Urban and rural pavement maintenance monitoring system based on intelligent well lid data linkage

Abstract

The invention discloses an urban and rural pavement maintenance monitoring system based on intelligent well lid data linkage, and the system comprises a data collection module which detects the acceleration data, angular motion data and inclination angle change data of a well lid in real time through a three-axis acceleration sensor, a gyroscope and an inclination angle sensor which are disposed on the well lid; the edge calculation module is used for calculating a vibration value of the well lid according to the acceleration data, the angular motion data and the inclination angle change data of the well lid and correcting the vibration value; the cloud data center obtains a risk index of a road surface where the well lid is located through a pre-constructed association model according to the corrected vibration value and the position of the well lid corresponding to the vibration value; and the maintenance decision terminal module performs maintenance decision on the road surface where the well lid is located according to the risk index of the road surface where the well lid is located. According to the invention, the well lid on the road surface is taken as the basis of data acquisition, and the maintenance condition of urban and rural road surfaces is detected in real time by acquiring the data on the well lid, so that the maintenance condition can be accurately obtained in real time.

Description

Technical Field

[0001] This invention relates to the field of road maintenance technology, and in particular to an urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage. Background Technology

[0002] Urban and rural roads are core components of regional transportation networks, and their health directly impacts traffic safety, transportation efficiency, and regional economic development. Timely and scientific maintenance can not only effectively extend the service life of road surfaces but also significantly reduce long-term maintenance costs. Whether in densely trafficked urban areas or in rural areas with complex geographical environments, road surface monitoring and maintenance are critical areas of infrastructure operation and maintenance. Currently, there is an urgent need for precise, comprehensive, and scenario-adaptable road surface maintenance solutions to support the efficient and safe operation of urban and rural transportation.

[0003] Currently, the method used for detecting urban and rural road surfaces is to collect vibration values ​​from vibration sensors mounted on vehicles, and then combine this with GPS to determine the vehicle's location. The vibration values ​​at the specific location of the vehicle are then used to determine whether the road surface needs maintenance. While this method can indeed achieve the goal of road maintenance monitoring, it has high technical and road surface requirements. First, GPS positioning must be real-time and accurate, without any time delay. Second, this method can only be used on roads without speed bumps or manhole covers, and the road surface needs to be as flat as possible, because speed bumps and manhole covers can affect vehicle vibrations, leading to misjudgments of road maintenance. Furthermore, when using this method, the shock absorbers of each vehicle vary greatly, even among cars of the same brand, model, and batch. These significant differences in shock absorbers result in substantial variations in the data collected by the vibration sensors, making accurate conclusions about road maintenance impossible. Using a single testing vehicle can overcome the errors caused by differences in vehicle shock absorbers, but this requires the same vehicle to continuously drive and test within the monitoring area. This is not only time-consuming and labor-intensive, but the detected data also has a delay, failing to meet the needs of real-time monitoring. Therefore, the current method, limited by technology, cannot provide real-time and accurate information on road maintenance. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the existing technology by providing an urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage. The system uses manhole covers on the road as the basis for data collection, and by collecting data from the manhole covers, it can detect the maintenance status of urban and rural roads in real time, thus enabling real-time and accurate information on the maintenance status of urban and rural roads.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage, comprising: The data acquisition module uses a triaxial accelerometer, gyroscope, and tilt sensor installed on the manhole cover to detect the acceleration data, angular motion data, and tilt angle change data of the manhole cover in real time. The edge computing module calculates the vibration value of the manhole cover based on its acceleration data, angular motion data, and tilt angle change data, and then corrects the vibration value. The cloud data center uses a pre-built correlation model to obtain the risk index of the road surface where the manhole cover is located, based on the corrected vibration value and the location of the manhole cover corresponding to that vibration value. The maintenance decision-making terminal module makes maintenance decisions for the road surface where the manhole cover is located based on the risk index of the road surface.

[0006] Furthermore, the edge computing module corrects the vibration value, including: Obtain the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole covers; Obtain the distance between the center of the manhole cover to be tested and the centerline of the road width; Based on the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover, as well as the distance between the center of the manhole cover to be tested and the center line of the road width, and in combination with the original vibration value, the corrected vibration value is calculated.

[0007] Furthermore, the calculated corrected vibration value is obtained using the following formula:

[0008] in, This is the vibration value after one correction. The original vibration value. The distance between the center of the manhole cover to be tested and the center line of the road width is given. The optimal vertical distance between the center of the manhole cover to be tested and the centerline of the road width is given. The maximum permissible vertical distance between the center of the manhole cover to be tested and the centerline of the road width. The distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is... The optimal vertical distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is defined as follows: The maximum permissible vertical distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover.

[0009] Furthermore, it also includes: The vehicle detection module determines the weight of the regional traffic flow on the road surface where the manhole cover to be tested and its adjacent manhole covers are located, based on the road characteristics of the road surface. The edge computing module corrects the vibration value, including the following steps: The vibration attenuation coefficient of the manhole cover to be tested is obtained according to the type of road surface on which the manhole cover to be tested and its adjacent manhole covers are located. Based on the weight of the regional traffic flow on the road surface where the manhole cover to be tested and its adjacent manhole covers are located, and the attenuation coefficient of the vibration of the manhole cover to be tested, combined with the vibration value after the first correction, the vibration value after the second correction is obtained.

[0010] Furthermore, the vibration value after secondary correction is calculated using the following formula:

[0011] in, This is the vibration value after secondary correction. This is the vibration value after one correction. Let be the attenuation coefficient of the vibration of the manhole cover to be tested. The weight of the regional traffic flow on the road surface where the manhole cover to be measured and its adjacent manhole covers are located.

[0012] Furthermore, the data acquisition module detects soil moisture data through soil moisture sensors deployed on the edge of the manhole cover.

[0013] Furthermore, the edge computing module determines the road surface soil stability based on soil moisture data and tilt angle change data, and marks the locations of manhole covers with low soil stability as high-risk points.

[0014] Furthermore, the cloud data center updates the risk index of the road surface based on the high-risk locations.

[0015] Compared with the prior art, the present invention has the following advantages: This invention constructs a fixed sensor network based on manhole covers widely distributed on urban and rural roads, effectively overcoming the inherent defects of traditional vehicle-mounted mobile detection methods. By deploying triaxial accelerometers, gyroscopes, and tilt sensors on the bottom of the manhole covers, it can directly and continuously collect multi-dimensional dynamic data (including acceleration, angular motion, and tilt changes) transmitted to the manhole covers as vehicles pass by. This ensures that the data source itself is not affected by differences in the shock absorption systems of different vehicles, solving the core problem of inconsistent monitoring data caused by individual vehicle differences in the prior art. Furthermore, the original vibration values ​​are corrected in real time through an edge computing module (e.g., compensation calculations based on factors such as the spacing between manhole covers and the distance between the center of the manhole cover and the centerline of the road width), significantly eliminating the influence of the manhole cover's own positional characteristics on the vibration signal and improving the accuracy of the data in representing the actual road surface condition. Based on the corrected vibration data and its location information, the cloud data center uses a pre-built correlation model for comprehensive analysis to generate a risk index reflecting the road surface health condition, realizing the intelligent transformation from "vibration signal" to "maintenance decision." This fixed, networked monitoring method enables the system to operate 24 / 7 without relying on repeated patrols by specific inspection vehicles, thus achieving real-time and continuous monitoring of urban and rural road maintenance conditions. Furthermore, for special scenarios such as rural roads, by adding soil moisture sensors and combining them with tilt data analysis, the system can also assess soil stability, further expanding the monitoring dimensions and accuracy. Therefore, this invention ultimately achieves the technical effect of real-time, accurate, and comprehensive perception of urban and rural road maintenance conditions, providing immediate and reliable data support for maintenance decisions.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] Figure 1 This is a bottom view of the smart manhole cover used in this invention.

[0018] Figure 2 This is a system block diagram of the invention.

[0019] Explanation of reference numerals in the attached figures: 1. Manhole cover; 2. Gyroscope; 3. Tilt sensor; 4. Triaxial accelerometer; 5. Humidity sensor. Detailed Implementation

[0020] like Figure 1As shown, the intelligent manhole cover used in this invention integrates a gyroscope 2, a tilt sensor 3, and a triaxial accelerometer 4 onto a regular manhole cover 1. The gyroscope 2, tilt sensor 3, and triaxial accelerometer 4 are arranged longitudinally on the bottom surface of the manhole cover 1, forming a multi-dimensional motion sensing unit. Simultaneously, a humidity sensor 5 is also installed on the edge of the manhole cover 1. The triaxial accelerometer 4 detects the linear acceleration transmitted to the manhole cover 1 in the X, Y, and Z directions when a vehicle drives over the manhole cover 1 or the road surface surrounding it; its value directly reflects the intensity of the impact. The gyroscope 2 detects the angular velocity of the manhole cover 1 rotating around its own coordinate axis, capturing the torsional or rolling isoangular motion of the manhole cover 1 that may be caused by a vehicle passing over it. This is particularly important for identifying abnormal states caused by non-perpendicular impacts (such as vehicle eccentricity or sideslip). The tilt sensor 3 is used to continuously monitor the change in the tilt angle of the manhole cover 1 relative to the horizontal reference plane. It can sensitively detect static or quasi-static deformations of the manhole cover 1, such as tilting or sinking, caused by uneven settlement of the roadbed, collapse of the road surface around the manhole, or vehicle rolling. The data from these three sensors are complementary, which can comprehensively and three-dimensionally characterize the dynamic response and structural state of the manhole cover 1 and the road surface it supports, ensuring the richness and multidimensionality of the data source.

[0021] like Figure 2 As shown, based on the aforementioned smart manhole cover, this invention proposes an urban and rural road maintenance monitoring system based on smart manhole cover data linkage, comprising: The data acquisition module uses a triaxial accelerometer 4, a gyroscope 2, and an inclination sensor 3 installed on the manhole cover 1 to detect the acceleration data, angular motion data, and inclination change data of the manhole cover in real time. The edge computing module calculates the vibration value of the manhole cover based on its acceleration data, angular motion data, and tilt angle change data, and then corrects the vibration value. The cloud data center uses a pre-built correlation model to obtain the risk index of the road surface where the manhole cover is located, based on the corrected vibration value and the location of the manhole cover corresponding to that vibration value. The maintenance decision-making terminal module makes maintenance decisions for the road surface where the manhole cover is located based on the risk index of the road surface.

[0022] The urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage provided by this invention is essentially about transforming the widely distributed and fixed-location manhole covers on urban and rural roads into intelligent sensing nodes, constructing a distributed, networked real-time road condition monitoring system. This system fundamentally changes the traditional mode of relying on mobile vehicles carrying sensors for inspection and testing. By using fixed nodes, it eliminates errors introduced by the characteristics of the monitoring carrier (vehicle) itself and achieves 24 / 7 uninterrupted data acquisition and processing.

[0023] The system mainly includes a data acquisition module, an edge computing module, a cloud data center, and a maintenance decision-making terminal module. The data flow originates from various sensors deployed on the manhole covers. After local preprocessing and initial correction by the edge computing module, the data is uploaded to the cloud data center for big data fusion analysis and model calculation. Finally, the visualized road risk index and decision suggestions are pushed to the maintenance decision-making terminal, forming a complete closed loop of "perception-computation-analysis-decision".

[0024] The data acquisition module is the nerve ending of the system, and it is completed through the aforementioned smart manhole cover.

[0025] Edge computing modules are typically integrated into intelligent computing units inside the manhole cover or into nearby gateway devices. Their main functions include: (1) Calculate the vibration value: Filter (e.g., remove high-frequency noise) and synthesize (e.g., calculate the synthesized acceleration or extract the energy of the characteristic frequency band) the raw time-series data collected by the triaxial accelerometer 4 to calculate a scalar "vibration value" that can represent the impact intensity of the current event. This value integrates information such as the amplitude and frequency components of the vibration.

[0026] (2) Preliminary data processing and correction: The original vibration value is corrected for the first time according to the preset algorithm to reduce the systematic deviation caused by the non-ideal installation position of the manhole cover 1.

[0027] (3) Local logic judgment: For example, threshold judgment is performed on the tilt angle data. If the tilt angle continues to exceed the safety threshold, a local alarm can be triggered immediately.

[0028] By performing calculations and initial corrections at the edge, the amount of data that needs to be uploaded to the cloud is significantly reduced, lowering network bandwidth requirements and cloud computing pressure, and enabling millisecond-level local rapid response.

[0029] The cloud-based data center receives corrected vibration values ​​and their unique location identifiers (such as geographic coordinates) uploaded from edge computing modules of all smart manhole covers across the network. The core of the cloud-based system is a pre-built correlation model. This model is trained using machine learning (such as neural network models) or big data statistical analysis of historical data. It establishes a mapping relationship between "vibration characteristics - location information" and "road surface health status (risk index)". For example, the model learns that: at a specific location (such as a bend on an urban main road), continuous high-frequency, moderate-intensity vibrations may indicate fatigue cracks in the road surface; while at another location (such as a soft soil section of a rural road), the correlation model is used to obtain the risk index of the road surface where the manhole cover is located based on the vibration value and the corresponding manhole cover location. This index is a normalized assessment value that intuitively reflects the urgency of road surface maintenance at that point.

[0030] The maintenance decision-making terminal module is the system's output interface, typically provided to road maintenance management departments as a web application or mobile app. This terminal receives and displays risk index maps for each road segment from the cloud data center. Decision-makers can clearly see which road segments within the entire monitoring area are in a high-risk (e.g., red alert), medium-risk (yellow alert), or low-risk (green, normal) state. Based on this visualized information, maintenance departments can scientifically formulate maintenance plans and prioritize resource allocation for high-risk road segments, thereby achieving intelligent decision-making from data to action.

[0031] Furthermore, since this invention is based on data obtained from manhole covers, in order to determine the accuracy of the data, the edge computing module corrects the vibration value, including: Obtain the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole covers; Obtain the distance between the center of the manhole cover to be tested and the centerline of the road width; Based on the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover, as well as the distance between the center of the manhole cover to be tested and the center line of the road width, and in conjunction with the vibration value of the manhole cover, a corrected vibration value is calculated.

[0032] The corrected vibration value is calculated using the following formula:

[0033] in, This is the vibration value after one correction. The original vibration value. The distance between the center of the manhole cover to be tested and the center line of the road width is given. The optimal vertical distance between the center of the manhole cover to be tested and the center line of the road width is (generally 3m). This is the maximum permissible vertical distance (generally 8m) between the center of the manhole cover to be tested and the centerline of the road width. The distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is... The optimal vertical distance (typically 75m) between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is defined. The maximum permissible vertical distance (typically 100m) between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover.

[0034] The above details the vibration value correction method in the edge computing module, aiming to eliminate the impact of differences in the physical installation location of manhole covers on the comparability of monitoring data and further optimize the accuracy of the vibration values ​​detected by manhole covers. The principle is that when the force exerted by a vehicle on the road surface is transmitted to the manhole cover, its attenuation is related to two key geometric factors: first, the distance (L) between the center of the manhole cover and the center line of the road width; the greater the distance, the longer the transmission path, the greater the vibration attenuation, and the smaller the monitored vibration value; second, the distance (D) between the center point of the manhole cover and the center points of its adjacent manhole covers. The distance reflects the density of monitoring points; too large a distance will miss local damage, while too small a distance may cause data redundancy, and the "representative" range of a single manhole cover needs to be calibrated.

[0035] During system deployment, it will be preset , Optimal vertical spacing and .

[0036] The effect of the above calculation formula is to perform weighted compensation: | | / Item: Measures the degree to which the current lateral position of the manhole cover deviates from the ideal position, with a percentage coefficient of 0.1. The greater the deviation, the smaller the correction coefficient, which is equivalent to "amplifying" and compensating for the signal attenuated due to the remote location, or "suppressing" the signal that is too close to the center.

[0037] | | / This item measures the degree to which the current monitoring point spacing deviates from the ideal density, with a percentage coefficient of 0.05. Non-ideal spacing can affect the spatial representativeness of the data at that point, and this correction aims to smooth out this effect.

[0038] This correction allows vibration values ​​measured from manhole covers at different locations and spacings to be assessed on a more unified and comparable benchmark, significantly improving the accuracy and fairness of subsequent risk index calculations. The proportion coefficients of 0.1 and 0.05 are empirical values ​​calibrated from experimental data and can be adjusted according to actual road conditions.

[0039] Meanwhile, this invention also introduces a dynamic influence factor of traffic load, making the system evaluation more refined. Its core is the addition of a vehicle detection module (whose function can be integrated into an edge computing module or the cloud), and a secondary correction of vibration values. Through this dual correction mechanism, the real-time performance and accuracy of the detected vibration values ​​are clearly guaranteed, ensuring that the data obtained when judging road maintenance is the most original data, without any contaminated data.

[0040] The system also includes a vehicle detection module, which determines the weight of regional traffic flow on the road surface where the manhole cover to be tested and its adjacent manhole covers are located, based on the road characteristics of the road surface. Meanwhile, the edge computing module corrects the vibration value, including the following steps: Obtain the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole covers, and the distance between the center of the manhole cover to be tested and the center line of the road width. The vibration attenuation coefficient of the manhole cover to be tested is obtained according to the type of road surface on which the manhole cover to be tested and its adjacent manhole covers are located. Based on the weight of the regional traffic flow on the road surface where the manhole cover to be tested and its adjacent manhole covers are located, and the attenuation coefficient of the vibration of the manhole cover to be tested, combined with the vibration value after the first correction, the vibration value after the second correction is obtained.

[0041] The vibration value after secondary correction is calculated using the following formula:

[0042] in, This is the vibration value after secondary correction. This is the vibration value after one correction. Let be the attenuation coefficient of the vibration of the manhole cover to be tested. The weight of the regional traffic flow of the manhole cover to be tested and the road surface where the adjacent manhole covers are located is generally 0.8 (roads with low traffic flow such as rural roads), 1.0 (roads with moderate traffic flow such as ordinary urban roads), and 1.2 (roads with high traffic flow such as urban centers or roads under construction such as roads with heavy vehicle weight).

[0043] The two revisions to the edge computing module are as follows: First correction: As described above, a correction is made based on geometric position, resulting in... .

[0044] Second revision: Introduce two new factors (α, β): 1. Attenuation coefficient (α) of the vibration of the manhole cover under test: Pre-set according to the type of road surface where the manhole cover is located (such as asphalt, cement concrete). Different materials have different absorption and attenuation characteristics for vibration waves. The α coefficient is used to normalize the sensing data of different road surface types.

[0045] 2. Weight (β) of traffic flow in the area where two adjacent manhole covers are located: provided by the vehicle detection module.

[0046] The calculation is performed using the above formula, followed by a secondary correction. The principle and effect of this formula are as follows: This represents the vibration response caused by the road surface condition itself under standard load. Multiplying by α is to eliminate sensor reading differences caused by variations in road material. Multiplying by β "restores" the actual, varying effects of traffic loads into the vibration data. For example, the same road surface in good condition will show different vibrations measured during the daytime when there are many heavy vehicles (higher β value). , compared to measurements taken at night when there are almost no cars (low β value) After ×β correction, its second correction value The system should tend towards a stable value that reflects the condition of the road surface itself, while filtering out interference from traffic flow fluctuations. This allows the system to more accurately separate the two variables, "traffic load" and "road surface health," enabling it to be based on... The calculated risk index more accurately reflects the inherent damage to the road surface structure, rather than just the appearance of high traffic volume, greatly improving the accuracy and scientific nature of the assessment.

[0047] Enhanced functionality is needed for special scenarios such as rural roads. However, the roadbed of rural roads may not be stable enough, and changes in soil conditions are an important factor affecting the health of the road surface.

[0048] The data acquisition module also detects soil moisture data through a soil moisture sensor 5 installed on the edge of the manhole cover. The soil moisture sensor 5 is installed on the manhole cover located on a rural road.

[0049] Furthermore, the edge computing module also determines the road surface soil stability based on soil moisture data and tilt angle change data, and marks the locations of manhole covers with low soil stability as high-risk points.

[0050] Furthermore, the cloud data center updates the risk index of the road surface based on the high-risk locations.

[0051] In practice, the data acquisition module is equipped with a soil moisture sensor 5 installed on the edge of the manhole cover on rural roads to detect the moisture content of the roadbed soil around the manhole. Excessive soil moisture can significantly reduce the bearing capacity and stability of the subgrade, easily leading to road subsidence and cracking.

[0052] The edge computing module integrates soil moisture data and manhole cover tilt angle change data (mainly from tilt sensor 3) for correlation analysis. For example, when soil moisture is detected to continuously exceed the threshold, and the lateral tilt angle of the manhole cover (indicating uneven settlement) changes significantly in a short period of time, the edge computing module can determine that the road surface soil stability at that location is "low" and mark its geographical location as a high-risk location.

[0053] When calculating the global road surface risk index, the cloud data center integrates high-risk location information directly determined by the edge computing side. Specifically, after the correlation model calculates the basic risk index of a road segment, if that segment contains marked high-risk locations, the cloud data center will adjust and update the risk index of that segment with a weighted average. For example, it may multiply the original risk index by a magnification factor greater than 1.

[0054] The advantage of this implementation method is that it expands the traditional monitoring that only focuses on road surface vibration to include the perception of the subgrade soil condition, achieving integrated "surface-subgrade" monitoring. It can provide early warning of pavement defects caused by subgrade instability, and is especially suitable for rural roads with complex geological conditions and relatively weak drainage systems, significantly improving the monitoring system's scenario adaptability and early warning foresight.

[0055] The maintenance decision-making terminal module also visualizes the road surface risk index. This visualization typically uses a Geographic Information System (GIS) map as a base, overlaying the risk indices of each road segment and location from the cloud data center into heat maps, layered color maps, or markers of different colors and sizes. For example, high-risk road segments are highlighted in dark red, medium-risk in orange, and low-risk in green. Decision-makers can click on specific manhole cover icons to view their historical vibration curves, tilt angle trends, real-time data, soil moisture, and calculated risk index details. This visualization provides global situational awareness and micro-data analysis capabilities, making complex data intuitive and easy to understand. It greatly facilitates maintenance managers in macro-level planning (such as developing annual maintenance budgets and plans) and micro-level emergency command (such as quickly locating and handling sudden severe road damage), making it an indispensable human-computer interaction element for achieving intelligent and precise maintenance decision-making.

[0056] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the present invention. Any simple modifications, alterations, or equivalent structural changes made to the above embodiments based on the technical essence of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage, characterized in that, include: The data acquisition module uses a triaxial accelerometer, gyroscope, and tilt sensor installed on the manhole cover to detect the acceleration data, angular motion data, and tilt angle change data of the manhole cover in real time. The edge computing module calculates the vibration value of the manhole cover based on the acceleration data, angular motion data, and tilt angle change data, and corrects the vibration value. The cloud data center uses a pre-built correlation model to obtain the risk index of the road surface where the manhole cover is located, based on the corrected vibration value and the location of the manhole cover corresponding to that vibration value. The maintenance decision-making terminal module makes maintenance decisions for the road surface where the manhole cover is located based on the risk index of the road surface.

2. The urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 1, characterized in that, The edge computing module corrects the vibration value, including: Obtain the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole covers; Obtain the distance between the center of the manhole cover to be tested and the centerline of the road width; Based on the distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover, as well as the distance between the center of the manhole cover to be tested and the center line of the road width, and in conjunction with the vibration value of the manhole cover, a corrected vibration value is calculated.

3. The urban and rural road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 2, characterized in that, The corrected vibration value is calculated using the following formula: in, This is the vibration value after one correction. The original vibration value. The distance between the center of the manhole cover to be tested and the center line of the road width is [missing information]. The optimal vertical distance between the center of the manhole cover to be tested and the centerline of the road width is given. The maximum permissible vertical distance between the center of the manhole cover to be tested and the centerline of the road width. The distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is [missing information]. The optimal vertical distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover is defined as follows: The maximum permissible vertical distance between the center point of the manhole cover to be tested and the center point of its adjacent manhole cover.

4. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 3, characterized in that, Also includes: The vehicle detection module determines the weight of the regional traffic flow of the manhole cover under test and its adjacent manhole covers based on the road characteristics of the road surface where the manhole cover under test and its adjacent manhole covers are located. The edge computing module corrects the vibration value after the first correction, including the following steps: The vibration attenuation coefficient of the manhole cover to be tested is obtained according to the type of road surface on which the manhole cover to be tested and its adjacent manhole covers are located. Based on the weight of the regional traffic flow on the road surface where the manhole cover to be tested and its adjacent manhole covers are located, and the attenuation coefficient of the vibration of the manhole cover to be tested, combined with the vibration value after the first correction, the vibration value after the second correction is obtained.

5. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 4, characterized in that, The vibration value after secondary correction is calculated using the following formula: in, This is the vibration value after secondary correction. This is the vibration value after one correction. Let be the attenuation coefficient of the vibration of the manhole cover to be tested. The weight of the regional traffic flow on the road surface where the manhole cover to be measured and its adjacent manhole covers are located.

6. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 1, characterized in that, The data acquisition module detects soil moisture data through soil moisture sensors installed on the edge of the manhole cover.

7. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 6, characterized in that, The edge computing module determines the stability of the road surface soil based on soil moisture data and tilt angle change data, and marks the locations of manhole covers with low soil stability as high-risk points.

8. A rural and urban road maintenance monitoring system based on intelligent manhole cover data linkage as described in claim 7, characterized in that, The cloud data center updates the risk index of the road surface based on the high-risk locations.

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