A road data anomaly alarm method and device, electronic equipment and storage medium

Abstract

The application provides a road data anomaly alarm method and device, electronic equipment and a storage medium, applied to the technical field of road safety monitoring and alarm, through acquiring basic point cloud data information of a monitoring target object, then acquiring real-time point cloud data information of the monitoring target object in real time, comparing the finally obtained real-time point cloud data information with the basic point cloud data information, judging whether data anomaly occurs in a monitoring road section, and issuing data anomaly alarm information, so that the road data anomaly alarm method has the beneficial effects of intelligent correction of monitoring errors and high timeliness of alarm.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road safety monitoring and alarming, and in particular to a road data anomaly alarming method and device, an electronic device and a storage medium. BACKGROUND

[0002] In recent years, road traffic safety problems have increasingly become the most concerned topic in people's production and life. With extreme weather changes, roads located along mountains are easily affected by natural disasters such as landslides, debris flow coverage and dangerous rock collapse.

[0003] In addition to traditional monitoring means, traffic, emergency rescue, safety monitoring, and geological disaster mapping departments in various places gradually introduce three-dimensional laser scanners (including airborne laser radar, vehicle-mounted laser radar, backpack handheld laser radar, ground laser radar, and ground-based monitoring SAR) as front-end data acquisition equipment for safety alarm measures.

[0004] For three-dimensional point cloud achievement data of laser scanning, the general processing means on the market is on-site data acquisition and rear-end manual software analysis and processing. This processing method has problems such as poor timeliness, inability to provide early warning, low automation, and inability to correct detection data errors when using different equipment for monitoring in different environments.

[0005] Therefore, the road safety monitoring in the prior art has the problems of low intelligence and poor timeliness of alarming. SUMMARY

[0006] In view of the above deficiencies in the prior art, the present application provides a road data anomaly alarming method and device, an electronic device and a storage medium, which are applied to the technical field of road safety monitoring and alarming. By acquiring basic point cloud data information of a monitoring target object, current point cloud data information and theoretical point cloud data information of the monitoring target object are acquired in real time. The theoretical point cloud data information is used to assist the alarm platform in intelligently determining whether the acquired current point cloud data information has errors. That is, by calculating the difference between the current point cloud data information and the theoretical point cloud data information, the difference is the first comparison result. The first comparison result is compared with a preset allowed error value. When the first comparison result is less than or equal to the preset allowed error value, the current point cloud data information is taken as real-time point cloud data information. When the first comparison result is greater than the preset allowed error value, the theoretical point cloud data information is taken as real-time point cloud data information. The finally obtained real-time point cloud data information is compared with the basic point cloud data information, so as to determine whether a data anomaly occurs in the monitored road section, and data anomaly alarm information is sent. Therefore, the road data anomaly alarming method has the beneficial effects of intelligent correction of monitoring errors and high timeliness of alarming.

[0007] In a first aspect, the application provides a road data anomaly alarm method, which comprises the following steps:

[0008] A1: acquiring basic point cloud data information of a monitoring target object;

[0009] A2: acquiring current point cloud data information and theoretical point cloud data information of the monitoring target object in real time;

[0010] A3: comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, when the first comparison result is less than or equal to a preset allowable error value, using the current point cloud data information as real-time point cloud data information, and when the first comparison result is greater than the preset allowable error value, using the theoretical point cloud data information as real-time point cloud data information;

[0011] A4: comparing the real-time point cloud data and the basic point cloud data information to generate a second comparison result;

[0012] A5: issuing an alarm information of road data anomaly according to the second comparison result.

[0013] Through the above road data anomaly alarm method, the basic point cloud data information of the monitoring target object is acquired, so as to use the basic point cloud data information as the comparison reference data of the subsequent real-time point cloud data information, and the alarm platform compares the data information to determine whether the road has data anomaly; then, the current point cloud data information and the theoretical point cloud data information of the monitoring target object are acquired in real time, and since different devices may cause errors in the current point cloud data information and the real data information when monitoring the same type of target object in different environments due to environmental shielding or device itself, a theoretical point cloud data information can be set to correct the current point cloud data information, if the difference between the current point cloud data information and the theoretical point cloud data information is too large, i.e. when the first comparison result is greater than the preset allowable error value, it indicates that the current point cloud data information acquired by the monitoring device has large error, and the theoretical point cloud data information is used as the real-time point cloud data information to reduce the error, if the difference between the current point cloud data information and the theoretical point cloud data information is small, i.e. when the first comparison result is less than or equal to the preset allowable error value, it indicates that the current point cloud data information is close to the real value and has small error, and the current point cloud data information can be used as the real-time point cloud data information, after the real-time point cloud data information is acquired, the real-time point cloud data information is compared with the basic point cloud data information when no data anomaly occurs to generate a second comparison result, and timely alarm is performed according to the second comparison result. Compared with the problems in the prior art, this method has the beneficial effects of intelligent correction of monitoring error and high timeliness of alarm.

[0014] Preferably, in the road data anomaly alarm method provided in the application, the step of acquiring the theoretical point cloud data information of the monitoring target in real time comprises:

[0015] Acquiring historical point cloud data information of the monitoring target, the historical point cloud data information being calculated according to at least a first angular resolution of a historical monitoring device, first measurement distance information and first elevation information of the monitoring target;

[0016] Establishing a point cloud data calculation model according to the calculation process;

[0017] Substituting a second angular resolution of a current monitoring device of the monitoring target, current measurement distance information and second elevation information of the monitoring target into the point cloud data calculation model to calculate the theoretical point cloud data information.

[0018] According to the road data anomaly alarm method, the acquisition of the theoretical point cloud data information can acquire the historical point cloud data information of the monitoring target in advance, the historical point cloud data information being calculated according to at least a first angular resolution of a historical monitoring device, first measurement distance information and first elevation information of the monitoring target, and the calculation process is established as a point cloud data calculation model, i.e., first elevation information / (first angular resolution*first measurement distance information)=historical point cloud data information. Therefore, when monitoring the same type of target in different environments of different monitoring sections, the second angular resolution of the current monitoring device, the current measurement distance information and the second elevation information are substituted into the calculation model to calculate the theoretical point cloud data information, so that the theoretical point cloud data information of different monitoring devices in different environments monitoring the same type of monitoring target can be obtained very conveniently.

[0019] Preferably, in the road data anomaly alarm method provided in the application, the step of establishing the point cloud data calculation model according to the calculation process comprises:

[0020] Classifying the monitoring target;

[0021] Establishing a point cloud data calculation model for the engineering construction object in the classified monitoring target.

[0022] By the road data abnormality alarm method, the monitoring target objects can be classified in the process of establishing the point cloud data calculation model, and the main types of the monitoring target objects include soil, vegetation, rock, and engineering construction object (man-made cement building). Different types of the monitoring target objects have different reflectivity when scanned by the monitoring device, and the point cloud data obtained is different. Since the engineering construction object is man-made, the point cloud data distribution of the engineering construction object on the image formed by the monitoring device is extremely obvious and easy to distinguish. Therefore, the point cloud data calculation model is established for the engineering construction object in the classified monitoring target objects, and the engineering construction object is used as the monitoring target object of the road data abnormality, which is beneficial to the accuracy and timeliness of the alarm.

[0023] Preferably, the application provides a road data abnormality alarm method, and the A4 step includes:

[0024] The first file size information of the basic point cloud data information and the second file size information of the real-time point cloud data information are obtained respectively.

[0025] The first file size information and the second file size information are compared.

[0026] When the first file size information and the second file size information are the same, the second comparison result is that the road does not have data abnormality.

[0027] When the first file size information and the second file size information are different, the second comparison result is that the road has data abnormality.

[0028] By the road data abnormality alarm method, the step of comparing the real-time point cloud data information and the basic point cloud data information can be comparing the first file size information and the second file size information, which is intuitive and simple, and is beneficial to judging whether the road has data abnormality.

[0029] Preferably, the application provides a road data abnormality alarm method, and when the first file size information and the second file size information are different, the step after the second comparison result that the road has data abnormality includes:

[0030] The data abnormality displacement of the road having data abnormality is calculated.

[0031] The step of calculating the data abnormality displacement of the road having data abnormality includes:

[0032] The first coordinate information of the basic point cloud data information in the geodetic coordinate system is obtained.

[0033] The second coordinate information of the real-time point cloud data information in the geodetic coordinate system is obtained.

[0034] According to the first coordinate information and the second coordinate information, a data abnormal displacement amount of the road data abnormality is calculated.

[0035] Preferably, the application provides a road data abnormality alarm method, and after calculating the data abnormal displacement amount of the road data abnormality according to the first coordinate information and the second coordinate information, the steps include:

[0036] The data abnormal displacement amount is compared with a preset allowable displacement amount, when the data abnormal displacement amount is less than or equal to the preset allowable displacement amount, the second comparison result is a first-level safety alarm information;

[0037] When the data abnormal displacement amount is greater than the preset allowable displacement amount, the second comparison result is a second-level safety alarm information.

[0038] Preferably, the application provides a road data abnormality alarm method, when the data abnormal displacement amount is greater than the preset allowable displacement amount, the step of making the second comparison result as the second-level safety alarm information includes:

[0039] Obtaining monitoring time information and geographical parameter information of the monitoring target object;

[0040] According to the monitoring time information and the geographical parameter information, a trend model of landslide rate-period is established;

[0041] According to the trend model, landslide time information and landslide deformation information of the monitoring target object are predicted;

[0042] The second-level safety alarm information includes the landslide time information and the landslide deformation information.

[0043] In a second aspect, the application provides a road data abnormality alarm device, which includes:

[0044] A first obtaining module is configured to obtain basic point cloud data information of a monitoring target object;

[0045] A second obtaining module is configured to obtain current point cloud data information and theoretical point cloud data information of the monitoring target object in real time;

[0046] A first comparison module is configured to compare the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, when the first comparison result is less than or equal to a preset allowable error value, the current point cloud data information is taken as real-time point cloud data information, and when the first comparison result is greater than the preset allowable error value, the theoretical point cloud data information is taken as real-time point cloud data information;

[0047] A second comparison module is configured to compare the real-time point cloud data information and the basic point cloud data information to generate a second comparison result;

[0048] The alarm module is configured to send an alarm information of the road data abnormality according to the second comparison result.

[0049] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the steps in the method provided in the first aspect are executed.

[0050] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0051] Advantages:

[0052] The road data abnormality alarm method, device, electronic device and storage medium provided by the present application can obtain the basic point cloud data information of the monitoring target object, then obtain the current point cloud data information and the theoretical point cloud data information of the monitoring target object in real time, the theoretical point cloud data information is used to assist the alarm platform to intelligently judge whether the current point cloud data information obtained exists error, that is, by calculating the difference between the current point cloud data information and the theoretical point cloud data information, the difference is the first comparison result, and the size of the first comparison result and the preset allowable error value is compared, when the first comparison result is less than or equal to the preset allowable error value, the current point cloud data information is taken as the real-time point cloud data information, when the first comparison result is greater than the preset allowable error value, the theoretical point cloud data information is taken as the real-time point cloud data information, the real-time point cloud data information obtained finally is compared with the basic point cloud data information, so as to judge whether the data of the monitoring road section is abnormal, and the data abnormality alarm information is sent, so that the road data abnormality alarm method has the beneficial effects of intelligent correction of monitoring error, high timeliness of alarm. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The flow chart of the road data abnormality alarm method provided by the present application.

[0054] Figure 2 The structural schematic diagram of the road data abnormality alarm device provided by the present application.

[0055] Figure 3 The structural schematic diagram of the electronic device provided by the present application.

[0056] Figure 4 The trend model graph of the landslide rate-period provided by the present application.

[0057] Label explanation: 201, first acquisition module; 202, second acquisition module; 203, first comparison module; 204, second comparison module; 205, alarm module; 301, processor; 302, memory; 303, communication bus; 3, electronic device. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and indicated in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0059] It should be noted that: similar labels and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first, second" and the like are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0060] The following disclosure provides many different embodiments or examples for achieving the purposes of the present application, solving the problems existing in the prior art. For the point cloud data of the current laser scanning, the general market processing means is on-site data acquisition and rear-end manual software analysis processing. This processing method has the problems of poor timeliness, inability to give early warning, low automation, and inability to intelligently correct the monitoring data errors of different devices used in different environments, etc. in the road scene, especially for geological disaster monitoring. In order to solve this problem, the present application provides a road data anomaly alarm method, device, electronic equipment and storage medium, specifically:

[0061] Please refer to Figure 1 The embodiments of the present application provide a road data anomaly alarm method, which is applied to the technical field of road safety monitoring and alarm. On the basis of obtaining the basic point cloud data information and real-time point cloud data information of the monitoring target object, by comparing the difference between the two, whether the data anomaly occurs in the monitoring road section is analyzed, and the time and displacement of landslide and other large road crises after the data anomaly alarm is analyzed, so as to arrange rescue and relief in time and effectively.

[0062] The road data anomaly alarm method of the embodiments of the present application includes the following steps:

[0063] A1: obtaining basic point cloud data information of a monitoring target object;

[0064] A2: obtaining current point cloud data information and theoretical point cloud data information of the monitoring target object in real time;

[0065] A3: comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, when the first comparison result is less than or equal to a preset allowable error value, taking the current point cloud data information as real-time point cloud data information, and when the first comparison result is greater than the preset allowable error value, taking the theoretical point cloud data information as real-time point cloud data information;

[0066] A4: comparing the real-time point cloud data and the basic point cloud data information to generate a second comparison result;

[0067] A5: issuing an alarm information of road data anomaly according to the second comparison result.

[0068] The road actually includes but is not limited to roads, interchanges, squares, traffic facilities, rail transit facilities such as railways and subways; the basic point cloud data information of the monitoring target object refers to the basic point cloud data information obtained by a three-dimensional laser scanning device installed along the road when no data anomaly occurs, or the basic point cloud data information obtained by a patrol means when no data anomaly occurs, wherein the monitoring target object mainly includes soil, vegetation, rock and engineering construction objects (man-made cement buildings, which have the characteristics of not easy to deform and uniform distribution of point cloud data information on the image scanned by the monitoring device).

[0069] In the A1 step, the basic point cloud data information of the monitoring target object is obtained. In order to facilitate subsequent calculation of the data anomaly displacement amount based on the basic point cloud data information and the real-time point cloud data information, and to help the technical personnel to judge whether the monitoring road section has data anomaly, the obtained basic point cloud data information can be transferred to the CGCS2000 geodetic coordinate system, so as to ensure that each basic point cloud data information (x, y, z) is the longitude, latitude and elevation information in the CGCS2000 geodetic coordinate system. If the way of obtaining the basic point cloud data information is a ground station type three-dimensional laser scanner, at the same time of erecting the station, a geodetic control network is established by using at least 4 point target station. The 4 point target station refers to each station being able to see 4 target points, and the selection of the target points is not the focus of the present application and is not described here. The basic three-dimensional space data information of the target points is provided by an RTK (real-time differential positioning measuring instrument) with GPS (global positioning system) and GNSS (global navigation satellite system) functions; if the way of obtaining the basic point cloud data information is a vehicle-mounted or airborne three-dimensional laser scanning device, the POS system (positioning and inertial navigation system) provided by the system is used to obtain the basic point cloud data information in the CGCS2000 geodetic coordinate system.

[0070] In some specific embodiments, for example, the monitored section is a section along a railway, about 2 kilometers long, with a mountain along the railway about 700 meters high, a slope of about 45 degrees, a wide river nearby, dense vegetation, a huge mountain, many landslide points, and a complex terrain of argillaceous rock. According to the conditions of the section, a ground three-dimensional laser scanner with a ranging distance of 1500 meters and an accuracy of 5 millimeters is used to establish a survey station at a fixed point on the river-crossing mountain on the opposite side of the railway. The point uses surveying equipment with a static horizontal accuracy of 2.5 millimeters and a vertical accuracy of 5 millimeters. At the same time, nine target points are arranged around the survey station. Similarly, RTK measurement is used to obtain the basic point cloud data information of the nine target points in the CGCS2000 geodetic coordinate system. After the acquisition-noise filtering-registration process provided by the three-dimensional laser scanner, the basic point cloud data information of the section in the CGCS2000 geodetic coordinate system under the entire environmental scene is obtained as (19.6, 267.54, -39.25), and the like. It should be noted that in order to make the specification more concise, only one example of the basic point cloud data information in the CGCS2000 geodetic coordinate system is given, and it does not mean that all the data of the monitoring target object is this information.

[0071] In step A2, the current point cloud data information of the monitoring target object is obtained in real time, which means that at a specific time point in the continuous change of time, the three-dimensional laser scanning device is used to scan the current point cloud data information of the monitoring target object. The way to obtain the theoretical point cloud data information in real time can be to obtain the historical point cloud data information of the monitoring target object in advance, which is calculated at least according to the first angular resolution of the historical monitoring device, the first measurement distance information, and the first elevation information of the monitoring target object, and the calculation process is established as a point cloud data calculation model, that is: first elevation information / (first angular resolution*first measurement distance information)=historical point cloud data information. Therefore, in different environments of different monitoring sections, when monitoring the same type of target object, the second angular resolution of the current monitoring device, the current measurement distance information, and the second elevation information are substituted into the calculation model to calculate the theoretical point cloud data information. The angular resolution of the monitoring device is the parameter information of the monitoring device itself, and different monitoring devices have different angular resolutions. Therefore, the first angular resolution of the historical monitoring device can be replaced by the second angular resolution of the current monitoring device to calculate the theoretical point cloud data information. The measurement distance information is the straight-line distance between the monitoring device and the monitoring target object in the horizontal direction, and the elevation information refers to the height information of the monitoring target object monitored by the monitoring device in the vertical direction. The terms "first" and "second" are used only to distinguish different monitoring devices, different monitoring target objects, and different measurement distance information. Thus, in some preferred embodiments, the step of obtaining the theoretical point cloud data information of the monitoring target object in real time comprises:

[0072] Acquire historical point cloud data information of the monitoring target object, which is calculated according to at least a first angular resolution of a historical monitoring device, first measurement distance information, and first elevation information of the monitoring target object;

[0073] Establish a point cloud data calculation model according to the calculation process;

[0074] Substitute second angular resolution of a current monitoring device of the monitoring target object, current measurement distance information, and second elevation information of the monitoring target object into the point cloud data calculation model to calculate theoretical point cloud data information.

[0075] In some preferred embodiments, the theoretical point cloud data information can also be calculated by using horizontal length information of the monitoring target object, which is measured by the monitoring device, to replace the elevation information of the monitoring target object, so that the point cloud data calculation model can still be obtained. In actual application, the point cloud data calculation model is first elevation information / (first angular resolution*first measurement distance information)=historical point cloud data information, and the first elevation information, the first angular resolution, and the first measurement distance information are all replaceable parameter information. However, in the actual use of the monitoring device to scan the monitoring target object to acquire point cloud data information of the target object, the environment and the reflectivity of the monitoring target object itself will also be affected, which may cause errors. For example, the monitoring target object is covered by vegetation or soil, and it can be known through a large amount of monitoring data that the reflectivity of soil<the reflectivity of vegetation<the reflectivity of rock<the reflectivity of engineering structures. If the monitoring target object is an engineering structure located on the side of a railway network, and it is covered by vegetation, the acquired point cloud data information will be affected, resulting in errors. Therefore, it is necessary to establish a point cloud data calculation model to calculate the theoretical point cloud data information of the monitoring target object and correct the acquired current point cloud data information.

[0076] In some preferred embodiments, the step of establishing a point cloud data calculation model according to the calculation process includes:

[0077] Classify the monitoring target object;

[0078] Establish a point cloud data calculation model for the engineering structure in the classified monitoring target object.

[0079] The main types of monitoring targets include soil, vegetation, rock, and engineering structures (man-made cement structures). Different types of monitoring targets have different reflectivities when scanned by the monitoring device, and the point cloud data obtained is different. Engineering structures are man-made, such as retaining walls and drainage channels, and have characteristics that make their point cloud data distribution very obvious in the image formed by the monitoring device, making them easy to distinguish. Therefore, a point cloud data calculation model is established for the classified engineering structures, and using engineering structures as the monitoring target for data anomaly of the road is beneficial to the accuracy and timeliness of the alarm.

[0080] In some preferred embodiments, after obtaining the basic point cloud data information of the monitoring target, the basic point cloud data information of the engineering structure can be obtained by classifying the type of the monitoring target. The engineering structure includes retaining walls, drainage channels, bridges, and other different shaped buildings. Since the engineering structure is made of cement, it has the same reflectivity, so the basic point cloud data information of the engineering structure of the same shape can be called by conversion. For example, after obtaining the basic point cloud data information of the retaining wall, drainage channel, and bridge for the first time, it can be stored in the engineering structure point cloud database. When the data of the current retaining wall is needed in other road sections, the basic point cloud data information of the retaining wall stored in the point cloud database can be directly called. According to the size and setting angle of the current retaining wall, the basic point cloud data information is stretched or compressed at the same angle to obtain the distribution of the basic point cloud data information of the current retaining wall, thereby saving the time for obtaining the basic point cloud data information of the monitoring target, improving the monitoring efficiency, and avoiding errors in the monitoring data caused by external environmental interference when using the monitoring device to directly monitor the target.

[0081] Wherein, in the A3 step, the current point cloud data information is the point cloud data information obtained by actually scanning the monitoring target object by the current monitoring device; the theoretical point cloud data information is obtained by substituting the second angular resolution of the current monitoring device, the current measurement distance information and the second elevation information of the monitoring target object into the point cloud data calculation model. The first comparison result is the absolute value of the difference between the current point cloud data information and the theoretical point cloud data information. When the absolute value of the difference is less than or equal to the preset allowable error value, it indicates that the error of the current point cloud data information is small, and it is closer to the true value. Therefore, the current point cloud data information is taken as the real-time point cloud data information to compare with the basic point cloud data information, so as to determine whether the road has data anomaly. When the absolute value of the difference is greater than the preset allowable error value, it indicates that the error of the current point cloud data information is large, and it deviates from the true value. Therefore, the theoretical point cloud data information is taken as the real-time point cloud data information to compare with the basic point cloud data information, so as to determine whether the road has data anomaly. Through the intelligent correction method, the data with the smallest error is obtained as the real-time point cloud data information, which is used as the basis for determining whether the road has data anomaly, thereby improving the monitoring accuracy.

[0082] Wherein, in some preferred embodiments, the A4 step comprises:

[0083] The first file size information of the basic point cloud data information and the second file size information of the real-time point cloud data information are obtained respectively.

[0084] The first file size information and the second file size information are compared.

[0085] When the first file size information and the second file size information are the same, the second comparison result is that the road has no data anomaly.

[0086] When the first file size information and the second file size information are different, the second comparison result is that the road has data anomaly.

[0087] Wherein, after obtaining the basic point cloud data information and the real-time point cloud data information, they are stored in the form of txt (text format) or las (three-dimensional format) file data. Therefore, in actual application, the first file size information is the size of the basic point cloud data saved as a txt file, for example, the first file size information is 3KB; the second file size information is the size of the real-time point cloud data information saved as a txt file, for example, the second file size information is 3KB.

[0088] Wherein, in actual application, if the road does not have data anomaly, the real-time point cloud data information obtained is the same as the basic point cloud data information, and after the two are saved as txt files, their file sizes are also the same, if the file sizes of the two are different, it indicates that the road data is abnormal, therefore, by comparing the first file size information and the second file size information, whether the monitored road has data anomaly can be determined, and the specific determination manner is that when the first file size information and the second file size information are the same, such as both are 3KB, no data anomaly occurs, when the first file size information and the second file size information are different, such as the first file size information is 4KB and the second file size information is 3KB, it can be determined that the monitored road has data anomaly, by comparing whether the file size information is the same, whether the road has data anomaly can be quickly judged, the judgment efficiency is improved, and the alarm is sent in time.

[0089] Wherein, when it is confirmed that the road has data anomaly, in order to make the road monitoring more accurate, the displacement amount of the road having data anomaly needs to be determined, therefore, in some preferred embodiments, when the first file size information and the second file size information are different, the second comparison result is the step after the road has data anomaly, which includes:

[0090] calculating the data anomaly displacement amount of the road having data anomaly;

[0091] The step of calculating the data anomaly displacement amount of the road having data anomaly includes:

[0092] obtaining the first coordinate information of the basic point cloud data information in the geodetic coordinate system;

[0093] obtaining the second coordinate information of the real-time point cloud data information in the geodetic coordinate system;

[0094] calculating the data anomaly displacement amount of the road having data anomaly according to the first coordinate information and the second coordinate information.

[0095] Wherein, the data anomaly displacement amount actually refers to the range of the road having data anomaly, and the data anomaly displacement amount is the difference value obtained by subtracting the first coordinate information from the second coordinate information. For example, in actual application, the second coordinate information obtained is (19.39, 267.79, -36.63), the first coordinate information is (19.38, 267.78, -37.81), and then the data anomaly displacement amount (0.01, 0.01, 1.18) can be obtained by calculation, and then alarm information can be sent according to the data anomaly displacement amount, therefore, in some preferred embodiments, the step after calculating the data anomaly displacement amount of the road having data anomaly according to the first coordinate information and the second coordinate information includes:

[0096] comparing the data abnormal displacement amount with a preset allowable displacement amount, when the data abnormal displacement amount is less than or equal to the preset allowable displacement amount, the second comparison result is a first safety alarm information;

[0097] when the data abnormal displacement amount is greater than the preset allowable displacement amount, the second comparison result is a second safety alarm information.

[0098] The preset allowable displacement amount is a range of data abnormality allowed by the monitoring target, and the data abnormal displacement amount is smaller in the range, which actually represents a smaller deformation amount of the monitoring target, and thus does not cause traffic accidents such as road landslide. In practical applications, the preset allowable displacement amount is set by technicians according to historical data in advance. Therefore, when the data abnormal displacement amount is less than or equal to the preset allowable displacement amount, the second comparison result is the first safety alarm information, and when the first safety alarm information is issued, the alarm platform can notify the technician to repair the monitoring target. When the data abnormal displacement amount is greater than the preset allowable displacement amount, it means that the deformation amount of the monitoring target is large, which may cause a landslide hazard. At this time, the second safety alarm information is issued to remind the technician that there is a landslide risk on the road. Further, after the second safety alarm information is issued, the alarm platform can establish a landslide rate-period trend model according to the data information returned by the monitoring device in real time, predict the landslide time information and landslide deformation information of the road landslide in advance, facilitate the technician to make a danger alarm and rescue preparation in advance, and ensure the safety of road personnel. Therefore, in some preferred embodiments, when the data abnormal displacement amount is greater than the preset allowable displacement amount, the step of setting the second comparison result to the second safety alarm information includes:

[0099] obtaining monitoring time information and geographical parameter information of the monitoring target;

[0100] establishing a landslide rate-period trend model according to the monitoring time information and the geographical parameter information;

[0101] predicting landslide time information and landslide deformation information of the monitoring target according to the trend model;

[0102] The second safety alarm information includes the landslide time information and the landslide deformation information.

[0103] The monitoring time information is a period of monitoring time set by the technician according to the actual situation after the monitoring device detects the data abnormality of the monitoring target, which meets the monitoring time length required by the alarm platform to establish the landslide rate-period trend model. The landslide rate-period trend model is specifically referred to in Figure 4The geographic parameter information includes a slope angle, a water content, and a soil viscosity of a topography where the monitoring target object is located. In actual application, the slope angle, the water content, and the soil viscosity of different topographies are different, so that the landslide time information and the landslide deformation information of the same type of monitoring target object under different topographies are different. For example, the monitoring target object of a certain railway section is a retaining wall A, and the geographic parameter information of the position where the retaining wall A is located is obtained through a sensor. A technical person presets 24 hours of monitoring time information, and establishes a landslide rate-period trend model according to the information. In the case of keeping other conditions unchanged, the retaining wall A occurs landslide at the 8th hour, and the landslide deformation is 50 meters. Therefore, the above landslide time information and landslide deformation information can be packaged into secondary safety alarm information to make an alarm reminder.

[0104] As can be seen from the above, the road data anomaly alarm method provided in the application obtains the basic point cloud data information of the monitoring target object, and then obtains the current point cloud data information and the theoretical point cloud data information of the monitoring target object in real time. The theoretical point cloud data information is used to assist the alarm platform to intelligently judge whether the obtained current point cloud data information has errors, that is, by calculating the difference between the current point cloud data information and the theoretical point cloud data information, the difference is the first comparison result, and the first comparison result is compared with the size of the preset allowable error value. When the first comparison result is less than or equal to the preset allowable error value, the current point cloud data information is taken as the real-time point cloud data information, and when the first comparison result is greater than the preset allowable error value, the theoretical point cloud data information is taken as the real-time point cloud data information. The finally obtained real-time point cloud data information is compared with the basic point cloud data information, so as to judge whether the monitoring section has data anomaly, and data anomaly alarm information is sent, so that the road data anomaly alarm method has the beneficial effects of intelligent correction of monitoring errors, high timeliness of alarm.

[0105] Please refer to Figure 2 The road data anomaly alarm device provided in the application comprises:

[0106] The first acquisition module 201 is configured to obtain the basic point cloud data information of the monitoring target object.

[0107] The second acquisition module 202 is configured to obtain the current point cloud data information and the theoretical point cloud data information of the monitoring target object in real time.

[0108] The first comparison module 203 is configured to compare the current point cloud data information and the theoretical point cloud data information to generate a first comparison result. When the first comparison result is less than or equal to a preset allowable error value, the current point cloud data information is taken as the real-time point cloud data information, and when the first comparison result is greater than the preset allowable error value, the theoretical point cloud data information is taken as the real-time point cloud data information.

[0109] The second comparison module 204 is configured to compare the real-time point cloud data information with the basic point cloud data information, and generate a second comparison result.

[0110] The alarm module 205 is configured to send an alarm information of road data abnormality according to the second comparison result.

[0111] In actual application, the first acquisition module 201 is configured to acquire the scanning data of the three-dimensional laser scanner, wherein the scanning data of the three-dimensional laser scanner is the basic point cloud data information.

[0112] In some specific embodiments, the first acquisition module 201 can also acquire the basic point cloud data information by means of patrol. In order to facilitate the second comparison module 204 to compare the basic point cloud data information with the real-time point cloud data information, in some preferred embodiments, the first acquisition module 201 can transfer the acquired basic point cloud data information to the CGCS2000 coordinate system, so as to ensure that each basic three-dimensional space data information (x, y, z) is the longitude, latitude and height information in the CGCS2000 coordinate system. For example, the monitored road section is a railway section along the railway, with a total length of about 2 kilometers, a mountain with a height of 700 meters along the railway, a slope of about 45 degrees, a wide river nearby, dense vegetation, huge mountains, many landslide points and complex mud rock terrain. According to the conditions of the road section, a ground three-dimensional laser scanner with a ranging distance of 1500 meters and an accuracy of 5 millimeters is used to establish a surveying station at a fixed point on the opposite side of the river across the mountain, and a surveying device with a static horizontal accuracy of 2.5 millimeters and a vertical accuracy of 5 millimeters is used at the point. At the same time, 9 target points are arranged around the surveying station, and RTK measurement is used to acquire the basic point cloud data information of the 9 target points in the CGCS2000 coordinate system. After the acquisition-noise filtering-registration process of the three-dimensional laser scanner is completed, the basic point cloud data information of the road section in the CGCS2000 coordinate system in the entire environment scene is (19.6, 267.54, -39.25) and the like. It should be noted that, in order to make the specification more concise, only one example of the basic point cloud data information in the CGCS2000 coordinate system is given, and it does not mean that all the data of the monitored target object is this information.

[0113] In some specific embodiments, the second acquisition module 202 acquires historical point cloud data information of the monitoring target object in advance, and the historical point cloud data information is calculated according to at least the first angular resolution of the historical monitoring device, the first measurement distance information and the first elevation information of the monitoring target object, and the calculation process is established as a point cloud data calculation model, that is, the first elevation information / (the first angular resolution*the first measurement distance information)=the historical point cloud data information. Therefore, when monitoring the same type of target object in different environments of different monitoring sections, the second angular resolution of the current monitoring device, the current measurement distance information and the second elevation information are substituted into the calculation model to calculate the theoretical point cloud data information. The angular resolution of the monitoring device is the parameter information of the monitoring device itself, and different monitoring devices have different angular resolutions. Therefore, the first angular resolution of the historical monitoring device can be replaced by the second angular resolution of the current monitoring device to calculate the theoretical point cloud data information. The measurement distance information is the straight-line distance between the monitoring device and the monitoring target object in the horizontal direction, and the elevation information refers to the height information of the monitoring target object monitored by the monitoring device in the vertical direction. The terms "first" and "second" are used to distinguish different monitoring devices, different monitoring target objects and different measurement distance information.

[0114] In some specific embodiments, the second angular resolution of the current monitoring device of the monitoring target object, the current measurement distance information and the second elevation information of the monitoring target object are substituted into the point cloud data calculation model to calculate the theoretical point cloud data information.

[0115] In some specific embodiments, the first comparison module 203 obtains a first comparison result by comparing the current point cloud data information with the theoretical point cloud data information, and the first comparison result is the absolute value of the difference between the current point cloud data information and the theoretical point cloud data information. Then, the real-time point cloud data information is obtained according to the first comparison result. The specific comparison method is as follows: when the absolute value of the difference is less than or equal to a preset allowed error value, it means that the error of the current point cloud data information is small, and it is closer to the true value. Therefore, the current point cloud data information is taken as the real-time point cloud data information to compare with the basic point cloud data information to determine whether the road has data anomaly. When the absolute value of the difference is greater than the preset allowed error value, it means that the error of the current point cloud data information is large, and it deviates from the true value. Therefore, the theoretical point cloud data information is taken as the real-time point cloud data information to compare with the basic point cloud data information to determine whether the road has data anomaly.

[0116] In some specific embodiments, the first comparison module 203 can obtain real-time point cloud data information and transmit the real-time point cloud data information to the second comparison module 204. The second comparison module 204 can filter and denoise the real-time point cloud data information and compare it with the basic point cloud data information to generate a second comparison result. For example, after the first acquisition module 201 obtains the basic point cloud data information, it is stored in a txt (text format) or las (three-dimensional format) file data to obtain a first file, and the first file size information is 3KB. The real-time point cloud data information is stored as a second file, and the second file size information is 3KB. The second comparison module 204 compares the first file size information with the second file size information. If they are equal, it can be concluded that the second comparison result is that the road has no data anomaly. However, if the first file size information is 4KB and the second file size information is 3KB, the second comparison module 204 can compare and obtain the second comparison result that the road has a data anomaly.

[0117] In some specific embodiments, after the second comparison module 204 generates a second comparison result that the road has a data anomaly, in order to enable the subsequent alarm module 205 to better issue an alarm information according to the second comparison result, the data anomaly displacement amount of the road can be calculated. For example, the second coordinate information is (19.39, 267.79, -36.63) and the first coordinate information is (19.38, 267.78, -37.81). The second comparison module 204 can calculate the data anomaly displacement amount as (0.01, 0.01, 1.18). Then, the alarm module 205 can issue an alarm information according to the data anomaly displacement amount. When the data anomaly displacement amount is less than or equal to a preset allowable displacement amount, the second comparison result is a first-level safety alarm information. When the first-level safety alarm information is issued, the alarm platform can notify the technical personnel to repair the monitoring target object. When the data anomaly displacement amount is greater than the preset allowable displacement amount, it means that the deformation amount of the monitoring target object is large, and there may be a landslide danger. At this time, a second-level safety alarm information is issued to remind the technical personnel that there is a landslide risk on the road. Further, after the second-level safety alarm information is issued, the alarm platform can establish a landslide rate-period trend model according to the data information returned by the monitoring device in real time, predict the landslide time information and landslide deformation information of the road in advance, facilitate the technical personnel to make danger alarm and rescue preparation in advance, and ensure the safety of road personnel.

[0118] From the above, the application provides a road data anomaly alarm device, which acquires the basic point cloud data information of the monitoring target object, then acquires the current point cloud data information and the theoretical point cloud data information of the monitoring target object in real time, the theoretical point cloud data information is used to assist the alarm platform to intelligently judge whether the current point cloud data information acquired exists error, that is, by calculating the difference between the current point cloud data information and the theoretical point cloud data information, the difference is the first comparison result, and the size of the first comparison result and the preset allowable error value is compared, when the first comparison result is less than or equal to the preset allowable error value, the current point cloud data information is taken as the real-time point cloud data information, when the first comparison result is greater than the preset allowable error value, the theoretical point cloud data information is taken as the real-time point cloud data information, the finally obtained real-time point cloud data information is compared with the basic point cloud data information, so as to judge whether the data of the monitoring road section is abnormal, and the data anomaly alarm information is sent, so that the road data anomaly alarm device has the beneficial effects of intelligent correction of monitoring error, high timeliness of alarm.

[0119] Please refer to Figure 3 , Figure 3 The application provides a structure schematic diagram of an electronic device, and provides an electronic device 3, which comprises a processor 301 and a memory 302. The processor 301 and the memory 302 are interconnected and communicate with each other through a communication bus 303 and / or other forms of connection mechanism (not marked). The memory 302 stores computer readable instructions executable by the processor 301. When the electronic device is running, the processor 301 executes the computer readable instructions to execute the method in any optional implementation manner of the above-mentioned embodiments, so as to realize the following functions: acquiring the basic point cloud data information of a monitoring target object; acquiring the current point cloud data information and the theoretical point cloud data information of the monitoring target object in real time; comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, when the first comparison result is less than or equal to a preset allowable error value, taking the current point cloud data information as the real-time point cloud data information, when the first comparison result is greater than the preset allowable error value, taking the theoretical point cloud data information as the real-time point cloud data information; comparing the real-time point cloud data information and the basic point cloud data information to generate a second comparison result; and sending the alarm information of the road data anomaly according to the second comparison result.

[0120] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute the method in any optional implementation manner of the above embodiment, so as to realize the following functions: obtaining basic point cloud data information of a monitoring target object; obtaining current point cloud data information and theoretical point cloud data information of the monitoring target object in real time; comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, when the first comparison result is less than or equal to a preset allowable error value, taking the current point cloud data information as real-time point cloud data information, when the first comparison result is greater than the preset allowable error value, taking the theoretical point cloud data information as real-time point cloud data information; comparing the real-time point cloud data information and the basic point cloud data information to generate a second comparison result; and issuing an alarm information of road data exception according to the second comparison result.

[0121] The computer readable storage medium can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0122] In the embodiments provided by the present application, it should be understood that the disclosed device and method can be implemented in other manners. The described device embodiments are merely schematic, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation; for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0123] In addition, the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0124] Furthermore, each functional module in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0125] In this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations.

[0126] The above is only an embodiment of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A road data abnormality alarm method characterized by comprising: The method comprises the steps of: A1: acquiring basic point cloud data information of a monitoring target object; A2: acquiring current point cloud data information and theoretical point cloud data information of the monitoring target object in real time; A3: comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, taking the current point cloud data information as real-time point cloud data information when the first comparison result is less than or equal to a preset allowable error value, and taking the theoretical point cloud data information as real-time point cloud data information when the first comparison result is greater than the preset allowable error value; A4: comparing the real-time point cloud data information and the basic point cloud data information to generate a second comparison result; The A4 step comprises: acquiring first file size information of the basic point cloud data information and second file size information of the real-time point cloud data information respectively; comparing the first file size information and the second file size information; when the first file size information and the second file size information are the same, the second comparison result is that no data anomaly occurs on the road; when the first file size information and the second file size information are different, the second comparison result is that data anomaly occurs on the road; when the second comparison result is that data anomaly occurs on the road, calculating a data anomaly displacement amount of the data anomaly occurring on the road; The step of calculating the data anomaly displacement amount of the data anomaly occurring on the road comprises: acquiring first coordinate information of the basic point cloud data information in a geodetic coordinate system; acquiring second coordinate information of the real-time point cloud data information in the geodetic coordinate system; calculating the data anomaly displacement amount of the data anomaly occurring on the road according to the first coordinate information and the second coordinate information; comparing the data anomaly displacement amount and a preset allowable displacement amount, taking the second comparison result as first-level safety alarm information when the data anomaly displacement amount is less than or equal to the preset allowable displacement amount; taking the second comparison result as second-level safety alarm information when the data anomaly displacement amount is greater than the preset allowable displacement amount; A5: issuing alarm information of road data anomaly according to the second comparison result.

2. The road data anomaly alerting method of claim 1, wherein The step of acquiring theoretical point cloud data information of the monitoring target object in real time comprises: acquiring historical point cloud data information of the monitoring target object, which is calculated at least according to first angular resolution, first measurement distance information of a historical monitoring device, and first elevation information of the monitoring target object; establishing a point cloud data calculation model according to a calculation process; substituting second angular resolution, current measurement distance information of a current monitoring device of the monitoring target object, and second elevation information of the monitoring target object into the point cloud data calculation model to calculate the theoretical point cloud data information.

3. The road data anomaly alerting method according to claim 2, characterized in that, The step of establishing the point cloud data calculation model according to the calculation process comprises: classifying the monitoring target object; establishing the point cloud data calculation model for engineering structures in the classified monitoring target object.

4. The road data anomaly alerting method of claim 1, wherein The step of setting the second comparison result as the secondary safety alarm information when the data abnormal displacement is greater than the preset allowable displacement comprises: acquiring monitoring time information and geographical parameter information of the monitoring target object; establishing a landslide rate-period trend model according to the monitoring time information and the geographical parameter information; predicting landslide time information and landslide deformation information of the monitoring target object according to the trend model; the secondary safety alarm information comprises the landslide time information and the landslide deformation information.

5. A road data abnormality alarm device characterized by comprising: The device for implementing the method of any one of claims 1-4 comprises: a first acquisition module for acquiring basic point cloud data information of a monitoring target object; a second acquisition module for acquiring current point cloud data information and theoretical point cloud data information of the monitoring target object in real time; a first comparison module for comparing the current point cloud data information and the theoretical point cloud data information to generate a first comparison result, and setting the current point cloud data information as real-time point cloud data information when the first comparison result is less than or equal to a preset allowable error value, and setting the theoretical point cloud data information as the real-time point cloud data information when the first comparison result is greater than the preset allowable error value; a second comparison module for comparing the real-time point cloud data information and the basic point cloud data information to generate a second comparison result; an alarm module for sending alarm information of the road data abnormality according to the second comparison result.

6. An electronic device, comprising: The device comprises a processor and a memory, and the memory stores computer readable instructions, and when the computer readable instructions are executed by the processor, the steps in the method of any one of claims 1-4 are executed.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to execute the steps in the method of any one of claims 1-4.

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