A material authentication traceability method, device, medium, and program product

Abstract

A material authentication traceability method, device, medium and program product relate to the field of building material supply chain. A destination-determined entry weighing event can be used as a trigger anchor point to automatically and accurately trace the complete history track of a single transportation task from massive spatiotemporal data. By combining supplier coordinate geofencing identification and loading time period analysis, the legality of the transportation task source is confirmed. By monitoring behaviors such as abnormally long stays, the trajectory of the load running stage from loading to entry can not only verify where the goods come from, but also intelligently identify whether the goods are replaced or mixed during transportation, thereby effectively preventing fraud in the material supply chain without the need for full-time manual vehicle following, and improving the anti-counterfeiting depth and credibility of material authentication traceability.

Description

Technical Field

[0001] This application relates to the field of building materials supply chain, and in particular to a material certification and traceability method, equipment, medium and procedure product. Background Technology

[0002] In the construction of large-scale infrastructure projects, in order to ensure project quality and prevent corruption risks, it has become an industry consensus to trace the authenticity of the source and the compliance of the transportation process of bulk raw materials such as concrete and sand.

[0003] In order to achieve unified supervision of heavy-duty freight vehicles, the state has established a national public supervision and service platform for road freight vehicles (i.e., the "National Freight Platform"). This platform utilizes vehicle-mounted BeiDou / GPS satellite positioning terminals to automatically collect real-time data on the location, speed, and direction of travel of freight vehicles nationwide according to standardized communication protocols and time intervals, and performs centralized cloud storage and processing. Enterprises or regulatory departments can send query requests to the platform to obtain the driving trajectory records of specified vehicles over a specific time period, thereby understanding the historical operating status and approximate activity range of the vehicles, providing a data foundation for industry supervision.

[0004] However, with the expansion of project scale and the tightening of schedules, the transportation of raw materials exhibits characteristics of high frequency and high throughput (e.g., 34-45 round trips per vehicle per day). Under relevant technologies, the national freight platform, as a universal regulatory infrastructure across the industry, focuses its data collection logic on the continuous recording of vehicle operating status. The output data is typically a continuous sequence of raw coordinates of vehicles over a long period. When faced with high-frequency round-trip transportation scenarios involving dozens of vehicles per day, simple trajectory playback is often obscured by massive amounts of positioning noise, making it difficult to extract truly matching valid road segments from the vast continuous trajectories, thus hindering the improvement of traceability efficiency and early warning accuracy. Summary of the Invention

[0005] This application provides a material certification traceability method, equipment, medium, and program product, which improves the accuracy and data credibility of material source traceability in complex supply chain scenarios by instantly reconstructing the trajectory of a single transportation task and dynamically investigating hidden risks.

[0006] In a first aspect, this application provides a material certification traceability method applied to traceability equipment. The method includes: in response to a target vehicle's entry and weighing event at a destination, obtaining the vehicle identity information and entry time of the target vehicle; determining the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle based on the vehicle identity information and the entry time; identifying the loading and stopping period of the target vehicle within the geofence of the supplier coordinates based on the historical spatiotemporal trajectory data; and generating a material mixing risk warning based on the vehicle's trajectory during the load operation phase between the loading and stopping period and the entry time, provided that the target vehicle's dwell time during the load operation phase exceeds a preset dwell threshold.

[0007] By adopting the above technical solution, the complete historical trajectory of a single transportation task can be automatically and accurately traced back from massive spatiotemporal data, using the arrival and weighing event at the destination as the trigger anchor point. By combining geofencing identification of supplier coordinates with loading period analysis, the legitimacy of the transportation task's origin is confirmed. Analyzing the load operation trajectory from loading to arrival, and monitoring behaviors such as abnormally long dwell times, not only verifies the origin of the goods but also intelligently identifies highly concealed risks such as whether goods have been switched or adulterated during transit. This effectively prevents fraud in the material supply chain without requiring full-time manual supervision, enhancing the anti-counterfeiting depth and credibility of material certification and traceability.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the step of determining the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle specifically includes: estimating the standard transportation time of the target vehicle based on the supplier coordinates and the geographical location of the destination; determining a search pattern from multiple preset search patterns according to the standard transportation time, the search pattern being used to determine a query time window for data backtracking; and determining the historical spatiotemporal trajectory data of the target vehicle based on the vehicle identity information and the query time window.

[0009] By adopting the above technical solution, a dynamic strategy based on geographical distance to estimate standard transportation time is introduced. This intelligently adjusts the time span of data retrieval according to the actual physical distance between the supplier and the destination, avoiding the problems of "short-distance data redundancy" or "long-distance trajectory truncation" that may occur with fixed time spans. This adaptive retrieval mechanism ensures that the acquired historical spatiotemporal trajectory data has a complete business context (covering pre-loading, loading, and the entire transportation process), guaranteeing the data integrity foundation for subsequent risk analysis while reducing the overhead of transmitting and processing invalid data. This, in turn, improves the system's response speed and data processing efficiency while ensuring traceability accuracy.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of identifying the loading and stopping time of the target vehicle within the geofence of the supplier coordinates specifically includes: based on the historical spatiotemporal trajectory data, using the supplier coordinates and a preset position error range as spatial matching conditions, identifying the first trajectory point whose spatial location falls within the geofence of the supplier coordinates; and determining the trajectory point as the loading completion time of the target vehicle's current transportation task.

[0011] By adopting the above technical solution, the historical spatiotemporal trajectory data traced back from the destination is spatially matched with the supplier's coordinate geofence. The trajectory point that first enters the fence is used as the anchoring basis for the loading completion time. This avoids the data credibility defects caused by relying on the supplier's proactive reporting of the factory time, and achieves an objective restoration of the starting time of this transportation task, providing an accurate time benchmark for compliance analysis in the subsequent load operation phase.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, after identifying the loading and stopping time period of the target vehicle within the geofence of the supplier's coordinates, the method further includes: extracting the empty preparation phase trajectory within a preset verification time window before the loading completion time based on the loading completion time of the loading and stopping time; and generating a source pollution risk warning if it is determined that abnormal stopping behavior of the target vehicle is detected in the empty preparation phase trajectory.

[0013] By adopting the above technical solution, the time window for quality compliance verification is shifted from "after loading" to "before loading." By tracing the trajectory of the empty preparation stage and monitoring abnormal stopping behavior, clues of possible violations by vehicles before loading qualified materials can be captured, such as abnormal stops near construction waste sites or mud pits, further ensuring the purity of the final materials entering the site.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of generating the material mixing risk warning, the method further includes: integrating the vehicle identity information of the target vehicle, the loading and parking period, the entry time, and the load operation phase trajectory to generate a structured digital ledger; and marking the material mixing risk warning in the digital ledger.

[0015] By adopting the above technical solution, heterogeneous data such as discrete vehicle identities, time nodes, trajectory segments, and risk warnings are deeply logically linked and structurally encapsulated. This processing method transforms the originally fragmented raw sensor data into "one vehicle, one file" digital assets with legal validity and business significance. The structured digital ledger not only solidifies the evidence of the entire process of a single transportation task, enabling any transaction to be accurately reconstructed and traced, but also provides a standardized information carrier for subsequent big data statistics and cross-system interaction, thereby reducing the cost of manual data processing and improving the digitalization and standardization of engineering supply chain management.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after marking the risk warning of mixed material loading in the digital ledger, the method further includes: combining multiple digital ledgers generated within a preset statistical period to form a comprehensive transportation database containing multiple suppliers and multiple project departments, with each project department corresponding to the destination.

[0017] By adopting the above technical solutions and leveraging the reusability of digital ledgers, massive amounts of discrete records are aggregated according to supplier and project department dimensions. This constructs a knowledge base that reflects the long-term operational status of the supply chain. By accumulating historical data containing rich risk markers, it provides solid data support for evaluating supplier credit, optimizing logistics scheduling, and formulating regional quality control strategies.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of forming an integrated transportation database containing multiple suppliers and multiple project departments, the method further includes: in response to a filtering instruction input by a user, retrieving from the integrated transportation database all target ledger records that satisfy the filtering instruction, the filtering instruction including at least one of supplier name, destination name, or date range; and visually aggregating and presenting the target ledger records on a user interface.

[0019] By adopting the above technical solution, and through responding to screening commands and presenting them in a visual aggregated format, the system transforms the dry and obscure backend database records into graphical intelligence (such as risk distribution maps and trajectory playback) that is easy for managers to understand. This presentation method allows regulatory personnel to accurately locate problematic vehicles and high-risk periods from thousands of records within seconds, without needing professional data mining skills, and to intuitively review the specific geographical environment in which the risks occurred. This lowers the technical threshold and time cost of supervision, and improves the speed of emergency response and decision-making efficiency for material quality issues at engineering sites.

[0020] In a second aspect, this application provides a traceability device comprising: one or more processors and a memory; the memory is coupled to the one or more processors and is used to store computer program code, the computer program code including computer instructions, wherein the one or more processors invoke the computer instructions to cause the traceability device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, this application provides a computer program product containing instructions that, when run on a traceability device, cause the traceability device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a tracing device, cause the tracing device to perform the method described in the first aspect and any possible implementation thereof.

[0023] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0024] 1. By adopting a historical trajectory reverse tracing and full-process risk dynamic investigation mechanism triggered by the destination entry weighing event, it can automatically cut out the complete single transportation task chain from massive data with the determined destination delivery behavior as the anchor point, and combine geofencing and abnormal stop analysis to conduct in-depth logical verification of the compliance of "loading end" and "transportation". Therefore, it effectively solves the technical problem of difficult to effectively identify highly concealed cheating behaviors such as "empty truck forged loading" and "concealed exchange of goods (AB goods) along the way". In this way, it realizes anti-counterfeiting traceability of material source and transportation process without the need for human full-time vehicle supervision, and improves the credibility of supply chain data.

[0025] 2. By adopting an adaptive retrieval strategy that dynamically plans the data query time window based on the geographical distance between the supplier and the destination, the standard transportation time can be estimated according to the actual physical distance, and the optimal data backtracking range can be intelligently matched accordingly. Therefore, it effectively solves the technical problems of "short-distance data redundancy (increasing invalid calculations)" or "long-distance trajectory truncation (loss of origin evidence)" caused by using a fixed query time. This achieves a balance between the integrity of data backtracking and the efficiency of system processing, ensuring that risk analysis is based on a complete business context.

[0026] 3. By employing structured ledger retrieval and visualization aggregation technology that responds to multi-dimensional filtering commands, fragmented background risk records and spatiotemporal trajectory data can be logically linked and transformed into intuitive maps and charts. Therefore, it effectively solves the technical problems of difficulty in manually reviewing each item, low efficiency in historical tracing, and difficulty in quickly locating the source of risk when facing massive discrete transportation data. This transforms dry data into visualized regulatory intelligence, lowers the decision-making threshold for managers, and improves the response speed and investigation efficiency of potential quality hazards in the engineering supply chain. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a material authentication and traceability method in an embodiment of this application;

[0028] Figure 2 This is another flowchart illustrating a material certification and traceability method in the embodiments of this application;

[0029] Figure 3 This is a schematic diagram of a digital ledger for a material certification and traceability method in an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of the vehicle trajectory route for a material certification and traceability method in this application embodiment;

[0031] Figure 5 This is a schematic diagram of an exemplary hardware structure of the traceability device in the embodiments of this application. Detailed Implementation

[0032] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0033] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0034] Please see Figure 1 This is a flowchart illustrating a material authentication and traceability method in an embodiment of this application.

[0035] S101. In response to the target vehicle's entry and weighing event at the destination, obtain the target vehicle's vehicle identity information and entry time.

[0036] The target vehicle refers to a mobile vehicle configured to perform material transportation tasks and registered in the system. It is associated with a unique vehicle identifier and location permission configuration. In this embodiment, it is specifically represented as a heavy-duty dump truck or concrete mixer truck that has been digitally registered and equipped with a positioning module. The destination refers to a preset material receiving geographical area, which is marked as the end point of the transportation task in the geographic information database of the traceability equipment. It usually corresponds to the physical coordinate range of a specific construction project department or mixing plant. The entry weighing event refers to a data processing interruption request triggered by physical sensor signals, indicating that the traceability equipment has detected a change in the status of the external weighing subsystem that conforms to the preset business logic. Specifically, it means that the analog or digital signals uploaded by the weighing sensors simultaneously meet the determination conditions of effective load in both amplitude and time domain.

[0037] Step S101 of the traceability device typically occurs when the transport vehicle arrives at the construction site and enters the weighbridge area for acceptance and handover. The traceability device maintains a long-term connection or polling state with the weighbridge control system deployed at the destination via a pre-set communication interface, continuously monitoring the real-time status stream fed back by the weighbridge sensors. This is triggered by receiving event push notifications from the weighbridge control system. Once the weighbridge system has completed monitoring and judgment of the physical weighing process, it generates an "entry weighing event" data packet containing key data such as vehicle identification information and entry time, and sends it to the traceability device. The traceability device parses this data packet, obtains the "vehicle identification information" and "entry time," and automatically queries the locally stored supplier association list.

[0038] S102. Based on vehicle identity information and entry time, determine the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle.

[0039] Among them, supplier coordinates refer to the geographical location information of manufacturers or transit stations that are pre-registered in the material supply chain management system and have legal supply qualifications. They are usually composed of longitude, latitude and their corresponding electronic fence radius parameters. Historical spatiotemporal trajectory data refers to a series of positioning point records collected and uploaded by the vehicle positioning terminal within a continuous time range before the target vehicle enters the site. Each record contains at least a timestamp, longitude coordinates, latitude coordinates and instantaneous speed. The collection of these data points reconstructs the vehicle's movement behavior in the spatiotemporal dimension.

[0040] Based on the entry time reference determined in step S101, the traceability device determines the supplier's coordinates by parsing the digital waybill carried with the vehicle or querying the local contract association table.

[0041] Specifically, the traceability device retrieves the current dispatch order based on the vehicle's identity information, extracts the pre-assigned shipping unit information, and retrieves the registered geographic coordinates and fence range of the shipping unit from the geographic information database. After determining the spatial endpoint (destination) and origin (supplier coordinates), the traceability device constructs a trajectory query request for the vehicle. The core of this request is setting a time range sufficient to cover the entire transportation process. Through its internal data interface, the traceability device uses the vehicle's identity information and the set time range to initiate a data retrieval command to a pre-set trajectory data storage center (which may be the company's self-built vehicle management platform database or a connected national-level freight supervision platform). After responding to the request, the data center returns the vehicle's original location data packet for that time period. The traceability device receives this data packet and performs basic data cleaning (such as removing duplicates and formatting timestamps).

[0042] In some embodiments, the determination of the supplier coordinates and the acquisition of trajectory data can be achieved in various ways, particularly regarding the determination of the query time window:

[0043] Optionally, to avoid querying too much useless data or causing trajectory truncation due to an overly short query range, the tracing device will invoke its internal route planning algorithm or query the historical transportation database to calculate an estimated standard transportation time based on the geographical distance between the supplier's coordinates and the destination (e.g., a calculated journey time of 2 hours). Subsequently, the device will match a search pattern from a preset strategy library based on this standard time. For example, if the transportation distance is long, a "long-distance mode" will be matched, setting the query window starting point to twice the standard transportation time prior to the entry time or a fixed 24 hours prior; if the distance is short, a "short-distance mode" will be matched, setting the starting point to once the standard transportation time prior to the entry time or a fixed 2 hours prior. The device uses this dynamically calculated query time window to determine historical spatiotemporal trajectory data, ensuring data integrity while optimizing query efficiency.

[0044] Optionally, a segmented progressive query method can also be used, that is, first query the most recent hour, and if the starting point is not found, continue to append queries forward. There is no limitation here.

[0045] S103. Based on historical spatiotemporal trajectory data, identify the loading and stopping times of the target vehicle within the geofence of the supplier's coordinates.

[0046] After obtaining the historical spatiotemporal trajectory data after basic cleaning in step S102, the traceability device needs to accurately locate the starting loading stage of this transportation task from the continuous coordinate sequence. Since the entry time has been anchored as the time endpoint in step S101, and the supplier coordinates have been determined in step S102, the traceability device has the conditions to perform constraint matching on both the time axis and the spatial axis simultaneously. The traceability device uses the supplier coordinates and its preset position error range (this range is used to absorb the drift error of satellite positioning itself and the actual coverage area of ​​the supplier's site, for example, set to 500 meters) as the target conditions for spatial matching. Starting from the entry time, the traceability device traverses the positioning records in the historical spatiotemporal trajectory data backward along the time axis, calculates the geographical distance between the latitude and longitude coordinates of each positioning record and the supplier coordinates, and compares this distance value with the preset position error range. When the traceability device detects for the first time during the backward traversal that the spatial position of a trajectory point falls within the geographical fence of the supplier coordinates, the precise timestamp corresponding to that trajectory point is determined as the loading completion time of this transportation task.

[0047] In some embodiments, the traceability device performs a preliminary spatial dimension screening on the historical spatiotemporal trajectory data acquired in S102, and uses a point-polygon geometric relationship algorithm (such as Ray Casting) to extract a subset of trajectory points whose latitude and longitude coordinates fall within the supplier's coordinate geofence. By scanning the time-series trajectory data, the device identifies the time point when the vehicle finally leaves the geofence and does not return as the loading completion time.

[0048] In other embodiments, if the vehicle terminal supports CAN bus data upload, the tracing device will combine the vehicle's ACC ignition status and engine speed data to make a judgment. The loading period is only confirmed when the trajectory location is within the fence and the vehicle engine is detected to be in an "idling" or "off" state, accompanied by a specific change pattern of the PTO (power take-off, commonly used in dump truck lifting or mixer truck rotation) signal; this is not limited here.

[0049] In some embodiments, the tracing device performs a start-up dynamics analysis on the initial driving phase after the vehicle leaves the supplier's geofence. According to Newton's second law, the acceleration of an object is directly proportional to the net external force acting on it and inversely proportional to its mass. The tracing device extracts several trajectory points (e.g., the first 500 meters or the first 2 minutes) from the vehicle's trajectory during the load operation phase. This segment corresponds to the process of the vehicle starting from a standstill or low speed and accelerating to normal driving speed. The device calculates the average acceleration value of the vehicle each time it accelerates from a standstill or low speed (speed close to zero) to normal driving speed within this segment. If the calculated average start-up acceleration value is lower than a preset heavy-load acceleration threshold (this threshold is calculated through a physical model, i.e., based on parameters such as the vehicle's rated load capacity, engine power, and road friction coefficient, and calculated according to Newton's second law to determine the maximum theoretical acceleration under full load conditions), it indicates that the vehicle has indeed overcome a huge gravitational load and meets the physical characteristics of heavy-load departure. Conversely, if an abnormally high acceleration is detected in the vehicle during the departure phase (e.g., exceeding the heavy-load acceleration threshold, approaching or reaching the typical acceleration level of an empty vehicle), it is reasonable to infer that although the vehicle has stayed at the supplier's location for a sufficient period of time, it may not have actually been loaded with goods and is still in an empty, lightweight state. This situation may correspond to abnormal scenarios such as invalid stay or suspected empty departure.

[0050] Understandably, according to Newton's second law, the acceleration of an object is directly proportional to the net external force acting on it and inversely proportional to its mass. When a heavy-duty truck is fully loaded with tens of tons of sand and gravel, its starting process exhibits a significant "hysteresis effect" due to the constraint of its enormous mass and inertia—that is, even with high engine torque, the vehicle's speed increases slowly. Simultaneously, fully loaded vehicles, due to their large mass and momentum, possess strong "anti-interference steady state" during operation, according to the principle of conservation of momentum and the law of inertia. Their speed curve typically exhibits "low-frequency, low-amplitude" fluctuations—that is, speed changes slowly and with small amplitudes, statistically showing a small speed distribution skewness (i.e., speed is concentrated in a certain low-speed range, and the distribution curve shows a central tendency). Conversely, unloaded vehicles, due to their smaller mass and more maneuverable handling, are more prone to frequent rapid acceleration and deceleration by drivers depending on road conditions, resulting in speed curves exhibiting high-frequency fluctuations and a high degree of dispersion in speed distribution.

[0051] The traceability equipment quantifies the "steady-state fluctuation" characteristic by calculating the standard deviation of the speed time series of the trajectory during the load operation phase, the span of the speed distribution from the decimal to the ninetieth digit (P10-P90 span), and the skewness coefficient of the speed distribution. If the equipment detects that the speed curve after leaving the vehicle conforms to the "low-frequency steady-state" characteristics unique to heavy loads (i.e., small standard deviation, narrow P10-P90 span, and skewness coefficient close to zero), it confirms the loading stop period initially identified in step S103, confirming that the vehicle has indeed completed the loading operation. If the speed curve after leaving the vehicle exhibits "high acceleration and high fluctuation" characteristics (i.e., large standard deviation, wide P10-P90 span, and frequent speed changes), the stop is marked as an invalid stop or a suspected empty-load departure.

[0052] The equipment compares three sets of dynamic feature vectors (starting acceleration, steady-state speed fluctuation, and braking smoothness) of the vehicle's trajectory during the loaded operation phase with the corresponding feature vectors of the vehicle's trajectory during the unloaded operation phase before entering the supplier's geofence. The degree of difference is quantified by calculating the statistical distance between the two sets of feature distributions (such as Kullback-Leibler divergence or feature overlap rate). If the difference is significant (e.g., exceeding a preset significance threshold), it indicates a fundamental difference in the vehicle's driving behavior between the two phases, conforming to the normal logistics process of "unloaded-loaded-loaded." If the difference is minimal, it suggests that the vehicle still exhibits unloaded characteristics during the so-called "loaded operation phase," indicating a possible lack of loading or severely insufficient loading.

[0053] In some embodiments, the traceability device uses the "loading completion time" determined in S103 as the time anchor point, and backtracks to extract a segment of historical location data with a preset verification time window to construct the empty preparation stage trajectory. A clustering algorithm is used to scan this trajectory to identify all dwelling events. The coordinates of these dwelling points are compared with a built-in "risk map." This risk map marks high-risk areas (such as construction waste disposal sites and river dredging points). If abnormal dwelling behavior is detected in the target vehicle's empty preparation stage trajectory (e.g., the vehicle stayed at a construction waste disposal point for more than 10 minutes 30 minutes before loading), the device immediately determines that the vehicle is highly suspected of "loading with contaminants"—that is, the driver may have deliberately left some waste residue unloaded or intentionally loaded some inferior filler material before driving to the supplier for weighing (the tare weight at this time includes the weight of the waste residue). In response to the anomaly detection, the source tracing equipment generates a source pollution risk warning and links the warning to the digital ledger of this transportation task, reminding on-site acceptance personnel that when the vehicle arrives, they should not only check the net weight, but also focus on checking whether there are any abnormal residues at the bottom of the cargo compartment after unloading.

[0054] S104. Based on the vehicle's trajectory during the loading and stopping period and the entry time, if it is determined that the target vehicle's dwell time during the loading and stopping period exceeds the preset dwell time threshold, a material mixing risk warning is generated.

[0055] After confirming vehicle loading in step S103, the traceability device extracts the spatiotemporal positioning data sequence of the target vehicle on the timeline between the end of the loading and stopping period and the entry time, as the trajectory for the load operation phase. The device performs point-by-point traversal analysis of the trajectory points. The engine first calculates the physical displacement vector and instantaneous velocity between adjacent positioning points. When the algorithm detects that the instantaneous velocity of a series of consecutive data points is lower than the preset stationary judgment velocity, or when the positioning points drift disorderly within a very small radius (e.g., 20 meters), it determines that the vehicle has entered a physically stationary state. The duration of this continuous stationary state, i.e., the dwell time, is compared with a preset dwell time threshold (it is understood that compliant material transportation is usually a point-to-point direct operation, and apart from necessary traffic control waiting, there should be no long-term delays). If the calculated dwell time exceeds the threshold, then during this excessively long period of invisible time, the driver has a time window to operate the self-unloading device to dump out high-quality materials and use equipment such as excavators to load inferior materials or construction waste (i.e., "AB goods" behavior). Therefore, the traceability equipment immediately generates a risk warning for mixed materials and marks the start and end times of the abnormal stay, as well as the latitude and longitude coordinates, in the digital ledger.

[0056] In some embodiments, the device queries the geographic point of interest (POI) attribute corresponding to the coordinate point and maintains an internal "whitelist of legal places of stay," covering categories such as "highway service areas," "gas stations," "CNG stations," "vehicle repair shops," and "traffic police checkpoints." If the POI type returned by the API falls within the whitelist and the stay duration is within a reasonable business range (e.g., less than 60 minutes at a service area or less than 15 minutes at a gas station), the tracing device will identify the driver's true intention as "physiological rest" or "vehicle refueling" and mark it as a "pre-compliant stay."

[0057] If the POI attribute indicates that the vehicle is parked on a regular road, in wasteland, or in a non-service area, the device continues to query the road congestion status of that road segment during the period of the stop. The tracing device sends a historical query request to the map traffic situation interface to obtain the average traffic speed or congestion level (e.g., "smooth traffic", "slow traffic", "congested") of that road segment within the exact time window. If the feedback result shows that the road segment was in a "severely congested" state at that time (average speed <10km / h), then the long-term stop is determined to be a passive behavior that may be caused by force majeure (traffic jam). When the geographic point of interest attribute is determined to be a non-formal service area (e.g., abandoned quarry, remote road), and the road congestion status is shown as smooth, the tracing device generates a high-confidence material mixing risk warning.

[0058] In this embodiment, by employing a joint verification mechanism of reverse trajectory backtracking and dynamic characteristic consistency triggered by the destination entry weighing event, a single complete transportation task chain can be automatically cut from massive amounts of data using a defined endpoint delivery behavior in the physical world as an "anchor point." This intelligently identifies the physical anomalies of highly concealed fraudulent behaviors involving concealed goods swapping (AB goods). This enhances the authenticity and anti-counterfeiting capabilities of material traceability data, ensuring the quality and safety of raw materials for engineering construction.

[0059] In the above embodiments, the device can achieve precise anti-counterfeiting and traceability for a single transportation task by integrating a reverse trajectory backtracking mechanism triggered by the destination entry weighing event with a dynamic characteristic consistency verification mechanism. However, in practical applications, when implementing the above method, since large-scale infrastructure construction projects typically involve hundreds or thousands of material transportation tasks, simple single-task analysis will generate massive amounts of discrete data records. Managers will find it difficult to intuitively grasp the macro-risk distribution of the entire project's supply chain in a short period of time, and it will be difficult to quickly review and collect evidence on historical issues of specific suppliers or specific time periods. This method can solve this technical problem by integrating the automatic generation of structured digital ledgers with multi-dimensional spatiotemporal data aggregation and visualization technology, thereby improving decision-making efficiency.

[0060] Please see Figure 2 This is another flowchart illustrating a material authentication and traceability method in this application.

[0061] S201. In response to the weighing event of the target vehicle at the destination, obtain the vehicle identity information and entry time of the target vehicle.

[0062] S202. Based on vehicle identity information and entry time, determine the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle.

[0063] S203. Based on historical spatiotemporal trajectory data, identify the loading and stopping times of the target vehicle within the geofence of the supplier's coordinates.

[0064] S204. Based on the vehicle's trajectory during the loading and stopping period and the entry time, if it is determined that the target vehicle's dwell time during the loading and stopping period exceeds the preset dwell time threshold, a material mixing risk warning is generated.

[0065] S205. Integrate the vehicle identity information, loading and parking time, entry time, and load operation trajectory of the target vehicle to generate a structured digital ledger.

[0066] Among them, structured digital ledgers refer to a type of electronic data carrier that has undergone standardized coding processing. It encapsulates and solidifies discrete vehicle transportation behaviors, spatiotemporal trajectory data, business transaction vouchers, and risk assessment results in the physical world at the logical level. It is usually represented as a JSON object or XML document with a unique index key value and is the basic unit for realizing full-process traceability of the supply chain.

[0067] After the risk assessment logic in step S104 is completed, the ledger generation instruction is triggered. The system retrieves the license plate number, vehicle type classification, millisecond-accurate entry weighing timestamp, net weight reading uploaded by the weighbridge sensor, supplier's registered name and geofence coordinates, precise loading start and end times, and vehicle dwell time during this period from the memory pool. The "spatiotemporal process data" generated in steps S102 and S104 is serialized, compressing the originally bulky GPS raw sampling point set into a lightweight vector trajectory string while retaining key dynamic characteristic parameters (such as the number of abnormal stops). The three types of data (entry end, loading end, and transportation end) are rigidly bound using a unique "waybill number" or "license plate + time window" composite primary key. The system automatically verifies the data integrity of each field. If any field is missing, it is marked as pending completion. If the verification passes, a digital ledger file containing header index information and main content information is generated according to the predefined metadata standard format. This file is then encrypted and written to local high-speed storage media or cloud object storage bucket, completing the transformation from "scattered data" to "high-value digital assets".

[0068] S206. After marking the risk warning of mixed material loading in the digital ledger, combine multiple digital ledgers generated within the preset statistical period to form a comprehensive transportation database containing multiple suppliers and multiple project departments.

[0069] At the start of each preset statistical period, the backend service scans all newly generated, labeled, discrete digital ledgers. Using a distributed computing framework (such as MapReduce), the service merges the ledger records scattered across different time points according to the two core dimensions of "supplier ID" and "project department ID," constructing a comprehensive transportation database. This database not only stores the original records but also creates multi-dimensional indexes and statistical summaries, such as key performance indicators (KPIs) for each supplier in each period, including total transport trips, total volume, number of anomaly warning trips, and risk incidence rate. These statistical results are then stored in a summary table within the database.

[0070] S207. In response to the user's input filtering instruction, retrieve all target ledger records that meet the filtering instruction from the integrated transportation database.

[0071] Among them, the filtering instructions refer to the combination of conditions entered by the user through the front-end interactive interface to narrow down the scope of data retrieval. They usually include multiple optional fields such as time dimension (such as start and end dates), spatial dimension (such as supplier name, project department name), and vehicle dimension (such as license plate number).

[0072] The front-end user interface of the traceability device (such as...) Figure 3 The digital ledger diagram shown provides managers with flexible multi-condition query capabilities. Users can construct complex filtering instructions through interactive controls such as drop-down selection boxes, date pickers, and text input boxes. For example, a user can select "Xinchang County XX Transportation Construction Co., Ltd." in the "Raw Material Manufacturer" field and set "January 1, 2025 to January 2, 2025" in the "Manufacture Date" field. When the user clicks the "Query" button, the front end encapsulates these discrete input parameters into a structured query request (such as a SQL WHERE clause or a NoSQL Filter expression) and sends it to the backend data retrieval engine via a RESTful API or RPC call.

[0073] S208. Visualize and aggregate the target ledger records on the user interface.

[0074] like Figure 3 The "Digital Ledger Diagram" shown first displays the core fields of the ledger in a row-column matrix format in the table area of ​​the main interface. Each row represents a complete transportation task record, and the column fields include, but are not limited to: "Project", "License Plate Number", "Raw Material Manufacturer (Supplier Name)", "Material Name", "Weight", "Arrival Time", "Time Taken", and "Trajectory".

[0075] When a user selects a track in the list and clicks the "Track" button, a new window will pop up and load a map view (e.g., ...). Figure 4 The "Entry Vehicle Trajectory Route Diagram" shown is used to illustrate this. This view is built on the JavaScript API of Gaode Maps or Baidu Maps and possesses the following core capabilities: extracting the latitude and longitude sequence of the "load operation phase trajectory" from the ledger, calling the Polyline drawing method of the map API, and overlaying the actual driving path of the vehicles onto satellite imagery or road base maps. The map automatically marks three key geographical locations: raw material manufacturer (labeled with supplier name, latitude and longitude, and material name), destination (project site) (labeled with project site name and latitude and longitude), parking warning list (displaying detailed information such as "stay start time," "stay stop time," "number of stops," and "parking address"), material arrival time, and estimated departure time.

[0076] Steps S201~S204 and Figure 1 The steps S101 to S104 in the illustrated embodiment are similar, and can be referred to the descriptions in steps S101 to S104, which will not be repeated here.

[0077] In this embodiment, by employing automatic generation of structured digital ledgers and multi-dimensional spatiotemporal data aggregation and visualization technology, the entire lifecycle data of each transportation task (from loading to arrival) can be presented through flexible multi-condition retrieval and intuitive map + list linkage, enabling managers to quickly locate target events in massive historical data. This effectively solves the technical problems of severe data fragmentation and low efficiency of historical traceability in related technologies.

[0078] The exemplary traceability device 300 provided in the embodiments of this application is described below. Figure 5 This is an exemplary hardware structure diagram of the traceability device 300 provided in this application embodiment.

[0079] In some embodiments, the traceability device 300 is a computer device or includes a computer device. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements the methods in the embodiments of this application.

[0080] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0081] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0082] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0083] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0084] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for material certification and traceability, characterized in that, Applied to traceability equipment, the method includes: In response to a target vehicle's entry and weighing event at the destination, obtain the vehicle's identity information and entry time. Based on the vehicle identity information and the entry time, the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle are determined; Based on the historical spatiotemporal trajectory data, identify the loading and stopping time periods of the target vehicle within the geofence of the supplier's coordinates; Based on the vehicle's trajectory during the load operation phase between the loading stop time and the entry time, a material mixing risk warning is generated if it is determined that the target vehicle has a dwell time exceeding a preset dwell threshold during the load operation phase.

2. The method according to claim 1, characterized in that, The steps of determining the supplier coordinates and the historical spatiotemporal trajectory data of the target vehicle specifically include: Based on the supplier's coordinates and the destination's geographical location, the standard transportation time for the target vehicle is estimated. Based on the standard transportation time, a search pattern is determined from multiple preset search patterns, and the search pattern is used to determine the query time window for data backtracking; Based on the vehicle identity information and the query time window, the historical spatiotemporal trajectory data of the target vehicle is determined.

3. The method according to claim 1, characterized in that, The step of identifying the loading and stopping time of the target vehicle within the geofence of the supplier's coordinates specifically includes: Based on the historical spatiotemporal trajectory data, the supplier coordinates and the preset location error range are used as spatial matching conditions to identify the first trajectory point whose spatial location falls within the geofence of the supplier coordinates. The trajectory points are determined as the loading completion time of the target vehicle's current transportation task.

4. The method according to claim 3, characterized in that, Following the step of identifying the loading and stopping time period of the target vehicle within the geofence of the supplier's coordinates, the method further includes: Based on the loading completion time during the loading docking period, extract the empty preparation phase trajectory within a preset verification time window before the loading completion time; If it is determined that the target vehicle exhibits abnormal stopping behavior in its trajectory during the empty preparation phase, a source pollution risk warning is generated.

5. The method according to claim 1, characterized in that, Following the step of generating a risk warning for mixed materials, the method further includes: By integrating the vehicle identity information, loading and parking time, entry time, and load operation trajectory of the target vehicle, a structured digital ledger is generated. The risk warning of mixed materials is marked in the digital ledger.

6. The method according to claim 5, characterized in that, After the step of marking the risk warning of mixed materials in the digital ledger, the method further includes: Multiple digital ledgers generated within a preset statistical period are combined to form a comprehensive transportation database containing multiple suppliers and multiple project departments, with each project department corresponding to a destination.

7. The method according to claim 6, characterized in that, Following the step of forming a comprehensive transportation database containing multiple suppliers and multiple project departments, the method further includes: In response to a user-inputted filtering instruction, all target ledger records that meet the filtering instruction are retrieved from the integrated transportation database. The filtering instruction includes at least one of a supplier name, a destination name, or a date range. The target ledger records are visualized and aggregated on the user interface.

8. A traceability device, characterized in that, The tracing device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the tracing device to perform the method as described in any one of claims 1-7.

9. A computer program product containing instructions, characterized in that, When the computer program product is run on the traceability device, the traceability device performs the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the traceability device, the traceability device performs the method as described in any one of claims 1-7.

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