Virtual electronic fence-based muck truck behavior studying and judging method, device and equipment

Abstract

The invention discloses a muck truck behavior research and judgment method, device and equipment based on virtual electronic fences, and relates to the technical field of vehicle state monitoring, the method comprises the following steps: obtaining real-time coordinate data of different sites, and generating dynamic electronic fences of the different sites according to the real-time coordinate data of the different sites; processing the multi-source driving data of the muck truck, and analyzing and recording the real-time driving state of the muck truck in combination with the dynamic electronic fences of the different sites; and based on the real-time driving state of the muck truck and the dynamic electronic fences of the different sites, studying and judging whether the muck truck has illegal behaviors or not. By establishing the dynamic electronic fence, a supervision blind area caused by mismatching of a traditional fence and a dynamic scene is solved, multi-source driving data is fused, the composite violation problem that a single data source is difficult to find is solved, matching calculation is performed on the driving state, violation behaviors are found immediately, evidence is stored immediately after finding, and the driving safety is improved. And the response time from behavior occurrence to supervision intervention is remarkably shortened.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle status monitoring, and more specifically, to a method, device and equipment for analyzing and judging the behavior of a dump truck based on a virtual electronic fence. Background Art

[0002] With the rapid development of urban construction, the supervision of construction waste transportation has become a major challenge for urban management. Traditional supervision methods rely primarily on manual inspections, vehicle registration, and post-event tracking, making it difficult to achieve real-time monitoring and accurate early warning of dumping by construction waste trucks. Especially in complex urban environments, illegal dumping is often concealed and instantaneous, necessitating the use of intelligent technologies to improve supervision efficiency.

[0003] In order to avoid illegal dumping in the existing technology, the main method used is fixed electronic fence monitoring: pre-set geographic fence boundaries in fixed areas such as disposal sites and construction sites, and use the on-board GPS equipment to determine whether the vehicle has entered the designated area. If the vehicle exceeds the fence range, a violation warning will be triggered. Trajectory deviation analysis: pre-plan the transportation route of the dump truck, and determine whether there is a violation by comparing the degree of deviation between the actual driving trajectory of the vehicle and the registered path (such as the distance deviation exceeds the threshold). Manual video verification: deploy cameras at key sections of the road, and manually conduct visual inspections of the vehicle loading status and dumping behavior in the video footage.

[0004] However, the above methods only rely on GPS positioning or video clips, lack the integrated analysis of multi-dimensional information such as load data and time rules, and cannot identify hidden dumping behaviors when the trajectory is compliant; the boundaries and rule parameters of the electronic fence are fixed and cannot dynamically adapt to changes in scenarios such as temporary construction sites and emergency control areas; the determination of violations relies on post-event trajectory playback or manual review, and there is a lag of several hours to several days from the occurrence of the behavior to the generation of the warning, which makes it difficult to collect evidence for law enforcement. Summary of the Invention

[0005] In response to at least one defect or improvement need in the prior art, the present invention provides a method, device and equipment for analyzing the behavior of dump trucks based on virtual electronic fences, which is used to solve the problems in the prior art that only rely on GPS positioning or video clips, lack of fusion analysis of multi-dimensional information such as load data and time rules, cannot identify hidden dumping behavior when the trajectory is compliant, the electronic fence boundaries and rule parameters are fixed, and cannot dynamically adapt to changes in scenarios such as temporary construction sites and emergency control areas, and manual verification has serious lags and difficulties in rounding.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence is provided, comprising:

[0007] Obtain real-time coordinate data of different sites, and generate dynamic electronic fences for different sites based on the real-time coordinate data of different sites;

[0008] Process multi-source driving data of muck trucks and analyze and record their real-time driving status in combination with dynamic electronic fences at different sites;

[0009] Based on the real-time driving status of the dump truck and the dynamic electronic fences in different sites, it is determined whether the dump truck has committed any illegal acts.

[0010] In one possible implementation, obtaining real-time coordinate data of different sites and generating dynamic electronic fences for the different sites based on the real-time coordinate data of the different sites also includes:

[0011] Determine the real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information;

[0012] Extract polygon vertices from real-time coordinate data of different sites through edge recognition algorithm and feature matching algorithm;

[0013] Generate dynamic electronic fences for different sites based on the real-time coordinate data and polygon vertices of different sites.

[0014] In one possible implementation, determining the real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information further includes:

[0015] Pre-process the recorded site coordinate boundaries, real-time positioning information, and real-time camera information, and perform coordinate conversion to obtain multi-source coordinate data in the same coordinate system;

[0016] The multi-source coordinate data of the same coordinate system are fused and dynamically updated to obtain real-time coordinate data of different sites.

[0017] In one possible implementation, generating dynamic electronic fences for different sites based on real-time coordinate data and polygon vertices of different sites also includes:

[0018] Connect polygon vertices in a preset order to generate initial polygonal electronic fences for different sites;

[0019] Calculate the overlap between the initial polygonal electronic fence and the real-time coordinate data of the corresponding site;

[0020] The initial polygonal electronic fence that is lower than the preset overlap degree is dynamically adjusted according to the real-time coordinate data.

[0021] In one possible implementation, the multi-source driving data includes the starting point coordinates and the ending point coordinates. The multi-source driving data of the muck truck is processed, and the real-time driving status of the muck truck is analyzed and recorded in combination with dynamic electronic fences at different sites. The following also applies:

[0022] Determine the driving trajectory of the muck truck based on the starting point coordinates and the end point coordinates, and calculate the average speed of the muck truck;

[0023] The position status of the dump truck and the dynamic electronic fence is analyzed based on the driving trajectory of the dump truck and the dynamic electronic fences in different sites.

[0024] In one possible implementation, the real-time driving status of the dump truck and the dynamic electronic fences at different sites are used to determine whether the dump truck is engaging in illegal activities, and the following steps may also be performed:

[0025] Monitor the real-time load data of the muck truck and calculate the load difference when the real-time load data changes;

[0026] Whether the dump truck is committing any illegal acts is determined based on its average speed, the position of the dump truck and the dynamic electronic fence, and the difference in load.

[0027] In one possible implementation, the dynamic electronic fence includes a construction site dynamic electronic fence and a disposal site dynamic electronic fence. The system determines whether the dump truck is engaging in illegal activities based on its average speed, the position between the dump truck and the dynamic electronic fence, and the difference in load. The system also includes:

[0028] If the real-time load data exceeds the preset maximum load, an overload alarm record will be generated and the muck truck will be prohibited from leaving the construction site dynamic electronic fence;

[0029] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the load difference exceeds the preset change threshold, the dump truck is performing illegal dumping and an illegal dumping warning record is generated;

[0030] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the load difference does not exceed the preset change threshold, the dump truck will be considered to have spilled soil on the road, and a road spill warning record will be generated;

[0031] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the average speed of the dump truck exceeds the preset speed threshold, a dump truck speeding warning record will be generated.

[0032] According to a second aspect of the present invention, a device for analyzing and judging the behavior of a muck truck based on a virtual electronic fence is provided, comprising:

[0033] A dynamic fence module is configured to obtain real-time coordinate data of different venues and generate dynamic electronic fences for different venues based on the real-time coordinate data of different venues;

[0034] A driving status module is configured to process multi-source driving data of the muck truck and analyze and record the real-time driving status of the muck truck in combination with dynamic electronic fences at different sites;

[0035] The behavior analysis module is configured to judge whether the dump truck has committed any illegal behavior based on the real-time driving status of the dump truck and the dynamic electronic fences in different sites.

[0036] According to the third aspect of the present invention, a device for analyzing the behavior of a dump truck based on a virtual electronic fence is also provided, which includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program. When the computer program is executed by the processing unit, the processing unit executes any step of the above-mentioned method for analyzing the behavior of a dump truck based on a virtual electronic fence.

[0037] According to the fourth aspect of the present invention, a storage medium is also provided, which stores a computer program that can be executed by a dump truck behavior analysis device based on a virtual electronic fence. When the computer program runs on the dump truck behavior analysis device based on a virtual electronic fence, the dump truck behavior analysis device based on a virtual electronic fence executes any step of the above-mentioned dump truck behavior analysis method based on a virtual electronic fence.

[0038] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0039] The proposed method for analyzing dump truck behavior based on virtual electronic fences breaks through the static limitations of traditional electronic fences in terms of scenario adaptability. By dynamically generating dynamic electronic fences by acquiring construction site coordinates in real time, the monitoring scope can be instantly adjusted to meet the needs of temporary construction sites and emergency control area adjustments. This effectively addresses the regulatory blind spots caused by the mismatch between traditional fences and dynamic scenarios, making it particularly suitable for the temporary construction projects that frequently occur in urban construction. In terms of data analysis depth, it integrates multi-source driving data to determine multi-dimensional characteristics such as the spatiotemporal trajectory, load status, and dwell duration of dump trucks. This method not only identifies overt violations such as over-boundary driving but also accurately captures covert dumping behaviors by coupling analysis of sudden load changes with unconventional stops, addressing the complex violations that are difficult to detect with a single data source. In terms of regulatory timeliness, a real-time streaming data processing architecture is established, upgrading the traditional post-incident manual verification model to an intelligent early warning and alert system that performs matching calculations on driving status, enabling the immediate detection and immediate preservation of violations. This significantly shortens the response time from the occurrence of the behavior to regulatory intervention, effectively preventing the loss of evidence and the escalation of the consequences of the violation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A flow chart of an embodiment of a method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence provided by the present invention;

[0042] Figure 2 The present invention provides Figure 1 A flow chart of an embodiment of step S101;

[0043] Figure 3 The present invention provides Figure 2 A flow chart of an embodiment of step S203;

[0044] Figure 4 A schematic structural diagram of an embodiment of a device for analyzing and judging the behavior of a muck truck based on a virtual electronic fence provided by the present invention;

[0045] Figure 5 A schematic diagram of the structure of a dump truck behavior analysis device based on a virtual electronic fence provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0047] The terms "first," "second," "third," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements, but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0048] The present invention provides a method, device and equipment for analyzing and judging the behavior of a muck truck based on a virtual electronic fence, which are described below respectively.

[0049] See also Figure 1 , Figure 1 This is a flow chart of an embodiment of a method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence provided by the present invention. In a specific embodiment of the present invention, a method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence is disclosed, comprising:

[0050] S101, acquiring real-time coordinate data of different sites, and generating dynamic electronic fences of different sites according to the real-time coordinate data of different sites;

[0051] S102: Processing multi-source driving data of the muck truck, and analyzing and recording the real-time driving status of the muck truck in combination with dynamic electronic fences at different sites;

[0052] S103. Determine whether the dump truck has committed any illegal activity based on the truck's real-time driving status and the dynamic electronic fences at different locations.

[0053] In the above embodiment, by accessing multiple data sources such as the municipal construction permit system, traffic control platform, GPS, etc., the boundary coordinate data of sites such as construction sites and slag disposal sites are obtained in real time, solving the problem that traditional electronic fences rely on fixed geographic fences and cannot adapt to temporary construction sites or emergency control areas (such as road repair areas). The geometric model of the electronic fence (such as a polygonal fence) is dynamically generated based on the site coordinates, supporting the automatic expansion or contraction of the fence range with the construction progress and control needs. For example, the fence is automatically generated when the temporary construction site is activated and automatically expires after the project is completed, avoiding the lag of frequent manual configuration. Rule parameters such as the effective period and the allowed operation type can be added to the electronic fence (such as only allowing specific vehicles to enter between 7:00 and 22:00), realizing the multi-dimensional constraints of "space + time + business rules", avoiding overtime operations, illegal intrusions and other behaviors from the source.

[0054] By integrating multiple sources of information such as the dump truck's GPS location data, load sensor data, and camera video streams, and through data cleaning, spatiotemporal alignment and other technologies, multi-dimensional vehicle operation data is constructed, breaking through the limitations of single GPS positioning.

[0055] The vehicle's trajectory is matched to the spatiotemporal patterns of the dynamic geo-fence, analyzing key behavioral characteristics. This includes, but is not limited to, determining whether the vehicle enters or leaves the geo-fence area during permitted time periods; and identifying suspected illegal dumping by combining the duration of time spent within the geo-fence with sudden drops in load (e.g., from a full load to an empty load). Finally, key indicators such as the time a vehicle enters and exits the fence and load change curves are persistently stored to provide data support for subsequent violation tracing.

[0056] Cross-validation is performed for complex scenarios such as legal trajectories but abnormal loads, legal time periods but abnormal locations, and so on. For example, a vehicle is within a legal fence but unloads cargo not within the permitted load limit. By comparing load sensor data with declared records, misreporting of loads and false dumping can be identified. The analysis results are linked to the original data (timestamps, coordinates, sensor readings) to generate a trusted evidence package with complete spatiotemporal context. This allows for one-click export of law enforcement reports, addressing the inefficiency and omissions of manual evidence collection.

[0057] Compared to existing technologies, the virtual electronic fence-based method for analyzing and assessing dump truck behavior, provided in this embodiment, breaks through the static limitations of traditional electronic fences in terms of scenario adaptability. By dynamically generating dynamic electronic fences by acquiring construction site coordinates in real time, the monitoring scope can be instantly adjusted to meet demands such as the establishment of temporary construction sites and the adjustment of emergency control areas. This effectively addresses the regulatory blind spots caused by the mismatch between traditional fences and dynamic scenarios, making it particularly suitable for the frequent temporary construction projects in urban construction. In terms of data analysis depth, it integrates multi-source driving data to determine multi-dimensional characteristics such as the spatiotemporal trajectory, load status, and dwell duration of dump trucks. This method not only identifies overt violations such as over-boundary driving, but also accurately captures covert dumping behaviors by coupling analysis of sudden changes in load and unconventional stops, addressing the complex violations that are difficult to detect with a single data source. In terms of regulatory timeliness, a real-time streaming data processing architecture is established, upgrading the traditional post-incident manual verification model to an intelligent early warning and alert system that matches and calculates driving status, enabling the immediate detection and immediate preservation of violations. This significantly shortens the response time from the occurrence of the behavior to regulatory intervention, effectively preventing the loss of evidence and the escalation of the consequences of the violation.

[0058] See also Figure 2 , Figure 2 The present invention provides Figure 1 In some embodiments of the present invention, obtaining real-time coordinate data of different sites and generating dynamic electronic fences for different sites based on the real-time coordinate data of different sites also includes:

[0059] S201, determining real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information;

[0060] S202, extracting polygon vertices from real-time coordinate data of different sites through edge recognition algorithm and feature matching algorithm;

[0061] S203: Generate dynamic electronic fences for different sites based on the real-time coordinate data and polygon vertices of the different sites.

[0062] In the above embodiment, a multi-source data verification mechanism is established by integrating the registered site coordinate boundaries, real-time positioning information, and real-time camera information, addressing the issue of single data sources being susceptible to environmental interference. In response to temporary site boundary changes (such as expansion of the construction area) or emergency control areas (such as road collapse emergency areas), the registered coordinates are dynamically corrected using real-time positioning and video data to ensure the spatiotemporal consistency of the site boundaries and avoid fence deviations caused by information lags.

[0063] Based on the image frames of the video stream, edge detection and semantic segmentation techniques are used to identify visual features such as construction site fencing and the distribution of construction machinery, extracting the pixel-level outline of the site's actual physical boundaries. This addresses the low efficiency of traditional manual labeling and its difficulty adapting to dynamic scenes. The visually recognized outline coordinates are spatially matched with the registered coordinates and real-time positioning data. By calculating the geometric relationship of feature points (such as building corners and road signs), the precise geographic coordinates of polygon vertices are determined, eliminating outline extraction errors caused by perspective distortion or occlusion, and improving the reliability of vertex coordinates.

[0064] Based on the polygon vertices extracted in S202, a geofencing algorithm (such as the ray method) is used to generate a minimum closed polygon, dynamically covering the actual working area of ​​the site and supporting the accurate description of complex terrain. Dynamic attributes (such as the fence's effective time period and permitted vehicle types) can be added to the electronic fence to achieve real-time synchronization of fence rules and business scenarios. Redundant vertices are removed through convex hull algorithms or vertex simplification strategies (such as the Douglas-Peucker algorithm), reducing the complexity of the fence model and improving the computational efficiency of subsequent trajectory matching and violation determination.

[0065] In some embodiments of the present invention, determining real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information further includes:

[0066] Pre-process the recorded site coordinate boundaries, real-time positioning information, and real-time camera information, and perform coordinate conversion to obtain multi-source coordinate data in the same coordinate system;

[0067] The multi-source coordinate data of the same coordinate system are fused and dynamically updated to obtain real-time coordinate data of different sites.

[0068] In the above embodiment, the recorded site coordinate boundaries, real-time positioning information, and real-time camera information are pre-processed to address data noise issues. For example, this includes filtering out jump points in GPS positioning (such as sudden changes in coordinates caused by signal occlusion) and repairing missing frames in the video stream caused by occlusion or lighting changes.

[0069] The coordinate systems of different data sources are uniformly converted to the same geographic reference system (such as the CGCS2000 National Geodetic Coordinate System) through affine transformation or deep learning pose estimation model to eliminate boundary deviations caused by coordinate system differences; perspective correction and geographic registration are performed on feature points in video images to achieve accurate mapping of pixel coordinates to geographic coordinates.

[0070] Multi-source coordinate data is weighted based on data confidence (e.g., GPS positioning accuracy, video resolution), and fused using Kalman filtering or Bayesian estimation algorithms. For example, the recorded coordinates serve as a benchmark, but their confidence decays over time; real-time positioning data is highly accurate but susceptible to interference, so it is assigned a dynamic weight; and video data, which has increased confidence within the visible range, is used for local calibration. The fusion results are used to correct coordinate points at the site boundary, addressing the limitations of a single data source (e.g., outdated recorded data, drifting positioning signals).

[0071] Based on the real-time positioning and timestamps of video data, the site boundaries are incrementally adjusted at the millisecond level to adapt to sudden scenarios; significant changes in coordinate data are detected by edge computing nodes, automatically triggering fence model reconstruction to avoid invalid calculations.

[0072] See also Figure 3 , Figure 3 The present invention provides Figure 2 FIG. 1 is a flow chart of an embodiment of step S203 in FIG. 1 . In some embodiments of the present invention, generating dynamic electronic fences for different sites based on real-time coordinate data and polygon vertices of different sites further includes:

[0073] S301, connecting polygon vertices in a preset order to generate initial polygonal electronic fences of different sites;

[0074] S302, calculating the coverage overlap between the initial polygonal electronic fence and the real-time coordinate data of the corresponding site;

[0075] S303: Dynamically adjust the initial polygonal electronic fence that is lower than the preset overlap according to the real-time coordinate data.

[0076] In the above embodiment, the extracted polygon vertices are connected in a preset order (e.g., clockwise / counterclockwise) to generate a closed initial polygonal electronic fence, ensuring geometric topological correctness (e.g., avoiding self-intersections and concave polygons), and providing a standardized geometric foundation for subsequent analysis. A convex hull algorithm is used to wrap the vertex set, eliminating recessed areas caused by feature extraction errors and ensuring that the fence covers the entire operating range of the actual site. For multi-plot scenarios (e.g., dispersed waste disposal areas), multiple independent polygonal fences can be generated to avoid overgeneralization of a single fence.

[0077] Based on real-time coordinate data, a spatial overlay analysis algorithm is used to quantify the coverage overlap between the initial polygonal fence and the actual site. For example, the percentage of dynamic monitoring points contained within the fence is calculated; the average distance deviation between the fence boundary and the actual site boundary is evaluated. The distance field algorithm is introduced to calculate the shortest distance distribution from the fence boundary to the real-time coordinate point and identify areas with insufficient local coverage. Differentiated overlap thresholds are set according to the site type (such as ≥95% for fixed sites and ≥85% for temporary construction sites) to avoid misjudgments due to scene differences; areas with overlap below the threshold are automatically marked as areas to be optimized, triggering the dynamic adjustment process of the fence.

[0078] For areas with insufficient coverage, a vertex interpolation optimization algorithm is used to adjust polygon vertex coordinates so that the fence boundary converges to the real-time coordinate point set. For newly added real-time coordinate clusters, an incremental vertex insertion strategy is used to expand the fence range while maintaining polygon closure. During the adjustment process, vertex movement is restricted according to site business rules to ensure fence compliance. For large-scale deformation scenarios, polygonal affine transformations are used to quickly reconstruct the fence and reduce computational overhead. After each adjustment, the coverage overlap is recalculated until a threshold is met or the maximum number of iterations is reached, forming a closed-loop iterative mechanism of "generate-evaluate-optimize." Historical adjustment data is recorded and used to train the fence optimization model to improve the efficiency of subsequent adjustments.

[0079] In some embodiments of the present invention, the multi-source driving data includes the starting point coordinates and the ending point coordinates; processing the multi-source driving data of the muck truck, and analyzing and recording the real-time driving status of the muck truck in combination with dynamic electronic fences at different sites, further includes:

[0080] Determine the driving trajectory of the muck truck based on the starting point coordinates and the end point coordinates, and calculate the average speed of the muck truck;

[0081] The position status of the dump truck and the dynamic electronic fence is analyzed based on the driving trajectory of the dump truck and the dynamic electronic fences in different sites.

[0082] In the above embodiment, based on the coordinates of the starting point and the end point, combined with the discrete GPS points reported by the vehicle, Bezier curve interpolation or map path planning API (such as Amap / Baidu Map) is used to fill in the missing trajectory points, solve the trajectory breakage problem caused by signal loss, and restore the complete driving path. According to the timestamp sequence of the trajectory points, the instantaneous speed (distance / time difference between adjacent points) is calculated in segments, and abnormal values ​​(such as sudden acceleration / deceleration noise) are filtered; the sliding window average algorithm is used to output the smoothed real-time average speed, which is used to identify behaviors such as speeding and abnormal stagnation. Logical verification is performed on abnormal start / end point coordinates, and they are automatically corrected or marked as suspicious data; for trajectory points with disordered or missing timestamps, time series repair is performed based on the vehicle motion model.

[0083] The trajectory points are spatially matched with the polygonal boundaries of the dynamic electronic fence in milliseconds, and the ray method or R-tree index is used to accelerate point-polygon inclusion detection to determine whether the vehicle enters / leaves the fence area. In areas with dense trajectory points, abnormal clusters are identified through kernel density estimation or DBSCAN clustering algorithm.

[0084] In some embodiments of the present invention, judging whether a muck truck has committed an illegal act based on its real-time driving status and dynamic electronic fences at different sites further includes:

[0085] Monitor the real-time load data of the muck truck and calculate the load difference when the real-time load data changes;

[0086] Whether the dump truck is committing any illegal acts is determined based on its average speed, the position of the dump truck and the dynamic electronic fence, and the difference in load.

[0087] In the above example, the vehicle's onboard weighing sensor collects real-time weight data from the dump truck, and a sliding window variance analysis or catastrophe detection algorithm is used to locate the timestamp of suspected dumping operations. The load difference (ΔW = W_front - W_back) is calculated and compared with a preset threshold (e.g., ΔW ≥ 8 tons) to filter out minor fluctuations caused by road bumps or sensor noise.

[0088] In some embodiments of the present invention, the dynamic electronic fence includes a construction site dynamic electronic fence and a disposal site dynamic electronic fence; judging whether the dump truck has committed an illegal act based on the average speed of the dump truck, the position between the dump truck and the dynamic electronic fence, and the load difference also includes:

[0089] If the real-time load data exceeds the preset maximum load, an overload alarm record will be generated and the muck truck will be prohibited from leaving the construction site dynamic electronic fence;

[0090] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the load difference exceeds the preset change threshold, the dump truck is performing illegal dumping and an illegal dumping warning record is generated;

[0091] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the load difference does not exceed the preset change threshold, the dump truck will be considered to have spilled soil on the road, and a road spill warning record will be generated;

[0092] If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the average speed of the dump truck exceeds the preset speed threshold, a dump truck speeding warning record will be generated.

[0093] In the above example, real-time monitoring of the load data of muck trucks within the dynamic electronic fence of the construction site is performed. If the load exceeds a preset maximum load (e.g., ≥30 tons), an overload alarm is triggered. The load data is synchronized with the fence status within milliseconds to prevent overloaded vehicles from illegally leaving the site. Overload records are uploaded to the supervision platform in real time and used as a basis for deducting points from the company's credit score.

[0094] If a dump truck does not enter any dynamic electronic fences at a disposal site and the load difference ΔW exceeds a preset threshold (e.g., ΔW ≥ 8 tons), it is considered illegal dumping. The illegal dumping location is located by temporally and spatially matching the load curve mutation points with the GPS trajectory. Video streams from cameras surrounding the dumping site are automatically retrieved to generate a visual chain of evidence demonstrating "load zero + no entry into a disposal site."

[0095] If the dump truck does not enter the disposal site fence and ΔW does not exceed the threshold (e.g., 1 ton ≤ ΔW < 8 tons), a road spill warning is triggered. Analysis is performed on the correlation between small changes in load and driving trajectory (e.g., slow load reduction on continuously bumpy roads). The system then links with the municipal sanitation system to initiate automatic cleaning or manual inspections of the alerted road sections.

[0096] If the dump truck does not enter the disposal site fence and its average speed exceeds a preset threshold (e.g., ≥60 km / h on urban roads), a speeding warning is triggered. Speed ​​limits are dynamically adjusted based on the road type (e.g., school zones, elevated roads) (e.g., a 30 km / h speed limit in school zones). Real-time speed limit information is obtained using a high-precision map API to avoid lags in static rules. Vehicles that repeatedly exceed the speed limit (e.g., three times within 10 minutes) are warned, and law enforcement officers are notified to handle the situation on the spot.

[0097] In order to better implement the method for analyzing the behavior of a muck truck based on a virtual electronic fence in the embodiment of the present invention, based on the method for analyzing the behavior of a muck truck based on a virtual electronic fence, please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of an embodiment of a device for analyzing and judging the behavior of a muck truck based on a virtual electronic fence provided by the present invention. The embodiment of the present invention provides a device 400 for analyzing and judging the behavior of a muck truck based on a virtual electronic fence, comprising:

[0098] A dynamic fence module 410 is configured to obtain real-time coordinate data of different venues and generate dynamic electronic fences for different venues based on the real-time coordinate data of different venues;

[0099] A driving status module 420 is configured to process multi-source driving data of the muck truck and analyze and record the real-time driving status of the muck truck in combination with dynamic electronic fences at different sites;

[0100] The behavior analysis module 430 is configured to analyze whether the muck truck has committed any illegal behavior based on the real-time driving status of the muck truck and the dynamic electronic fences of different sites.

[0101] It should be noted here that the device 400 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above method embodiments, which will not be repeated here.

[0102] See also Figure 5 , Figure 5 A schematic diagram of the structure of a device for analyzing and assessing muck truck behavior based on a virtual electronic fence, provided in an embodiment of the present invention. Based on the aforementioned method for analyzing and assessing muck truck behavior based on a virtual electronic fence, the present invention also provides a device for analyzing and assessing muck truck behavior based on a virtual electronic fence. The device can be a computing device such as a mobile terminal, desktop computer, laptop, PDA, or server. The device 500 includes a processor 510, a memory 520, and a display 530. Figure 5 Only some components of the device for analyzing the behavior of a muck truck based on a virtual electronic fence are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0103] In some embodiments, the memory 520 can be an internal storage unit of the virtual electronic fence-based dump truck behavior analysis and judgment device 500, such as a hard drive or memory of the virtual electronic fence-based dump truck behavior analysis and judgment device 500. In other embodiments, the memory 520 can also be an external storage device of the virtual electronic fence-based dump truck behavior analysis and judgment device 500, such as a plug-in hard drive, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped with the virtual electronic fence-based dump truck behavior analysis and judgment device 500. Furthermore, the memory 520 can also include both the internal storage unit of the virtual electronic fence-based dump truck behavior analysis and judgment device 500 and an external storage device. The memory 520 is used to store application software and various data installed in the virtual electronic fence-based dump truck behavior analysis and judgment device 500, such as the program code for installing the virtual electronic fence-based dump truck behavior analysis and judgment device 500. The memory 520 can also be used to temporarily store data that has been output or is about to be output. In one embodiment, a dump truck behavior analysis program 540 based on a virtual electronic fence is stored in the memory 520. The dump truck behavior analysis program 540 based on a virtual electronic fence can be executed by the processor 510, thereby realizing the dump truck behavior analysis method based on a virtual electronic fence of each embodiment of the present application.

[0104] In some embodiments, the processor 510 can be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 520, such as executing a method for analyzing the behavior of a dump truck based on a virtual electronic fence.

[0105] In some embodiments, the display 530 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 530 is used to display information on the virtual electronic fence-based dump truck behavior analysis device 500 and to display a visual user interface. The components 510-530 of the virtual electronic fence-based dump truck behavior analysis device 500 communicate with each other via a system bus.

[0106] In one embodiment, when the processor 510 executes the muck truck behavior analysis program 540 based on virtual electronic fence in the memory 520, the steps in the above muck truck behavior analysis method based on virtual electronic fence are implemented.

[0107] This embodiment further provides a computer-readable storage medium storing a program for analyzing and determining the behavior of a muck truck based on a virtual electronic fence. When the program is executed by a processor, the following steps are implemented:

[0108] Obtain real-time coordinate data of different sites, and generate dynamic electronic fences for different sites based on the real-time coordinate data of different sites;

[0109] Process multi-source driving data of muck trucks and analyze and record their real-time driving status in combination with dynamic electronic fences at different sites;

[0110] Based on the real-time driving status of the dump truck and the dynamic electronic fences in different sites, it is determined whether the dump truck has committed any illegal acts.

[0111] In summary, the proposed method for analyzing dump truck behavior based on virtual electronic fences breaks through the static limitations of traditional electronic fences in terms of scenario adaptability. By dynamically generating dynamic electronic fences by acquiring construction site coordinates in real time, the scope of supervision can be instantly adjusted based on needs such as the establishment of temporary construction sites and the adjustment of emergency control areas. This effectively addresses the regulatory blind spots caused by the mismatch between traditional fences and dynamic scenarios, making it particularly suitable for the temporary construction projects that frequently occur in urban construction. In terms of data analysis depth, it integrates multi-source driving data to determine multi-dimensional characteristics such as the spatiotemporal trajectory, load status, and dwell duration of dump trucks. This method not only identifies overt violations such as over-the-counter driving but also accurately captures covert dumping behaviors through coupled analysis of sudden load changes and unconventional stops, addressing the complex violations that are difficult to detect with a single data source. In terms of regulatory timeliness, a real-time streaming data processing architecture is established, upgrading the traditional post-incident manual verification model to an intelligent early warning and alert system that performs matching calculations on driving status, enabling the immediate detection and immediate preservation of violations. This significantly shortens the response time from the occurrence of the behavior to regulatory intervention, effectively preventing the loss of evidence and the escalation of the consequences of the violation.

[0112] The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0113] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0114] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of the device or unit can be electrical or other forms.

[0116] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0117] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0118] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0119] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0120] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure herein, those skilled in the art will easily think of the implementation scheme of the present disclosure. This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

[0121] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence, characterized in that: include: Obtain real-time coordinate data of different sites, and generate dynamic electronic fences for different sites based on the real-time coordinate data of different sites; Processing multi-source driving data of the muck truck, and analyzing and recording the real-time driving status of the muck truck in combination with the dynamic electronic fences of the different sites; Based on the real-time driving status of the dump truck and the dynamic electronic fences of the different sites, it is determined whether the dump truck has committed any illegal acts.

2. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 1, characterized in that: The method of obtaining real-time coordinate data of different sites and generating dynamic electronic fences of different sites according to the real-time coordinate data of different sites also includes: Determine the real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information; Extracting polygon vertices from the real-time coordinate data of the different sites by using an edge recognition algorithm and a feature matching algorithm; Dynamic electronic fences of different sites are generated according to the real-time coordinate data of different sites and the polygon vertices.

3. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 2, characterized in that: The method of determining the real-time coordinate data of different sites based on the recorded site coordinate boundaries, real-time positioning information, and real-time camera information further includes: Pre-process the recorded site coordinate boundaries, real-time positioning information, and real-time camera information, and perform coordinate conversion to obtain multi-source coordinate data in the same coordinate system; The multi-source coordinate data of the same coordinate system are fused and dynamically updated to obtain real-time coordinate data of different sites.

4. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 2, characterized in that: The method of generating dynamic electronic fences for different sites according to the real-time coordinate data of different sites and the polygon vertices further includes: Connecting the polygonal vertices in a preset order to generate initial polygonal electronic fences of different sites; Calculate the overlap between the initial polygonal electronic fence and the real-time coordinate data of the corresponding site; The initial polygonal electronic fence that is lower than a preset overlap is dynamically adjusted according to the real-time coordinate data.

5. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 1, characterized in that: The multi-source driving data includes the starting point coordinates and the end point coordinates; the multi-source driving data of the muck truck is processed, and the real-time driving status of the muck truck is analyzed and recorded in combination with the dynamic electronic fences of the different sites, and further includes: Determine the driving trajectory of the muck truck according to the starting point coordinates and the end point coordinates, and calculate the average speed of the muck truck; The position status of the muck truck and the dynamic electronic fence is analyzed based on the driving trajectory of the muck truck and the dynamic electronic fences of different sites.

6. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 5, characterized in that: The determining whether the muck truck has committed any illegal act based on the real-time driving status of the muck truck and the dynamic electronic fences at different sites further includes: Monitoring the real-time load data of the muck truck, and calculating the load difference when the real-time load data changes; Whether the dump truck has committed any illegal act is determined based on the average speed of the dump truck, the position of the dump truck and the dynamic electronic fence, and the load difference.

7. The method for analyzing and judging the behavior of a muck truck based on a virtual electronic fence according to claim 6, characterized in that: The dynamic electronic fence includes a construction site dynamic electronic fence and a waste disposal site dynamic electronic fence; The determining whether the muck truck has committed an illegal act based on the average speed of the muck truck, the position of the muck truck and the dynamic electronic fence, and the load difference also includes: If the real-time load data exceeds the preset maximum load, an overload alarm record is generated, prohibiting the muck truck from leaving the construction site dynamic electronic fence; If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site, and the load difference exceeds the preset change threshold, the dump truck is performing illegal dumping, and an illegal dumping warning record is generated; If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site, and the load difference does not exceed the preset change threshold, the dump truck has a road spillage behavior, and a road spillage warning record is generated; If the dump truck does not enter the corresponding dynamic electronic fence of the disposal site and the average speed of the dump truck exceeds the preset speed threshold, a dump truck speeding warning record is generated.

8. A device for analyzing and judging the behavior of a muck truck based on a virtual electronic fence, characterized in that: include: A dynamic fence module is configured to obtain real-time coordinate data of different venues and generate dynamic electronic fences for different venues based on the real-time coordinate data of different venues; A driving status module is configured to process multi-source driving data of the muck truck and analyze and record the real-time driving status of the muck truck in combination with the dynamic electronic fences of the different sites; The behavior analysis module is configured to judge whether the muck truck has committed any illegal behavior based on the real-time driving status of the muck truck and the dynamic electronic fences of the different sites.

9. A device for analyzing and judging the behavior of a muck truck based on a virtual electronic fence, characterized in that: It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program. When the computer program is executed by the processing unit, the processing unit executes the steps of the method for analyzing the behavior of a dump truck based on a virtual electronic fence as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: It stores a computer program that can be executed by a dump truck behavior analysis device based on a virtual electronic fence. When the computer program runs on the dump truck behavior analysis device based on a virtual electronic fence, the dump truck behavior analysis device based on a virtual electronic fence executes the steps of the dump truck behavior analysis method based on a virtual electronic fence as described in any one of claims 1 to 7.

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