Pole tower inclination prediction method based on linear fitting algorithm and amplitude limiting filtering

Abstract

The invention relates to a tower inclination prediction method based on a linear fitting algorithm and amplitude limiting filtering, and the method specifically comprises the steps: starting Beidou positioning, connecting a differential service platform, carrying out the differential positioning, obtaining a primary positioning result, and then powering off a positioning module, and reducing the power consumption; performing difference processing according to the number of the historical records; taking the longitude, the latitude and the time as three coordinate axes x, y and z of a space rectangular coordinate system, placing historically recorded coordinate points into the coordinate axes, and calculating a fitting space linear equation through a least square method of a space straight line; historical data generated due to instability of a differential service platform or high-voltage line magnetic field interference are eliminated through an optimized amplitude limiting filtering algorithm; the fitting result is stored, the inclination state of the tower in the future is predicted according to the fitting result model, and the prediction result is reported; the method has the advantages that the future state prediction of the tower is realized by adopting the amplitude limiting filtering method based on the spatial linear fitting algorithm.

Description

Technical Field

[0001] The invention belongs to the technical field of power tower prediction, and in particular relates to a tower inclination prediction method based on a linear fitting algorithm and limiting filtering. Background Art

[0002] With the development of the national economy, the coverage area of ​​power and communication networks is getting wider and wider. In the transmission lines and communication lines, a large number of iron tower structures are used, such as high-voltage transmission line towers, communication base station towers, etc. The towers play an important role in the transmission lines and communication network lines. However, due to some natural disasters (such as extreme weather such as rain, snow, and strong winds), and human activities (coal mining, civil construction, man-made destruction, etc.), the environment around the towers will be damaged to a certain extent, so the towers often tilt. With the completion of my country's Beidou satellite navigation system, Beidou positioning is gradually used in the monitoring system of transmission towers. Through the "Beidou + power" mode, the tilt state of the tower can be effectively calculated through the positioning information of the positioning terminal and uploaded to the data center; At present, the prediction of the tilt of the tower is mostly based on the external environment. By sorting and calculating the external environment information, the influence of external factors on the tower is finally obtained. In addition, since the monitoring equipment of the transmission tower is mostly battery-powered, it also has certain requirements on power consumption and cannot support too many peripherals to run continuously for a long time; such as The Chinese patent "A transmission tower tilt prediction method and system" with publication number CN117473813A proposes that by collecting various data in the soil and combining the rainfall information in the target area, the tilt of the tower is predicted and calculated in the form of an algorithm, and a threshold judgment is performed; however, this method requires a large amount of feature data collected on site, and in the long-term operation process, due to seasonal changes, human activities and other factors, the rainfall information, soil information and other data in the same area will change to a certain extent, thereby affecting the accuracy of the algorithm, and the update and maintenance of the on-site information requires a lot of manpower costs; and the method is less versatile, and the degree of environmental impact on different types of towers is also different. For a certain type of tower, the algorithm can meet its requirements, while for towers of other structures, the algorithm needs to be modified for adaptation; therefore, it is very necessary to provide a tower tilt prediction method based on a linear fitting algorithm and a limiting filter that is based on a spatial linear fitting algorithm and adopts a limiting filter method to realize the prediction of the future state of the tower. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. Currently, most predictions of tower tilt are based on external conditions;

[0006] 2. Most monitoring devices are battery-powered and cannot support long-term continuous operation of too many peripherals;

[0007] 3. Traditional methods require a large amount of on-site feature data to be collected. In addition, changes in rainfall information, soil information and other data will affect the accuracy of the algorithm during long-term operation. In addition, a large amount of manpower is required to update and maintain on-site information.

[0008] 4. The traditional method has low versatility and the algorithm needs to be modified to adapt to different types of towers.

[0009] (II) Technical solution

[0010] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a tower inclination prediction method based on a linear fitting algorithm and limiting filtering, which is based on a spatial linear fitting algorithm and adopts a limiting filtering method to realize the prediction of the future state of the tower.

[0011] The object of the present invention is achieved by: a tower inclination prediction method based on linear fitting algorithm and limiting filtering, the method comprising the following steps:

[0012] Step 1: Start Beidou positioning, connect to the differential service platform, perform differential positioning, obtain a positioning result, and then power off the positioning module to reduce power consumption;

[0013] Step 2: Perform difference processing based on the number of historical records;

[0014] Step 3: Use longitude, latitude, and time as the three coordinate axes of the spatial rectangular coordinate system x, y, and z, place the coordinate points of the historical records into the coordinate axes, and calculate the fitting spatial straight line equation through the least squares method of the spatial straight line;

[0015] Step 4: Use the optimized limiting filter algorithm to remove historical data caused by the instability of the differential service platform or the interference of the high-voltage line magnetic field;

[0016] Step 5: Store the results of this fitting, and predict the future inclination state of the tower based on the result model of this fitting, and report the prediction results.

[0017] Furthermore, step 2 is specifically as follows: when the number of historical records is less than a certain value, record the current positioning result, wait for a certain time interval, and then return to step 1; when the number of historical records is greater than a certain value, store the current positioning result, take out the historical records, and enter step 3.

[0018] Furthermore, the step 3 specifically includes the following steps:

[0019] Step 3.1: First, the point-to-point equation of the space line: Among them, m, n, p are a set of direction numbers of the line; x0, y0, z0 are the three-axis coordinates of any point on the line; perform equivalent transformations to obtain the following expressions for x, z and y, z: in,

[0020] Step 3.2: Consider expression (1) and expression (2) as two planes in a three-dimensional coordinate system, and then convert the spatial straight line equation to be calculated into the intersection of the two planes. Then, fitting the spatial straight line equation is converted into fitting the two planes represented by expression (1) and expression (2), that is, fitting expression (1) by the projection of the coordinate point on the xz plane, and fitting expression (2) by the projection of the coordinate point on the yz plane.

[0021] Step 3.3: For the two planes of the above formula (1) and expression (2), calculate k1 and k2 respectively by the least square method; that is: in, is the average value of the x-coordinate of the data; is the average value of the y coordinate of the data; is the average value of the z coordinate of the data;

[0022] Step 3.4: Finally, substitute the obtained k1 and k2 back into expressions (1) and (2), substitute the coordinate points to calculate b1 and b2, and determine the spatial straight line equation of the fitted spatial rectangular coordinate system;

[0023] Step 3.5: Based on the coordinate points and the calculated straight line equation of the spatial rectangular coordinate system, use the distance formula from a point in space to a straight line to calculate the distance from each positioning point to the fitted straight line.

[0024] Furthermore, step 4 is specifically as follows: if there is a distance from a positioning point to a straight line that exceeds a set value, an amplitude filtering algorithm is used to filter and delete the points that exceed the set value, and then the remaining data is used to return to step 1; if there is no distance from a positioning point to a straight line that exceeds the set value, step 5 is entered.

[0025] (III) Beneficial effects

[0026] 1. By filtering the positioning results according to the needs and deleting some abnormal data through filtering algorithms, it can effectively avoid the influence of positioning deviation on the calculation results caused by factors such as unstable differential platform, poor antenna quality, and electromagnetic field interference of high-voltage lines;

[0027] 2. The collection interval can be adjusted according to the actual situation on site to control power consumption, which is more friendly to machines installed in environments with poor weather;

[0028] 3. The tilt state of the tower is calculated and predicted through the historical data recorded inside the machine. Different types of towers can be automatically adapted according to historical records, which is highly versatile.

[0029] 4. The tower tilt angle can be calculated using only positioning information, without the need to add other sensors for auxiliary calculations. The demand for information around the tower is not large, the overall cost of the system is controllable, and it is easy to promote;

[0030] 5. The algorithm reduces the amount of data transmitted to the data center by performing local calculations on individual towers in the terminal, reduces traffic consumption, alleviates the computing pressure of the data center, and makes the data more intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0032] The present invention proposes a pole tower inclination prediction method based on a spatial linear fitting algorithm and a limiting filtering method. The algorithm predicts the future state of the pole tower inclination angle through long-term historical monitoring data.

[0033] The present invention is further described below in conjunction with embodiments and / or drawings.

[0034] Example 1

[0035] like Figure 1 As shown, a tower inclination prediction method based on linear fitting algorithm and limiting filtering comprises the following steps:

[0036] Step 1: Start Beidou positioning, connect to the differential service platform, perform differential positioning, obtain a positioning result, and then power off the positioning module to reduce power consumption;

[0037] Step 2: Perform difference processing based on the number of historical records;

[0038] Step 3: Use longitude, latitude, and time as the three coordinate axes of the spatial rectangular coordinate system x, y, and z, place the coordinate points of the historical records into the coordinate axes, and calculate the fitting spatial straight line equation through the least squares method of the spatial straight line;

[0039] Step 4: Use the optimized limiting filter algorithm to remove historical data caused by the instability of the differential service platform or the interference of the high-voltage line magnetic field;

[0040] Step 5: Store the results of this fitting, and predict the future inclination state of the tower based on the result model of this fitting, and report the prediction results.

[0041] The present invention is a pole tower inclination prediction method based on linear fitting algorithm and limiting filtering. In use, firstly, by performing spatial linear fitting on historical data of pole tower inclination, the changing trend of pole tower inclination state in the past period of time is obtained; then, the limiting filtering method is used to eliminate the data with positioning offset caused by external factors such as electromagnetic interference in the past time period, and then the remaining data is subjected to spatial linear fitting again; after multiple fitting iterations, a spatial straight line that meets the requirements is finally obtained, and finally, by data substitution, the future inclination state of the pole tower is predicted and calculated, and finally, before the disaster occurs, the prediction result is reported to the data center, and the pole tower is maintained according to the prediction result, so as to avoid the occurrence of disasters; the present invention has the advantages of being based on a spatial linear fitting algorithm and adopting a limiting filtering method to realize the prediction of the future state of the pole tower.

[0042] Example 2

[0043] like Figure 1 As shown, a tower inclination prediction method based on linear fitting algorithm and limiting filtering comprises the following steps:

[0044] Step 1: Start Beidou positioning, connect to the differential service platform, perform differential positioning, obtain a positioning result, and then power off the positioning module to reduce power consumption;

[0045] Step 2: Perform difference processing based on the number of historical records;

[0046] In the present invention, step 2 is specifically as follows: when the number of historical records is less than a certain value, record the current positioning result, wait for a certain time interval, and then return to step 1; when the number of historical records is greater than a certain value, store the current positioning result, take out the historical records, and enter step 3.

[0047] Step 3: Use longitude, latitude, and time as the three coordinate axes of the spatial rectangular coordinate system x, y, and z, place the coordinate points of the historical records into the coordinate axes, and calculate the fitting spatial straight line equation through the least squares method of the spatial straight line;

[0048] In the present invention, step 3 specifically includes the following steps:

[0049] Step 3.1: First, the point-to-point equation of the space line: Among them, m, n, p are a set of direction numbers of the line; x0, y0, z0 are the three-axis coordinates of any point on the line; perform equivalent transformations to obtain the following expressions for x, z and y, z: in,

[0050] Step 3.2: Consider expression (1) and expression (2) as two planes in a three-dimensional coordinate system, and then convert the spatial straight line equation to be calculated into the intersection of the two planes. Then, fitting the spatial straight line equation is converted into fitting the two planes represented by expression (1) and expression (2), that is, fitting expression (1) by the projection of the coordinate point on the xz plane, and fitting expression (2) by the projection of the coordinate point on the yz plane.

[0051] Step 3.3: For the two planes of the above formula (1) and expression (2), calculate k1 and k2 respectively by the least square method; that is: in, is the average value of the x-coordinate of the data; is the average value of the y coordinate of the data; is the average value of the z coordinate of the data;

[0052] Step 3.4: Finally, substitute the obtained k1 and k2 back into expressions (1) and (2), substitute the coordinate points to calculate b1 and b2, and determine the spatial straight line equation of the fitted spatial rectangular coordinate system;

[0053] Step 3.5: Based on the coordinate points and the calculated straight line equation of the spatial rectangular coordinate system, use the distance formula from a point in space to a straight line to calculate the distance from each positioning point to the fitted straight line.

[0054] Step 4: Use the optimized limiting filter algorithm to remove historical data caused by the instability of the differential service platform or the interference of the high-voltage line magnetic field;

[0055] In the present invention, step 4 is specifically as follows: if there is a distance from a positioning point to a straight line that exceeds a set value, an amplitude filtering algorithm is used to filter and delete the points that exceed the set value, and then the remaining data is used to return to step 1; if there is no distance from a positioning point to a straight line that exceeds the set value, step 5 is entered.

[0056] Step 5: Store the results of this fitting, and predict the future inclination state of the tower based on the result model of this fitting, and report the prediction results.

[0057] The present invention is a pole tower inclination prediction method based on linear fitting algorithm and limiting filtering. In use, firstly, by performing spatial linear fitting on historical data of pole tower inclination, the changing trend of pole tower inclination state in the past period of time is obtained; then, the limiting filtering method is used to eliminate the data with positioning offset caused by external factors such as electromagnetic interference in the past time period, and then the remaining data is subjected to spatial linear fitting again; after multiple fitting iterations, a spatial straight line that meets the requirements is finally obtained, and finally, by data substitution, the future inclination state of the pole tower is predicted and calculated, and finally, before the disaster occurs, the prediction result is reported to the data center, and the pole tower is maintained according to the prediction result, so as to avoid the occurrence of disasters; the present invention has the advantages of being based on a spatial linear fitting algorithm and adopting a limiting filtering method to realize the prediction of the future state of the pole tower.

Claims

1. A tower inclination prediction method based on linear fitting algorithm and limiting filtering, characterized in that: The method comprises the following steps: Step 1: Start Beidou positioning, connect to the differential service platform, perform differential positioning, obtain a positioning result, and then power off the positioning module to reduce power consumption; Step 2: Perform difference processing based on the number of historical records; Step 3: Use longitude, latitude, and time as the three coordinate axes of the spatial rectangular coordinate system x, y, and z, place the coordinate points of the historical records into the coordinate axes, and calculate the fitting spatial straight line equation through the least squares method of the spatial straight line; Step 4: Use the optimized limiting filter algorithm to remove historical data caused by the instability of the differential service platform or the interference of the high-voltage line magnetic field; Step 5: Store the results of this fitting, and predict the future inclination state of the tower based on the result model of this fitting, and report the prediction results.

2. A tower inclination prediction method based on linear fitting algorithm and limiting filtering as claimed in claim 1, characterized in that: The specific step 2 is: when the number of historical records is less than a certain value, record the current positioning result, wait for a certain time interval, and then return to step 1; when the number of historical records is greater than a certain value, store the current positioning result, take out the historical records, and enter step 3.

3. The tower inclination prediction method based on linear fitting algorithm and limiting filtering as claimed in claim 1, characterized in that: The step 3 specifically includes the following steps: Step 3.1: First, the point-to-point equation of the space line: Among them, m, n, p are a set of direction numbers of the line; x0, y0, z0 are the three-axis coordinates of any point on the line; perform equivalent transformations to obtain the following expressions for x, z and y, z: in, Step 3.2: Consider expression (1) and expression (2) as two planes in a three-dimensional coordinate system, and then convert the spatial straight line equation to be calculated into the intersection of the two planes. Then, fitting the spatial straight line equation is converted into fitting the two planes represented by expression (1) and expression (2), that is, fitting expression (1) by the projection of the coordinate point on the xz plane, and fitting expression (2) by the projection of the coordinate point on the yz plane. Step 3.3: For the two planes of the above formula (1) and expression (2), calculate k1 and k2 respectively by the least square method; that is: in, is the average value of the x-coordinate of the data; is the average value of the y coordinates of the data; z is the average value of the z coordinates of the data; Step 3.4: Finally, substitute the obtained k1 and k2 back into expressions (1) and (2), substitute the coordinate points to calculate b1 and b2, and determine the spatial straight line equation of the fitted spatial rectangular coordinate system; Step 3.5: Based on the coordinate points and the calculated straight line equation of the spatial rectangular coordinate system, use the distance formula from a point in space to a straight line to calculate the distance from each positioning point to the fitted straight line.

4. The tower inclination prediction method based on linear fitting algorithm and limiting filtering as claimed in claim 1, characterized in that: The specific step 4 is as follows: if there is a distance from a positioning point to a straight line that exceeds a set value, an amplitude filtering algorithm is used to filter and delete the points that exceed the set value, and then the remaining data is used to return to step 1; if there is no distance from a positioning point to a straight line that exceeds the set value, step 5 is entered.

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