assessment includes the following specific steps:

[0053] S211. Obtain the size and depth data of the surface defect of the corresponding tower foundation, and at the same time obtain the average distance data from each position of the defect to the connection between the tower bottom and the foundation. Here, the calculation method of the average distance is to obtain the contour and boundary of the defect, and obtain the average of the distances from each point of the boundary to the connection between the tower bottom and the foundation. Because the closer the defect is to the connection between the tower bottom and the foundation, the greater the impact on the tower stability, so it is necessary to analyze the danger degree of the surface defect by weighting the importance based on the reciprocal of the average distance data. This is mainly because the tower bottom bears the maximum bending moment and shear force, and defects (such as cracks and rust) in this area are prone to cause stress concentration and significantly reduce the overall structural stability. Among them, the calculation formula for the danger degree of the surface defect can be: Wherein, G is the number of basic surface defects, Lm is the perimeter of the upper surface of the pole tower foundation, Lc is the average distance from each position of the c-th surface defect of the foundation to the connection between the bottom of the pole tower and the foundation, dc is the length of the c-th defect, hc is the width of the widest position of the c-th defect, lc is the depth of the c-th defect position, and Vm is the safe volume of the defect;

[0054] S212. Obtain the settlement data of each position of the pole tower foundation, and analyze the settlement anomalies based on the settlement magnitude and settlement uniformity of each position. Among them, the calculation formula for settlement anomaly analysis is: Wherein, ci is the settlement data of the i-th position of the foundation, n is the number of detection positions, cm is the maximum value of the safe settlement range, cv is the average settlement of the foundation detection positions, and λ is the proportion weight of settlement uniformity. The benefits of analyzing based on the magnitude and uniformity of the pole tower foundation settlement data: In the health monitoring of the pole tower foundation, consider both the settlement magnitude and settlement uniformity of each position for anomaly analysis, comprehensively identify risks, and avoid missed detections. Settlement magnitude analysis: Identify the points exceeding the safety threshold. Uniformity analysis: Quantify the differential settlement through the standard deviation or coefficient of variation, and discover potential hazards of uneven distribution;

[0055] S213. Perform weighted summation based on the surface defect danger level and the settlement anomaly analysis results to obtain the foundation anomaly assessment result. Setting it like this can dynamically capture compound risks and improve the comprehensiveness of the assessment. Relying solely on settlement data may ignore local stress concentrations caused by surface defects (such as cracks and rust); only analyzing surface defects cannot reflect the overall stability of the foundation;

[0056] S22. Obtain the change rate of the foundation anomaly assessment result over time during the monitoring period, and at the same time obtain the real-time foundation anomaly assessment result. Standardize and sum the change rate of the foundation anomaly assessment result over time during the monitoring period and the real-time foundation anomaly assessment result to obtain the foundation change anomaly analysis result. Standardize and sum the change rate of the foundation anomaly assessment result over time during the monitoring period and the real-time foundation anomaly assessment result to obtain the foundation change anomaly analysis result; This can reflect the acceleration of abnormal development, and can more accurately warn of potential risks than static data at a single time point. After unifying the dimensions of the rate (trend) and the real-time value (current situation) and superimposing them, a comprehensive index is formed, which can not only identify high risks of slow accumulation but also capture early risks of rapid deterioration. By standardizing and integrating the abnormal change rate (trend) and the real-time assessment result (current situation), the foundation health risks can be identified earlier, more accurately, and more comprehensively, providing dynamic and quantitative decision-making basis for the safe operation and maintenance of infrastructure, and significantly improving the initiative and economy of risk prevention and control;

[0057] S3. Import the pole tower operation data into the pole tower operation anomaly assessment model for pole tower operation anomaly analysis;

[0058] In this embodiment, importing the operating data of the pole tower into the pole tower operating anomaly assessment model for pole tower operating anomaly analysis includes the following specific steps:

[0059] S31. Obtain the swing data of the connection part of the pole tower line and the line tension data transmitted by the circuit, and conduct a pole tower volatility assessment under environmental influence based on the swing data of the connection part of the pole tower line. Among them, the pole tower volatility assessment formula is: where w is the number of swings of the connection part of the pole tower line within the monitoring period, Yz is the swing amplitude of the z-th swing, Ym is the safety value of the swing amplitude, and F is the wind force level;

[0060] S32. Conduct a circuit transmission volatility assessment under environmental influence based on the line tension data transmitted by the circuit. Among them, the circuit transmission volatility assessment formula is: where T is the duration of the monitoring period, ft is the line tension of the corresponding line of the pole tower at time t, fm is the maximum value of the line tension that the line can withstand, and dt is the time integral constant;

[0061] S33. Obtain the pole tower volatility assessment result and the circuit transmission volatility assessment result, and perform a weighted sum to obtain the pole tower operating anomaly analysis result;

[0062] S4. Conduct a pole tower hazard assessment for future time based on the basic change anomaly analysis result, the pole tower operating anomaly analysis result, and the weather data of future time;

[0063] In this embodiment, conducting a pole tower hazard assessment for future time based on the basic change anomaly analysis result, the pole tower operating anomaly analysis result, and the weather data of future time includes the following specific contents:

[0064] Obtain the basic change anomaly analysis result, the pole tower operating anomaly analysis result, and the weather data of future time, and evaluate the pole tower operating anomaly under future environmental influence through the pole tower operating anomaly analysis result and the weather data of future time. Among them, the calculation formula for the pole tower operating anomaly under future environmental influence is preferably: Ht＝(Wd+δXl)×Fh , where Fh is the average wind force data of the future monitoring period, δ is the proportion weight of the circuit transmission volatility;

[0065] Obtain the weighted sum of the pole tower operating anomaly under future environmental influence and the basic change anomaly analysis result to obtain the pole tower hazard assessment result for the future period

[0066] S5. Conduct a pole tower reinforcement warning based on the pole tower hazard assessment result;

[0067] In this embodiment, the warning for pole reinforcement based on the pole risk assessment results includes the following specific contents: if the obtained pole risk assessment result for the future period is greater than or equal to the set risk threshold, a warning for pole reinforcement is issued to remind the staff to carry out pole reinforcement; if the obtained pole risk assessment result for the future period is less than the set risk threshold, no warning for pole reinforcement is issued.

[0068] It should also be noted in this embodiment that the value-taking method of each set parameter in this embodiment is as follows: obtain the pole risk assessment result for the future time through the operation data of the pole, the monitoring data of the pole foundation, and the weather data at each time obtained from the evaluation history, obtain the judgment result on whether the pole can work in the next period, import the evaluation result and the judgment result into the fitting software correspondingly, and output the value-taking of each set parameter that meets the highest judgment result accuracy rate;

[0069] The advantages of this embodiment compared with the prior art are as follows: obtain the monitoring data of the pole foundation, perform abnormal analysis of the foundation change based on the monitoring data of the pole foundation, import the pole operation data into the pole operation abnormal assessment model to perform pole operation abnormal analysis, perform pole risk assessment for the future time based on the abnormal analysis result of the foundation change, the abnormal analysis result of the pole operation, and the weather data for the future time, issue a warning for pole reinforcement based on the pole risk assessment result, combine the change of the foundation structure and the abnormality of the operation state, cover the two dimensions of "static stability" and "dynamic operation health" of the pole, introduce the future weather data, quantify the dynamic impact of the external environment on the risk, and avoid misjudgment caused by meteorological mutations. This technical solution significantly improves the accuracy, initiative, and economy of pole safety management through multi-dimensional data fusion, dynamic risk assessment, and predictive maintenance, and solves the problems of data isolation, lagging response, and high cost in traditional methods.

[0070] Embodiment 2

[0071] As Figure 4 shown, the pole foundation settlement and deformation prediction system based on the sensor network is implemented based on the above-mentioned pole foundation settlement and deformation prediction method based on the sensor network. Acquisition module: used to obtain the operation data of the pole, the monitoring data of the pole foundation, and the weather data at each time, and store them respectively;

[0072] Foundation change abnormal analysis module: used to obtain the monitoring data of the pole foundation and perform abnormal analysis of the foundation change based on the monitoring data of the pole foundation;

[0073] Pole operation abnormal analysis module: used to import the pole operation data into the pole operation abnormal assessment model to perform pole operation abnormal analysis;

[0074] Tower danger assessment module: Based on the analysis results of abnormal foundation changes, abnormal tower operation analysis results, and weather data for future time, conduct tower danger assessment for future time;

[0075] Reinforcement warning module: Based on the tower danger assessment results, conduct tower reinforcement warning. The specific steps of the above modules in this embodiment are specifically described in the above method embodiment and will not be elaborated in detail in this embodiment.

[0076] Embodiment 3

[0077] This embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory;

[0078] The processor executes the above-mentioned tower foundation settlement and deformation prediction method based on the sensor network by calling the computer program stored in the memory.

[0079] This electronic device may have relatively large differences due to different configurations or performances, and can include one or more processors and one or more memories. Among them, at least one computer program is stored in the memory, and this computer program is loaded and executed by the processor to implement the tower foundation settlement and deformation prediction method provided by the above method embodiment. This electronic device can also include other components for realizing device functions. For example, this electronic device can also have components such as wired or wireless network interfaces and input / output interfaces for data input and output. This embodiment will not be elaborated here.

[0080] Embodiment 4

[0081] This embodiment proposes a computer-readable storage medium, on which a rewritable computer program is stored;

[0082] When the computer program runs on a computer device, it enables the computer device to execute the above-mentioned tower foundation settlement and deformation prediction method based on the sensor network.

[0083] For example, the computer-readable storage medium can be a read-only memory, a random access memory, a read-only optical disc, magnetic tape, floppy disk, and optical data storage device, etc.

[0084] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more sets of available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

Claims

1. A method for predicting the settlement and deformation of tower foundations based on a sensor network, characterized in that, It includes the following specific steps: Obtain the operation data of the pole tower, the monitoring data of the pole tower foundation, and the weather data at each time, and store them separately; Obtain the monitoring data of the pole tower foundation, and conduct abnormal analysis of foundation changes based on the monitoring data of the pole tower foundation; Import the operation data of the pole tower into the pole tower operation abnormal evaluation model to conduct abnormal analysis of the pole tower operation; Conduct risk assessment of the pole tower at future times based on the results of abnormal analysis of foundation changes, the results of abnormal analysis of pole tower operation, and the weather data at future times; Conduct early warning of pole tower reinforcement based on the results of pole tower risk assessment.

2. The method for predicting the settlement and deformation of the tower foundation based on the sensor network according to claim 1, wherein The abnormal analysis of foundation changes based on the monitoring data of the pole tower foundation includes the following specific steps: Obtain the surface defect data of the corresponding pole tower foundation and the settlement data of each position of the pole tower foundation, and conduct abnormal evaluation of the foundation based on the surface defect data of the corresponding pole tower foundation and the settlement data of each position of the pole tower foundation; Obtain the change rate of the abnormal evaluation result of the foundation over time during the monitoring period, and at the same time obtain the real-time abnormal evaluation result of the foundation. Sum the standardized change rate of the abnormal evaluation result of the foundation over time during the monitoring period and the real-time abnormal evaluation result of the foundation to obtain the result of abnormal analysis of foundation changes.

3. The method for predicting the settlement and deformation of the pole tower foundation based on the sensor network according to claim 2, wherein The abnormal evaluation of the foundation includes the following specific steps: Obtain the size and depth data of the surface defects of the corresponding pole tower foundation, and at the same time obtain the average distance data from each position of the defect to the connection between the pole tower bottom and the foundation. Analyze the risk degree of the surface defects by weighting with the reciprocal of the average distance data as the importance; Obtain the settlement data of each position of the pole tower foundation, and conduct abnormal analysis of the settlement based on the settlement size and uniformity of each position; Conduct weighted summation based on the risk degree of the surface defects and the results of abnormal analysis of the settlement to obtain the result of abnormal evaluation of the foundation.

4. The method for predicting the settlement and deformation of the tower foundation based on the sensor network according to claim 3, wherein, The abnormal analysis of the pole tower operation by importing the operation data of the pole tower into the pole tower operation abnormal evaluation model includes the following specific steps: Obtain the swing data of the connection part of the pole tower line and the line tension data of the circuit transmission, and conduct volatility evaluation of the pole tower under environmental influence based on the swing data of the connection part of the pole tower line; Conduct volatility evaluation of the circuit transmission under environmental influence based on the line tension data of the circuit transmission; Obtain the volatility evaluation result of the pole tower and the volatility evaluation result of the circuit transmission, and conduct weighted summation to obtain the result of abnormal analysis of the pole tower operation.

5. The method for predicting the settlement and deformation of the tower foundation based on the sensor network according to claim 4, characterized in that, The risk assessment of the pole tower at future times based on the results of abnormal analysis of foundation changes, the results of abnormal analysis of pole tower operation, and the weather data at future times includes the following specific contents: Obtain the results of abnormal analysis of foundation changes, the results of abnormal analysis of pole tower operation, and the weather data at future times, and evaluate the abnormal operation of the pole tower under future environmental influence through the results of abnormal analysis of pole tower operation and the weather data at future times; Obtain the abnormal operation of the pole tower under future environmental influence and the results of abnormal analysis of foundation changes, and conduct weighted summation to obtain the risk assessment result of the pole tower in the future period.

6. The method for predicting the settlement and deformation of the tower foundation based on the sensor network according to claim 5, characterized in that, The warning of tower pole reinforcement based on the risk assessment result of the tower pole includes the following specific contents: if the risk assessment result of the tower pole in the future period is greater than or equal to the set risk threshold, a warning of tower pole reinforcement is given to remind the staff to reinforce the tower pole; if the risk assessment result of the tower pole in the future period is less than the set risk threshold, no warning of tower pole reinforcement is given.

7. The method for predicting the settlement and deformation of the pole tower foundation based on the sensor network according to claim 6, wherein The calculation formula for settlement anomaly analysis is as follows: Wherein, ci is the settlement data at the i-th position of the foundation, n is the number of detection positions, cm is the maximum value of the safe settlement range, cv is the average settlement of the foundation detection positions, and λ is the weight of the settlement uniformity ratio.

8. The pole tower foundation settlement and deformation prediction system based on a sensor network is implemented based on the pole tower foundation settlement and deformation prediction method based on a sensor network according to any one of claims 1-7, and is characterized in that, Specifically, it includes the following modules: Collection module: used to obtain the operation data of the tower pole, the monitoring data of the tower pole foundation, and the weather data at each time, and store them separately; Foundation change abnormality analysis module: used to obtain the monitoring data of the tower pole foundation and conduct foundation change abnormality analysis based on the monitoring data of the tower pole foundation; Tower pole operation abnormality analysis module: used to import the tower pole operation data into the tower pole operation abnormality assessment model for tower pole operation abnormality analysis; Tower pole risk assessment module: conduct risk assessment of the tower pole at future time based on the foundation change abnormality analysis result, the tower pole operation abnormality analysis result, and the weather data at future time; Reinforcement warning module: give a warning of tower pole reinforcement based on the risk assessment result of the tower pole.

9. An electronic device, comprising: A processor and a memory, wherein, computer programs that can be called by the processor are stored in the memory; It is characterized in that the processor executes the method for predicting the settlement and deformation of the tower pole foundation based on the sensor network according to any one of claims 1-7 by calling the computer programs stored in the memory.

10. A computer-readable storage medium, characterized in that, Instructions are stored, and when the instructions run on a computer, the computer is made to execute the method for predicting the settlement and deformation of the tower pole foundation based on the sensor network according to any one of claims 1-7.

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