A nail exploration intelligent road hidden danger detection vehicle based on RFID technology, a detection method and a computer readable storage medium

Abstract

The application discloses a kind of based on RFID technology's nail exploration intelligent road hidden danger detection vehicle, detection method and computer readable storage medium, belong to road detection technical field;Including vehicle main body, RFID interaction module, multidimensional detection module, data processing module, positioning communication module and power module;RFID interaction module includes RFID label, RFID read-write device and label activation unit;Multidimensional detection module includes road surface flatness sensor, ultrasonic wave roadbed detector, high-definition crack identification camera and environmental sensor;Data processing module includes data receiving unit, fusion analysis unit and hidden danger evaluation unit;Data receiving unit supports data format standardization processing, eliminates invalid data;Hidden danger evaluation unit establishes evaluation model based on gradient boosting decision tree algorithm, and hidden danger evaluation value is obtained by processing and calculating monitoring data through evaluation model;According to evaluation value, hidden danger grade is divided, and hidden danger evaluation report is generated.

Description

Technical Field

[0001] This invention relates to the field of road inspection technology, specifically to a DingTan intelligent road hazard detection vehicle based on RFID technology, a detection method, and a computer-readable storage medium. Background Technology

[0002] With the long-term operation of transportation infrastructure, roads are prone to problems such as road surface cracks, loosening, potholes, roadbed voids, and settlement under the erosion of the natural environment and repeated vehicle loads. If these problems are not detected and addressed in a timely manner, they will seriously threaten driving safety and shorten the service life of roads. Traditional road inspections rely on manual patrols, which suffer from low efficiency, high missed detection rates, and unclear hazard location. Existing intelligent inspection equipment mostly uses single sensors to collect data, lacking correlation and integration with basic road information, and cannot achieve traceable management of hazards. At the same time, the construction parameters and historical maintenance records of different road sections vary greatly, resulting in a lack of targeted inspection standards and affecting the accuracy of hazard assessment. Based on this, this invention combines the advantages of wireless identification and data storage of RFID technology to propose a DingTan intelligent road hazard detection vehicle and detection method, achieving efficient, accurate, and traceable detection of road hazards. Summary of the Invention

[0003] The purpose of this invention is to provide a road hazard detection vehicle based on RFID technology, a detection method, and a computer-readable storage medium to solve the technical problems mentioned in the background section.

[0004] The objective of this invention can be achieved through the following technical solutions: A smart road hazard detection vehicle based on RFID technology includes a vehicle body, an RFID interaction module, a multi-dimensional detection module, a data processing module, a positioning and communication module, and a power supply module. The vehicle-mounted main body adopts a lightweight modification design, with an electrically adjustable detection component mounting bracket at the bottom and a communication antenna and positioning device mounting base integrated at the top. It also has a reserved 19-inch standard rack mounting space inside, which can adapt to the detection operation needs of different road conditions. The RFID interaction module includes an RFID tag, an RFID reader / writer, and a tag activation unit; RFID tags are pre-installed according to road segment zones, with tag spacing set according to road grade; the top of the tag is 8-10cm away from the road surface. Each RFID tag stores a unique EPC code, which is associated with the construction parameters, historical inspection data and maintenance records of the corresponding road section; The RFID reader / writer communicates with the data processing module via an RS485 interface; The tag activation unit uses a high-power radio frequency wake-up module, which works synchronously with the reader / writer; The multi-dimensional detection module includes a road surface smoothness sensor, an ultrasonic roadbed detector, a high-definition crack recognition camera, and an environmental sensor. The data processing module includes a data receiving unit, a fusion analysis unit, and a hazard assessment unit; The data receiving unit supports data format standardization processing and removes invalid data; The fusion analysis unit associates the road infrastructure information read by RFID tags with multi-dimensional detection data according to the positioning coordinates, and establishes a three-dimensional data model of infrastructure information, real-time detection data and historical data. A preset data threshold standard is established, which is dynamically adjusted based on the road design parameters stored in the RFID tags. By comparing real-time detection data with threshold standards, the improved YOLOv8 target detection algorithm is used to filter out abnormal data that exceed the threshold and mark them as suspected potential hazards. The hazard assessment unit establishes an assessment model based on the gradient boosting decision tree algorithm, and processes and calculates the hazard assessment value by the monitoring data through the assessment model; Based on the assessment values, hazard levels are classified, and a hazard assessment report is generated. The positioning and communication module integrates a GPS / BeiDou dual-mode positioning unit and a 5G communication unit; The location unit is linked to hazard data and timestamps; The 5G communication unit uses the TCP / IP encrypted transmission protocol to upload positioning data, detection data and evaluation results to the cloud management platform in real time; It supports resume function and automatically switches to 4G network in areas with weak signal to ensure that no data is lost; Receive instructions from the cloud platform to adjust the detection route, calibrate parameters, and pause tasks; The power module adopts a vehicle-mounted 12V / 24V dual voltage input, paired with a 200Ah lithium iron phosphate backup lithium battery, and outputs a stable 12V / 16.7A voltage through the Mean Well SD-200B-12 power converter; it has overvoltage, overcurrent and overtemperature protection functions, and supports the testing equipment to work continuously for no less than 8 hours.

[0005] Preferably, the pre-setting and information storage of RFID tags meet the specific parameter requirements of the RFID interaction module.

[0006] Preferably, the data acquisition of the multi-dimensional detection module meets the specific parameter requirements of the multi-dimensional detection module.

[0007] Preferably, the fusion analysis of the data processing module conforms to the specific parameter requirements of the data processing module.

[0008] Preferably, the assessment process of the hazard assessment unit conforms to the specific parameter requirements of the hazard assessment unit.

[0009] Preferably, the operation of the positioning communication module conforms to the specific parameter requirements of the positioning communication module.

[0010] A method for detecting road hazards based on RFID technology, applied to the aforementioned RFID-based intelligent road hazard detection vehicle, includes: Detection preparation phase: The positioning and communication module receives the detection route, area range, and parameter requirements from the cloud management platform; the RFID reader starts preheating, the tag activation unit enters standby mode, the multi-dimensional detection module completes equipment self-test, and the IMU is initialized; Data acquisition phase: The inspection vehicle travels along a preset route, with the speed set according to road grade: 60-80 km / h for highways, 40-60 km / h for urban arterial roads, and 20-40 km / h for rural roads; RFID readers, triggered by the tag activation unit, establish communication with the preset RFID tags on the road and read the basic information and historical data stored in the tags; simultaneously, multi-dimensional detection modules collect data synchronously: the road surface smoothness sensor samples at 100Hz, the ultrasonic roadbed detector collects one set of data every 0.5 meters, the high-definition crack recognition camera collects images at 30fps, and the environmental sensor collects temperature, humidity, and light data at 1Hz; the positioning unit records the latitude and longitude coordinates of the collection points in real time; Data fusion and analysis phase: The data processing module receives the collected data at 10ms / frame, performs format standardization and removes invalid data; the fusion analysis unit uses the improved YOLOv8 algorithm to associate and fuse RFID tag information with real-time detection data according to positioning coordinates, compares it with dynamic threshold standards, and screens suspected potential hazards; Hazard assessment and traceability phase: The assessment value Y is calculated using the GBDT hazard assessment model to classify hazard levels and generate an assessment report; the hazard information and assessment results of this inspection are written into the corresponding RFID tags, the tag storage data is updated, and the full lifecycle traceability of hazards is realized; Data upload and feedback phase: The positioning and communication module uploads complete test data to the cloud platform in batches; the cloud platform generates a visual test report and rectification suggestions, which are fed back to the relevant management departments, and at the same time, instructions for subsequent test tasks are issued.

[0011] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned RFID-based intelligent road hazard detection vehicle.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention combines the advantages of RFID technology in wireless identification and data storage to achieve efficient, accurate, and traceable detection of road hazards. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a block diagram of a smart road hazard detection vehicle based on RFID technology according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, as Figure 1 As shown, the present invention is a Dingtan intelligent road hazard detection vehicle based on RFID technology, including a vehicle body, an RFID interaction module, a multi-dimensional detection module, a data processing module, a positioning and communication module, and a power supply module; The vehicle body adopts a lightweight modification design, for example, based on the Jiangling Yuhu 7 pickup truck chassis, with a curb weight of ≤2.2t, a wheelbase of 3085mm, an electric liftable detection component mounting rack at the bottom with a travel range of 0-30cm, a load capacity of ≥50kg, and a lifting accuracy of ±1mm. The top integrates a communication antenna and positioning device mounting base, and the interior is reserved with a 19-inch standard rack mounting space with a depth of 600mm to adapt to the detection operation needs of different road conditions. The RFID interaction module includes an RFID tag, an RFID reader / writer, and a tag activation unit; The RFID tag uses the Impinj Monza R6-P chip, is encapsulated in corrosion-resistant 304 stainless steel, has an IP68 protection rating, operates at a frequency of 902-928MHz, has a read / write distance of 0.3-1.5m, a storage capacity of 2KB, supports 100,000 erase / write cycles, and is suitable for working environments from -40℃ to 85℃. RFID tags are pre-installed according to road sections and zones. The spacing between tags is set according to the road grade: one tag every 50 meters for highways, one tag every 100 meters for urban main roads, and one tag every 200 meters for rural roads. The top of the tag should be 8-10 cm away from the road surface. A special positioning clamp is used during installation to ensure that the horizontal error is ≤0.5°. Each RFID tag stores a unique EPC code (64 bits), which is associated with the construction parameters of the corresponding road section (subgrade and pavement material type, structural layer thickness ±0.1cm, design load-bearing capacity), historical inspection data (inspection timestamp, hazard type code, and handling results), and maintenance records (maintenance time, maintenance method, and material type). The RFID reader / writer selected is Impinj Speedway R220, which supports EPC Gen2 protocol, has an adjustable output power of 16-30dBm, a reading rate of ≥1200 tags / second, and communicates with the data processing module via RS485 interface; The tag activation unit uses a high-power radio frequency wake-up module, model SYN500R, with a wake-up distance ≥2m and a response time ≤10ms, and works synchronously with the reader / writer; The multi-dimensional detection module includes a road surface smoothness sensor, an ultrasonic roadbed detector, a high-definition crack recognition camera, and an environmental sensor. The road surface evenness sensor uses a Trimble SPS985 GNSS receiver with a positioning accuracy of ±3mm +0.5ppm; the inertial measurement unit (IMU) uses an MTi-680G with a sampling frequency of 100Hz, and the error in calculating the International Roughness Index (IRI) is ≤0.1m / km. The ultrasonic roadbed detector used is the Panametrics-NDT 5072PR, with a continuously adjustable transmission frequency from 20kHz to 1MHz. The probe is a 2.25MHz dual-crystal straight probe, with a detection depth range of 0-3m, a resolution of ±2mm, and an acoustic impedance measurement range of 1.0×10⁻⁶. 6 ~1.5×10 7 kg / (m²・s), data is transmitted via Ethernet interface; The high-definition crack detection camera uses a Basler acA2500-14uc (2592×1944 resolution, 14fps frame rate, CMOS sensor, 3.45μm pixel size), paired with a Fujinon HF35SA-1 lens with a focal length of 35mm and an aperture of F1.4. A polarizing filter is added to eliminate road surface reflection interference. The crack width detection accuracy is 0.1mm, and the depth detection error is ≤0.5mm. The camera is installed at a height of 1.2m, at an angle of 60° to the road surface, and transmits image data through a GigE Vision interface. The environmental sensors used are the SHT30 temperature and humidity sensor (accuracy ±0.5℃, ±2%RH) and the TSL2591 light intensity sensor (measurement range 0-65535 lux, accuracy ±10 lux), which are integrated into the camera housing. The data update rate is 1Hz and communication is via I2C interface. The data processing module includes a data receiving unit, a fusion analysis unit, and a hazard assessment unit; The data receiving unit uses an Advantech UNO-2484G industrial PC (Intel Core i7-11850H processor, 16GB DDR4 memory, 512GB NVMe SSD), equipped with 4 RS485 interfaces, 2 Gigabit Ethernet interfaces, and 2 USB 3.0 interfaces. It supports standardized data format processing (JSON format encapsulation), removes invalid data, and uses the 3σ criterion to filter out abnormal values ​​caused by sensor failure and signal interference. The fusion analysis unit is developed using the Python programming language and integrates the TensorFlow 2.8 deep learning framework. It associates the basic road information read by RFID tags with multi-dimensional detection data according to the positioning coordinates (error ≤ 5m) to establish a three-dimensional data model of "basic information - real-time detection data - historical data". A preset data threshold standard is established, which is dynamically adjusted based on the road design parameters stored in the RFID tag; for example: 0.3 mm for the width of cracks in highway pavement, 0.5 mm for urban arterial roads, and 0.8 mm for rural roads. By comparing real-time detection data with threshold standards, the improved YOLOv8 target detection algorithm filters out abnormal data exceeding the threshold and marks them as suspected potential hazards. The algorithm's recognition accuracy is ≥95% and the false detection rate is ≤3%. The hazard assessment unit establishes an assessment model based on the Gradient Boosting Decision Tree (GBDT) algorithm. The assessment model expression is as follows: Y= \alpha \cdot X_1 + \beta \cdot X_2 + \gamma \cdot X_3; In the formula: Y is the hazard assessment value, 0-1; X1 is the deviation coefficient between real-time detection data and the threshold, calculated as: (measured value - threshold) / threshold; X2 is the historical hazard recurrence probability, calculated using Bayes' theorem based on historical data stored in RFID tags; X3 is the road service life coefficient, service life / design service life; α, β, and γ are weighting coefficients, set according to the road grade. Highway: α=0.4, β=0.3, γ=0.3; Urban main roads: α=0.35, β=0.35, γ=0.3; Rural roads: α=0.3, β=0.3, γ=0.4; For roads that have been in use for more than 10 years, the γ coefficient increases by 0.1, with a maximum of 0.5. Hazard levels are classified according to the assessment value Y: Y < 0.3 is a minor hazard, 0.3 ≤ Y < 0.7 is a moderate hazard, and Y ≥ 0.7 is a serious hazard. Combine location data to determine the precise location of the hazard (latitude and longitude error ≤ 5m) and its range of influence (classified by crack length / cavity diameter: minor ≤ 1m, moderate 1-3m, severe > 3m), and generate a hazard assessment report; The positioning and communication module integrates a GPS / BeiDou dual-mode positioning unit and a 5G communication unit; The positioning unit uses Huaxin Antenna Ublox F9P module, which supports GPS L1 / L5 and Beidou B1C / B2a frequency bands, with positioning accuracy ≤5m (single point positioning) and ≤1m (RTK differential positioning), and data update rate of 10Hz. It is bound to hidden danger data and timestamp (accurate to milliseconds). The 5G communication unit uses Huawei ME909s-821, which supports SA / NSA dual mode, downlink speed ≥1Gbps, uplink speed ≥100Mbps, and adopts TCP / IP encrypted transmission protocol (AES-256 encryption) to upload positioning data, detection data and evaluation results to the cloud management platform in real time. It supports breakpoint resume function, data cache capacity ≥10GB, and automatically switches to 4G network in areas with weak signal (signal-to-noise ratio <10dB) to ensure no data loss; Receive instructions from the cloud platform such as adjusting the detection route, calibrating parameters, and pausing tasks, with an instruction response time of ≤500ms; The power module adopts a dual voltage input of 12V / 24V for vehicle use, adaptable to different vehicle models. It is equipped with a 200Ah lithium iron phosphate backup lithium battery and outputs a stable voltage of 12V / 16.7A through the Mean Well SD-200B-12 power converter. It has overvoltage (protection threshold 16V), overcurrent (protection threshold 20A), and overtemperature (protection threshold 85℃) protection functions, and supports the continuous operation of the testing equipment for no less than 8 hours (≥4 hours when powered by lithium battery alone).

[0017] The pre-installation and information storage of RFID tags meet the specific parameter requirements of the aforementioned RFID interaction module; The data acquisition of the multi-dimensional detection module meets the specific parameter requirements of the multi-dimensional detection module mentioned above. The fusion analysis of the data processing module meets the specific parameter requirements of the aforementioned data processing module; The assessment process of the hazard assessment unit conforms to the specific parameter requirements of the aforementioned hazard assessment unit; The operation of the positioning communication module conforms to the specific parameter requirements of the positioning communication module mentioned above. Example 2: A method for detecting hidden dangers on roads using RFID technology, comprising: Detection preparation phase: The positioning communication module receives the detection route (KML format file), area range (latitude and longitude coordinate selection), and parameter requirements (detection speed, sampling frequency, threshold standard) from the cloud management platform; the RFID reader starts warm-up (warm-up time 30 seconds), the tag activation unit enters standby state, the multi-dimensional detection module completes equipment self-test (including sensor communication link, data acquisition accuracy calibration), and IMU initialization (static calibration time 60 seconds, calibration error ≤0.1°); Data acquisition phase: The inspection vehicle travels along a preset route at a speed set according to road grade: 60-80 km / h for highways, 40-60 km / h for urban arterial roads, and 20-40 km / h for rural roads. Triggered by the tag activation unit, the RFID reader establishes communication with the pre-set RFID tags on the road, reading the basic information and historical data stored in the tags (reading success rate ≥98%). Simultaneously, multi-dimensional detection modules collect data: the road surface smoothness sensor samples at 100Hz, the ultrasonic roadbed detector collects one set of data every 0.5 meters, the high-definition crack recognition camera captures images at 30fps, and the environmental sensor collects temperature, humidity, and light data at 1Hz. The positioning unit records the latitude and longitude coordinates of the collection points in real time (update rate 10Hz). Data fusion and analysis phase: The data processing module receives collected data at 10ms / frame, performs format standardization (JSON encapsulation, fields include device ID, timestamp, location coordinates, detection value, environmental parameters) and invalid data removal (3σ criterion); The fusion analysis unit uses the improved YOLOv8 algorithm to associate and fuse RFID tag information with real-time detection data according to location coordinates, compares it with dynamic threshold standards, and filters suspected potential hazards (identification delay ≤500ms). Hazard assessment and traceability phase: The assessment value Y is calculated using the GBDT hazard assessment model, the hazard level is classified, and an assessment report is generated (including hazard location, type, level, impact range, and development trend prediction); the hazard information and assessment results of this inspection are written into the corresponding RFID tag (writing success rate ≥99%), the tag storage data is updated, and the full life cycle traceability of the hazard is realized; Data upload and feedback phase: The positioning and communication module uploads complete test data (raw data + fused data + evaluation report) to the cloud platform in batches (100 sets of data constitute one batch); the cloud platform generates a visualized test report (PDF format) and rectification suggestions (including rectification priority, technical solutions, and material selection), and feeds them back to the relevant management departments, while issuing instructions for subsequent test tasks.

[0018] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0019] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0020] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0021] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0022] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A road hazard detection vehicle based on RFID technology, characterized in that, It includes the vehicle-mounted main unit, RFID interaction module, multi-dimensional detection module, data processing module, positioning and communication module, and power supply module; The vehicle-mounted main body adopts a lightweight modification design, with an electrically adjustable detection component mounting bracket at the bottom and a communication antenna and positioning device mounting base integrated at the top. It also has a reserved 19-inch standard rack mounting space inside, which can adapt to the detection operation needs of different road conditions. The RFID interaction module includes an RFID tag, an RFID reader / writer, and a tag activation unit; RFID tags are pre-installed according to road segment zones, with tag spacing set according to road grade; the top of the tag is 8-10cm away from the road surface. Each RFID tag stores a unique EPC code, which is associated with the construction parameters, historical inspection data and maintenance records of the corresponding road section; The RFID reader / writer communicates with the data processing module via an RS485 interface; The tag activation unit uses a high-power radio frequency wake-up module, which works synchronously with the reader / writer; The multi-dimensional detection module includes a road surface smoothness sensor, an ultrasonic roadbed detector, a high-definition crack recognition camera, and an environmental sensor. The data processing module includes a data receiving unit, a fusion analysis unit, and a hazard assessment unit; The data receiving unit supports data format standardization processing and removes invalid data; The fusion analysis unit associates the road infrastructure information read by RFID tags with multi-dimensional detection data according to the positioning coordinates, and establishes a three-dimensional data model of infrastructure information, real-time detection data and historical data. A preset data threshold standard is established, which is dynamically adjusted based on the road design parameters stored in the RFID tags. By comparing real-time detection data with threshold standards, the improved YOLOv8 target detection algorithm is used to filter out abnormal data that exceed the threshold and mark them as suspected potential hazards. The hazard assessment unit establishes an assessment model based on the gradient boosting decision tree algorithm, and processes and calculates the hazard assessment value by the monitoring data through the assessment model; Based on the assessment values, hazard levels are classified, and a hazard assessment report is generated. The positioning and communication module integrates a GPS / BeiDou dual-mode positioning unit and a 5G communication unit; The location unit is linked to hazard data and timestamps; The 5G communication unit uses the TCP / IP encrypted transmission protocol to upload positioning data, detection data and evaluation results to the cloud management platform in real time; It supports resume function and automatically switches to 4G network in areas with weak signal to ensure that no data is lost; Receive instructions from the cloud platform to adjust the detection route, calibrate parameters, and pause tasks; The power module adopts a vehicle-mounted 12V / 24V dual voltage input, paired with a 200Ah lithium iron phosphate backup lithium battery, and outputs a stable 12V / 16.7A voltage through the Mean Well SD-200B-12 power converter; it has overvoltage, overcurrent and overtemperature protection functions, and supports the testing equipment to work continuously for no less than 8 hours.

2. The RFID-based intelligent road hazard detection vehicle according to claim 1, characterized in that, The pre-installation and information storage of RFID tags meet the specific parameter requirements of the RFID interaction module.

3. The RFID-based intelligent road hazard detection vehicle according to claim 1, characterized in that, The data acquisition of the multi-dimensional detection module meets the specific parameter requirements of the multi-dimensional detection module.

4. The RFID-based intelligent road hazard detection vehicle according to claim 1, characterized in that, The fusion analysis of the data processing module meets the specific parameter requirements of the data processing module.

5. The RFID-based intelligent road hazard detection vehicle according to claim 1, characterized in that, The assessment process of the hazard assessment unit conforms to the specific parameter requirements of the hazard assessment unit.

6. The RFID-based intelligent road hazard detection vehicle according to claim 1, characterized in that, The operation of the positioning communication module conforms to the specific parameter requirements of the positioning communication module.

7. A method for detecting potential road hazards using RFID-based pin-type sensors, characterized in that, The method, applied to an RFID-based intelligent road hazard detection vehicle as described in any one of claims 1 to 6, comprises: Detection preparation phase: The positioning and communication module receives the detection route, area range, and parameter requirements from the cloud management platform; the RFID reader starts preheating, the tag activation unit enters standby mode, the multi-dimensional detection module completes equipment self-test, and the IMU is initialized; Data acquisition phase: The inspection vehicle travels along a preset route, with the speed set according to road grade: 60-80 km / h for highways, 40-60 km / h for urban arterial roads, and 20-40 km / h for rural roads; RFID readers, triggered by the tag activation unit, establish communication with the preset RFID tags on the road and read the basic information and historical data stored in the tags; simultaneously, multi-dimensional detection modules collect data synchronously: the road surface smoothness sensor samples at 100Hz, the ultrasonic roadbed detector collects one set of data every 0.5 meters, the high-definition crack recognition camera collects images at 30fps, and the environmental sensor collects temperature, humidity, and light data at 1Hz; the positioning unit records the latitude and longitude coordinates of the collection points in real time; Data fusion and analysis phase: The data processing module receives the collected data at 10ms / frame, performs format standardization and removes invalid data; the fusion analysis unit uses the improved YOLOv8 algorithm to associate and fuse RFID tag information with real-time detection data according to positioning coordinates, compares it with dynamic threshold standards, and screens suspected potential hazards; Hazard assessment and traceability phase: The assessment value Y is calculated using the GBDT hazard assessment model to classify hazard levels and generate an assessment report; the hazard information and assessment results of this inspection are written into the corresponding RFID tags, the tag storage data is updated, and the full lifecycle traceability of hazards is realized; Data upload and feedback phase: The positioning and communication module uploads complete test data to the cloud platform in batches; the cloud platform generates a visual test report and rectification suggestions, which are fed back to the relevant management departments, and at the same time, instructions for subsequent test tasks are issued.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a RFID-based intelligent road hazard detection vehicle as described in any one of claims 1 to 6.

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