Three-in-one digital data acquisition and processing equipment

Abstract

The present invention provides a three-in-one digital data acquisition and processing device, which belongs to the technical field of image data processing, and specifically comprises: when determining that the data quality of the collected data of a road section meets the requirements by using weather data and the signal disappearance conditions of different types of collected data, determining the alignment processing requirement values ​​and data processing priorities of different road sections based on the total mileage and historical collected data, determining the corresponding deviation conditions of different types of collected data in different time periods by using the collected data, determining the data processing order of different road sections at the corresponding data processing priority based on the corresponding deviation conditions, the alignment processing requirement values ​​and the data quality, determining the data analysis processing order of the collected data of different road sections based on the data processing priority and the data processing order, and determining whether the road section needs to be re-collected and processed according to the data analysis results of the collected data, thereby ensuring the reliability of the collected data of the road section.

Description

Technical Field

[0001] The present invention belongs to the technical field of image data processing, and in particular relates to a three-in-one digital data acquisition and processing device. Background Art

[0002] Highways need to carry out three annual tasks every year, namely, road network condition detection and assessment, basic database review, and infrastructure digitization. Therefore, in order to realize the collection, analysis and processing of data, in the invention patent application CN202311736644.2 "A method, device, equipment and medium for image acquisition based on road conditions", the camera parameters of the image acquisition device are adjusted by adjusting parameters based on road condition images, thereby improving the image quality and accuracy of road condition images, making the image acquisition method more stable, reliable and efficient. However, it is not difficult to find the following technical problems through analysis:

[0003] In the process of collecting and analyzing various types of data, laser point cloud data, GPS positioning data, road ahead image data, etc. are often integrated. Therefore, in the process of data collection and analysis, if different types of data cannot be time-aligned, the accuracy of the statistical analysis results of different types of data cannot be guaranteed.

[0004] In view of the above technical problems, the present invention provides a three-in-one digital data acquisition and processing device. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a three-in-one digital data acquisition and processing device is provided.

[0007] A three-in-one digital data acquisition and processing device, with a base fixed on a support frame of a detection vehicle, the base rigidly connected to a laser scanning measurement system, wherein the laser scanning measurement system hardware integrates a multifunctional road condition detection and laser scanning measurement dual system, specifically including:

[0008] S1: acquiring the collected data of the collection and processing equipment at different road sections, determining the weather data of the collection period corresponding to the collected data, and using the weather data and the signal disappearance of different types of collected data to determine that the data quality of the collected data of the road section meets the requirements, and then proceeding to the next step;

[0009] S2 determines the total mileage of different road sections based on the collected data, and determines the alignment processing requirement values ​​and data processing priorities of different road sections based on the total mileage and historical collected data;

[0010] S3 uses the collected data to determine corresponding deviations of different types of collected data at different time periods, and determines a data processing order of different road sections at corresponding data processing priorities based on the corresponding deviations, alignment processing requirement values, and data quality;

[0011] S4 determines the data analysis and processing order of the collected data of different road sections based on the data processing priority and the data processing order, and determines whether the road section needs to be re-collected and processed according to the data analysis results of the collected data.

[0012] The beneficial effects of the present invention are:

[0013] 1. In the present invention, weather data and the signal disappearance of different types of collected data are used to determine whether the data quality of the collected data of a road section meets the requirements, that is, the influence of abnormal weather data on the image quality of the collected data is taken into account, and the difference in data validity of the collected data of the road section caused by the signal disappearance of the collected data is also taken into account, thereby realizing the screening of the collected data of the road section with abnormal data quality, and also laying a foundation for determining differentiated re-collection processing strategies in different road sections.

[0014] 2. In the present invention, whether a road section needs to be re-collected is determined based on the data analysis results of the collected data, thereby avoiding the technical problem that the collected data of the road section cannot accurately evaluate the road condition data due to abnormalities in data alignment or deviations in data quality, ensuring the reliability and effectiveness of the collected data of the road section, and laying the foundation for further digitization, road condition detection, database review and other tasks.

[0015] 3. In the present invention, field data collection is realized once, and three tasks including digitization, road condition detection and database review are completed simultaneously.

[0016] A further technical solution is that the collected data includes laser point cloud data, GPS positioning data, and road front image data.

[0017] A further technical solution is that the weather data includes light intensity and weather type, wherein the weather type includes snow, rain, cloudy and sunny.

[0018] A further technical solution is that the signal disappearance of the collected data is determined based on the disappearance of the collected signal of the collection and processing device obtained by the cloud, specifically including the number of signal disappearances and the signal disappearance durations for different signal disappearance times.

[0019] A further technical solution is to determine whether a road section needs to be re-collected, which specifically includes:

[0020] Determine alignment processing results of different types of collected data of the road section at different times using the data analysis results of the collected data, determine suspected data deviation moments according to the alignment processing results, and determine the data deviation coefficient of the road section based on the number ratio of suspected data deviation moments;

[0021] Determine a sub-section of the road section with poor collected data based on the data analysis result of the collected data, and determine the data quality deviation coefficient of the road section according to the mileage ratio of the sub-section with poor collected data and the overlapping mileage of the sub-section with poor collected data and the sub-section with poor collected data in the historical collection process;

[0022] The data comprehensive deviation coefficient of the road section is determined by taking the average value of the data deviation coefficient and the data quality deviation coefficient of the road section, and whether the road section needs to be re-collected is determined based on the data comprehensive deviation coefficient.

[0023] A further technical solution is that when the comprehensive deviation coefficient of the data of the road section does not meet the requirements, it is determined that the road section needs to be re-collected and processed.

[0024] A base is fixed on a support frame of the inspection vehicle, and the base is rigidly connected to a laser scanning measurement system, wherein the laser scanning measurement system hardware integrates a multifunctional road condition detection system and a laser scanning measurement dual system.

[0025] A further technical solution is that the laser scanning measurement system includes road condition detection, road front image acquisition, point cloud data acquisition, image data acquisition, and positioning and attitude measurement functions.

[0026] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0027] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings;

[0029] Figure 1 It is a flow chart of a three-in-one digital data acquisition and processing device;

[0030] Figure 2 It is a flow chart for determining whether the data quality of the collected data of the road section meets the requirements;

[0031] Figure 3 is a flow chart of a method for determining an alignment processing requirement value of a road segment;

[0032] Figure 4 The present invention is a flowchart of a method for determining a data processing order of road sections at corresponding data processing priorities. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0034] The "three" in "three-in-one" refers to the three annual tasks currently being carried out in the highway field, namely, highway network condition inspection and assessment, basic database review, and infrastructure digitization.

[0035] (1) Road condition detection and assessment

[0036] Road condition detection uses automated comprehensive road surface detection equipment to detect indicators such as road surface flatness, road damage and road rutting, while also collecting landscape images and GPS information ahead of the road.

[0037] (2) Review of highway basic database

[0038] The highway basic database review is the basic work to find out the status quo of highways, accurately judge the current status and development trend of highways, and scientifically determine the development goals and construction priorities of highways in the future. The highway basic database review work is carried out. The basic database review data collection uses the highway front landscape image and geographic location information collection device of the road surface automatic comprehensive detection equipment.

[0039] "Three-in-one" working mode

[0040] The essence of "three-in-one" digital collection is the three-in-one collection of front-end hardware equipment and the three-in-one fusion of back-end data. One-time collection, multiple generation, and multi-source data fusion are not only the mechanical assembly and integration of the field collection hardware equipment of the three projects, but also interactive and interrelated, with an inherent logical relationship. First, road condition detection is the basic hardware carrier. Through road condition detection, the field collection work of highway basic database review and infrastructure digitization is completed simultaneously; second, the highway basic database review results provide a detailed and reliable basis for the highway network structure for maintenance assessment and infrastructure digitization. Secondly, by completing the information and expanding the attributes of the review result data elements (line tracks, bridges and tunnels, etc.), the digitalization results of infrastructure can be formed. At the same time, the road condition assessment and maintenance data are used as dynamic data of infrastructure digital attributes. In turn, the digitalization results of highway infrastructure can verify the quality of the basic database review.

[0041] By focusing on the digitalization of highway infrastructure and adopting a "three-in-one" collection model of road condition detection, basic database review and infrastructure digital collection, we can achieve high-precision, low-cost collection, reduce the workload of field collection and data processing, effectively reduce promotion costs, and support the digitalization of road network-level infrastructure.

[0042] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a three-in-one digital data acquisition and processing device is provided. A base is fixed on the support frame of the inspection vehicle. The base is rigidly connected to the laser scanning measurement system. The laser scanning measurement system hardware integrates multifunctional road condition detection and laser scanning measurement dual systems, specifically including:

[0043] S1: acquiring the collected data of the collection and processing equipment at different road sections, determining the weather data of the collection period corresponding to the collected data, and using the weather data and the signal disappearance of different types of collected data to determine that the data quality of the collected data of the road section meets the requirements, and then proceeding to the next step;

[0044] Furthermore, the collected data includes laser point cloud data, GPS positioning data, and road front image data.

[0045] It should be noted that the weather data includes light intensity and weather type, wherein the weather type includes snow, rain, cloudy and sunny.

[0046] Furthermore, the signal disappearance condition of the collected data is determined based on the disappearance condition of the collected signal of the collection and processing device obtained by the cloud, specifically including the number of signal disappearances and the signal disappearance durations for different signal disappearance times.

[0047] It is understandable that if Figure 2As shown, determining whether the data quality of the collected data of the road section meets the requirements specifically includes:

[0048] Determine the illumination intensity of the collected data at different time periods by using the weather data, determine the illumination abnormality time period in the time period by using the illumination intensity, and determine the illumination data abnormality value of the road section based on the proportion of the illumination abnormality time period;

[0049] Determine the signal disappearance period based on the signal disappearance situation of different types of collected data, and determine the signal abnormality value of the road section according to the proportion of the signal disappearance period;

[0050] The data quality of the collected data of the road section is determined based on the abnormal values ​​of the illumination data and the abnormal values ​​of the signal, and whether the data quality of the collected data of the road section meets the requirements is determined based on a preset quality threshold.

[0051] Further, when the number of signal disappearance moments of the collected data within a unit time length is greater than a preset number of moments, the unit time length is determined to be a signal disappearance period.

[0052] It should also be noted that the data quality of the collected data of the road section is determined according to the weighted sum of the illumination data abnormal value and the signal abnormal value.

[0053] It can be understood that when the data quality of the collected data of the road section is greater than a preset management threshold, it is determined that the data quality of the collected data of the road section meets the requirements.

[0054] Furthermore, when the data quality of the road section does not meet the requirements, it is determined that the road section needs to be re-collected.

[0055] It should also be noted that determining that the data quality of the collected data of the road section meets the requirements specifically includes:

[0056] Determine the light intensity of the collected data at different time periods by using the weather data, and determine the abnormal light period in the time period by using the light intensity;

[0057] Determine the signal disappearance period based on the signal disappearance situation of different types of collected data;

[0058] The data quality of the collected data of the road section is determined based on the duration of the signal disappearance period and the abnormal lighting period and their duration ratio, and whether the data quality of the collected data of the road section meets the requirements is determined based on a preset quality threshold.

[0059] It should also be noted that determining that the data quality of the collected data of the road section meets the requirements specifically includes:

[0060] S11 determines whether there is a signal disappearance period on the road section based on the signal disappearance conditions of different types of collected data. If yes, proceed to the next step; if no, proceed to step S13;

[0061] S12 determines the signal abnormality value of the road section according to the proportion of the signal disappearance period, and judges whether the signal abnormality value of the road section meets the requirements. If so, enter the step; if not, determine that the data quality of the collected data of the road section does not meet the requirements;

[0062] S13: using the weather data to determine the light intensity of the collected data at different time periods, and using the light intensity to determine whether there is an abnormal light period on the road section, if yes, proceeding to the next step, if no, determining that the data quality of the collected data on the road section does not meet the requirements, and using the preset data quality to determine the data quality of the collected data on the road section;

[0063] S14 determines whether the sum of the duration of the abnormal illumination period and the duration of the signal disappearance period meets the requirement, if so, proceeds to the next step, if not, determines that the data quality of the collected data of the road section does not meet the requirement;

[0064] S15 determines the abnormal value of the illumination data according to the illumination intensity and duration of the collected data of different abnormal illumination periods, and judges whether the abnormal value of the illumination data meets the requirements. If so, proceeds to the next step; if not, determines that the data quality of the collected data of the road section does not meet the requirements;

[0065] S16 determines the validity of the illumination intensity of the collected data based on the illumination intensity of the collected data at different times and the abnormal value of the illumination data, determines the data quality of the collected data of the road section in combination with the signal abnormal value, and determines whether the data quality of the collected data of the road section meets the requirements based on a preset quality threshold.

[0066] S2 determines the total mileage of different road sections based on the collected data, and determines the alignment processing requirement values ​​and data processing priorities of different road sections based on the total mileage and historical collected data;

[0067] Furthermore, the historical collection data is determined based on the reading result of the data record of the historical collection data of the road section, specifically including the historical collection time, collection mileage and sub-section with poor collection data of the road section.

[0068] It can be understood that the sub-section with poor collected data is a sub-section in the section where collected data is lost or image quality is poor.

[0069] Specifically, Figure 3 As shown, the method for determining the alignment processing requirement value of the road section is:

[0070] Determining a mileage processing requirement value of the road section based on the total mileage of the road section;

[0071] Acquire historical acquisition data of the road section, and determine sub-sections of the road section with poor image quality based on the historical acquisition data, and determine the historical image acquisition quality of the road section by using the number of sub-sections of the road section with poor image quality;

[0072] The alignment processing requirement value of the road section is determined by utilizing the historical image acquisition quality and the mileage processing requirement value.

[0073] Furthermore, the data processing priority is determined according to a preset priority corresponding to the alignment processing requirement value of the road section.

[0074] It should be noted that the data processing priority includes a first priority, a second priority and a third priority, wherein the first priority is greater than the second priority, and the second priority is greater than the third priority.

[0075] It can also be understood that the method for determining the alignment processing requirement value of the road section is:

[0076] Determining a preset processing demand value corresponding to the road section based on the total mileage of the road section;

[0077] Acquire historical collection data of the road section, and determine sub-sections of the road section with poor image quality based on the historical collection data, and use the sub-sections of the road section with poor image quality to determine the mileage ratio of the sub-sections with poor image quality;

[0078] The alignment processing requirement value of the road section is determined by multiplying the mileage ratio of the sub-road section with poor image and a preset processing requirement value.

[0079] S3 uses the collected data to determine corresponding deviations of different types of collected data at different time periods, and determines a data processing order of different road sections at corresponding data processing priorities based on the corresponding deviations, alignment processing requirement values, and data quality;

[0080] Further, such as Figure 4 As shown, the method for determining the data processing order of the road section at the corresponding data processing priority is:

[0081] Determine the time deviation period of the road section according to the alignment deviation of different types of collected data at different time periods, and determine the difficulty of alignment processing of the road section according to the proportion of the number of the time deviation periods;

[0082] Based on the alignment processing difficulty, the alignment processing requirement value and the data quality, determine the weight sum of the alignment processing difficulty, the alignment processing requirement value and the data quality of the road section, and use it as the processing priority weight value;

[0083] The data processing order of the road section at the corresponding data processing priority level is determined according to the processing priority weight value.

[0084] It should also be noted that the method for determining the data processing order of the road sections at the corresponding data processing priorities is as follows:

[0085] S21: obtaining the data quality of the collected data of the road section, determining whether the data quality of the collected data of the road section is within a preset quality interval, if yes, dividing the road section into a first road section group, and determining the data processing order of the road section at the corresponding data processing priority by using the data quality of the collected data of the road section, if no, proceeding to the next step;

[0086] S22 determines whether there is a time deviation period for the road section based on the alignment deviation of different types of collected data at different time periods. If yes, proceed to the next step; if no, proceed to step S26;

[0087] S23 determines whether the total mileage of the road section corresponding to the time deviation period of the road section is less than the preset mileage threshold, if so, proceeds to step S25, if not, proceeds to the next step;

[0088] S24: determining the alignment processing difficulty of the road section by using the number of the time deviation periods and the total mileage of the road section corresponding to the time deviation period, judging whether the alignment processing difficulty of the road section meets the requirement, if so, proceeding to the next step, if not, dividing the road section into a second road section group, and determining the data processing order of the road section in the corresponding data processing priority by using the alignment processing difficulty of the road section;

[0089] S25 determines the time period deviation amounts of different time period deviations according to the total mileage of the road sections corresponding to the different time period deviations and the number of types of collected data with time deviations, and determines whether there is a time period deviation amount that does not meet the requirements. If so, the road section is divided into a third road section group, and the number of time period deviation amounts of the road section that does not meet the requirements is used to determine the data processing order of the road section at the corresponding data processing priority level;

[0090] S26 determines the weight and sum of the alignment processing difficulty, alignment processing requirement value and data quality of the road section based on the alignment processing difficulty, alignment processing requirement value and data quality, and uses it as the processing priority weight value, and determines the data processing order of the road section at the corresponding data processing priority according to the processing priority weight value.

[0091] Further, the data processing order of the road section at the corresponding data processing priority is determined by using the number of time deviation time periods in which the time deviation amount of the road section does not meet the requirement, specifically including:

[0092] The first processing order of the road section is determined by using the road section group in which the road section is located, and the processing order of the road section in the road section group is determined according to the number of time deviation time periods in which the time period deviation amount of the road section does not meet the requirements.

[0093] S4 determines the data analysis and processing order of the collected data of different road sections based on the data processing priority and the data processing order, and determines whether the road section needs to be re-collected and processed according to the data analysis results of the collected data.

[0094] Specifically, determining whether a road section needs to be re-collected includes:

[0095] Determine alignment processing results of different types of collected data of the road section at different times using the data analysis results of the collected data, determine suspected data deviation moments according to the alignment processing results, and determine the data deviation coefficient of the road section based on the number ratio of suspected data deviation moments;

[0096] Determine a sub-section of the road section with poor collected data based on the data analysis result of the collected data, and determine the data quality deviation coefficient of the road section according to the mileage ratio of the sub-section with poor collected data and the overlapping mileage of the sub-section with poor collected data and the sub-section with poor collected data in the historical collection process;

[0097] The data comprehensive deviation coefficient of the road section is determined by taking the average value of the data deviation coefficient and the data quality deviation coefficient of the road section, and whether the road section needs to be re-collected is determined based on the data comprehensive deviation coefficient.

[0098] Furthermore, when the comprehensive deviation coefficient of the data of the road section does not meet the requirements, it is determined that the road section needs to be re-collected.

[0099] It should also be noted that determining whether a road section needs to be re-collected includes:

[0100] S11 uses the data analysis results of the collected data to determine the alignment processing results of different types of collected data of the road section at different times, and determines whether there is a suspected data deviation moment in the road section according to the alignment processing results. If yes, proceed to the next step, if not, proceed to step S13;

[0101] S12 determines the data deviation coefficient of the road section based on the number ratio of the suspected data deviation moments, and judges whether the data deviation coefficient of the road section meets the requirements. If so, proceed to the next step; if not, determine that the road section needs to be re-collected;

[0102] S13 determines whether there is a sub-section with poor collected data in the section based on the data analysis result of the collected data, if yes, proceeds to the next step, if no, determines that the section does not need to be re-collected;

[0103] S14 determines whether the mileage ratio of the sub-section with poor data collection meets the requirement, if so, proceeds to the next step, if not, determines that the section needs to be re-collected;

[0104] S15 determines the data quality deviation coefficient of the road section based on the mileage ratio of the sub-section with poor data collection and the overlapping mileage of the sub-section with poor data collection and the sub-section with poor data collection in the historical collection process, and judges whether the data quality deviation coefficient of the road section meets the requirements. If so, proceed to the next step; if not, determine that the road section needs to be re-collected;

[0105] S16 determines the data comprehensive deviation coefficient of the road section based on the average value of the data deviation coefficient and the data quality deviation coefficient of the road section, and determines whether the road section needs to be re-collected based on the data comprehensive deviation coefficient.

[0106] A further technical solution is that the laser scanning measurement system includes road condition detection, road front image acquisition, point cloud data acquisition, image data acquisition, and positioning and attitude measurement functions.

[0107] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0108] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A three-in-one digital data acquisition and processing device, with a base fixed on a support frame of a detection vehicle, the base being rigidly connected to a laser scanning measurement system, wherein the laser scanning measurement system hardware integrates a multifunctional road condition detection and laser scanning measurement dual system, characterized in that: Specifically include: Acquire the collected data of the collection and processing equipment at different road sections, determine the weather data of the collection period corresponding to the collected data, and use the weather data and the signal disappearance of different types of collected data to determine that the data quality of the collected data of the road section meets the requirements, and then proceed to the next step; Determine the total mileage of different road sections based on the collected data, and determine the alignment processing requirement values ​​and data processing priorities of different road sections based on the total mileage and historical collected data; Determine corresponding deviations of different types of collected data at different time periods using the collected data, and determine a data processing order of different road sections at corresponding data processing priorities based on the corresponding deviations, alignment processing requirement values, and data quality; Determine the data analysis and processing order of the collected data of different road sections based on the data processing priority and the data processing order, and determine whether the road section needs to be re-collected and processed according to the data analysis results of the collected data; The method for determining the alignment processing requirement value of the road section is: Determining a mileage processing requirement value of the road section based on the total mileage of the road section; Acquire historical acquisition data of the road section, and determine sub-sections of the road section with poor image quality based on the historical acquisition data, and determine the historical image acquisition quality of the road section by using the number of sub-sections of the road section with poor image quality; Determine the alignment processing requirement value of the road section by using the historical image acquisition quality and the mileage processing requirement value; The data processing priority is determined according to a preset priority corresponding to the alignment processing requirement value of the road section; The data processing priority includes a first priority, a second priority and a third priority, wherein the first priority is greater than the second priority, and the second priority is greater than the third priority; Determine whether the road section needs to be re-collected, including: Determine alignment processing results of different types of collected data of the road section at different times using the data analysis results of the collected data, determine suspected data deviation moments according to the alignment processing results, and determine the data deviation coefficient of the road section based on the number ratio of suspected data deviation moments; Determine a sub-section of the road section with poor collected data based on the data analysis result of the collected data, and determine the data quality deviation coefficient of the road section according to the mileage ratio of the sub-section with poor collected data and the overlapping mileage of the sub-section with poor collected data and the sub-section with poor collected data in the historical collection process; The data comprehensive deviation coefficient of the road section is determined by taking the average value of the data deviation coefficient and the data quality deviation coefficient of the road section, and whether the road section needs to be re-collected is determined based on the data comprehensive deviation coefficient.

2. The three-in-one digital data acquisition and processing device according to claim 1, characterized in that: The collected data includes laser point cloud data, GPS positioning data, and road front image data.

3. The three-in-one digital data acquisition and processing device according to claim 1, characterized in that: The weather data includes light intensity and weather type, wherein the weather type includes snow, rain, cloudy and sunny.

4. The three-in-one digital data acquisition and processing device according to claim 1, characterized in that: Determining that the data quality of the collected data of the road section meets the requirements specifically includes: Determine the illumination intensity of the collected data at different time periods by using the weather data, determine the illumination abnormality time period in the time period by using the illumination intensity, and determine the illumination data abnormality value of the road section based on the proportion of the illumination abnormality time period; Determine the signal disappearance period based on the signal disappearance situation of different types of collected data, and determine the signal abnormality value of the road section according to the proportion of the signal disappearance period; The data quality of the collected data of the road section is determined based on the abnormal values ​​of the illumination data and the abnormal values ​​of the signal, and whether the data quality of the collected data of the road section meets the requirements is determined based on a preset quality threshold.

5. The three-in-one digital data acquisition and processing device according to claim 4, characterized in that: When the number of signal disappearance moments of the collected data within a unit time length is greater than the preset number of moments, the unit time length is determined to be a signal disappearance period.

6. The three-in-one digital data acquisition and processing device according to claim 1, characterized in that: The historical collection data is determined based on the reading result of the data record of the historical collection data of the road section, and specifically includes the historical collection time, collection mileage and sub-section with poor collection data of the road section.

7. The three-in-one digital data acquisition and processing device according to claim 1, characterized in that: The laser scanning measurement system includes road condition detection, road front image acquisition, point cloud data acquisition, image data acquisition, and positioning and attitude measurement functions.

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