Intelligent evaluation method and system of road quality based on smooth road index

Abstract

The present invention discloses a road quality intelligent evaluation method and system based on the Smooth Road Index. The method comprises: obtaining collected data of urban roads to be evaluated; dividing the urban roads to be evaluated into different evaluation units; obtaining health status indicators according to the collected data, and calculating the Smooth Road Index corresponding to different evaluation units according to the health status indicators, wherein the health status indicators at least include road damage parameters and bump impact parameters; dividing according to the Smooth Road Index corresponding to different evaluation units to obtain at least a multi-color evaluation map; and outputting the at least multi-color evaluation map. By implementing the method of the present invention, the scientificity and accuracy of road health evaluation can be improved, and the defects of traditional evaluation methods in terms of low coverage frequency and untimely update can be effectively compensated.

Description

Technical Field

[0001] The present invention relates to a road quality evaluation method, and more specifically to an intelligent road quality evaluation method and system based on a smooth road index. Background Art

[0002] With the rapid development of urban economy and the continuous expansion of population size, the number of cars per capita has increased dramatically, which has brought tremendous pressure to municipal roads. In order to ensure the service performance of roads, maintaining their good condition has become the top priority of road maintenance. Accurate assessment of road health is the key decision-making basis for effective road maintenance.

[0003] At present, road health assessment mainly relies on two indicators: RQI (Road Quality Index) and PCI (Pavement Condition Index). These two assessment methods each focus on specific assessment areas. For example, RQI focuses on the smoothness and driving comfort of the road surface, reflecting the bumps during driving; PCI focuses on the degree of damage to the road surface and assesses the damage of the road.

[0004] However, both have obvious limitations. RQI and PCI each only consider one aspect, lack comprehensiveness, and have a narrow assessment scope. These two methods are usually inspected once a year, and the updates are not timely, and they cannot reflect the real-time status of the road. In addition, professional equipment is required for inspection, which increases maintenance costs.

[0005] Therefore, it is necessary to design a new method to improve the scientificity and accuracy of road health assessment, and effectively make up for the shortcomings of traditional assessment methods such as low coverage frequency and untimely updates. Summary of the invention

[0006] The purpose of the present invention is to overcome the defects of the prior art and provide a road quality intelligent evaluation method and system based on the smooth road index.

[0007] To achieve the above object, the present invention adopts the following technical solution: a road quality intelligent evaluation method based on the smooth road index, comprising:

[0008] Obtaining collected data of the urban roads to be evaluated;

[0009] Divide the urban roads to be evaluated into different evaluation units;

[0010] Acquire a health status index according to the collected data, and calculate a smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index at least includes a road damage parameter and a bump impact parameter;

[0011] Divide according to the smooth road index corresponding to different evaluation units to obtain at least a multi-color evaluation map;

[0012] The at least multi-color evaluation map is output.

[0013] Its further technical solution is: the evaluation units include grid points, road units, roads, sections and regions; wherein, the road units include several grid points; the roads include several road units integrated based on road names; the sections include all road units involved after the division according to the project construction sections; the regions include all road units involved based on the urban areas.

[0014] A further technical solution is: obtaining health status indicators according to the collected data, and calculating the smooth road index corresponding to different evaluation units according to the health status indicators, including:

[0015] Acquire a health status indicator corresponding to a single grid according to the collected data;

[0016] Determine the health status index corresponding to the road unit according to the health status index corresponding to the single grid point combined with the internal factors, wherein the internal factors include road maintenance information, road grade, and road age;

[0017] Calculate the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit;

[0018] According to the smooth road index corresponding to the road unit, the smooth road index corresponding to the road, section and area is calculated respectively to obtain the smooth road index corresponding to different evaluation units.

[0019] A further technical solution is: the step of obtaining the health status indicator corresponding to a single grid according to the collected data includes:

[0020] Applying a target recognition model and a depth estimation model to the collected data, combined with camera parameters, to determine the type of damage, the number of damages, and the area of ​​damages to obtain road damage parameters;

[0021] The collected data is analyzed, and a mathematical model is constructed to identify high-frequency components. The mathematical model is used to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain the impact strength.

[0022] Its further technical solution is: the road damage parameters also include a disease area coefficient, the disease area coefficient is set by the disease type according to different disease severity levels and determined by the maintenance ratio; the bumpy impact parameters also include a bumpy impact force coefficient; the bumpy impact force coefficient is adjusted according to the road damage parameters.

[0023] A further technical solution is: the method of determining the health status index corresponding to the road unit according to the health status index corresponding to the single grid point combined with the internal factors includes:

[0024] The health status index corresponding to each grid point in the road unit is accumulated to obtain the health status index corresponding to the road unit.

[0025] A further technical solution is: the calculation of the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit includes:

[0026] Calculate a score factor according to the health status index corresponding to the road unit;

[0027] The corresponding smooth road index is calculated according to the score factor and the set road grade weight coefficient and empirical parameters.

[0028] A further technical solution is: the smoothness indexes corresponding to the road, section and region are calculated according to the smoothness index corresponding to the road unit to obtain the smoothness indexes corresponding to different evaluation units, including:

[0029] Accumulating the smoothness indexes and corresponding color coefficients corresponding to the road units involved in the road, section and area, and combining the smoothness indexes corresponding to the road units to obtain the smoothness indexes corresponding to different evaluation units;

[0030] The color coefficient refers to the coefficient corresponding to the serious unevenness of the road, the presence of ups and downs and disease points, and the good road condition in the road unit.

[0031] A further technical solution is that the at least multi-color evaluation map includes images showing the unevenness of the entire road section, individual serious disease points on the road, and good road conditions using different colors.

[0032] The present invention also provides a road quality intelligent evaluation device based on the smooth road index, comprising:

[0033] A data acquisition unit, used to acquire collected data of the urban road to be evaluated;

[0034] An evaluation unit division unit is used to divide the urban roads to be evaluated into different evaluation units;

[0035] An index calculation unit, used to obtain a health status index according to the collected data, and calculate a smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index includes a road damage parameter and a bump impact parameter;

[0036] A division unit, used for dividing according to the smooth road index corresponding to different evaluation units to obtain at least a multi-color evaluation map;

[0037] An output unit is used to output the at least multi-color evaluation diagram.

[0038] Compared with the prior art, the present invention has the following beneficial effects: the present invention collects data of urban roads to be evaluated to ensure the comprehensiveness and accuracy of information, so as to improve the scientific nature of health status indicators; divides urban roads into multiple evaluation units, so as to be able to more carefully analyze and monitor road conditions in different areas, and increase the coverage frequency of evaluation; based on the collected health status indicators such as road damage parameters and bump impact parameters, accurately calculates the smooth road index of each evaluation unit, and improves the credibility of the evaluation results; presents the smooth road index of different evaluation units in the form of multi-color evaluation diagrams, so that the road health status is clear at a glance, and it is convenient to quickly identify problem areas; realizes dynamic updating of data, ensures that the evaluation results reflect the latest road conditions, and thus effectively overcomes the defects of traditional methods in that they are not updated in a timely manner.

[0039] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0041] Figure 1 A schematic diagram of a process flow of a road quality intelligent evaluation method based on a smooth road index provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a sub-process of a road quality intelligent evaluation method based on a smooth road index provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a sub-process of a road quality intelligent evaluation method based on a smooth road index provided in an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of a sub-process of a road quality intelligent evaluation method based on a smooth road index provided in an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of a grid point provided by an embodiment of the present invention;

[0046] Figure 6 A schematic diagram of an evaluation map corresponding to a road section composed of road units provided in an embodiment of the present invention;

[0047] Figure 7A schematic block diagram of a road quality intelligent evaluation device based on a smooth road index provided by an embodiment of the present invention;

[0048] Figure 8 A schematic block diagram of an index calculation unit of a road quality intelligent evaluation device based on a smooth road index provided by an embodiment of the present invention;

[0049] Fig. 9 A schematic block diagram of a grid health status index acquisition subunit of a road quality intelligent evaluation device based on a smooth road index provided by an embodiment of the present invention;

[0050] Fig.10 A schematic block diagram of a road unit health status index acquisition subunit of a road quality intelligent evaluation device based on a smooth road index provided by an embodiment of the present invention;

[0051] Fig.11 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0054] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0055] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0056] Existing road health assessments mainly rely on the Ride Quality Index (RQI) and the Pavement Condition Index (PCI), which focus on the smoothness and comfort of the road surface and the degree of road damage, respectively. However, the RQI and PCI have a narrow assessment scope, lack comprehensiveness, and only consider a single aspect. They are usually tested once a year, are not updated in a timely manner, and cannot reflect the real-time status of the road. The need for specialized equipment also increases maintenance costs.

[0057] To this end, an embodiment of the present invention provides an intelligent road quality evaluation method based on the Smooth Road Index, which comprehensively considers road bumps and road damage, and divides the evaluation units into grid points-road units-roads-sections-regions. The classification range is increased, and the evaluation range is wider and more unified. Since the grid point is the smallest unit, the update of the grid point will drive the update of a larger range, so as to obtain evaluations of different ranges, and the Smooth Road Index is calculated based on real-time data to evaluate the road quality. The most accurate data can be fed back at any time, thereby improving the scientificity and accuracy of road health assessment, and effectively making up for the shortcomings of traditional assessment methods in terms of low coverage frequency and untimely updates.

[0058] Specifically, see Figure 1 , Figure 1 The figure is a flow chart of a method for intelligently evaluating road quality based on the smooth road index provided by an embodiment of the present invention. Figure 1 As shown, the method includes the following steps S110 to S150.

[0059] S110: Acquire collected data of the urban roads to be evaluated.

[0060] In this embodiment, the collected data includes real-time collected road damage image data, road facility data, road bump and vibration data and other related data of the urban roads to be evaluated. Of course, it also includes non-real-time collected road damage image data, road facility data, road bump and vibration data and other related data of the urban roads to be evaluated.

[0061] Specifically, the smart platform system intelligently assigns inspection tasks to inspection vehicles based on the needs of road maintenance. These tasks usually include designated road sections for inspection, inspection frequency, and the type of data to be collected; the inspection vehicles are equipped with advanced inspection equipment, including high-definition cameras, sensors, and data recorders. These devices can comprehensively collect road status information, such as image data of road diseases (such as cracks, potholes, etc.), status data of road facilities (such as traffic signs, street lights, etc.), and real-time monitoring data of road bumps and vibrations. During the inspection process, the inspection equipment will collect various information on the road surface in real time. When the vehicle travels along the designated route, the equipment will automatically capture images and record data to ensure that the collected content is comprehensive and accurate. After the inspection is completed, the inspection equipment will upload the collected data to the smart platform system in real time via the wireless network. This process ensures the timeliness and effectiveness of the data, allowing managers to quickly obtain the latest road condition information.

[0062] S120. Divide the urban roads to be evaluated into different evaluation units.

[0063] In this embodiment, the evaluation units include grid points, road units, roads, sections and regions; wherein the road units include a number of grid points; the roads include a number of road units integrated based on road names; the sections include all road units involved after the division according to the project construction sections; the regions include all road units involved based on the urban areas.

[0064] In this embodiment, the grid point is the smallest unit for data collection, and a 3m x 3m square grid is used to divide the urban roads. Each grid point can accurately record specific information in the area, such as road conditions, facility locations, etc. This detailed division can improve the accuracy of data collection and facilitate subsequent analysis and processing.

[0065] Road units are linear data units formed by dividing urban roads into 200-meter sections. Each 200-meter section constitutes a complete road unit; if a road section is less than 200 meters, it is divided into a separate unit. This division method ensures that the status information of each road section can be fully recorded, making it easier to analyze and manage specific road sections.

[0066] The road as a whole is defined by the road name. Each road includes all road units within its scope. For example, a road named "Sunshine Avenue" will include all road units along the route with a length of 200 meters. This division ensures that the maintenance and management of the entire road can be handled centrally, improving management efficiency.

[0067] The bidding section is divided based on the construction section of the project. Each bidding section contains all relevant road units within its scope. This management method facilitates the unified management and monitoring of various road units in the construction area during road maintenance or construction, ensuring that data collection and situation feedback during the construction process can be timely and effective.

[0068] The division of regions is based on the administrative or geographical areas of the city. Each region includes all road units within its scope. Such division facilitates macro-urban road management and planning, enables effective monitoring and coordination of the situation of each road unit at the regional level, and provides a basis for policy formulation and resource allocation.

[0069] Through the above division method, from grid points to regions, each level has its specific function and significance. This structured data collection and management method not only improves the accuracy and efficiency of urban road management, but also provides a good foundation for data analysis, decision support and daily maintenance.

[0070] S130, obtaining a health status index according to the collected data, and calculating a smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index at least includes a road damage parameter and a bump impact parameter.

[0071] In this embodiment, the smoothness index is a comprehensive indicator for evaluating the health of roads, which mainly considers factors such as road driving comfort, disease monitoring, bumps, and road service life. By quantifying these influencing factors, the smoothness index can reflect the smoothness and safety of roads, providing a scientific basis for road maintenance and management. The smoothness index is a percentage score.

[0072] Road damage parameters are mainly used to quantify the damage of the road surface. Common forms of damage include:

[0073] Manhole cover: A manhole cover is a cover plate used to cover underground wells (such as drainage wells, communication wells, etc.) on urban roads. If the height of the manhole cover is not flush with the road surface, or the road surface around the manhole cover settles, it may cause unstable driving and even cause danger to pedestrians and vehicles.

[0074] Line cracks: Line cracks are long and thin cracks that appear on asphalt concrete pavement, usually in a straight line. Line cracks are mostly caused by factors such as temperature changes and traffic loads that cause asphalt concrete materials to shrink or expand. Although line cracks are relatively small, they may further develop into larger cracks if not treated in time.

[0075] Web cracks: Web cracks refer to cracks that appear in a grid pattern on the road surface, usually due to failure of the road structure, material aging or temperature changes. Web cracks usually affect the overall performance of the road surface and increase the bumpy feeling of driving.

[0076] Cracking: Cracking refers to cracks formed on the pavement surface, which are shaped and arranged like a turtle shell, usually caused by temperature changes and moisture intrusion. Such cracks not only affect the appearance, but may also lead to the peeling of pavement materials and structural damage.

[0077] Bumping: Bumping refers to the phenomenon of local bulges on the road surface, which is often caused by factors such as uneven settlement of the foundation, aging of the asphalt layer or the influence of traffic loads. Bumping will cause vibration when the vehicle is driving, affecting driving safety.

[0078] Potholes: Potholes are depressions formed on the road surface, usually caused by roadbed settlement, traffic loads or water soaking. Potholes pose a serious threat to driving safety and may cause vehicle damage or traffic accidents.

[0079] Spalling: Spalling refers to the phenomenon that the surface layer of the pavement material falls off due to aging, erosion or other external factors. This situation will reduce the strength and durability of the pavement, and affect the comfort and safety of driving.

[0080] These diseases are important factors affecting road performance and safety. Timely monitoring and maintenance can effectively extend the service life of roads and ensure traffic safety.

[0081] These damage conditions can be obtained through on-site detection, sensor monitoring, etc. The specific parameters include:

[0082] Disease types: manhole covers, linear cracks, web cracks, cracks, lumps, potholes and peeling.

[0083] Disease quantity: the quantity corresponding to the disease type.

[0084] Damaged area: the proportion of damaged area to the road surface;

[0085] Disease area coefficient: set by disease type according to different disease severity levels and determined by maintenance ratio.

[0086] In this embodiment, according to the actual needs of road maintenance, the diseases can be divided into three levels according to the severity: level one, level two and level three. Each level corresponds to a different disease area coefficient, reflecting the degree of influence of different types of diseases on road health; specifically, level one diseases: the severity of such diseases is relatively low, usually referring to diseases with a large number but small impact, such as loose manhole covers, linear cracks, etc. Although the number is large, the harm to road health is small, so the proportion and corresponding area coefficient required for maintenance are low; level two diseases: such diseases have a more significant impact on the road, although their harm is lower than level one diseases, they still need regular maintenance and monitoring. For example, moderate bulging or partial cracks. The maintenance ratio and disease area coefficient of such diseases are between level one and level three; level three diseases: such diseases are extremely harmful to the health and safety of the road, usually including large areas of potholes, severe bulging, etc. Such diseases need to be treated first and usually occupy a larger disease area coefficient.

[0087] The disease area coefficient is an indicator used to quantify the impact of diseases on road health, usually determined by the maintenance ratio of the actual maintenance unit.

[0088] Key maintenance damage: For damages with a severity level of three (such as potholes, bumps, etc.), because they pose a greater threat to road health, the maintenance ratio is high, and the corresponding damage area coefficient is also large. Ensure timely repairs to reduce accident risks and improve road safety.

[0089] Large number of defects: For defects with a severity level of one (such as manhole covers, cracks, etc.), although there are a large number of them, since the harm of a single defect to the road is relatively small, their maintenance ratio is relatively high, resulting in a large corresponding defect area coefficient. This shows that within a certain period of time, these defects require more maintenance work, but because their impact is relatively low, they are usually not given priority.

[0090] Medium impact diseases: Level 2 diseases are usually moderate in maintenance, and their disease area coefficient is between level 1 and level 3. The maintenance ratio is determined based on actual conditions.

[0091] By classifying diseases and determining their severity levels and disease area coefficients, maintenance units can give priority to dealing with diseases with high severity (level three) to ensure road safety; rationally allocate maintenance resources to ensure that maintenance work is carried out on diseases with the greatest impact; and regularly monitor diseases of different levels to adjust maintenance strategies in a timely manner to ensure the overall health of the road.

[0092] Bump impact parameters are used to evaluate the impact of roads on vehicle driving, especially the impact caused by driving on uneven roads. It usually involves impact intensity, bump impact force coefficient, and bump times, among which impact intensity refers to the magnitude of instantaneous impact force caused by unevenness or road defects (such as potholes, cracks, etc.) when a vehicle is driving on the road.

[0093] In addition, the bump impact force coefficient is adjusted according to the road damage parameter. Specifically, the bump impact force coefficient is used to quantify the intensity of bumps and impacts suffered by the vehicle during driving. This coefficient is usually closely related to factors such as the flatness, damage type, and damage degree of the road. The bump impact force coefficient is adjusted by the following steps.

[0094] Based on the collected road damage parameters, the bump impact coefficient is preliminarily calculated. The following model can be used: [K=K0+alpha*D+beta*C], where K is the adjusted bump impact coefficient; K0 is the bump impact coefficient under ideal conditions (no damage), D is the quantitative value of the road surface disease degree (such as disease area or severity level); C is the influencing factor of vehicle driving conditions (such as speed, load, etc.); alpha and beta are empirical coefficients obtained through experiments.

[0095] Adjustments are made based on the calculated initial bumpy impact force coefficient: when the road damage level is high, the bumpy impact force coefficient is increased to reflect the deterioration of driving conditions; if the road conditions improve after repairs or maintenance, the bumpy impact force coefficient is reduced accordingly.

[0096] In addition, a dynamic monitoring system can be established to monitor road conditions in real time through road surface sensors and data acquisition equipment, and input the data into the calculation model of the bump impact force coefficient:

[0097] The bump impact coefficient is automatically adjusted based on the road status data collected in real time to ensure the timeliness and accuracy of the value. The effectiveness of the bump impact coefficient is evaluated through vehicle driving data and user feedback, and the parameters in the model are continuously adjusted.

[0098] Use machine learning technology to analyze historical data and find the relationship between bump impact coefficient and road damage parameters to improve prediction accuracy;

[0099] The influence of different variables on the bump impact coefficient is further analyzed through the multivariate regression model.

[0100] The adjusted bump impact force coefficient is applied to road maintenance decisions and verified through actual road driving tests to ensure the effectiveness and accuracy of the model.

[0101] The bump impact coefficient can be effectively adjusted according to the road damage parameters. This adjustment can not only improve the road management efficiency, but also improve driving comfort and safety.

[0102] In one embodiment, see Figure 2 The above-mentioned step S130 may include steps S131~S134.

[0103] S131. Obtain a health status indicator corresponding to a single grid according to the collected data.

[0104] In one embodiment, see Figure 3 , the above-mentioned step S131 may include steps S1311~S1312.

[0105] S1311. Apply a target recognition model and a depth estimation model to the collected data, and determine the type of damage, the number of damages, and the area of ​​damage in combination with camera parameters to obtain road damage parameters.

[0106] In this embodiment, in actual application, the collected data is deduplicated to prevent the influence of duplicate data on the analysis results. Next, the road surface image to be evaluated is processed using the trained semantic segmentation network to segment the areas of different road surface elements. This process can identify road surface elements such as manhole covers, linear cracks, network cracks, cracks, bumps, potholes, and peeling. Each element is assigned a mask to form a corresponding segmentation result. The image in the collected data is input into the target recognition model to detect the location and type of each road surface element. At this point, the model can identify different objects on the road surface and return the location information and category label of each object. The depth estimation model is then used to obtain the depth value of each pixel in the image. This process includes the following steps:

[0107] Preprocessing: Perform channel conversion, size adjustment, tensor conversion, etc. on the input image to meet the input requirements of the deep model.

[0108] Feature extraction and depth map generation: The encoder and decoder of the depth estimation model generate a depth map for each pixel. The depth map shows the distance of each point from the camera.

[0109] Next, the generated depth map is overlapped and matched with the road surface feature position information obtained by target detection and segmentation, the area overlapping with the road surface features is screened out, and the corresponding depth value is extracted.

[0110] The actual area of ​​each road feature is calculated using the depth value in combination with the focal length, photosensitive element size, and pixel size of the camera. The calculation formula is as follows: Area = (d÷f) × s × Depth value corresponding to the pixel of the road feature, where f is the focal length; s is the photosensitive element size; and d is the pixel size.

[0111] Finally, by analyzing the inspection results and depth information, the types of defects (such as cracks, potholes, etc.) and their quantity are identified. The defect detection model can classify and count the defects in the image.

[0112] It is also possible to integrate the type, quantity and area of ​​the defects into the road surface image to be evaluated, so that users can intuitively view the distribution and detailed parameters of the road surface defects. For example, the name of the defect and its calculated area can be marked in the image.

[0113] The road damage parameters can be obtained by using the disease parameters obtained by the above method.

[0114] S1312, analyzing the collected data, and constructing a mathematical model to identify high-frequency components, and using the mathematical model to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain impact strength.

[0115] In this embodiment, the impact strength refers to reading the vibration sensor data during the vehicle's travel, extracting the high-frequency abnormal data in the vibration data, and the high-frequency component is related to the bumps and vibrations of the road surface. By calculating the root mean square value of the high-frequency part of the frequency data The result obtained, where RMShigh refers to the root mean square value of the high-frequency frequency data, v i refers to the vehicle speed; a i Refers to the vehicle's vertical acceleration information; θ i Refers to the horizontal attitude angle of the vehicle; N is the number of data points; x i represents the road elevation; b is the damping coefficient.

[0116] Specifically, the collected data includes vibration sensor data, GPS data, vehicle horizontal attitude angle, vehicle vertical acceleration information and magnetic field information. The vibration sensor is used to detect the vibration of the vehicle during driving. It can record various vibrations to the vehicle, including bumps from the road. GPS data is obtained through the global positioning system (GPS). It provides the specific location information of the vehicle on the earth, including longitude, latitude and altitude, which is used to locate the geographical location of the vehicle. The vehicle horizontal attitude angle refers to the tilt angle of the vehicle relative to the horizontal plane, also known as the yaw angle. This angle can reflect the tilt of the vehicle during driving, such as the tilt angle when the vehicle turns. The vehicle vertical acceleration information is the acceleration data of the vehicle in the vertical direction. It can reflect the movement state of the vehicle in the vertical direction, including acceleration, deceleration and external vibration. Magnetic field information refers to the magnetic field strength data around the vehicle. These data can be used for navigation or environmental perception, such as determining whether the vehicle is in a specific magnetic field area.

[0117] First, the coordinate system of the collected data is transformed, and a mathematical model between the vehicle vertical acceleration, the vehicle horizontal attitude angle, the vehicle speed and the road elevation change is constructed based on the transformation results. The mathematical model is: ;in, is the vertical acceleration of the vehicle at time t; is the elevation of the road surface at time t; is the vehicle's horizontal attitude angle; is the speed of the vehicle at time t; is the effect of road elevation change on vehicle acceleration; and is the damping coefficient of the vehicle, which respectively represents the influence of speed on acceleration and the influence of acceleration change rate on acceleration; then, the frequency domain corresponding to the vehicle vertical acceleration information in the collected data is analyzed, and the high-frequency components in the frequency domain are determined in combination with the set threshold, specifically, the vehicle vertical acceleration information in the initial data is preprocessed, and Fourier transform is used The preprocessing result (a(t)) is converted from the time domain to the frequency domain (x(f)) to obtain data in the frequency domain. The high-frequency component is identified based on the frequency domain data and the initial data in combination with a set threshold, and the weight coefficient corresponding to the high-frequency component is determined. The high-frequency component refers to a component with a higher frequency, which usually corresponds to a rapidly changing part or a rapidly oscillating part in the data. In the context of vehicle driving, the high-frequency component may represent a short-term change or rapid vibration in the vehicle's motion, such as bumps and vibrations on the road. Specifically, the frequency domain data is subjected to spectral analysis, and the high-frequency component is identified in combination with the vehicle's horizontal attitude angle, the vehicle speed, and the set threshold; the proportion of the high-frequency component in the total is calculated to obtain the weight coefficient corresponding to the weight coefficient corresponding to the high-frequency component. Finally, the RMS value of the frequency data corresponding to the high-frequency component is calculated in combination with the mathematical model to obtain the impact strength. The high-frequency component is usually related to the slight bumps and vibrations on the road surface. The vehicle speed will affect the frequency domain distribution of the collected data. When the vehicle passes through slight bumps and vibrations, a large acceleration change will occur. The increase in vehicle speed will cause the amplitude of the high-frequency component to change. These bumps and vibrations will cause the vehicle to have a large attitude angle change. By calculating the RMS value of the high-frequency part of the frequency data , and the impact strength is obtained.

[0118] S132. Determine the health status index corresponding to the road unit according to the health status index corresponding to the single grid point in combination with internal factors, wherein the internal factors include road maintenance information, road grade, and road age.

[0119] In this embodiment, factors such as road maintenance information, road grade, and road age are feedback data from the maintenance unit after each repair, and are historical data.

[0120] In this embodiment, calculations are performed based on indicators such as road driving comfort, road hazard monitoring and analysis results, road service life, and road maintenance conditions in the on-site road condition information. Reasonable weight ratios are set for these indicators, and the weighted sum is used to obtain the road health score corresponding to a single road grid point.

[0121] Specifically, points are deducted according to the disease type, quantity, severity, bump impact intensity and internal factors according to the corresponding scoring table. The scoring table is obtained based on the maintenance unit's previous scoring experience, so as to determine the score of the health status indicator corresponding to a single grid point.

[0122] Specifically, the health status index corresponding to each grid point in the road unit is accumulated to obtain the health status index corresponding to the road unit.

[0123] S133. Calculate a smooth road index corresponding to the road unit according to the health status index corresponding to the road unit.

[0124] In one embodiment, see Figure 4 , the above-mentioned step S133 may include steps S1331~S1332.

[0125] S1331. Calculate a score factor according to the health status index corresponding to the road unit.

[0126] In this embodiment, the score factor is a numerical index obtained by comprehensive evaluation of a specific road unit (such as a section of road or a grid area). The calculation of this index takes into account a variety of factors that affect road health and comfort, such as road diseases, bumps and shocks, etc.

[0127] The calculation of fractional factors involves the following main aspects:

[0128] Number of damage types: refers to the number of different damage types present in the road unit. More damage types mean the road is in a worse condition.

[0129] Disease area coefficient of specified type: used to reflect the severity of a specific disease. This coefficient will affect the final score factor value.

[0130] Number of specified types of defects: The number of a specific defect in a road unit. The greater the number, the greater the impact on the road.

[0131] The area of ​​the designated disease is the actual area occupied by the disease in the road unit. The larger the area, the more significant the impact on the road.

[0132] Bump Impact Coefficient: This is a constant used to quantify the impact force caused by bumps during road use.

[0133] The total number of bumpy impacts generated within a road unit: reflects the frequency of bumps caused by vehicles passing by during road use.

[0134] Bump impact intensity: This parameter characterizes the intensity of the impact force. The greater the intensity, the greater the impact on the road.

[0135] Effectiveness of bump impact force: used to indicate whether the impact force is effective. A value of 1 indicates effective, and a value of 0 indicates normal sensation with no significant impact.

[0136] In this embodiment, the higher the value of the score factor, the better the condition of the road unit. Conversely, it means that there may be more diseases and unsuitable driving conditions. Road managers can use this indicator to determine which road units need to be maintained or repaired first. According to the score factors of each road unit, maintenance resources and funds are reasonably allocated to improve the efficiency and effect of road maintenance.

[0137] Specifically, using Calculate the fractional factor where is the specified road unit fraction factor; is the number of disease types; is the disease area coefficient of the specified type; The number of diseases of a given type; The area for the specified disease; is the bump impact coefficient, which can be taken as 0.0178; It is the total number of times the bump impact force is generated in the road unit; It is the intensity of bump impact force, reflecting the magnitude of impact force; The effectiveness of the bumpy impact force: effective is 1, and normal sensation is 0.

[0138] S1332. Calculate a corresponding smooth road index according to the score factor and the set road grade weight coefficient and empirical parameters.

[0139] In this embodiment, the Calculate the corresponding smooth road index, where is the smoothness index of a specified road unit; It is an empirical parameter, and its general value is 0.0072; is the road grade weight coefficient: 0.414 for main roads, 0.362 for secondary roads, and 0.224 for branch roads.

[0140] S134. Calculate the smoothness indexes corresponding to the road, section and region respectively according to the smoothness index corresponding to the road unit, so as to obtain the smoothness indexes corresponding to different evaluation units.

[0141] In this embodiment, the smoothness indexes and the corresponding color coefficients corresponding to the road units involved in the road, section and area are accumulated, and the smoothness indexes corresponding to the road units are combined to obtain the smoothness indexes corresponding to different evaluation units;

[0142] The color coefficient refers to the coefficient corresponding to the serious unevenness of the road, the presence of ups and downs and disease points, and the good road condition in the road unit.

[0143] Specifically ,use , Calculate the smooth road index corresponding to the corresponding evaluation unit, where: is the road smoothness index; It is the smooth road index of the bidding section; It is the regional smooth road index; It refers to the smoothness index of each road unit in the road, section and area. It is the smoothness index of the road unit. is the number of road units within a given road; is the number of road units within the specified section; is the number of road units in the specified area; is the unit color adjustment coefficient: the red unit is , the yellow unit is , the green unit is .

[0144] Specifically, a single grid point is selected for health status index calculation, and the starting point of the evaluation is the road unit, which usually refers to a specific road section or area. These units are the basic units for health status evaluation. The status of each road unit will directly affect the health assessment results of the larger area. When performing the evaluation, the health status of each road unit must first be determined. When the health status of a road unit reaches a predetermined coverage threshold (for example, a certain number of road units meet the health standard), the evaluation can be expanded outward to a larger area. This expansion method helps ensure the accuracy and effectiveness of the evaluation.

[0145] In this embodiment, the smoothness index is a quantitative index used to evaluate the driving comfort and safety of a road, which usually takes into account multiple factors, such as the smoothness of the road, the quality of the road surface, and the presence of damage.

[0146] In each level of area (such as a single road section, the entire section, the region, etc.), the smoothness index of each road unit is calculated comprehensively. This calculation process includes the following aspects:

[0147] Summarization of smooth road index values: Summarize all smooth road index values ​​within the road unit to obtain the average value or weighted value of the area.

[0148] Quantity and corresponding color coefficient: The number of road units and their corresponding color coefficients may also be taken into account during the calculation process (the color coefficient can be used to indicate how good the road condition is). For example, healthy road units may be marked green, while units with serious problems may be marked red.

[0149] Finally, through comprehensive analysis of road units in different levels of regions, a smooth road index for roads, sections and regions is formed. This process not only provides an intuitive health status assessment, but also helps decision makers make more scientific choices in maintaining and improving roads.

[0150] The resulting smooth road index can be used to formulate maintenance plans, allocate resources, and identify areas that require key repairs, thereby effectively improving the safety and comfort of road use.

[0151] Through the layer-by-layer expansion of the assessment method, from the basic road unit to the comprehensive assessment of a larger area, the scientificity and reliability of the assessment results are ensured. This method can provide effective data support for road management, thereby promoting the efficient implementation of road maintenance work.

[0152] S140, dividing according to the smooth road indexes corresponding to different evaluation units to obtain at least a multi-color evaluation diagram.

[0153] In this embodiment, at least the multi-color evaluation diagram refers to a picture that uses different colors to represent the status, performance or quality of a system or area. This graphical representation method can intuitively display data and help users quickly understand and analyze the situation.

[0154] Specifically, the at least multi-color evaluation map includes images using different colors to present the unevenness of the entire road section, the occurrence of individual serious disease points on the road, and the good road conditions.

[0155] By systematically calculating the smoothness index of road units, roads, sections and regions, the evaluation results can be divided into three colors: red, yellow and green, representing different road health conditions:

[0156] The red assessment result indicates that the road section is uneven as a whole, which seriously affects the comfort and safety of driving. It is necessary to carry out major or medium repairs to comprehensively improve the smoothness of the road to ensure driving safety.

[0157] The yellow assessment result indicates that there are several undulations on the road, which affect the driving experience, and there are some serious disease points. It is recommended to carry out regional centralized maintenance to improve the driving experience and ensure the normal function of the road.

[0158] The green assessment result indicates that the overall driving experience of the road is good, the road has few defects, and it is suitable for normal traffic. Daily inspections and regular pre-maintenance are needed to maintain the good condition of the road and prevent the further deterioration of the defects.

[0159] The red, yellow and green three-color evaluation system allows managers and drivers to understand the health status of the road at a glance, so that they can take appropriate measures in a timely manner. This hierarchical evaluation method provides data support for road maintenance, helps optimize resource allocation, and makes maintenance work more efficient and accurate. Through timely maintenance and repair, traffic accidents caused by road diseases can be reduced, and driving safety and comfort can be improved. Regular inspections and maintenance can effectively extend the service life of roads and reduce long-term maintenance costs. Provide a good road driving experience, improve public satisfaction with road management and maintenance, and promote social trust in transportation infrastructure.

[0160] Through such an evaluation system, roads can be better managed and maintained, improving the efficiency and safety of the overall transportation system.

[0161] Specifically, a range value corresponding to each smooth road index of different colors is set, and the calculated range value is compared with the set range value corresponding to the smooth road index of different colors, so as to determine the color of the road unit.

[0162] For example, Figure 5 and Figure 6 As shown in the figure, the color of each grid point and the image after the quality assessment of the road unit are presented respectively. From the figure, it can be clearly seen which grid points are overall undulating, which seriously affects the comfort and safety of driving. Major and medium repairs are needed to comprehensively improve the flatness of the road to ensure driving safety. Which grid points have several undulating points that affect the driving experience, and there are individual serious disease points. It is recommended to carry out regional centralized maintenance to improve the driving experience and ensure the normal function of the road. Which grid points have a good overall driving experience, fewer diseases, and are suitable for normal traffic. Daily inspections and regular pre-maintenance are needed to maintain the good condition of the road and prevent further deterioration of the disease. The same is true for the road image, which can show the quality of different roads at a glance.

[0163] S150: Output the at least multi-color evaluation diagram.

[0164] The above-mentioned intelligent road quality evaluation method based on the Smooth Road Index collects data on urban roads to be evaluated to ensure the comprehensiveness and accuracy of information, so as to improve the scientific nature of health status indicators; divides urban roads into multiple evaluation units, which can more carefully analyze and monitor road conditions in different areas and increase the coverage frequency of evaluation; based on the collected health status indicators such as road damage parameters and bump impact parameters, accurately calculates the Smooth Road Index of each evaluation unit to improve the credibility of the evaluation results; presents the Smooth Road Index of different evaluation units in the form of multi-color evaluation charts, so that the road health status is clear at a glance and it is easy to quickly identify problem areas; realizes dynamic updating of data to ensure that the evaluation results reflect the latest road conditions, thereby effectively overcoming the shortcomings of traditional methods in terms of untimely updates.

[0165] Figure 7 is a schematic block diagram of a road quality intelligent evaluation device 300 based on a smooth road index provided by an embodiment of the present invention. Figure 7 As shown, corresponding to the above-mentioned road quality intelligent evaluation method based on the smooth road index, the present invention also provides a road quality intelligent evaluation device 300 based on the smooth road index. The road quality intelligent evaluation device 300 based on the smooth road index includes a unit for executing the above-mentioned road quality intelligent evaluation method based on the smooth road index, and the device can be configured in a server. Figure 7 The road quality intelligent evaluation device 300 based on the smooth road index includes a data acquisition unit 301, an evaluation unit division unit 302, an index calculation unit 303, a division unit 304 and an output unit 305.

[0166] The data acquisition unit 301 is used to acquire the collected data of the urban road to be evaluated; the evaluation unit division unit 304302 is used to divide the urban road to be evaluated into different evaluation units; the index calculation unit 303 is used to obtain the health status index according to the collected data, and calculate the smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index includes a road damage parameter and a bump impact parameter; the division unit 304 is used to divide according to the smooth road index corresponding to different evaluation units to obtain at least a multi-color evaluation map; the output unit 305 is used to output the at least multi-color evaluation map.

[0167] In one embodiment, if Figure 8 As shown, the index calculation unit 303 includes a grid health status index acquisition subunit 3031, a road unit health status index acquisition subunit 3032, a road unit smooth road index calculation subunit 3033 and an evaluation unit smooth road index calculation subunit 3034.

[0168] The grid health status indicator acquisition subunit 3031 is used to acquire the health status indicator corresponding to a single grid according to the collected data; the road unit health status indicator acquisition subunit 3032 is used to determine the health status indicator corresponding to the road unit according to the health status indicator corresponding to a single grid point combined with internal factors, wherein the internal factors include road maintenance information, road grade, and road age; the road unit smooth road index calculation subunit 3033 is used to calculate the smooth road index corresponding to the road unit according to the health status indicator corresponding to the road unit; the evaluation unit smooth road index calculation subunit 3034 is used to calculate the smooth road index corresponding to the road, section, and region respectively according to the smooth road index corresponding to the road unit, so as to obtain the smooth road index corresponding to different evaluation units.

[0169] In one embodiment, if Fig. 9 As shown, the grid health status indicator acquisition subunit 3031 includes a road damage determination module 30311 and a bump impact determination module 30312 .

[0170] The road damage determination module 30311 is used to apply the target recognition model and the depth estimation model to the collected data, and determine the type, number and area of ​​damage in combination with the camera parameters to obtain road damage parameters; the bump impact determination module 30312 is used to analyze the collected data, and construct a mathematical model to identify high-frequency components, and use the mathematical model to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain the impact intensity.

[0171] In one embodiment, the road unit health status indicator acquisition subunit 3032 is used to accumulate the health status indicator corresponding to each grid point in the road unit to obtain the health status indicator corresponding to the road unit.

[0172] In one embodiment, if Fig.10 As shown, the road unit smooth road index calculation subunit 3033 includes a fractional factor calculation module 30331 and a smooth road index calculation module 30332 .

[0173] The score factor calculation module 30331 is used to calculate the score factor according to the health status index corresponding to the road unit; the smooth road index calculation module 30332 is used to calculate the corresponding smooth road index according to the score factor and the set road grade weight coefficient and empirical parameters.

[0174] In one embodiment, the evaluation unit smooth road index calculation subunit 3034 is used to accumulate the smooth road indexes and corresponding color coefficients corresponding to the road units involved in the roads, sections and areas, and combine the smooth road indexes corresponding to the road units to obtain the smooth road indexes corresponding to different evaluation units; wherein the color coefficient refers to the coefficient corresponding to the road unit with severe unevenness, ups and downs and damage points, and the road condition being good.

[0175] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned intelligent road quality evaluation device 300 based on the smooth road index and each unit can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and brevity of description, it will not be repeated here.

[0176] The above-mentioned road quality intelligent evaluation device 300 based on the smooth road index can be implemented in the form of a computer program. The computer program can be used in Fig.11 Runs on the computer device shown.

[0177] See also Fig.11 , Fig.11 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a server, wherein the server may be an independent server or a server cluster composed of multiple servers.

[0178] See also Fig.11 The computer device 500 includes a processor 502 , a memory and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0179] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a road quality intelligent evaluation method based on a smooth road index.

[0180] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0181] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a road quality intelligent evaluation method based on the smooth road index.

[0182] The network interface 505 is used to communicate with other devices over the network. Fig.11The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0183] The processor 502 is used to run the computer program 5032 stored in the memory to implement the following steps:

[0184] Acquire collected data of the urban road to be evaluated; divide the urban road to be evaluated into different evaluation units; acquire health status indicators according to the collected data, and calculate smooth road indexes corresponding to different evaluation units according to the health status indicators, wherein the health status indicators at least include road damage parameters and bump impact parameters; divide the evaluation units according to the smooth road indexes corresponding to different evaluation units to obtain at least a multi-color evaluation map; and output the at least multi-color evaluation map.

[0185] Among them, the evaluation units include grid points, road units, roads, sections and regions; among them, the road units include several grid points; the roads include several road units integrated based on road names; the sections include all road units involved after the division according to the project construction sections; the regions include all road units involved based on the urban areas.

[0186] The at least multi-color evaluation map includes images using different colors to present the unevenness of the entire road section, individual serious disease points on the road, and good road conditions.

[0187] In one embodiment, when the processor 502 implements the steps of acquiring the health status index according to the collected data and calculating the smooth road index corresponding to different evaluation units according to the health status index, the processor 502 specifically implements the following steps:

[0188] The health status index corresponding to a single grid is obtained according to the collected data; the health status index corresponding to the road unit is determined according to the health status index corresponding to the single grid point combined with internal factors, wherein the internal factors include road maintenance information, road grade, and road age; the smooth road index corresponding to the road unit is calculated according to the health status index corresponding to the road unit; the smooth road indexes corresponding to the road, section, and region are calculated according to the smooth road index corresponding to the road unit, so as to obtain the smooth road indexes corresponding to different evaluation units.

[0189] In one embodiment, when the processor 502 implements the step of acquiring the health status indicator corresponding to a single grid according to the collected data, the processor 502 specifically implements the following steps:

[0190] Apply a target recognition model and a depth estimation model to the collected data, and combine the camera parameters to determine the type of damage, the number of damages and the area of ​​damage to obtain road damage parameters; analyze the collected data, and construct a mathematical model to identify high-frequency components, and use the mathematical model to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain the impact strength.

[0191] Among them, the road damage parameters also include a disease area coefficient, which is set by the disease type according to different disease severity levels and determined by the maintenance ratio; the bumpy impact parameters also include a bumpy impact force coefficient; the bumpy impact force coefficient is adjusted according to the road damage parameters.

[0192] In one embodiment, when the processor 502 implements the step of determining the health status indicator corresponding to the road unit according to the health status indicator corresponding to the single grid point in combination with the internal factors, the processor 502 specifically implements the following steps:

[0193] The health status index corresponding to each grid point in the road unit is accumulated to obtain the health status index corresponding to the road unit.

[0194] In one embodiment, when the processor 502 implements the step of calculating the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit, the processor 502 specifically implements the following steps:

[0195] A score factor is calculated according to the health status index corresponding to the road unit; and a corresponding smooth road index is calculated according to the score factor and a set road grade weight coefficient and empirical parameters.

[0196] In one embodiment, when the processor 502 implements the step of calculating the smoothness indexes corresponding to the road, section, and region according to the smoothness indexes corresponding to the road unit to obtain the smoothness indexes corresponding to different evaluation units, the processor 502 specifically implements the following steps:

[0197] Accumulating the smoothness indexes and corresponding color coefficients corresponding to the road units involved in the road, section and area, and combining the smoothness indexes corresponding to the road units to obtain the smoothness indexes corresponding to different evaluation units;

[0198] The color coefficient refers to the coefficient corresponding to the serious unevenness of the road, the presence of ups and downs and disease points, and the good road condition in the road unit.

[0199] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0200] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0201] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein when the computer program is executed by a processor, the processor executes the following steps:

[0202] Acquire collected data of the urban road to be evaluated; divide the urban road to be evaluated into different evaluation units; acquire health status indicators according to the collected data, and calculate smooth road indexes corresponding to different evaluation units according to the health status indicators, wherein the health status indicators at least include road damage parameters and bump impact parameters; divide the evaluation units according to the smooth road indexes corresponding to different evaluation units to obtain at least a multi-color evaluation map; and output the at least multi-color evaluation map.

[0203] Among them, the evaluation units include grid points, road units, roads, sections and regions; among them, the road units include several grid points; the roads include several road units integrated based on road names; the sections include all road units involved after the division according to the project construction sections; the regions include all road units involved based on the urban areas.

[0204] The at least multi-color evaluation map includes images using different colors to present the unevenness of the entire road section, individual serious disease points on the road, and good road conditions.

[0205] In one embodiment, when the processor executes the computer program to implement the steps of acquiring the health status index according to the collected data and calculating the smooth road index corresponding to different evaluation units according to the health status index, the processor specifically implements the following steps:

[0206] The health status index corresponding to a single grid is obtained according to the collected data; the health status index corresponding to the road unit is determined according to the health status index corresponding to the single grid point combined with internal factors, wherein the internal factors include road maintenance information, road grade, and road age; the smooth road index corresponding to the road unit is calculated according to the health status index corresponding to the road unit; the smooth road indexes corresponding to the road, section, and region are calculated according to the smooth road index corresponding to the road unit, so as to obtain the smooth road indexes corresponding to different evaluation units.

[0207] In one embodiment, when the processor executes the computer program to implement the step of acquiring the health status indicator corresponding to a single grid according to the collected data, the processor specifically implements the following steps:

[0208] Apply a target recognition model and a depth estimation model to the collected data, and combine the camera parameters to determine the type of damage, the number of damages and the area of ​​damage to obtain road damage parameters; analyze the collected data, and construct a mathematical model to identify high-frequency components, and use the mathematical model to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain the impact strength.

[0209] Among them, the road damage parameters also include a disease area coefficient, which is set by the disease type according to different disease severity levels and determined by the maintenance ratio; the bumpy impact parameters also include a bumpy impact force coefficient; the bumpy impact force coefficient is adjusted according to the road damage parameters.

[0210] In one embodiment, when the processor executes the computer program to implement the step of determining the health status index corresponding to the road unit according to the health status index corresponding to the single grid point combined with the internal factors, the processor specifically implements the following steps:

[0211] The health status index corresponding to each grid point in the road unit is accumulated to obtain the health status index corresponding to the road unit.

[0212] In one embodiment, when the processor executes the computer program to implement the step of calculating the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit, the processor specifically implements the following steps:

[0213] A score factor is calculated according to the health status index corresponding to the road unit; and a corresponding smooth road index is calculated according to the score factor and a set road grade weight coefficient and empirical parameters.

[0214] In one embodiment, when the processor executes the computer program to implement the step of calculating the smoothness indexes corresponding to the road, section and area respectively according to the smoothness index corresponding to the road unit to obtain the smoothness indexes corresponding to different evaluation units, the processor specifically implements the following steps:

[0215] Accumulating the smoothness indexes and corresponding color coefficients corresponding to the road units involved in the road, section and area, and combining the smoothness indexes corresponding to the road units to obtain the smoothness indexes corresponding to different evaluation units;

[0216] The color coefficient refers to the coefficient corresponding to the serious unevenness of the road, the presence of ups and downs and disease points, and the good road condition in the road unit.

[0217] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0218] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0219] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0220] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0221] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0222] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. The intelligent road quality evaluation method based on the smooth road index is characterized by: include: Obtaining collected data of the urban roads to be evaluated; Divide the urban roads to be evaluated into different evaluation units; Acquire a health status index according to the collected data, and calculate a smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index at least includes a road damage parameter and a bump impact parameter; Divide according to the smooth road index corresponding to different evaluation units to obtain at least a multi-color evaluation map; outputting the at least multi-color evaluation map; The step of obtaining a health status index according to the collected data and calculating a smooth road index corresponding to different evaluation units according to the health status index includes: Acquire a health status indicator corresponding to a single grid according to the collected data; Determine the health status index corresponding to the road unit according to the health status index corresponding to the single grid point combined with the internal factors, wherein the internal factors include road maintenance information, road grade, and road age; Calculate the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit; According to the smoothness index corresponding to the road unit, the smoothness index corresponding to the road, section and area is calculated to obtain the smoothness index corresponding to different evaluation units; The evaluation units include grid points, road units, roads, sections and regions; wherein the road units include several grid points; the roads include several road units integrated based on road names; the sections include all road units involved after the division according to the construction sections; the regions include all road units involved based on the urban areas; The method of determining the health status index corresponding to the road unit according to the health status index corresponding to the single grid point in combination with the internal factors includes: Accumulate the health status index corresponding to each grid point in the road unit to obtain the health status index corresponding to the road unit; The calculating of the smooth road index corresponding to the road unit according to the health status index corresponding to the road unit includes: Calculate a score factor according to the health status index corresponding to the road unit; Calculate the corresponding smooth road index according to the score factor and the set road grade weight coefficient and empirical parameters; The method of calculating the smoothness indexes corresponding to the road, section and region respectively according to the smoothness index corresponding to the road unit to obtain the smoothness indexes corresponding to different evaluation units includes: Accumulating the smoothness indexes and corresponding color coefficients corresponding to the road units involved in the road, section and area, and combining the smoothness indexes corresponding to the road units to obtain the smoothness indexes corresponding to different evaluation units; Among them, the color coefficient refers to the coefficient corresponding to the serious unevenness of the road, the presence of ups and downs and disease points, and the good road condition in the road unit; use Calculate the fractional factor where is the specified road unit fraction factor; is the number of disease types; is the disease area coefficient of the specified type; The number of diseases of a given type; The area for the specified disease; is the bump impact coefficient; It is the total number of times the bump impact force is generated in the road unit; It is the intensity of bump impact force, reflecting the magnitude of impact force; For the effectiveness of bump impact; use Calculate the corresponding smooth road index, where is the smoothness index of a specified road unit; is an empirical parameter, is the road grade weight coefficient.

2. The road quality intelligent evaluation method based on the smooth road index according to claim 1 is characterized in that: The obtaining of a health status indicator corresponding to a single grid according to the collected data includes: Applying a target recognition model and a depth estimation model to the collected data, combined with camera parameters, to determine the type of damage, the number of damages, and the area of ​​damages to obtain road damage parameters; The collected data is analyzed, and a mathematical model is constructed to identify high-frequency components. The mathematical model is used to calculate the root mean square value of the frequency data corresponding to the high-frequency components to obtain the impact strength.

3. The road quality intelligent evaluation method based on the smooth road index according to claim 2 is characterized in that: The road damage parameters also include a disease area coefficient, which is set by the disease type according to different disease severity levels and determined by the maintenance ratio; the bumpy impact parameters also include a bumpy impact force coefficient; the bumpy impact force coefficient is adjusted according to the road damage parameters.

4. The road quality intelligent evaluation method based on the smooth road index according to claim 1 is characterized in that: The at least multi-color evaluation map includes images using different colors to present the unevenness of the entire road section, individual serious disease points on the road, and good road conditions.

5. The intelligent road quality evaluation device based on the smooth road index is characterized by: The device uses the road quality intelligent evaluation method based on the smooth road index according to any one of claims 1 to 4, and the device comprises: A data acquisition unit, used to acquire collected data of the urban road to be evaluated; An evaluation unit division unit is used to divide the urban roads to be evaluated into different evaluation units; An index calculation unit, used to obtain a health status index according to the collected data, and calculate a smooth road index corresponding to different evaluation units according to the health status index, wherein the health status index includes a road damage parameter and a bump impact parameter; A division unit, used for dividing according to the smooth road index corresponding to different evaluation units to obtain at least a multi-color evaluation map; an output unit, configured to output the at least multi-color evaluation diagram; The index calculation unit includes a grid health status index acquisition subunit, a road unit health status index acquisition subunit, a road unit smooth road index calculation subunit and an evaluation unit smooth road index calculation subunit; A grid health status indicator acquisition subunit is used to acquire the health status indicator corresponding to a single grid based on the collected data; a road unit health status indicator acquisition subunit is used to determine the health status indicator corresponding to the road unit based on the health status indicator corresponding to a single grid point in combination with internal factors, wherein the internal factors include road maintenance information, road grade, and road age; a road unit smooth road index calculation subunit is used to calculate the smooth road index corresponding to the road unit based on the health status indicator corresponding to the road unit; an evaluation unit smooth road index calculation subunit is used to calculate the smooth road index corresponding to the road, section, and region respectively based on the smooth road index corresponding to the road unit, so as to obtain the smooth road index corresponding to different evaluation units.

Images