CN117848303B - Intelligent mapping planning method and system for constructional engineering - Google Patents
Intelligent mapping planning method and system for constructional engineering Download PDFInfo
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Abstract
The invention relates to an intelligent planning method and system for surveying and mapping of constructional engineering, which belong to the technical field of surveying and mapping of constructional engineering. According to the invention, the mapping points can be evaluated according to the image definition shot under different environmental characteristics, so that shot mapping data can meet the image definition requirement, thereby avoiding the occurrence of events of which the image data does not meet the preset definition requirement, further avoiding the repeated operation of the mapping unmanned aerial vehicle, and improving the working efficiency of the mapping unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of building mapping, in particular to an intelligent mapping planning method and system for building engineering.
Background
Compared with the traditional mapping technology, the three-dimensional laser scanning technology has the advantages of high precision, full coverage, flexibility and three-dimensional advantage, and the technology can be used for directly scanning the building surface and acquiring the power supply data of the building surface. Thereby alleviate relevant staff's working strength and operation degree of difficulty when effectively promoting building survey and drawing work precision, realize building survey and drawing work efficiency and the comprehensive promotion of quality. The three-dimensional laser scanning technology belongs to a high-new technology which is produced in the 90 th century of the 20 th century, and the basic principle is that a high-frequency laser scanning measuring instrument is utilized to effectively acquire three-dimensional space point information of a surface to be measured. The three-dimensional laser scanner mainly comprises a laser emitter, a laser receiver, a phase discriminator, a camera, a filter, a microprocessor, software connected with the microprocessor and the like. The working principle of building mapping by applying the three-dimensional laser scanning technology is as follows: the laser transmitter emits laser beams with regular geometric shape characteristics to the surface of the object to be mapped, the laser beams scan the surface of the object to be mapped through rotating the reflecting mirror, the time difference of the reflected laser beams at different points is obtained by means of the phase discriminator to infer the distance between the laser beams and the measured point, the encoder is used for calculating the actual three-dimensional coordinates of each scanning point on the surface of the object to be mapped, and the microprocessor integrates and calculates the related data to obtain a sampling point set, namely 'point cloud data', of the surface of the object to be mapped. The point cloud data contains three-dimensional information, RGB information and reflection intensity information of the surface of the target object. In contrast, in high-rise buildings, space-to-ground comprehensive scanning is generally performed on a region through an unmanned aerial vehicle-mounted laser scanner, and finally, comprehensive acquisition of various data information is realized through internal computing. However, the laser scanner is also easily affected by the environment, for example, due to inconsistent penetrability of the laser beam under different environmental characteristics (such as temperature, humidity, visibility, haze, etc.), the sharpness of the image photographed under different environmental characteristics is different, when the laser scanner is not adjusted, partial data can not be collected or the collected data is blurred, so that the image data does not meet the preset sharpness requirement, and the laser scanner needs to be collected again through the surveying and mapping unmanned aerial vehicle, which is time-consuming and labor-consuming and has low working efficiency.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an intelligent mapping planning method and system for constructional engineering.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The first aspect of the invention provides an intelligent mapping planning method for constructional engineering, which comprises the following steps:
Acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
Acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
Acquiring abnormal mapping points and normal mapping points based on image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
and obtaining the geographical position information of the abnormal mapping point, and optimizing the mapping point according to the geographical position information of the abnormal mapping point.
Further, in the method, a plurality of mapping points are constructed according to mapping task data information and building drawing information of the building to be mapped, and the method specifically comprises the following steps:
Constructing a three-dimensional building model diagram through three-dimensional modeling software according to building drawing information, acquiring the position information of a region to be mapped and the area information of a shooting region to be mapped based on mapping task data information of a building to be mapped, introducing a genetic algorithm, and setting a genetic algebra according to the genetic algorithm;
Initializing the position information of a plurality of laser scanners and the number of shooting points, and carrying out shooting area simulation through three-dimensional modeling software according to the position information of the laser scanners to obtain shooting area information corresponding to the position information of the laser scanners;
Counting shooting area information corresponding to the position information of the laser scanner, acquiring total shooting area information, adjusting the position information of the laser scanner and the number of shooting points according to the genetic algebra if the total shooting area information is not larger than the total shooting area information, outputting the position information of the laser scanner and the number of the shooting points if the total shooting area information is larger than the total shooting area information, and randomly selecting a datum point in a building to be mapped;
Acquiring coordinate point data of a datum point in a world coordinate system and coordinate point data of the datum point in a building three-dimensional model diagram, and constructing a coordinate relationship according to the coordinate point data of the datum point in the world coordinate system and the coordinate point data of the datum point in the building three-dimensional model diagram;
Converting the position information of the laser scanner into coordinate point data information in a world coordinate system according to the coordinate relation, acquiring converted coordinate point data information, taking a point position corresponding to the converted coordinate point data information as a mapping point, and outputting a plurality of mapping points.
Further, in the method, environmental characteristic data information of each mapping point within a preset range is obtained, and image definition characteristic data information of each mapping point under different laser scanner working parameters is predicted according to the environmental characteristic data information of the mapping point within the preset range, specifically including:
acquiring image definition characteristic data information of images acquired by a laser scanner under each environmental characteristic data information and different working parameters of the laser scanner through big data, introducing the image neural network, and taking the environmental characteristic data information as a first image node of the image neural network;
Taking the image definition characteristic data information as a second graph node of the graph neural network, taking the working parameter of the laser scanner as a third graph node, constructing an undirected edge description relationship, and connecting the first graph node, the second graph node and the third graph node based on the undirected edge description relationship to generate a topological structure diagram;
acquiring related adjacency matrixes according to a topological structure diagram, constructing a knowledge graph, sequentially inputting the related adjacency matrixes into the knowledge graph for storage, and acquiring environmental characteristic data information of each mapping point within a preset range and a working parameter range threshold of a laser scanner;
And inputting the environmental characteristic data information of each mapping point within a preset range and the working parameter range threshold value of the laser scanner into a knowledge graph for data matching, and obtaining the image definition characteristic data information of each mapping point under different working parameters of the laser scanner.
Further, in the method, obtaining abnormal mapping points and normal mapping points based on the image definition characteristic data information of each mapping point under different working parameters of the laser scanner specifically includes:
presetting image definition threshold data, and judging whether at least one piece of image definition characteristic data information exists in image definition characteristic data information of mapping points under different working parameters of a laser scanner, wherein the image definition characteristic data information is larger than the image definition threshold data;
when at least one image definition characteristic data information exists in the image definition characteristic data information of the mapping points under different working parameters of the laser scanner, the corresponding mapping points are used as normal mapping points;
When any image definition characteristic data information of the mapping points under different working parameters of the laser scanner does not exist, and the image definition characteristic data information is larger than image definition threshold data, the corresponding mapping points are used as abnormal mapping points;
and outputting the normal mapping points and the abnormal mapping points as output results.
Further, in the method, the optimal working parameters of the laser scanner are configured for each normal mapping point, and the method specifically comprises the following steps:
Acquiring image definition characteristic data information of each normal mapping point under different working parameters of a laser scanner, and constructing an image definition sequencing table;
inputting the image definition characteristic data information of the normal mapping points under different working parameters of the laser scanner into an image definition sorting table for sorting;
Acquiring an image definition sequencing result of each mapping point under different laser scanner working parameters according to the image definition sequencing table, and acquiring a laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result;
and taking the laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result as the optimal laser scanner working parameter of each mapping point, and carrying out parameter control on the laser scanner according to the optimal laser scanner working parameter.
Further, in the method, geographical position information of an abnormal mapping point is obtained, mapping point optimization is performed according to the geographical position information of the abnormal mapping point, and the method specifically includes:
acquiring geographical position information of an abnormal mapping point, and carrying out shooting simulation on a laser scanner according to the geographical position information of the abnormal mapping point to acquire shooting area range information of the abnormal mapping point;
dividing the shooting area range information of the abnormal mapping point into a plurality of sub shooting areas, calculating Euclidean distance values among the sub shooting areas of the abnormal mapping point, merging the sub shooting areas with the same Euclidean distance value, and obtaining the merged sub shooting areas;
Taking sub-shooting areas with different Euclidean distance values as independent sub-shooting areas, and counting the combined sub-shooting areas and the independent sub-shooting areas to obtain the range information of the non-shooting areas of the current building to be painted;
And carrying out genetic iteration on the number of mapping points and the alternative mapping positions of the abnormal mapping points according to the range information of the non-shot area of the current building to be mapped by using a genetic algorithm, obtaining the alternative mapping positions of the abnormal mapping points, and carrying out mapping point optimization according to the alternative mapping positions of the abnormal mapping points.
Further, in the method, the intelligent planning method for mapping of the constructional engineering further comprises the following steps:
Obtaining geographical position information of a mapping point, planning a path of the mapping unmanned aerial vehicle according to the geographical position information of the mapping point, generating one or more flight paths of the unmanned aerial vehicle, and obtaining flight information of each flight path;
Obtaining estimated energy consumption values of unit flight information, and calculating the estimated energy consumption value of each flight path according to the flight distance information of the flight path and the energy consumption values of the unit flight distance information;
the method comprises the steps of obtaining an energy consumption value sequencing result of the flight paths by sequencing the estimated energy consumption value of each flight path, and obtaining the minimum estimated energy consumption value in the energy consumption value sequencing result of the flight paths;
And acquiring a flight path corresponding to the smallest estimated energy consumption value in the energy consumption value sequencing result of the flight paths, and taking the flight path corresponding to the smallest estimated energy consumption value as a navigation path of the mapping unmanned aerial vehicle.
The second aspect of the present invention provides a mapping intelligent planning system for a building engineering, the mapping intelligent planning system for a building engineering comprises a memory and a processor, the memory comprises a mapping intelligent planning method program for a building engineering, and the mapping intelligent planning method program for a building engineering realizes the following steps when executed by the processor:
Acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
Acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
Acquiring abnormal mapping points and normal mapping points based on image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
and obtaining the geographical position information of the abnormal mapping point, and optimizing the mapping point according to the geographical position information of the abnormal mapping point.
The invention solves the defects existing in the background technology, and has the following beneficial effects:
According to the invention, a plurality of mapping points are constructed according to the mapping task data information and the building drawing information of the building to be mapped, and then the environmental characteristic data information of each mapping point in a preset range is obtained, and the image definition characteristic data information of each mapping point under different laser scanner working parameters is predicted according to the environmental characteristic data information of each mapping point in the preset range, so that abnormal mapping points and normal mapping points are obtained based on the image definition characteristic data information of each mapping point under different laser scanner working parameters, and optimal laser scanner working parameters are configured for each normal mapping point, and finally the mapping points are optimized according to the geographic position information of each abnormal mapping point. According to the invention, the mapping points can be evaluated according to the image definition shot under different environmental characteristics, so that shot mapping data can meet the image definition requirement, thereby avoiding the occurrence of events of which the image data does not meet the preset definition requirement, further avoiding the repeated operation of the mapping unmanned aerial vehicle, and improving the working efficiency of the mapping unmanned aerial vehicle.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of a mapping intelligent planning method of a construction project;
FIG. 2 shows a first method flow diagram of a mapping intelligent planning method of a construction project;
FIG. 3 shows a second method flow diagram of a mapping intelligent planning method of a construction project;
Fig. 4 shows a system block diagram of a mapping intelligent planning system for construction engineering.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the first aspect of the present invention provides an intelligent mapping planning method for constructional engineering, which includes the following steps:
S102, acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
in the step S102, a plurality of mapping points are constructed according to mapping task data information and building drawing information of the building to be mapped, and the method specifically includes the following steps:
Constructing a three-dimensional building model diagram through three-dimensional modeling software according to building drawing information, acquiring the position information of a region to be mapped and the area information of a shooting region to be mapped based on mapping task data information of a building to be mapped, introducing a genetic algorithm, and setting a genetic algebra according to the genetic algorithm;
In this step, the three-dimensional modeling software includes maya software, BIM technology related software, solidWorks, and other three-dimensional modeling software, and the construction drawing information includes shape size information, contour information, positioning size new type data, and the mapping task data includes data such as shot area information, position area information, length information, width information, and height information to be mapped.
Initializing the position information of a plurality of laser scanners and the number of shooting points, and carrying out shooting area simulation through three-dimensional modeling software according to the position information of the laser scanners to obtain shooting area information corresponding to the position information of the laser scanners;
The area information of the region that can be imaged by the laser scanner is simulated based on the position of the laser scanner by the three-dimensional modeling technique and the virtual reality display technique.
Counting shooting area information corresponding to the position information of the laser scanner, acquiring total shooting area information, adjusting the position information of the laser scanner and the number of shooting points according to the genetic algebra if the total shooting area information is not larger than the total shooting area information, outputting the position information of the laser scanner and the number of the shooting points if the total shooting area information is larger than the total shooting area information, and randomly selecting a datum point in a building to be mapped;
It should be noted that the genetic algorithm is a search algorithm for solving optimization in computational mathematics, and is one of evolutionary algorithms. Evolution starts from a population of completely random individuals, with the next generation occurring. In each generation, the fitness of the entire population is evaluated, a number of individuals are randomly selected from the current population, and a new living population is generated by natural selection and mutation, which becomes the current population in the next iteration of the algorithm. The position information of the laser scanner and the number of shooting points can be obtained through a genetic algorithm, so that the optimal position and the number of mapping points are selected, and the mapping efficiency of the mapping unmanned aerial vehicle is optimized.
Acquiring coordinate point data of a datum point in a world coordinate system and coordinate point data of the datum point in a building three-dimensional model diagram, and constructing a coordinate relationship according to the coordinate point data of the datum point in the world coordinate system and the coordinate point data of the datum point in the building three-dimensional model diagram;
It should be noted that, one point of the limit position should be selected in the three-dimensional building model, and the point of the reference point in the coordinates (0, 0), (x, y, 0) (0, y, z), (x, 0, z) should be avoided, where the values of x, y, z are not equal to 0, so that the coordinate relationship between the coordinates in the three-dimensional model and the coordinates in the world coordinate system can be avoided from being unable to be obtained. If the coordinates in the three-dimensional coordinate system are (1, 1), and the same reference point in the world coordinate system is the coordinates of 2 degrees in longitude, 2 degrees in latitude and 2 meters in altitude, the relationship of the x-direction coordinates, the y-direction coordinates and the z-direction coordinates to the coordinates in the real coordinate system is 0.5 times can be obtained.
Converting the position information of the laser scanner into coordinate point data information in a world coordinate system according to the coordinate relation, acquiring converted coordinate point data information, taking a point position corresponding to the converted coordinate point data information as a mapping point, and outputting a plurality of mapping points.
It should be noted that, through the above description of the coordinate relationship, several mapping points can be obtained, so as to obtain the geographic position information of the mapping points in the world coordinate system.
S104, acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
as shown in fig. 2, in the step S104, the main embodiments are as follows:
S202, acquiring image definition characteristic data information of images acquired by a laser scanner under each environmental characteristic data information and different working parameters of the laser scanner through big data, introducing the image neural network, and taking the environmental characteristic data information as a first image node of the image neural network;
In recent years, research into analyzing a graph by a machine learning method has been paid more and more attention due to strong expressive force of the graph structure. Graph Neural Networks (GNNs) are a class of deep learning-based methods of processing graph domain information. GNN has recently become a widely used graph analysis method due to its better performance and interpretability. The environment characteristic data information, the working parameters of the laser scanner and the image definition characteristic data information can be converted into a topological structure diagram through the image neural network, and the topological structure diagram can clearly describe the relationship among the environment characteristic data information, the working parameters of the laser scanner and the image definition characteristic data information. The environmental characteristic data information includes temperature, humidity, visibility, haze and the like, and in practice, the penetrability of the laser beam under different environmental characteristic conditions is inconsistent, so that the sharpness of the images photographed under different environmental characteristics has a certain difference. The laser scanner operating parameters include parameters such as power, wavelength, type of laser beam, etc.
S204, taking the image definition characteristic data information as a second graph node of the graph neural network, taking the working parameter of the laser scanner as a third graph node, constructing an undirected edge description relationship, and connecting the first graph node, the second graph node and the third graph node based on the undirected edge description relationship to generate a topological structure diagram;
S206, acquiring related adjacency matrixes according to the topological structure diagram, constructing a knowledge graph, sequentially inputting the related adjacency matrixes into the knowledge graph for storage, and acquiring environmental characteristic data information of each mapping point within a preset range and a working parameter range threshold of the laser scanner;
S208, inputting the environmental characteristic data information of each mapping point within a preset range and the working parameter range threshold value of the laser scanner into a knowledge graph for data matching, and obtaining the image definition characteristic data information of each mapping point under different working parameters of the laser scanner.
It should be noted that the topology structure diagram can be converted into a related adjacency matrix, so that the related adjacency matrix is input into the knowledge graph for storage, and thus, the image definition characteristic data information of each mapping point under different working parameters of the laser scanner is directly identified according to the difference of the parameters. The image definition characteristic data information comprises parameter descriptions such as resolution, resolution and the like.
S106, acquiring abnormal mapping points and normal mapping points based on image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
In the step S106, abnormal mapping points and normal mapping points are obtained based on the image sharpness characteristic data information of each mapping point under different laser scanner working parameters, and specifically the method includes the following steps:
presetting image definition threshold data, and judging whether at least one piece of image definition characteristic data information exists in image definition characteristic data information of mapping points under different working parameters of a laser scanner, wherein the image definition characteristic data information is larger than the image definition threshold data;
when at least one image definition characteristic data information exists in the image definition characteristic data information of the mapping points under different working parameters of the laser scanner, the corresponding mapping points are used as normal mapping points;
when at least one image definition characteristic data information is larger than the image definition threshold data, the mapping point is specified to be the image data with the image definition characteristic data information larger than the image definition threshold data by the specific working parameters of the laser scanner, and the mapping requirement of the mapping point is met.
When any image definition characteristic data information of the mapping points under different working parameters of the laser scanner does not exist, and the image definition characteristic data information is larger than image definition threshold data, the corresponding mapping points are used as abnormal mapping points;
It should be noted that, when there is no image definition characteristic data information greater than the image definition threshold value data, it is indicated that the mapping point is not provided with a specific laser scanner working parameter to obtain the image data whose image definition characteristic data information is greater than the image definition threshold value data, and even if the working parameter of the laser scanner is adjusted, the collected image data is still blurred or unclear, and it is impossible to meet the mapping requirement of the mapping point.
And outputting the normal mapping points and the abnormal mapping points as output results.
In the step S106, the optimal laser scanner operating parameters are configured for each normal mapping point, including the steps of:
Acquiring image definition characteristic data information of each normal mapping point under different working parameters of a laser scanner, and constructing an image definition sequencing table;
inputting the image definition characteristic data information of the normal mapping points under different working parameters of the laser scanner into an image definition sorting table for sorting;
Acquiring an image definition sequencing result of each mapping point under different laser scanner working parameters according to the image definition sequencing table, and acquiring a laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result;
and taking the laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result as the optimal laser scanner working parameter of each mapping point, and carrying out parameter control on the laser scanner according to the optimal laser scanner working parameter.
By the method, the optimal working parameters of the laser scanner of the normal mapping points can be screened, so that the acquired image data are clearer, and the effectiveness of the acquired data of the mapping unmanned aerial vehicle is improved.
S108, obtaining the geographical position information of the abnormal mapping points, and optimizing the mapping points according to the geographical position information of the abnormal mapping points.
As shown in fig. 3, further, in step S108 of the method, the method specifically includes:
S302, acquiring geographical position information of an abnormal mapping point, and carrying out shooting simulation on a laser scanner according to the geographical position information of the abnormal mapping point to acquire shooting area range information of the abnormal mapping point;
The imaging simulation of the laser scanner is performed according to the geographical position information of the abnormal mapping point, and the actual imaging area range information of the abnormal mapping point is obtained by performing the simulation in the three-dimensional modeling technology and the virtual reality technology, so that the acquired data range area is determined.
S304, dividing the shooting area range information of the abnormal mapping point into a plurality of sub shooting areas, calculating Euclidean distance values among the sub shooting areas of the abnormal mapping point, combining the sub shooting areas with the same Euclidean distance value, and obtaining the combined sub shooting areas;
The calculation formula of the euclidean distance is as follows:
wherein d is a Euclidean distance value, For the i < th > sub-photographing region/>，/>For the j-th sub-shooting area/>Wherein/>≠/>. The Euclidean distance value between the neutron shooting areas of the abnormal mapping points can be calculated through the method.
S306, taking sub-shooting areas with different Euclidean distance values as independent sub-shooting areas, and counting the combined sub-shooting areas and the independent sub-shooting areas to obtain the range information of the non-shooting areas of the current building to be painted;
It should be noted that, when the euclidean distance values are the same, the related sub-shooting areas are repeated shooting areas, and the independent sub-shooting areas represent that no repeated shooting areas exist.
And S308, carrying out genetic iteration on the number of mapping points and the alternative mapping positions of the abnormal mapping points according to the range information of the non-shot area of the building to be mapped according to a genetic algorithm, obtaining the alternative mapping positions of the abnormal mapping points, and carrying out mapping point optimization according to the alternative mapping positions of the abnormal mapping points.
It should be noted that, setting an image definition threshold, searching an optimal mapping position in the range information of the non-shot area through a genetic algorithm, so that definition data of an image acquired in the optimal mapping position accords with the definition threshold, and thus searching an optimal mapping point. The genetic iteration of mapping point number and mapping position substitution of abnormal mapping points is performed on the range information of the non-shot area of the building to be mapped according to the genetic algorithm, and the genetic iteration can be realized by the same principle similar to the step in the step S102.
Further, in the method, the intelligent planning method for mapping of the constructional engineering further comprises the following steps:
Obtaining geographical position information of a mapping point, planning a path of the mapping unmanned aerial vehicle according to the geographical position information of the mapping point, generating one or more flight paths of the unmanned aerial vehicle, and obtaining flight information of each flight path;
the flight information includes data such as flight resistance and flight distance.
Obtaining estimated energy consumption values of unit flight information, and calculating the estimated energy consumption value of each flight path according to the flight distance information of the flight path and the energy consumption values of the unit flight distance information;
the method comprises the steps of obtaining an energy consumption value sequencing result of the flight paths by sequencing the estimated energy consumption value of each flight path, and obtaining the minimum estimated energy consumption value in the energy consumption value sequencing result of the flight paths;
And acquiring a flight path corresponding to the smallest estimated energy consumption value in the energy consumption value sequencing result of the flight paths, and taking the flight path corresponding to the smallest estimated energy consumption value as a navigation path of the mapping unmanned aerial vehicle.
It should be noted that, by the method, the optimal mapping path of the mapping unmanned aerial vehicle can be selected.
It should be noted that, the invention can evaluate the mapping points according to the image definition shot under different environmental characteristics, and can make the shot mapping data meet the image definition requirement, so as to avoid the occurrence of events that the image data does not meet the preset definition requirement, further avoid the repeated operation of the mapping unmanned aerial vehicle, and improve the working efficiency of the mapping unmanned aerial vehicle.
In addition, in the practical application process, the method can further comprise the following steps:
After genetic algebra iteration is carried out through a genetic algorithm, if the alternative mapping position of the abnormal mapping point is not found, acquiring meteorological characteristic data information in a preset range of a building to be mapped within a preset time period; presetting a plurality of time stamps, and acquiring environmental characteristic data information of each time stamp in a preset range of a building to be mapped according to the meteorological characteristic data information in the preset range of the building to be mapped in the preset time period; inputting the environmental characteristic data information of each time stamp in the preset range of the building to be mapped into the knowledge graph for data matching, and obtaining the corresponding image definition characteristic data information under the environmental characteristic data information of the building to be mapped in each time stamp; and selecting a time stamp corresponding to image definition characteristic data information which is larger than image definition threshold data and is corresponding to environmental characteristic data information within a preset range of the building to be mapped as a mapping period of the unmanned mapping plane, and carrying out mapping planning according to the mapping period of the unmanned mapping plane.
It should be noted that, in the actual process, after the genetic algebra iteration is performed through the genetic algorithm, the alternative mapping position of the abnormal mapping point may not be found, and the mapping period of the unmanned mapping unmanned aerial vehicle can be further improved to optimize through the method, so that the mapping efficiency of the unmanned mapping unmanned aerial vehicle is improved.
In addition, during actual operation, the following may be included:
acquiring electromagnetic wave characteristic data information of a building to be drawn within a preset range, constructing a search tag according to the electromagnetic wave characteristic data information of the building to be drawn within the preset range, and searching through a big data network based on the search tag; acquiring flight interference characteristic data information of the current to-be-painted building under electromagnetic wave characteristic data information within a preset range through retrieval, and presetting flight interference characteristic threshold value data; judging whether the flight disturbance characteristic data information is larger than the flight disturbance characteristic threshold value data or not, and taking the current time stamp as a non-mapping period if the flight disturbance characteristic data information is larger than the flight disturbance characteristic threshold value data; and when the flight disturbance characteristic data information is not larger than the flight disturbance characteristic threshold value data, taking the current time stamp as a mapping period.
It should be noted that, because the unmanned aerial vehicle of survey and drawing receives electromagnetic wave's interference easily for the unmanned aerial vehicle of survey and drawing appears the confusing phenomenon of control logic easily in the survey and drawing process, thereby lead to the unmanned aerial vehicle of survey and drawing not fly according to predetermined flight path easily, on the other hand leads to the phenomenon that laser scanner appears in disorder easily. The electromagnetic wave characteristic data information includes data such as the type of electromagnetic wave, the wavelength of the electromagnetic wave, the frequency, and the like. The interference characteristic data includes low interference, medium interference, high interference, and the like. The rationality of the unmanned surveying and mapping plane during surveying and mapping can be further improved through the method.
As shown in fig. 4, the second aspect of the present invention provides a mapping intelligent planning system 4 for a building engineering, where the mapping intelligent planning system 4 for a building engineering includes a memory 41 and a processor 42, and the memory 41 includes a mapping intelligent planning method program for a building engineering, and when the mapping intelligent planning method program for a building engineering is executed by the processor 42, the following steps are implemented:
Acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
Acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
Acquiring abnormal mapping points and normal mapping points based on image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
and obtaining the geographical position information of the abnormal mapping point, and optimizing the mapping point according to the geographical position information of the abnormal mapping point.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of 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.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The intelligent mapping planning method for the constructional engineering is characterized by comprising the following steps of:
Acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
Acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
Acquiring abnormal mapping points and normal mapping points based on the image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
And obtaining the geographical position information of the abnormal mapping point, and optimizing the mapping point according to the geographical position information of the abnormal mapping point.
2. The intelligent planning method for mapping of constructional engineering according to claim 1, wherein the construction of a plurality of mapping points according to the mapping task data information and the construction drawing information of the building to be mapped specifically comprises:
Constructing a three-dimensional building model diagram through three-dimensional modeling software according to the building drawing information, acquiring the position information of a region to be mapped and the area information of a shooting region to be mapped based on the mapping task data information of the building to be mapped, introducing a genetic algorithm, and setting a genetic algebra according to the genetic algorithm;
Initializing the position information of a plurality of laser scanners and the number of shooting points, and carrying out shooting area simulation through three-dimensional modeling software according to the position information of the laser scanners to obtain shooting area information corresponding to the position information of the laser scanners;
Counting shooting area information corresponding to the position information of the laser scanner, acquiring total shooting area information, adjusting the position information of the laser scanner and the number of shooting points according to the genetic algebra if the position information of the laser scanner is not larger than the total shooting area information, outputting the position information of the laser scanner and the number of the shooting points if the position information of the laser scanner is larger than the total shooting area information, and randomly selecting a datum point in a building to be mapped;
Acquiring coordinate point data of the datum point in a world coordinate system and coordinate point data of the datum point in a building three-dimensional model graph, and constructing a coordinate relationship according to the coordinate point data of the datum point in the world coordinate system and the coordinate point data of the datum point in the building three-dimensional model graph;
And converting the position information of the laser scanner into coordinate point data information in a world coordinate system according to the coordinate relation, acquiring converted coordinate point data information, taking a point position corresponding to the converted coordinate point data information as a mapping point, and outputting a plurality of mapping points.
3. The intelligent planning method for mapping of constructional engineering according to claim 1, wherein the method is characterized by obtaining environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range, and specifically comprises the following steps:
acquiring image definition characteristic data information of images acquired by a laser scanner under each environmental characteristic data information and different working parameters of the laser scanner through big data, introducing the image definition characteristic data information into a graph neural network, and taking the environmental characteristic data information as a first graph node of the graph neural network;
Taking the image definition characteristic data information as a second graph node of the graph neural network, taking the working parameter of the laser scanner as a third graph node, constructing an undirected edge description relationship, and connecting the first graph node, the second graph node and the third graph node based on the undirected edge description relationship to generate a topological structure diagram;
Acquiring a related adjacency matrix according to the topological structure diagram, constructing a knowledge graph, sequentially inputting the related adjacency matrix into the knowledge graph for storage, and acquiring environmental characteristic data information of each mapping point within a preset range and a working parameter range threshold of a laser scanner;
And inputting the environmental characteristic data information of each mapping point within a preset range and the working parameter range threshold value of the laser scanner into the knowledge graph for data matching, and obtaining the image definition characteristic data information of each mapping point under different working parameters of the laser scanner.
4. The intelligent planning method for mapping of constructional engineering according to claim 1, wherein the acquiring of the abnormal mapping point and the normal mapping point based on the image definition characteristic data information of each mapping point under different working parameters of the laser scanner specifically comprises:
Presetting image definition threshold data, and judging whether at least one piece of image definition characteristic data information exists in image definition characteristic data information of mapping points under different working parameters of a laser scanner, wherein the image definition characteristic data information is larger than the image definition threshold data;
When at least one image definition characteristic data information exists in the image definition characteristic data information of the mapping points under different working parameters of the laser scanner, the corresponding mapping points are used as normal mapping points;
When any image definition characteristic data information of the mapping points under different working parameters of the laser scanner does not exist, and the image definition characteristic data information is larger than the image definition threshold value data, the corresponding mapping points are used as abnormal mapping points;
and outputting the normal mapping points and the abnormal mapping points as output results.
5. The intelligent planning method for mapping of constructional engineering according to claim 1, wherein the configuring of the optimal laser scanner operating parameters for each normal mapping point specifically comprises:
Acquiring image definition characteristic data information of each normal mapping point under different working parameters of a laser scanner, and constructing an image definition sequencing table;
Inputting the image definition characteristic data information of the normal mapping points under different working parameters of the laser scanner into the image definition sequencing table for sequencing;
acquiring an image definition sequencing result of each mapping point under different laser scanner working parameters according to the image definition sequencing table, and acquiring a laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result;
And taking the laser scanner working parameter corresponding to the maximum image definition in the image definition sequencing result as the optimal laser scanner working parameter of each mapping point, and carrying out parameter control on the laser scanner according to the optimal laser scanner working parameter.
6. The intelligent planning method for mapping of constructional engineering according to claim 1, wherein the method is characterized by obtaining geographic position information of an abnormal mapping point and optimizing the mapping point according to the geographic position information of the abnormal mapping point, and specifically comprises the following steps:
acquiring geographical position information of an abnormal mapping point, carrying out shooting simulation on a laser scanner according to the geographical position information of the abnormal mapping point, and acquiring shooting area range information of the abnormal mapping point;
Dividing the shooting area range information of the abnormal mapping point into a plurality of sub shooting areas, calculating Euclidean distance values among the sub shooting areas of the abnormal mapping point, merging the sub shooting areas with the same Euclidean distance value, and obtaining the merged sub shooting areas;
Taking sub-shooting areas with different Euclidean distance values as independent sub-shooting areas, and counting the combined sub-shooting areas and the independent sub-shooting areas to obtain the range information of the non-shooting areas of the current building to be painted;
And carrying out genetic iteration on the number of mapping points and the alternative mapping positions of the abnormal mapping points according to the range information of the non-shot area of the current building to be mapped by a genetic algorithm, obtaining the alternative mapping positions of the abnormal mapping points, and carrying out mapping point optimization according to the alternative mapping positions of the abnormal mapping points.
7. The intelligent planning method for mapping of constructional engineering according to claim 1, further comprising the steps of:
obtaining geographical position information of a mapping point, planning a path of a mapping unmanned aerial vehicle according to the geographical position information of the mapping point, generating one or more flight paths of the unmanned aerial vehicle, and obtaining flight information of each flight path;
obtaining estimated energy consumption values of unit flight information, and calculating the estimated energy consumption value of each flight path according to the flight distance information of the flight path and the energy consumption values of the unit flight distance information;
the estimated energy consumption value of each flight path is sequenced, an energy consumption value sequencing result of the flight path is obtained, and the minimum estimated energy consumption value in the energy consumption value sequencing result of the flight path is obtained;
and acquiring a flight path corresponding to the smallest estimated energy consumption value in the energy consumption value sequencing result of the flight paths, and taking the flight path corresponding to the smallest estimated energy consumption value as a navigation path of the mapping unmanned aerial vehicle.
8. The intelligent mapping planning system for the building engineering is characterized by comprising a memory and a processor, wherein the memory comprises an intelligent mapping planning method program for the building engineering, and the intelligent mapping planning method program for the building engineering realizes the following steps when being executed by the processor:
Acquiring mapping task data information and building drawing information of a building to be mapped, and constructing a plurality of mapping points according to the mapping task data information and the building drawing information of the building to be mapped;
Acquiring environmental characteristic data information of each mapping point within a preset range, and predicting image definition characteristic data information of each mapping point under different laser scanner working parameters according to the environmental characteristic data information of the mapping point within the preset range;
Acquiring abnormal mapping points and normal mapping points based on the image definition characteristic data information of each mapping point under different laser scanner working parameters, and configuring optimal laser scanner working parameters for each normal mapping point;
And obtaining the geographical position information of the abnormal mapping point, and optimizing the mapping point according to the geographical position information of the abnormal mapping point.
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