Land survey method and system adaptive to complex ground feature space

Abstract

The invention discloses a land surveying method and system adaptive to a complex ground object space, and relates to the technical field of land surveying and mapping data acquisition, and the method comprises the steps: selecting a plurality of data acquisition points with the data certainty exceeding a set value as first reference sites; calculating and generating a measurement deviation ratio of the survey data acquired at each current first reference site, and analyzing and generating a first incidence relation between the measurement deviation ratio and the data acquisition position by combining the spatial position distribution of each first reference site; acquiring at least two data acquisition points of which the measurement deviation rates are lower than a set value as second reference sites as data acquisition points, acquiring at least two pieces of target survey data, performing associated storage on the target survey data and the corresponding measurement deviation rates, and configuring confidence weights for the target survey data according to the measurement deviation rates, according to the scheme of the application, the measurement deviation during data acquisition can be reduced, the accuracy of the geological exploration data can be improved, and meanwhile, the efficiency of land exploration work can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of land surveying and mapping data collection and analysis and processing, and to a land surveying method and system that is adaptive to complex land object space. Background Art

[0002] Before building a new project or implementing old town renovation in a city, it is necessary to accurately survey the topography, underground pipelines and other data of the project implementation area to ensure that the project design meets the actual conditions. At the same time, if demolition is encountered, the current status of the building, ownership boundaries and other information will be known to provide a basis for calculating the demolition amount and compensation.

[0003] When conducting urban land surveys, we not only need to accurately obtain spatial geometric data, such as land boundary points, ground elevations, and steep slopes on the terrain, but also need to collect data related to environmental geology and three-dimensional images, such as groundwater levels and real-life building models.

[0004] In actual applications, urban buildings are densely populated and underground pipelines are complexly distributed, making it difficult to efficiently and accurately obtain various geological survey data. In addition, due to differences in data collection methods, collection time, and collection environment, the accuracy and reliability of various types of geological survey data are different. Even if the same type of data is obtained through the same data collection method, the errors generated by data collected in different environments during the later data processing are also different. For example, the accuracy of detection data collected by ground penetrating radar under different soil moisture conditions is different.

[0005] Faced with the complex topography in cities, how to optimize the collection methods of various types of geological survey data and reduce or compensate for the deviation of geological survey data is very important for improving the accuracy and efficiency of urban land surveys. Summary of the invention

[0006] In view of the problem that the current geological survey data is difficult to collect and the data accuracy is difficult to ensure due to the complex spatial distribution of urban objects, the first purpose of this application is to provide a land survey method that is adaptive to complex object spaces. The corresponding relationship between target data and related data is established based on historical geological survey data. When collecting target data, relevant related data is collected in conjunction, and then the target data is corrected based on the related data, thereby reducing the difficulty of collecting geological survey data under complex object space conditions and improving the accuracy of various types of geological survey data. In order to realize the above-mentioned land survey method, the second purpose of this application is to provide a land survey system that is adaptive to complex object spaces. The specific scheme is as follows: A land survey method adaptive to complex land space, comprising: For target survey data, multiple data collection points with data confidence exceeding a set value are selected from historical survey data as first reference points; Collect and obtain the survey data corresponding to each first reference point currently, and compare it with the historical survey data to obtain the measurement deviation rate of the survey data collected at each first reference point currently; Combine the spatial position distribution and measurement deviation rate of each first reference point, and analyze and generate the first correlation relationship between the measurement deviation rate of the survey data and the data collection position; According to the first correlation relationship, obtain at least two data collection points with a measurement deviation rate lower than the set value as the second reference points; Use the second reference points as data collection points to collect and obtain at least two target survey data related to the target ground feature points, and store the target survey data and its corresponding measurement deviation rate in an associated manner; Configure confidence weights for each target survey data according to the data deviation rate corresponding to the target survey data, and calculate and generate geological exploration data according to the set fusion algorithm; Among them, the geological exploration data includes spatial geometric data and environmental geological data.

[0007] Through the above technical solution, multiple measurement control points with relatively high confidence, that is, data collection points, can be selected from the historical survey data as the first reference points, and then multiple survey data are collected and obtained at the above multiple first reference points. By comparing the data deviation rate between the currently obtained survey data and the historical survey data, the law of the change of the data deviation rate with the data collection position is obtained, so as to find the second reference point with the lowest data deviation rate. By using the above second reference point as the data collection point and collecting the survey data for calculation, the accuracy of the finally obtained geological exploration data can be greatly improved.

[0008] Further, if it is impossible to directly collect and obtain the target survey data by using the second reference point as the data collection point, the method further includes: Select a third reference point that can directly measure both the second reference point and the target ground feature point in the self-test area as the collection relay point; Obtain the intermediate survey data between the collection relay point, the target ground feature point and the second reference point; Obtain the measurement deviation rate at the collection relay point based on the first correlation relationship; Assign confidence weights to each intermediate survey data according to the measurement deviation rates of the collection relay point and the second reference point, and calculate and generate geological exploration data according to the set fusion algorithm.

[0009] Through the above technical solution, when there are obstacle factors that hinder measurement between the second reference point and the target ground feature point, the third reference point can be used as a measurement relay point to indirectly obtain the survey data between the second reference point and the target ground feature point, and after the data deviation rate is corrected, the accuracy of the generated geological exploration data can be guaranteed.

[0010] Further, divide the surveyed area plots into multiple confidence regions according to the first association relationship and configure corresponding confidence probability values for each confidence region; Store the corresponding relationship between different confidence probability values and the number of data collection times; Obtain the position coordinates of each second reference site and the data collection relay point, and find the confidence probability value of the region where they are located; Determine the number of data collection times required at each second reference site and / or data collection relay point according to the corresponding relationship, and store them in association with the data collection point position coordinates; Collect and store the target survey data and / or intermediate survey data in multiple times according to the number of data collection times associated with the data collection points; Calculate and generate the target survey data and / or intermediate survey data corresponding to the data collection points according to the mean algorithm or weighted fusion algorithm.

[0011] Through the above technical solution, when the confidence probability value of the data collection point is too low, the survey data can be obtained by calculating through the set algorithm after collecting data multiple times, which can greatly improve the accuracy of the survey data and avoid or reduce the data deviation caused by single data collection.

[0012] Further, the land survey method further includes: Obtain and analyze the correlation between the measurement deviation rate of the target survey data, the data collection time, and the data collection environmental conditions based on historical survey data, and generate the second association relationship and the third association relationship respectively; When collecting the target survey data at the second reference site, it further includes: Record the time point when the target survey data is obtained and collect the current environmental conditions of the second reference site; Based on the above time point and environmental conditions, combine the first association relationship and the second association relationship to configure the time-associated deviation rate and the environment-associated deviation rate for the currently obtained survey data respectively; Comprehensively calculate and generate the time and environment deviation rate of the target survey data according to the time-associated deviation rate and the environment-associated deviation rate; Adjust the confidence weights of each target survey data according to the time and environment deviation rate and the measurement deviation rate, and calculate and generate the geological exploration data according to the set fusion algorithm; Wherein, the data collection environmental conditions include the intensity of rain, snow, haze, tide height, surface and underground temperature, air flow or water flow intensity that affect the survey data.

[0013] Through the above technical solution, the currently collected target survey data can be corrected according to the deviation rate of data collection at different time periods and different environmental conditions of the data collection point, so that the final generated geological exploration data result is more accurate.

[0014] Furthermore, the measurement deviation rate includes the data deviation magnitude and the deviation direction; In the land surveying method, generating the first correlation relationship includes: Statistically analyze the deviation magnitude and deviation direction corresponding to the measurement deviation rate at each first reference point, obtain the position coordinates of each first reference point and the relative position distribution relationship between each first reference point, combine the deviation magnitude and deviation direction, analyze the change trend of the measurement deviation rate in the survey area, fit and generate a first correlation function formula or a first data table for characterizing the corresponding relationship between each position coordinate in the survey area and the deviation magnitude and deviation direction, and store it as the first correlation relationship; Generating the second correlation relationship includes: Select a time point from the collection time points included in the historical survey data as the time reference point, obtain the time reference survey data at the above time reference point, compare the target survey data collected in each period with the time reference survey data, statistically analyze the measurement deviation rate of the target survey data collected in each period compared with the time reference survey data, fit and generate a second correlation function formula or a second data table for characterizing the corresponding relationship between each collection time point and the deviation magnitude and deviation direction, and store it as the second correlation relationship; Generating the third correlation relationship includes: Calibrate an environmental condition as the reference environmental condition and store it. Obtain the environmental condition data corresponding to the survey data from the historical survey data, determine the environmental reference survey data corresponding to the reference environmental condition, and compare the remaining historical survey data and its corresponding environmental condition data with the environmental reference survey data. Statistically generate a third correlation function formula or a third data table for characterizing the corresponding relationship between the measurement deviation rate of the survey data and the change of the environmental condition data, and store it as the third correlation relationship.

[0015] Through the above technical solution, target survey data can be obtained at any data collection point and environmental condition as needed. The obtained data is corrected and calculated to generate corresponding geological exploration data, with high accuracy. It is also convenient for on-site survey personnel to flexibly select measurement control points, which is beneficial to improving the efficiency of the survey.

[0016] Furthermore, the land surveying method further includes an automatic data collection scheme generation step, including: Obtain and based on the type of target survey data to be collected and the allowable measurement deviation rate range; Automatically select and store the second reference point according to the first correlation relationship; Select the data collection time according to the second correlation relationship; Select the data collection environment according to the third correlation relationship; Obtain and determine the number of times of collecting the target survey data at the second reference point according to the confidence region where the second reference point is located; Mark data collection points, their corresponding data collection times and / or data collection environments, and the number of data collection times on the survey area map according to the second reference point.

[0017] Through the above technical solution, surveyors only need to input the type of target survey data to be collected and the range of allowable measurement deviation rates, such as the position coordinates of boundary points and their deviation ranges. After completing the data collection work at the first reference point, the system will automatically plan the implementation plan for this survey work based on historical survey data, significantly improving the efficiency of land survey while ensuring the accuracy of geological exploration data.

[0018] A land survey system adapted to the space of complex ground features, including: A first reference point acquisition unit configured to select multiple data collection points with data confidence exceeding a set value from historical survey data as the first reference points and store them according to the target survey data to be collected currently; A first correlation relationship generation unit configured to collect and obtain the survey data corresponding to each first reference point currently, and then compare it with historical survey data to obtain the measurement deviation rate of collecting survey data at each current first reference point. According to the spatial position distribution of each first reference point and the above measurement deviation rate, analyze and generate a first correlation relationship between the measurement deviation rate of survey data and the data collection position; A second reference point acquisition unit configured to obtain at least two data collection points with a measurement deviation rate lower than the set value as the second reference points according to the first correlation relationship; A target survey data acquisition unit configured to collect and obtain at least two target survey data related to target ground feature points with the second reference point as the data collection point, and store the target survey data in association with its corresponding measurement deviation rate; A geological exploration data generation unit configured to obtain and configure a confidence weight for each target survey data according to the data deviation rate corresponding to the target survey data, and calculate and generate geological exploration data according to a set fusion algorithm; Wherein, the geological exploration data includes spatial geometric data and environmental geological data.

[0019] Through the above technical solution, first, a position with a lower measurement deviation rate is obtained based on historical survey data as the data collection point, which can reduce the data deviation existing during the collection of target survey data; by setting multiple data collection points to collect the same target survey data, and then calculating and generating survey data through a set fusion algorithm according to the measurement deviation rate of each data collection point, the accuracy of geological exploration data can be greatly improved.

[0020] Further, the system further includes: A data acquisition occlusion detection unit configured to detect and determine whether target survey data can be directly acquired with the second reference point as the data acquisition point, and output determination result data; An acquisition relay point generation unit configured to receive the determination result data. If the target survey data cannot be directly acquired, a third reference point that can achieve direct measurement between the second reference point and the target feature point is selected from the self-survey area as the acquisition relay point and stored; An intermediate survey data acquisition unit configured to acquire intermediate survey data between the acquisition relay point, the target feature point, and the second reference point; In the geological exploration data generation unit, it is further configured to: obtain the measurement deviation rate at the acquisition relay point based on the first association relationship, assign confidence weights to each intermediate survey data according to the measurement deviation rates of the acquisition relay point and the second reference point, and calculate and generate geological exploration data according to the set fusion algorithm.

[0021] Further, the system further includes: A region division unit configured to divide the survey area plot into multiple confidence regions according to the first association relationship and configure corresponding confidence probability values for each confidence region, and at the same time, associate and store the data acquisition times corresponding to each confidence probability value; An acquisition times confirmation unit configured to obtain the regional positions where each data acquisition point is located and thereby determine the data acquisition times corresponding to each data acquisition point; A data acquisition output unit configured to acquire target data in multiple times according to the data acquisition times associated with the data acquisition points, and then calculate and generate the target survey data and / or intermediate survey data corresponding to the data acquisition points according to the mean algorithm or the weighted fusion algorithm.

[0022] Further, the system further includes a data acquisition plan automatic generation unit, including: A requirement acquisition module configured to acquire the required target survey data and the range of allowable measurement deviation rates thereof; An acquisition parameter setting module configured to automatically select the second reference point, the data acquisition time, and the data acquisition environment according to the first association relationship, the second association relationship, and the third association relationship and store them; An acquisition times determination module configured to acquire and determine the acquisition times of the target survey data at the second reference point according to the confidence region where the second reference point is located; An acquisition plan generation module configured to mark the data acquisition points and their corresponding data acquisition times and / or data acquisition environments, and the data acquisition times on the survey area map according to the second reference point.

[0023] Through the above technical solution, surveyors can quickly know the position coordinates of each data collection point, as well as the corresponding collection time and / or collection environment, which can significantly improve the efficiency of land surveying.

[0024] This application has at least one of the following beneficial effects: (1) The solution of this application can accurately determine the optimal data collection points corresponding to the current survey work based on historical survey data, reduce the measurement deviation during data collection, and improve the accuracy of geological exploration data; (2) By setting multiple data collection points and assigning confidence weights to the target survey data collected at each data collection point according to the corresponding measurement deviation rate at each data collection point, and finally obtaining the geological exploration data through the set data fusion algorithm, it can significantly reduce the impact of obtaining survey data from a single data collection point on the final result and improve the accuracy of geological exploration data; (3) By associating influencing factors such as data collection location, data collection time, and data collection environment with the measurement deviation rate and automatically determining the collection scheme for target survey data, it can not only ensure the accuracy of data collection, but also greatly improve the efficiency of land surveying work. Description of the Drawings

[0025] Figure 1 It is a schematic diagram of the overall process of the land surveying method of this application; Figure 2 It is a schematic diagram of obtaining target survey data (distance between boundary points A and B) through the second reference point; Figure 3 It is a schematic diagram of the method for determining the number of data collections according to the position coordinates of the data collection points; Figure 4 It is a schematic diagram of the connection of the functional units of this application system.

[0026] Reference Signs: 1. First reference point acquisition unit; 2. First association relationship generation unit; 3. Second reference point acquisition unit; 4. Target survey data acquisition unit; 5. Geological exploration data generation unit; 6. Data collection occlusion detection unit; 7. Relay point generation unit during collection; 8. Intermediate survey data acquisition unit. Detailed Embodiments

[0027] The following details the embodiments of this application, and the examples of the embodiments are shown in the appendices Figures 1-4 as follows.

[0028] In the description of this specification, the description referring to terms such as "certain embodiments", "one embodiment", "some embodiments", "schematic embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiments or examples are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiments or examples. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] An adaptive land surveying method for complex terrain space, as Figure 1 shown, mainly includes the following steps: S100, select multiple data collection points with data confidence levels exceeding a set value from historical survey data for the target survey data as the first reference sites.

[0030] S200, collect the survey data corresponding to each of the first reference sites currently and compare it with the historical survey data to obtain the measurement deviation rate of the survey data collected at each of the first reference sites currently. S300, combine the spatial position distribution and measurement deviation rate of each of the first reference sites, and analyze and generate the first correlation relationship between the measurement deviation rate of the survey data and the data collection position. S400, obtain at least two data collection points with measurement deviation rates lower than the set value as the second reference sites according to the first correlation relationship. S500, use the second reference sites as data collection points to collect at least two target survey data related to the target ground object points, and store the target survey data and its corresponding measurement deviation rate in an associated manner. S600, configure confidence weights for each target survey data according to the data deviation rate corresponding to the target survey data, and calculate and generate geological exploration data according to the set fusion algorithm.

[0031] In the embodiments of this application, in the above step S100, the target survey data refers to the data required to be collected in the current survey work, such as the distance data between two boundary points in a land parcel, the height data of a certain ground bulge or building, etc. The historical survey data includes the survey data already stored related to the current survey area, such as existing national control points or available control points established by other engineering departments near the survey area, the distance between adjacent two ground object sites, the height of a specific building, etc. Judging the data confidence level of each data collection point includes comparing the deviation rates of the survey data in previous surveys in the historical survey data. If it is less than the set value, it can be determined that the survey data of the above data collection point can be trusted and used as a standard reference, such as the position coordinate data corresponding to the landmarks buried in the survey area.

[0032] The reference points described in the embodiments of the present application include position coordinate points or feature reference points for positioning reference within or outside the survey area, including but not limited to survey control points in the prior art.

[0033] In step S200, relevant survey equipment is used to survey each first reference point to obtain the latest survey data, such as the position coordinate data of a certain landmark. Due to the influence of newly built urban buildings, there will be a certain deviation between the currently collected position coordinate data and the historical survey data. For example, due to the urban canyon effect caused by urban buildings, GNSS signals will be interfered, resulting in changes in the currently collected survey data compared with the historical survey data. The purpose of step S200 is to obtain the measurement deviation rates of the relevant survey data collected at multiple first reference points inside and outside the survey area.

[0034] In practical applications, the above-mentioned measurement deviation rates are often related to the distribution positions of buildings and underground spaces. To obtain the above associations, step S300 analyzes the measurement deviation rates at multiple first reference points, and finally obtains a first functional relationship representing the corresponding relationship between the measurement deviation rate of the survey data and the position where the data collection point is located, which is stored as the first association relationship. Theoretically, based on the first association relationship, the measurement deviation rate corresponding to the survey data collected at any point in the survey area can be obtained, which is convenient for correcting the above-mentioned survey data in the later stage.

[0035] Specifically, the measurement deviation rates described in the embodiments of the present application include the data deviation amplitude and the deviation direction, that is, the measured data value increases or decreases. Generating the above first association relationship includes: statistically counting the deviation amplitude and deviation direction corresponding to the measurement deviation rates at each first reference point, obtaining the position coordinates of each first reference point and the relative position distribution relationship between each first reference point, combining the deviation amplitude and deviation direction, analyzing the change trend of the measurement deviation rates in the survey area, and fitting to generate a first association function formula or a first data table representing the corresponding relationship between each position coordinate in the survey area and the deviation amplitude and deviation direction, which is stored as the first association relationship. In subsequent steps, when the position coordinates of the data collection point are known, the measurement deviation rate corresponding to the target survey data collected at the data collection point can be calculated through the above first association function formula or directly found through the first data table.

[0036] In step S500, such as Figure 2As shown in the figure, in order to measure the distance between boundary point A and boundary point B, the distances of AC and BC can be measured at point C by using a total station. Then, based on the included angle formed by the connection lines of AC and BC, the distance between boundary points A and B can be calculated according to the trigonometric function relationship. The above method is especially applicable to the situation where there is an obstruction between boundary points A and B. By introducing point D as a control point, that is, a data acquisition point, two sets of data can be measured, and then the required geological exploration data can be calculated through the fusion algorithm in step S600.

[0037] It should be noted that the geological exploration data in the embodiments of the present application includes multiple different types of survey data, such as spatial geometric data and environmental geological data. The spatial geometric data includes plane coordinate data, such as the position coordinates of plot boundary points and building corner points, and also includes elevation data, such as ground elevation and the buried depth of underground facilities. The environmental geological data includes data such as the groundwater level line.

[0038] In the embodiments of the present application, the set fusion algorithm involved in the above step S600 is configured as weighted summation and then averaging. For example, the distance L1 between points A and B calculated from the survey data collected at point C is 200m, and its confidence weight is w1 = 0.98. The distance L2 between points A and B calculated from the survey data collected at point D is 201m, and the confidence weight is w2 = 0.99. Then the spacing value L between points A and B is L=(L1*w1+L2*w2) / (w1+w2) / 2 = 200.503m. If there are n second reference sites, then the spacing value L = ((L1*w1+L2*w2+...+L n *w n ) / (w1+w2+...+w n )) / n.

[0039] By using the above second reference sites as data acquisition points to collect survey data, and then calculating the required geological exploration data through the set fusion algorithm, the accuracy of the finally obtained geological exploration data can be greatly improved.

[0040] In practical applications, for different types of survey data, the involved data fusion algorithm can be adjusted. For example, when measuring elevation data, if the overall ground in the survey area subsides, a correction coefficient can be configured in the algorithm to correct the calculation result. Special cases are not listed one by one here.

[0041] Due to the complex urban topography, if it is impossible to directly collect the target survey data by using the second reference site as the data acquisition point during the land survey process, the land survey method of the present application further includes: S510, selecting a third reference site that can be directly measured with both the second reference site and the target feature point in the survey area as a collection relay point; S511, acquiring intermediate survey data between the acquisition relay point and the target ground feature point and the second reference point; S512, obtaining a measurement deviation rate at the acquisition relay point based on the first association relationship; S610, assigning confidence weights to each intermediate survey data according to the measurement deviation rate of the acquisition relay point and the second reference point, and generating geological survey data according to the set fusion algorithm.

[0042] The direct measurement described in step S510 means that the acquired data does not need to be converted and generated through data collected from other reference points, for example, the distance between two points is directly measured by laser ranging. When selecting the third reference point in step S510, the data collection point with a lower measurement deviation rate is preferentially selected under the premise that direct measurement can be achieved. The setting fusion algorithm described in step S610 can adopt the data fusion algorithm in the aforementioned disclosed step S600.

[0043] The above technical solution uses the third reference point as a measurement relay point to indirectly obtain the survey data between the second reference point and the target object point, and then corrects the data deviation rate to ensure the accuracy of the generated geological survey data. At the same time, it can also enable the land survey method described in this application to meet the survey needs under complex urban terrain conditions.

[0044] In the implementation manner of the present application, in view of the complex terrain conditions in the city, the above-mentioned collection relay point can be configured as a drone equipped with relevant instruments and equipment.

[0045] In the implementation mode of this application, Figure 3 As shown, in order to further improve the accuracy of the survey data, the land survey method also includes: S410, dividing the survey area into multiple confidence areas according to the first association relationship and configuring a corresponding confidence probability value for each confidence area. For example, when using a satellite positioning system to locate the coordinates of a boundary point, the location close to a high-rise building is easily disturbed, while the coordinate positioning data collected in an open area far away from the high-rise building is more accurate, that is, the confidence probability is higher; similarly, when using a ground penetrating radar to detect underground space and pipelines, the higher the soil moisture content, the lower the detection accuracy, that is, the lower the confidence probability value of the collected survey data.

[0046] S411, storing the corresponding relationship between different confidence probability values and the number of data collection times.

[0047] S412, obtaining the position coordinates of each second reference point, searching for the confidence probability value of the area where the second reference point is located, and if there is a collection relay point, obtaining the confidence probability value of the area where the collection relay point is located.

[0048] S413. Determine the required number of data acquisitions at each second reference point and / or acquisition relay point according to the corresponding relationship, and store them in association with the position coordinates of the data acquisition points.

[0049] S414. Acquire and store the target survey data and / or intermediate survey data in multiple acquisitions according to the number of data acquisitions associated with the data acquisition points.

[0050] S415. Calculate and generate the target survey data and / or intermediate survey data corresponding to the data acquisition points according to the mean algorithm or weighted fusion algorithm. In the implementation manner of this application, it is preferably to calculate the values of the target survey data or intermediate survey data acquired multiple times by using the mean algorithm.

[0051] Based on the above solution, when the confidence probability value of the data acquisition point is too low, the survey data can be obtained by calculating through a set algorithm after multiple data acquisitions, thereby greatly improving the accuracy of the survey data and avoiding or reducing the data deviation caused by single data acquisition.

[0052] During the survey of urban land, there are some survey data that are sensitive to the data acquisition time and data acquisition environment. For example, the intensity of rain, snow, haze, and vegetation coverage at the survey site will affect the detection results of lidar. If the survey area is close to the sea, the elevation survey results of the coastal beach will also be affected by the tide time.

[0053] In order to reduce the influence of the data acquisition time and data acquisition environment on the accuracy of the target survey data, the land survey method further includes: S310. Obtain and analyze the correlation between the measurement deviation rate of the target survey data and the data acquisition time and data acquisition environment conditions according to the historical survey data, and generate a second correlation relationship and a third correlation relationship respectively. The above data acquisition environment conditions include but are not limited to the intensity of rain, snow, haze, tide height, surface and underground temperature, air flow or water flow intensity that affect the survey data.

[0054] Generating the second correlation relationship includes: selecting a time point from the acquisition time points included in the historical survey data as the time reference point, obtaining the time reference survey data at the above time reference point, comparing the target survey data collected in each period with the time reference survey data, statistically calculating the measurement deviation rate of the target survey data collected in each period compared with the time reference survey data, fitting and generating a second correlation functional formula or a second data table for characterizing the corresponding relationship between each acquisition time point and the deviation amplitude and deviation direction, and storing it as the second correlation relationship. Generating the third correlation relationship includes: calibrating an environmental condition as the reference environmental condition and storing it, obtaining the environmental condition data corresponding to the survey data from the historical survey data, determining the environmental reference survey data corresponding to the reference environmental condition, and comparing the remaining historical survey data and its corresponding environmental condition data with the environmental reference survey data, statistically generating a third correlation functional formula or a third data table for characterizing the corresponding relationship between the measurement deviation rate of the survey data and the change in the environmental condition data, and storing it as the third correlation relationship.

[0055] When acquiring the target survey data at the second reference site, it further includes: S520, recording the time point when the target survey data is obtained and collecting the current environmental conditions of the second reference site; S521, based on the above time point and environmental conditions, combining the first correlation relationship and the second correlation relationship, respectively configuring a time correlation deviation rate and an environmental correlation deviation rate for the currently obtained survey data; S522, comprehensively calculating and generating a time - environment deviation rate of the target survey data according to the time correlation deviation rate and the environmental correlation deviation rate; S523, adjusting the confidence weight of each target survey data according to the time - environment deviation rate in combination with the measurement deviation rate, and calculating and generating geological exploration data according to the set fusion algorithm.

[0056] The above - mentioned technical solution can correct the currently collected target survey data according to the deviation rates of data collection at different periods and different environmental conditions at the data collection points, making the final generated geological exploration data results more accurate.

[0057] In order to improve the efficiency of geological exploration work, in the embodiment of the present application, the land survey method further includes a step S700 for automatically generating a data collection plan, based on the first correlation relationship, the second correlation relationship, the third correlation relationship, and the corresponding relationship between the confidence region and the number of data collections determined in the foregoing steps, specifically including: S710, obtaining and based on the type of target survey data to be collected and the allowable measurement deviation rate range; S711, automatically selecting and storing the second reference site according to the first correlation relationship; S712. Select the data collection time according to the second association relationship; S713. Select the data collection environment according to the third association relationship; S714. Obtain and determine the number of times of collecting the target survey data at the second reference point according to the confidence region where the second reference point is located; S715. Mark the data collection points and their corresponding data collection times and / or data collection environments, and the number of times of data collection on the survey area map according to the second reference point.

[0058] After the surveyor only needs to input the type of target survey data to be collected and the range of allowable measurement deviation rates, such as the position coordinates of the boundary points and their deviation ranges, and complete the data collection work at the first reference point, the system will automatically plan the implementation plan for this survey work according to the historical survey data, and significantly improve the efficiency of land survey while ensuring the accuracy of geological exploration data.

[0059] To implement the above land survey method adapted to the complex terrain space, the embodiment of the present application also discloses a land survey system adapted to the complex terrain space, such as Figure 4 shown, mainly including: a first reference point acquisition unit 1, a first association relationship generation unit 2, a second reference point acquisition unit 3, a target survey data acquisition unit 4, and a geological exploration data generation unit 5.

[0060] The first reference point acquisition unit 1 is configured to select multiple data collection points with data confidence exceeding a set value from historical survey data as the first reference points and store them according to the target survey data to be collected currently. The first association relationship generation unit 2 is configured to be data-connected to the first reference point acquisition unit 1, and is used to collect and obtain the survey data corresponding to each first reference point currently, such as the position coordinate data of the boundary points measured currently, and then compare it with the historical survey data to obtain the measurement deviation rate of collecting the survey data at each current first reference point. According to the spatial position distribution of each first reference point and the above measurement deviation rate, obtain the law that the above measurement deviation rate changes with the position of the data collection point, that is, analyze and generate the first association relationship between the measurement deviation rate of the survey data and the data collection position.

[0061] The second reference point acquisition unit 3 is configured to obtain at least two data acquisition points with a measurement deviation rate lower than a set value as the second reference points according to the first association relationship and store them. The target survey data acquisition unit 4 is configured to collect at least two pieces of target survey data related to the target ground points with the second reference points as the data acquisition points, and store the target survey data in association with their corresponding measurement deviation rates. The geological exploration data generation unit 5 is connected to the target survey data acquisition unit 4 for data, and is configured to obtain and configure confidence weights for each piece of target survey data according to the data deviation rate corresponding to the target survey data, and calculate and generate geological exploration data according to a set fusion algorithm.

[0062] In practical applications, according to the different types of target survey data, the target survey data acquisition unit 4 is configured with different instrument devices and data processing modules. For example, when the target survey data is the distance between two boundary points, the target survey data acquisition unit 4 includes a total station and a data processor connected to it.

[0063] In the embodiment of the present application, the relevant data storage and processing components included in the system are all configured in a tablet computer or a PC.

[0064] In order to overcome the difficulties caused by the complex urban landform environment to land survey work, the land survey system described in the present application further includes: a data acquisition occlusion detection unit 6, a relay point generation unit 7 during acquisition, and an intermediate survey data acquisition unit 8.

[0065] The data acquisition occlusion detection unit 6 is configured to detect and determine whether the target survey data can be directly acquired with the second reference point as the data acquisition point, and output the determination result data. In specific applications, the above data acquisition occlusion detection unit 6 is configured as a software module corresponding to the hardware. The relay point generation unit 7 during acquisition is connected to the data acquisition occlusion detection unit 6 for data, and is configured to receive the determination result data. If the target survey data cannot be directly acquired, a third reference point that can achieve direct measurement between the self-survey area, the second reference point, and the target ground point is selected as the relay point during acquisition and stored. The intermediate survey data acquisition unit 8 is configured to obtain the intermediate survey data between the relay point during acquisition, the target ground point, and the second reference point, such as the distance between the boundary points AB obtained via point C as described above.

[0066] The geological exploration data generation unit 5 is further configured to: obtain the measurement deviation rate at the relay point during acquisition based on the first association relationship, assign confidence weights to each piece of intermediate survey data according to the measurement deviation rates of the relay point during acquisition and the second reference point, and calculate and generate geological exploration data according to a set fusion algorithm. The relevant fusion algorithms have been publicly disclosed before and will not be elaborated here.

[0067] To reduce the impact of measurement deviations contained in a single data acquisition on the overall survey results, the land survey system of the solution of this application further includes a regional division unit, a data acquisition times confirmation unit, and a data acquisition output unit.

[0068] Specifically, the regional division unit is data-connected to the first association relationship generation unit 2, and is configured to divide the surveyed area plots into multiple confidence regions according to the first association relationship and configure corresponding confidence probability values for each confidence region, and at the same time associate and store the data acquisition times corresponding to each confidence probability value. The data acquisition times confirmation unit is configured to obtain the regional positions where each data acquisition point is located and thereby determine the data acquisition times corresponding to each data acquisition point. The data acquisition output unit is configured to collect and obtain target data in multiple times according to the data acquisition times associated with the data acquisition points, and then calculate and generate the target survey data and / or intermediate survey data corresponding to the data acquisition points according to the mean algorithm or the weighted fusion algorithm.

[0069] To improve the efficiency of land survey, the land survey system described in the solution of this application further includes a data acquisition plan automatic generation unit, which is used to automatically mark parameters such as data acquisition points and data acquisition times on the surveyed area according to the required target survey data, so as to improve the acquisition efficiency of the staff. Specifically, it includes a requirement acquisition module, a collection parameter setting module, a collection times determination module, and a collection plan generation module.

[0070] The requirement acquisition module is configured to obtain the required target survey data and the range of allowable measurement deviation rates thereof. The above requirement acquisition module includes a human-computer interaction component for receiving information data input by the staff. The collection parameter setting module is configured to retrieve and, according to the first association relationship, the second association relationship, and the third association relationship, automatically select the second reference point, the data acquisition time, and the data acquisition environment through function relation generation or data table lookup and store them.

[0071] The collection times determination module is configured to obtain and, according to the confidence region where the second reference point is located, combine the data acquisition times corresponding to the confidence region to match and determine the data acquisition times of the target survey data at the second reference point. The collection plan generation module is configured to receive the outputs of the collection parameter setting module and the collection times determination module, and mark the data acquisition points, their corresponding data acquisition times, data acquisition environments, and data acquisition times on the surveyed area map according to the second reference point. In the specific implementation process of the above collection plan, the data acquisition time and the data acquisition environment usually only need to meet one of them. The above solution can significantly improve the efficiency of land survey.

[0072] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A land surveying method adapted to the complex ground object space, characterized in that, Including: Selecting multiple data acquisition points with data confidence exceeding a set value from historical survey data according to the target survey data as the first reference points; Collecting and obtaining the survey data corresponding to each first reference point currently and comparing it with the historical survey data to obtain the measurement deviation rate of collecting survey data at each current first reference point; Combining the spatial position distribution and measurement deviation rate of each first reference point to analyze and generate the first correlation relationship between the measurement deviation rate and the data acquisition position; Obtaining at least two data acquisition points with a measurement deviation rate lower than the set value as the second reference points according to the first correlation relationship; Using the second reference points as data acquisition points to collect and obtain at least two target survey data related to the target ground object points, and associatively storing the target survey data with its corresponding measurement deviation rate; Configuring confidence weights for each target survey data according to the data deviation rate corresponding to the target survey data, and calculating and generating geological exploration data according to the set fusion algorithm; Wherein, the geological exploration data includes spatial geometric data and environmental geological data.

2. The land survey method according to claim 1, characterized in that If it is impossible to directly collect and obtain the target survey data by using the second reference points as data acquisition points, the method further includes: Selecting a third reference point that can directly measure between the second reference point and the target ground object point in the survey area as the acquisition relay point; Obtaining the intermediate survey data between the acquisition relay point, the target ground object point and the second reference point; Obtaining the measurement deviation rate at the acquisition relay point based on the first correlation relationship; Assigning confidence weights to each intermediate survey data according to the measurement deviation rates of the acquisition relay point and the second reference point, and calculating and generating geological exploration data according to the set fusion algorithm.

3. The land surveying method according to claim 2, characterized in that, The land survey method further includes: Dividing the survey area plot into multiple confidence regions according to the first correlation relationship and configuring corresponding confidence probability values for each confidence region; Storing the corresponding relationship between different confidence probability values and the number of data acquisitions; Obtaining the position coordinates of each second reference point and the acquisition relay point, and searching for and matching the confidence probability value of the area where they are located; Determining the number of data acquisitions required at each second reference point and / or the acquisition relay point according to the corresponding relationship, and associatively storing it with the data acquisition point position coordinates; Collecting and obtaining the target survey data and / or intermediate survey data at the data acquisition point in multiple times according to the number of data acquisitions associated with the data acquisition point and storing them; Calculating and generating the target survey data and / or intermediate survey data corresponding to the data acquisition point according to the mean algorithm or the weighted fusion algorithm.

4. The land surveying method according to claim 3, characterized in that, The land survey method further includes: Obtaining and analyzing the correlation between the measurement deviation rate of the target survey data, the data acquisition time and the data acquisition environmental conditions according to the historical survey data, and respectively generating the second correlation relationship and the third correlation relationship; When collecting and obtaining the target survey data at the second reference point, it further includes: Recording the time point of obtaining the target survey data and collecting the current environmental conditions of the second reference point; Based on the above time point and environmental conditions, combining the first correlation relationship and the second correlation relationship, respectively configuring the time correlation deviation rate and the environmental correlation deviation rate for the currently obtained survey data; Comprehensively calculate and generate the time - environment deviation rate of the target survey data based on the time - related deviation rate and the environment - related deviation rate; Based on the time - environment deviation rate and in combination with the measurement deviation rate, adjust the confidence weights of each target survey data, and calculate and generate geological exploration data according to the set fusion algorithm; Among them, the data acquisition environmental conditions include the intensity of rain, snow, haze, tide height, surface and underground temperature, air flow or water flow intensity that affect the survey data.

5. The land survey method according to claim 4, characterized in that, The measurement deviation rate includes the data deviation amplitude and the deviation direction; In the land survey method, generating the first correlation relationship includes: Statistically analyze the deviation amplitude and deviation direction corresponding to the measurement deviation rate at each first reference point, obtain the position coordinates of each first reference point and the relative position distribution relationship between each first reference point, combine the deviation amplitude and deviation direction, analyze the change trend of the measurement deviation rate in the survey area, and fit and generate a first correlation function formula or a first data table representing the corresponding relationship between each position coordinate in the survey area and the deviation amplitude and deviation direction, and store it as the first correlation relationship; Generating the second correlation relationship includes: Select a time point from the collection time points included in the historical survey data as the time reference point, obtain the time - reference survey data at the above - mentioned time reference point, compare the target survey data collected in each period with the time - reference survey data, statistically analyze the measurement deviation rate of the target survey data collected in each period compared with the time - reference survey data, and fit and generate a second correlation function formula or a second data table representing the corresponding relationship between each collection time point and the deviation amplitude and deviation direction, and store it as the second correlation relationship; Generating the third correlation relationship includes: Calibrate an environmental condition as the reference environmental condition and store it. Obtain the environmental condition data corresponding to the survey data from the historical survey data, determine the environmental reference survey data corresponding to the reference environmental condition, and compare the remaining historical survey data and their corresponding environmental condition data with the environmental reference survey data, and statistically generate a third correlation function formula or a third data table representing the corresponding relationship between the measurement deviation rate of the survey data and the change of the environmental condition data, and store it as the third correlation relationship.

6. The land survey method according to claim 5, characterized in that The land survey method further includes an automatic data acquisition plan generation step, including: Obtain and be based on the type of target survey data to be collected and the allowable measurement deviation rate range; Automatically select and store the second reference points according to the first correlation relationship; Select the data collection time according to the second correlation relationship; Select the data collection environment according to the third correlation relationship; Obtain and determine the number of times of collecting the target survey data at the second reference point according to the confidence region where the second reference point is located; Mark the data collection points and their corresponding data collection time and / or data collection environment, and the number of data collection times on the survey area map according to the second reference points.

7. An adaptive land surveying system for complex terrain space, characterized in that, Include: The first reference point acquisition unit (1), configured to select multiple data collection points with data confidence exceeding the set value from the historical survey data as the first reference points and store them according to the currently required target survey data to be collected; The first correlation relationship generation unit (2) is configured to collect and obtain the survey data corresponding to each first reference point currently, and then compare it with the historical survey data to obtain the measurement deviation rate of the survey data collected at each first reference point currently. According to the spatial position distribution of each first reference point and the above measurement deviation rate, analyze and generate the first correlation relationship between the measurement deviation rate of the survey data and the data collection position; The second reference point acquisition unit (3) is configured to obtain at least two data collection points with a measurement deviation rate lower than the set value as the second reference points according to the first correlation relationship; The target survey data acquisition unit (4) is configured to collect and obtain at least two target survey data related to the target ground object points with the second reference points as the data collection points, and store the target survey data in association with its corresponding measurement deviation rate; The geological exploration data generation unit (5) is configured to obtain and configure confidence weights for each target survey data according to the data deviation rate corresponding to the target survey data, and calculate and generate geological exploration data according to the set fusion algorithm; Among them, the geological exploration data includes spatial geometric data and environmental geological data.

8. The land surveying system according to claim 7, characterized in that, The system further includes: The data collection occlusion detection unit (6) is configured to detect and determine whether the target survey data can be directly collected with the second reference point as the data collection point, and output the determination result data; The data collection relay point generation unit (7) is configured to receive the determination result data. If the target survey data cannot be directly collected, select a third reference point that can achieve direct measurement between the second reference point and the target ground object point in the self-survey area as the data collection relay point and store it; The intermediate survey data acquisition unit (8) is configured to acquire the intermediate survey data between the data collection relay point, the target ground object point and the second reference point; In the geological exploration data generation unit (5), it is also configured to: obtain the measurement deviation rate at the data collection relay point based on the first correlation relationship, assign confidence weights to each intermediate survey data according to the measurement deviation rates of the data collection relay point and the second reference point, and calculate and generate geological exploration data according to the set fusion algorithm.

9. The land surveying system according to claim 8, wherein, The system further includes: The area division unit is configured to divide the survey area plot into multiple confidence areas according to the first correlation relationship and configure corresponding confidence probability values for each confidence area, and at the same time store the data collection times corresponding to each confidence probability value in association; The data collection times confirmation unit is configured to obtain the area positions where each data collection point is located and thereby determine the data collection times corresponding to each data collection point; The data collection output unit is configured to collect and obtain the target data in multiple times according to the data collection times associated with the data collection points, and then calculate and generate the target survey data and / or intermediate survey data corresponding to the data collection points according to the mean algorithm or the weighted fusion algorithm.

10. The land surveying system according to claim 9, characterized in that, The system further includes a data collection scheme automatic generation unit, including: The requirement acquisition module is configured to acquire the required target survey data and the range of its allowed measurement deviation rate; The acquisition parameter setting module is configured to automatically select and store a second reference site, a data acquisition time, and a data acquisition environment according to the first association relationship, the second association relationship, and the third association relationship; The acquisition times determination module is configured to obtain and determine the acquisition times of the target survey data at the second reference site according to the confidence region where the second reference site is located; The acquisition plan generation module is configured to mark data acquisition points on the survey area map according to the second reference site, and their corresponding data acquisition times and / or data acquisition environments, as well as the data acquisition times.

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