Banded area geoid refinement method and device, electronic equipment and medium

Abstract

The invention relates to the field of elevation measurement of strip-shaped areas of line engineering, and provides a strip-shaped area geoid refinement method and device, electronic equipment and a medium, and the method comprises the following steps: determining actual elevation abnormal values of a plurality of measurement control points on a line center line of a pipeline; determining a model elevation abnormal value of each measurement control point by using an EGM2008 gravity field model, and further determining an elevation abnormal difference of each measurement control point; determining the elevation abnormal difference of a plurality of interpolation points of each measurement control point on the line center line; determining the elevation abnormal difference of a plurality of outer measurement points on two sides of the line center line; determining refined elevation abnormal values of the measurement control points, the interpolation points and the outer measurement points; and generating elevation abnormal value grid data of the target banded region, and further generating a geoid-like model. The problems of high cost, high difficulty, long period, low precision and poor result consistency of a traditional elevation measurement method are solved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of elevation measurement for strip areas in line projects, and particularly to a method and device for refining the quasi-geoid of a strip area, an electronic device, and a medium. Background Art

[0002] Long-distance pipeline projects have long routes and large spans, with their route lengths often reaching thousands of kilometers, but the required construction periods are generally short. To complete the elevation control measurement work for long-distance pipelines within a limited construction period, in addition to the need for sufficient investment in human and equipment resources, it is also necessary to adopt elevation measurement methods and corresponding technologies that adapt to the characteristics of long-distance pipeline projects.

[0003] At present, the positioning measurement method of the Global Navigation Satellite System (GNSS) has been widely used. Especially with the establishment of the Continuously Operating Reference Stations (CORS) network of satellite navigation positioning, it has become very easy to obtain accurate geodetic heights. Therefore, if a high-precision quasi-geoid model of the long-distance pipeline survey area can be obtained, the normal height of the points to be measured on the long-distance pipeline can be directly obtained through calculation.

[0004] In the existing measurement methods for long-distance oil and gas pipelines, elevation control measurement generally adopts methods such as GNSS elevation fitting measurement, total station trigonometric leveling measurement, and GNSS Real Time Kinematic (RTK) elevation measurement to complete. The elevation control network is usually arranged in a hierarchical manner and controlled step by step. Before topographic map measurement, elevation control points that meet the accuracy requirements must be arranged in advance. To ensure the accuracy of the results, whether it is the GNSS elevation fitting measurement method or the total station trigonometric leveling measurement method, the requirements for the number and distribution of elevation connection points are relatively strict. Among them, the total station trigonometric leveling measurement method also requires the use of reciprocal observation methods, which are time-consuming and laborious. In hilly and mountainous areas, when the number of known elevation connection points is insufficient or the distribution is not good, it is also necessary to use the leveling measurement method to transfer the elevation, which makes the entire elevation measurement work more difficult.

[0005] Generally speaking, the existing measurement methods for long-distance pipelines have the following disadvantages in elevation measurement: 1. Poor result consistency: In line projects, for controlled and other important monomers, line surveys are generally carried out in advance. For elevation control points arranged at different times, their elevations may have systematic inconsistencies, resulting in the inability to connect the elevations on the later drawings, which has an important impact on design and construction.

[0006] 2. Low efficiency: When conducting leveling or trigonometric leveling surveys in mountainous areas, the operation is difficult and time-consuming, resulting in a long construction period and low efficiency.

[0007] 3. High cost: The density of traditional elevation control points is generally 5 km / pair, with a large layout density. The burial and measurement of on-site reference points require a large amount of materials, labor, and other related resource costs.

[0008] 4. Low accuracy: When using the GNSS elevation fitting measurement method, it is necessary to perform elevation fitting on the obtained geodetic height. If the selected fitting points and fitting methods are inappropriate, the fitting accuracy will be greatly reduced, making it difficult to meet the requirements of relevant specifications.

[0009] 5. Difficult to maintain: Traditional elevation measurement methods rely on on-site elevation control reference points. The cycle from the design to the construction of long-distance pipelines is relatively long, generally up to several years. In addition, the on-site buried elevation control reference points are extremely vulnerable to damage due to economic construction activities and other construction impacts. When in use, the damaged elevation control reference points need to be re-buried and measured, making maintenance extremely difficult. Summary of the Invention

[0010] The present disclosure aims to at least solve one of the problems existing in the prior art, and provides a method and device for refining the quasi-geoid of a strip area, an electronic device, and a medium.

[0011] In one aspect of the present disclosure, a method for refining the quasi-geoid of a strip area is provided, and the method includes: Select a number of measurement control points from the center line of the pipeline, and respectively determine the actual height anomalies of each of the measurement control points; wherein, each of the measurement control points is evenly distributed along the center line and conforms to the terrain change trend of the center line; Using the EGM2008 gravity field model, respectively determine the model height anomalies of each of the measurement control points; According to the actual height anomaly and the model height anomaly, determine the height anomaly difference corresponding to each of the measurement control points; According to the terrain change trend, select a number of interpolation points of each of the measurement control points from the center line at a first preset distance, and determine the height anomaly difference corresponding to each of the interpolation points; Select a number of outer measurement points within a second preset distance on both sides of the center line, and determine the height anomaly difference corresponding to each of the outer measurement points; Using the EGM2008 gravity field model, determine the model height anomalies corresponding to each of the interpolation points and each of the outer measurement points; Add the height anomaly differences corresponding to each of the measurement control points, each of the interpolation points, and each of the external measurement points to the corresponding model height anomaly values to obtain the refined height anomaly values corresponding to each of the measurement control points, each of the interpolation points, and each of the external measurement points; Generate grid data of height anomaly values for the target strip area where the center line of the line is located based on the refined height anomaly values; Generate a quasi-geoid model for the target strip area based on the grid data of height anomaly values.

[0012] Optionally, the step of separately determining the actual height anomaly values of each of the measurement control points includes: Obtain the geodetic height and normal height of each of the measurement control points; Subtract the normal height of each of the measurement control points from its geodetic height to obtain the actual height anomaly value of each of the measurement control points.

[0013] Optionally, the step of determining the height anomaly differences corresponding to each of the interpolation points includes: Adopt a quadratic polynomial moving curve fitting algorithm and a least squares algorithm to fit the height anomaly differences corresponding to each of the interpolation points by using the height anomaly differences corresponding to each of the measurement control points.

[0014] Optionally, the step of adopting a quadratic polynomial moving curve fitting algorithm and a least squares algorithm to fit the height anomaly differences corresponding to each of the interpolation points by using the height anomaly differences corresponding to each of the measurement control points includes: Select a number of the measurement control points from the upstream and downstream of each of the interpolation points as the respective reference points corresponding to each of the interpolation points, and calculate the mileage differences between each of the interpolation points and their corresponding respective reference points according to the following formula: ; wherein, represents the mileage difference between the interpolation point p and its corresponding i-th reference point; represents the mileage of the interpolation point p; represents the mileage of the i-th reference point corresponding to the interpolation point p; i represents the reference point number and i = 1, 2,..., t; t represents the total number of reference points corresponding to the interpolation point p; Determine the fitting curve, and represent the fitting curve as: ; wherein, represents the height anomaly difference matrix and , respectively represent the height anomaly differences of the 1st, 2nd,..., t-th reference points corresponding to the interpolation point p; represents the mileage difference matrix and , respectively represent the mileage differences between the interpolation point p and its corresponding 1st, 2nd, …, t-th reference points; , , respectively represent the first coefficient, the second coefficient, and the third coefficient; According to the following formula, calculate the weights of the respective reference points corresponding to each of the interpolation points: ; wherein, represents the weight of the i-th reference point corresponding to the interpolation point p; represents the natural logarithm; represents a constant; Let the coefficient matrix , and solve the coefficient matrix according to the following formula: ; wherein, represents the parameter matrix and , represents the weight matrix and ; Let , and substitute it together with the solved , , into the fitting curve to obtain the height anomaly difference corresponding to the interpolation point p; wherein, represents the mileage difference between the interpolation point p and itself.

[0015] Optionally, the determining the height anomaly differences respectively corresponding to each of the external measurement points includes: Project each of the external measurement points onto the center line of the line to obtain the corresponding projected points; Take the height anomaly differences corresponding to each of the projected points as the height anomaly differences corresponding to each of the external measurement points corresponding to the projected points.

[0016] Optionally, the generating the grid data of the height anomaly values of the target strip area where the center line of the line is located according to the refined height anomaly values includes: Perform grid division on the target strip area according to a preset grid point interval; Determine the smallest rectangle covering the target strip area to obtain the grid range corresponding to the target strip area; wherein, the longitude and latitude corresponding to each side of the smallest rectangle meet the preset accuracy requirements; Within the grid range, calculate the coordinates of each grid point in sequence and determine the refined height anomaly value corresponding to each grid point to obtain the grid data of the height anomaly values.

[0017] Optionally, after determining the height anomaly difference corresponding to each of the measurement control points according to the actual height anomaly value and the model height anomaly value, the method further includes: Select N continuously arranged measurement control points as a control point group, and fit them with a quadratic polynomial to obtain the corresponding expression: ; where represents the height anomaly difference of the measurement control point, represents the mileage difference between the measurement control point and the measurement reference point on the center line of the line, , , are all quadratic polynomial coefficients to be solved; Solve the quadratic polynomial coefficients , , and the residuals of the selected N measurement control points, and calculate the sum of squared residuals of the control point group according to the following formula: ; where represents the sum of squared residuals of the control point group, represents the number of the measurement control points in the control point group and , represents the number of the measurement control point, represents the th measurement control point's height anomaly difference, represents the th measurement control point's mileage difference from the measurement reference point; Update step: Remove the measurement control point arranged at the forefront from the current control point group, and add the next measurement control point after the measurement control point arranged at the end of the current control point group to the control point group to obtain a new control point group, and calculate the sum of squared residuals of the new control point group; Take the new control point group as the current control point group, and repeat the update step until no more measurement control points are added to the control point group; If the sum of squared residuals of all the control point groups where a certain measurement control point is located is higher than a target multiple of the standard deviation of the height anomaly differences of all the measurement control points, then remove this measurement control point.

[0018] Another aspect of the present disclosure provides a quasi-geoid refinement device for a strip-shaped area, and the device includes: The first determination module is used to select a number of measurement control points from the center line of the pipeline, and respectively determine the actual height anomaly values of each of the measurement control points; wherein, each of the measurement control points is evenly distributed along the center line and conforms to the terrain change trend of the center line; The second determination module is used to respectively determine the model height anomaly values of each of the measurement control points by using the EGM2008 gravity field model; The third determination module is used to determine the height anomaly differences respectively corresponding to each of the measurement control points according to the actual height anomaly values and the model height anomaly values; The fourth determination module is used to select a number of interpolation points of each of the measurement control points from the center line according to the terrain change trend at a first preset distance, and determine the height anomaly differences respectively corresponding to each of the interpolation points; The fifth determination module is used to select a number of external measurement points within a second preset distance on both sides of the center line, and determine the height anomaly differences respectively corresponding to each of the external measurement points; The sixth determination module is used to use the EGM2008 gravity field model to determine the model height anomaly values respectively corresponding to each of the interpolation points and each of the external measurement points; The refinement module is used to add the height anomaly differences respectively corresponding to each of the measurement control points, each of the interpolation points, and each of the external measurement points to the corresponding model height anomaly values to obtain the refined height anomaly values respectively corresponding to each of the measurement control points, each of the interpolation points, and each of the external measurement points; The first generation module is used to generate grid data of height anomaly values of the target strip area where the center line of the pipeline is located according to the refined height anomaly values; The second generation module is used to generate a quasi-geoid model of the target strip area according to the grid data of height anomaly values;

[0019] Optionally, the device further includes an elimination module; the elimination module is used to perform the following steps after the third determination module determines the height anomaly differences respectively corresponding to each of the measurement control points: Select N continuously arranged measurement control points as a control point group, and fit them with a quadratic polynomial to obtain the corresponding expression: ; wherein, represents the height anomaly difference of the measurement control point, represents the mileage difference between the measurement control point and the measurement reference point on the center line of the pipeline, , , are all quadratic polynomial coefficients to be solved; Solve the coefficients of the quadratic polynomial using the least squares method , , and the residuals of the selected N measurement control points, and calculate the sum of the squared residuals of the control point group according to the following formula: ; wherein, represents the sum of the squared residuals of the control point group, represents the number of the measurement control point in the control point group and , represents the number of the measurement control point, represents the th elevation anomaly difference of the measurement control point, represents the th mileage difference between the measurement control point and the measurement reference point; Update step: Remove the measurement control point arranged at the forefront from the current control point group, and add the next measurement control point of the measurement control point arranged at the end of the current control point group to the control point group to obtain a new control point group, and calculate the sum of the squared residuals of the new control point group; Take the new control point group as the current control point group, and repeat the update step until no more measurement control points are added to the control point group; If the sum of the squared residuals of all the control point groups where a certain measurement control point is located is higher than the target multiple of the standard deviation of the elevation anomaly differences of all the measurement control points, then remove the measurement control point.

[0020] Another aspect of the present disclosure provides an electronic device, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the strip area quasi-geoid refinement method described above.

[0021] Another aspect of the present disclosure provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the strip area quasi-geoid refinement method described above is implemented.

[0022] Compared with the prior art, the present disclosure provides an efficient, economical, safe and environmentally friendly elevation measurement solution for the pipeline design industry. By combining GNSS technology for elevation measurement, it solves the problems of high cost, great difficulty, long cycle, low accuracy and poor result consistency of traditional elevation measurement methods, improves the flexibility and efficiency of elevation measurement work, provides the availability of the whole life cycle of design, construction and operation through the finally obtained quasi-geoid model, can meet the more stringent standard requirements of the owner, effectively guarantees the quality of elevation results, and promotes the development of pipeline measurement towards the direction of no ground control benchmark. At the same time, the method for refining the quasi-geoid of a strip area provided by the implementation manner of the present disclosure also improves the elevation measurement accuracy, reduces the elevation measurement cost, can effectively enhance the competitiveness of enterprises and promote sustainable development. Brief Description of the Drawings

[0023] One or more embodiments are exemplarily illustrated by the pictures in the corresponding drawings. These exemplary illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the drawings in the figures do not constitute a scale limitation.

[0024] Figure 1 It is a flowchart of a method for refining the quasi-geoid of a strip area provided by an embodiment of the present disclosure; Figure 2 It is a schematic diagram of a fitting curve provided by another embodiment of the present disclosure; Figure 3 It is a flowchart of a method for refining the quasi-geoid of a strip area provided by another embodiment of the present disclosure; Figure 4 It is a schematic structural diagram of a device for refining the quasi-geoid of a strip area provided by another embodiment of the present disclosure; Figure 5 It is a schematic structural diagram of an electronic device provided by another embodiment of the present disclosure. Detailed Embodiments

[0025] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the following will elaborate on the various embodiments of the present disclosure with reference to the drawings. However, those of ordinary skill in the art can understand that in the various embodiments of the present disclosure, many technical details are provided to help the reader better understand the present disclosure. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions required to be protected by the present disclosure can still be implemented. The following division of the various embodiments is for convenience of description and should not constitute any limitation on the specific implementation manners of the present disclosure. The various embodiments can be combined and cross-referenced with each other on the premise of not being contradictory.

[0026] One embodiment of the present disclosure relates to a method for refining the quasigeoid in a strip area, and its process is as follows Figure 1 shown, including steps S110 to S190.

[0027] Step S110: Select a number of measurement control points from the center line of the pipeline, and respectively determine the actual height anomalies of each measurement control point; wherein, each measurement control point is evenly distributed along the center line of the pipeline and conforms to the terrain change trend of the center line of the pipeline.

[0028] Specifically, the center line of the pipeline refers to the geometric central axis along the pipeline path. The pipeline here can be any type of long-distance pipeline. A long-distance pipeline refers to a pipeline system used for long-distance transportation of oil, gas, water or other fluid media. Such pipelines usually span hundreds or even thousands of kilometers. Therefore, when selecting measurement control points, it is necessary to fully consider the terrain change trend of the center line of the pipeline to adapt to terrain changes and improve accuracy.

[0029] Exemplarily, in step S110, respectively determining the actual height anomalies of each measurement control point includes: obtaining the geodetic height and normal height of each measurement control point; subtracting the normal height of each measurement control point from its geodetic height to obtain the actual height anomaly of each measurement control point.

[0030] Specifically, when obtaining the geodetic height and normal height of each measurement control point, the geodetic height H and normal height h of the measurement control point can be obtained by performing GNSS static observations on the measurement control point. Denote the actual height anomaly of the measurement control point as ξ, then ξ = H - h.

[0031] Step S120: Using the EGM2008 gravity field model, respectively determine the model height anomalies of each measurement control point.

[0032] Specifically, the EGM2008 gravity field model is a global gravity field model designed to provide high-precision gravity field information on the Earth's surface and its vicinity. Therefore, in step S120, the corresponding point of the measurement control point in the EGM2008 gravity field model can be determined first according to the longitude and latitude, and then the height anomaly of the corresponding point is extracted from the EGM2008 gravity field model, and this height anomaly is used as the model height anomaly of its corresponding measurement control point.

[0033] For example, the model height anomaly of the measurement control point can be expressed as: .

[0034] Wherein, T represents the disturbing potential. represents the normal gravity value of the measurement control point. represents the geocentric gravitational constant. respectively represent the geocentric radius, reduced latitude, and longitude of the measurement control points. represents the degree of the EGM2008 gravity field model, represents the order. represents the semi-major axis of the ellipsoid. respectively represent the first fully normalized potential coefficient and the second fully normalized potential coefficient. represents the fully normalized associated Legendre function.

[0035] Step S130: Determine the height anomaly difference corresponding to each measurement control point according to the actual height anomaly value and the model height anomaly value.

[0036] Specifically, subtract the model height anomaly value of the measurement control point from its actual height anomaly value to obtain the height anomaly difference corresponding to the measurement control point. In other words, when the actual height anomaly value of the measurement control point is ξ and the model height anomaly value of the measurement control point is at this time, the height anomaly difference is represented as = ξ - .

[0037] Particularly, in order to further improve the model accuracy, after step S130, the method for refining the quasi-geoid in the strip area further includes a process of removing abnormal measurement control points to reduce the influence of abnormal measurement control points on the model accuracy.

[0038] The process of removing abnormal measurement control points mainly includes the following steps.

[0039] Select N continuously arranged measurement control points as a control point group, fit them with a quadratic polynomial, and obtain the corresponding expression: . Among them, represents the height anomaly difference of the measurement control point, represents the mileage difference between the measurement control point and the measurement reference point on the center line of the line, , , are all quadratic polynomial coefficients to be solved. Among them, the specific value of N can be selected and set according to actual needs. For example, the value of N can be 3, 4, 5, etc. Among them, the preferred value of N is 4.

[0040] Solve the quadratic polynomial coefficients , , and the residuals of the selected N measurement control points, and calculate the sum of the squared residuals of the control point group according to the following formula: .

[0041] Among them, represents the sum of squared residuals of the control point group, represents the number of the measured control points in the control point group and , represents the number of the measured control points, represents the th height anomaly difference of the measured control points, represents the th mileage difference between the measured control point and the measurement reference point.

[0042] Update step: Remove the first measured control point from the current control point group, and add the next measured control point after the last measured control point in the current control point group to the control point group to obtain a new control point group, and calculate the sum of squared residuals of the new control point group.

[0043] Take the new control point group as the current control point group, and repeat the update step until no more measured control points are added to the control point group.

[0044] If the sum of squared residuals of all control point groups where a certain measured control point is located is higher than the target multiple of the standard deviation of the height anomaly differences of all measured control points, then remove the measured control point. Among them, the target multiple can be set according to actual needs. For example, the target multiple is preferably 3, that is, if the sum of squared residuals of all control point groups where a certain measured control point is located is higher than 3 times the standard deviation of the height anomaly differences of all measured control points, then remove the measured control point.

[0045] Among them, the standard deviation of the height anomaly differences of all measured control points can be calculated according to the existing standard deviation calculation formula. For example, when the total number of measured control points selected in step S110 is L, and the th height anomaly difference of the measured control point is , the average value of the height anomaly differences of L measured control points can be calculated first , where . On this basis, the standard deviation of the height anomaly differences of L measured control points can be obtained.

[0046] For example, assume L = 8, and the 8 measured control points are respectively , select as the control point group, then in the way of a sliding window, four control point groups can also be obtained, which are respectively: The control point group composed of The control point group composed of The control point group composed of The control point group composed of. Assume The sum of the squared residuals of all control point groups is higher than three times the standard deviation of the height anomaly differences of all measured control points, i.e., to three times the standard deviation of the height anomaly differences, then will be excluded and will not be used in subsequent steps .

[0047] Step S140: According to the terrain change trend, select several interpolation points for each measured control point from the center line of the line, and determine the height anomaly differences corresponding to each interpolation point.

[0048] Specifically, in order to further increase the density of points on the center line of the line and improve the model fineness, several interpolation points need to be selected. Among them, the first preset distance can be determined according to the actual situation to meet the accuracy requirements.

[0049] Exemplarily, in step S140, determining the height anomaly differences corresponding to each interpolation point includes: using the quadratic polynomial moving curve fitting algorithm and the least squares algorithm to fit the height anomaly differences corresponding to each interpolation point by using the height anomaly differences corresponding to each measured control point.

[0050] By using the quadratic polynomial moving curve fitting algorithm and the least squares algorithm to fit the height anomaly differences corresponding to each interpolation point, the fitted height anomaly differences can be made closer to the actual height anomaly differences of the interpolation points, greatly improving the fitting accuracy.

[0051] Exemplarily, using the quadratic polynomial moving curve fitting algorithm and the least squares algorithm to fit the height anomaly differences corresponding to each interpolation point by using the height anomaly differences corresponding to each measured control point includes: selecting several measured control points from the upstream and downstream of each interpolation point as the respective reference points corresponding to each interpolation point, and calculating the mileage differences between each interpolation point and its corresponding reference points according to the following formula: .

[0052] Among them, represents the mileage difference between the interpolation point p and its corresponding i-th reference point. represents the mileage of the interpolation point p. represents the mileage of the i-th reference point corresponding to the interpolation point p. i represents the reference point number and i = 1, 2,..., t. t represents the total number of reference points corresponding to the interpolation point p.

[0053] Determine the fitting curve and represent the fitting curve as: .

[0054] Among them, represents the height anomaly difference matrix and , respectively represent the height anomaly differences of the 1st, 2nd, …, t-th reference points corresponding to the interpolation point p. represents the mileage difference matrix and , respectively represent the mileage differences between the interpolation point p and its corresponding 1st, 2nd, …, t-th reference points. 、 、 respectively represent the first coefficient, the second coefficient, and the third coefficient.

[0055] For example, according to Figure 2 the 1st, 2nd, 3rd, t-th reference points corresponding to the interpolation point p shown , the corresponding fitting curve can be obtained. Among them, respectively represent the mileage differences between the interpolation point p and the reference point , respectively represent the height anomaly differences of the reference point .

[0056] Calculate the weights of each reference point corresponding to each interpolation point according to the following formula: .

[0057] Among them, represents the weight of the i-th reference point corresponding to the interpolation point p. represents the natural logarithm. represents a constant.

[0058] Let the coefficient matrix , and solve the coefficient matrix according to the following formula: .

[0059] Among them, represents the parameter matrix and . represents the weight matrix and .

[0060] Let , and substitute it together with the obtained , , into the fitting curve to obtain the height anomaly difference corresponding to the interpolation point p. Among them, represents the mileage difference between the interpolation point p and itself.

[0061] For example, as Figure 2 shown, let , and substitute it together with the obtained , , into the fitting curve corresponding to the reference point ​ , the height anomaly difference corresponding to the interpolation point p can be obtained .

[0062] Step S150: Select a number of external measurement points within the second preset distance on both sides of the line centerline, and determine the height anomaly differences corresponding to the respective external measurement points.

[0063] Specifically, after obtaining the height anomaly differences of each measurement control point and each interpolation point on the line centerline, it is also necessary to determine the height anomaly differences of a number of external measurement points other than the line centerline in the target strip area where the line centerline is located, so as to comprehensively understand the topographic features and gravity field changes in the target strip area, and further improve the accuracy of the quasi-geoid model of the target strip area finally obtained.

[0064] Exemplarily, in step S150, determining the height anomaly differences corresponding to the respective external measurement points includes: projecting the respective external measurement points onto the line centerline to obtain the corresponding projection points; using the height anomaly differences corresponding to the respective projection points as the height anomaly differences corresponding to the external measurement points corresponding to the respective projection points.

[0065] Specifically, if the projection point corresponding to the external measurement point is a certain measurement control point or a certain interpolation point, the height anomaly difference corresponding to the measurement control point or the interpolation point can be directly used as the height anomaly difference corresponding to the projection point, and thus as the height anomaly difference corresponding to the external measurement point. If the projection point corresponding to the external measurement point is neither a measurement control point nor an interpolation point, the projection point can be used as a new interpolation point, and the same method as in step S140 is used to obtain the height anomaly difference corresponding to the new interpolation point, and the height anomaly difference corresponding to the new interpolation point is used as the height anomaly difference corresponding to the projection point, and thus as the height anomaly difference corresponding to the external measurement point.

[0066] By using the projection points of the respective external measurement points to obtain the height anomaly differences corresponding to the respective external measurement points, the height anomaly differences corresponding to the external measurement points can be made closer to their actual height anomaly differences, and the accuracy of the quasi-geoid model of the target strip area finally obtained can be further improved.

[0067] Step S160: Use the EGM2008 gravity field model to determine the model height anomaly values corresponding to the respective interpolation points and the respective external measurement points.

[0068] Specifically, similar to step S120, in step S160, the corresponding points of the interpolation points and the external measurement points in the EGM2008 gravity field model can be determined first according to the longitude and latitude, and then the height anomaly values of the corresponding points are extracted from the EGM2008 gravity field model, and the height anomaly value is used as the model height anomaly value of the corresponding interpolation point or external measurement point.

[0069] Step S170: Add the height anomaly differences corresponding to each measurement control point, each interpolation point, and each external measurement point to their corresponding model height anomaly values respectively to obtain the refined height anomaly values corresponding to each measurement control point, each interpolation point, and each external measurement point respectively.

[0070] Specifically, by using the height anomaly differences to refine the model height anomaly values corresponding to each measurement control point, each interpolation point, and each external measurement point respectively, the accuracy of the quasi-geoid model of the target strip area finally obtained can be further improved.

[0071] Step S180: Generate grid data of height anomaly values for the target strip area where the center line of the line is located according to the refined height anomaly values.

[0072] Exemplarily, step S180 includes: dividing the target strip area into grids according to a preset grid point interval; determining the smallest rectangle covering the target strip area to obtain the grid range corresponding to the target strip area; wherein, the longitude and latitude corresponding to each side of the smallest rectangle meet the preset accuracy requirements; within the grid range, calculate the coordinates of each grid point in turn and determine the refined height anomaly value corresponding to each grid point to obtain the grid data of height anomaly values.

[0073] Specifically, the preset grid point interval can be set according to actual accuracy requirements. For example, the preset grid point interval can be determined according to units of longitude and latitude such as degrees, minutes, and seconds. For example, in order to balance accuracy requirements and computational amount, the preset grid point interval can be set to 1 minute × 1 minute, that is, the longitude interval and latitude interval of the grid points are both 1 minute.

[0074] In order to ensure that the grid points completely cover the entire target strip area, after determining the smallest rectangle covering the target strip area, the smallest rectangle needs to be updated according to the preset grid point interval so that the updated smallest rectangle meets the preset accuracy requirements. For example, when the preset grid point interval is set to 1 minute × 1 minute, if the longitude and latitude corresponding to a certain initial side of the smallest rectangle covering the target strip area are not integer multiples of 1 minute, then this initial side needs to be translated to the integer longitude line or integer latitude line closest to it in the direction away from the center of the smallest rectangle, and at the same time, the two sides adjacent to this initial side are extended to intersect with this initial side, so as to realize the update of the smallest rectangle and make the updated smallest rectangle meet the preset accuracy requirements.

[0075] When determining the refined height anomaly value corresponding to each grid point, if the grid point corresponds to a certain survey control point, interpolation point or external survey point, the refined height anomaly value of the survey control point, interpolation point or external survey point can be directly used as the refined height anomaly value of the grid point. If the grid point does not correspond to a certain survey control point, interpolation point or external survey point, the grid point can be used as a new interpolation point, and the refined height anomaly value of the grid point can be obtained by the same method as in step S140.

[0076] Step S190: Generate a quasi-geoid model of the target strip area according to the height anomaly value grid data.

[0077] Specifically, step S190 can use the Grid Factory software tool to generate a quasi-geoid model in GGF format according to the height anomaly value grid data, so as to facilitate the use of GNSS static adjustment software.

[0078] The method for refining the quasi-geoid of the strip area provided by the embodiment of the present disclosure provides an efficient, economical, safe and environmentally friendly height measurement solution for the pipeline design industry compared with the prior art. Combining GNSS technology for height measurement solves the problems of high cost, great difficulty, long cycle, low accuracy and poor result consistency of traditional height measurement methods, improves the flexibility and efficiency of height measurement work, provides the usability of the whole life cycle of design, construction and operation through the finally obtained quasi-geoid model, can meet the more stringent standard requirements of the owner, effectively guarantees the quality of height measurement results, and promotes the development of pipeline measurement in the direction of no ground control benchmark. At the same time, the method for refining the quasi-geoid of the strip area provided by the embodiment of the present disclosure also improves the height measurement accuracy, reduces the height measurement cost, can effectively enhance the competitiveness of enterprises, and promotes sustainable development.

[0079] To enable those skilled in the art to better understand the above embodiments, a specific example is described below.

[0080] Combined with Figure 3 , a method for refining the quasi-geoid of a strip area includes the following steps.

[0081] The known geodetic height H and normal height h of the survey control point: Perform GNSS static observation on the survey control point to obtain the geodetic height H and normal height h of the survey control point.

[0082] The height anomaly value ξ of the control point = H - h: Subtract the normal height h of each survey control point from its geodetic height H to obtain the actual height anomaly value ξ of each survey control point, that is, ξ = H - h.

[0083] Extract the elevation anomaly value ξ(EGM) of the control points: Using the EGM2008 gravity field model, determine the model elevation anomaly value ξ(EGM) of each measurement control point respectively.

[0084] Find the elevation anomaly difference of the control points as the elevation anomaly value of the control points - the elevation anomaly value extracted by the EGM2008 model: Subtract the actual elevation anomaly value of the measurement control point from its model elevation anomaly value to obtain the corresponding elevation anomaly difference of the measurement control point.

[0085] Fit to find the elevation anomaly differences of all points on the center line and grid of the line: According to the terrain change trend, select several interpolation points of each measurement control point from the center line of the line at the first preset distance, and use the quadratic polynomial moving curve fitting algorithm and the least square algorithm to fit the elevation anomaly differences corresponding to each interpolation point by using the elevation anomaly differences corresponding to each measurement control point respectively. Select several external measurement points within the second preset distance on both sides of the center line of the line, project each external measurement point onto the center line of the line respectively to obtain the corresponding projection points; use the elevation anomaly differences corresponding to each projection point as the elevation anomaly differences corresponding to the external measurement points corresponding to each projection point.

[0086] Extract the elevation anomaly values of all points on the center line and grid of the line: Using the EGM2008 gravity field model, determine the model elevation anomaly values corresponding to each interpolation point and each external measurement point respectively.

[0087] Obtain the refined elevation anomaly of all grid points after refinement: Add the elevation anomaly differences corresponding to each measurement control point, each interpolation point, and each external measurement point to their corresponding model elevation anomaly values respectively to obtain the refined elevation anomaly values corresponding to each measurement control point, each interpolation point, and each external measurement point respectively.

[0088] Generate the grid data of the elevation anomaly values of the target area: According to the refined elevation anomaly values, generate the grid data of the elevation anomaly values of the target strip area where the center line of the line is located.

[0089] Generate the geoid model in GGF format: Using the Grid Factory software tool, generate the quasi-geoid model in GGF format according to the grid data of the elevation anomaly values.

[0090] Another embodiment of the present disclosure relates to a quasi-geoid refinement device for a strip area, as Figure 4 shown, including a first determination module 410, a second determination module 420, a third determination module 430, a fourth determination module 440, a fifth determination module 450, a sixth determination module 460, a refinement module 470, a first generation module 480, and a second generation module 490.

[0091] The first determination module 410 is used to select a number of measurement control points from the center line of the pipeline, and respectively determine the actual height anomaly values of each measurement control point; among them, each measurement control point is evenly distributed along the center line of the pipeline and conforms to the terrain change trend of the center line of the pipeline.

[0092] The second determination module 420 is used to respectively determine the model height anomaly values of each measurement control point by using the EGM2008 gravity field model.

[0093] The third determination module 430 is used to determine the height anomaly difference corresponding to each measurement control point according to the actual height anomaly value and the model height anomaly value.

[0094] The fourth determination module 440 is used to select a number of interpolation points of each measurement control point from the center line of the pipeline according to the terrain change trend at a first preset distance, and determine the height anomaly difference corresponding to each interpolation point.

[0095] The fifth determination module 450 is used to select a number of external measurement points within a second preset distance on both sides of the center line of the pipeline, and determine the height anomaly difference corresponding to each external measurement point.

[0096] The sixth determination module 460 is used to use the EGM2008 gravity field model to determine the model height anomaly values corresponding to each interpolation point and each external measurement point.

[0097] The refinement module 470 is used to add the height anomaly difference corresponding to each measurement control point, each interpolation point, and each external measurement point to its corresponding model height anomaly value to obtain the refined height anomaly value corresponding to each measurement control point, each interpolation point, and each external measurement point.

[0098] The first generation module 480 is used to generate grid data of height anomaly values of the target strip area where the center line of the pipeline is located according to the refined height anomaly values.

[0099] The second generation module 490 is used to generate a quasi-geoid model of the target strip area according to the grid data of height anomaly values.

[0100] Exemplarily, the strip area quasi-geoid refinement device further includes a rejection module.

[0101] The rejection module is used to perform the following steps after the third determination module determines the height anomaly difference corresponding to each measurement control point.

[0102] Select N continuously arranged measurement control points as a control point group, and fit them with a quadratic polynomial to obtain the corresponding expression: .

[0103] Among them, represents the height anomaly difference of the measurement control point, represents the mileage difference between the survey control point and the survey reference point on the center line of the line, , , are all coefficients of the quadratic polynomial to be solved.

[0104] The coefficients of the quadratic polynomial are solved by the least squares method , , and the residuals of the selected N survey control points. According to the following formula, the sum of the squared residuals of the control point group is calculated: .

[0105] Among them, represents the sum of the squared residuals of the control point group, represents the number of the survey control point in the control point group and , represents the number of the survey control point, represents the th height anomaly difference of the survey control point, represents the th mileage difference between the survey control point and the survey reference point.

[0106] Update step: Remove the survey control point arranged at the front from the current control point group, and add the next survey control point of the survey control point arranged at the end of the current control point group to the control point group to obtain a new control point group, and calculate the sum of the squared residuals of the new control point group.

[0107] Take the new control point group as the current control point group, and repeat the update step until no more survey control points are added to the control point group.

[0108] If the sum of the squared residuals of all control point groups where a certain survey control point is located is higher than the target multiple of the standard deviation of the height anomaly differences of all survey control points, then remove this survey control point.

[0109] For the specific implementation method of the quasigeoid refinement device in the strip area provided by the embodiments of the present disclosure, reference can be made to the quasigeoid refinement method in the strip area provided by the embodiments of the present disclosure, which will not be elaborated here.

[0110] The strip area quasi-geoid refinement device provided by the embodiments of the present disclosure provides an efficient, economical, safe and environmentally friendly elevation measurement solution for the pipeline design industry compared with the prior art. By combining GNSS technology for elevation measurement, it solves the problems of high cost, great difficulty, long cycle, low accuracy and poor result consistency of traditional elevation measurement methods, improves the flexibility and efficiency of elevation measurement work, provides the usability of the whole life cycle of design, construction and operation through the finally obtained quasi-geoid model, can meet the more stringent standard requirements of the owner, effectively guarantees the quality of elevation results, and promotes the development of pipeline measurement towards the direction of no ground control benchmark. At the same time, the strip area quasi-geoid refinement method provided by the embodiments of the present disclosure also improves the elevation measurement accuracy, reduces the elevation measurement cost, can effectively enhance the competitiveness of enterprises and promote sustainable development.

[0111] Another embodiment of the present disclosure relates to an electronic device, as Figure 5 shown, including: At least one processor 501; and, A memory 502 communicatively connected to the at least one processor 501; wherein, The memory 502 stores instructions executable by the at least one processor 501, and the instructions are executed by the at least one processor 501 to enable the at least one processor 501 to execute the strip area quasi-geoid refinement method described in the above embodiments.

[0112] Wherein, the memory and the processor are connected by a bus. The bus can include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripheral devices, voltage regulators and power management circuits, which are well known in the art, so they will not be further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on the transmission medium. The data processed by the processor is transmitted on the wireless medium through the antenna. Further, the antenna also receives data and transmits the data to the processor.

[0113] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management and other control functions. The memory can be used to store the data used by the processor when executing operations.

[0114] Another embodiment of the present disclosure relates to a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the strip area quasi-geoid refinement method described in the above embodiments is implemented.

[0115] That is, those skilled in the art can understand that all or part of the steps in the methods described in the above embodiments can be completed by instructing relevant hardware through a program. This program is stored in a storage medium and includes several instructions for causing a device (which can be a single-chip microcomputer, a chip, etc.) or a processor to execute all or part of the steps in the methods described in various embodiments of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0116] Those of ordinary skill in the art can understand that the above embodiments are specific implementation manners for implementing the present disclosure, and in actual applications, various changes can be made to them in form and details without departing from the spirit and scope of the present disclosure.

Claims

1. A method for refining the quasigeoid of a strip-shaped area, characterized in that, The method includes: Selecting a number of measurement control points from the center line of the pipeline, and respectively determining the actual height anomalies of the measurement control points; wherein, the measurement control points are evenly distributed along the center line and conform to the terrain change trend of the center line; Using the EGM2008 gravity field model, respectively determining the model height anomalies of the measurement control points; According to the actual height anomalies and the model height anomalies, determining the height anomaly differences respectively corresponding to the measurement control points; According to the terrain change trend, selecting a number of interpolation points of the measurement control points from the center line at a first preset distance, and determining the height anomaly differences respectively corresponding to the interpolation points; Selecting a number of external measurement points within a second preset distance on both sides of the center line, and determining the height anomaly differences respectively corresponding to the external measurement points; Using the EGM2008 gravity field model, determining the model height anomalies respectively corresponding to the interpolation points and the external measurement points; Adding the height anomaly differences respectively corresponding to the measurement control points, the interpolation points, and the external measurement points to their corresponding model height anomalies, to obtain the refined height anomalies respectively corresponding to the measurement control points, the interpolation points, and the external measurement points; According to the refined height anomalies, generating grid data of height anomalies of the target strip area where the center line is located; According to the grid data of height anomalies, generating a quasi-geoid model of the target strip area.

2. The method according to claim 1, wherein The respectively determining the actual height anomalies of the measurement control points includes: Obtaining the geodetic height and normal height of each measurement control point; Subtracting the normal height of each measurement control point from its geodetic height to obtain the actual height anomaly of each measurement control point.

3. The method according to claim 1, characterized in that, The determining the height anomaly differences respectively corresponding to the interpolation points includes: Adopting a quadratic polynomial moving curve fitting algorithm and a least squares algorithm, and using the height anomaly differences respectively corresponding to the measurement control points to fit the height anomaly differences respectively corresponding to the interpolation points.

4. The method according to claim 3, wherein The adopting a quadratic polynomial moving curve fitting algorithm and a least squares algorithm, and using the height anomaly differences respectively corresponding to the measurement control points to fit the height anomaly differences respectively corresponding to the interpolation points includes: Selecting a number of the measurement control points from the upstream and downstream of each interpolation point as the respective reference points corresponding to each interpolation point, and calculating the mileage differences between each interpolation point and its corresponding reference points according to the following formula: ； Among them, represents the mileage difference between the interpolation point p and the i-th reference point corresponding to it; represents the mileage of the interpolation point p; represents the mileage of the i-th reference point corresponding to the interpolation point p; i represents the reference point number and i = 1, 2, …, t; t represents the total number of reference points corresponding to the interpolation point p; Determining a fitting curve, and expressing the fitting curve as: ； Among them, represents the matrix of height anomaly differences and , respectively represent the height anomaly differences of the 1st, 2nd, …, t-th reference points corresponding to the interpolation point p; represents the matrix of mileage differences and , respectively represent the mileage differences between the interpolation point p and its corresponding 1st, 2nd, …, t-th reference points; , , respectively represent the first coefficient, the second coefficient, and the third coefficient; According to the following formula, calculating the weights of the respective reference points corresponding to each interpolation point: ； Among them, represents the weight of the i-th reference point corresponding to the interpolation point p; represents the natural logarithm; represents a constant; Let the coefficient matrix , solve the coefficient matrix according to the following formula : ； Among them, represents a parameter matrix and , represents a weight matrix and ; Let , and substitute it together with the obtained , , into the fitting curve to obtain the height anomaly difference corresponding to the interpolation point p; among them, represents the mileage difference between the interpolation point p and itself.

5. The method according to claim 1, wherein The determining the height anomaly differences respectively corresponding to the external measurement points includes: Projecting each external measurement point onto the center line to obtain the corresponding projection points; Taking the height anomaly differences corresponding to the projection points as the height anomaly differences corresponding to the external measurement points corresponding to the projection points.

6. The method according to claim 1, characterized in that The generating the grid data of height anomalies of the target strip area where the center line is located according to the refined height anomalies includes: Gridding the target strip area according to a preset grid point interval; Determine a minimum rectangle covering the target strip area, and obtain a grid range corresponding to the target strip area; wherein the longitude and latitude corresponding to each side of the minimum rectangle meet a preset accuracy requirement; Within the grid range, the coordinates of each grid point are calculated in turn, and the refined elevation anomaly value corresponding to each grid point is determined to obtain the elevation anomaly value grid data.

7. The method according to any one of claims 1 to 6, characterized in that After determining the elevation anomaly differences corresponding to the respective measurement control points according to the actual elevation anomaly values ​​and the model elevation anomaly values, the method further comprises: Select N continuously arranged said measurement control points as a control point group, and fit them with a quadratic polynomial to obtain the corresponding expression: ; Among them, represents the elevation anomaly difference of the said measurement control point, represents the mileage difference between the said measurement control point and the measurement reference point on the said center line of the line, , , are all coefficients of the quadratic polynomial to be solved; Solve the coefficients of the quadratic polynomial using the least squares method , , and the residuals of the selected N measurement control points, and calculate the sum of squared residuals of the control point group according to the following formula: ； Among them, represents the sum of squared residuals of the control point group, represents the number of the measured control points in the control point group and , represents the number of the measured control points, represents the th elevation anomaly difference of the measured control points, represents the mileage difference between the th measured control point and the measurement reference point; Update step: removing the measured control point arranged at the front from the current control point group, and adding the next measured control point of the measured control point arranged at the back in the current control point group to the control point group to obtain a new control point group, and calculating the residual sum of squares of the new control point group; Using the new control point group as the current control point group, repeating the updating step until no more of the measured control points are added to the control point group; If the residual sum of squares of all the control point groups where a certain survey control point is located is higher than the target multiple of the standard deviation of the elevation anomaly errors of all the survey control points, the survey control point will be eliminated.

8. A device for refining the quasi-geoid of a strip-shaped area, characterized in that, The device comprises: The first determination module is used to select a number of measurement control points from the pipeline line centerline and respectively determine the actual elevation anomaly value of each of the measurement control points; wherein each of the measurement control points is evenly distributed along the line centerline and conforms to the terrain change trend of the line centerline; The second determination module is used to determine the model elevation anomaly value of each of the measurement control points using the EGM2008 gravity field model; A third determination module is used to determine the elevation anomaly difference corresponding to each of the measurement control points according to the actual elevation anomaly value and the model elevation anomaly value; A fourth determination module is used to select a plurality of interpolation points of each of the measurement control points from the line center line according to the terrain change trend and a first preset distance, and determine the elevation anomaly difference corresponding to each of the interpolation points; A fifth determination module is used to select a number of external measurement points within a second preset distance from both sides of the line centerline, and determine the elevation anomaly difference corresponding to each of the external measurement points; A sixth determination module, for determining the model elevation anomaly values ​​corresponding to each of the interpolation points and each of the external measurement points respectively by using the EGM2008 gravity field model; A refinement module, used for adding the elevation anomaly differences corresponding to each of the survey control points, each of the interpolation points, and each of the external survey points to the corresponding model elevation anomaly values, to obtain the refined elevation anomaly values ​​corresponding to each of the survey control points, each of the interpolation points, and each of the external survey points; A first generating module is used to generate elevation anomaly value grid data of a target strip area where the line centerline is located according to the refined elevation anomaly value; A second generation module, configured to generate a quasi-geoid model of the target strip area according to the grid data of the height anomaly values.

9. An electronic device, characterized in that, Comprising: At least one processor; And, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images