Monitoring data processing method and system based on measurement robot

Abstract

The invention provides a monitoring data processing method and system based on a measurement robot, and relates to the technical field of automatic monitoring. Aiming at the problem that monitoring data is greatly influenced by an abnormal datum point due to the fact that a single adjustment algorithm is adopted for the monitoring data in the prior art, the method comprises the following steps: acquiring corner data and datum point data of a plurality of to-be-monitored points, and generating a plurality of datum point combinations according to the datum point data; in each reference point combination, calculating an adjustment result of the corner data according to an adjustment algorithm; and according to the precision in the adjustment result, determining the weight corresponding to the coordinate data in the adjustment result, and carrying out weighted calculation on the coordinate data set. And comparing the weighted coordinate data with the historical coordinate data, when the difference between the weighted coordinate data and the historical coordinate data is greater than a set gross error threshold value, determining the weighted coordinate data as gross error sites, and outputting the monitoring data after removing the gross error sites. Monitoring data under different reference point combinations are comprehensively considered, the influence of abnormal data is weakened, and the precision of the monitoring data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic monitoring, and in particular to a monitoring data processing method and system based on a measurement robot. BACKGROUND

[0002] High-precision automatic monitoring technology based on a measurement robot is gradually applied in real-time deformation monitoring of tunnels, subway structures, bridges and the like. The measurement robot is used to realize automatic data acquisition and processing of a target structure, so as to accurately capture the deformation of the structure.

[0003] Patent No. CN113916260B discloses a measurement robot real-time networking automatic adjustment calculation method, which includes establishing an intelligent measurement site, a reference point and a monitoring point, real-time angle and distance acquisition, monitoring point approximate coordinate calculation, determining the weight of the observation angle and distance, establishing an angle and distance error equation, and monitoring point position error calculation, and a series of calculation methods for obtaining the adjustment calculation method of the monitoring point coordinate. During the calculation process, the selection and combination mode of the reference point is fixed, and the adjustment calculation method only uses a single adjustment calculation. When individual reference points are displaced due to geological activities or construction influence, forced fixation will distort the whole network deformation field, resulting in magnification of the monitoring data error. In summary, the single adjustment algorithm in the prior art greatly affects the monitoring data of the abnormal reference point.

[0004] Therefore, it is of great significance to develop a monitoring data processing method based on a measurement robot for improving the accuracy of the monitoring data processing result. SUMMARY

[0005] In view of the problem that the single adjustment algorithm for monitoring data in the prior art greatly affects the monitoring data of the abnormal reference point, the present application proposes a monitoring data processing method based on a measurement robot, which specifically includes the following steps: S1, controlling the measurement robot to perform data observation to obtain edge and angle data of a plurality of monitoring points; S2, obtaining reference point data and generating a plurality of reference point combinations according to the reference point data; S3, in each reference point combination, calculating an adjustment result for the edge and angle data of each monitoring point according to an adjustment algorithm, wherein the adjustment result includes a coordinate data set of the monitoring point and a precision corresponding to each coordinate data in the coordinate data set; S4, determining a weight corresponding to each coordinate data according to the precision of each coordinate data, and performing weighted calculation on each coordinate data in the coordinate data set according to the weight to obtain weighted coordinate data of each coordinate data set; S5, compare the weighted coordinate data of each coordinate data set with the historical coordinate data, and determine that the coordinate data set corresponding to the weighted coordinate data is a gross error point when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set gross error threshold value; S6, remove the coordinate data set corresponding to the gross error point, and output the final monitoring data.

[0006] Further, the calculation of the adjustment result of the side and angle data of each to-be-monitored point in each reference point combination according to the adjustment algorithm comprises: calculating a plane adjustment result and a height adjustment result of the side and angle data of each to-be-monitored point according to the adjustment algorithm respectively; wherein the plane adjustment result comprises a plane coordinate data set and the accuracy of each plane coordinate data in the plane coordinate data set; and the height adjustment result comprises a height coordinate data set and the accuracy of each height coordinate data in the height coordinate data set.

[0007] Further, after the calculation of the plane adjustment result and the height adjustment result of the side and angle data of each to-be-monitored point according to the adjustment algorithm, it further comprises: comparing the accuracy of each plane coordinate data with a preset plane accuracy, and removing the plane coordinate data in the plane coordinate data set when the accuracy of the plane coordinate data is less than the preset plane accuracy; and comparing the accuracy of each height coordinate data with a preset height accuracy, and removing the height coordinate data in the height coordinate data set when the accuracy of the height coordinate data is less than the preset height accuracy.

[0008] Further, after the control of the surveying robot to perform data observation to obtain the side and angle data of the to-be-monitored points, it comprises: S11, performing a missing check on the side and angle data of the plurality of to-be-monitored points to determine missing to-be-monitored points; S12, determining unqualified to-be-monitored points by comparing the side and angle data with a preset range; S13, removing the unqualified to-be-monitored points and performing re-measurement on the unqualified to-be-monitored points and the missing to-be-monitored points; S14, continuing to perform S11-S13 after the re-measurement is completed until the side and angle data of the plurality of to-be-monitored points meet the preset range.

[0009] Further, the missing check on the side and angle data of the plurality of to-be-monitored points to determine missing to-be-monitored points comprises: detecting the side and angle data of each to-be-monitored point one by one; and determining the to-be-monitored points corresponding to the unrecorded or missing data of the side and angle data as missing to-be-monitored points.

[0010] Further, the control measurement robot carries out data observation to obtain the edge angle data of the plurality of monitoring points, specifically comprising: obtaining the target point position information of the monitoring points, and the observation rule of the measurement robot and the position information thereof; calculating the azimuth and elevation angle of the monitoring points according to the target point position information of the monitoring points and the position information of the measurement robot; controlling the mechanical structure of the measurement robot to rotate to the corresponding angle according to the azimuth and elevation angle of the monitoring points, and setting the observation parameters of the measurement robot according to the target point position information; controlling the measurement robot to search for the monitoring points according to the set observation parameters, and controlling the measurement robot to measure the edge angle data of the monitoring points according to the observation rule after the measurement robot locks the monitoring points; and using the full-circle observation method to measure the edge angle data of the plurality of monitoring points.

[0011] Further, the control measurement robot carries out data observation to obtain the edge angle data of the plurality of monitoring points, specifically comprising: obtaining the target point position information of the monitoring points, and the observation rule of the measurement robot and the position information thereof; calculating the azimuth and elevation angle of the monitoring points according to the target point position information of the monitoring points and the position information of the measurement robot; controlling the mechanical structure of the measurement robot to rotate to the corresponding angle according to the azimuth and elevation angle of the monitoring points, and setting the observation parameters of the measurement robot according to the target point position information; controlling the measurement robot to search for the monitoring points according to the set observation parameters, and controlling the measurement robot to measure the edge angle data of the monitoring points according to the observation rule after the measurement robot locks the monitoring points; and using the full-circle observation method to measure the edge angle data of the plurality of monitoring points.

[0012] Further, after the edge angle data of the plurality of monitoring points meet the preset range, the method further comprises: performing meteorological correction on the edge angle data of the plurality of monitoring points according to the meteorological data.

[0013] Further, the comparison of the weighted coordinate data of each coordinate data set and the historical coordinate data is performed, and when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set rough error threshold, the coordinate data set corresponding to the weighted coordinate data is determined as a rough error point position, which comprises: comparing the difference between the weighted coordinate data in each coordinate data set and the coordinate data in the target point position information, and / or comparing the difference between the weighted coordinate data in each coordinate data set and the historical coordinate data, and when any difference is greater than a set rough error threshold, the coordinate data set corresponding to the weighted coordinate data is determined as a rough error point position.

[0014] The application also provides a monitoring data processing system based on a measurement robot, which adopts the monitoring data processing method based on a measurement robot as any one of the above, and specifically comprises the following modules: A data acquisition module is configured to control the measurement robot to carry out data observation and obtain the edge angle data of the plurality of monitoring points. A reference point combination module is configured to obtain reference point data and generate a plurality of reference point combinations according to the reference point data. A adjustment calculation module is connected with the data acquisition module and the reference point combination module, and is configured to calculate the adjustment result of the edge angle data of each monitoring point according to each reference point combination, wherein the adjustment result comprises a coordinate data set of the monitoring point and the accuracy of each coordinate data in the coordinate data set. The weighting calculation module is connected with the adjustment calculation module, and is configured to determine a weight corresponding to each coordinate data according to the accuracy of each coordinate data, and perform weighting calculation on each coordinate data in the coordinate data set according to the weight, to obtain weighted coordinate data of each coordinate data set. The gross error detection module is connected with the weighting calculation module, and is configured to compare the weighted coordinate data of each coordinate data set with historical coordinate data, and determine that the coordinate data set corresponding to the weighted coordinate data is a gross error point when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set gross error threshold. The data output module is connected with the gross error detection module, and is configured to eliminate the coordinate data set corresponding to the gross error point, and output final monitoring data.

[0015] Compared with the prior art, the present application has the following advantages: Firstly, the present application obtains edge angle data and reference point data of a plurality of monitoring points, generates a plurality of reference point combinations according to the reference point data, calculates adjustment results according to the adjustment algorithm in each reference point combination, the adjustment results include coordinate data sets of the monitoring points and the accuracy of each coordinate data in the coordinate data set, determines the weight of the coordinate data in the adjustment results according to the accuracy in the adjustment results of each reference point combination for each monitoring point, performs weighting calculation on the coordinate data set according to the weight, to obtain weighted coordinate data, compares the weighted coordinate data of each coordinate data set with historical coordinate data, and determines that the coordinate data set corresponding to the weighted coordinate data is a gross error point when the difference between the weighted coordinate data and the historical coordinate data is greater than a set gross error threshold, and finally outputs the final monitoring data after eliminating the coordinate data set corresponding to the gross error point. According to the accuracy in the adjustment results of each reference point combination, the weight of the coordinate data in the adjustment results is determined, the coordinate data set is weighted calculated, and the weighted coordinate data is output, which comprehensively considers the monitoring data under different reference point combinations, the abnormal reference point only has an impact in the combination containing the reference point, and other combinations can provide relatively accurate reference. Through subsequent weighting calculation, the abnormal result with large deviation will be weakened due to the low weight, the proportion of abnormal data in the final result can be reduced, and the influence of abnormal data can be weakened. At the same time, the weighted coordinate data and the historical coordinate data are subjected to gross error detection, the gross error point is determined and eliminated according to the gross error setting value, the influence of the gross error data on the monitoring result can be effectively avoided, and the accuracy of the monitoring data is improved.

[0016] Secondly, the plane adjustment result and the height adjustment result of each to-be-monitored point are calculated according to the adjustment algorithm, the plane coordinate data set of the to-be-monitored point is obtained according to the plane adjustment result, and the height coordinate data set of the to-be-monitored point is obtained according to the height adjustment result. According to the accuracy of each coordinate data in the plane adjustment result, the coordinate data with accuracy less than the preset plane accuracy is removed to obtain the plane coordinate data set of the to-be-monitored point, and according to the accuracy of each coordinate data in the height adjustment result, the coordinate data with accuracy less than the preset height accuracy is removed to obtain the height coordinate data set of the to-be-monitored point. By calculating the plane and height adjustment results respectively and screening the qualified coordinate data according to the accuracy, the low-accuracy data is removed pertinently, the interference of unqualified data on the final result is reduced, a high-quality data basis is provided for subsequent weighted processing, the low-accuracy data is prevented from distorting the result when participating in the weighting, and the accuracy of the monitoring data is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0018] Figure 1 is a flow chart of the monitoring data processing method based on the measurement robot provided by the embodiment of the present application; Figure 2 is a structural schematic diagram of the data processing system provided by the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] The specific embodiments of the present application will be described below.

[0021] In view of the problem in the prior art that a single adjustment algorithm is used for monitoring data, resulting in that the monitoring data is greatly affected by an abnormal reference point, the application obtains edge and angle data and reference point data of multiple monitoring points, generates multiple reference point combinations according to the reference point data; in each reference point combination, calculates an adjustment result according to the edge and angle data by using an adjustment algorithm; according to the accuracy in the adjustment result, determines the corresponding weight of the coordinate data in the adjustment result, and performs weighted calculation on the coordinate data set. Compare the weighted coordinate data with the historical coordinate data, when the difference between the weighted coordinate data and the historical coordinate data is greater than a set gross error threshold, determine that the weighted coordinate data is a gross error point, and output the monitoring data after removing the gross error point. The monitoring data under different reference point combinations is comprehensively considered to weaken the influence of abnormal data and improve the accuracy of the monitoring data.

[0022] Embodiment 1 The embodiment of the application provides a monitoring data processing method based on a measurement robot, Figure 1 The embodiment of the application provides a monitoring data processing method based on a measurement robot, Figure 1 The embodiment of the application provides a monitoring data processing method based on a measurement robot, S1, control the measurement robot to perform data observation, and obtain edge and angle data of multiple monitoring points.

[0023] The monitoring point refers to a target point that needs to be deformed and monitored, and is a key position on an engineering structure, for example, a tunnel wall, a bridge component, etc. The edge and angle data include horizontal angle, height angle, slant distance and other measurement data, and the edge and angle data are basic original data for calculating the coordinates of the monitoring points.

[0024] Specifically, the measurement robot is controlled to perform data observation, and the edge and angle data of multiple monitoring points are obtained, including: obtaining target point information of the monitoring points, observation rules of the measurement robot and position information of the measurement robot; calculating the azimuth angle and the height angle of the monitoring points according to the target point information of the monitoring points and the position information of the measurement robot; controlling the mechanical structure of the measurement robot to rotate to the corresponding angle according to the azimuth angle and the height angle of the monitoring points, and setting the observation parameters of the measurement robot according to the target point information; controlling the measurement robot to search for the monitoring points according to the set observation parameters, and controlling the measurement robot to measure the edge and angle data of the monitoring points according to the observation rules after the measurement robot locks the monitoring points; and measuring the edge and angle data of multiple monitoring points by using a full-circle observation method.

[0025] In this embodiment, the target point information is the preset coordinate information of the to-be-monitored point, which is used to determine the position reference of the to-be-monitored point. The measuring robot is a high-precision total station with automatic aiming, tracking and measuring functions, and can automatically complete angle and distance measurement. It is the core equipment for deformation monitoring of tunnels, bridges and other engineering projects. The observation rules of the robot include observation measurement times, observation accuracy levels, repeated measurement intervals and other parameters to ensure data reliability. The azimuth angle refers to the horizontal angle from the measuring robot station to the to-be-monitored point, and the elevation angle refers to the vertical angle from the horizontal line of the measuring robot station to the to-be-monitored point, which is used to determine the height position of the target. The full-circle observation method is a high-precision angle measurement method. For example, the measuring robot successively observes multiple target points in one measurement, rotates one circle in clockwise or counterclockwise direction to complete the observation of all to-be-monitored points, and reduces the influence of instrument error on the result.

[0026] The azimuth angle and the elevation angle are calculated by the acquired target point information of the to-be-monitored point and the position information of the measuring robot. The observation parameters of the measuring robot are set according to the target point information. The target point information refers to the inherent attributes or preset information of the to-be-monitored point, such as the type of the installed reflection device, the prism constant, the installation height of the point, etc. The target point information directly affects the distance measurement accuracy. The observation parameters are the configuration parameters of the measuring robot during observation, which are set according to the target point information and directly affect the measurement process and data results. The observation parameters mainly include: the search and locking mode of the measuring robot to the target, the prism constant correction value, the observation accuracy level and other parameters. Based on the observation parameters, the azimuth angle and the elevation angle, the aiming navigation is provided for the measuring robot. The measuring robot automatically finds the reflection device of the to-be-monitored point in the preset azimuth angle and elevation angle range through laser scanning or image recognition technology. When the measuring robot identifies the reflection device of the to-be-monitored point, the lens is accurately aligned to the target through a precision motor driven mechanical structure, ensuring that the target is always tracked during the measurement process. After the target is locked, the robot automatically collects the edge angle data according to the preset observation rules. The full-circle observation method is adopted to successively observe multiple target points, and one circle is rotated in clockwise or counterclockwise direction to complete the observation of all to-be-monitored points. Through multiple measurement and clockwise or counterclockwise rotation observation, the tilt errors of the horizontal and vertical axes of the instrument and the aiming errors are offset.

[0027] After the measuring robot is controlled to perform data observation and the edge angle data of the to-be-monitored points are acquired, the following steps are included: S11, performing missing check on the edge angle data of the multiple to-be-monitored points to determine the missing to-be-monitored points. S12, determining the unqualified to-be-monitored points by comparing the edge angle data with the preset range. S13, rejecting the unqualified to-be-monitored points and performing supplementary measurement on the unqualified to-be-monitored points and the missing to-be-monitored points. S14, continuing to perform S11-S13 after the supplementary measurement is completed, until the edge angle data of the multiple to-be-monitored points meet the preset range.

[0028] The integrity of the edge angle data of all to-be-monitored points is checked, and it is confirmed whether there are points that have not collected data. The edge angle data of each to-be-monitored point is detected one by one. The to-be-monitored point corresponding to the edge angle data that is not recorded or has missing data is determined as a missed to-be-monitored point. For example, 10 to-be-monitored points are preset for monitoring, but only the data of 9 points is collected, and the point that is not collected is marked as a missed to-be-monitored point and needs to be included in the supplementary measurement list.

[0029] The collected edge angle data is screened according to the accuracy threshold set in the observation rule to determine whether the data meets the accuracy requirement. The screening indicators include single measurement regression zero difference, twice collimation difference, vertical indicator difference, measurement interval angle difference, and measurement interval distance difference. The unqualified coordinate data that has been marked is excluded, and the unqualified to-be-monitored point missing point is re-observed. The supplementary measurement needs to strictly follow the original observation rule such as the number of measurement returns and the observation method to ensure the consistency of the supplementary measurement data and the original data. The data is checked in a loop until it is qualified, ensuring zero omission and high quality of the original data, and providing reliable input for subsequent meteorological correction and adjustment processing.

[0030] In addition to controlling the measurement robot to observe data and obtain edge angle data of multiple to-be-monitored points, the method also includes: acquiring meteorological data at fixed intervals during the observation of the measurement robot. After the edge angle data of the multiple to-be-monitored points meets the preset range, the method further includes: performing meteorological correction on the edge angle data of the multiple to-be-monitored points according to the meteorological data.

[0031] Meteorological data refers to atmospheric parameters related to the measurement environment, mainly including temperature, humidity, air pressure, etc. Since the laser or infrared wave emitted by the measurement robot during distance measurement, the meteorological data will directly affect the propagation speed and path of the electromagnetic wave, and then cause distance measurement error. By synchronously collecting meteorological data through sensors and using meteorological correction formulas such as the international standard atmospheric correction model, the original slant range is corrected and converted into distance values under standard meteorological conditions, thereby eliminating environmental interference. By eliminating the influence of environmental factors on distance measurement through meteorological correction, the accuracy of the slant range data is ensured, and reliable original data is provided for subsequent adjustment calculation and processing.

[0032] The meteorological corrected edge angle data needs to be converted through a standard format to provide uniform input for subsequent adjustment calculation. Specifically, the meteorological corrected observation data, including the horizontal angle, the height angle, the corrected slant distance, the station information of each measuring return, etc., are written into a.SUC file in a fixed format. The.SUC file is a standardized intermediate format of measurement data, which contains the original records of observation values, such as measuring return number, target point number, angle and distance values, etc. According to the.SUC file, the.in1 file dedicated to plane adjustment and the.in2 file dedicated to height adjustment are further generated. The.in1 file contains the plane coordinate observation relationship of the monitoring points, such as horizontal angle, horizontal distance related data, which is used to calculate the plane coordinates (X, Y). The.in2 file contains the height related observation data, such as height angle, vertical distance converted from slant distance, which is used to calculate the height (H). The.SUC,.in1 and.in2 format files are uniformly saved to the database as the input data for subsequent multi-scheme adjustment.

[0033] S2, obtaining reference point data and generating multiple groups of reference point combinations according to the reference point data.

[0034] The reference point data refers to the coordinate data serving as the coordinate reference. The reference points are generally considered to be stable and immovable, and are the reference for calculating the coordinates of the monitoring points. The reference point combination refers to multiple groups of reference points formed by selecting different subsets from all the reference points. Compared with the traditional method which adopts the one-time adjustment method and relies on a fixed reference point combination, when individual reference points are displaced due to geological activities, construction and other factors, it will lead to adjustment distortion and magnify the error of the monitoring points. In the embodiment, different subsets are selected from all the reference points in different combination modes to form multiple groups of reference point combinations, each combination will serve as an independent adjustment reference, and multiple groups of reference point combinations are generated. When adjusting, the results from different combinations are verified with each other to weaken the influence of abnormal reference points. If a reference point is displaced, the adjustment result of the combination containing it may be abnormal, while the combination result not containing the reference point is relatively reliable. Through the comprehensive results of multiple groups, more accurate adjustment results can be obtained. At the same time, in a complex monitoring environment, it is difficult to avoid the interference or abnormality of the reference points. The multiple groups of reference point combination mode enables the system to complete the adjustment calculation using other normal reference point combinations even if some reference points are abnormal, ensuring the continuity of the monitoring work and the usability of the data, and preventing the entire monitoring system from failing due to the abnormality of individual reference points.

[0035] S3, in each reference point combination, calculating the adjustment result of each monitoring point according to the adjustment algorithm based on the edge angle data of each monitoring point. The adjustment result includes the coordinate data set of the monitoring point and the precision of each coordinate data in the coordinate data set.

[0036] The adjustment algorithm refers to a mathematical method for processing measurement data errors, for example, the adjustment algorithm includes the least squares method. The adjustment result is the output of the adjustment algorithm after processing the side angle data of the points, including the coordinate data of the points to be monitored (i.e., the coordinate data set), and the accuracy index of each coordinate data, which is calculated by the observation residual in the adjustment process. The residual is the difference between the observation value and the theoretical value after adjustment. By adjusting the deviation between the observation value and the theoretical value, the optimal coordinates of the points to be monitored are calculated. For each group of reference point combinations, the side angle data after meteorological correction and format conversion preprocessing is adjusted and calculated, and the adjustment result is calculated for each point to be monitored. Each group of reference point combinations corresponds to a set of adjustment results, including the coordinate data of all points to be monitored, and multiple groups of combinations form multiple sets of coordinate data sets.

[0037] Specifically, in each reference point combination, the adjustment result of each point to be monitored is calculated according to the adjustment algorithm, and the coordinate data set of the point to be monitored in the adjustment result is obtained, including: the plane adjustment result and the height adjustment result of each point to be monitored are calculated according to the adjustment algorithm. Among them, the plane adjustment result includes the plane coordinate data set and the accuracy of each plane coordinate data in the plane coordinate data set; the height adjustment result includes the height coordinate data set and the accuracy of each height coordinate data in the height coordinate data set.

[0038] The plane adjustment result refers to the plane coordinates (X, Y) of the point to be monitored calculated by the adjustment algorithm, reflecting the position of the point in the horizontal direction. The height adjustment result refers to the height (H) of the point to be monitored calculated by the adjustment algorithm, reflecting the height of the point in the vertical direction. The plane coordinate data set is a data set composed of the plane coordinates (X, Y) of all points to be monitored, corresponding to the plane adjustment result of a certain reference point combination. The height coordinate data set is a data set composed of the height (H) of all points to be monitored, corresponding to the height adjustment result of a certain reference point combination.

[0039] For each group of reference point combinations, the horizontal angle, horizontal distance and other data are used to calculate the plane coordinates of the points to be monitored by the adjustment algorithm, forming the plane coordinate data set. The height angle, slant distance and other data are used to calculate the height of the points to be monitored by the adjustment algorithm, forming the height coordinate data set. Each group of reference point combinations outputs the plane coordinate data set and the height coordinate data set, providing basic data for subsequent weighted calculation.

[0040] After the plane adjustment result and the height adjustment result of each to-be-monitored point are calculated respectively according to the adjustment algorithm, the method further comprises: comparing the accuracy of each plane coordinate data with a preset plane accuracy, and when the accuracy of the plane coordinate data is less than the preset plane accuracy, eliminating the plane coordinate data in the plane coordinate data set; comparing the accuracy of each height coordinate data with a preset height accuracy, and when the accuracy of the height coordinate data is less than the preset height accuracy, eliminating the height coordinate data in the height coordinate data set.

[0041] The accuracy of each plane coordinate data in the plane coordinate data set is compared with the preset plane accuracy, if the accuracy of the plane coordinate data is greater than or equal to the preset plane accuracy, the plane coordinate data is retained, and if the accuracy of the plane coordinate data is less than the preset plane accuracy, the plane coordinate data is eliminated. The accuracy of each height coordinate data in the height coordinate data set is compared with the preset height accuracy, if the accuracy of the height coordinate data is greater than or equal to the preset height accuracy, the height coordinate data is retained, and if the accuracy of the height coordinate data is less than the preset height accuracy, the height coordinate data is eliminated.

[0042] The screened plane coordinate data and the height coordinate data respectively form a qualified plane coordinate data set and a qualified height coordinate data set, and provide high-quality input for subsequent weighted mean calculation. By respectively calculating the plane and height adjustment results and screening the qualified coordinate data according to the accuracy, the low-accuracy data is eliminated in a targeted manner, the interference of unqualified data on the final result is reduced, a high-quality data basis is provided for subsequent weighted processing, the low-accuracy data is avoided to distort the result when participating in the weighting, and the accuracy of the monitoring data is further improved.

[0043] S4, determining a weight corresponding to each coordinate data according to the accuracy of each coordinate data set, and performing weighted calculation on each coordinate data in the coordinate data set according to the weight to obtain weighted coordinate data of each coordinate data set.

[0044] In this embodiment, for each to-be-monitored point, the coordinate data and the corresponding accuracy thereof are extracted from the adjustment results of all reference point combinations, the weight is determined according to the accuracy index, and the calculation formula is as follows: wherein is the weight, and the accuracy is represented by the mean error m.

[0045] For example, the coordinate (X, Y, Z) is subjected to weighted calculation, and the calculation formula is as follows: Coordinate The final point coordinate calculated by weighting calculation is represented as X, and the coordinate data set includes n coordinate points, respectively 、 ... , and the value range of i is 1≤i≤n.

[0046] After the calculation is completed, the final point coordinate needs to be evaluated for accuracy, and the calculation formula is as follows: wherein M is the accuracy evaluation result of the final point coordinate, for the user's reference. For example, when the accuracy evaluation result of the final point coordinate meets the preset condition, the final point coordinate is retained, and when the accuracy evaluation result of the final point coordinate does not meet the preset condition, the final point coordinate is eliminated.

[0047] By weighting calculation on the coordinate data set, abnormal results with large deviations can be weakened due to low weight, the proportion of abnormal data in the final result can be reduced, and the influence of abnormal data can be weakened. The adjustment result accuracy of different reference point combinations can be different, the introduction of weight makes the high-precision result have a higher proportion in the final calculation, and ensures that the result is closer to the true value. In addition, the embodiment does not need to manually judge the reliability of the reference point combination, but automatically realizes the fusion of the optimal result through mathematical algorithm, and improves the data processing efficiency.

[0048] S5, comparing the weighted coordinate data of each coordinate data set with the historical coordinate data, when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set gross error threshold, determining that the coordinate data set corresponding to the weighted coordinate data is a gross error point.

[0049] Specifically, comparing the weighted coordinate data of each coordinate data set with the historical coordinate data, when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set gross error threshold, determining that the coordinate data set corresponding to the weighted coordinate data is a gross error point, including: comparing the difference between the weighted coordinate data in each coordinate data set and the coordinate data in the target point information, and / or comparing the difference between the weighted coordinate data in each coordinate data set and the historical coordinate data, when any difference is greater than a set gross error threshold, determining that the coordinate data set corresponding to the weighted coordinate data is a gross error point.

[0050] The weighted coordinate data is the optimal coordinate of the monitoring point obtained by weighting calculation on the adjustment results of multiple reference point combinations. The coordinate data in the target point information is the initial reference data of the monitoring point, for example, the coordinates at the first monitoring or the design coordinates, which are used as the original reference value of deformation monitoring. The historical coordinate data is the point coordinate output by the monitoring point in the past monitoring, reflecting the historical deformation state.

[0051] For each to-be-monitored point, initial data and historical monitoring data of the weighted coordinate data and target point information are obtained from the system, the difference between the weighted coordinate data and the target point information data is calculated, the difference between the weighted coordinate data and the historical coordinate data is calculated, and the calculated difference is compared with a preset gross error setting value. When any difference exceeds the setting value, it is determined that the point is a gross error point.

[0052] S6, the coordinate data set corresponding to the gross error point is removed, and final monitoring data is output.

[0053] On the basis of the above embodiment, by comparing the weighted coordinate data with the initial reference and historical data, the gross error caused by instrument measurement error, target shielding and other sudden conditions can be accurately detected, and the gross error point is removed to output the final monitoring data, avoiding abnormal data into the final result, and ensuring that the monitoring result can truly reflect the actual deformation state of the structure.

[0054] Embodiment 2 The embodiment of the application provides a kind of monitoring data processing system based on measurement robot, data processing system adopts the monitoring data processing method based on measurement robot in the above embodiment, Figure 2 It is a kind of structure schematic diagram of data processing system provided in the embodiment of the application, as Figure 2 As shown, the data processing system specifically includes the following modules: The data acquisition module 110 is used to control the measurement robot to observe data, and the edge angle data of a plurality of to-be-monitored points is obtained. The reference point combination module 120 is used to obtain reference point data, and generate a plurality of reference point combinations according to the reference point data. The adjustment calculation module 130 is connected with the data acquisition module 110 and the reference point combination module 120, and is used to calculate the adjustment result of the edge angle data of each to-be-monitored point according to each reference point combination, wherein the adjustment result includes the coordinate data set of the to-be-monitored point, and the accuracy of each coordinate data in the coordinate data set. The weighting calculation module 140 is connected with the adjustment calculation module 130, and is used to determine the weight corresponding to each coordinate data according to the accuracy of each coordinate data, and perform weighted calculation on each coordinate data in the coordinate data set according to the weight to obtain the weighted coordinate data of each coordinate data set. The gross error detection module 150 is connected with the weighting calculation module 140, and is used to compare the weighted coordinate data of each coordinate data set with the historical coordinate data, and when the difference between the weighted coordinate data of the coordinate data set and the historical coordinate data is greater than a set gross error threshold, the coordinate data set corresponding to the weighted coordinate data is determined as a gross error point. The data output module 160 is connected with the gross error detection module 150, and is used to remove the data set corresponding to the gross error point, and output final monitoring data.

[0055] The weighted calculation module 140 determines the weight corresponding to the coordinate data in the adjustment result according to the accuracy in the adjustment result of each reference point combination, performs weighted calculation on the coordinate data set, and outputs weighted coordinate data, which comprehensively considers the monitoring data under different reference point combinations. Abnormal reference points only have an impact in the combination containing the reference point, and other combinations can provide relatively accurate references. Through subsequent weighted calculation, abnormal results with large deviations will be weakened due to low weights, which can reduce the proportion of abnormal data in the final results and weaken the influence of abnormal data. At the same time, the gross error detection module 150 performs gross error detection on the weighted coordinate data, and when the difference between the weighted coordinate data and the historical coordinate data is greater than the set gross error threshold, the weighted coordinate data is determined as a gross error point, which can effectively avoid the influence of gross error data on the monitoring results and improve the accuracy of the monitoring data.

[0056] Embodiment 3 An electronic device, comprising: a processor and a memory; The processor, by invoking the program or instruction stored in the memory, is configured to execute the steps of the monitoring data processing method based on the measuring robot according to any one of the embodiments 1.

[0057] Embodiment 4 A computer readable storage medium, comprising computer program instructions, the computer program instructions causing a computer to execute the steps of the monitoring data processing method based on the measuring robot according to any one of the embodiments 1.

[0058] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of readable storage medium include: electrical connection with one or more conductive wires, portable disk, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can still be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present application.

Claims

1. A monitoring data processing method based on a measurement robot, characterized in that, include: S1. Control the measurement robot to observe data and acquire corner data of multiple points to be monitored; S2. Obtain benchmark data and generate multiple sets of benchmark combinations based on the benchmark data; S3. In each combination of reference points, the adjustment result is calculated for the corner data of each point to be monitored according to the adjustment algorithm. The adjustment result includes the coordinate dataset of the point to be monitored and the accuracy of each coordinate data in the coordinate dataset. S4. Determine the weight corresponding to each coordinate data according to the precision of each coordinate data, and perform weighted calculation on each coordinate data in the coordinate dataset according to the weight to obtain the weighted coordinate data of each coordinate dataset; S5. Compare the weighted coordinate data of each coordinate dataset with the historical coordinate data. When the difference between the weighted coordinate data of the coordinate dataset and the historical coordinate data is greater than the set gross error threshold, determine the coordinate dataset corresponding to the weighted coordinate data as the gross error point. S6. Remove the coordinate datasets corresponding to the gross error points and output the final monitoring data.

2. The monitoring data processing method based on a measurement robot according to claim 1, characterized in that, In each reference point combination, the adjustment result is calculated for the corner data of each monitoring point according to the adjustment algorithm, including: Based on the adjustment algorithm, the plane adjustment result and the elevation adjustment result are calculated for the corner data of each monitoring point; The plane adjustment results include a plane coordinate dataset and the accuracy of each plane coordinate data in the plane coordinate dataset; the elevation adjustment results include an elevation coordinate dataset and the accuracy of each elevation coordinate data in the elevation coordinate dataset.

3. The monitoring data processing method based on a measurement robot according to claim 2, characterized in that, After calculating the plane adjustment results and elevation adjustment results for the corner data of each monitoring point according to the adjustment algorithm, the following is also included: Compare the precision of each plane coordinate data with the preset plane precision. If the precision of the plane coordinate data is less than the preset plane precision, remove the plane coordinate data from the plane coordinate dataset. Compare the accuracy of each elevation coordinate data with the preset elevation accuracy. If the accuracy of the elevation coordinate data is less than the preset elevation accuracy, remove the elevation coordinate data from the elevation coordinate dataset.

4. The monitoring data processing method based on a measurement robot according to claim 1, characterized in that, After the control and measurement robot performs data observation and acquires the corner data of the point to be monitored, it includes: S11. Perform a check on the corner data of the multiple monitoring points to identify any missing monitoring points; S12. By comparing the corner data with a preset range, unqualified monitoring points are identified; S13. Remove unqualified monitoring points and conduct supplementary monitoring on unqualified and missed monitoring points. S14. After the supplementary measurement is completed, continue to execute S11-S13 until the corner data of multiple monitoring points meet the preset range.

5. The monitoring data processing method based on a measurement robot according to claim 4, characterized in that, The step of performing a check for omissions in the corner data of the multiple monitoring points to determine the omission monitoring points includes: Each corner data point to be monitored is checked one by one; The monitoring points corresponding to the corner data that were not recorded or were missing data were identified as the missed monitoring points.

6. The monitoring data processing method based on a measurement robot according to claim 1, characterized in that, The controlled measurement robot performs data observation and acquires corner data of multiple points to be monitored, specifically including: Acquire the target location information of the points to be monitored, as well as the observation rules and location information of the measuring robot; Based on the target point location information of the point to be monitored and the location information of the measuring robot, calculate the azimuth and elevation angle of the point to be monitored. Based on the azimuth and elevation angles of the point to be monitored, the mechanical structure of the measuring robot is controlled to rotate to the corresponding angle, and the observation parameters of the measuring robot are set according to the target point information; The control measurement robot searches for the monitoring point according to the set observation parameters. After the measurement robot locks the monitoring point, the control measurement robot measures the corner data of the monitoring point according to the observation rules. The corner data of multiple monitoring points were measured using the full-circle observation method.

7. The monitoring data processing method based on a measuring robot according to claim 4, characterized in that, While the controlled measurement robot performs data observation and acquires corner data of multiple points to be monitored, it also includes: Meteorological data is acquired at fixed intervals during the observation process of the measurement robot.

8. The monitoring data processing method based on a measurement robot according to claim 7, characterized in that, After the corner data of multiple monitoring points meet the preset range, the process further includes: Meteorological corrections are performed on the corner data of the multiple monitoring points based on the meteorological data.

9. The monitoring data processing method based on a measuring robot according to claim 6, characterized in that, Compare the weighted coordinate data of each coordinate dataset with historical coordinate data. When the difference between the weighted coordinate data of a coordinate dataset and the historical coordinate data exceeds a set gross error threshold, the coordinate dataset corresponding to the weighted coordinate data is determined to be a gross error point, including: Compare the weighted coordinate data in each coordinate dataset with the coordinate data in the target point information, and / or compare the weighted coordinate data in each coordinate dataset with the historical coordinate data. When any difference is greater than a set gross error threshold, the coordinate dataset corresponding to the weighted coordinate data is determined to be a gross error point.

10. A monitoring data processing system based on a measurement robot, characterized in that, The data processing system employs the monitoring data processing method based on a measurement robot as described in any one of claims 1-9, and specifically includes the following modules: The data acquisition module is used to control the measuring robot to observe data and acquire the corner data of multiple monitoring points; The benchmark point combination module is used to acquire benchmark point data and generate multiple sets of benchmark point combinations based on the benchmark point data. The adjustment calculation module is connected to the data acquisition module and the benchmark point combination module. It is used to calculate the adjustment result for the corner data of each monitoring point based on each benchmark point combination. The adjustment result includes the coordinate dataset of the monitoring point and the precision of each coordinate data in the coordinate dataset. The weighted calculation module, connected to the adjustment calculation module, is used to determine the weight corresponding to each coordinate data according to the precision of each coordinate data, and to perform weighted calculation on each coordinate data in the coordinate dataset according to the weight to obtain the weighted coordinate data of each coordinate dataset; The gross error detection module, connected to the weighted calculation module, is used to compare the weighted coordinate data of each coordinate dataset with the historical coordinate data. When the difference between the weighted coordinate data of the coordinate dataset and the historical coordinate data is greater than a set gross error threshold, the coordinate dataset corresponding to the weighted coordinate data is determined to be a gross error point. The data output module, connected to the gross error detection module, is used to remove the coordinate datasets corresponding to the gross error points and output the final monitoring data.

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