Interactive communication line survey and design method and system

Abstract

The present invention discloses an interactive communication line survey and design method and system, which relates to the field of communication line survey and design technology, and includes the following steps: the three-dimensional laser scanner collects relevant operating parameter information in real time during the actual scanning process to provide basic data for subsequent abnormality analysis. The present invention collects and analyzes the operating parameter information of the three-dimensional laser scanner in real time, uses a machine learning model for intelligent evaluation, and timely detects potential accuracy problems, ensuring high-precision acquisition of three-dimensional terrain data and object surface information, avoiding line planning and design errors caused by low-precision data, and reducing design deviations and safety hazards. In addition, through intelligent evaluation of scanning accuracy quantization coefficients and abnormality type judgment, it quickly distinguishes sudden, unstable and continuous abnormalities, takes corresponding measures for different abnormalities, improves problem solving efficiency, and optimizes equipment maintenance and maintenance strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of communication line survey and design, and in particular to an interactive communication line survey and design method and system. Background Art

[0002] Interactive communication line survey and design is a method that combines traditional communication line design with modern computer technology, using interactive software tools to plan, design and optimize lines. Its core concept is to use computer-aided design (CAD) software and geographic information system (GIS) and other technologies to process and analyze line data in real time and generate accurate line maps and design plans. This method not only improves the efficiency and accuracy of the design, but also can better cope with complex terrain and environmental conditions, providing comprehensive technical support for engineers.

[0003] In actual operation, interactive communication line survey and design involves multiple steps, including site survey, data collection, preliminary design, optimization adjustment and final design. First, engineers need to conduct detailed surveys on site to collect relevant data such as topography, geology and environment. Then, these data are input into the system through professional design software to carry out preliminary design of the line. During this process, engineers can view and adjust the design plan in real time to ensure the safety and economy of the line. Finally, after multiple optimizations and verifications, the final design drawings and construction plans are generated. This interactive method not only improves work efficiency, but also significantly reduces design errors and ensures the smooth construction of communication lines.

[0004] In the process of interactive communication line survey and design, 3D laser scanners are often used. The specific function of 3D laser scanners is to quickly obtain high-precision 3D terrain data and object surface information by emitting laser beams and receiving reflected signals. This equipment can generate detailed terrain models and environmental data to help engineers accurately analyze site conditions and optimize line planning and design, especially in complex terrain and densely built areas, providing comprehensive visual and data support, thereby improving the accuracy of design and the feasibility of construction.

[0005] During the interactive communication line survey and design process, if the accuracy of the 3D terrain data and object surface information obtained by the 3D laser scanner deteriorates and is not discovered in time, low-precision data may lead to errors in line planning and design. For example, the line may avoid obstacles that do not actually exist, or fail to avoid obstacles that do exist, which will cause deviations in the implementation of the design and affect the layout and function of the communication line. Secondly, insufficiently accurate data may lead to the failure to identify and avoid potential geological risks in the design, such as underground pipelines, rock layers, or unstable soil. If these unexpected risks are encountered during the construction process, serious safety accidents may occur and endanger the lives of construction workers.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide an interactive communication line survey and design method and system, which collects and analyzes the operating parameter information of the three-dimensional laser scanner in real time, uses the machine learning model for intelligent evaluation, and promptly detects potential accuracy problems, ensuring high-precision acquisition of three-dimensional terrain data and object surface information, avoiding line planning and design errors caused by low-precision data, and reducing design deviations and safety hazards. In addition, through intelligent evaluation of the scanning accuracy quantification coefficient and abnormality type judgment, it is possible to quickly distinguish between sudden, unstable and continuous abnormalities, take corresponding measures for different abnormalities, improve problem solving efficiency, and optimize equipment maintenance and maintenance strategies to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: an interactive communication line survey and design method, comprising the following steps:

[0009] During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time, providing basic data for subsequent abnormal analysis;

[0010] Store the collected operating parameter information in a structured database, and organize and clean the data to ensure data integrity and consistency;

[0011] Perform anomaly analysis on the sorted operating parameter information, input the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluate the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information;

[0012] The machine learning model analyzes the input operating parameter information to detect potential accuracy issues when the 3D laser scanner acquires 3D terrain data and object surface information;

[0013] After detecting potential accuracy issues, the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information is comprehensively analyzed again to determine the type of potential anomalies, and different types of measures are taken according to different types of potential anomalies.

[0014] Preferably, the relevant operating parameter information includes laser beam collimation information and scanning frequency information. The scanning frequency refers to the number of times the three-dimensional laser scanner emits and receives laser pulses per unit time. The laser beam collimation refers to the ability of the laser beam to remain parallel and not diverge during the emission process, that is, the accuracy of the laser beam propagating along the designed trajectory.

[0015] Preferably, the collected operating parameter information is stored in a structured database, and the data is sorted and cleaned to ensure data integrity and consistency. The specific steps are as follows:

[0016] During the actual scanning process of the 3D laser scanner, the operating parameter information is collected in real time and transmitted to the central server through the transmission protocol;

[0017] storing the operating parameter information transmitted to the central server in a structured database;

[0018] Conduct preliminary checks on raw data stored in the database to verify the integrity and accuracy of the data;

[0019] Clean and process abnormal data found in the initial inspection;

[0020] The cleaned data is standardized to ensure that the data formats and units from different sources are consistent to facilitate subsequent analysis and use.

[0021] Preferably, after performing an abnormal analysis on the sorted scanning frequency, a scanning frequency instability index is generated, and after performing an abnormal analysis on the sorted laser beam collimation, a laser beam collimation index is generated. The scanning frequency instability index and the laser beam collimation index after the abnormal analysis are input into a pre-trained machine learning model to generate a scanning accuracy quantization coefficient, and the accuracy of the three-dimensional terrain data and the object surface information is intelligently evaluated through the scanning accuracy quantization coefficient.

[0022] Preferably, a scanning accuracy quantization coefficient generated by a machine learning model when a 3D laser scanner acquires 3D terrain data and object surface information is compared and analyzed with a preset scanning accuracy quantization coefficient reference threshold to detect potential accuracy problems when the 3D laser scanner acquires 3D terrain data and object surface information. The specific steps are as follows:

[0023] If the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold, an efficient scanning signal is generated, indicating that the three-dimensional laser scanner can efficiently obtain three-dimensional terrain data and object surface information;

[0024] If the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold, an abnormal scanning signal is generated, indicating that there is a potential accuracy problem when the 3D laser scanner acquires 3D terrain data and object surface information.

[0025] Preferably, when the 3D laser scanner acquires 3D terrain data and object surface information to generate an abnormal scanning signal, a number of scanning accuracy quantization coefficients generated subsequently are acquired to establish an analysis set, and the scanning accuracy quantization coefficient mean and the scanning accuracy quantization coefficient standard deviation are calculated through the scanning accuracy quantization coefficients in the data set, and the scanning accuracy quantization coefficient mean and the scanning accuracy quantization coefficient standard deviation are respectively compared and analyzed with the pre-set scanning accuracy quantization coefficient reference threshold and the standard deviation reference threshold to determine the type of potential anomaly. The specific determination results are as follows:

[0026] If the mean value of the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold and the scanning accuracy quantization coefficient standard is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a sudden anomaly;

[0027] If the standard deviation of the scanning accuracy quantization coefficient is greater than or equal to the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as an unstable anomaly;

[0028] If the mean value of the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold and the scanning accuracy quantization coefficient standard is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a persistent anomaly.

[0029] Preferably, after performing abnormal analysis on the sorted scanning frequency, the specific steps of generating the scanning frequency instability index are as follows:

[0030] Collect all the scanning frequency data of the scanner within the fixed detection window, and establish a scanning frequency data set F with all the scanning frequency data, then: F = {f i}={f1,f2,f3,…,fn}, where f i represents the i-th scanning frequency data point;

[0031] Calculate the mean and standard deviation of the scan frequency data set. The calculation expression is: In the formula, μ represents the mean of the scanning frequency data set, and σ represents the standard deviation of the scanning frequency data set;

[0032] Calculate the coefficient of variation of the scanning frequency data. The coefficient of variation measures the relative change of the standard deviation of the scanning frequency data set and the mean of the scanning frequency data set. The calculation expression is: Where, CV represents the coefficient of variation of scanning frequency;

[0033] Calculate the scanning frequency change rate, which reflects the degree of change between each consecutive data point. All scanning frequency change rate data are used to establish a scanning frequency change rate data set RoC, then: RoC = {r i} = {r1, r2, r3, ..., r n},in,

[0034] Calculate the mean value and standard deviation of the scanning frequency change rate. The calculation expression is: In the formula, μ RoC represents the mean value of the scanning frequency change rate, σ RoC Indicates the standard deviation of the scanning frequency change rate;

[0035] Calculate the scanning frequency instability index, the calculation expression is: SFII = α·CV + β·μ RoC +γ·σ RoC , where SFII represents the scanning frequency instability index, α, β, and γ are the scanning frequency coefficient of variation CV and the scanning frequency change rate mean μ respectively. RoC , standard deviation of scanning frequency change rate σ RoC The weight coefficient of .

[0036] Preferably, after performing abnormal analysis on the collimation of the laser beam, the specific steps of generating the laser beam collimation index are as follows:

[0037] The actual position data point set D of the laser beam is collected within the fixed detection window, which represents the actual position of the laser beam. D = {d k}={d1,d2,d3,…,d N}, obtain the theoretical position data point set T on the preset trajectory of the laser beam, representing the preset trajectory of the laser beam, T = {t k}={t1,t2,t3,…,t N}, N represents the total number of data points;

[0038] Calculate the deviation between the actual position and the theoretical position of each data point to obtain a deviation set, and represent the deviation set with Δ, then Δ={Δ k}={Δ1,Δ2,Δ3,…,Δ N}, (x k ,y k , z k ) represents the actual position coordinate of the laser beam at the kth data point, (x' k , y' k , z' k ) represents the coordinates of the kth data point of the preset trajectory of the laser beam, Δ k represents the deviation of the kth data point calculated using the Euclidean distance;

[0039] Calculate the standard score for each deviation using the expression: In the formula, Z k represents the standard score of the kth data point, μ Δ represents the mean of the deviation set, σ Δ represents the standard deviation of the deviation set;

[0040] The standard score of the data point is compared with the preset standard score reference threshold. If the standard score is greater than or equal to the standard score reference threshold, the data point is marked as an outlier, and the outlier set is marked as A, then A = {a1, a2, a3, ..., a m}, m represents the total number of abnormal points. If the standard score is less than the standard score reference threshold, the data point is marked as a normal point;

[0041] Calculate the laser beam collimation index. The calculation expression is: Where LCI is the laser beam collimation index.

[0042] An interactive communication line survey and design system, comprising a data acquisition module, a data storage and sorting module, an abnormality analysis and evaluation module, an intelligent analysis module, and a comprehensive analysis and response module;

[0043] Data acquisition module: During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time to provide basic data for subsequent abnormal analysis;

[0044] The data storage and organization module stores the collected operating parameter information in a structured database, and organizes and cleans the data to ensure data integrity and consistency;

[0045] The anomaly analysis and evaluation module performs an anomaly analysis on the sorted operating parameter information, inputs the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluates the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information;

[0046] Intelligent analysis module, which analyzes the input operating parameter information through machine learning models to detect potential accuracy problems when the 3D laser scanner obtains 3D terrain data and object surface information;

[0047] After detecting potential accuracy problems, the comprehensive analysis and response module will conduct a comprehensive analysis of the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information, determine the type of potential anomalies, and take different types of measures according to different types of potential anomalies.

[0048] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0049] The present invention collects and analyzes the operating parameter information of the 3D laser scanner in real time, and uses a pre-trained machine learning model for intelligent evaluation, so as to detect potential accuracy problems in a timely manner, ensure that high accuracy can be maintained when acquiring 3D terrain data and object surface information, effectively avoid line planning and design errors caused by low-precision data, ensure that the layout and function of communication lines are more reasonable, and reduce deviations in design implementation and potential safety hazards.

[0050] The present invention can quickly distinguish sudden, unstable and continuous anomalies by performing intelligent evaluation on the scanning accuracy quantization coefficient and judging the anomaly type, and take corresponding measures for different types of anomalies. This classification processing method not only improves the efficiency of problem solving, but also optimizes the maintenance and care strategy of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0052] Figure 1 The present invention is a method flow chart of an interactive communication line survey and design method.

[0053] Figure 2 The present invention is a module schematic diagram of an interactive communication line survey and design system. DETAILED DESCRIPTION

[0054] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0055] The present invention provides Figure 1 An interactive communication line survey and design method shown includes the following steps:

[0056] During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time, providing basic data for subsequent abnormal analysis;

[0057] Relevant operating parameter information includes laser beam collimation information and scanning frequency information. The scanning frequency refers to the number of times the 3D laser scanner emits and receives laser pulses per unit time. The laser beam collimation refers to the ability of the laser beam to remain parallel and not diverge during the emission process, that is, the accuracy of the laser beam propagating along the designed trajectory.

[0058] Store the collected operating parameter information in a structured database, and organize and clean the data to ensure data integrity and consistency;

[0059] The collected operating parameter information is stored in a structured database, and the data is sorted and cleaned to ensure data integrity and consistency. The specific steps are as follows:

[0060] During the actual scanning process of the 3D laser scanner, the operating parameter information is collected in real time and transmitted to the central server through a transmission protocol (such as Wi-Fi, Bluetooth or wired connection);

[0061] The purpose of transmission via a transport protocol is to ensure that data is not lost or damaged during transmission, and to record transmission logs for subsequent inspection and auditing.

[0062] storing the operating parameter information transmitted to the central server in a structured database;

[0063] A structured database such as MySQL, PostgreSQL, or SQL Server. Design an appropriate database table structure, define field types, primary keys, and foreign key relationships, and ensure that data can be stored and retrieved efficiently. For example, create a table to store parameters such as timestamp, laser emission intensity, scanning frequency, ambient temperature, etc.

[0064] Conduct preliminary checks on raw data stored in the database to verify the integrity and accuracy of the data;

[0065] Verifying the completeness and accuracy of data includes:

[0066] Check whether the data is missing, in the correct format, and within a reasonable range, and identify and record abnormal data points. For example, check whether the ambient temperature is within a reasonable range (such as 0 to 50 degrees Celsius).

[0067] Clean and process abnormal data found in the initial inspection;

[0068] Processing methods include filling missing values ​​(such as using the mean or median), correcting erroneous data (such as adjusting outliers to values ​​within a reasonable range), and deleting irreparable erroneous data to ensure data integrity and consistency. For example, if the laser emission intensity during a period of time is abnormally low, it may be necessary to adjust it to an average value within a reasonable range.

[0069] Standardize the cleaned data to ensure that the data formats and units from different sources are consistent, which is convenient for subsequent analysis and use;

[0070] Standardization processing includes unifying data formats (such as timestamp format), converting units (such as unifying all temperature units to Celsius), and standardizing field names. Finally, a consistency check is performed to ensure that all data conforms to the expected format and range, and a data cleaning report is generated to record the cleaning process and results for subsequent auditing and tracking.

[0071] Perform anomaly analysis on the sorted operating parameter information, input the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluate the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information;

[0072] After abnormal analysis of the sorted scanning frequency, a scanning frequency instability index is generated. After abnormal analysis of the sorted laser beam collimation, a laser beam collimation index is generated. The scanning frequency instability index and the laser beam collimation index after abnormal analysis are input into a pre-trained machine learning model to generate a scanning accuracy quantization coefficient. The scanning accuracy quantization coefficient is used to perform an intelligent evaluation of the accuracy of three-dimensional terrain data and object surface information.

[0073] Unstable scanning frequency will indeed lead to poor accuracy of 3D terrain data and object surface information. Scanning frequency refers to the number of times a 3D laser scanner transmits and receives laser pulses per unit time. If the scanning frequency is unstable, it means that the number of laser pulses emitted and received at the same time is inconsistent, which will directly affect the point cloud data acquisition process. Point cloud data is measured by the signal reflected back after the laser pulse interacts with the surface of the object. If the scanning frequency is unstable, the number and distribution of collected data points will be uneven, resulting in unstable density and quality of point cloud data. This uneven point cloud data will lead to a decrease in the accuracy of terrain and object surface information, which is manifested as local data loss and increased errors, which in turn affects the accuracy and reliability of the overall 3D model.

[0074] More specifically, the instability of the scanning frequency will lead to the following problems. First, when the scanning frequency is high, the data point density is high and more details can be captured; when the scanning frequency is low, the data point density is low and some details may be missed. This density change will cause discontinuous or non-smooth areas in the generated 3D model, which cannot accurately reflect the actual terrain and object surface. In addition, the instability of the scanning frequency will also affect the time synchronization, resulting in the inability to accurately align the data collected at different time points, thereby causing errors in the stitching and alignment process. Especially in dynamic environments, such as moving vehicles or personnel, the error in time synchronization will be more significant, further affecting the accuracy and consistency of the data. Therefore, ensuring the stability of the scanning frequency is crucial to improving the accuracy of 3D terrain data and object surface information, and is an important prerequisite for achieving high-quality 3D laser scanning.

[0075] After performing abnormal analysis on the sorted scanning frequency, the specific steps to generate the scanning frequency instability index are as follows:

[0076] Collect all the scanning frequency data of the scanner within the fixed detection window, and establish a scanning frequency data set F with all the scanning frequency data, then: F = {f i}={f1,f2,f3,…,f n}, where f i represents the i-th scanning frequency data point;

[0077] Calculate the mean and standard deviation of the scan frequency data set. The calculation expression is: In the formula, μ represents the mean of the scanning frequency data set, and σ represents the standard deviation of the scanning frequency data set;

[0078] Calculate the coefficient of variation of the scanning frequency data. The coefficient of variation measures the relative change of the standard deviation of the scanning frequency data set and the mean of the scanning frequency data set. The calculation expression is: Where, CV represents the coefficient of variation of scanning frequency;

[0079] Calculate the scanning frequency change rate, which reflects the degree of change between each consecutive data point. All scanning frequency change rate data are used to establish a scanning frequency change rate data set RoC, then: RoC = {r i} = {r1, r2, r3, ..., r n},in,

[0080] Calculate the mean value and standard deviation of the scanning frequency change rate. The calculation expression is: In the formula, μ RoC represents the mean value of the scanning frequency change rate, σ RoC Indicates the standard deviation of the scanning frequency change rate;

[0081] Calculate the scanning frequency instability index, the calculation expression is: SFII = α·CV + β·μ RoC +γ·σ RoC , where SFII represents the scanning frequency instability index, α, β, and γ are the scanning frequency coefficient of variation CV and the scanning frequency change rate mean μ respectively. RoC , standard deviation of scanning frequency change rate σ RoC The weight coefficient is used to adjust the scanning frequency variation coefficient CV and the scanning frequency change rate mean μ RoC , standard deviation of scanning frequency change rate σ RoC The degree of impact in SFII, these coefficients can be adjusted through historical data and experience to best suit actual applications, and no specific restrictions are made here.

[0082] When a 3D laser scanner acquires 3D terrain data and object surface information, the greater the value of the scanning frequency instability index, the greater the fluctuation of the scanning frequency, the higher the instability of data acquisition, and the worse the accuracy. This means that when the scanning frequency instability index is high, the scanner cannot stably transmit and receive laser pulses, the data points are unevenly distributed, and data may be missing or errors may increase. Conversely, the smaller the value of the scanning frequency instability index, the higher the stability of the scanning frequency, the more reliable and consistent the data acquisition process, the higher the accuracy, and the more accurate and reliable the generated 3D terrain data and object surface information. Therefore, maintaining a low scanning frequency instability index is crucial to ensuring the accuracy of 3D laser scanning.

[0083] Poor laser beam collimation, that is, the laser beam deviates from the designed trajectory, which will lead to poor accuracy of 3D terrain data and object surface information. Laser beam collimation refers to the ability of the laser to remain parallel and not diverge during the emission process. When the laser beam has good collimation, the laser can propagate along the designed straight path, thereby ensuring the accurate position of the measurement point. When the laser beam deviates from the designed trajectory, it will cause the laser beam to scatter or bend during propagation, affecting the accuracy of the laser's arrival and return paths. This deviation will cause the position deviation of the measurement point, resulting in inaccurate generated 3D point cloud data, which in turn affects the accuracy of the overall terrain data and object surface information.

[0084] Specifically, when the laser beam has poor collimation, the laser beam will scatter or diverge during the emission process, resulting in deviations in the received reflected signal. This deviation can lead to the following problems: First, the position deviation of the measurement point will lead to inaccurate generated 3D point cloud data, especially in fine structures and edge areas, where the deviation is more obvious. Secondly, when a laser beam with poor collimation propagates over long distances, the error will be further amplified, resulting in a significant decrease in the data accuracy of long-distance measurements. In addition, due to the deviation of the laser beam from the designed trajectory, the laser scanning coverage may be insufficient, resulting in blind areas that are not fully covered, making the final generated 3D topographic map and object surface information incomplete.

[0085] Therefore, the collimation of the laser beam is crucial for the 3D laser scanner to obtain high-precision data. In order to ensure the accuracy of the 3D terrain data and the surface information of the object, the high collimation of the laser beam must be maintained. The collimation of the laser beam is a key factor in ensuring the quality of 3D laser scanning data, and directly affects the accuracy of the final generated terrain data and the surface information of the object.

[0086] After abnormal analysis of the collimated laser beam collimation, the specific steps to generate the laser beam collimation index are as follows:

[0087] The actual position data point set D of the laser beam is collected within the fixed detection window, which represents the actual position of the laser beam. D = {d k}={d1,d2,d3,…,d N}, obtain the theoretical position data point set T on the preset trajectory of the laser beam, representing the preset trajectory of the laser beam, T = {t k}={t1,t2,t3,…,t N}, N represents the total number of data points;

[0088] It should be noted that the preset trajectory data are theoretical trajectory points set by the software during the initial setup of the scanner. These data are usually provided by the manufacturer or generated according to the predetermined scanning path before the scanning task begins. This method ensures that the trajectory data is consistent with the requirements of the actual scanning task. If the CAD model of the scan target already exists, the theoretical trajectory points can be extracted from the CAD model. The CAD model provides high-precision target geometry information and can provide accurate data support for the theoretical trajectory of the laser beam.

[0089] Calculate the deviation between the actual position and the theoretical position of each data point to obtain a deviation set, and represent the deviation set with Δ, then Δ={Δ k}={Δ1,Δ2,Δ3,…,Δ N}, (x k ,y k , z k ) represents the actual position coordinate of the laser beam at the kth data point, (x' k , y' k , z' k ) represents the coordinates of the kth data point of the preset trajectory of the laser beam, Δ k represents the deviation of the kth data point calculated using the Euclidean distance;

[0090] Calculate the standard score for each deviation using the expression: In the formula, Z k represents the standard score of the kth data point, μ Δ represents the mean of the deviation set, σ Δ represents the standard deviation of the deviation set (the calculation process of the deviation set mean and the deviation set standard deviation is not described in detail here, refer to the above);

[0091] The standard score of the data point is compared with the preset standard score reference threshold. If the standard score is greater than or equal to the standard score reference threshold, the data point is marked as an outlier, and the outlier set is marked as A, then A = {a1, a2, a3, ..., a m}, m represents the total number of abnormal points. If the standard score is less than the standard score reference threshold, the data point is marked as a normal point;

[0092] Calculate the laser beam collimation index. The calculation expression is: Where LCI is the laser beam collimation index.

[0093] When a 3D laser scanner acquires 3D terrain data and object surface information, the larger the value of the laser beam collimation index, the higher the collimation of the laser beam during the scanning process, thus ensuring that the acquired data has a higher accuracy; on the contrary, the smaller the value of the laser beam collimation index, the greater the degree of deviation of the laser beam from the designed trajectory during the scanning process, resulting in lower accuracy of the acquired data. Therefore, the laser beam collimation index can be used as an important indicator to measure the accuracy of scanning data, directly reflecting the performance quality of the 3D laser scanner during the working process.

[0094] The scanning frequency instability index SFII and laser beam collimation index LCI generated by the 3D laser scanner in a fixed detection window when acquiring 3D terrain data and object surface information are input into a pre-trained machine learning model to generate a scanning accuracy quantization coefficient Scan accuracy , by scanning precision quantization coefficient Scan accuracy Intelligently evaluate the accuracy of 3D terrain data and object surface information.

[0095] The machine learning model is not specifically limited here, and can realize comprehensive analysis of the scanning frequency instability index SFII and the laser beam collimation index LCI to generate the scanning accuracy quantization coefficient Scan accuracy In order to realize the technical solution of the present invention, the present invention provides a specific implementation method;

[0096] Scanning precision quantization coefficient Scan accuracy The resulting calculation formula is:

[0097]

[0098] , where w1 and w2 are the preset proportional coefficients of the scanning frequency instability index SFII and the laser beam collimation index LCI, respectively, and both w1 and w2 are greater than 0.

[0099] It can be seen from the scanning accuracy quantification coefficient that within the fixed detection window, the smaller the scanning frequency instability index generated by the 3D laser scanner when acquiring 3D terrain data and object surface information, the larger the laser beam collimation index, that is, the larger the performance value of the scanning accuracy quantification coefficient generated within the fixed detection window, indicating that the 3D laser scanner has higher accuracy when acquiring 3D terrain data and object surface information, and vice versa.

[0100] The machine learning model analyzes the input operating parameter information to detect potential accuracy issues when the 3D laser scanner acquires 3D terrain data and object surface information;

[0101] The scanning accuracy quantization coefficient generated by the machine learning model when the 3D laser scanner acquires 3D terrain data and object surface information is compared and analyzed with the pre-set scanning accuracy quantization coefficient reference threshold to detect potential accuracy problems when the 3D laser scanner acquires 3D terrain data and object surface information. The specific steps are as follows:

[0102] If the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold, an efficient scanning signal is generated, indicating that the three-dimensional laser scanner can efficiently obtain three-dimensional terrain data and object surface information;

[0103] If the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold, an abnormal scanning signal is generated, indicating that there is a potential accuracy problem when the 3D laser scanner acquires 3D terrain data and object surface information.

[0104] After detecting potential accuracy problems, the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information is comprehensively analyzed again to determine the type of potential anomalies, and different types of measures are taken according to different types of potential anomalies;

[0105] When the 3D laser scanner obtains 3D terrain data and object surface information to generate abnormal scanning signals, obtain several scanning accuracy quantization coefficients generated subsequently to establish an analysis set, calculate the mean value of the scanning accuracy quantization coefficient and the standard deviation of the scanning accuracy quantization coefficient through the scanning accuracy quantization coefficients in the data set (the calculation process of the mean value of the scanning accuracy quantization coefficient and the standard deviation of the scanning accuracy quantization coefficient is not described in detail here, refer to the above), compare and analyze the mean value of the scanning accuracy quantization coefficient and the standard deviation of the scanning accuracy quantization coefficient with the pre-set scanning accuracy quantization coefficient reference threshold and standard deviation reference threshold, respectively, to determine the type of potential anomaly, and the specific determination results are as follows:

[0106] If the mean value of the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold and the scanning accuracy quantization coefficient standard is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a sudden anomaly;

[0107] If the standard deviation of the scanning accuracy quantization coefficient is greater than or equal to the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as an unstable anomaly;

[0108] If the mean value of the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold and the scanning accuracy quantization coefficient standard is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a persistent anomaly.

[0109] For sudden abnormalities, it means that a significant but short-term abnormality occurred at a certain moment. This may be caused by sudden external interference (such as strong light exposure, instantaneous vibration) or a momentary failure inside the equipment. Countermeasures include:

[0110] Real-time monitoring and recording: Immediately start the real-time monitoring system to record in detail the environment and equipment status when the abnormality occurs, and capture the specific time point and possible cause of the sudden abnormality.

[0111] Equipment Inspection and Calibration: Perform a comprehensive inspection and necessary calibration of the scanner to ensure that all components are working properly and eliminate the possibility of hardware failure.

[0112] Environmental interference elimination: Analyze environmental factors and eliminate or reduce possible external interference, such as blocking strong light sources, isolating vibration sources, etc., to ensure the stability of the scanning environment.

[0113] For unstable anomalies, it means that the scanning accuracy fluctuates greatly and is unstable. This situation is usually caused by equipment aging, sensor failure or continuous external interference. Countermeasures include:

[0114] Equipment maintenance and replacement: Regularly perform equipment maintenance and service, check the status of sensors and other key components, and replace aging or damaged parts in a timely manner.

[0115] Stable working environment: Ensure the stability of the scanner's working environment and reduce factors that may cause interference in the environment, such as temperature, humidity, and electromagnetic interference.

[0116] Software adjustment and upgrade: Check and upgrade the scanner's software system, optimize the scanning algorithm and data processing flow, and improve the adaptability and stability of the device in different environments.

[0117] For persistent abnormalities, it means that the scanning accuracy is low for a long time and the fluctuation is small, which may be caused by equipment configuration error, improper calibration or system failure. Countermeasures include:

[0118] Detailed Diagnosis and Adjustment: Perform detailed troubleshooting on the scanner, check configuration parameters and calibration status, and ensure the device is operating correctly according to predetermined standards.

[0119] Systematic optimization: Optimize the overall system, including hardware and software, to ensure that each link meets the requirements of high-precision scanning, such as adjusting laser power, optimizing scanning paths, etc.

[0120] Professional repair and support: Contact the device manufacturer or professional technical support team for in-depth repair and technical support to resolve systemic faults and ensure that the device is restored to optimal working condition.

[0121] The present invention collects and analyzes the operating parameter information of the 3D laser scanner in real time, and uses a pre-trained machine learning model for intelligent evaluation, so as to detect potential accuracy problems in a timely manner, ensure that high accuracy can be maintained when acquiring 3D terrain data and object surface information, effectively avoid line planning and design errors caused by low-precision data, ensure that the layout and function of communication lines are more reasonable, and reduce deviations in design implementation and potential safety hazards.

[0122] The present invention can quickly distinguish sudden, unstable and continuous abnormalities by intelligently evaluating the scanning accuracy quantization coefficient and judging the abnormality type, and take corresponding measures for different types of abnormalities. This classification and processing method not only improves the efficiency of problem solving, but also optimizes the maintenance and maintenance strategy of the equipment. For example, for sudden abnormalities, environmental investigation and equipment calibration can be carried out quickly; for unstable abnormalities, regular maintenance and environmental monitoring of the equipment can be strengthened; and for continuous abnormalities, in-depth system diagnosis and optimization can be carried out. This precise and targeted maintenance strategy not only extends the service life of the equipment, but also ensures the continuity and efficiency of the scanning work, greatly improving the overall work efficiency.

[0123] An interactive communication line survey and design system, comprising a data acquisition module, a data storage and sorting module, an abnormality analysis and evaluation module, an intelligent analysis module, and a comprehensive analysis and response module;

[0124] Data acquisition module: During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time to provide basic data for subsequent abnormal analysis;

[0125] The data storage and organization module stores the collected operating parameter information in a structured database, and organizes and cleans the data to ensure data integrity and consistency;

[0126] The anomaly analysis and evaluation module performs an anomaly analysis on the sorted operating parameter information, inputs the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluates the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information;

[0127] Intelligent analysis module, which analyzes the input operating parameter information through machine learning models to detect potential accuracy problems when the 3D laser scanner obtains 3D terrain data and object surface information;

[0128] The comprehensive analysis and response module, after detecting potential accuracy problems, conducts a comprehensive analysis of the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information, determines the type of potential anomalies, and takes different types of measures according to different types of potential anomalies;

[0129] An interactive communication line survey and design method provided in an embodiment of the present invention is implemented by the above-mentioned interactive communication line survey and design system. The specific method and process of an interactive communication line survey and design system are detailed in the embodiment of the above-mentioned interactive communication line survey and design method, which will not be repeated here.

[0130] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0131] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0132] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0133] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0134] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An interactive communication line survey and design method, characterized in that: The following steps are involved: During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time, providing basic data for subsequent abnormal analysis; Store the collected operating parameter information in a structured database, and organize and clean the data to ensure data integrity and consistency; Perform anomaly analysis on the sorted operating parameter information, input the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluate the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information; The machine learning model analyzes the input operating parameter information to detect potential accuracy issues when the 3D laser scanner acquires 3D terrain data and object surface information; After detecting potential accuracy problems, the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information is comprehensively analyzed again to determine the type of potential anomalies, and different types of measures are taken according to different types of potential anomalies; The relevant operating parameter information includes laser beam collimation information and scanning frequency information. The scanning frequency refers to the number of times the 3D laser scanner emits and receives laser pulses per unit time. The laser beam collimation refers to the ability of the laser beam to remain parallel and not diverge during the emission process, that is, the accuracy of the laser beam propagating along the designed trajectory. After abnormal analysis of the sorted scanning frequency, a scanning frequency instability index is generated. After abnormal analysis of the sorted laser beam collimation, a laser beam collimation index is generated. The scanning frequency instability index and the laser beam collimation index after abnormal analysis are input into a pre-trained machine learning model to generate a scanning accuracy quantization coefficient. The scanning accuracy quantization coefficient is used to intelligently evaluate the accuracy of three-dimensional terrain data and object surface information. The scanning accuracy quantization coefficient generated by the machine learning model when the 3D laser scanner acquires 3D terrain data and object surface information is compared and analyzed with the pre-set scanning accuracy quantization coefficient reference threshold to detect potential accuracy problems when the 3D laser scanner acquires 3D terrain data and object surface information. The specific steps are as follows: If the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold, an efficient scanning signal is generated, indicating that the three-dimensional laser scanner can efficiently obtain three-dimensional terrain data and object surface information; If the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold, an abnormal scanning signal is generated, indicating that there is a potential accuracy problem when the 3D laser scanner acquires 3D terrain data and object surface information; When the 3D laser scanner obtains 3D terrain data and object surface information to generate abnormal scanning signals, obtain several scanning accuracy quantization coefficients generated subsequently to establish an analysis set, calculate the mean value of the scanning accuracy quantization coefficient and the standard deviation of the scanning accuracy quantization coefficient through the scanning accuracy quantization coefficients in the data set, and compare and analyze the mean value of the scanning accuracy quantization coefficient and the standard deviation of the scanning accuracy quantization coefficient with the pre-set scanning accuracy quantization coefficient reference threshold and standard deviation reference threshold, respectively, to determine the type of potential anomalies. The specific determination results are as follows: If the mean value of the scanning accuracy quantization coefficient is greater than or equal to the scanning accuracy quantization coefficient reference threshold and the standard deviation of the scanning accuracy quantization coefficient is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a sudden anomaly; If the standard deviation of the scanning accuracy quantization coefficient is greater than or equal to the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as an unstable anomaly; If the mean value of the scanning accuracy quantization coefficient is less than the scanning accuracy quantization coefficient reference threshold and the standard deviation of the scanning accuracy quantization coefficient is less than the standard deviation reference threshold, the potential anomaly when the 3D laser scanner acquires 3D terrain data and object surface information is classified as a persistent anomaly.

2. An interactive communication line survey and design method according to claim 1, characterized in that: The collected operating parameter information is stored in a structured database, and the data is sorted and cleaned to ensure data integrity and consistency. The specific steps are as follows: During the actual scanning process of the 3D laser scanner, the operating parameter information is collected in real time and transmitted to the central server through the transmission protocol; storing the operating parameter information transmitted to the central server in a structured database; Conduct preliminary checks on raw data stored in the database to verify the integrity and accuracy of the data; Clean and process abnormal data found in the initial inspection; The cleaned data is standardized to ensure that the data formats and units from different sources are consistent to facilitate subsequent analysis and use.

3. The interactive communication line survey and design method according to claim 1, characterized in that: After performing abnormal analysis on the sorted scanning frequency, the specific steps to generate the scanning frequency instability index are as follows: Collect all the scanning frequency data of the scanner within the fixed detection window, and establish a scanning frequency data set F with all the scanning frequency data, then: F = {f i }={f1,f2,f3,…,f n }, where f i represents the i-th scanning frequency data point; Calculate the mean and standard deviation of the scan frequency data set. The calculation expression is: In the formula, μ represents the mean of the scanning frequency data set, and σ represents the standard deviation of the scanning frequency data set; Calculate the coefficient of variation of the scanning frequency data. The coefficient of variation measures the relative change of the standard deviation of the scanning frequency data set and the mean of the scanning frequency data set. The calculation expression is: Where, CV represents the coefficient of variation of scanning frequency; Calculate the scanning frequency change rate, which reflects the degree of change between each consecutive data point. All scanning frequency change rate data are used to establish a scanning frequency change rate data set RoC, then: RoC = {r i } = {r1, r2, r3, ..., r n },in, Calculate the mean value and standard deviation of the scanning frequency change rate. The calculation expression is: In the formula, μ RoC represents the mean value of the scanning frequency change rate, σ RoC Indicates the standard deviation of the scanning frequency change rate; Calculate the scanning frequency instability index, the calculation expression is: SFII = α·CV + β·μ RoC +γ·σ RoC , where SFII represents the scanning frequency instability index, α, β, and γ are the scanning frequency coefficient of variation CV and the scanning frequency change rate mean μ respectively. RoC , standard deviation of scanning frequency change rate σ RoC The weight coefficient of .

4. The interactive communication line survey and design method according to claim 1, characterized in that: After abnormal analysis of the collimated laser beam collimation, the specific steps to generate the laser beam collimation index are as follows: The actual position data point set D of the laser beam is collected within the fixed detection window, which represents the actual position of the laser beam. D = {d k }={d1,d2,d3,…,d N }, obtain the theoretical position data point set T on the preset trajectory of the laser beam, representing the preset trajectory of the laser beam, T = {t k }={t1,t2,t3,…,t N }, N represents the total number of data points; Calculate the deviation between the actual position and the theoretical position of each data point to obtain a deviation set, and represent the deviation set with Δ, then Δ={Δ k }={Δ1,Δ2,Δ3,…,Δ N }, (x k ,y k , z k ) represents the actual position coordinate of the laser beam at the kth data point, (x' k , y' k , z' k ) represents the coordinates of the kth data point of the preset trajectory of the laser beam, Δ k represents the deviation of the kth data point calculated using the Euclidean distance; Calculate the standard score for each deviation using the expression: In the formula, Z k represents the standard score of the kth data point, μ Δ represents the mean of the deviation set, σ Δ represents the standard deviation of the deviation set; The standard score of the data point is compared with the preset standard score reference threshold. If the standard score is greater than or equal to the standard score reference threshold, the data point is marked as an outlier, and the outlier set is marked as A, then A = {a1, a2, a3, ..., a m }, m represents the total number of abnormal points. If the standard score is less than the standard score reference threshold, the data point is marked as a normal point; Calculate the laser beam collimation index. The calculation expression is: Where LCI represents the laser beam collimation index.

5. An interactive communication line survey and design system, used to implement an interactive communication line survey and design method as described in any one of claims 1 to 4, characterized in that: It includes data collection module, data storage and organization module, abnormal analysis and evaluation module, intelligent analysis module and comprehensive analysis and response module; Data acquisition module: During the actual scanning process, the 3D laser scanner collects relevant operating parameter information in real time to provide basic data for subsequent abnormal analysis; The data storage and organization module stores the collected operating parameter information in a structured database, and organizes and cleans the data to ensure data integrity and consistency; The anomaly analysis and evaluation module performs an anomaly analysis on the sorted operating parameter information, inputs the operating parameter information after anomaly analysis into a pre-trained machine learning model, and intelligently evaluates the accuracy of the 3D laser scanner in acquiring 3D terrain data and object surface information; Intelligent analysis module, which analyzes the input operating parameter information through machine learning models to detect potential accuracy problems when the 3D laser scanner obtains 3D terrain data and object surface information; After detecting potential accuracy problems, the comprehensive analysis and response module will conduct another comprehensive analysis of the subsequent process of the 3D laser scanner acquiring 3D terrain data and object surface information, determine the type of potential anomalies, and take different types of measures according to different types of potential anomalies.

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