Staking data acquisition and transmission method based on Beidou RTK measurement technology

Abstract

The present invention relates to the field of data filtering technology, and specifically to a method for collecting and transmitting staking data based on Beidou RTK measurement technology; determining whether the current staking point is an abnormal point according to the position data of the staking point and the difference characteristics between the position data of the staking point and the position prediction value; if it is an abnormal point, obtaining the predicted difference characteristic value and the difference sequence according to the position data and the position prediction value of the staking point; obtaining the humidity influence value according to the humidity change characteristics of the historical staking point, the humidity data sequence of the historical staking point and the change trend of the difference sequence; adjusting the matrix parameters according to the humidity influence value to obtain the initial matrix parameters. The present invention optimizes the initial matrix parameters according to the predicted difference characteristic value and humidity difference characteristics of the current staking point and the adjacent historical staking point, obtains an adaptive Kalman filter and performs filtering, transmits the filtered position data, and improves the accuracy of the staking point position measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of data filtering, and in particular to a method for collecting and transmitting layout data based on Beidou RTK measurement technology. Background Art

[0002] Beidou RTK real-time dynamic positioning is a technology that uses the Beidou satellite navigation system for high-precision positioning. It uses differential correction technology to achieve centimeter-level positioning accuracy and is widely used in surveying, mapping, engineering construction and other fields. In actual measurement, it is necessary to obtain the position data of different stakeout points measured by Beidou RTK through a handheld mobile station. In order to make the position data closer to the true value and reduce noise interference, the existing Kalman filter can be used for filtering.

[0003] However, in the process of collecting the staked position data, abnormal weather may be encountered, such as rain and thunderstorms, which will affect the humidity in the troposphere, resulting in low accuracy of Beidou RTK measurement. Since the accuracy of the position data collected at different staked points will decrease, the predicted value obtained by the position data of the historical staked points will deviate, which will eventually lead to a decrease in the filtering effect of the Kalman filter, making the accuracy of the staked point position data obtained in abnormal weather low. Summary of the invention

[0004] In order to solve the technical problem that the filtering effect of the position data of the stakeout point using the Kalman filter in abnormal weather is poor, the object of the present invention is to provide a stakeout data acquisition and transmission method based on Beidou RTK measurement technology, and the technical scheme adopted is as follows:

[0005] Obtain humidity data sequences of different stakeout points and position measurement sequences measured by Beidou RTK; obtain position prediction values ​​of different stakeout points through a Kalman filter according to the position measurement sequences;

[0006] Obtaining the degree of measurement anomaly according to the difference characteristics of the position data of the current staked point and the historical staked points in the position measurement sequence, and the difference characteristics of the position data of the current staked point and the position prediction value; determining whether the current staked point is an abnormal point according to the degree of measurement anomaly;

[0007] If the current stakeout point is an abnormal point, a first humidity influence is obtained according to the humidity change characteristics of the historical stakeout points in the humidity data sequence; a predicted difference characteristic value and a difference sequence are obtained according to the difference characteristics of the position data and the position prediction value of the stakeout point; a second humidity influence is obtained according to the difference characteristics of the change trends of the humidity data sequence and the difference sequence corresponding to the historical stakeout point; a humidity influence value of the current stakeout point is obtained according to the first humidity influence and the second humidity influence; and the matrix parameters of the Kalman filter are adjusted according to the humidity influence value to obtain initial matrix parameters;

[0008] The initial matrix parameters are optimized according to the difference characteristics of the predicted difference eigenvalues ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics to obtain an adaptive Kalman filter; the position data of the current stakeout point is filtered according to the adaptive Kalman filter, and the filtered position data is transmitted.

[0009] Furthermore, the step of obtaining the degree of measurement abnormality according to the difference characteristics of the position data of the current staked point and the historical staked points in the position measurement sequence and the difference characteristics of the position data of the current staked point and the position prediction value includes:

[0010] According to the position measurement sequence, the sum of the absolute values ​​of the difference between the three-axial position data of the current staked point and any historical staked point is calculated to obtain a position difference characteristic value; the inverse of the difference between the acquisition position of the current staked point and any historical staked point is calculated to obtain a distance weight; the average value of the product of the position difference characteristic value and the distance weight is calculated to obtain a weighted average measurement difference value between the current staked point and the corresponding historical staked point; the sum of the absolute values ​​of the difference between the three-axial position data of the current staked point and the position prediction value is calculated to obtain a prediction deviation value; the sum of the weighted average measurement difference value and the prediction deviation value is calculated to obtain the measurement abnormality degree of the current staked point.

[0011] Further, the step of determining whether the current stakeout point is an abnormal point according to the measurement abnormality degree includes:

[0012] When the measurement abnormality exceeds a preset abnormality threshold, the current stakeout point is an abnormal point.

[0013] Furthermore, the step of obtaining a first humidity influence degree according to the humidity change characteristics of the historical sampling points in the humidity data sequence includes:

[0014] In the stakeout point range of the current stakeout point and the corresponding historical stakeout point, the sum of the humidity differences between any stakeout point and the previous historical stakeout point is calculated according to the humidity data sequence and normalized to obtain the first humidity influence degree of the current stakeout point.

[0015] Furthermore, the step of obtaining the predicted difference feature value and the difference sequence according to the difference features of the position data and the position prediction value of the stakeout point includes:

[0016] The difference between the position data of the staked point and the corresponding position prediction value is calculated to obtain the prediction difference characteristic value of the staked point; the corresponding prediction difference characteristic values ​​are sorted according to the order of the historical staked points to obtain the difference sequence of the historical staked points.

[0017] Furthermore, the step of obtaining the second humidity influence degree according to the difference characteristics of the change trends of the humidity data sequence and the difference sequence corresponding to the historical sampling points includes:

[0018] The average value of the Pearson correlation coefficients of the humidity data sequence corresponding to the historical stakeout point and the difference sequences of the three axes is calculated and normalized to obtain the second humidity influence degree of the current stakeout point.

[0019] Furthermore, the step of obtaining the humidity influence value of the current stakeout point according to the first humidity influence degree and the second humidity influence degree includes:

[0020] An average value of the first humidity influence degree and the second humidity influence degree is calculated to obtain a humidity influence value of the current stakeout point.

[0021] Furthermore, the step of adjusting the matrix parameters of the Kalman filter according to the humidity impact value to obtain the initial matrix parameters includes:

[0022] The product of the humidity influence value and the preset coefficient is calculated to obtain the adjustment factor; the sum of the adjustment factor and a constant 1 is calculated to obtain the adjustment coefficient; the product of the adjustment coefficient and the matrix parameter is calculated to obtain the initial matrix parameter of the current stakeout point.

[0023] Furthermore, the step of optimizing the initial matrix parameters according to the difference characteristics of the predicted difference characteristic values ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics to obtain the adaptive Kalman filter includes:

[0024] The sum of the difference values ​​of the predicted difference eigenvalues ​​of the three axes between the current stakeout point and the previous historical stakeout point is calculated and normalized to obtain the predicted difference degree; the humidity difference between the current stakeout point and the previous historical stakeout point is calculated and normalized to obtain the humidity difference eigenvalue; the average value of the predicted difference degree and the humidity difference eigenvalue is calculated to obtain the optimization factor; the sum of the optimization factor and a constant 1 is calculated to obtain the optimization coefficient; the product of the optimization coefficient and the initial matrix parameters is calculated to obtain the final matrix parameters of the current stakeout point; the final matrix parameters are used to replace the original matrix parameters in the Kalman filter to obtain the adaptive Kalman filter.

[0025] The present invention has the following beneficial effects:

[0026] In the present invention, obtaining the position prediction value of the stakeout point can be used to analyze the difference characteristics of the position data and the position prediction value measured at the stakeout point, so as to determine the abnormality degree of the stakeout point and the adjustment degree of the Kalman filter; obtaining the measured abnormality degree can determine whether the current stakeout point is an abnormal point, and then determine whether the filtering process needs to be adjusted. Obtaining the first humidity influence degree can reflect the reason why the current stakeout point becomes an abnormal point; obtaining the difference characteristic value and the difference sequence can be used to further analyze the reason why the current stakeout point becomes an abnormal point; obtaining the second humidity influence degree can reflect the reason why the current stakeout point becomes an abnormal point according to the change correlation characteristics of the humidity change characteristics and the difference sequence. Then, obtaining the humidity influence value according to the first humidity influence degree and the second humidity influence degree can determine the adjustment degree of the matrix parameters in the filtering process; obtaining the initial matrix parameters can preliminarily improve the accuracy of the Kalman filter measuring the position data of the stakeout point under abnormal weather; obtaining the adaptive Kalman filter can further improve the filtering accuracy of the position data measured at the stakeout point, so that the filtering result is closer to the actual value of the stakeout point position, and finally improves the accuracy of the stakeout point position measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A flow chart of a method for collecting and transmitting layout data based on Beidou RTK measurement technology is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a method for collecting and transmitting layout data based on Beidou RTK measurement technology proposed by the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0031] The following is a detailed description of a specific scheme of a method for collecting and transmitting layout data based on Beidou RTK measurement technology provided by the present invention in conjunction with the accompanying drawings.

[0032] See also Figure 1 , which shows a flow chart of a method for collecting and transmitting laying-out data based on Beidou RTK measurement technology provided by an embodiment of the present invention, the method comprising the following steps:

[0033] Step S1, obtaining humidity data sequences of different stakeout points and position measurement sequences of Beidou RTK measurement; obtaining position prediction values ​​of different stakeout points through a Kalman filter according to the position measurement sequence.

[0034] In an embodiment of the present invention, the implementation scenario is to filter the position data of the stakeout points measured by Beidou RTK to improve the accuracy of the position measurement of the stakeout points. First, the humidity data sequence of different stakeout points and the position measurement sequence measured by Beidou RTK are obtained, and the position data of the stakeout points measured by Beidou RTK are measured by a handheld mobile station. The position data of each stakeout point includes data of three axes, namely, horizontal, vertical position and elevation. The handheld mobile station obtains position data once each time it moves to a stakeout point, thereby constructing a three-dimensional position measurement sequence based on the position data of different stakeout points; a humidity sensor is installed on the handheld mobile station, and the humidity data at each stakeout point is collected at the same time to construct a humidity data sequence. It should be noted that the overall stakeout points are used to describe the building area, so the distance between adjacent stakeout points in the position measurement sequence is close, and the position data of adjacent stakeout points are relatively similar. Abnormal weather such as rain and thunderstorms can cause high humidity in the troposphere, resulting in low accuracy of the position data of the stakeout points. The Kalman filter filters the position data of the current stakeout point based on the position data of the historical stakeout points, and the accuracy of the position data of consecutive stakeout points is low, resulting in the filtering result of the current stakeout point being difficult to approach the actual position data. Therefore, it is necessary to improve the position data filtering process under abnormal weather conditions to improve the accuracy of measurement.

[0035] First, the position prediction values ​​of different stakeout points are obtained through the Kalman filter according to the position measurement sequence. It should be noted that the Kalman filter belongs to the existing technology, and the specific calculation steps of the position prediction value are not repeated; the position prediction value is used for subsequent analysis of abnormal characteristics of the position data.

[0036] Step S2, obtaining the degree of measurement anomaly according to the difference characteristics between the position data of the current staked point and the historical staked points in the position measurement sequence, and the difference characteristics between the position data of the current staked point and the position prediction value; and determining whether the current staked point is an abnormal point according to the degree of measurement anomaly.

[0037] Because it is necessary to optimize the filtering process when the accuracy of the position data is low due to abnormal weather, it is necessary to determine whether the position data of the staked point is abnormal; then, the degree of measurement abnormality is obtained according to the difference characteristics of the position data of the current staked point and the historical staked points in the position measurement sequence, and the difference characteristics of the position data of the current staked point and the position prediction value. Preferably, in the embodiment of the present invention, the step of obtaining the degree of abnormality includes: calculating the sum of the absolute values ​​of the difference between the position data of the current staked point and any historical staked point in three axes according to the position measurement sequence to obtain the position difference characteristic value, and the three axes are the horizontal coordinate, the vertical coordinate and the elevation of the staked point in the position measurement sequence respectively; it should be noted that the current staked point refers to the staked point that currently needs to be filtered, and the historical staked point refers to the staked point in the position measurement sequence that is connected to the current staked point and has obtained position data. In the embodiment of the present invention, the number of historical staked points is 10, that is, the first 10 staked points in the position measurement sequence that have obtained position data adjacent to the current staked point, and the current staked point is any staked point after the 10th staked point in the position measurement sequence, which can be determined by the implementer according to the implementation scenario. Since the actual positions of the current staked point and the historical staked point are close, the position data are similar. The larger the position difference characteristic value, the greater the difference between the position data of the current staked point and any historical staked point; if the position difference characteristic value of the current staked point and all historical staked points is larger, the position data of the current staked point is more likely to be measured incorrectly. Calculate the inverse of the difference between the acquisition position of the current staked point and any historical staked point to obtain the distance weight; the acquisition position refers to the position of different staked points in the position measurement sequence. The closer the acquisition position between the two staked points is, the closer the actual positions of the two staked points are; the closer any historical staked point is to the current staked point, the greater the distance weight, and the position difference characteristic value of any historical staked point can better reflect whether the position data of the current staked point is abnormal. Calculate the average value of the product of the position difference characteristic value and the distance weight to obtain the weighted average measurement difference value between the current stakeout point and the corresponding historical stakeout point; the larger the weighted average measurement difference value, the greater the difference in position data between the current stakeout point and the corresponding historical stakeout point, and the more abnormal the position data measured by the current stakeout point is.

[0038] Furthermore, the sum of the absolute values ​​of the difference between the three-axis position data of the current stakeout point and the position prediction value is calculated to obtain the prediction deviation value; the position prediction value of the current stakeout point is obtained by Kalman filtering through the position data of the historical stakeout points. The larger the prediction deviation value, the more abnormal the position data of the current stakeout point is. Calculate the sum of the weighted average measurement difference value and the prediction deviation value to obtain the measurement abnormality degree of the current stakeout point; the greater the measurement abnormality degree, the less accurate the position data of the current stakeout point is. The formula for obtaining the measurement abnormality degree includes:

[0039]

[0040] Where W represents the measurement anomaly degree of the current stakeout point, D represents the absolute value of the difference between the position data of the horizontal axis of the current stakeout point and the position prediction value, F represents the absolute value of the difference between the position data of the vertical axis of the current stakeout point and the position prediction value, G represents the absolute value of the difference between the position data of the elevation of the current stakeout point and the position prediction value, Indicates the predicted deviation value of the current stakeout point; N indicates the number of historical stakeout points. Indicates the distance weight of the nth historical stakeout point, Indicates the absolute value of the difference between the nth historical stakeout point and the horizontal axis position data of the current stakeout point. Indicates the absolute value of the difference between the vertical axis position data of the nth historical stakeout point and the current stakeout point. Indicates the absolute value of the difference between the nth historical stakeout point and the current stakeout point elevation. represents the position difference eigenvalue, Represents the weighted average measurement difference value.

[0041] The greater the measurement anomaly of the current staked point, the more likely the position data of the current staked point is inaccurate. Therefore, whether the current staked point is an abnormal point is determined based on the measurement anomaly, specifically including: when the measurement anomaly exceeds the preset abnormal threshold, the current staked point is an abnormal point; the preset abnormal threshold in the embodiment of the present invention is 0.8, and the implementer can determine the preset abnormal threshold according to the implementation scenario. If the current staked point is not an abnormal point, it means that the position data of the staked point is normal, and accurate filtering results can be obtained without optimizing the filtering process.

[0042] Step S3, if the current stakeout point is an abnormal point, obtain the first humidity influence degree according to the humidity change characteristics of the historical stakeout points in the humidity data sequence; obtain the predicted difference characteristic value and the difference sequence according to the difference characteristics of the position data and the position prediction value of the stakeout point; obtain the second humidity influence degree according to the difference characteristics of the change trends of the humidity data sequence and the difference sequence corresponding to the historical stakeout point; obtain the humidity influence value of the current stakeout point according to the first humidity influence degree and the second humidity influence degree; adjust the matrix parameters of the Kalman filter according to the humidity influence value to obtain the initial matrix parameters.

[0043] Since it is impossible to ensure that the terrain of the geographical location of the staked point is completely regular during the measurement of the position data of the staked point, and there may be undulating terrain or obstructions, the horizontal and vertical positions or elevation positions of adjacent staked points due to the geographical environment may be too different in the acquisition position measurement sequence, so that the current staked point is identified as an abnormal point. Therefore, it is necessary to further analyze the cause of the abnormality. If the position data abnormality is caused by abnormal weather, the filtering process needs to be improved. If the weather is abnormal, the humidity in the air will gradually increase; therefore, the first humidity influence degree is obtained according to the humidity change characteristics of the historical staked points in the humidity data sequence. Preferably, in an embodiment of the present invention, the step of obtaining the first humidity influence degree includes: in the staked point range of the current staked point and the corresponding historical staked point, the sum of the humidity difference between any staked point and the previous historical staked point is calculated according to the humidity data sequence and normalized to obtain the first humidity influence degree of the current staked point. When the first humidity influence is greater, it means that within the stakeout point range between the current stakeout point and the corresponding historical stakeout point, as the stakeout point position data measurement proceeds, the humidity value increases, and the inaccurate position data of the current stakeout point is more likely to be caused by the high humidity in the troposphere, which leads to errors in Beidou RTK measurement.

[0044] Furthermore, the abnormal points caused by the geographical environment are usually limited by the terrain and cause a large displacement of adjacent staked points, which is random and has no correlation with the humidity data. The increase in humidity caused by weather changes such as rainfall will gradually affect the position data measurement of the staked points. As the humidity increases, the position data measured at the staked points will gradually deviate from the actual position data. Therefore, the difference between the position data and the position prediction value of the staked points is getting bigger and bigger, showing certain correlation characteristics with the humidity increase process. Therefore, according to the difference characteristics of the position data and the position prediction value of the staked points, the predicted difference characteristic value and the difference sequence are obtained. Preferably, in an embodiment of the present invention, the step of obtaining the predicted difference characteristic value and the difference sequence includes: calculating the difference between the position data of the staked point and the corresponding position prediction value to obtain the predicted difference characteristic value of the staked point; sorting the corresponding predicted difference characteristic values ​​according to the order of the historical staked points to obtain the difference sequence of the historical staked points, each axis corresponds to a difference sequence, and a total of three different axis difference sequences are obtained. Because the position prediction value is calculated by the Kalman filter based on the position data of the previous staked point, the larger the predicted difference characteristic value, the greater the deviation between the position data of the historical staked point and the actual position data; and the difference sequence reflects the deviation characteristics between the position data measured by different historical staked points and the actual position data. After obtaining the difference sequence, the abnormal cause of the current staked point can be analyzed according to the degree of correlation between the difference sequence and the humidity change; therefore, the second humidity influence degree is obtained according to the difference characteristics of the change trend of the humidity data sequence corresponding to the historical staked point and the difference sequence.

[0045] Preferably, in an embodiment of the present invention, the step of obtaining the second humidity influence degree includes: calculating the average value of the Pearson correlation coefficient of the humidity data sequence corresponding to the historical stakeout point and the difference sequence of the three axes and normalizing them to obtain the second humidity influence degree of the current stakeout point. The humidity data sequence corresponding to the historical stakeout point refers to a fragment of the humidity value of the historical stakeout point intercepted from the entire humidity data sequence; the calculation of the Pearson correlation coefficient belongs to the prior art, and the specific calculation steps are no longer repeated. The more correlated the change trends of the two sequences are, the less close the Pearson correlation coefficient is to 0; the less close the change trends of the two sequences are, the closer the Pearson correlation coefficient is to 0. Therefore, when the second humidity influence degree is closer to 1, it means that the change trends of the humidity data sequence corresponding to the historical stakeout point and the difference sequence are more correlated, and the inaccurate position data measured at the current stakeout point is more due to the increase in humidity.

[0046] Furthermore, if the humidity is getting higher and higher, and the correlation between the difference sequence and the humidity data sequence is stronger, the first humidity influence degree and the second humidity influence degree are larger, and the abnormal reason of the current stakeout point is more likely to be the increase in humidity caused by weather changes; therefore, the humidity influence value of the current stakeout point is obtained according to the first humidity influence degree and the second humidity influence degree. Preferably, in an embodiment of the present invention, the step of obtaining the humidity influence value includes: calculating the average of the first humidity influence degree and the second humidity influence degree to obtain the humidity influence value of the current stakeout point. When the humidity influence value is larger, it means that the reason for the inaccurate position data measured at the current stakeout point is more likely to be due to the increase in humidity caused by weather changes, so it is necessary to improve the filtering process of the current stakeout point; when the humidity influence value is smaller, it is more likely that the terrain factors cause the current stakeout point to be recognized as an abnormal point, and there is no need to improve the filtering process.

[0047] There is an observation noise covariance matrix parameter in the Kalman filter, which is used to represent the uncertainty or noise in the observation data. The larger the matrix parameter is, the greater the uncertainty of the position data measured at the current stakeout point is, which means that the filter has a lower degree of trust in the position data of the current stakeout point, so that the filter is more dependent on the prediction of the historical position data. Therefore, the matrix parameters of the Kalman filter can be adjusted according to the humidity influence value to obtain the initial matrix parameters; preferably, in an embodiment of the present invention, the step of obtaining the initial matrix parameters includes: calculating the product of the humidity influence value and the preset coefficient to obtain the adjustment factor. In an embodiment of the present invention, the preset coefficient is 0.1, the purpose is to reduce the degree of regulation and avoid the matrix parameter being too large to cause the filtering result to deviate further from the actual value; calculating the sum of the adjustment factor and the constant 1 to obtain the adjustment coefficient; the larger the humidity influence value is, the larger the adjustment coefficient is, and the filtering process needs to consider the position data of the historical stakeout point more. Calculate the product of the adjustment coefficient and the matrix parameter to obtain the initial matrix parameter of the current stakeout point. It should be noted that the matrix parameter is the observation noise covariance matrix parameter in the Kalman filter, and its acquisition process belongs to the prior art, and the specific steps are not repeated.

[0048] Step S4, optimizing the initial matrix parameters according to the difference characteristics of the predicted difference eigenvalues ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics, and obtaining an adaptive Kalman filter; filtering the position data of the current stakeout point according to the adaptive Kalman filter, and transmitting the filtered position data.

[0049] After the initial adjustment of the matrix parameters, there may be an adjustment degree that causes inaccurate filtering results. Therefore, the initial matrix parameters are further adjusted according to the predicted difference eigenvalues ​​between the current stakeout point and the adjacent historical stakeout points. If the predicted difference eigenvalue of the current stakeout point is greater than the predicted difference eigenvalue of the adjacent historical stakeout point, it means that the adjustment degree is too small and the adjustment range needs to be increased. At the same time, if the humidity increases further, the adjustment range also needs to be increased. Therefore, the initial matrix parameters are optimized according to the difference characteristics of the predicted difference eigenvalues ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics to obtain an adaptive Kalman filter.

[0050] Preferably, in an embodiment of the present invention, the step of obtaining an adaptive Kalman filter includes: calculating the sum of the difference values ​​of the predicted difference eigenvalues ​​of the three axes between the current stakeout point and the previous historical stakeout point and normalizing them to obtain the predicted difference degree; when the predicted difference degree is larger, it means that the difference between the position data predicted by the filter and the measured position data increases more, and the initial matrix parameters need to be increased more. Calculating the humidity difference between the current stakeout point and the previous historical stakeout point and normalizing them to obtain the humidity difference eigenvalue; when the humidity difference eigenvalue is larger, it means that the humidity increases more, the more it affects the measurement of the position data of the stakeout point, the filtering process cannot trust the position data measured at the current stakeout point, and the value of the matrix parameter should be larger. Calculating the average value of the predicted difference degree and the humidity difference eigenvalue to obtain the optimization factor; when the value of the optimization factor is larger, the initial matrix parameters need to be further increased. Calculating the sum of the optimization factor and the constant 1 to obtain the optimization coefficient; calculating the product of the optimization coefficient and the initial matrix parameter to obtain the final matrix parameter of the current stakeout point; replacing the original matrix parameter in the Kalman filter with the final matrix parameter to obtain the adaptive Kalman filter. The adaptive Kalman filter can filter the position data measured at the current stakeout point more accurately when the humidity is high, so that the filtered position data is closer to the actual value. Finally, the position data of the current stakeout point is filtered according to the adaptive Kalman filter, and the filtered position data is transmitted. It should be noted that the filtering by the adaptive Kalman filter belongs to the prior art, and the specific steps are not repeated; the filtered position data can be closer to the actual position data, which improves the accuracy of the position measurement of the stakeout point.

[0051] In summary, the embodiment of the present invention provides a method for collecting and transmitting staking data based on Beidou RTK measurement technology; determine whether the current staking point is an abnormal point according to the position data of the staking point and the difference characteristics between the position data of the staking point and the position prediction value; if it is an abnormal point, obtain the predicted difference characteristic value and the difference sequence according to the position data and the position prediction value of the staking point; obtain the humidity influence value according to the humidity change characteristics of the historical staking point, the humidity data sequence of the historical staking point and the change trend of the difference sequence; adjust the matrix parameters according to the humidity influence value to obtain the initial matrix parameters. The present invention optimizes the initial matrix parameters according to the predicted difference characteristic value and humidity difference characteristics of the current staking point and the adjacent historical staking points, obtains an adaptive Kalman filter and performs filtering, transmits the filtered position data, and improves the accuracy of the staking point position measurement.

[0052] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for collecting and transmitting lofting data based on Beidou RTK measurement technology, characterized in that: The method comprises the following steps: Obtain humidity data sequences of different stakeout points and position measurement sequences measured by Beidou RTK; obtain position prediction values ​​of different stakeout points through a Kalman filter according to the position measurement sequences; Obtaining the degree of measurement anomaly according to the difference characteristics of the position data of the current staked point and the historical staked points in the position measurement sequence, and the difference characteristics of the position data of the current staked point and the position prediction value; determining whether the current staked point is an abnormal point according to the degree of measurement anomaly; If the current stakeout point is an abnormal point, a first humidity influence is obtained according to the humidity change characteristics of the historical stakeout points in the humidity data sequence; a predicted difference characteristic value and a difference sequence are obtained according to the difference characteristics of the position data and the position prediction value of the stakeout point; a second humidity influence is obtained according to the difference characteristics of the change trends of the humidity data sequence and the difference sequence corresponding to the historical stakeout point; a humidity influence value of the current stakeout point is obtained according to the first humidity influence and the second humidity influence; and the matrix parameters of the Kalman filter are adjusted according to the humidity influence value to obtain initial matrix parameters; The initial matrix parameters are optimized according to the difference characteristics of the predicted difference eigenvalues ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics to obtain an adaptive Kalman filter; the position data of the current stakeout point is filtered according to the adaptive Kalman filter, and the filtered position data is transmitted.

2. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of obtaining the degree of measurement abnormality according to the difference characteristics of the position data of the current staked point and the historical staked points in the position measurement sequence, and the difference characteristics of the position data of the current staked point and the position prediction value comprises: According to the position measurement sequence, the sum of the absolute values ​​of the difference between the three-axial position data of the current staked point and any historical staked point is calculated to obtain a position difference characteristic value; the inverse of the difference between the acquisition position of the current staked point and any historical staked point is calculated to obtain a distance weight; the average value of the product of the position difference characteristic value and the distance weight is calculated to obtain a weighted average measurement difference value between the current staked point and the corresponding historical staked point; the sum of the absolute values ​​of the difference between the three-axial position data of the current staked point and the position prediction value is calculated to obtain a prediction deviation value; the sum of the weighted average measurement difference value and the prediction deviation value is calculated to obtain the measurement abnormality degree of the current staked point.

3. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of determining whether the current stakeout point is an abnormal point according to the measurement abnormality degree comprises: When the measurement abnormality exceeds a preset abnormality threshold, the current stakeout point is an abnormal point.

4. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of obtaining the first humidity influence degree according to the humidity change characteristics of the historical sampling points in the humidity data sequence comprises: In the stakeout point range of the current stakeout point and the corresponding historical stakeout point, the sum of the humidity differences between any stakeout point and the previous historical stakeout point is calculated according to the humidity data sequence and normalized to obtain the first humidity influence degree of the current stakeout point.

5. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of obtaining predicted difference feature values ​​and difference sequences according to the difference features between the position data of the set-out points and the position prediction values ​​comprises: The difference between the position data of the staked point and the corresponding position prediction value is calculated to obtain the prediction difference characteristic value of the staked point; the corresponding prediction difference characteristic values ​​are sorted according to the order of the historical staked points to obtain the difference sequence of the historical staked points.

6. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of obtaining the second humidity influence degree according to the difference characteristics of the change trends of the humidity data sequence and the difference sequence corresponding to the historical sampling points comprises: The average value of the Pearson correlation coefficients of the humidity data sequence corresponding to the historical stakeout point and the difference sequences of the three axes is calculated and normalized to obtain the second humidity influence degree of the current stakeout point.

7. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of obtaining the humidity influence value of the current setting out point according to the first humidity influence degree and the second humidity influence degree comprises: An average value of the first humidity influence degree and the second humidity influence degree is calculated to obtain a humidity influence value of the current stakeout point.

8. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of adjusting the matrix parameters of the Kalman filter according to the humidity impact value to obtain the initial matrix parameters comprises: The product of the humidity influence value and the preset coefficient is calculated to obtain the adjustment factor; the sum of the adjustment factor and a constant 1 is calculated to obtain the adjustment coefficient; the product of the adjustment coefficient and the matrix parameter is calculated to obtain the initial matrix parameter of the current stakeout point.

9. The method for collecting and transmitting lofting data based on Beidou RTK measurement technology according to claim 1, characterized in that: The step of optimizing the initial matrix parameters according to the difference characteristics of the predicted difference characteristic values ​​between the current stakeout point and the adjacent historical stakeout points and the humidity difference characteristics to obtain the adaptive Kalman filter comprises: The sum of the difference values ​​of the predicted difference eigenvalues ​​of the three axes between the current stakeout point and the previous historical stakeout point is calculated and normalized to obtain the predicted difference degree; the humidity difference between the current stakeout point and the previous historical stakeout point is calculated and normalized to obtain the humidity difference eigenvalue; the average value of the predicted difference degree and the humidity difference eigenvalue is calculated to obtain the optimization factor; the sum of the optimization factor and a constant 1 is calculated to obtain the optimization coefficient; the product of the optimization coefficient and the initial matrix parameters is calculated to obtain the final matrix parameters of the current stakeout point; the final matrix parameters are used to replace the original matrix parameters in the Kalman filter to obtain the adaptive Kalman filter.

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