A portable distance measurement method and system for engineering surveying

Abstract

This invention discloses a portable ranging method and system for engineering surveying. The method includes: acquiring distance measurement points of a target object and the attitude parameters of the measuring equipment using a measuring device; processing the distance measurement points using a density clustering algorithm to obtain valid measurement points, marking these valid measurement points to obtain a ranging node sequence; calculating the Euclidean distance between adjacent valid measurement points in the ranging node sequence, constructing a ranging chain branch, and adjusting the ranging chain branch when the directional angle between adjacent valid measurement points satisfies a first spatial constraint condition to obtain a continuous ranging chain; based on the curvature change of adjacent valid measurement points in the continuous ranging chain, obtaining abnormal node segments, acquiring compensated ranging values ​​for abnormal node segments and base ranging values ​​for non-abnormal node segments, and obtaining the target ranging value. This invention more comprehensively reflects the actual situation of the measurement scenario and can better adapt to various challenges in complex measurement environments.

Description

Technical Field

[0001] This invention relates to the field of engineering surveying technology, specifically to a portable distance measuring method and system for engineering surveying. Background Technology

[0002] In engineering construction, geological exploration, and building construction, accurately obtaining distance information of the target area is a prerequisite for carrying out various tasks. Traditional engineering surveying and distance measurement methods mostly rely on large-scale surveying equipment. This equipment is often bulky and heavy, requiring multiple people to carry and operate it, making it difficult to adapt to the measurement needs of complex terrain or confined spaces. For example, in scenarios such as interior decoration measurement and pipeline laying and positioning, the entry and erection of large equipment are very inconvenient, greatly limiting the flexibility and efficiency of surveying work.

[0003] With technological advancements, portable ranging devices have gradually emerged on the market, alleviating the limitations of large-scale equipment to some extent. However, existing portable ranging methods still have numerous problems in data acquisition and processing. Some methods rely solely on a single ranging unit to acquire distance data, ignoring the impact of changes in the measuring device's own attitude on the measurement results, leading to a significant increase in measurement errors when the device shakes or tilts. Other methods, while considering device attitude, lack effective filtering and constraint mechanisms during data processing, resulting in a large amount of redundant or abnormal data being included in the calculations, affecting the accuracy of the final ranging results.

[0004] Existing portable ranging methods often employ simple time-series stitching when constructing ranging chains, failing to adequately incorporate spatial constraints based on motion trajectories. This leads to issues such as broken ranging chains or chaotic topological relationships. When obstacles or electromagnetic interference exist in the measurement environment, measurement data may exhibit jumps or gaps. Traditional methods are weak in identifying and processing such anomalies, often directly discarding outlier segments, resulting in incomplete ranging results that fail to accurately reflect the true distance information of the target area. These problems make it difficult for existing portable ranging methods to balance convenience, accuracy, and completeness in practical applications, failing to meet the requirements of engineering surveying for high-precision, high-reliability ranging results. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a portable ranging method and system for engineering measurement, so as to avoid invalid data occupying computing resources, ranging chain logic confusion caused by simple splicing, and abnormal data affecting the overall results, so that the final output ranging value is closer to the actual distance of the target area.

[0006] To solve the above technical problems, the present invention adopts the following technical solution:

[0007] A portable distance measurement method for engineering surveying, comprising:

[0008] S1. Use measuring equipment to obtain the distance measurement points of the target object and the attitude parameters of the measuring equipment in engineering measurement.

[0009] S2. Use density clustering algorithm to process the distance measurement points to obtain valid measurement points, mark the valid measurement points, and obtain the distance measurement node sequence.

[0010] S3. Calculate the Euclidean distance between adjacent valid measurement points in the ranging node sequence. When the Euclidean distance is greater than the maximum allowable offset or the timestamp is interrupted, construct a ranging chain branch. When the directional angle between adjacent valid measurement points satisfies the first spatial constraint condition, adjust the ranging chain branch to obtain a continuous ranging chain.

[0011] S4. Based on the curvature change of adjacent effective measurement points in the continuous ranging chain, abnormal node segments are obtained. Compensated ranging values ​​of abnormal node segments and basic ranging values ​​of non-abnormal node segments are obtained to obtain the target ranging value.

[0012] Furthermore, in step S1, the measuring device integrates a laser ranging unit and an inertial measurement unit.

[0013] The laser ranging unit emits a modulated laser signal at a set fixed sampling frequency. After the signal is reflected by the target object, the reflected signal is obtained. The phase difference between the modulated laser signal and the reflected signal is calculated to obtain the original distance measurement value.

[0014] The inertial measurement unit (IMU) acquires triaxial acceleration and triaxial angular velocity data in real time. The data is processed using a noise reduction filter, and the processed triaxial acceleration data is double-integrated using a dead reckoning algorithm. Combined with the triaxial angular velocity data, the attitude parameters of the measurement equipment are obtained, including pitch angle, roll angle, and yaw angle.

[0015] The attitude parameters of the measuring equipment are input into the dynamic compensation model. The distance compensation coefficient of the measuring equipment in the vertical direction is calculated based on the pitch angle offset, and the correction factor of the measuring equipment in the horizontal direction is calculated based on the yaw angle offset. The original distance measurement value is multiplied by the distance compensation coefficient to obtain the distance measurement point.

[0016] Furthermore, in step S2, the obtained ranging node sequence includes the following:

[0017] The elevation angle and azimuth angle are obtained from the attitude parameters of the measuring equipment. The distance measurement point is then mapped to a three-dimensional coordinate system to obtain the spatial coordinates of the distance measurement point in the three-dimensional coordinate system.

[0018] Centered on the current distance measurement point and within a preset neighborhood radius, a density clustering algorithm is used to identify spatial regions within this range where the number of other distance measurement points exceeds the minimum density threshold. These regions are then designated as spatial clustering areas, and the distance measurement points within these spatial clustering areas are considered as valid measurement points.

[0019] Add timestamps to valid measurement points to generate a sequence of distance measurement nodes arranged in ascending order of timestamps.

[0020] Furthermore, in step S3, the resulting continuous ranging chain includes the following:

[0021] S301. Calculate the Euclidean distance between the current valid measurement point and its next adjacent valid measurement point in the ranging node sequence based on the three-dimensional coordinate system; when the Euclidean distance is less than or equal to the maximum allowable offset, add the next adjacent valid measurement point to the end of the current ranging chain; when the Euclidean distance is greater than the maximum allowable offset or the timestamp is interrupted, create a ranging chain branch for the current valid measurement point.

[0022] S302. Based on the attitude parameters of the measuring device obtained in step S1, extract the historical motion trajectory data, use the recursive least squares method to obtain the historical movement direction vector of the measuring device, and combine the historical movement direction vector to calculate the direction angle between the current effective measuring point and its adjacent next effective measuring point. When the direction angle is less than the preset angle tolerance, it indicates that the first spatial constraint condition is met. Add the adjacent next effective measuring point to the ranging chain branch corresponding to the current effective measuring point to obtain the adjusted ranging chain branch.

[0023] S303. Perform steps S301-S302 on all valid measurement points in the ranging node sequence, and all the adjusted ranging chain branches obtained constitute a continuous ranging chain.

[0024] Furthermore, the spatial coordinates of adjacent valid measurement points are compared with the displacement calculation value of the measuring device in real time; when the deviation between the spatial coordinates and the displacement calculation value exceeds the calibration trigger threshold, the measuring device is started to reacquire the distance measurement point at the current position, and the reacquired distance measurement point is used as the calibration node to update the continuous ranging chain.

[0025] Furthermore, in step S4, the target ranging value obtained includes the following:

[0026] Calculate the curvature change of adjacent valid measurement points in the continuous ranging chain. When the curvature change of three consecutive valid measurement points exceeds the dynamic curvature threshold, mark the interval formed by the three valid measurement points as an abnormal node segment.

[0027] Extract the coordinates of the first and last measurement points of the abnormal node segment, take the straight-line distance between the first and last measurement points as the relative displacement, input the relative displacement into the chain integrity verification model, output the effective distance weight of the abnormal node segment, multiply the effective distance weight by the relative displacement to obtain the compensation distance value.

[0028] The cumulative distance of all non-abnormal node segments in the continuous ranging chain is traversed to obtain the base ranging value; the base ranging value and the compensation ranging value are superimposed to obtain the target ranging value.

[0029] Furthermore, the present invention also proposes a portable ranging system for engineering surveying, including a measuring end, a data processing module, a data analysis module, and a display terminal.

[0030] The measurement end includes a laser ranging unit, an inertial measurement unit, and a wireless transmission unit. It is used to collect the original distance measurement value of the target object in the engineering measurement using the laser ranging unit, collect the attitude parameters of the measuring equipment of the target object in the engineering measurement using the inertial measurement unit, compensate the original distance measurement value using the attitude parameters of the measuring equipment to obtain the distance measurement point, and transmit the collected distance measurement point and the attitude parameters of the measuring equipment to the data processing module through the wireless transmission unit.

[0031] The data processing module is used to process the distance measurement points using a density clustering algorithm to obtain a distance measurement node sequence; calculate the Euclidean distance between adjacent valid measurement points in the distance measurement node sequence; when the Euclidean distance is greater than the maximum allowable offset or the timestamp is interrupted, construct a distance measurement chain branch; when the directional angle between adjacent valid measurement points satisfies the first spatial constraint condition, adjust the distance measurement chain branch to obtain a continuous distance measurement chain.

[0032] The data analysis module is used to obtain abnormal node segments based on the curvature change of adjacent effective measurement points in the continuous ranging chain, acquire the compensated ranging value of the abnormal node segments and the basic ranging value of the non-abnormal node segments, and obtain the target ranging value.

[0033] The display terminal is used to receive target ranging values ​​and generate a visual ranging report.

[0034] Furthermore, the data processing module and the measurement end establish a data channel via Bluetooth protocol, the data analysis module receives the continuous ranging chain data packets output by the data processing module via IoT protocol, and the display terminal obtains the target ranging value through the data analysis module and updates the three-dimensional ranging trajectory map in real time.

[0035] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the steps of the portable ranging method for engineering measurement.

[0036] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, performs the portable ranging method for engineering measurements.

[0037] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0038] 1. This invention can detect the tilt and shaking of the equipment in a timely manner during the measurement process, realize the fusion and acquisition of multi-dimensional data, more comprehensively reflect the actual situation of the measurement scenario, and provide richer original evidence for subsequent data processing.

[0039] 2. This invention reduces the interference of redundant and obviously erroneous data on subsequent processing, avoids invalid data occupying computing resources, and lays a good data foundation for building an accurate ranging chain.

[0040] 3. This invention fully considers the spatial correlation in the measurement process, enabling the ranging chain to truly reflect the movement path of the measuring end and the spatial structure of the target area, avoiding the logical confusion of the ranging chain caused by simple splicing, and reflecting the continuity and rationality of the ranging chain.

[0041] 4. This invention utilizes the effective information of abnormal segments through reasonable calculation methods, which reduces the impact of abnormal data on the overall results while ensuring the integrity of the ranging results. This makes the final output ranging value more consistent with the actual distance of the target area and can better adapt to various challenges in complex measurement environments. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the overall implementation of the method proposed in this invention.

[0043] Figure 2 This is a flowchart illustrating the generation of the ranging node sequence in this invention.

[0044] Figure 3 This is a flowchart illustrating the generation and adjustment of the ranging chain branches in this invention.

[0045] Figure 4 This is a three-dimensional trajectory diagram of the effective measurement points in an embodiment of the present invention.

[0046] Figure 5 This is an architecture diagram of the system proposed in this invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0049] To achieve the above objectives, this invention proposes a portable distance measurement method for engineering surveying, such as... Figure 1 As shown, it includes:

[0050] S1. Obtain the distance measurement points of the target object and the attitude parameters of the measurement equipment during engineering surveying using measuring equipment. Specifically:

[0051] The measuring equipment integrates a laser ranging unit and an inertial measurement unit to achieve collaborative data acquisition.

[0052] The laser ranging unit emits a modulated laser signal of a specific wavelength at a set fixed sampling frequency. This signal is reflected by the target object and captured by the receiving sensor to obtain the reflected signal. The phase difference between the modulated laser signal and the reflected signal is calculated to obtain the original distance measurement value. A signal gain controller is used to adjust the receiving sensitivity to adapt to different ambient lighting conditions.

[0053] The inertial measurement unit operates synchronously, with its built-in three-axis accelerometer continuously acquiring linear motion data of the measuring equipment to obtain three-axis acceleration data. The three-axis gyroscope captures the angular velocity changes of the measuring equipment in real time to obtain three-axis angular velocity data. The data is processed using a noise reduction filter, and the processed three-axis acceleration data is double-integrated using a dead reckoning algorithm. Combined with the three-axis angular velocity data, the attitude parameters of the measuring equipment are obtained, including pitch angle, roll angle, and yaw angle.

[0054] The attitude parameters of the measuring device are strictly synchronized with the original distance measurement values ​​through a hardware clock to ensure timestamp alignment.

[0055] The attitude parameters of the measuring equipment are input into the dynamic compensation model. The distance compensation coefficient of the measuring equipment in the vertical direction is calculated based on the pitch angle offset, and the correction factor of the measuring equipment in the horizontal direction is calculated based on the yaw angle offset. The original distance measurement value is multiplied by the distance compensation coefficient to obtain the distance measurement point.

[0056] The distance measurement point transmission uses differential coding technology, where adjacent data packets only send coordinate changes instead of complete data, effectively reducing the wireless channel load.

[0057] S2. The distance measurement points are processed using a density clustering algorithm to obtain valid measurement points. These valid measurement points are then marked to obtain a sequence of distance measurement nodes. For example... Figure 2 As shown, specifically:

[0058] The elevation and azimuth angles are obtained from the pitch, roll, and yaw angles in the attitude parameters of the measuring equipment. Elevation angle = 90° - pitch angle, azimuth angle = yaw angle. The elevation angle is used to determine the Z-axis component, and the azimuth angle is used to calculate the XY plane projection. The distance measurement point is mapped to a three-dimensional coordinate system to obtain its spatial coordinates in the three-dimensional coordinate system. The coordinate array records the X, Y, and Z axis values ​​in single-precision floating-point format.

[0059] Centered on the current distance measurement point and within a preset neighborhood radius, a density clustering algorithm is used to identify spatial regions where the number of other distance measurement points exceeds a minimum density threshold. These regions are designated as spatial clusters, and distance measurement points within these clusters are considered valid measurement points. The density clustering algorithm optimizes the spatial partitioning index by dividing the 3D space into a cubic grid, performing distance calculations only on points within adjacent grids. Valid measurement points are stored using a sparse matrix structure, and storage space for filtered-out points is removed to improve memory efficiency.

[0060] The preset neighborhood radius is set according to the measurement accuracy requirements, and the minimum density threshold is set according to the sampling frequency and environmental complexity. For example, if the preset neighborhood radius is set to 0.3 meters and the minimum density threshold is set to 5, then if there are at least 5 other distance measurement points within 0.3 meters of a certain distance measurement point, the spatial area is identified as a spatial clustering area.

[0061] Timestamps are added to valid measurement points, generating a ranging node sequence arranged in ascending order of timestamps. A clock source assigns a unique time identifier to each point in the ranging node sequence, accurate to the millisecond level and associated with an absolute time reference. The timestamp index table uses 64-bit integers to store time offsets. The timestamp marking process incorporates a time synchronization protocol, periodically performing nanosecond-level time calibration with the measurement end clock to eliminate clock drift errors between devices. The ranging node sequence includes timestamps, 3D coordinate values, and node index numbers.

[0062] A double-buffering mechanism is employed during the generation of the ranging node sequence: a front-end buffer receives new data, while a back-end buffer executes the ranging node sequence generation operation. Once generation is complete, data is transmitted via a shared memory interface. This double-buffering mechanism ensures real-time data transmission while preventing data loss during the generation process. Data compression is performed when outputting the ranging node sequence, using run-length encoding to reduce the transmission of duplicate coordinate values. A data stream pipeline mode is used to transmit the ranging node sequence, allowing real-time reading of node data without waiting for the entire sequence to be generated.

[0063] The laser ranging unit incorporates an automatic gain control loop to monitor the laser reflection signal intensity, inertial data noise level, and ambient light intensity in real time. The inertial measurement unit (IMU) has a built-in temperature sensor that monitors chip temperature changes in real time. An environmental complexity assessment model outputs an environmental level, categorized as low, medium, and high. When the environmental level is high (e.g., strong light interference causing the reflected signal intensity to fall below the threshold of -60 dBm, or the noise variance of the data collected by the IMU exceeding 0.02 g²), a dynamic threshold adjustment mechanism is automatically triggered: the laser emission power is reduced to decrease background light interference, while the signal amplification at the receiver is increased to maintain the signal-to-noise ratio; if the reflected signal intensity remains below the detection threshold, a pulse width modulation (PWM) mode is triggered to enhance echo energy by extending the laser pulse duration; an online compensation for chip temperature is performed using a lookup table containing correction coefficients for the measuring device under different temperatures and humidity conditions; the minimum density threshold is increased by 20% from its base value to reduce false filtering of valid measurement points; and the preset neighborhood radius for density clustering is reduced to 0.2 meters to enhance the ability to identify sparse valid points.

[0064] S3. Calculate the Euclidean distance between adjacent valid measurement points in the ranging node sequence. When this Euclidean distance is greater than the maximum allowable offset or the timestamp is interrupted, construct a ranging chain branch. When the directional angle between adjacent valid measurement points satisfies the first spatial constraint condition, adjust the ranging chain branch to obtain a continuous ranging chain. Specifically:

[0065] S301. Calculate the Euclidean distance between the current valid measurement point and its next adjacent valid measurement point in the distance measurement node sequence based on a three-dimensional coordinate system. This distance is used to determine the continuity of valid measurement points. The specific formula is as follows:

[0066] ;

[0067] in, Let represent the Euclidean distance between the i-th valid measurement point and its next adjacent valid measurement point. , , Let X, Y, and Z represent the X, Y, and Z coordinate components of the i-th valid measurement point, respectively. , , These represent the X, Y, and Z coordinate components of the next adjacent valid measurement point, respectively.

[0068] The calculated Euclidean distance is stored in a temporary buffer for comparison with the maximum allowable offset. The maximum allowable offset is loaded by the project configuration file. This offset is preset to a fixed floating-point value according to different measurement scenarios. For example, when the sampling frequency of the laser ranging unit is 10Hz, the maximum allowable offset is set to 0.5 meters.

[0069] When the Euclidean distance is less than or equal to the maximum allowable offset, the next adjacent valid measurement point is added to the tail of the current ranging chain. The current ranging chain includes head and tail pointers and a node counter, and the node counter increments to record the current chain length. When the Euclidean distance is greater than the maximum allowable offset or the timestamp is interrupted, the current ranging chain state is frozen and stored in the chain set. A ranging chain branch is created for the current valid measurement point, and a unique chain identifier is assigned to the ranging chain branch. The identifier generation rule adopts an incremental sequence number pattern. Specifically, when the timestamp difference between the i-th valid measurement point and the (i-1)-th valid measurement point exceeds the timestamp threshold, it is marked as a timestamp interruption. The timestamp threshold is set based on the sampling interval of the measurement device. If the sampling rate is 100ms, the timestamp threshold is set to 200ms.

[0070] The ranging chain branch creation process sets up a state lock mechanism, and in a multi-threaded environment, mutex locks are used to ensure data consistency.

[0071] S302, such as Figure 3 As shown, based on the attitude parameters of the measuring device obtained in step S1, historical motion trajectory data is extracted. This data includes time-series-based device position coordinates, stored in a circular buffer with a fixed depth of the latest 100 position points. Vertical fluctuations in the data are ignored. Linear regression analysis is performed on the horizontal coordinate sequence of this data using the recursive least squares method to obtain the historical movement direction vector of the measuring device with a fixed length of 1.0. The direction represents the main direction of the measuring device's movement trend. The cosine of the angle between the current valid measuring point and its adjacent next valid measuring point is calculated by combining this historical movement direction vector. The specific formula is as follows:

[0072] ;

[0073] in, This represents the displacement vector from the current valid measurement point to the next adjacent valid measurement point. When... and When all vectors are zero, the result is considered invalid. The dot product result ranges from -1 to 1, and the direction angle is obtained after inverse cosine transformation. .

[0074] When the included angle in this direction is less than the preset angle tolerance, it indicates that the first spatial constraint condition is met. The next adjacent valid measurement point is then added to the ranging chain branch corresponding to the current valid measurement point, resulting in an adjusted ranging chain branch. The preset angle tolerance is loaded from the configuration file, with a typical value of 15 degrees.

[0075] In addition, a dual-caching strategy is adopted for storing historical motion trajectory data: the front-end cache updates the latest location points in real time, and the back-end cache is used by the fitting algorithm.

[0076] S303. Perform steps S301-S302 on all valid measurement points in the ranging node sequence. All the adjusted ranging chain branches obtained constitute a continuous ranging chain including topological relationships. When the spatial distance and time interval between the first and last nodes of two adjusted ranging chain branches are both less than the threshold, they are automatically connected into a single continuous ranging chain and the topological relationship graph is updated.

[0077] The initial ranging chain set is constructed using all the obtained ranging chain branches. This set adopts a hash table structure, with the key being the chain identifier and the content being an array of node indices. For example, identifier L1 corresponds to the valid measurement point index [1,3,5], and identifier L2 corresponds to the valid measurement point index [2,4,6]. The metadata model stores the spatial location statistics of all ranging chain branches, including the timestamp of the first node, the timestamp of the last node, and the spatial envelope boundary value. The chain identifier marks the chain status as valid, paused, or pending. The memory management model adopts a linked storage structure, and each valid measurement point entity contains spatial coordinates, a timestamp, a pointer to its chain, and a backward pointer.

[0078] The continuous ranging chain is converted into binary data blocks and transmitted directly using zero-copy technology. During transmission, the complete chain structure information is preserved, including the global index number of each node, chain branch connections, and chain topological feature values. Upon completion of transmission, a memory reclamation thread is triggered to delete the persistent chain data copy on disk. A chain structure verification model is used to periodically verify the integrity of the topological relationships. Broken chains or invalid pointers are detected by traversing valid measurement points. If the chain length exceeds a preset upper limit, it is automatically segmented, and a segmented time-related index is established. When spatial distance values ​​change abruptly but do not exceed the limit, a continuous trajectory smoothing algorithm automatically corrects the position coordinates of valid measurement points to maintain path consistency.

[0079] Implement a resource recycling mechanism: Frozen historical ranging chains are moved to the secondary cache storage, freeing up main memory space for continuous ranging chains.

[0080] In addition, the spatial coordinates of adjacent valid measurement points are compared with the displacement calculation value of the inertial measurement unit in real time; when the deviation between the spatial coordinates and the displacement calculation value exceeds the calibration trigger threshold, the laser ranging unit is activated to reacquire the distance measurement point at the current position, and the reacquired distance measurement point is used as the calibration node to update the continuous ranging chain.

[0081] S4. Based on the curvature change of adjacent valid measurement points in the continuous ranging chain, abnormal node segments are obtained. Compensated ranging values ​​for abnormal node segments and base ranging values ​​for non-abnormal node segments are acquired to obtain the target ranging value. Specifically:

[0082] Calculate the curvature change of adjacent valid measurement points in a continuous ranging chain, when three consecutive valid measurement points... , , When the curvature change exceeds the dynamic curvature threshold, the interval formed by the three valid measurement points is marked as an abnormal node segment. The abnormal node segment is written to non-volatile memory in real time to ensure data is not lost during abnormal power outages. A batch packaging mechanism is used during data transmission; every five identified abnormal node segments are packaged, compressed, and sent to the next stage.

[0083] Curvature change The calculation formula is:

[0084] ;

[0085] in, V1 represents the spatial angle between the first vector V1 and the second vector V2. point to V2 represents point to ; This represents the average length of two valid measurement point segments, specifically taken as... The value of .

[0086] Dynamic curvature threshold The calculation is adaptively based on the length of the continuous ranging chain, and a lookup table is used to achieve fast conversion, avoiding real-time floating-point operation latency. Specifically, the total number of valid measurement points in the continuous ranging chain is N. .

[0087] Extract the coordinates of the first measurement point of the abnormal node segment. Coordinates of tail measurement points The straight-line distance between the first and last measurement points is taken as the relative displacement. The specific formula is as follows:

[0088] .

[0089] Register-level parallel optimization is implemented during spatial coordinate difference calculation, and the three-axis differences are performed synchronously to shorten the processing cycle. Newton's iterative acceleration algorithm is used for square root calculation, with a fixed number of iterations to achieve the required accuracy. The unit of relative displacement is meters, and floating-point precision is preserved to the millimeter level. When the index ranges of adjacent abnormal node segments overlap, they are merged into a single abnormal node segment, and the start and end indices and displacement are recalculated.

[0090] The relative displacement is input into the chain integrity verification model, and the effective distance weight of the abnormal node segment is output. The effective distance weight is multiplied by the relative displacement to obtain the compensation distance value.

[0091] The cumulative distance of all non-abnormal node segments in the continuous ranging chain is traversed to obtain the base ranging value; the base ranging value and the compensation ranging value are superimposed to obtain the target ranging value.

[0092] Example:

[0093] The method proposed in this invention is used to measure a certain tunnel cross-section, such as... Figure 4 As shown, the coordinates of the effective measurement point with index 17 are (3.85, 0.97, 1.05). The inertial measurement unit records the triaxial acceleration data within the effective measurement points from index 16 to 17, and outputs a displacement estimate of 0.63 meters after double integration. The actual spatial distance between the effective measurement points from index 16 to 17 is 0.58 meters, and the estimated value deviates from the actual spatial distance by 0.05 meters. The trigger threshold during calibration is preset to 0.04 meters; exceeding the deviation limit activates the calibration procedure. The laser ranging unit immediately emits 16 modulated laser pulses, collects the reflected signal from the current position, calculates the average distance value, updates the coordinates of the effective measurement point with index 17 to (3.83, 0.96, 1.04), and recalculates the position coordinates of the subsequent effective measurement points with indices 18-20 based on this calibration point.

[0094] After acquiring the updated valid measurement point coordinates, the time-aligned displacement estimate is immediately read from the inertial measurement unit's buffer. Coordinate-estimated value pairs with perfectly matching timestamps are prioritized; if the time deviation of a coordinate-estimated value pair exceeds 50 milliseconds, it is automatically discarded, awaiting the arrival of the next data pair. Upon triggering the calibration signal, the laser ranging unit initiates priority scheduling, interrupting any low-priority measurement tasks currently in progress. The re-acquisition phase lasts 200 milliseconds, during which ranging chain expansion is paused. Locking is used to protect valid measurement points written to shared memory, preventing the topology building thread from reading inconsistent data.

[0095] The continuous ranging chain contains 62 valid measurement points, arranged in index 1-62, with three anomalous node segments marked: indices 8-10, 23-26, and 45-47. Non-anomalous node segments are processed sequentially starting from the valid measurement point at index 1. These non-anomalous node segments are divided into six consecutive intervals: 1-7, 11-22, 27-44, 48-51, 52-57, and 58-62. The coordinates of the valid measurement point at index 11 are (2.31, 1.05, 0.98), and the coordinates of the valid measurement point at index 12 are (2.38, 1.12, 0.99), with a Euclidean distance of 0.087 meters between them. Processing sequentially up to the valid measurement point at index 22, the cumulative distance of segment 11-22 reaches 3.217 meters. The distances of the remaining non-anomalous node segments are accumulated in the same way, resulting in a final base ranging value of 38.526 meters.

[0096] In the abnormal node segment with indices 8-10, the valid measurement point coordinates for index 8 are (1.02, 3.17, 1.15), and the valid measurement point coordinates for index 10 are (1.11, 3.28, 1.18). The relative displacement of this abnormal node segment is 0.131 meters. This relative displacement is input into the chain integrity verification model. The model's built-in weight factor table contains the displacement range and corresponding weights: the weight factor for the 0-0.2 meter interval is 0.92. The compensation distance value is calculated as: 0.131 × 0.92 = 0.1205 meters.

[0097] In the abnormal node segment with indices 23-26, the valid measurement point coordinates for index 23 are (4.07, 0.23, 1.02), and the valid measurement point coordinates for index 26 are (4.25, 0.31, 1.06). The relative displacement of this abnormal node segment is 0.196 meters, and the weighting factor is 0.87. The compensation distance calculation is: 0.196 × 0.87 = 0.1705 meters.

[0098] In the abnormal node segment with index 45-47, the relative displacement is 0.085 meters, the weight factor is 0.95, and the compensation distance value is calculated as follows: 0.085 × 0.95 = 0.0808 meters.

[0099] For the three abnormal node segments, the cumulative compensation distance value is 0.3718 meters.

[0100] The target distance measurement is 38.526 + 0.3718 = 38.8978 meters. After rounding to two decimal places, the target distance measurement is output as 38.90 meters to the display terminal. The terminal interface synchronously updates the values ​​on the 3D trajectory map. The abnormal node segment with index 8-10 displays a floating compensation value of 0.1205 meters. The terminal interface displays the occurrence time and coordinate correction amount of the calibration event for the valid measurement point with index 17. Operators can click on the abnormal node segment to view a detailed diagnostic report, including a comparison diagram of the coordinates of the first and last nodes, the displacement calculation process, and the basis for the selection of weighting factors.

[0101] like Figure 5 As shown, this embodiment of the invention also proposes a portable ranging system for engineering surveying, including a measuring end, a data processing module, a data analysis module, a display terminal, and a computer program that can run on a processor. It should be noted that each module in the above system corresponds to a specific step of the method provided in this embodiment of the invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this embodiment of the invention.

[0102] This invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0103] This invention also proposes a computer-readable storage medium storing a computer program. It should be noted that when the computer program is executed by a processor, it corresponds to the specific steps of the method provided in this invention, possessing the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in this invention.

[0104] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A portable ranging method for engineering surveying, characterized in that, The method comprises the following steps: S1, obtaining a distance measurement point of a target object in engineering surveying and a measurement device posture parameter by using a measurement device; S2, processing the distance measurement point by using a density clustering algorithm to obtain an effective measurement point, and marking the effective measurement point to obtain a ranging node sequence; S3, calculating the Euclidean distance between adjacent effective measurement points in the ranging node sequence, and when the Euclidean distance is greater than a maximum allowed offset or a timestamp interruption, constructing a ranging chain branch, and when the direction angle between adjacent effective measurement points satisfies a first spatial constraint condition, adjusting the ranging chain branch to obtain a continuous ranging chain; In step S3, obtaining the continuous ranging chain comprises the following contents: S301, calculating the Euclidean distance between a current effective measurement point and a next adjacent effective measurement point in the ranging node sequence based on a three-dimensional coordinate system; when the Euclidean distance is less than or equal to the maximum allowed offset, the next adjacent effective measurement point is added to the tail of the current ranging chain; when the Euclidean distance is greater than the maximum allowed offset or the timestamp interruption, a ranging chain branch is created for the current effective measurement point; S302, extracting historical motion trajectory data according to the measurement device posture parameter obtained in step S1, obtaining a historical movement direction vector of the measurement device by using a recursive least squares method, calculating the direction angle between the current effective measurement point and the next adjacent effective measurement point in combination with the historical movement direction vector, and when the direction angle is less than a preset angle tolerance, it is indicated that the first spatial constraint condition is satisfied, the next adjacent effective measurement point is added to the ranging chain branch corresponding to the current effective measurement point, and an adjusted ranging chain branch is obtained; S303, performing steps S301-S302 on all effective measurement points in the ranging node sequence, and all adjusted ranging chain branches obtained constitute a continuous ranging chain; S304, comparing the spatial coordinates of adjacent effective measurement points and the displacement calculation value of the measurement device in real time; when the deviation between the spatial coordinates and the displacement calculation value exceeds a calibration trigger threshold, the measurement device is started to reacquire a distance measurement point of the current position, and the continuous ranging chain is updated with the reacquired distance measurement point as a calibration node; S4, obtaining an abnormal node segment based on the curvature change amount of adjacent effective measurement points in the continuous ranging chain, obtaining a compensation ranging value of the abnormal node segment and a basic ranging value of a non-abnormal node segment, and obtaining a target ranging value; In step S4, obtaining the target ranging value comprises the following contents: calculating the curvature change amount of adjacent effective measurement points in the continuous ranging chain, and when the curvature change amount of three consecutive effective measurement points exceeds a dynamic curvature threshold, marking the interval formed by the three effective measurement points as an abnormal node segment; extracting the first measurement point coordinates and the tail measurement point coordinates of the abnormal node segment, taking the straight line distance between the first and tail measurement points as a relative displacement amount, inputting the relative displacement amount into a chain integrity verification model, outputting an effective distance weight of the abnormal node segment, multiplying the effective distance weight by the relative displacement amount, and obtaining a compensation ranging value; traversing the cumulative distance of all non-abnormal node segments in the continuous ranging chain to obtain a basic ranging value, and superimposing the basic ranging value and the compensation ranging value to obtain a target ranging value.

2. The portable ranging method for engineering surveying according to claim 1, characterized in that, In step S1, a laser ranging unit and an inertial measurement unit are integrated on the measuring device; The laser ranging unit emits a modulated laser signal at a fixed sampling frequency, and after the signal is reflected by the target object, a reflected signal is obtained. The phase difference between the modulated laser signal and the reflected signal is calculated to obtain an original distance measurement value; The inertial measurement unit collects three-axis acceleration data and three-axis angular velocity data in real time, processes the data using a noise reduction filter, performs double integration processing on the processed three-axis acceleration data using a dead reckoning algorithm, and combines the three-axis angular velocity data to obtain a measuring device attitude parameter, which includes a pitch angle, a roll angle, and a yaw angle; The measuring device attitude parameter is input into a dynamic compensation model, a distance compensation coefficient of the measuring device in the vertical direction is calculated according to the offset of the pitch angle, a correction factor of the measuring device in the horizontal direction is calculated according to the offset of the yaw angle, and the original distance measurement value is multiplied by the distance compensation coefficient to obtain a distance measurement point.

3. The portable ranging method for engineering surveying according to claim 1, wherein, In step S2, the ranging node sequence includes the following contents: According to the measuring device attitude parameter, an elevation angle and an azimuth angle are obtained, the distance measurement point is mapped to a three-dimensional coordinate system, and the spatial coordinates of the distance measurement point in the three-dimensional coordinate system are obtained; Taking the current distance measurement point as the center and a preset neighborhood radius as the range, a density clustering algorithm is used to identify a spatial region in which the number of other distance measurement points contained in the range exceeds a minimum density threshold, as a spatial aggregation region. The distance measurement points in the spatial aggregation region are taken as valid measurement points. A time stamp is added to the valid measurement points to generate a ranging node sequence arranged in ascending order of the time stamp.

4. System for applying the portable ranging method for engineering surveying according to any one of claims 1-3, characterized in that, The system includes a measuring end, a data processing module, a data analysis module, and a display terminal; The measuring end includes a laser ranging unit, an inertial measurement unit, and a wireless transmission unit. The laser ranging unit is used to collect original distance measurement values of target objects in engineering measurement. The inertial measurement unit is used to collect measuring device attitude parameters of target objects in engineering measurement. The measuring device attitude parameters are used to compensate the original distance measurement values to obtain distance measurement points. The wireless transmission unit is used to transmit the collected distance measurement points and measuring device attitude parameters to the data processing module. The data processing module is used to process the distance measurement points using a density clustering algorithm to obtain a ranging node sequence. The Euclidean distance between adjacent valid measurement points in the ranging node sequence is calculated. When the Euclidean distance is greater than a maximum allowed offset or the time stamp is interrupted, a ranging chain branch is constructed. When the direction angle between adjacent valid measurement points satisfies a first spatial constraint condition, the ranging chain branch is adjusted to obtain a continuous ranging chain. The data analysis module is used to obtain an abnormal node segment based on the curvature change amount of adjacent valid measurement points in the continuous ranging chain, obtain a compensation ranging value of the abnormal node segment and a basic ranging value of a non-abnormal node segment, and obtain a target ranging value. The display terminal is used to receive the target ranging value and generate a visual ranging report.

5. The system for portable ranging method for engineering surveying according to claim 4, characterized in that, The data processing module and the measuring end establish a data channel through Bluetooth protocol, the data analysis module receives the continuous ranging chain data packet output by the data processing module through the Internet of Things protocol, and the display terminal obtains the target ranging value through the data analysis module and updates the three-dimensional ranging trajectory graph in real time.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program, when executed by the processor, implements the steps of the portable ranging method for engineering surveying according to any one of claims 1 to 3.

7. A computer-readable storage medium storing a computer program, the computer-readable storage medium being characterized by, The computer program, when executed by the processor, implements the steps of the portable ranging method for engineering surveying according to any one of claims 1 to 3.

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