System for estimating lead precision

Abstract

The invention relates to the technical field of surveying and mapping engineering, in particular to a lead precision estimation system which comprises a data acquisition module, a coordinate conversion module, a lead design module, a precision estimation module, a visual display module, a data interaction module and a data storage module. The data acquisition module collects geographic space data of a to-be-processed area and parameters of a measuring instrument; the coordinate conversion module converts the latitude and longitude coordinates into plane projection coordinates through Gaussian positive calculation; the wire design module constructs an edge measurement net, an angle measurement net or a corner net, sets an edge length threshold value, and matches instrument parameters corresponding to a second-level control net and a fourth-level control net; the precision estimation module calculates a weakest point median error and a weakest edge relative median error, and decomposes and calculates a plane through error; the visualization module draws a lead element and an error ellipse on the online map; data interaction supports CSV format import and export, and the storage module stores data support operation through a unified array. The method improves the estimation efficiency and precision, adapts to multiple engineering scenes, and reduces the achievement application threshold.

Description

Technical Field

[0001] This invention relates to the field of surveying and mapping engineering technology, specifically to a system for estimating the accuracy of traverse lines. Background Technology

[0002] In the field of surveying and mapping engineering, traverse surveying is a core technical means to determine the coordinates of ground control points and achieve precise engineering positioning. It is widely used in urban road construction, industrial park planning, power transmission line surveying in mountainous areas, and water conservancy engineering construction. As the requirements for spatial positioning accuracy in various projects continue to increase—for example, urban underground utility tunnel construction requires millimeter-level positioning accuracy, and high-voltage transmission tower site layout requires controlling centimeter-level deviations—traverse accuracy estimation, as a crucial step in predicting the reliability of measurement results and avoiding the risk of rework, is becoming increasingly important in terms of efficiency and accuracy. However, current techniques for traverse accuracy estimation still have many limitations and cannot meet the demands of modern engineering for efficient, accurate, and flexible surveying. Specific problems include: 1. Data processing has a low degree of automation, resulting in low efficiency and a high risk of errors. Traditional traverse accuracy estimation relies on manual data processing across multiple stages, resulting in a cumbersome process with low error tolerance. On one hand, coordinate transformations of geospatial data, such as converting latitude and longitude coordinates to Gaussian plane projection coordinates, require manual application of the Gaussian forward calculation formula, involving the derivation of complex parameters such as meridian arc length X and trochanteric radius of curvature N. This is not only time-consuming (e.g., 10-20 minutes for a single control point conversion), but also prone to errors due to human calculation, such as incorrect conversion between arcs and angles or omissions in formula coefficients, leading to coordinate deviations. On the other hand, measuring instrument parameters, such as angular mean square error and fixed error A, must be manually entered into the calculation system. If operators confuse the parameter standards corresponding to second-order and fourth-order control networks—for example, mistakenly entering the fourth-order angular mean square error of 2.5″ instead of the second-order 8″—it will directly lead to distorted subsequent accuracy estimation results, requiring repeated verification and correction, further reducing work efficiency.

[0003] 2. The wire design lacks flexibility and is limited in its applicability to various scenarios. Traditional traverse design relies heavily on experience, making it difficult to flexibly adjust traverse network types and parameters according to engineering scenarios. Firstly, the control network is limited to a single form; most traditional tools only support one type of network—angle or lateral—failing to adapt to terrain conditions. For example, angle measurements in mountainous areas are easily obstructed by vegetation, making lateral networks more suitable; while industrial parks offer open views, making lateral networks more appropriate. This mismatch between the measurement scheme and the actual scenario increases additional errors. Secondly, traverse side length control lacks systematic constraints. Traditional designs rely on manual experience to set side length thresholds. For instance, relying solely on engineers' subjective judgment that side lengths should not be too short, without a standardized threshold control mechanism, easily leads to short sides being included in the traverse network. For example, a short side less than 50m significantly amplifies the impact of angle measurement errors on lateral continuity errors. A 10″ angle measurement error on a short side results in a lateral deviation 2-3 times greater than that on a long side, ultimately leading to substandard overall accuracy.

[0004] 3. The accuracy estimation logic is complex, and the accuracy of the results is difficult to guarantee. Traverse accuracy estimation involves specialized theories such as the error propagation law and variance-covariance matrix operations. Traditional manual calculations or calculations using simple tools have significant shortcomings. On the one hand, the calculation of the weakest point error requires simultaneous processing of the principal direction variance, covariance, and x / y direction errors of the point error ellipse, such as Qxx, Qyy, Qxy, σx, and σy. When manually expanding the formula, it is easy to omit the covariance term or make calculation errors, such as errors in square root calculations or unit conversion deviations, leading to biases in the identification of the weakest point. On the other hand, the estimation of planar penetration error often adopts a simplified method of accumulating the overall error, without deconstructing the independent influence of angle measurement error and distance measurement error on the lateral penetration error. This makes it impossible to accurately locate the source of error. For example, attributing deviations caused by distance measurement error to the angle measurement stage is not conducive to the optimization of subsequent measurement schemes.

[0005] 4. Weak ability to visualize results and interact with data Traditional methods for presenting and managing traverse accuracy estimation results are insufficient to meet the collaborative needs of engineering teams. Firstly, visualization is poor. Most results rely on static traverse diagrams drawn in CAD, requiring manual annotation of error ellipses with inconsistent scales. For example, the semi-major axis scales of ellipses drawn by different engineers can differ by up to 30%. Furthermore, online maps cannot be integrated to intuitively display the geographical location of elements, making it difficult for non-professionals, such as construction teams, to quickly identify the spatial distribution of the weakest points and sides. Secondly, data interaction compatibility is low. Traditional tools support diverse data source formats, such as point coordinates stored in Excel spreadsheets and angle data in TXT documents. There is a lack of a unified CSV format for import and adaptation, and exported results only contain the final error values, without archiving process data such as Qss and σ. Subsequent reviews or data reuse require recalculation, increasing management costs.

[0006] 5. Low level of data storage structure and insufficient computing support capabilities. Traditional traverse surveying data is often stored in a decentralized manner, such as storing point numbers and coordinates in one file and edge numbers and lengths in another, lacking a standardized data organization format. On the one hand, when accuracy estimation requires accessing point, edge, and angle data, the data format must be manually organized, such as entering the decentralized data into matrix operation tables. This is not only time-consuming but also prone to errors due to data misalignment, such as mismatched point numbers and coordinates. On the other hand, matrix operations, as a core step in accuracy estimation, such as solving error parameters, traditional methods rely on specialized software like MATLAB for manual execution. This cannot be seamlessly integrated with the traverse design and accuracy calculation stages, resulting in a broken overall process and making it difficult to achieve a closed loop of design-calculation-verification.

[0007] In summary, existing traverse accuracy estimation technologies have significant shortcomings in terms of automation, scenario adaptability, result accuracy, visualization interaction, and data management, and can no longer meet the demands of modern engineering for efficient, accurate, and collaborative surveying and mapping. Therefore, developing a traverse accuracy estimation system that integrates automated data processing, intelligent traverse design, precise accuracy estimation, intuitive visualization, and structured data management has become an urgent technical problem to be solved in the field of surveying and mapping engineering. Summary of the Invention

[0008] The purpose of this invention is to provide a system for estimating the accuracy of conductors, in order to solve the problems mentioned in the background art, such as low automation, poor adaptability to different scenarios, weak visualization of results, and inefficient data management in existing conductor accuracy estimation methods.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A system for estimating the accuracy of traverse lines includes a data acquisition module, a coordinate transformation module, a traverse design module, an accuracy estimation module, a visualization module, and a data interaction module. The data acquisition module is used to acquire geospatial data and measuring instrument parameters of the area to be processed. The coordinate transformation module is used to convert the latitude and longitude coordinates in the geospatial data into planar projected coordinates using Gaussian forward calculation. The Gaussian forward calculation formula is:

[0010]

[0011] in From the equator to latitude The meridian arc length, The radius of curvature of the circle is denoted as . Latitude Difference in longitude; The traverse design module is used to construct a planar traverse network in the form of a perihelion network, a protractor network, or a lateral-angle network based on the planar projection coordinates; The accuracy estimation module is used to calculate the standard error of the weakest point, the relative standard error of the weakest side, and the plane continuity error based on the type of the planar traverse network and the parameters of the measuring instrument. The calculation of the standard error of the weakest point uses the error propagation law, and the formula is:

[0012] , , These represent the variance and covariance of the principal directions of the positional error ellipse, respectively. , These are the mean square errors in the x and y directions, respectively; the visualization module is used to draw the point, line, and angle features and error ellipses of the planar traverse network on the online map, and to distinguish feature attributes by different shapes or colors; The data interaction module is used to import and export data.

[0013] Preferably, the measuring instrument parameters include angular mean square error, fixed error A, and error coefficient B. The traverse design module can match the corresponding measuring instrument parameters according to the preset control network level. The control network level includes at least level two and level four. The angular mean square error corresponding to level two control network is 8, the fixed error A is 12, and the error coefficient B is 12. The angular mean square error corresponding to level four control network is 2.5, and the relative mean square error of the side is 1 / 14000.

[0014] Preferably, when the conductor design module constructs a planar conductor network, it can set a conductor side length threshold, which is not less than 50m, in order to avoid the influence of short sides on angle measurement error and lateral continuity error.

[0015] Preferably, the planar penetration error calculated by the accuracy estimation module includes lateral penetration error, angle penetration error, and distance penetration error, wherein the formula for calculating the lateral penetration error is:

[0016] M represents the lateral penetration error caused by angular measurement error. This refers to the lateral penetration error caused by the ranging error.

[0017] Preferably, when the visualization module draws the error ellipse, it uses the line drawing function of the online map API to fit the ellipse outline through multi-segment lines, and the ellipse scale parameter can be adjusted to control the display size of the error ellipse on the map.

[0018] Preferably, the data interaction module supports importing data sources in CSV format, and supports exporting data including process data and result data for accuracy estimation. The result data includes the point number corresponding to the weakest point, the edge number corresponding to the weakest edge, and the standard error corresponding to both.

[0019] Preferably, the formula for calculating the radius of curvature N of the maxima and minima in the coordinate transformation module is as follows:

[0020] in For the Earth's semi-major axis, It has the highest eccentricity on Earth.

[0021] Preferably, the accuracy estimation module uses the following formula when calculating the relative mean square error of the weakest side:

[0022] in The variance factor is the side length. The error is the unit weight. Let be the length of the weakest side.

[0023] Preferably, the visualization module distinguishes element attributes in the following ways: known points are marked with circles, and test stations are marked with squares; known edges are marked with red lines, and edges to be tested are marked with blue lines.

[0024] Preferably, the system also includes a data storage module, which uses a unified array to store the point number, point coordinates, edge number, edge length, and angle data of the planar traverse network, and performs matrix operations based on the unified array to calculate the error parameters in the accuracy estimation module.

[0025] Compared with the prior art, the beneficial effects of the present invention are: I. Automating the entire process of traverse accuracy estimation to improve surveying efficiency. This invention constructs a complete automated workflow from geospatial data collection to accuracy output by coordinating modules for data acquisition, coordinate transformation, traverse design, accuracy estimation, visualization, and data interaction. It eliminates the need for manual step-by-step handling of complex steps such as coordinate transformation and error calculation. For example, the coordinate transformation module automatically converts latitude and longitude to planar projected coordinates using the Gaussian forward calculation formula, and the accuracy estimation module automatically calculates the mean square error of the weakest point, the relative mean square error of the weakest side, and the planar continuity error based on preset formulas. This significantly reduces manual operation time and human calculation errors, and substantially improves the efficiency of traverse accuracy estimation in surveying engineering.

[0026] II. Adapt to surveying and mapping needs in multiple scenarios to enhance system practicality This invention supports accuracy estimation for control networks at multiple levels, including second-order and fourth-order networks. It can automatically match corresponding measuring instrument parameters based on the preset control network level. For example, a second-order control network is matched with an angular mean square error of 8″, a fixed error A=12mm, and an error coefficient B=12mm / km; a fourth-order control network is matched with an angular mean square error of 2.5″ and a relative side mean square error of 1 / 14000, meeting the accuracy requirements of different engineering scenarios such as urban roads, industrial parks, and mountain power transmission lines. Simultaneously, the traverse design module can flexibly construct planar traverse networks in the form of side-measuring networks, angle-measuring networks, or side-angle networks, and supports setting a traverse side length threshold of not less than 50m to avoid the influence of short sides on angular measurement errors and lateral continuity errors, further adapting to complex terrain and diverse engineering needs.

[0027] III. The accuracy estimation results are precise and reliable, ensuring the quality of engineering positioning. This invention employs professional and rigorous calculation methods in the accuracy estimation process. For example, the calculation of the weakest point's mean square error is based on the law of error propagation; the planar penetration error is decomposed into angle measurement error and distance measurement error, calculated separately and then synthesized; the relative mean square error of the weakest side is calculated by combining the side length variance factor and the unit weight mean square error, ensuring that the calculation logic of each accuracy indicator conforms to surveying and mapping industry standards. Simultaneously, the radius of curvature N of the ramusoidal circle in the coordinate transformation module is accurately calculated using the Earth's semi-major axis, the Earth's first eccentricity, and latitude parameters, providing a reliable foundation for planar projection coordinate transformation. From the data source to the result calculation, this comprehensive approach ensures the accuracy of the accuracy estimation results, thereby providing a reliable basis for point layout and facility positioning in engineering construction and avoiding engineering deviations caused by insufficient accuracy.

[0028] IV. Convenient visualization of results and data interaction lower the threshold for applying the results. This invention's visualization module intuitively presents the point, line, and angle elements and error ellipses of a planar traverse network on an online map. It distinguishes element attributes through different shapes (circles for known points, squares for measured points) and colors (red for known edges, blue for edges to be measured). The display size can be controlled by adjusting the error ellipse's scaling parameters, making the accuracy estimation results easier to understand. Even non-professionals can quickly identify the location and error range of the weakest point and weakest edge, facilitating communication and decision-making within the engineering team. Furthermore, the data interaction module supports importing CSV format data sources, exporting process and result data for accuracy estimation (including the point number and mean square error corresponding to the weakest point, and the edge number and mean square error corresponding to the weakest edge). This facilitates subsequent data analysis, archiving, and reuse, lowering the barrier to application and management of accuracy results.

[0029] V. Structured data storage supports efficient computing and data management. The data storage module added in this invention uses a unified array to store the point numbers, point coordinates, edge numbers, edge lengths, and angle data of the planar traverse network. The array structure is clear and standardized, which facilitates matrix operations based on the array to quickly calculate error parameters in the accuracy estimation module, and also benefits the unified management and retrieval of data. Compared with traditional distributed data storage, unified array storage reduces redundant operations during data retrieval, improves calculation speed, and avoids data loss or corruption, ensuring the integrity and security of data throughout the accuracy estimation process. Attached Figure Description

[0030] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are explained in detail together with the embodiments of the invention, but do not constitute a limitation thereof.

[0031] Figure 1 This is a block diagram of the system composition for estimating conductor accuracy in this invention; Figure 2 This is a bar chart showing the surveying accuracy indicators for newly constructed urban roads in Embodiment 1 of the present invention. Figure 3 This is a bar chart comparing the number of testing stations and the error at the weakest point in three embodiments of the present invention; Figure 4 This is a bar chart comparing the accuracy of the angle measuring instruments in three different embodiments of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: Accuracy estimation of secondary control network traverse in urban new road surveying This embodiment is applied to the surveying and mapping project of newly built urban main roads. It is necessary to ensure the accuracy of the road construction layout by estimating the accuracy of the traverse accuracy. The plane traverse network is constructed using the secondary control network standard. The specific implementation process is as follows: 1. Data Acquisition Module Working Process The data acquisition module collects geospatial data of 5 known control points (numbered K1-K5) and 8 survey stations (numbered P1-P8) along the road to be surveyed using a GNSS receiver. The latitude and longitude range of the known control points is 116.32°-116.35° east longitude and 39.91°-39.93° north latitude. At the same time, it acquires the measuring instrument parameters of the total station, matching the parameters according to the secondary control network level, specifically the angular mean square error of 8″, fixed error A=12mm, and error coefficient B=12mm / km.

[0034] 2. Working process of the coordinate transformation module The coordinate transformation module uses the Gaussian forward calculation formula to convert latitude and longitude coordinates into planar projected coordinates (Gaussian 3-degree zone, central meridian longitude 117°). The radius of curvature N of the circumpolar region is calculated using the formula... Calculations were performed, taking the Earth's semi-major axis a = 6378137m and the Earth's first eccentricity e = 0.0818191908426. Taking a known point K2 (116.33°E, 39.92°N) as an example, the meridian arc length from the equator to this point was calculated to be X = 4418920.5m, and the longitude difference l = 1.47° (converted to radians before calculation). Finally, the plane projection coordinates were obtained as x = 4418925.3m and y = 506820.7m.

[0035] 3. Working process of the conductor design module The traverse design module constructs a planar traverse network in the form of a side-angle network based on the transformed planar projected coordinates. The threshold for traverse side length is set to 60m (to meet the requirement of not less than 50m and avoid the influence of short sides on angular measurement errors and lateral continuity errors). This side-angle network uses K1-K3 as known sides and connects P1-P8 as measurement stations sequentially to form a closed traverse loop. The side length of each traverse is between 65m and 120m, meeting the side length threshold requirement.

[0036] 4. Working process of the accuracy estimation module The accuracy estimation module calculates various accuracy indicators based on the type of edge and corner mesh and the parameters of the measuring instrument: The error at the weakest point: using the law of error propagation, the formula is as follows. The weakest point is station P3, and the variance of the principal direction of its positional error ellipse is Qxx = 0.8 mm. 2 Qyy=0.9mm 2 The covariance Qxy = 0.3mm 2 The mean square error in the x-direction is σx = 1.5 mm, and the mean square error in the y-direction is σy = 1.6 mm, so Mp = ±5.2 mm is obtained. Relative mean square error of the weakest edge: using the formula The variance factor of the side length, Qss, is 1.2 mm. 2 / m 2The unit weight error σ = 1.1 mm, the weakest side is P3-P4 (side length S = 85 m), and Ms / S = 1 / 12000 is calculated. Planar penetration error: The formula for calculating transverse penetration error is as follows The lateral penetration error M_angle_horizontal caused by the angle measurement error is ±2.8mm, and the lateral penetration error M_distance_horizontal caused by the distance measurement error is ±2.5mm, so the final result is M_horizontal = ±3.8mm.

[0037] 5. Visualization of the module's operation process The visualization module calls the Baidu online map API to draw traverse network elements and error ellipses on the map: Point elements: Known points K1-K5 are marked with red circles (radius 8px), and survey points P1-P8 are marked with blue squares (side length 6px). Line elements: Known edges are represented by red lines (3px wide), and edges to be measured are represented by blue lines (2px wide). Error Ellipse: The error ellipse is fitted to the weakest point P3 by 36 line segments. The ellipse scale parameter is adjusted to 1:5000, with a major semi-axis of 8mm and a minor semi-axis of 6mm (corresponding to 40px and 30px on the map).

[0038] 6. Data Interaction and Storage Module Working Process Data interaction module: Import point coordinate data in CSV format and automatically verify its integrity; export process data (such as Qxx, σx) and result data (point number and mean square error of the weakest point P3, and edge number and relative mean square error of the weakest edge P3-P4). Data storage module: A unified array is used to store point number, point coordinates, edge number, edge length and angle data. The array structure is [[point number, x, y], [edge number, start point number, end point number, length], [angle number, vertex number, left angle value]]. Matrix operations are performed based on this array to calculate error parameters.

[0039] Core parameter table for Example 1:

[0040] Example 2: Precision estimation of fourth-order control network traverse in industrial park construction This embodiment is applied to the construction surveying project of a large industrial park, which needs to meet the high-precision positioning requirements of facilities such as factory buildings and pipelines within the park. A plane traverse network is constructed using the fourth-order control network standard. The specific implementation process is as follows: 1. Data Acquisition Module Working Process The data acquisition module uses a high-precision GNSS receiver (accuracy level ≤ 5mm + 1ppm) to collect geospatial data of 4 known high-level control points (numbered G1-G4) and 10 measuring stations (numbered Z1-Z10) in the industrial park. The latitude and longitude range of the known control points is 120.15°-120.20° east longitude and 30.25°-30.30° north latitude. At the same time, it acquires the measuring instrument parameters of the high-precision total station, matching the parameters according to the fourth-order control network level, specifically the angular mean square error of 2.5″ and the relative mean square error of sides of 1 / 14000.

[0041] 2. Working process of the coordinate transformation module The coordinate transformation module uses the Gaussian forward calculation formula to convert latitude and longitude coordinates into planar projected coordinates (Gaussian 3-degree zone, central meridian longitude 120°). The calculation parameters for the radius of curvature N of the circumpolar region are taken as Earth's semi-major axis a = 6378137m and Earth's first eccentricity e = 0.0818191908426. Taking a known point G2 (120.17°E, 30.28°N) as an example, the meridian arc length X = 3356890.2m is calculated, the longitude difference l = 1.83° (converted to radians), and the final planar projected coordinates are x = 3356895.6m and y = 412560.9m.

[0042] 3. Working process of the conductor design module The traverse design module constructs a planar traverse network in the form of a goniometric network based on planar projected coordinates, setting the traverse side length threshold to 80m (greater than 50m to reduce the impact of short sides on measurement errors). This goniometric network uses G1-G2 and G3-G4 as known sides, forming three interconnected traverse loops from Z1 to Z10 along the path G1-Z1-Z2-Z3-G2G3-Z4-Z5-Z6-Z7-G4Z2-Z8-Z9-Z10-Z5. The side length of each traverse loop is between 85m and 150m, meeting the side length threshold requirement.

[0043] 4. Working process of the accuracy estimation module The accuracy estimation module calculates various accuracy indicators based on the type of angle measurement network and the instrument parameters of the fourth-order control network: Weakest point error: Using the law of error propagation, station Z6 is the weakest point, and the positional error ellipse Qxx = 0.5mm. 2 Qyy=0.6mm 2 The covariance Qxy = 0.2 mm 2 The mean square error in the x-direction is σx = 1.0 mm, and the mean square error in the y-direction is σy = 1.1 mm, so Mp = ±3.1 mm is obtained. Relative mean square error of the weakest edge: using the formula The variance factor of the side length, Qss, is 0.8 mm. 2 / m 2The unit weight error σ = 0.9 mm, the weakest side is Z6-Z7 (side length S = 110 m), and Ms / S = 1 / 14500 is calculated (better than the requirement of 1 / 14000 for fourth-order control networks). Planar penetration error: The formula for calculating transverse penetration error is as follows The lateral penetration error M_angle_horizontal caused by the angle measurement error is ±1.6mm, and the lateral penetration error M_distance_horizontal caused by the distance measurement error is ±1.7mm, so the final result is M_horizontal = ±2.2mm.

[0044] 5. Visualization of the module's operation process The visualization module uses the Gaode online map API to display elements. Point elements: Known points G1-G4 are marked with orange circles (radius 10px), and survey points Z1-Z10 are marked with green squares (side length 8px). Line elements: Known edges are represented by red lines (4px wide), and edges to be measured are represented by blue lines (3px wide). Error Ellipse: The error ellipse of the weakest point Z6 is fitted by 48 line segments. The ellipse scale parameter is adjusted to 1:8000, with a major semi-axis of 6mm and a minor semi-axis of 4mm (corresponding to 48px and 32px on the map).

[0045] 6. Data Interaction and Storage Module Working Process Data interaction module: Import point data in CSV format (including point number, latitude and longitude, and elevation), automatically remove abnormal data; export process data (such as Qss, σ) and result data (point number and mean square error of the weakest point Z6, and edge number and relative mean square error of the weakest edge Z6-Z7). Data storage module: Data is stored using a unified array with the following structure: [[point number, longitude, latitude, x, y], [side number, start number, end number, length], [angle number, vertex number, angle value]]. Precision estimation is performed by matrix operations based on this array.

[0046] Core parameter table for Example 2:

[0047] Example 3: Precision estimation of conductor accuracy for secondary control network in mountainous transmission line surveying This embodiment is applied to the surveying and mapping project of high-voltage transmission lines in mountainous areas with complex terrain (slope of 15°-30°). Accurate positioning of transmission towers requires conductor accuracy estimation. A two-level control network standard is used to construct a planar conductor network. The specific implementation process is as follows: 1. Data Acquisition Module Working Process The data acquisition module uses a portable GNSS receiver (with strong anti-obstruction capability) to collect geospatial data of 6 known control points (numbered T1-T6) and 12 measuring stations (numbered L1-L12) along the transmission line. The latitude and longitude range of the known control points is 103.50°-103.60° east longitude and 29.70°-29.80° north latitude. At the same time, it acquires the parameters of a total station with prism-free distance measurement function, and matches the parameters according to the secondary control network level, specifically the angular mean square error of 8″, fixed error A=12mm, and error coefficient B=12mm / km.

[0048] 2. Working process of the coordinate transformation module The coordinate transformation module uses the Gaussian forward calculation formula to convert latitude and longitude coordinates into planar projected coordinates (Gaussian 3-degree zone, central meridian longitude 105°). The calculation parameters for the radius of curvature N of the circumpolar region are taken as Earth's semi-major axis a = 6378137m and Earth's first eccentricity e = 0.0818191908426. Taking a known point T3 (103.55°E, 29.75°N) as an example, the meridian arc length X = 3298760.8m is calculated, the longitude difference l = 2.45° (converted to radians), and the final planar projected coordinates are x = 3298766.1m and y = 368920.4m.

[0049] 3. Working process of the conductor design module The conductor design module constructs a planar conductor network in the form of a trilateration network based on planar projected coordinates (angle measurement in mountainous areas is easily obstructed, while trilateration is more convenient), setting a conductor side length threshold of 70m (not less than 50m to avoid the influence of short side errors). This trilateration network uses T1-T2 and T4-T5 as known sides, and forms a chain-like conductor network along L1-L12 according to the transmission line route. The side length of each conductor is between 75m and 130m, meeting the side length threshold requirement.

[0050] 4. Working process of the accuracy estimation module The accuracy estimation module calculates various accuracy indicators based on the type of the perimetric network and the instrument parameters of the secondary control network: Weakest point error: Using the law of error propagation, station L9 is the weakest point, and the positional error ellipse Qxx = 0.7mm. 2 Qyy=0.8mm 2 The covariance Qxy = 0.3mm 2 The mean square error in the x-direction is σx = 1.4 mm, and the mean square error in the y-direction is σy = 1.5 mm, so Mp = ±4.8 mm is obtained. Relative mean square error of the weakest edge: using the formula The variance factor of the side length, Qss, is 1.1 mm. 2 / m 2The unit weight error σ = 1.0 mm, the weakest side is L9-L10 (side length S = 95 m), and Ms / S = 1 / 11500 is calculated. Planar penetration error: The formula for calculating transverse penetration error is as follows M-angle horizontal = ±2.6mm, M-distance horizontal = ±2.7mm, and finally M-horizontal = ±3.5mm.

[0051] 5. Visualization of the module's operation process The visualization module uses the Tianditu API (which supports mountainous terrain display) to display elements: Point elements: Known points T1-T6 are marked with red circles (radius 9px), and survey stations L1-L12 are marked with blue squares (side length 7px). Line elements: Known edges are marked with red lines (3px wide), and edges to be measured are marked with blue lines (2px wide), with the edge length labeled; Error Ellipse: The error ellipse of the weakest point L9 is fitted by 40 line segments. The ellipse scale parameter is adjusted to 1:6000, with a major semi-axis of 7mm and a minor semi-axis of 5mm (corresponding to 42px and 30px on the map).

[0052] 6. Data Interaction and Storage Module Working Process Data interaction module: Import station data in CSV format (including point number, latitude and longitude, and terrain slope), and support manual correction of the coordinates of obscured points; export process data (such as M-angle horizontal and M-distance horizontal) and result data (point number and mean square error of the weakest point L9, and edge number and relative mean square error of the weakest side L9-L10). Data storage module: Data is stored using a unified array with the following structure: [[point number, longitude, latitude, x, y, slope], [edge number, starting point number, ending point number, length, occlusion status], [angle number, vertex number, angle value]]. Matrix operations are performed based on this array to adapt to the accuracy estimation in mountainous areas.

[0053] Third Embodiment Core Parameter Table

[0054] The advantages of the system for estimating conductor accuracy proposed in this invention are as follows: I. Automating the entire process of traverse accuracy estimation to improve surveying efficiency. This invention constructs a complete automated workflow from geospatial data collection to accuracy output by coordinating modules for data acquisition, coordinate transformation, traverse design, accuracy estimation, visualization, and data interaction. It eliminates the need for manual step-by-step handling of complex steps such as coordinate transformation and error calculation. For example, the coordinate transformation module automatically converts latitude and longitude to planar projected coordinates using the Gaussian forward calculation formula, and the accuracy estimation module automatically calculates the mean square error of the weakest point, the relative mean square error of the weakest side, and the planar continuity error based on preset formulas. This significantly reduces manual operation time and human calculation errors, and substantially improves the efficiency of traverse accuracy estimation in surveying engineering.

[0055] II. Adapt to surveying and mapping needs in multiple scenarios to enhance system practicality This invention supports accuracy estimation for control networks at multiple levels, including second-order and fourth-order networks. It can automatically match corresponding measuring instrument parameters based on the preset control network level. For example, a second-order control network is matched with an angular mean square error of 8″, a fixed error A=12mm, and an error coefficient B=12mm / km; a fourth-order control network is matched with an angular mean square error of 2.5″ and a relative side mean square error of 1 / 14000, meeting the accuracy requirements of different engineering scenarios such as urban roads, industrial parks, and mountain power transmission lines. Simultaneously, the traverse design module can flexibly construct planar traverse networks in the form of side-measuring networks, angle-measuring networks, or side-angle networks, and supports setting a traverse side length threshold of not less than 50m to avoid the influence of short sides on angular measurement errors and lateral continuity errors, further adapting to complex terrain and diverse engineering needs.

[0056] III. The accuracy estimation results are precise and reliable, ensuring the quality of engineering positioning. This invention employs professional and rigorous calculation methods in the accuracy estimation process. For example, the calculation of the weakest point's mean square error is based on the law of error propagation; the planar penetration error is decomposed into angle measurement error and distance measurement error, calculated separately and then synthesized; the relative mean square error of the weakest side is calculated by combining the side length variance factor and the unit weight mean square error, ensuring that the calculation logic of each accuracy indicator conforms to surveying and mapping industry standards. Simultaneously, the radius of curvature N of the ramusoidal circle in the coordinate transformation module is accurately calculated using the Earth's semi-major axis, the Earth's first eccentricity, and latitude parameters, providing a reliable foundation for planar projection coordinate transformation. From the data source to the result calculation, this comprehensive approach ensures the accuracy of the accuracy estimation results, thereby providing a reliable basis for point layout and facility positioning in engineering construction and avoiding engineering deviations caused by insufficient accuracy.

[0057] IV. Convenient visualization of results and data interaction lower the threshold for applying the results. This invention's visualization module intuitively presents the point, line, and angle elements and error ellipses of a planar traverse network on an online map. It distinguishes element attributes through different shapes (circles for known points, squares for measured points) and colors (red for known edges, blue for edges to be measured). The display size can be controlled by adjusting the error ellipse's scaling parameters, making the accuracy estimation results easier to understand. Even non-professionals can quickly identify the location and error range of the weakest point and weakest edge, facilitating communication and decision-making within the engineering team. Furthermore, the data interaction module supports importing CSV format data sources, exporting process and result data for accuracy estimation (including the point number and mean square error corresponding to the weakest point, and the edge number and mean square error corresponding to the weakest edge). This facilitates subsequent data analysis, archiving, and reuse, lowering the barrier to application and management of accuracy results.

[0058] V. Structured data storage supports efficient computing and data management. The data storage module added in this invention uses a unified array to store the point numbers, point coordinates, edge numbers, edge lengths, and angle data of the planar traverse network. The array structure is clear and standardized, which facilitates matrix operations based on the array to quickly calculate error parameters in the accuracy estimation module, and also benefits the unified management and retrieval of data. Compared with traditional distributed data storage, unified array storage reduces redundant operations during data retrieval, improves calculation speed, and avoids data loss or corruption, ensuring the integrity and security of data throughout the accuracy estimation process.

[0059] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for estimating the accuracy of conductors, characterized in that, It includes a data acquisition module, a coordinate transformation module, a traverse design module, a precision estimation module, a visualization module, and a data interaction module; The data acquisition module is used to acquire geospatial data and measuring instrument parameters of the area to be processed. The coordinate transformation module is used to convert the latitude and longitude coordinates in the geospatial data into planar projected coordinates using Gaussian forward calculation. The Gaussian forward calculation formula is: in From the equator to latitude The meridian arc length, Let be the radius of curvature of the circle. Latitude Due to the difference in longitude; The traverse design module is used to construct a planar traverse network in the form of a perihelion network, a protractor network, or a lateral-angle network based on the planar projection coordinates; The accuracy estimation module is used to calculate the standard error of the weakest point, the relative standard error of the weakest side, and the plane continuity error based on the type of the planar traverse network and the parameters of the measuring instrument. The calculation of the standard error of the weakest point uses the error propagation law, and the formula is: , , These represent the variance and covariance of the principal directions of the positional error ellipse, respectively. , These are the mean square errors in the x and y directions, respectively; the visualization module is used to draw the point, line, and angle features and error ellipses of the planar traverse network on the online map, and to distinguish feature attributes by different shapes or colors; The data interaction module is used to import and export data.

2. The system for estimating conductor accuracy according to claim 1, characterized in that, The measuring instrument parameters include the angular measurement error, the fixed error A, and the error coefficient B. The traverse design module can match the corresponding measuring instrument parameters according to the preset control network level. The control network level includes at least level two and level four. The angular measurement error corresponding to level two control network is 8, the fixed error A is 12, and the error coefficient B is 12. The angular measurement error corresponding to level four control network is 2.5, and the relative side error is 1 / 14000.

3. The system for estimating conductor accuracy according to claim 1, characterized in that, When constructing a planar traverse network, the traverse design module can set a traverse side length threshold, which is not less than 50m, to avoid the influence of short sides on angle measurement errors and lateral continuity errors.

4. The system for estimating conductor accuracy according to claim 1, characterized in that, The accuracy estimation module calculates the planar penetration error, including lateral penetration error, angle penetration error, and distance penetration error. The formula for calculating the lateral penetration error is as follows: M represents the lateral penetration error caused by angular measurement error. This refers to the lateral penetration error caused by the ranging error.

5. The system for estimating conductor accuracy according to claim 1, characterized in that, When the visualization module draws the error ellipse, it uses the line drawing function of the online map API to fit the ellipse outline through multi-segment lines, and can adjust the ellipse scale parameters to control the display size of the error ellipse on the map.

6. The system for estimating conductor accuracy according to claim 1, characterized in that, The data interaction module supports importing data sources in CSV format and supports exporting data including process data and result data for accuracy estimation. The result data includes the point number corresponding to the weakest point, the edge number corresponding to the weakest edge, and the standard error corresponding to each.

7. The system for estimating conductor accuracy according to claim 1, characterized in that, The formula for calculating the radius of curvature N of the ramidal circle in the coordinate transformation module is as follows: in For the Earth's semi-major axis, It has the highest eccentricity on Earth.

8. The system for estimating conductor accuracy according to claim 1, characterized in that, When calculating the relative mean square error of the weakest side, the accuracy estimation module uses the following formula: in The variance factor is the side length. The error is the unit weight. Let be the length of the weakest side.

9. The system for estimating conductor accuracy according to claim 1, characterized in that, The specific way the visualization module distinguishes element attributes is as follows: known points are marked with circles, and test stations are marked with squares; known edges are marked with red lines, and edges to be measured are marked with blue lines.

10. The system for estimating conductor accuracy according to claim 1, characterized in that, It also includes a data storage module, which uses a unified array to store the point number, point coordinates, edge number, edge length and angle data of the planar traverse network, and performs matrix operations based on the unified array to realize the calculation of error parameters in the accuracy estimation module.

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