A multi-survey integrated surveying and mapping indoor and outdoor data automatic processing system and method

Abstract

The present invention discloses a multi-survey integrated surveying and mapping indoor and outdoor data automatic processing system and method, and relates to the technical field of automatic surveying and mapping; the technical key points are: by analyzing indoor and outdoor data in the surveying and mapping process, a three-dimensional task model is constructed, and also for environmental data in the surveying and mapping process, especially for the flight environment of unmanned aerial vehicles, a number of influence modes are constructed, the stability coefficient in the automatic surveying and mapping process is analyzed, and the optimal three-dimensional task model is selected; according to the three-dimensional task model, settlement parameters and deformation parameters are extracted, and a nonlinear fitting model is built to generate corresponding element time variation curves; different analysis strategies are executed according to the generated normal reference element values ​​and abnormal reference element values, and the three-dimensional task model is further calibrated to improve the precision and accuracy in the data acquisition process; at the same time, the process focuses on the whole process of the construction operation stage, avoiding the safety hazards caused by foundation settlement and building deformation.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic surveying and mapping, and in particular to a system and method for automatically processing field and indoor data of multi-survey integrated surveying and mapping. Background Art

[0002] Surveying and mapping is the most critical tool in the construction industry. Comprehensiveness and accuracy are the key points of surveying work. In existing technologies, the "multi-measurement in one" technology can organically combine a variety of measurements, a variety of equipment and computer-aided software to achieve accurate measurement. For example, laser scanning technology, oblique photogrammetry technology, satellite navigation technology, etc. are used to obtain multi-source data and generate visualization results such as three-dimensional models.

[0003] However, in the surveying and mapping process, drones are often used as carriers for laser scanning and oblique photography, which are easily affected by the environment, resulting in inaccurate data collection, which in turn leads to inaccurate generated three-dimensional models that cannot accurately describe the terrain or building features. When construction work is to be carried out, the surveying and mapping process often only focuses on the construction preparation stage, ignoring the problems of foundation settlement and building deformation that may occur during the construction process or the completion stage, leading to safety hazards during the construction process or the acceptance process. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-survey integrated surveying and mapping indoor and outdoor data automatic processing system and method, which analyzes indoor and outdoor data in the surveying and mapping process, constructs a three-dimensional task model, and constructs a number of influence modes for the environmental data in the surveying and mapping process, especially for the flight environment of the unmanned aerial vehicle, analyzes and processes to select the optimal three-dimensional task model, obtains settlement parameters and deformation parameters according to the three-dimensional task model, builds a nonlinear fitting model, generates corresponding element time variation curves, and marks normal reference element values ​​and abnormal reference element values ​​to execute different analysis strategies, further calibrates the three-dimensional task model, and improves the precision and accuracy in the data acquisition process; at the same time, the process focuses on the entire process of the construction operation stage, avoids the safety hazards caused by foundation settlement and building deformation during the construction operation, and solves the problems raised in the background technology.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] An automatic processing system for surveying and mapping indoor and outdoor data in a multi-survey integration mode comprises a data acquisition module, a data processing module and an automatic feedback module, wherein the data acquisition module, the data processing module and the automatic feedback module are communicatively connected with each other;

[0009] The data collection module collects environmental data, indoor data and outdoor data of different construction work stages in the construction work task; wherein the construction work task uniquely corresponds to the construction work stage and the stage is marked, and the construction work stage includes the construction preparation stage, the construction process stage and the construction completion stage;

[0010] The data processing module constructs a three-dimensional task model corresponding to the construction operation stage according to the environmental data, the indoor data and the outdoor data, determines the regional scope of the construction operation task according to the three-dimensional task model, monitors the parameter set corresponding to the construction operation task according to the regional scope, and performs preprocessing analysis on the parameter set to generate a standard parameter set; wherein the standard parameter set includes a settlement standard parameter and a deformation standard parameter;

[0011] The process of preprocessing and analyzing the parameter set is as follows:

[0012] Constructing a parametric analysis model:

[0013] Mark the input data as parameter Qa, wherein the parameter Qa includes several data indicators, mark any data indicator as q, and the data value of marking any data indicator q is Lq, and set the standard interval of the data indicator q as QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one;

[0014] By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index;

[0015] The formula for calculating the standard reference index BZQ is:

[0016] ;

[0017] In the formula, σ is the weight coefficient;

[0018] Applying the parameter analysis model: Substituting the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4;

[0019] The automatic feedback module, by building a nonlinear fitting model, performs data fitting on the standard parameter set, constructs a curve model between the standard parameter set and time, executes a processing and analysis strategy, obtains an execution result, performs data correction or early warning processing according to the execution result, and issues corresponding feedback instructions back to the data acquisition module.

[0020] Furthermore, the construction operation tasks include non-construction operations, demolition operations and land development operations, and the construction process of the three-dimensional task model is:

[0021] Data preparation: Set the collection cycle to collect the internal data and field data corresponding to the construction operation stage at regular intervals; wherein the collection cycle of the internal data is T1, and the collection cycle of the field data is T2, and both T1 and T2 are natural numbers greater than 0;

[0022] Model construction: construct a three-dimensional task model, generate a three-dimensional terrain model based on historically collected internal data, and the historically collected internal data is obtained by analyzing the historically corresponding field data, use the three-dimensional terrain model as the basic model of the three-dimensional task model, add the corresponding construction work tasks as the elements of the three-dimensional task model, and map the regularly collected field data to the three-dimensional task model;

[0023] Feature extraction: extracting acquisition data parameters in the three-dimensional task model, wherein the acquisition data parameters include image frame rate and image voxels; at the same time, calculating the accuracy coefficient according to the acquisition data parameters;

[0024] The formula on which the coefficient of accuracy is based is: ;

[0025] Where precision is the precision coefficient, pr is the image frame rate, vo is the image voxel, μ1 and μ2 are the weight coefficients of the image frame rate pr and the image voxel vo, respectively, and both μ1 and μ2 are greater than 0;

[0026] Model optimization: construct several influence patterns based on the environmental data, and obtain them through reinforcement learning exploration of the sample environment corresponding to the collected data parameters, wherein the sample environment is composed of environmental data corresponding to at least two historical collected data parameters; analyze the stability coefficient based on several influence patterns, and select the optimal three-dimensional task model based on the stability coefficient.

[0027] Furthermore, the process of selecting the optimal three-dimensional task model is:

[0028] First, the environmental level coefficient is calculated according to the environmental data, and the environmental level interval is matched according to the environmental level coefficient, and the corresponding acquisition data parameters obtained under the environmental data are monitored to construct a three-dimensional monitoring vector, including: precision coefficient precision, environmental level interval S, and acquisition period time;

[0029] Then, the average value and fluctuation value of the precision coefficient within the preset time are obtained. For any time period Ta, the environmental level interval of the time period Ta is marked as Sa, the average value and fluctuation value are pre_a and per_b respectively, and the fluctuation value is the average value of the difference between the maximum precision coefficient and the minimum precision coefficient within the time period Ta; a number of judgment vectors are constructed according to different combinations, including a first judgment vector and a second judgment vector;

[0030] The first judgment vector is expressed as: ;

[0031] The second judgment vector is expressed as: ;

[0032] Construct the first judgment vector - the change function h1 of the environmental level coefficient, and determine it as the first influence mode of environmental data on the collected data parameters;

[0033] Constructing the second judgment vector - the variation function h2 of the environmental level coefficient, and determining it as the second influence mode of the environmental data on the collected data parameters;

[0034] Finally, the corresponding stability coefficient is calculated according to the change function h1 or the change function h2;

[0035] The formula for calculating the stability coefficient is: ;

[0036] Where h is the variation function h1 or h2, and α is the influence coefficient;

[0037] Compare the stability coefficients corresponding to the first impact mode and the second impact mode, select the stability coefficient with a larger value, and use it as the optimal three-dimensional task model.

[0038] Furthermore, the environmental data includes light intensity value, air humidity value and drone speed, and the environmental level coefficient is based on the following formula:

[0039] ;

[0040] Where Lx is the light intensity value, Rh is the air humidity value, and Vu is the UAV speed; β1, β2, and β3 are the influence coefficients of the light intensity value Lx, the air humidity value Rh, and the UAV speed Vu, respectively, and β1, β2, and β3 are all greater than 0;

[0041] The formula on which the environmental grade intervals are based is: ;

[0042] Where k is the preset fluctuation ratio, 0<k<0.35, de b is the preset environmental grade coefficient, ω is the environmental grade coefficient de a The preset environmental level coefficient b The conversion factor between .

[0043] Furthermore, the parameter set includes settlement parameters and deformation parameters, and the settlement parameters are composed of settlement amounts, and the deformation parameters include deformation amounts, strains, and stresses.

[0044] Furthermore, the process of building a nonlinear fitting model and executing the processing and analysis strategy is as follows:

[0045] Set the standard parameter set Qb = {settlement standard parameter, deformation standard parameter}, input any element in the set Qb, set the measurement period T3, the number of acquisitions in the measurement period T3 is nc, obtain the values ​​of nc elements in the measurement period T3, and establish a rectangular coordinate system with the corresponding acquisition period T1 or acquisition period T2 as the horizontal coordinate and the value of the current element as the vertical coordinate, and draw the element time change curve in the rectangular coordinate system; the element time change curve includes the settlement change curve, the deformation change curve, the strain change curve and the stress change curve;

[0046] At the same time, a corresponding standard threshold curve is drawn on the rectangular coordinate system, and the distance value between the two points is calculated by analyzing the corresponding points of the element time variation curve and the standard threshold curve;

[0047] Distance threshold A and distance threshold B are preset, and according to the rectangular coordinate system, normal reference elements and abnormal reference elements are extracted for separate determination, specifically:

[0048] In the case of a normal reference element, if the obtained distance value exceeds the distance threshold A, correction processing is performed; in the case of an abnormal reference element, if the obtained distance value exceeds the distance threshold B, a state analysis is performed according to the element time change curve to obtain the corresponding state cycle, and early warning processing is performed according to the state cycle; otherwise, no processing is performed.

[0049] Furthermore, the state cycle includes an initial period, an active period and a decay period.

[0050] A method for automatically processing indoor and outdoor surveying and mapping data by integrating multiple surveys into one includes the following steps:

[0051] S1, in the construction work task, collects environmental data, indoor data and outdoor data at different construction work stages;

[0052] S2, constructing a three-dimensional task model corresponding to the construction operation stage according to the environmental data, the indoor data and the outdoor data; determining the regional scope of the construction operation task according to the three-dimensional task model, monitoring the parameter set corresponding to the construction operation task according to the regional scope, and performing preprocessing analysis on the parameter set to generate a standard parameter set; wherein the parameter set includes settlement parameters and deformation parameters, and the standard parameter set includes settlement standard parameters and deformation standard parameters;

[0053] The process of preprocessing and analyzing the parameter set is as follows:

[0054] Constructing a parametric analysis model:

[0055] Mark the input data as parameter Qa, wherein the parameter Qa includes several data indicators, mark any data indicator as q, and the data value of marking any data indicator q is Lq, and set the standard interval of the data indicator q as QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one;

[0056] By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index;

[0057] The formula for calculating the standard reference index BZQ is:

[0058] ;

[0059] In the formula, σ is the weight coefficient;

[0060] Applying the parameter analysis model: Substituting the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4;

[0061] S3, building a nonlinear fitting model, performing data fitting on the standard parameter set, constructing a curve model between the standard parameter set and time, and executing a processing and analysis strategy to obtain an execution result, and performing data correction or early warning processing according to the execution result.

[0062] (III) Beneficial effects

[0063] The present invention provides a multi-survey integrated surveying and mapping indoor and outdoor data automatic processing system and method, which has the following beneficial effects:

[0064] 1. The present invention uses big data processing and storage technology to map indoor and outdoor data on a large scale, improving the system processing capacity and efficiency; through intelligent data processing and analysis functions, it can identify the structure of the building during the mapping process, further analyze the foundation settlement and building deformation process, and monitor and feedback in real time, so as to timely discover data anomalies and problems, and timely process and correct them to ensure the accuracy and reliability of the data, so as to improve the efficiency and accuracy of mapping data processing and ensure the safety during the construction process;

[0065] 2. The present invention analyzes the environmental data during the surveying and mapping process, and establishes several influence modes for the flight environment of the UAV, thereby selecting a three-dimensional task model generation mode with good stability, ensuring the accuracy of the surveying and mapping data, achieving precise analysis, and not being restricted by the environment. It also realizes automatic control of UAV flight, improving the defects of low accuracy and great environmental influence in the data collection process in the three-dimensional task model. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a schematic diagram of a module of a multi-survey-in-one surveying and mapping field data automatic processing system according to an exemplary embodiment;

[0067] Figure 2 is a graph showing a change in sedimentation according to an exemplary embodiment;

[0068] Figure 3 It is a flowchart of a method for automatically processing multi-survey integrated surveying and mapping field data according to an exemplary embodiment. DETAILED DESCRIPTION

[0069] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] Embodiment 1: This embodiment provides a multi-survey integrated surveying and mapping field data automatic processing system. Figure 1 is a schematic diagram of a module of a multi-survey integrated surveying and mapping field data automatic processing system according to an exemplary embodiment. Figure 2 is a graph showing a change in sedimentation according to an exemplary embodiment. Figure 1 - Figure 2The multi-survey integrated surveying and mapping indoor and outdoor data automatic processing system includes: a data acquisition module, a data processing module and an automatic feedback module, and the data acquisition module, the data processing module and the automatic feedback module are connected in communication;

[0071] The following are specific descriptions and explanations of each module or component:

[0072] Data acquisition module:

[0073] In the construction operation task, environmental data, internal data and external data of different construction operation stages are collected; among them, the construction operation task uniquely corresponds to the construction operation stage, and the stage is marked. The construction operation stage includes the construction preparation stage, the construction process stage and the construction completion stage;

[0074] First, the meanings of environmental data, field data, and office data are explained as follows:

[0075] Field data: refers to the data collected during field measurements, which is usually highly accurate and real-time, and directly reflects the actual situation on site, including the original data corresponding to the terrain, landforms, building locations and sizes obtained on site through GPS, total stations, radar laser scanning, and drones;

[0076] In-office data: refers to the information obtained through data collation, processing and analysis in surveying and mapping work, including data editing, correction, modeling and other processes, which are usually completed in the office or laboratory and can be used for subsequent applications and decision support, including maps, reports and 3D terrain models generated based on field data; for example: by scanning the plane pattern inside and outside the building, the relevant dimensional data of the building is obtained, and after drawing a single-line sketch according to the measured dimensions, the required data and graphics are generated in one click through software automation processing, which greatly reduces the workload;

[0077] Environmental data: refers to data that affects the collected field data due to weather or equipment reasons during the surveying and mapping process; in this embodiment, for equipment data: drones are used as carriers of laser radar and oblique photography, and drones are directly selected as analysis objects for subsequent processing;

[0078] Then, the phases are marked based on the internal and external data, and the construction phases are divided according to the progress of the construction tasks. The construction phases are construction preparation phase, construction process phase and construction completion phase. Among them, the construction preparation phase indicates that the construction work has not yet been carried out, the construction process phase indicates that the construction work is in progress, and the construction completion phase indicates that the construction work has been completed.

[0079] Data processing module:

[0080] A three-dimensional task model corresponding to the construction operation stage is constructed according to the environmental data, the indoor data and the outdoor data, the regional scope of the construction operation task is determined according to the three-dimensional task model, a parameter set corresponding to the construction operation task is monitored according to the regional scope, and the parameter set is preprocessed and analyzed to generate a standard parameter set; wherein the standard parameter set includes a settlement standard parameter and a deformation standard parameter;

[0081] Among them, construction work tasks include non-construction work, demolition work and development land work;

[0082] Explanation of the relevant terms involved in construction tasks:

[0083] No construction work includes work involving safety surveys of existing buildings;

[0084] Demolition operations include residential demolition, industrial demolition, large infrastructure demolition, and historical building demolition;

[0085] Development land operations include residential development, industrial development, commercial development and public facilities development;

[0086] In this embodiment, for construction tasks, the building structure includes concrete, stone, brick, steel skeleton, yard and open space, and it is used as the basic element of the three-dimensional task model for subsequent analysis and processing of the three-dimensional task model; a set of indoor and outdoor data corresponding to the construction task is obtained through drone surveying and photography technology, for example, for the image data in the data, by introducing convolutional neural network for feature extraction and classification, the structure, material and use of the building can be identified, and building feature classification information can be generated, and combined with total station, radar technology, GPS, point cloud processing algorithm, the three-dimensional terrain model can be reconstructed to achieve high-precision building and foundation identification, provide data basis and data support for subsequent data analysis, and improve the accuracy of the surveying and mapping process;

[0087] The construction process of the three-dimensional task model is as follows:

[0088] Data preparation: Set the collection cycle to collect the internal and external data corresponding to the construction phase at regular intervals; the collection cycle for internal data is T1, and the collection cycle for external data is T2, and both T1 and T2 are natural numbers greater than 0;

[0089] Model construction: construct a 3D task model, generate a 3D terrain model based on historically collected internal data, and the historically collected internal data is obtained by analyzing the historically corresponding field data. The 3D terrain model is used as the basic model of the 3D task model, and the corresponding construction work tasks are added as elements of the 3D task model, and the regularly collected field data is mapped to the 3D task model;

[0090] Feature extraction: extracting acquisition data parameters in the three-dimensional task model, wherein the acquisition data parameters include image frame rate and image voxels; at the same time, calculating the accuracy coefficient according to the acquisition data parameters;

[0091] The coefficient of precision is based on the formula: ;

[0092] Where precision is the precision coefficient, pr is the image frame rate, vo is the image voxel, μ1 and μ2 are the weight coefficients of the image frame rate pr and the image voxel vo, respectively, and both μ1 and μ2 are greater than 0;

[0093] Model optimization: construct several impact models based on environmental data, and obtain them through reinforcement learning exploration of sample environments corresponding to the collected data parameters. The sample environment is composed of at least two environmental data corresponding to the historical collected data parameters; analyze the stability coefficient based on several impact models, and select the optimal three-dimensional task model based on the stability coefficient;

[0094] The process of selecting the optimal 3D task model is:

[0095] First, the environmental grade coefficient is calculated based on the environmental data, which includes the light intensity value, air humidity value and drone speed;

[0096] The environmental grade coefficient is based on the formula: ;

[0097] Where Lx is the light intensity value, Rh is the air humidity value, and Vu is the UAV speed; β1, β2, and β3 are the influence coefficients of the light intensity value Lx, the air humidity value Rh, and the UAV speed Vu, respectively, and β1, β2, and β3 are all greater than 0;

[0098] Match the environmental level range according to the environmental level coefficient;

[0099] The formula for the environmental grade interval is: ;

[0100] Where k is the preset fluctuation ratio, 0<k<0.35, de b is the preset environmental grade coefficient, ω is the environmental grade coefficient de a The preset environmental level coefficient b Conversion factor between ;

[0101] The corresponding data collection parameters obtained under the monitoring environment data are used to construct a three-dimensional monitoring vector, including: precision coefficient precision, environmental level interval S, and collection cycle time;

[0102] Then, the average value and fluctuation value of the precision coefficient within the preset time are obtained. For any time period Ta, the environmental level interval of the time period Ta is marked as Sa, the average value and fluctuation value are pre_a and per_b respectively, and the fluctuation value is the average value of the difference between the maximum precision coefficient and the minimum precision coefficient within the time period Ta; a number of judgment vectors are constructed according to different combinations, including a first judgment vector and a second judgment vector;

[0103] It should be noted that the definition of the preset time is:

[0104] According to the collection period T1, the collection period T1 is divided into i sub-time nodes, where i is an integer greater than 0. Similarly, according to the collection period T2, the collection period T2 is divided into j sub-time nodes, where j is an integer greater than 0. Several sub-time nodes are randomly selected to form a time period and marked as a preset time;

[0105] Then the first judgment vector is expressed as: ;

[0106] Then the second judgment vector is expressed as: ;

[0107] Construct the first judgment vector - the change function h1 of the environmental level coefficient: ;

[0108] and determine it as the first impact mode of environmental data on the collected data parameters;

[0109] Construct the second judgment vector - the change function h2 of the environmental level coefficient: ;

[0110] and determine it as the second influence mode of environmental data on the collected data parameters;

[0111] In the formula, ε1 and ε2 are conversion functions;

[0112] Finally, the corresponding stability coefficient is calculated according to the change function h1 or the change function h2;

[0113] The formula for calculating the stability factor is: ;

[0114] Where h is the variation function h1 or h2, and α is the influence coefficient;

[0115] comparing the stability coefficients corresponding to the first impact mode and the second impact mode, selecting the stability coefficient with a larger value as the optimal three-dimensional task model;

[0116] In this process, by relying on UAV mapping technology to analyze environmental data, the acquisition data parameters during the mapping process are regularly collected to select the optimal 3D task model, ensuring that the data (such as image data) is collected with high accuracy and is not restricted by the environment;

[0117] Determine the regional scope of the construction task according to the optimized three-dimensional task model, and monitor the parameter set corresponding to the construction task according to the regional scope, wherein the parameter set includes a settlement parameter and a deformation parameter, wherein the settlement parameter is composed of the settlement amount, and the deformation parameter includes the deformation amount, strain, and stress;

[0118] In this embodiment, for settlement parameters, the focus is on the ground slope and displacement information within the regional range, which is used for subsequent settlement analysis and calculation; for deformation parameters, the focus is on the deformation and displacement information of the building within the regional range, which is used to evaluate the bearing capacity and stability during the construction phase to ensure the safe progress of the construction task; and the ground slope and displacement information within the regional range, and the building deformation and displacement information can be obtained by analyzing the three-dimensional task model, and there are many analysis methods, and this process will not be described in detail in the text;

[0119] The process of preprocessing and analyzing the parameter set is as follows:

[0120] Constructing a parametric analysis model:

[0121] The input data is marked as parameter Qa, which contains several data indicators. Any data indicator is marked as q, and the data value of the marked data indicator q is Lq. The standard interval of the data indicator q is set to QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one.

[0122] By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index;

[0123] The formula for calculating the standard reference index BZQ is: ;

[0124] It should be noted that σ is the weight coefficient, and the value of σ is 1~3, and the general value is 2; the standard reference index BZQ can be regarded as a parabola with an opening upward. When the standard reference index calculated by the data indicator q is less than or equal to , it is a normal reference index; otherwise, it is an abnormal reference index;

[0125] Applying the parameter analysis model: Substituting the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4;

[0126] Automatic feedback module:

[0127] By building a nonlinear fitting model, the standard parameter set is fitted with data, a curve model between the standard parameter set and time is constructed, and the processing and analysis strategy is executed to obtain the execution results. Data correction or early warning processing is performed according to the execution results, and corresponding feedback instructions are sent back to the data acquisition module;

[0128] The process of building a nonlinear fitting model and executing the processing and analysis strategy is as follows:

[0129] Set the standard parameter set Qb = {settlement standard parameter, deformation standard parameter}, input any element in the set Qb, set the measurement period T3, the number of acquisitions in the measurement period T3 is nc, obtain the values ​​of nc elements in the measurement period T3, and establish a rectangular coordinate system with the corresponding acquisition period T1 or acquisition period T2 as the horizontal coordinate and the value of the current element as the vertical coordinate, and draw the element time change curve in the rectangular coordinate system; wherein the element time change curve includes the settlement change curve, the deformation change curve, the strain change curve and the stress change curve;

[0130] At the same time, a corresponding standard threshold curve is drawn on the rectangular coordinate system, and the distance value between the two points is calculated by analyzing the corresponding points of the element time variation curve and the standard threshold curve;

[0131] Distance threshold A and distance threshold B are preset, and according to the rectangular coordinate system, normal reference elements and abnormal reference elements are extracted for separate determination, specifically:

[0132] When it is a normal reference element, if the distance value obtained exceeds the distance threshold A, a correction process is performed, and a correction instruction is issued and displayed; otherwise, no process is performed;

[0133] It should be noted that the purpose of the correction process is to ensure the precision and accuracy of the relevant data. When the correction instruction is received, the text is immediately edited and displayed, and the text content is "abnormality occurs and correction processing is required"; in this embodiment, the main purpose is to correct the collected data;

[0134] For example, the correction process often involves image correction. For image correction, the specific process is:

[0135] Matching image feature points: When the drone captures the original image data, the scale-invariant feature transformation matching algorithm is used to extract fuzzy feature points, and the similarity between the feature points is calculated. The two feature points with the highest similarity are used as the feature points to be matched; the fuzzy feature points in the original image data are grouped together, and the set is marked as Jh1. The set consisting of all feature points in the target image is Jh2, and the similarity between sets Jh1 and Jh2 is calculated;

[0136] Two-dimensional feature point correction: Based on the extracted fuzzy feature points, a registration triangular mesh is constructed, and the original image data is marked as Δabc, and the corresponding coordinates are: (xa, ya), (xb, yb), (xc, yc), and the calibrated image data is marked as ΔABC, (xA, yA), (xB, yB), (xC, yC);

[0137] According to the formula: ;

[0138] Where (X, Y) is the calibration image, sim is the similarity, ΔX is the translation in the X direction, ΔY is the translation in the Y direction, θ is the matching parameter of the rotation angle, and py is the offset coefficient;

[0139] Improve the matching parameters for calculating the offset of the image in the horizontal and vertical directions and the rotation angle of the coordinate system, and obtain the image correction result by adjusting the matching parameters;

[0140] Curve adjustment: According to the image correction results, the sedimentation change curve is adjusted in real time, as shown below:

[0141] ;

[0142] Where (X, Y) is the image matching parameter, S is the sedimentation variation curve, and S 1 is the settlement at time t=1, S 0 is the settlement at time t=0, φ is the parameter to be identified, ψ is the step length of data acquisition time, and φ and ψ are both constants;

[0143] It should be noted that this process focuses on the correction of feature points of the two-dimensional image data to avoid the reprojection error values ​​of the viewpoints sd1 and sd2 taken by the drone in the three-dimensional reconstruction, which reduces the accuracy of the evaluation match. This process will not be described in detail in this article.

[0144] In the case of an abnormal reference element, if the obtained distance value exceeds the distance threshold B, a state analysis is performed according to the element time variation curve to obtain a corresponding state cycle; otherwise, no processing is performed;

[0145] The process of determining the distance threshold A or the distance threshold B is as follows:

[0146] Based on historical data curves: analyze the normal reference data and abnormal reference data of elements (including settlement, deformation, strain and stress) in past operation, and the normal reference data corresponds to the normal reference elements, and the abnormal reference data corresponds to the abnormal reference elements; respectively understand the range of fluctuations of normal reference elements and the data range of fluctuations of abnormal reference elements;

[0147] Statistical historical data curve: Collect the corresponding values ​​of the elements under normal reference elements and abnormal reference elements, perform statistical analysis on the collected data, determine the average value and standard deviation of the corresponding values, set the standard threshold curve according to the statistical results, and set the standard threshold curve to the average value of the element value plus 2 times the standard deviation;

[0148] Determine the distance threshold range: Based on the distance value between the element time variation curve and the standard threshold curve, determine the fluctuation range of the historical distance value data, and determine the average value and standard deviation of the corresponding distance value; set the standard threshold according to the statistical results, and set the standard threshold to the average value of the distance value plus 3 times the standard deviation;

[0149] In this embodiment, it is assumed that there are M0 points on the element time change curve, and the slope of the ordinate of the M0 points is obtained; the curve change rate is obtained by performing difference calculation on the slopes of any two points in the M0; an ascending set and a descending set are established according to the curve change rate, specifically: when the curve change rate is greater than 0, it means that the curve segment is on an ascending trend and is classified as an ascending set; when the curve change rate is less than 0, it means that the curve segment is on a descending trend and is classified as a descending set; when the curve change rate is equal to 0, it means that the curve segment has no change trend; thereby, the change speeds corresponding to the settlement change curve, the deformation change curve, the strain change curve and the stress change curve are calculated. In this embodiment, all element time change curves are expressed as an ascending set, and the change speed is used to define the state period of the foundation settlement or building deformation in the construction task, including the initial period, the active period and the decay period;

[0150] The meanings of the initial period, active period and decline period are explained as follows:

[0151] Initial stage: the initial state when the foundation settles or the building deforms. The corresponding element time change curve shows a sudden increase, usually in the form of a broken line.

[0152] Active period: The active state in which the foundation settles or the building deforms. The time variation curve of the corresponding element shows an increasing state, usually in the form of a parabola.

[0153] Decline stage: the decline state in which the foundation settles or the building deforms. The time change of the corresponding element is a slow increase, usually in the form of a parabola.

[0154] For example: foundation settlement is mainly the result of building load, and the process of building settlement under load is divided into three parts, instantaneous settlement L1, consolidation settlement L2 and stress settlement L3; suppose there is a settlement change curve, the settlement S at t=0, 1, 2, 3, 4, 5, 6, 7, 8 is 5, 6, 9, 15, 20, 18, 14, 10, 8 respectively; the initial period is t=0 to t=3, and its change rate gradually increases. This stage is the stage when the building just starts to apply load, and the settlement is mainly dominated by instantaneous settlement L1;

[0155] The active period is from t=3 to t=5, and its change rate peaks between t=3 and t=4 and then begins to decrease. During this stage, the building is under construction, the load continues to act, and the consolidation settlement L2 and stress settlement L3 begin to significantly affect the settlement state of the building.

[0156] The decline period is from t=5 to t=8, and the rate of change gradually decreases. After many years of use, the settlement state of the building tends to be stable, and the settlement rate slows down or stops.

[0157] When the foundation settlement amount obtained within the status cycle is greater than the corresponding standard threshold, an early warning instruction is immediately generated and displayed. The text content corresponding to the early warning instruction is: "The status cycle is XXX, and early warning processing is required." Using 5G technology, the early warning instruction is transmitted back to the unit in real time. After receiving the early warning instruction, the professionals will perform corresponding risk avoidance work to ensure the safety of the final completion and acceptance stage. The system also transmits back the processed data during the surveying and mapping process, generates a surveying and mapping report and data package for completion acceptance, and supports printing technology.

[0158] In summary, the system includes a data acquisition module, a data processing module and an automatic feedback module. The key technical points are as follows: by analyzing the indoor and outdoor data during the surveying and mapping process, a three-dimensional task model is constructed. At the same time, several influence models are constructed for the environmental data in the surveying and mapping process, especially for the flight environment of the UAV, and the stability coefficient in the automatic surveying and mapping process is analyzed to select the optimal three-dimensional task model. According to the three-dimensional task model, settlement parameters and deformation parameters are extracted, and a nonlinear fitting model is built to generate the corresponding element time variation curve. Different analysis strategies are executed according to the generated normal reference element values ​​and abnormal reference element values, and the three-dimensional task model is further calibrated to improve the precision and accuracy of the data acquisition process. At the same time, the process focuses on the entire process of the construction operation stage, avoiding the safety hazards caused by foundation settlement and building deformation.

[0159] Embodiment 2: Figure 3 This is a flowchart of a method for automatically processing field and indoor data of multi-survey integration surveying and mapping according to an exemplary embodiment. Figure 3 This embodiment provides a method for automatically processing field and indoor data of multi-survey integration surveying and mapping. The main steps of the processing method are as follows:

[0160] S1, in the construction work task, collects environmental data, indoor data and outdoor data at different construction work stages;

[0161] S2, constructing a three-dimensional task model corresponding to the construction operation stage according to the environmental data, the indoor data and the outdoor data; determining the regional scope of the construction operation task according to the three-dimensional task model, monitoring the parameter set corresponding to the construction operation task according to the regional scope, and performing preprocessing analysis on the parameter set to generate a standard parameter set; wherein the parameter set includes settlement parameters and deformation parameters, and the standard parameter set includes settlement standard parameters and deformation standard parameters;

[0162] The process of preprocessing and analyzing the parameter set is as follows:

[0163] Constructing a parametric analysis model:

[0164] Mark the input data as parameter Qa, wherein the parameter Qa includes several data indicators, mark any data indicator as q, and the data value of marking any data indicator q is Lq, and set the standard interval of the data indicator q as QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one;

[0165] By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index;

[0166] The formula for calculating the standard reference index BZQ is:

[0167] ;

[0168] In the formula, σ is the weight coefficient;

[0169] Applying the parameter analysis model: Substituting the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4;

[0170] S3, building a nonlinear fitting model, performing data fitting on the standard parameter set, constructing a curve model between the standard parameter set and time, and executing a processing and analysis strategy to obtain an execution result, and performing data correction or early warning processing according to the execution result.

[0171] In the application, the several formulas involved are all calculated by removing dimensions and taking their numerical values, and the formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The formula is set by technical personnel in this field according to actual conditions.

[0172] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in 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.

[0173] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A multi-survey integrated surveying and mapping field data automatic processing system, characterized in that: include: A data acquisition module, a data processing module and an automatic feedback module, wherein the data acquisition module, the data processing module and the automatic feedback module are communicatively connected with each other; The data collection module collects environmental data, indoor data and outdoor data of different construction work stages in the construction work task; wherein the construction work task uniquely corresponds to the construction work stage and the stage is marked, and the construction work stage includes the construction preparation stage, the construction process stage and the construction completion stage; The data processing module constructs a three-dimensional task model corresponding to the construction operation stage according to the environmental data, the indoor data and the outdoor data, determines the regional scope of the construction operation task according to the three-dimensional task model, monitors the parameter set corresponding to the construction operation task according to the regional scope, and performs preprocessing analysis on the parameter set to generate a standard parameter set; wherein the standard parameter set includes a settlement standard parameter and a deformation standard parameter; The process of preprocessing and analyzing the parameter set is as follows: Constructing a parametric analysis model: Mark the input data as parameter Qa, wherein the parameter Qa includes several data indicators, mark any data indicator as q, and the data value of marking any data indicator q is Lq, and set the standard interval of the data indicator q as QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one; By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index; The formula for calculating the standard reference index BZQ is: ； In the formula, σ is the weight coefficient; Applying the parameter analysis model: Substituting the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4; The automatic feedback module, by building a nonlinear fitting model, performs data fitting on the standard parameter set, constructs a curve model between the standard parameter set and time, executes a processing and analysis strategy, obtains an execution result, performs data correction or early warning processing according to the execution result, and issues corresponding feedback instructions back to the data acquisition module.

2. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 1 is characterized by: The construction operation tasks include non-construction operations, demolition operations and land development operations, and the construction process of the three-dimensional task model is: Data preparation: Set the collection cycle to collect the internal data and field data corresponding to the construction operation stage at regular intervals; wherein the collection cycle of the internal data is T1, and the collection cycle of the field data is T2, and both T1 and T2 are natural numbers greater than 0; Model construction: construct a three-dimensional task model, generate a three-dimensional terrain model based on historically collected internal data, and the historically collected internal data is obtained by analyzing the historically corresponding field data, use the three-dimensional terrain model as the basic model of the three-dimensional task model, add the corresponding construction work tasks as the elements of the three-dimensional task model, and map the regularly collected field data to the three-dimensional task model; Feature extraction: extracting acquisition data parameters in the three-dimensional task model, wherein the acquisition data parameters include image frame rate and image voxels; at the same time, calculating the accuracy coefficient according to the acquisition data parameters; The formula on which the coefficient of accuracy is based is: ; Where precision is the precision coefficient, pr is the image frame rate, vo is the image voxel, μ1 and μ2 are the weight coefficients of the image frame rate pr and the image voxel vo, respectively, and both μ1 and μ2 are greater than 0; Model optimization: construct several influence patterns based on the environmental data, and obtain them through reinforcement learning exploration of the sample environment corresponding to the collected data parameters, wherein the sample environment is composed of environmental data corresponding to at least two historical collected data parameters; analyze the stability coefficient based on several influence patterns, and select the optimal three-dimensional task model based on the stability coefficient.

3. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 2 is characterized by: The process of selecting the optimal 3D task model is: First, the environmental level coefficient is calculated according to the environmental data, and the environmental level interval is matched according to the environmental level coefficient, and the corresponding acquisition data parameters obtained under the environmental data are monitored to construct a three-dimensional monitoring vector, including: precision coefficient precision, environmental level interval S, and acquisition period time; Then, the average value and fluctuation value of the precision coefficient within the preset time are obtained. For any time period Ta, the environmental level interval of the time period Ta is marked as Sa, the average value and fluctuation value are pre_a and per_b respectively, and the fluctuation value is the average value of the difference between the maximum precision coefficient and the minimum precision coefficient in the time period Ta; a number of judgment vectors are constructed according to different combinations, including a first judgment vector and a second judgment vector; The first judgment vector is expressed as: ; The second judgment vector is expressed as: ; Construct the first judgment vector - the change function h1 of the environmental level coefficient, and determine it as the first influence mode of the environmental data on the collected data parameters; Constructing the second judgment vector - the variation function h2 of the environmental level coefficient, and determining it as the second influence mode of the environmental data on the collected data parameters; Finally, the corresponding stability coefficient is calculated according to the change function h1 or the change function h2; The formula for calculating the stability coefficient is: ; Where h is the variation function h1 or h2, and α is the influence coefficient; Compare the stability coefficients corresponding to the first impact mode and the second impact mode, select the stability coefficient with a larger value, and use it as the optimal three-dimensional task model.

4. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 3 is characterized by: The environmental data includes light intensity value, air humidity value and drone speed, and the formula of the environmental level coefficient is as follows: ； Where Lx is the light intensity value, Rh is the air humidity value, and Vu is the UAV speed; β1, β2, and β3 are the influence coefficients of the light intensity value Lx, the air humidity value Rh, and the UAV speed Vu, respectively, and β1, β2, and β3 are all greater than 0; The formula for the environmental grade interval is: ; Where k is the preset fluctuation ratio, 0<k<0.35, de b is the preset environmental level coefficient, ω is the environmental level coefficient de a The preset environmental level coefficient b The conversion factor between .

5. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 1 is characterized by: The parameter set includes a settlement parameter and a deformation parameter, wherein the settlement parameter is composed of a settlement amount, and the deformation parameter includes a deformation amount, a strain, and a stress.

6. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 2 is characterized by: The process of building a nonlinear fitting model and executing the processing and analysis strategy is as follows: Set the standard parameter set Qb = {settlement standard parameter, deformation standard parameter}, input any element in the set Qb, set the measurement period T3, the number of acquisitions in the measurement period T3 is nc, obtain the values ​​of nc elements in the measurement period T3, and establish a rectangular coordinate system with the corresponding acquisition period T1 or acquisition period T2 as the horizontal coordinate and the value of the current element as the vertical coordinate, and draw the element time change curve in the rectangular coordinate system; the element time change curve includes the settlement change curve, the deformation change curve, the strain change curve and the stress change curve; At the same time, a corresponding standard threshold curve is drawn on the rectangular coordinate system, and the distance value between the two points is calculated by analyzing the corresponding points of the element time variation curve and the standard threshold curve; Distance threshold A and distance threshold B are preset, and according to the rectangular coordinate system, normal reference elements and abnormal reference elements are extracted for separate determination, specifically: When it is a normal reference element, if the obtained distance value exceeds the distance threshold A, correction processing is performed; when it is an abnormal reference element, if the obtained distance value exceeds the distance threshold B, state analysis is performed according to the element time change curve to obtain the corresponding state cycle, and early warning processing is performed according to the state cycle; Otherwise, no action is taken.

7. The multi-survey integrated surveying and mapping field data automatic processing system according to claim 6 is characterized by: The state cycle includes an initial period, an active period, and a decay period.

8. A method for automatically processing indoor and outdoor data of multi-survey integration surveying and mapping, the method being applied to the automatic processing system for indoor and outdoor data of multi-survey integration surveying and mapping as described in any one of claims 1 to 7, characterized in that: The following steps are involved: S1, in the construction work task, collects environmental data, indoor data and outdoor data at different construction work stages; S2, constructing a three-dimensional task model corresponding to the construction operation stage according to the environmental data, the indoor data and the outdoor data; determining the regional scope of the construction operation task according to the three-dimensional task model, monitoring the parameter set corresponding to the construction operation task according to the regional scope, and performing preprocessing analysis on the parameter set to generate a standard parameter set; wherein the parameter set includes settlement parameters and deformation parameters, and the standard parameter set includes settlement standard parameters and deformation standard parameters; The process of preprocessing and analyzing the parameter set is as follows: Constructing a parametric analysis model: Mark the input data as parameter Qa, wherein the parameter Qa includes several data indicators, mark any data indicator as q, and the data value of marking any data indicator q is Lq, and set the standard interval of the data indicator q as QZ[QZ1, QZ2], and the standard interval QZ corresponds to the data indicator q one by one; By combining the data value Lq with the standard interval QZ, a standard reference index BZQ of the data indicator q is obtained; wherein the data indicator q corresponds to a standard reference index BZQ, and the standard reference index BZQ is expressed as a normal reference index and an abnormal reference index; The formula for calculating the standard reference index BZQ is: ； In the formula, σ is the weight coefficient; Apply the parameter analysis model: substitute the parameter set into the parameter analysis model to obtain the corresponding standard parameter set, including the settlement standard parameters and the deformation standard parameters, specifically including the settlement reference index c1, the deformation reference index c2, the strain reference index c3 and the stress reference index c4; S3, building a nonlinear fitting model, performing data fitting on the standard parameter set, constructing a curve model between the standard parameter set and time, and executing a processing and analysis strategy to obtain an execution result, and performing data correction or early warning processing according to the execution result.

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