Measurement method, system, equipment and medium
By measuring the same target point at multiple locations and performing data fusion and confidence assessment, the problem of low measurement accuracy in existing technologies is solved, achieving high-precision and high-reliability measurement results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTH SURVEYING & MAPPING INSTR
- Filing Date
- 2026-02-06
- Publication Date
- 2026-05-12
AI Technical Summary
Existing laser measurement technology suffers from low accuracy and cannot identify or eliminate random errors when encountering interference such as atmospheric disturbances, multipath effects, or slight equipment vibrations.
The same target point is measured at at least two different measurement locations. Through data fusion and confidence assessment, an adjustment scheme is determined until the confidence level reaches a threshold, thereby achieving high-precision measurement.
Through multiple measurements and data fusion, the accuracy and reliability of the measurements were improved, measurement errors were reduced, and the credibility and precision of the measurement results were enhanced.
Smart Images
Figure CN122015785A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of engineering measurement technology, and in particular to a measurement method, system, equipment and medium. Background Technology
[0002] In fields requiring high-precision spatial positioning, such as engineering surveying, deformation monitoring, and equipment installation, accurate and reliable measurement of the coordinates of target points is a core requirement.
[0003] Current laser measurement technologies mostly rely on single measurements, making it impossible to identify and eliminate random errors. When encountering interference such as atmospheric disturbances, multipath effects, or slight equipment vibrations, the measurement results may deviate, resulting in low measurement accuracy. Summary of the Invention
[0004] This invention provides a measurement method, system, device, and medium that can solve the problem of low measurement accuracy.
[0005] This invention provides a measurement method, comprising: The same target point is measured at at least two different measurement locations to obtain the measurement coordinates corresponding to each measurement location. The measurement coordinates are then fused to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement locations. A confidence assessment is performed based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, an adjustment plan is determined. The measurement is repeated according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, at which point the adjustment is stopped and the target coordinates of the target point are obtained.
[0006] This invention measures the same target point from multiple different stations, and the spatial intersection geometric synchronization amplifies signal redundancy, which can improve measurement accuracy. Data fusion of the measured coordinates from each station can average random errors, compress coordinate standard deviations, and significantly reduce measurement errors. Confidence assessment based on all measured coordinates and initial coordinates can improve the reliability of measurement results, thereby improving accuracy. When the confidence level is lower than a threshold, an adjustment scheme is generated, forming a closed-loop iteration that quickly approaches the lower limit of error and improves convergence efficiency. Iteration stops when the confidence level reaches the threshold, achieving high-precision and high-reliability measurement coordinate output.
[0007] Further, the confidence assessment based on all the measured coordinates and the initial coordinates to obtain the confidence level specifically involves: The mean coordinates are obtained by calculating the average value based on all the measured coordinates. The standard deviation is calculated based on all the measured coordinates and the mean coordinate to obtain a first value that characterizes the degree of dispersion among the measured coordinates. The second value is obtained by calculating the deviation between the initial coordinates and the mean coordinates. Based on the geometric relationship between each measurement location and the target point, a third value is calculated to characterize the geometric strength of the current measurement layout scheme; The confidence level is obtained by weighting the first value, the second value, and the third value according to the preset weighting coefficients.
[0008] By calculating the mean coordinates, a stable reference benchmark can be provided, improving the accuracy of subsequent discrepancies and bias calculations. The first value is used to quantify internal consistency; the smaller the value, the better the observation repeatability, reflecting the data precision in real time. The second value is used to quantify fusion bias; the smaller the value, the better the model matches the statistical results, improving accuracy. The third value is used to quantify graphic intensity; the higher the value, the greater the spatial intersection sharpness, with elevation and planar resolution enhanced simultaneously. Weighted composite confidence improves the credibility of the measurement results.
[0009] Furthermore, the weighting coefficients are determined based on data analysis or information entropy weighting.
[0010] In this way, weights can be automatically inverted through data analysis to achieve a precise match between weights and error contributions, thereby improving the accuracy of the assessment; or, weights can be assigned using the information entropy weighting method to dynamically highlight key indicators and enhance the sensitivity of the assessment.
[0011] Furthermore, the determination of the adjustment plan specifically includes: If the first value is greater than the preset standard deviation threshold, the adjustment scheme is obtained by increasing the preset number of measurements. If the third value is less than the preset intensity threshold, the adjustment scheme is obtained by adjusting each of the measurement positions or increasing the preset number of measurements. If the number of times the confidence level is less than the preset confidence threshold is greater than or equal to a preset quantity threshold, a prompt message will be generated to prompt the measurement equipment to perform fault detection.
[0012] In this way, when the first value exceeds the standard, the number of measurements can be increased to average random errors and reduce the standard deviation of coordinates; when the third value is low, the intersection angle can be increased by adjusting or adding stations to improve spatial resolution and simultaneously reduce plane and elevation errors; when the measurement fails to meet the standard continuously, the equipment self-check is triggered to quickly locate hardware or environmental anomalies, restore observation accuracy, and reduce invalid iterations.
[0013] Furthermore, the step of measuring the same target point at at least two different measurement locations to obtain the measurement coordinates corresponding to each measurement location specifically involves: For each measurement location, obtain the device coordinates, device attitude, and measurement value corresponding to each measurement location; The measured values are processed based on the device's attitude, and combined with the device coordinates, to obtain the measured coordinates of the target point at the measured position.
[0014] This method simultaneously acquires device coordinates, attitude, and slant range, forming a three-dimensional spatial vector in one step, maximizing the completeness of observation information. Target coordinates are calculated based on three-source observations, and the errors of each sensor are cross-validated, improving coordinate accuracy.
[0015] Furthermore, the process of fusing the measured coordinates to obtain the initial coordinates of the target point specifically involves: Based on the measured coordinates and the corresponding measured values, the observation model is determined using the least squares adjustment method. The observation model is solved using the least squares algorithm, and the initial coordinates are obtained by minimizing the observation error.
[0016] By constructing a least-squares adjustment model, coordinate errors are reduced, initial coordinates are output in one step, computational delay is low, and real-time coordinate accuracy is improved.
[0017] Furthermore, the process of fusing the measured coordinates to obtain the initial coordinates of the target point specifically involves: Obtain the objective function, wherein the objective function takes the initial coordinates as the variable to be optimized, and the objective function is obtained by calculating the sum of the squared residuals between each measured value and the corresponding observed value, wherein the observed value is obtained by calculating the squared difference of the distance between the measured coordinates and the initial coordinates; The initial coordinates are obtained by iteratively optimizing the objective function using a nonlinear optimization algorithm.
[0018] By constructing a residual sum of squares objective function, nonlinear observations are directly optimized, and the convergent value approaches the true extreme value, thus improving coordinate accuracy. Iterative optimization of the initial coordinates results in high accuracy and low error for large intersection angles and long-distance scenes.
[0019] Another embodiment of the present invention provides a measurement system, including: a first measurement module, a confidence assessment module, and a second measurement module; The first measurement module is used to measure the same target point at at least two different measurement positions, obtain the measurement coordinates corresponding to each measurement position, and perform data fusion on each measurement coordinate to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement positions; The confidence assessment module is used to perform a confidence assessment based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, an adjustment plan is determined. The second measurement module is used to remeasure according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, then stop the adjustment and obtain the target coordinates of the target point.
[0020] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the measurement method of the present invention.
[0021] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the measurement method of the present invention. Attached Figure Description
[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0023] Figure 1 This is a flowchart illustrating a measurement method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a measurement system provided in an embodiment of the present invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0026] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0028] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0030] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0031] See Figure 1 To address the problem of low measurement accuracy in existing technologies, an embodiment of the present invention provides a measurement method comprising: Step S101: Measure the same target point at at least two different measurement locations to obtain the measurement coordinates corresponding to each measurement location, and perform data fusion on each measurement coordinate to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement location.
[0032] In the above steps, the measuring device can perform observations on the same target point from at least two different spatial locations. At each location, the measuring device acquires a set of observation data containing spatial state information of that location and measurement information to the target point. Based on the observation data from each location, a spatial position estimate of the target point from that observation perspective is calculated, i.e., the measured coordinates. Subsequently, a data fusion algorithm is used to comprehensively process all measured coordinates to eliminate the random errors of a single observation and obtain a more robust position estimate, i.e., the initial coordinates of the target point.
[0033] As an example of an embodiment of the present invention, the step of measuring the same target point at at least two different measurement positions to obtain the measurement coordinates corresponding to each measurement position specifically involves: for each measurement position, obtaining the device coordinates, device attitude, and measurement value corresponding to each measurement position; processing the measurement value based on the device attitude, and combining it with the device coordinates to obtain the measurement coordinates of the target point at the measurement position.
[0034] In this embodiment, the measuring equipment is moved to the i-th station (i≥2) position (forming a certain angle θ with the previous station, the recommended angle is 30°≤θ≤150°), and the same target point is measured again. The current coordinates S of the measuring equipment are obtained through the satellite positioning module (such as a GNSS / RTK receiver) integrated into the measuring equipment. i This coordinate system defines the current three-dimensional position of the measuring equipment in a global reference coordinate system (such as the WGS-84 coordinate system or the National Geodetic Coordinate System). The current attitude of the measuring equipment is acquired through its integrated Inertial Measurement Unit (IMU). The equipment attitude is typically represented by a set of Euler angles (roll, pitch, and yaw) or quaternions. This attitude data defines the spatial orientation of the equipment's body coordinate system relative to the global reference coordinate system. The equipment attitude (Euler angles or quaternions) output by the IMU is then converted into a 3×3 rotation matrix R. i The measured value d is obtained through the laser ranging module integrated into the measuring equipment. i The measured coordinates P of the target point are calculated. i That is, the straight-line spatial distance from the laser emission point of the device to the reflection point on the surface of the target.
[0035] Based on the above three observation data, the measurement coordinates of the target point at the measurement location are calculated in real time using a spatial geometric calculation model. Specifically, in the equipment's coordinate system, it is assumed that the laser ranging direction is along the principal axis of the equipment's coordinate system (e.g., the positive Z-axis). Therefore, the laser measurement value d... i In the device coordinate system, this can be represented as a vector [0, 0, d]. iUsing the rotation matrix R, the local measurement vector [0, 0, d] in the above equipment coordinate system is transformed. i Transform to the global reference coordinate system to obtain the offset vector of the target point relative to the device coordinates. Then, convert the absolute coordinates S of the device itself. i The absolute coordinates P of the target point in the global reference coordinate system are obtained by adding the calculated global offset vector to the target point. i Among them, P i The calculation formula is as follows: .
[0036] Step S102: Perform a confidence assessment based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, determine an adjustment scheme.
[0037] In the above steps, using all the measurement coordinates obtained in step S101 and the fused initial coordinates as input, a confidence assessment calculation is performed. This assessment process comprehensively calculates a quantitative confidence index by analyzing at least one aspect of the consistency of the measurement data itself, the difference between the fused result and each independent observation, and the geometric configuration of the measurement location. This confidence index is compared with a preset confidence threshold: if it is not lower than the threshold, the current initial coordinates are considered to meet the reliability requirements and can be directly output as the final result; if it is lower than the threshold, an adjustment plan to guide the next measurement operation will be automatically generated based on the data obtained from the assessment calculation (e.g., whether the data is too discrete or the geometric configuration is poor).
[0038] Step S103: Remeasure according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, then stop the adjustment and obtain the target coordinates of the target point.
[0039] In the above steps, based on the adjustment scheme generated in step S102, corresponding measurement optimization operations are performed. The core of this operation is to change the measurement configuration, including adding new observation positions or adjusting existing observation positions. Based on the new measurement scheme, the target point is observed again to obtain new observation data and new measurement coordinates. Afterward, an iterative loop is entered: the new and old measurement coordinates are merged to form an updated dataset, and steps S101 and S102 are repeated until the confidence level calculated in a certain iteration is greater than or equal to the confidence threshold. The loop then terminates, and the initial coordinates obtained in this iteration are taken as the final confirmed target coordinates of the target point that meet the reliability requirements.
[0040] As an example of an embodiment of the present invention, the confidence assessment based on all the measured coordinates and the initial coordinates to obtain the confidence level specifically involves: calculating the average value based on all the measured coordinates to obtain the mean coordinates; calculating the standard deviation based on all the measured coordinates and the mean coordinates to obtain a first value characterizing the dispersion among the measured coordinates; calculating the deviation based on the initial coordinates and the mean coordinates to obtain a second value; calculating a third value characterizing the geometric strength of the current measurement layout scheme based on the geometric relationship between each measured position and the target point; and weighting the first value, the second value, and the third value according to a preset weighting coefficient to obtain the confidence level.
[0041] In this embodiment, the average value of multiple measured coordinates is calculated to obtain the mean coordinate Pˉ. The formula for calculating the mean coordinate is as follows: ; The standard deviation is calculated based on the above mean coordinates to obtain the first value. The formula for calculating the first value is as follows: ; The deviation between the initial coordinates P obtained from the intersection measurement and the mean coordinates Pˉ is calculated to obtain the second value. The formula for calculating the second value is as follows: Δ=|P−Pˉ|; f(θ) is the geometric intensity function related to the intersection angle θ, with a recommended angle of [30°, 150°]. θ is adjustable. The third value is calculated based on the geometric intensity function, which is expressed as follows: ; Where a, b, and c are confidence parameters, which are adjustable values. If there are three or more measurement locations, the intersection angle θ between each pair of measurement points can be different, but it is recommended that they all be within the above range; when the number is greater than a certain number (e.g., more than 5), consider reducing the minimum value of the recommended angle θ range (e.g., [15°, 150°]); in this case, calculate f(θ) between each pair of measurement points. i Then take the average value to get f(θ).
[0042] Taking into account the impact of the first value (data consistency), the second value (intersection precision), and the third value (measurement geometry strength) on the confidence level, for example, setting a standard deviation threshold σ. max When σ > σ max When the consistency of measurement results is poor and the confidence level is reduced, a deviation threshold Δ is set. max When Δ>Δ maxWhen the difference between the intersection measurement result and the average measurement result is too large, the confidence level is reduced. Therefore, a calculation is performed based on the preset weighting coefficient, expressed by the formula (this formula is only an example, and the calculation method is not limited to this formula): ; Where w1, w2, and w3 are weighting coefficients, and the objective weighting method can be used to determine the weighting coefficients: As an example of an embodiment of the present invention, the weighting coefficient is determined based on data analysis or information entropy weighting.
[0043] In this embodiment, when determining weight coefficients based on data analysis, a large amount of historical measurement data with known correct results is collected, and statistical methods (such as regression analysis) are used to deduce which evaluation dimensions have the greatest impact on the accuracy of the results, and the weights are adjusted accordingly. When determining weight coefficients based on the information entropy weighting method, the weights are determined according to the dispersion of the data of each evaluation dimension. If the data difference of a certain dimension is greater (the greater the entropy), it means that it carries more information, and the weight assigned should be higher.
[0044] As an example of an embodiment of the present invention, the determination of the adjustment scheme specifically involves: if the first value is greater than a preset standard deviation threshold, the adjustment scheme is obtained by increasing the number of measurements by a preset number; if the third value is less than a preset intensity threshold, the adjustment scheme is obtained by adjusting each of the measurement positions or increasing the number of measurements by a preset number; if the number of times the confidence level is less than the preset confidence threshold is greater than or equal to a preset quantity threshold, a prompt message is generated to prompt fault detection of the measuring equipment.
[0045] In this embodiment, when the standard deviation σ is too large (σ>σ), max To improve statistical reliability, it is recommended to increase the number of measurements (e.g., add two more measurements, remove the measurement with the largest deviation, and then recalculate the confidence level). When the intersection geometry is insufficient (f(θ) is a between every pair of measurement points), it is recommended to adjust the station position or increase the number of measurements (e.g., add one more measurement so that the range of θ is greater than the minimum of the recommended angle range) to improve the intersection angle. When a persistent anomaly is detected (the station layout has been increased and adjusted, but the recalculated confidence level is still below the threshold), it is recommended to check the equipment calibration status or the influence of environmental factors.
[0046] As an example of an embodiment of the present invention, the initial coordinates of the target point are obtained by data fusion of the measured coordinates, specifically by: determining the observation model using the least squares adjustment method based on the measured coordinates and the corresponding measured values; solving the observation model using the least squares algorithm; and obtaining the initial coordinates by minimizing the observation error.
[0047] In this embodiment, for measurement data obtained from different stations, the spatial intersection measurement method is used to calculate the initial coordinates of the target point. Specifically, N measurement coordinates {P} obtained from N different stations are aggregated. i | i=1...N}, and the corresponding N distance measurement values {d} acquired synchronously at each station. i | i=1...N}. Meanwhile, the equipment coordinates of each station are known {S}. i | i=1...N}. Let the three-dimensional unknown coordinates of the target point be set as the parameter vector to be determined. For the i-th station, its observed distance d i Theoretically, it should be equal to the target point coordinates X and the station coordinates S. i The Euclidean distance between them is used to construct a set of nonlinear observation equations for each station, resulting in an observation model, expressed as: ; Where (X,Y,Z) are the coordinates of the target point, (X... i ,Y i Z i Let be the coordinates of the target point measured in the i-th measurement, and let di be the distance from the device to the target point measured in the i-th measurement. i This represents the observation error.
[0048] The target point coordinates are set as parameters to be determined. The mean of the measured coordinates or the measurement result of any station is used as the initial approximation. A first-order Taylor expansion of the nonlinear observation equation is performed at the approximation to obtain the linearized error equation, and the design matrix and closed difference vector are constructed. Based on the least squares criterion—minimizing the sum of squares of all distance measurement observation residuals—the normal equation is established and solved to obtain the optimal estimate of the target point coordinate correction. The correction is added to the initial approximation to obtain the initial coordinates after one adjustment calculation. To improve accuracy, this result can be used as a new approximation to iteratively perform linearization and adjustment calculations until the coordinate correction converges to below a preset threshold. The final stable coordinate value output is the initial coordinate.
[0049] As an example of an embodiment of the present invention, the step of fusing data from each of the measured coordinates to obtain the initial coordinates of the target point specifically involves: obtaining an objective function, wherein the objective function uses the initial coordinates as a variable to be optimized, and the objective function is obtained by calculating the sum of the squared residuals between each measured value and the corresponding observed value, wherein the observed value is obtained by calculating the squared difference of the distance between the measured coordinates and the initial coordinates; and iteratively optimizing the objective function through a nonlinear optimization algorithm to obtain the initial coordinates.
[0050] In this embodiment, a nonlinear least squares problem with the target point coordinates as the optimization variable is constructed and solved. First, the initial coordinates X=[X,Y,Z] to be solved are used as the optimization variable, and the objective function is constructed based on spatial geometric relationships, expressed as: ; Where (X,Y,Z) are the coordinates of the target point, (X... i ,Y i Z i ) represents the coordinates of the target point measured in the i-th measurement, and di represents the distance from the device to the target point measured in the i-th measurement.
[0051] The above extreme value problem is solved using nonlinear optimization methods such as least squares, gradient descent, or Gauss-Newton, to obtain the optimal coordinate estimate of the target point. For example, the mean of the measured coordinates is used as the initial iteration value. In each iteration, the objective function value and its gradient (or Jacobian matrix) are calculated. The optimization variable X is updated according to strategies such as gradient descent, Gauss-Newton method, or Levenberg-Marquardt method until the function value converges or the change is lower than the set threshold. The X obtained at this time is the initial coordinate that satisfies the minimum sum of squared residuals.
[0052] It should be noted that the two data fusion algorithms mentioned above can be used separately or in combination.
[0053] like Figure 2 As shown, based on the above-described method embodiments, an embodiment of the present invention provides a measurement system 200, including: a first measurement module 201, a confidence assessment module 202, and a second measurement module 203; The first measurement module 201 is used to measure the same target point at at least two different measurement positions, obtain the measurement coordinates corresponding to each measurement position, and perform data fusion on each measurement coordinate to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement position; The confidence assessment module 202 is used to perform a confidence assessment based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, an adjustment plan is determined. The second measurement module 203 is used to remeasure according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, then stop the adjustment and obtain the target coordinates of the target point.
[0054] It is understood that the above system embodiments correspond to the method embodiments of the present invention, and can implement the measurement method provided by any of the above method embodiments of the present invention.
[0055] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0056] For ease of description and brevity, the system embodiments of the present invention include all the implementation methods described in the above measurement method embodiments, and will not be repeated here.
[0057] Based on the above-described embodiments of the measurement method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the measurement method of any embodiment of the present invention.
[0058] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0059] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0060] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0061] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the measurement method described in any of the above-described method embodiments of the present invention.
[0062] Based on the above-described method embodiments, this invention also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of any of the above-described method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.
[0063] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0064] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. A measurement method, characterized in that, include: The same target point is measured at at least two different measurement locations to obtain the measurement coordinates corresponding to each measurement location. The measurement coordinates are then fused to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement locations. A confidence assessment is performed based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, an adjustment plan is determined. The measurement is repeated according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, at which point the adjustment is stopped and the target coordinates of the target point are obtained.
2. The measurement method as described in claim 1, characterized in that, The confidence assessment based on all the measured coordinates and the initial coordinates to obtain the confidence level is specifically as follows: The mean coordinates are obtained by calculating the average value based on all the measured coordinates. The standard deviation is calculated based on all the measured coordinates and the mean coordinate to obtain a first value that characterizes the degree of dispersion among the measured coordinates. The second value is obtained by calculating the deviation between the initial coordinates and the mean coordinates. Based on the geometric relationship between each measurement location and the target point, a third value is calculated to characterize the geometric strength of the current measurement layout scheme; The confidence level is obtained by weighting the first value, the second value, and the third value according to the preset weighting coefficients.
3. The measurement method as described in claim 2, characterized in that, The weighting coefficients are determined based on data analysis or information entropy weighting.
4. The measurement method as described in claim 2, characterized in that, The specific details of determining the adjustment plan are as follows: If the first value is greater than the preset standard deviation threshold, the adjustment scheme is obtained by increasing the preset number of measurements. If the third value is less than the preset intensity threshold, the adjustment scheme is obtained by adjusting each of the measurement positions or increasing the preset number of measurements. If the number of times the confidence level is less than the preset confidence threshold is greater than or equal to a preset quantity threshold, a prompt message will be generated to prompt the measurement equipment to perform fault detection.
5. The measurement method as described in claim 1, characterized in that, The step of measuring the same target point at at least two different measurement locations to obtain the measurement coordinates corresponding to each measurement location specifically involves: For each measurement location, obtain the device coordinates, device attitude, and measurement value corresponding to each measurement location; The measured values are processed based on the device's attitude, and combined with the device coordinates, to obtain the measured coordinates of the target point at the measured position.
6. The measurement method as described in claim 5, characterized in that, The initial coordinates of the target point are obtained by fusing the data of each of the measured coordinates, specifically as follows: Based on the measured coordinates and the corresponding measured values, the observation model is determined using the least squares adjustment method. The observation model is solved using the least squares algorithm, and the initial coordinates are obtained by minimizing the observation error.
7. The measurement method as described in claim 5, characterized in that, The initial coordinates of the target point are obtained by fusing the data of each of the measured coordinates, specifically as follows: Obtain the objective function, wherein the objective function takes the initial coordinates as the variable to be optimized, and the objective function is obtained by calculating the sum of the squared residuals between each measured value and the corresponding observed value, wherein the observed value is obtained by calculating the squared difference of the distance between the measured coordinates and the initial coordinates; The initial coordinates are obtained by iteratively optimizing the objective function using a nonlinear optimization algorithm.
8. A measurement system, characterized in that, include: First measurement module, confidence assessment module, and second measurement module; The first measurement module is used to measure the same target point at at least two different measurement positions, obtain the measurement coordinates corresponding to each measurement position, and perform data fusion on each measurement coordinate to obtain the initial coordinates of the target point, wherein the measurement coordinates are the coordinates of the target point relative to the measurement positions; The confidence assessment module is used to perform a confidence assessment based on all the measured coordinates and the initial coordinates to obtain a confidence level. When the confidence level is less than a preset confidence threshold, an adjustment plan is determined. The second measurement module is used to remeasure according to the adjustment scheme until the confidence level is greater than or equal to the preset threshold, then stop the adjustment and obtain the target coordinates of the target point.
9. A terminal device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the measurement method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the measurement method as described in any one of claims 1-7.