Intelligent logistics waybill authenticity inspection model system

Abstract

The invention belongs to the technical field of intelligent logistics, and particularly relates to an intelligent logistics waybill authenticity inspection model system which comprises the following specific steps: S1, automatically generating a dynamic fence based on waybill information; when the loading and unloading place of the waybill is changed, receiving an updating instruction issued by the platform, completing fence coordinate updating within 10 seconds, and pushing a fence change prompt to a driver end to avoid card punching failure caused by place change; s2, acquiring key original data of a waybill full life cycle from multiple dimensions, constructing a data basic library for waybill authenticity inspection, and ensuring that inspection dimensions cover a waybill performance core link; according to the invention, through the precise spatial constraint of the dynamic electronic fence module and the cooperative verification of the identity sensing unit of the multi-dimensional sensing module, the cheating behaviors of card punching, virtual positioning and waybill progress forgery are completely eradicated.

Description

Technical Field

[0001] This invention relates to the field of smart logistics technology, specifically to a smart logistics waybill authenticity verification model system. Background Technology

[0002] With the rapid development of e-commerce and new retail, smart logistics has become a core link supporting the efficient operation of the supply chain. Waybills, as the core credential for logistics services, directly impact order settlement, cargo traceability, liability determination, and platform reputation. Currently, while mainstream smart logistics platforms have achieved online management of waybills, significant technical shortcomings remain in the waybill authenticity verification process.

[0003] 1. Significant issues with manual check-in fraud: Current waybill check-in systems largely rely on drivers manually triggering location uploads or manually filling out forms, leading to scenarios such as proxy check-ins and virtual location fraud. For example, in a cross-provincial trunk line transport, a driver did not actually arrive at the loading point in location A, but used mobile phone virtual location software to change their location to the loading point coordinates and manually submitted a check-in record to indicate that loading was completed. This caused the platform to misjudge the waybill progress, and the discrepancy between the waybill information and the actual transport was only discovered when subsequent investigations were conducted to determine responsibility for cargo delays.

[0004] 2. The limitations of electronic fence technology: Existing logistics electronic fences are mostly static fences with a fixed radius (such as a circular area with a radius of 100 meters centered on the loading point), which cannot adapt to complex scenarios. For example, in cold chain logistics, if the loading point is a large cold chain warehouse park (over 10,000 square meters), the static fence only covers the park gate. Drivers can trigger their attendance record by uploading their location from the parking lot outside the park, without actually entering the warehouse to complete the goods handover; or when the loading point is temporarily changed, the static fence cannot be updated in real time, causing the attendance record to fail.

[0005] 3. Single dimension for assessing the authenticity of waybills: Existing verification methods mostly rely on location and time dual-factor verification, without linking key data during transportation. For example, in a certain city distribution waybill, although the driver arrived at the loading site within the fenced area within the specified time, the vehicle was not turned off (no loading or unloading action), and the cargo weight sensor showed no change (no actual loading). The platform only judged the waybill valid based on location, ultimately leading to empty waybills being mistakenly judged as normal waybills, resulting in settlement losses for the platform.

[0006] 4. Low efficiency of post-transportation review: Most platforms adopt a post-transportation verification model that relies on manual spot checks of waybill tracks and attendance records. When the daily waybill volume exceeds 100,000, the manual review coverage rate is less than 5%, and the discovery of abnormal waybills is delayed (on average, more than 24 hours), making it impossible to intercept the risks brought by fake waybills in a timely manner.

[0007] In summary, existing technologies cannot achieve real-time, multi-dimensional, and adaptive verification of waybill authenticity. Therefore, an intelligent logistics waybill authenticity verification model system is invented. Summary of the Invention

[0008] This invention provides the following technical solution:

[0009] A smart logistics waybill authenticity verification model method includes the following specific steps:

[0010] S1: First, automatically generate a dynamic fence based on the waybill information; then, when the loading and unloading location of the waybill changes, receive the update instruction issued by the platform, complete the fence coordinate update within 10 seconds, and push the fence change reminder to the driver's end to avoid the failure of check-in due to location change;

[0011] S2: Collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill to build a data foundation library for verifying the authenticity of the waybill, ensuring that the verification dimensions cover the core aspects of waybill fulfillment;

[0012] S3: Focusing on sensing the operating status of equipment and the validity of data, it diagnoses the health of equipment in real time, compensates for failed / abnormal data, and triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, eliminating the risk of inspection misjudgment caused by equipment problems;

[0013] S4: Using multi-dimensional perception data as input, a scoring model is built based on a fusion machine learning algorithm to output a quantitative score of the authenticity of waybills and to classify them as real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback from manual review results to ensure that the inspection accuracy is continuously improved with the accumulation of scenarios, thus replacing traditional manual review and realizing efficient and intelligent judgment of large-scale waybills.

[0014] S5: A closed-loop mechanism is built around the model output results, which not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration and scenario adaptation upgrade through data accumulation.

[0015] As a preferred embodiment of the intelligent logistics waybill authenticity verification model method described in this invention, the specific steps of step S2 are as follows:

[0016] S21: Integrates driver facial recognition and vehicle terminal device ID binding to prevent proxy attendance;

[0017] S22: Collect the real-time location and motion status of the vehicle through the vehicle-mounted GPS and gyroscope to determine whether the vehicle is in a stationary working state;

[0018] S23: Collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors and temperature and humidity sensors to verify the authenticity of loading and unloading actions;

[0019] S24: Synchronize satellite time with platform server time to ensure that the check-in timestamp cannot be tampered with.

[0020] As a preferred embodiment of the intelligent logistics waybill authenticity verification model method described in this invention, the specific steps of S3 are as follows:

[0021] S31: First, collect the operating parameters of the devices under the multi-dimensional sensing module in real time; then generate the device health status identifier.

[0022] S32: First, for scenarios with weak or transient signal loss, if the GPS signal is lost, the vehicle's movement trajectory collected by the gyroscope is matched with historical routes to calculate the vehicle's position during the period of loss and complete the trajectory data; then, for scenarios with single device failure, if the weight sensor fails, the equivalent weight change value is generated by fusion calculation of vehicle fuel consumption changes and loading / unloading time windows to replace the failed sensor data; finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor shows instantaneous jumps, abnormal values ​​are automatically removed, and the average value of the preceding and following 1 minute is used to complete the data to avoid misjudging abnormalities in cold chain goods;

[0023] S33: When an equipment malfunction is detected, an equipment fault reminder is immediately pushed to the driver's end, and simultaneously synchronized to the platform administrator; and if the equipment fault cannot be repaired in time, a backup inspection plan is automatically triggered.

[0024] As a preferred embodiment of the intelligent logistics waybill authenticity verification model method described in this invention, the specific steps of step S4 are as follows:

[0025] S41: Extract the core features of the multi-dimensional perception module;

[0026] S42: First, a random forest + gradient boosting tree fusion algorithm is used to train the model with historical real and fake waybills as training data to output a waybill authenticity score; then, a score threshold is set. If the score is ≥80, it is a real waybill and automatically passes the verification; if the score is 40-79, it is a suspicious waybill and triggers a light manual review; if the score is <40, it is a fake waybill and immediately intercepts and freezes the waybill settlement.

[0027] S43: Use manual review results and subsequent fulfillment data of waybills as feedback data to iterate and optimize the model weekly to improve the accuracy of feature weights.

[0028] As a preferred embodiment of the intelligent logistics waybill authenticity verification model method described in this invention, the specific steps of S5 are as follows:

[0029] S51: When the model determines a suspicious / fake waybill, it will push a warning message to the platform administrator and the shipper in real time;

[0030] S52: Provides a visual audit interface that displays multi-dimensional perception data of waybills. Auditors can mark real / fake with one click and fill in audit remarks.

[0031] S53: Store the inspection results and audit records of all waybills in a distributed database and synchronize them to blockchain nodes to ensure that the data is tamper-proof and can be used for subsequent accountability.

[0032] S54: Only genuine waybills trigger automatic settlement. Fake waybills require manual review and confirmation of rectification before the settlement process can be unfrozen to avoid economic losses for the platform.

[0033] A smart logistics waybill authenticity verification model system includes:

[0034] The dynamic electronic fence module is used to automatically generate a dynamic fence based on the waybill information. Then, when the loading and unloading location of the waybill changes, it receives the update instruction issued by the platform, completes the fence coordinate update within 10 seconds, and pushes the fence change reminder to the driver's end to avoid the failure of check-in due to the change of location.

[0035] The multi-dimensional perception module is used to collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill, build a data foundation library for verifying the authenticity of the waybill, and ensure that the verification dimensions cover the core links of waybill fulfillment.

[0036] The equipment health monitoring and adaptive compensation module is used to focus on sensing the operating status of equipment and the validity of data. On the one hand, it diagnoses the health of equipment in real time, and on the other hand, it compensates for failure / abnormal data. At the same time, it triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, and eliminates the risk of inspection misjudgment caused by equipment problems.

[0037] The waybill authenticity verification model module is used to take multi-dimensional perception data as input, build a scoring model based on fusion machine learning algorithms, output a quantitative score of waybill authenticity, and realize the classification of real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback of manual review results to ensure that the verification accuracy is continuously improved with the accumulation of scenarios, and replace the traditional manual review to realize efficient and intelligent judgment of large-scale waybills.

[0038] The closed-loop management module is used to build a closed-loop mechanism around the model output results. It not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration, and scenario adaptation upgrades through data accumulation.

[0039] As a preferred embodiment of the intelligent logistics waybill authenticity verification model system described in this invention, the multi-dimensional perception module includes:

[0040] The identity perception unit is used to integrate driver facial recognition and vehicle terminal device ID binding to prevent proxy attendance.

[0041] The position and motion sensing unit is used to collect the real-time position and motion status of the vehicle through the vehicle GPS and gyroscope to determine whether the vehicle is in a stationary working state.

[0042] The cargo sensing unit is used to collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors and temperature and humidity sensors to verify the authenticity of loading and unloading actions;

[0043] The time sensing unit is used to synchronize satellite time with the platform server time to ensure that the check-in timestamp cannot be tampered with.

[0044] As a preferred embodiment of the intelligent logistics waybill authenticity verification model system described in this invention, the equipment health monitoring and adaptive compensation module includes:

[0045] The equipment health monitoring unit is used to first collect the operating parameters of the equipment under the multi-dimensional sensing module in real time; then it generates the equipment health status identifier.

[0046] The adaptive data compensation unit is used to first address scenarios with weak or transient signal loss. If the GPS signal is lost, it matches the vehicle's movement trajectory collected by the gyroscope with historical routes to calculate the vehicle's position during the lost period and complete the trajectory data. Next, for single device failure scenarios, if the weight sensor fails, it calculates an equivalent weight change value by fusing changes in vehicle fuel consumption with loading and unloading time windows to replace the failed sensor data. Finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor shows a sudden jump, it automatically removes outliers and uses the average of the preceding and following minutes to complete the data, avoiding misjudging abnormalities in cold chain goods.

[0047] The equipment early warning and linkage unit is used to immediately push equipment fault reminders to the driver's terminal when an equipment abnormality is detected, and simultaneously synchronize with the platform administrator; and automatically trigger the backup inspection plan when the equipment fault cannot be repaired in time.

[0048] As a preferred embodiment of the intelligent logistics waybill authenticity verification model system described in this invention, the waybill authenticity verification model module includes:

[0049] Feature input unit, used to extract core features of the multi-dimensional perception module;

[0050] The model training and inference unit first uses a random forest + gradient boosting tree fusion algorithm to train the model to output a waybill authenticity score using historical real waybills and fake waybills as training data. Then, a score threshold is set: if the score is ≥80, the waybill is real and automatically passes the verification; if the score is 40-79, the waybill is suspicious and triggers a light manual review; if the score is <40, the waybill is fake and the waybill settlement is immediately blocked.

[0051] The model iteration unit is used to iterate and optimize the model weekly, taking the results of manual review and subsequent fulfillment data of waybills as feedback data, to improve the accuracy of feature weights.

[0052] As a preferred embodiment of the intelligent logistics waybill authenticity verification model system described in this invention, the closed-loop management module includes:

[0053] The anomaly warning unit is used to push warning information to the platform administrator and the shipper in real time when the model determines that the waybill is suspicious / fake.

[0054] The manual review unit provides a visual review interface that displays multi-dimensional perception data of the waybill. Reviewers can mark it as real or fake with one click and fill in review remarks.

[0055] The data storage unit is used to store the inspection results and audit records of all waybills into a distributed database and synchronize them to the blockchain node to ensure that the data is tamper-proof and can be used for subsequent accountability.

[0056] The settlement linkage unit is used to trigger automatic settlement only for genuine waybills. Fake waybills must be manually reviewed and confirmed for rectification before the settlement process can be unfrozen, thus avoiding economic losses for the platform.

[0057] Compared with existing technologies:

[0058] 1. Through the precise spatial constraints of the dynamic electronic fence module and the collaborative verification of the identity perception unit of the multi-dimensional perception module, it can effectively prevent cheating behaviors such as proxy attendance, virtual positioning, and falsification of waybill progress.

[0059] 2. The dynamic electronic fence module adaptively generates polygonal fences based on waybill information, supports manual selection of latitude and longitude of temporary loading and unloading locations, and updates fence coordinates in real time within 10 seconds, enabling it to adapt to complex loading and unloading scenarios and temporary location changes.

[0060] 3. By collecting full-process data such as driver identity, vehicle location and actions, cargo status, timestamps, and transportation trajectories through the multi-dimensional perception module, and combining the waybill authenticity verification model module to perform fusion analysis of multiple features, it can realize waybill authenticity assessment from location + time dual elements to multi-dimensional full-process assessment, avoiding misjudgment caused by a single dimension;

[0061] 4. Through the real-time scoring and classification of the waybill authenticity verification model module and the real-time anomaly warning and automatic settlement linkage of the closed-loop management module, it can realize the transformation from the post-event mode of transportation first and then review to the efficient review of real-time verification and instant interception, and improve the timeliness of abnormal waybill detection and processing. Attached Figure Description

[0062] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0063] Figure 2 This is a schematic diagram of the multi-dimensional perception module framework of the present invention;

[0064] Figure 3 This is a schematic diagram of the device health monitoring and adaptive compensation module framework of the present invention;

[0065] Figure 4 This is a schematic diagram of the module framework for the waybill authenticity verification model of the present invention;

[0066] Figure 5 This is a schematic diagram of the closed-loop management module framework of the present invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0068] This invention provides a smart logistics waybill authenticity verification model and method. Please refer to [link / reference]. Figures 1-5 The specific steps are as follows:

[0069] S1: First, automatically generate a dynamic fence based on the waybill information; then, when the loading and unloading location of the waybill changes, receive the update instruction issued by the platform, complete the fence coordinate update within 10 seconds, and push the fence change reminder to the driver's end to avoid the failure of check-in due to location change;

[0070] S2: Collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill to build a data foundation library for verifying the authenticity of the waybill, ensuring that the verification dimensions cover the core aspects of waybill fulfillment;

[0071] The specific steps of S2 are as follows:

[0072] S21: Integrates driver facial recognition and vehicle terminal device ID binding to prevent proxy attendance;

[0073] S22: Collect the real-time location and motion status of the vehicle through the vehicle-mounted GPS and gyroscope to determine whether the vehicle is in a stationary working state;

[0074] S23: Collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors and temperature and humidity sensors to verify the authenticity of loading and unloading actions;

[0075] S24: Synchronize satellite time with platform server time to ensure that the check-in timestamp cannot be tampered with;

[0076] S3: Focusing on sensing the operating status of equipment and the validity of data, it diagnoses the health of equipment in real time, compensates for failed / abnormal data, and triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, eliminating the risk of inspection misjudgment caused by equipment problems;

[0077] The specific steps of S3 are as follows:

[0078] S31: First, collect the operating parameters of the devices under the multi-dimensional sensing module in real time; then generate the device health status identifier.

[0079] S32: First, for scenarios with weak or transient signal loss, if the GPS signal is lost, the vehicle's movement trajectory collected by the gyroscope is matched with historical routes to calculate the vehicle's position during the period of loss and complete the trajectory data; then, for scenarios with single device failure, if the weight sensor fails, the equivalent weight change value is generated by fusion calculation of vehicle fuel consumption changes and loading / unloading time windows to replace the failed sensor data; finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor shows instantaneous jumps, abnormal values ​​are automatically removed, and the average value of the preceding and following 1 minute is used to complete the data to avoid misjudging abnormalities in cold chain goods;

[0080] S33: When an equipment malfunction is detected, an equipment fault alert is immediately pushed to the driver's end, and simultaneously synchronized to the platform administrator; and if the equipment fault cannot be repaired in time, the backup inspection plan is automatically triggered.

[0081] S4: Using multi-dimensional perception data as input, a scoring model is built based on a fusion machine learning algorithm to output a quantitative score of the authenticity of waybills and to classify them as real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback from manual review results to ensure that the inspection accuracy is continuously improved with the accumulation of scenarios, thus replacing traditional manual review and realizing efficient and intelligent judgment of large-scale waybills.

[0082] The specific steps of S4 are as follows:

[0083] S41: Extract the core features of the multi-dimensional perception module;

[0084] S42: First, a random forest + gradient boosting tree fusion algorithm is used to train the model with historical real and fake waybills as training data to output a waybill authenticity score; then, a score threshold is set. If the score is ≥80, it is a real waybill and automatically passes the verification; if the score is 40-79, it is a suspicious waybill and triggers a light manual review; if the score is <40, it is a fake waybill and immediately intercepts and freezes the waybill settlement.

[0085] S43: Use manual review results and subsequent fulfillment data of waybills as feedback data to iterate and optimize the model weekly to improve the accuracy of feature weights;

[0086] S5: A closed-loop mechanism is built around the model output results, which not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration and scenario adaptation upgrade through data accumulation;

[0087] The specific steps of S5 are as follows:

[0088] S51: When the model determines a suspicious / fake waybill, it will push a warning message to the platform administrator and the shipper in real time;

[0089] S52: Provides a visual audit interface that displays multi-dimensional perception data of waybills. Auditors can mark real / fake with one click and fill in audit remarks.

[0090] S53: Store the inspection results and audit records of all waybills in a distributed database and synchronize them to blockchain nodes to ensure that the data is tamper-proof and can be used for subsequent accountability.

[0091] S54: Only genuine waybills trigger automatic settlement. Fake waybills require manual review and confirmation of rectification before the settlement process can be unfrozen to avoid economic losses for the platform.

[0092] A smart logistics waybill authenticity verification model system includes:

[0093] The dynamic electronic fence module is used to automatically generate a dynamic fence based on waybill information (loading / unloading location type, cargo attributes, and transportation mode). If the loading / unloading location is a fixed site (such as a logistics park or warehouse), the actual operating area of ​​the site (such as the coordinate range of the warehouse loading / unloading platform) is obtained through a high-precision map, and a polygonal fence (not a circular fence) is generated with an accuracy controlled within ±5 meters. If the loading / unloading location is a temporary location (such as a construction site or market), the shipper is allowed to upload the latitude and longitude range of the temporary location through the platform (manual selection is supported), and the fence is synchronized to the driver's device in real time after it is generated. If the cargo is a special category (such as dangerous goods or cold chain), the fence radius is reduced (such as 20 meters), and area permissions are associated (only designated vehicles can trigger check-in). Then, when the loading / unloading location of the waybill changes, the module receives an update instruction from the platform, completes the fence coordinate update within 10 seconds, and pushes a fence change reminder to the driver's device to avoid check-in failure due to location change.

[0094] The multi-dimensional perception module is used to collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill, build a data foundation library for verifying the authenticity of the waybill, and ensure that the verification dimensions cover the core links of waybill fulfillment.

[0095] The equipment health monitoring and adaptive compensation module is used to focus on sensing the operating status of equipment and the validity of data. On the one hand, it diagnoses the health of equipment in real time, and on the other hand, it compensates for failure / abnormal data. At the same time, it triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, and eliminates the risk of inspection misjudgment caused by equipment problems.

[0096] The waybill authenticity verification model module is used to take multi-dimensional perception data as input, build a scoring model based on fusion machine learning algorithms, output a quantitative score of waybill authenticity, and realize the classification of real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback of manual review results to ensure that the verification accuracy is continuously improved with the accumulation of scenarios, and replace the traditional manual review to realize efficient and intelligent judgment of large-scale waybills.

[0097] The closed-loop management module is used to build a closed-loop mechanism around the model output results. It not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration, and scenario adaptation upgrades through data accumulation.

[0098] The multi-dimensional perception module includes:

[0099] The identity perception unit is used to integrate driver facial recognition (supports offline recognition, false recognition rate ≤0.001%) and vehicle terminal device ID binding (uniquely associated with the driver's account) to prevent proxy clocking in;

[0100] The position and motion sensing unit is used to collect the real-time position and motion status of the vehicle (such as engine off / start, steering angle) through the vehicle-mounted GPS (positioning accuracy ±1 meter) and gyroscope to determine whether the vehicle is in a stationary working state.

[0101] The cargo sensing unit is used to collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors (accuracy ±0.5kg) and temperature and humidity sensors (suitable for cold chain) to verify the authenticity of loading and unloading actions.

[0102] The time sensing unit is used to synchronize satellite time with platform server time to ensure that the check-in timestamp cannot be tampered with (using blockchain for evidence storage, each check-in record generates a unique hash value).

[0103] The device health monitoring and adaptive compensation module includes:

[0104] The equipment health monitoring unit is used to collect the operating parameters of the equipment under the multi-dimensional sensing module in real time, such as GPS signal strength (below -120dBm is judged as weak signal), weight sensor calibration status (deviating from the standard value ±2% is judged as calibration failure), face recognition camera frame rate (below 15fps is judged as equipment lag), temperature and humidity sensor response delay (more than 1 second is judged as fault); then generate equipment health status identifiers and output "normal (1) / abnormal (0)" identifiers for each type of equipment. If ≥2 types of equipment in a single shipment are abnormal at the same time, an equipment warning will be triggered immediately.

[0105] The adaptive data compensation unit is used to first address scenarios with weak or transient signal loss. If the GPS signal is lost (e.g., in a tunnel), the vehicle's movement trajectory (speed, direction) collected by the gyroscope is matched with historical routes to calculate the vehicle's position during the lost period (error ≤ 10 meters) and complete the trajectory data. Next, for single-device failure scenarios, if the weight sensor fails, the unit calculates an equivalent weight change value by fusing changes in vehicle fuel consumption (fuel consumption increases by 15%-20% after loading) with the loading and unloading time window (compared with warehouse operation records) to replace the failed sensor data. Finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor experiences a sudden jump (e.g., from 3℃ to 20℃, and the duration is < 10 seconds), the unit automatically removes outliers and uses the average of the preceding and following minutes to complete the data, avoiding misjudgment of abnormal cold chain goods.

[0106] The equipment early warning and linkage unit is used to immediately push equipment fault reminders (including fault type and temporary operation suggestions, such as "please manually calibrate the weight sensor") to the driver's terminal when an equipment abnormality is detected (such as sensor calibration failure), and simultaneously synchronize with the platform administrator; and automatically trigger backup inspection schemes when equipment faults cannot be repaired in time (such as camera damage); for example, when the face recognition equipment fails, it switches to dual identity verification of "driver's ID card OCR recognition + vehicle terminal password verification" to ensure that the perception data is not interrupted.

[0107] The waybill authenticity verification model module includes:

[0108] The feature input unit is used to extract the core features of the multi-dimensional perception module. The core features include:

[0109] Fence matching feature (whether the vehicle is within the dynamic fence, if yes, record 1, otherwise record 0);

[0110] Authentication features (facial recognition pass rate, device binding consistency);

[0111] Action verification features (vehicle engine shutdown duration, loading and unloading action duration);

[0112] Cargo verification characteristics (weight change rate, whether temperature and humidity meet cargo requirements);

[0113] Time matching characteristics (the deviation between the actual clock-in time and the planned time on the waybill).

[0114] Track continuity characteristics (whether there are "jump points" or "disconnections" in the transportation track; jump points with a distance exceeding 500 meters are considered abnormal);

[0115] The model training and inference unit first uses a random forest + gradient boosting tree fusion algorithm to train the model using historical real waybills (labeled as "positive samples") and fake waybills (such as virtual location and fake attendance orders, labeled as "negative samples") as training data to output a waybill authenticity score (0-100 points). Then, a scoring threshold is set: if the score is ≥80 points, the waybill is real and automatically passes the verification; if the score is 40-79 points, the waybill is suspicious and triggers a light manual review; if the score is <40 points, the waybill is fake and the waybill settlement is immediately blocked.

[0116] The model iteration unit is used to iterate and optimize the model weekly using the results of manual review and subsequent fulfillment data of waybills (such as cargo receipt feedback) as feedback data, thereby improving the accuracy of feature weights (such as increasing the weight of the "trajectory jump point" feature for the "virtual location" scenario).

[0117] The closed-loop management module includes:

[0118] The anomaly warning unit is used to push warning information (including screenshots of abnormal features, such as location jump records and records of no change in weight) to the platform administrator and the shipper in real time when the model determines that the waybill is suspicious or fake.

[0119] The manual review unit provides a visual review interface that displays multi-dimensional perception data of the waybill (such as trajectory playback and sensor curves). Reviewers can mark the waybill as real or fake with one click and fill in review notes.

[0120] The data storage unit is used to store the inspection results and audit records of all waybills into a distributed database and synchronize them to the blockchain node to ensure that the data is tamper-proof and can be used for subsequent accountability.

[0121] The settlement linkage unit is used to trigger automatic settlement only for genuine waybills. Fake waybills must be manually reviewed and confirmed for rectification before the settlement process can be unfrozen, thus avoiding economic losses for the platform.

[0122] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A smart logistics waybill authenticity verification model method, characterized in that, The specific steps are as follows: S1: First, automatically generate a dynamic fence based on the waybill information; then, when the loading and unloading location of the waybill changes, receive the update instruction issued by the platform, complete the fence coordinate update within 10 seconds, and push the fence change reminder to the driver's end to avoid the failure of check-in due to location change; S2: Collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill to build a data foundation library for verifying the authenticity of the waybill, ensuring that the verification dimensions cover the core aspects of waybill fulfillment; S3: Focusing on sensing the operating status of equipment and the validity of data, it diagnoses the health of equipment in real time, compensates for failed / abnormal data, and triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, eliminating the risk of inspection misjudgment caused by equipment problems; S4: Using multi-dimensional perception data as input, a scoring model is built based on a fusion machine learning algorithm to output a quantitative score of the authenticity of waybills and to classify them as real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback from manual review results to ensure that the inspection accuracy is continuously improved with the accumulation of scenarios, thus replacing traditional manual review and realizing efficient and intelligent judgment of large-scale waybills. S5: A closed-loop mechanism is built around the model output results, which not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration and scenario adaptation upgrade through data accumulation.

2. The intelligent logistics waybill authenticity verification model method according to claim 1, characterized in that, The specific steps of S2 are as follows: S21: Integrates driver facial recognition and vehicle terminal device ID binding to prevent proxy attendance; S22: Collect the real-time location and motion status of the vehicle through the vehicle-mounted GPS and gyroscope to determine whether the vehicle is in a stationary working state; S23: Collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors and temperature and humidity sensors to verify the authenticity of loading and unloading actions; S24: Synchronize satellite time with platform server time to ensure that the check-in timestamp cannot be tampered with.

3. The intelligent logistics waybill authenticity verification model method according to claim 1, characterized in that, The specific steps of S3 are as follows: S31: First, collect the operating parameters of the devices under the multi-dimensional sensing module in real time; then generate the device health status identifier. S32: First, for scenarios with weak or transient signal loss, if the GPS signal is lost, the vehicle's movement trajectory collected by the gyroscope is matched with historical routes to calculate the vehicle's position during the period of loss and complete the trajectory data; then, for scenarios with single device failure, if the weight sensor fails, the equivalent weight change value is generated by fusion calculation of vehicle fuel consumption changes and loading / unloading time windows to replace the failed sensor data; finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor shows instantaneous jumps, abnormal values ​​are automatically removed, and the average value of the preceding and following 1 minute is used to complete the data to avoid misjudging abnormalities in cold chain goods; S33: When an equipment malfunction is detected, an equipment fault reminder is immediately pushed to the driver's end, and simultaneously synchronized to the platform administrator; and if the equipment fault cannot be repaired in time, a backup inspection plan is automatically triggered.

4. The intelligent logistics waybill authenticity verification model method according to claim 1, characterized in that, The specific steps of S4 are as follows: S41: Extract the core features of the multi-dimensional perception module; S42: First, a random forest + gradient boosting tree fusion algorithm is used to train the model with historical real and fake waybills as training data to output a waybill authenticity score; then, a score threshold is set. If the score is ≥80, the waybill is real and automatically passes the verification; if the score is 40-79, the waybill is suspicious and triggers a light manual review. If the score is less than 40, the waybill is considered fraudulent; the waybill settlement will be immediately intercepted and frozen. S43: Use manual review results and subsequent fulfillment data of waybills as feedback data to iterate and optimize the model weekly to improve the accuracy of feature weights.

5. The intelligent logistics waybill authenticity verification model method according to claim 1, characterized in that, The specific steps of S5 are as follows: S51: When the model determines a suspicious / fake waybill, it will push a warning message to the platform administrator and the shipper in real time; S52: Provides a visual audit interface that displays multi-dimensional perception data of waybills. Auditors can mark real / fake with one click and fill in audit remarks. S53: Store the inspection results and audit records of all waybills in a distributed database and synchronize them to blockchain nodes to ensure that the data is tamper-proof and can be used for subsequent accountability. S54: Only genuine waybills trigger automatic settlement. Fake waybills require manual review and confirmation of rectification before the settlement process can be unfrozen to avoid economic losses for the platform.

6. A smart logistics waybill authenticity verification model system, characterized in that, include: The dynamic electronic fence module is used to automatically generate dynamic fences based on waybill information. Then, when the loading and unloading location of the waybill changes, the system receives an update instruction from the platform, completes the fence coordinate update within 10 seconds, and pushes a fence change reminder to the driver's end to avoid the check-in failing due to the change of location; The multi-dimensional perception module is used to collect key raw data from multiple dimensions throughout the entire lifecycle of the waybill, build a data foundation library for verifying the authenticity of the waybill, and ensure that the verification dimensions cover the core links of waybill fulfillment. The equipment health monitoring and adaptive compensation module is used to focus on sensing the operating status of equipment and the validity of data. On the one hand, it diagnoses the health of equipment in real time, and on the other hand, it compensates for failure / abnormal data. At the same time, it triggers equipment fault warnings and backup inspection plans to ensure that the data input into the intelligent model is always reliable and complete, and eliminates the risk of inspection misjudgment caused by equipment problems. The waybill authenticity verification model module is used to take multi-dimensional perception data as input, build a scoring model based on fusion machine learning algorithms, output a quantitative score of waybill authenticity, and realize the classification of real / suspicious / fake. At the same time, the model parameters are continuously iterated through feedback of manual review results to ensure that the verification accuracy is continuously improved with the accumulation of scenarios, and replace the traditional manual review to realize efficient and intelligent judgment of large-scale waybills. The closed-loop management module is used to build a closed-loop mechanism around the model output results. It not only intercepts the risk of fake waybills in real time and protects the rights and interests of the platform and users, but also provides a basis for equipment management optimization, model algorithm iteration, and scenario adaptation upgrades through data accumulation.

7. The intelligent logistics waybill authenticity verification model system according to claim 6, characterized in that, The multi-dimensional perception module includes: The identity perception unit is used to integrate driver facial recognition and vehicle terminal device ID binding to prevent proxy attendance. The position and motion sensing unit is used to collect the real-time position and motion status of the vehicle through the vehicle GPS and gyroscope to determine whether the vehicle is in a stationary working state. The cargo sensing unit is used to collect cargo weight changes and environmental parameters through vehicle-mounted weight sensors and temperature and humidity sensors to verify the authenticity of loading and unloading actions; The time sensing unit is used to synchronize satellite time with the platform server time to ensure that the check-in timestamp cannot be tampered with.

8. The intelligent logistics waybill authenticity verification model system according to claim 6, characterized in that, The device health monitoring and adaptive compensation module includes: The equipment health monitoring unit is used to first collect the operating parameters of the equipment under the multi-dimensional sensing module in real time; then it generates the equipment health status identifier. The adaptive data compensation unit is used to first address scenarios with weak or transient signal loss. If the GPS signal is lost, it matches the vehicle's movement trajectory collected by the gyroscope with historical routes to calculate the vehicle's position during the lost period and complete the trajectory data. Next, for single device failure scenarios, if the weight sensor fails, it calculates an equivalent weight change value by fusing changes in vehicle fuel consumption with loading and unloading time windows to replace the failed sensor data. Finally, for scenarios with abnormal data fluctuations, if the temperature and humidity sensor shows a sudden jump, it automatically removes outliers and uses the average of the preceding and following minutes to complete the data, avoiding misjudging abnormalities in cold chain goods. The equipment early warning and linkage unit is used to immediately push equipment fault reminders to the driver's terminal when an equipment abnormality is detected, and simultaneously synchronize with the platform administrator; and automatically trigger the backup inspection plan when the equipment fault cannot be repaired in time.

9. The intelligent logistics waybill authenticity verification model system according to claim 6, characterized in that, The waybill authenticity verification model module includes: Feature input unit, used to extract core features of the multi-dimensional perception module; The model training and inference unit first uses a random forest + gradient boosting tree fusion algorithm to train the model to output a waybill authenticity score using historical real waybills and fake waybills as training data. Then, a score threshold is set: if the score is ≥80, the waybill is real and automatically passes the verification; if the score is 40-79, the waybill is suspicious and triggers a light manual review; if the score is <40, the waybill is fake and the waybill settlement is immediately blocked. The model iteration unit is used to iterate and optimize the model weekly, taking the results of manual review and subsequent fulfillment data of waybills as feedback data, to improve the accuracy of feature weights.

10. The intelligent logistics waybill authenticity verification model system according to claim 6, characterized in that, The closed-loop management module includes: The anomaly warning unit is used to push warning information to the platform administrator and the shipper in real time when the model determines that the waybill is suspicious / fake. The manual review unit provides a visual review interface that displays multi-dimensional perception data of the waybill. Reviewers can mark it as real or fake with one click and fill in review remarks. The data storage unit is used to store the inspection results and audit records of all waybills into a distributed database and synchronize them to the blockchain node to ensure that the data is tamper-proof and can be used for subsequent accountability. The settlement linkage unit is used to trigger automatic settlement only for genuine waybills. Fake waybills must be manually reviewed and confirmed for rectification before the settlement process can be unfrozen, thus avoiding economic losses for the platform.

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