Method and system for managing whole process of fire coal transportation and metering

Abstract

The invention provides a fire coal transportation metering whole process management method and system, and belongs to the technical field of mining business management, and the method comprises the steps: collecting the metering process data of a fire coal transportation vehicle in real time, and recording the weighing data of the fire coal transportation vehicle in real time; the metering process data and the weighing data are analyzed, metering weight deviation identification and metering process deviation identification are carried out, and a metering deviation identification result is determined; carrying out metering fraud identification and metering violation identification on all the coal-fired transport vehicles whose weighing states are marked as non-metering anomalies, and determining a metering fraud identification result and a metering violation identification result; and generating a metering report according to the metering deviation identification result, the metering fraud identification result, the metering violation identification result and the weighing state marks of all the fire coal transport vehicles. According to the invention, the transparency and traceability of data can be improved, the intelligence and refinement level of measurement management can be enhanced, the depth and breadth of measurement anomaly detection can be improved, and the accuracy, high efficiency and transparency of measurement management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mining business management, and particularly to a full-process management method and system for coal transportation metering. Background Art

[0002] Traditional coal transportation metering management methods mainly rely on weighbridge weighing and manual operations. The metering process includes static weighing of the tare weight and gross weight of vehicles, with calculating the net weight as the core, but there are obvious limitations. On the one hand, the collection and recording of weighing data mostly adopt manual methods, which are prone to human errors, tampering, or data loss, resulting in insufficient data credibility. On the other hand, traditional technologies simply rely on weight data and lack the monitoring and analysis of multi-dimensional data such as the video and behavior of the metering process, making it difficult to identify complex abnormal situations. In addition, for common fraud behaviors, traditional methods lack effective monitoring and identification means, making it difficult to trace problems. Abnormal detection often relies on manual review or simple deviation judgment, which is not only inefficient but also difficult to meet the requirements of large-scale transportation scenarios, lacking automation, intelligence, and transparency.

[0003] Therefore, the present invention provides a full-process management method and system for coal transportation metering. Summary of the Invention

[0004] The present invention provides a full-process management method and system for coal transportation metering. By analyzing the metering process data and weighing data collected or obtained in real time, it conducts metering weight deviation identification, metering process deviation identification, metering fraud identification, and metering violation identification, determines the metering deviation identification result, metering fraud identification result, and metering violation identification result, and generates a metering report. It can improve the transparency and traceability of data, strengthen the intelligence and refinement level of metering management, enhance the depth and breadth of metering anomaly detection, and improve the accuracy, efficiency, and transparency of metering management.

[0005] On the one hand, the present invention provides a full-process management method for coal transportation metering, including: 101: Collect the metering process data of coal transportation vehicles in real time and record the weighing data of coal transportation vehicles in real time; 102: Analyze the metering process data and weighing data, conduct metering weight deviation identification and metering process deviation identification, and determine the metering deviation identification result; 103: Conduct metering fraud identification and metering violation identification for all coal transportation vehicles with the weighing status marked as non-metering abnormal, and determine the metering fraud identification result and metering violation identification result; 104: Generate a metering report based on the metering deviation identification result, metering fraud identification result, metering violation identification result, and the weighing status marks of all coal transportation vehicles.

[0006] A full-process management method for coal transportation metering provided by the present invention collects the metering process data of coal transportation vehicles in real time, including: Determine all key areas for the weighbridge to collect metering process data, and determine multiple camera installation positions for each key area; Install camera groups at all camera installation positions in all key areas, and collect the metering sub-process data of each coal transportation vehicle based on the camera groups; Determine the metering process data based on all metering sub-process data.

[0007] A full-process management method for coal transportation metering provided by the present invention, the light vehicle on-the-scale video, light vehicle off-the-scale video, heavy vehicle on-the-scale video, and heavy vehicle off-the-scale video of the coal transportation vehicle corresponding to the metering sub-process data; The weighing data includes the vehicle identification, heavy vehicle weighing time, vehicle gross weight, coal information, vehicle tare weight, light vehicle weighing time, and weighing status of all coal transportation vehicles.

[0008] A full-process management method for coal transportation metering provided by the present invention analyzes the metering process data and the weighing data to perform metering weight deviation identification and metering process deviation identification, including: Based on the vehicle identification, vehicle gross weight, and vehicle tare weight of all coal transportation vehicles in the weighing data, determine the net weight of each coal transportation vehicle; Judge whether the net weight of each coal transportation vehicle is within the preset transportation weight deviation range, mark the weighing status of the coal transportation vehicle whose net weight is not within the preset transportation weight deviation range as metering abnormal, and determine the abnormal label as metering weight deviation; Perform metering process deviation identification on the coal transportation vehicles whose net weight is within the preset transportation weight deviation range based on the metering process data.

[0009] A full-process management method for coal transportation metering provided by the present invention performs metering process deviation identification on the coal transportation vehicles whose net weight is within the preset transportation weight deviation range based on the metering process data, including: Based on the metering sub-process data of the coal transportation vehicles whose net weight is within the preset transportation weight deviation range, calculate the tare weight fitting process deviation and the gross weight fitting process deviation of each coal transportation vehicle; Judge whether the tare weight fitting process deviation and the gross weight fitting process deviation of each coal transportation vehicle are within the process preset deviation range; If any of the tare weight fitting process deviation and the gross weight fitting process deviation is not within the process preset deviation range, determine that the weighing status of the corresponding coal transportation vehicle is marked as metering abnormal, and determine the abnormal label as metering process deviation; Determine the metering sub - deviation identification result of each coal - fired transportation vehicle based on the tare weight fitting process deviation and the gross weight fitting process deviation of each coal - fired transportation vehicle; Determine the metering deviation identification result based on the metering sub - deviation identification results of all coal - fired transportation vehicles.

[0010] According to a full - process management method for coal - fired transportation metering provided by the present invention, based on the metering sub - process data of coal - fired transportation vehicles with vehicle net weights within a preset transportation weight deviation range, calculate the tare weight fitting process deviation and the gross weight fitting process deviation of each coal - fired transportation vehicle, including: ; ; ; ; ; ; Wherein, represents the tare weight fitting process deviation of the coal - fired transportation vehicle, represents the gross weight fitting process deviation of the coal - fired transportation vehicle, D1 represents the first sub - fitting process deviation of the coal - fired transportation vehicle, D2 represents the second sub - fitting process deviation of the coal - fired transportation vehicle, D3 represents the third sub - fitting process deviation of the coal - fired transportation vehicle, represents the vehicle tare weight, represents the vehicle gross weight, represents the acceleration due to gravity, N1 represents the number of weight sensors, represents the weight of the first sub - fitting process deviation, represents the weight of the second sub - fitting process deviation, represents the weight of the third sub - fitting process deviation, represents the weight of the first sub - fitting process deviation and the second sub - fitting process deviation, represents the offset sensitivity coefficient, represents the maximum horizontal coordinate of the weighing area, represents the minimum horizontal coordinate of the weighing area, 、 、 represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the vehicle center of gravity respectively, 、 、 represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the i - th sensor respectively, represents the position vector of the i - th sensor, represents the modulus of the position vector of the i - th sensor, Denote the position vector of the j-th sensor, Denote the magnitude of the position vector of the j-th sensor, Denote the first angular difference sensitivity coefficient, Denote the first maximum allowable angular difference, Denote the overall horizontal tilt angle of the vehicle, Denote the overall vertical tilt angle of the vehicle, Denote the height difference based on the tilt angle in the horizontal direction, Denote the height difference based on the tilt angle in the vertical direction, Denote the reference height of the vehicle, Denote the second angular difference sensitivity coefficient, Denote the second maximum allowable angular difference, Denote the maximum vertical coordinate of the weighing area, Denote the minimum vertical coordinate of the weighing area, Denote the theoretical height of the i-th sensor, Denote the angular difference between the i-th sensor and the j-th sensor.

[0011] According to a whole-process management method for coal transportation metering provided by the present invention, conduct metering fraud identification and metering violation identification for all coal transportation vehicles with the weighing status marked as non-metering abnormal, including: Determine the number of people on the light vehicle based on the video of the light vehicle entering the list and the video of the light vehicle leaving the list in the metering process data; Determine the number of people on the heavy vehicle based on the video of the heavy vehicle entering the list and the video of the heavy vehicle leaving the list in the metering process data; Judge whether the number of people on the light vehicle is the same as the number of people on the heavy vehicle. Mark the weighing status of the coal transportation vehicle with inconsistent numbers of people on the light vehicle and the heavy vehicle as metering abnormal, and determine the abnormal label as metering fraud; Based on the number of people on the light vehicle and the number of people on the heavy vehicle of each coal transportation vehicle, determine the metering sub-fraud identification result of each coal transportation vehicle; Based on the metering sub-fraud identification results of all coal transportation vehicles, determine the metering fraud identification result; Analyze the metering process data, judge whether there are any personnel violation activities. Mark the weighing status of the coal transportation vehicle with personnel violation activities as metering abnormal, and determine the abnormal label as metering violation. Record the personnel violation activities of the coal transportation vehicle without personnel violation activities as none; Based on the personnel violation activities of each coal transportation vehicle, determine the metering sub-violation identification result of each coal transportation vehicle; Based on the metering sub-violation identification results of all coal transportation vehicles, determine the metering violation identification result; For all coal transportation vehicles whose weighing status is marked as non - measurement anomaly after measurement deviation identification, measurement fraud identification, and measurement violation identification, mark the weighing status of each coal transportation vehicle as measurement successful.

[0012] On the other hand, the present invention also provides a full - process management system for coal transportation metrology, including: Collection module: Real - time collect the measurement process data of coal transportation vehicles and record the weighing data of coal transportation vehicles in real - time. Analysis module: Analyze the measurement process data and weighing data, conduct measurement weight deviation identification and measurement process deviation identification, and determine the measurement deviation identification result. Identification module: Conduct measurement fraud identification and measurement violation identification on all coal transportation vehicles whose weighing status is marked as non - measurement anomaly, and determine the measurement fraud identification result and measurement violation identification result. Generation module: Generate a measurement report based on the measurement deviation identification result, measurement fraud identification result, measurement violation identification result, and the weighing status marks of all coal transportation vehicles.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: By analyzing the real - time collected or obtained measurement process data and weighing data, conducting measurement weight deviation identification, measurement process deviation identification, measurement fraud identification, and measurement violation identification, determining the measurement deviation identification result, measurement fraud identification result, and measurement violation identification result, and generating a measurement report. It can improve the transparency and traceability of data, strengthen the intelligence and refinement level of measurement management, enhance the depth and breadth of measurement anomaly detection, and improve the accuracy, efficiency, and transparency of measurement management. Brief Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 is a schematic flowchart of a full - process management method for coal transportation metrology provided by an embodiment of the present invention.

[0016] Figure 2 is a schematic structural diagram of a full - process management system for coal transportation metrology provided by an embodiment of the present invention. Detailed Embodiments

[0017] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a full-process management method for coal transportation metering, as Figure 1 shown, including: 101: Real-time collect the metering process data of coal transportation vehicles and record the weighing data of coal transportation vehicles in real time; 102: Analyze the metering process data and the weighing data, conduct metering weight deviation identification and metering process deviation identification, and determine the metering deviation identification result; 103: Conduct metering fraud identification and metering violation identification for all coal transportation vehicles with the weighing status marked as non-metering abnormal, and determine the metering fraud identification result and the metering violation identification result; 104: Generate a metering report based on the metering deviation identification result, the metering fraud identification result, the metering violation identification result, and the weighing status marks of all coal transportation vehicles.

[0019] In this embodiment, based on the collected data, analyze whether there are metering weight deviations and metering process deviations during the vehicle metering process, and determine the metering deviation identification result according to the analysis result.

[0020] In this embodiment, for vehicles not marked as metering abnormal, further analyze whether there are metering fraud behaviors or metering violation operations during their metering process, and determine the metering fraud identification result and the metering violation identification result through the above identifications.

[0021] In this embodiment, comprehensively analyze the metering deviation identification result, the metering fraud identification result, and the metering violation identification result, and combine the weighing status marks of all vehicles to generate a comprehensive metering report.

[0022] Beneficial effects of the above technical solution: By analyzing the metering process data and the weighing data collected or obtained in real time, conduct metering weight deviation identification, metering process deviation identification, metering fraud identification, and metering violation identification, determine the metering deviation identification result, the metering fraud identification result, and the metering violation identification result, and generate a metering report. It can improve the transparency and traceability of data, strengthen the intelligence and refinement level of metering management, enhance the depth and breadth of metering anomaly detection, and improve the accuracy, efficiency, and transparency of metering management.

[0023] Embodiment 2: An embodiment of the present invention provides a full-process management method for coal transportation metering, which collects the metering process data of coal transportation vehicles in real time, including: Determine all key areas for the weighbridge to collect metering process data, and determine the installation positions of multiple cameras in each key area; Install camera groups at all camera installation positions in all key areas, and collect the metering sub-process data of each coal transportation vehicle based on the camera groups; Determine the metering process data based on all metering sub-process data.

[0024] In this embodiment, according to the structure and metering process of the weighbridge, key areas that need to be monitored with emphasis during the metering process are identified, such as vehicle entry and exit areas, weighing areas, carriage closure status inspection areas, etc.

[0025] In this embodiment, for each key area, determine the installation positions of multiple cameras to ensure that all important monitoring angles can be covered and visual blind spots are avoided.

[0026] In this embodiment, install camera groups (composed of multiple cameras) at predetermined positions in all key areas to ensure that the metering process images and status data of each coal transportation vehicle passing through this area can be collected from multiple angles and comprehensively.

[0027] In this embodiment, the camera group collects the sub-process data during the metering process of each coal transportation vehicle in real time.

[0028] In this embodiment, summarize all metering sub-process data to form a complete metering process record.

[0029] The beneficial effects of the above technical solutions: Collecting the metering process data of coal transportation vehicles in real time can improve the comprehensiveness, accuracy and traceability of metering process records.

[0030] Embodiment 3: An embodiment of the present invention provides a full-process management method for coal transportation metering, where the metering sub-process data corresponds to the light vehicle on-the-scale video, light vehicle off-the-scale video, heavy vehicle on-the-scale video, and heavy vehicle off-the-scale video of the coal transportation vehicle; The weighing data includes the vehicle identification, heavy vehicle weighing time, vehicle gross weight, coal information, vehicle tare weight, light vehicle weighing time, and weighing status of all coal transportation vehicles.

[0031] In this embodiment, a light vehicle refers to an empty coal transportation vehicle, which is used to calculate the vehicle tare weight during weighing.

[0032] In this embodiment, a heavy vehicle refers to a coal-loaded transportation vehicle, which is used to calculate the vehicle gross weight during weighing.

[0033] In this embodiment, the on-board / off-board videos record the monitoring videos of the vehicle when it gets on and off the weighbridge (scale), reflecting the status and actions of the vehicle during the weighing process.

[0034] In this embodiment, the gross vehicle weight represents the total weight of a fully loaded vehicle, including the vehicle's own weight (tare weight) and the weight of the coal.

[0035] In this embodiment, the vehicle tare weight represents the vehicle's own weight when it is empty, which is used to deduct the gross weight to calculate the net weight of the coal.

[0036] In this embodiment, the weighing status represents the marked information on whether there are any abnormalities in the weighing process.

[0037] In this embodiment, the vehicle identification represents the unique identification of each transport vehicle (such as the license plate number or the system-assigned number); In this embodiment, the heavy vehicle weighing time represents the specific time when the vehicle is fully loaded (heavy vehicle) for weighing, and the light vehicle weighing time represents the specific time when the vehicle is empty (light vehicle) for weighing.

[0038] In this embodiment, the coal information represents the relevant information of the transported coal (such as the coal type, coal source or destination); The beneficial effects of the above technical solution: Determining the metering sub-process data and weighing data can improve the transparency and traceability of the data.

[0039] Embodiment 4: The embodiment of the present invention provides a method for the whole-process management of coal transportation metering, analyzing the metering process data and weighing data, and performing metering weight deviation identification and metering process deviation identification, including: Based on the vehicle identification, vehicle gross weight, and vehicle tare weight of all coal transportation vehicles in the weighing data, determine the vehicle net weight of each coal transportation vehicle; Judge whether the vehicle net weight of each coal transportation vehicle is within the preset transportation weight deviation range, mark the weighing status of the coal transportation vehicle whose vehicle net weight is not within the preset transportation weight deviation range as metering abnormal, and determine the abnormal label as metering weight deviation; Based on the metering process data, perform metering process deviation identification on the coal transportation vehicles whose vehicle net weights are within the preset transportation weight deviation range.

[0040] In this embodiment, based on the vehicle identification, vehicle gross weight, and vehicle tare weight of each coal transportation vehicle in the weighing data, calculate the vehicle net weight of each coal transportation vehicle (net weight = gross weight - tare weight), that is, the actual weight of the transported coal.

[0041] In this embodiment, it is checked whether the net vehicle weight of each vehicle is within the transport weight deviation range preset by the system (used to judge the reasonable fluctuation range of the transport weight): If the net vehicle weight is not within this range, its weighing status is marked as metering anomaly; at the same time, an anomaly label for the abnormal vehicle is determined as metering weight deviation, indicating that this anomaly is caused by the transport weight exceeding the preset deviation range.

[0042] In this embodiment, for vehicles with net vehicle weight within the deviation range, deviation identification of the metering process is further performed based on the metering process data.

[0043] Beneficial effects of the above technical solution: By analyzing the metering process data and weighing data, and performing metering weight deviation identification and metering process deviation identification, dual monitoring of metering anomalies can be achieved, strengthening the intelligent and refined level of metering management.

[0044] Embodiment 5: The embodiment of the present invention provides a method for the whole-process management of coal transportation metering, which performs deviation identification of the metering process on coal transportation vehicles with net vehicle weight within the preset transport weight deviation range based on the metering process data, including: Based on the metering sub-process data of coal transportation vehicles with net vehicle weight within the preset transport weight deviation range, calculate the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle; Judge whether the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle are within the process preset deviation range; If any one of the tare weight fitting process deviation and gross weight fitting process deviation is not within the process preset deviation range, determine that the weighing status of the corresponding coal transportation vehicle is marked as metering anomaly, and determine the anomaly label as metering process deviation; Based on the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle, determine the metering sub-deviation identification result of each coal transportation vehicle; Based on the metering sub-deviation identification results of all coal transportation vehicles, determine the metering deviation identification result.

[0045] In this embodiment, based on vehicles with net vehicle weight within the preset transport weight deviation range, from their metering sub-process data, calculate the tare weight fitting process deviation and gross weight fitting process deviation of each vehicle respectively.

[0046] In this embodiment, it is checked whether the tare weight fitting process deviation and gross weight fitting process deviation of each vehicle are within the process preset deviation range: If any deviation exceeds the range, mark the weighing status of this vehicle as metering anomaly, and determine the anomaly label as metering process deviation, indicating that the anomaly of this vehicle comes from problems in the metering process itself.

[0047] In this embodiment, based on the tare weight fitting process deviation and the gross weight fitting process deviation of each vehicle, the abnormal characteristics of the vehicle in the metering process are comprehensively analyzed to form the identification result of the metering sub-deviation of the vehicle.

[0048] In this embodiment, the process preset deviation range represents the reasonable fluctuation range of the tare weight and gross weight deviations allowed in the metering process. If the range is exceeded, it is considered abnormal.

[0049] In this embodiment, the identification results of the metering sub-deviations of all vehicles are summarized to determine the overall metering deviation identification result, which is used to judge the overall deviation characteristics of the metering data of the current batch of coal transportation vehicles and whether there are systematic metering problems.

[0050] Beneficial effects of the above technical solution: Based on the metering process data, the metering process deviation of coal transportation vehicles with the net weight of the vehicle within the preset transportation weight deviation range can be identified, and the metering anomalies of the vehicle can be accurately identified, improving the intelligence and accuracy of metering management, and making the metering process more transparent, standardized and efficient.

[0051] Embodiment 6: The embodiment of the present invention provides a method for managing the whole process of coal transportation metering. Based on the metering sub-process data of coal transportation vehicles with the net weight of the vehicle within the preset transportation weight deviation range, the tare weight fitting process deviation and the gross weight fitting process deviation of each coal transportation vehicle are calculated, including: ; ; ; ; ; ; Among them, represents the tare weight fitting process deviation of the coal transportation vehicle, represents the gross weight fitting process deviation of the coal transportation vehicle, D1 represents the first sub-fitting process deviation of the coal transportation vehicle, D2 represents the second sub-fitting process deviation of the coal transportation vehicle, D3 represents the third sub-fitting process deviation of the coal transportation vehicle, represents the vehicle tare weight, represents the vehicle gross weight, represents the acceleration of gravity, N1 represents the number of weight sensors, represents the weight of the first sub-fitting process deviation, represents the weight of the second sub-fitting process deviation, represents the weight of the third sub-fitting process deviation, Represents the weights of the first sub-fitting process deviation and the second sub-fitting process deviation, Represents the offset sensitivity coefficient, Represents the maximum horizontal coordinate of the weighing area, Represents the minimum horizontal coordinate of the weighing area, 、 、 Respectively represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the vehicle's center of gravity, 、 、 Respectively represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the i-th sensor, Represents the position vector of the i-th sensor, Represents the magnitude of the position vector of the i-th sensor, Represents the position vector of the j-th sensor, Represents the magnitude of the position vector of the j-th sensor, Represents the first angular difference sensitivity coefficient, Represents the first maximum allowable angular difference, Represents the overall horizontal tilt angle of the vehicle, Represents the overall vertical tilt angle of the vehicle, Represents the height difference based on the tilt angle in the horizontal direction, Represents the height difference based on the tilt angle in the vertical direction, Represents the reference height of the vehicle, Represents the second angular difference sensitivity coefficient, Represents the second maximum allowable angular difference, Represents the maximum vertical coordinate of the weighing area, Represents the minimum vertical coordinate of the weighing area, Represents the theoretical height of the i-th sensor, Represents the angle difference between the i-th sensor and the j-th sensor.

[0052] In this embodiment, the first sub-fitting process deviation D1 represents the deviation caused by the offset of the center of gravity position, the second sub-fitting process deviation D2 represents the deviation caused by the tilt angle based on the height, and the third sub-fitting process deviation D3 represents the deviation caused by the tilt angle based on the normal vector.

[0053] In this embodiment, Represents the included angle between the normal vectors of the i-th sensor and the j-th sensor, In this embodiment, Represents the actual height of the weighing area.

[0054] In this embodiment, Represents the actual length of the weighing area.

[0055] Beneficial effects of the above technical solution: Based on the metering sub-process data of coal transportation vehicles with the vehicle net weight within the preset transportation weight deviation range, the tare weight fitting process deviation and the gross weight fitting process deviation of each coal transportation vehicle can be calculated, which can provide data basis for accurately identifying the metering anomalies of single vehicles.

[0056] Example 7: The embodiment of the present invention provides a method for the whole-process management of coal transportation metering, which performs metering fraud identification and metering violation identification on all coal transportation vehicles with the metering status marked as non-metering anomaly, including: Determining the number of people on the empty vehicle based on the video of the empty vehicle entering the list and the video of the empty vehicle leaving the list in the metering process data; Determining the number of people on the loaded vehicle based on the video of the loaded vehicle entering the list and the video of the loaded vehicle leaving the list in the metering process data; Judging whether the number of people on the empty vehicle is the same as the number of people on the loaded vehicle, marking the weighing status of the coal transportation vehicle with inconsistent number of people on the empty vehicle and the loaded vehicle as metering anomaly, and determining the anomaly label as metering fraud; Based on the number of people on the empty vehicle and the number of people on the loaded vehicle of each coal transportation vehicle, determining the metering sub-fraud identification result of each coal transportation vehicle; Determining the metering fraud identification result based on the metering sub-fraud identification results of all coal transportation vehicles; Analyzing the metering process data, judging whether there are personnel violation activities, marking the weighing status of the coal transportation vehicle with personnel violation activities as metering anomaly, and determining the anomaly label as metering violation, and recording the personnel violation activities of the coal transportation vehicle without personnel violation activities as none; Based on the personnel violation activities of each coal transportation vehicle, determining the metering sub-violation identification result of each coal transportation vehicle; Determining the metering violation identification result based on the metering sub-violation identification results of all coal transportation vehicles; For all coal transportation vehicles with the weighing status marked as non-metering anomaly after metering deviation identification, metering fraud identification and metering violation identification, marking the weighing status of each coal transportation vehicle as metering successful.

[0057] In this embodiment, compare whether the number of people on the empty vehicle and the loaded vehicle is the same: If the number of people is inconsistent, then mark the weighing status of the vehicle as metering anomaly and assign the anomaly label metering fraud, indicating that there may be a fraud behavior where personnel interfere with the metering result by changing the vehicle weight when getting on and off the vehicle.

[0058] In this embodiment, based on the number of light and heavy vehicle personnel in each vehicle, the metering sub-fraud identification result of the vehicle is determined to mark whether there is fraud behavior in the vehicle.

[0059] In this embodiment, the metering sub-fraud identification results of all vehicles are summarized to obtain the overall metering fraud identification result, which is used to judge whether there is a systematic fraud problem in the metering system.

[0060] In this embodiment, by analyzing the metering process data, it is judged whether there are any personnel violation activities (such as unauthorized getting on and off the vehicle, etc.) during the metering process of the vehicle. If violation activities are found, the weighing status of the vehicle is marked as metering abnormal and an abnormal label of metering violation is assigned; if there are no violation activities, the situation of personnel violation activities is recorded as none.

[0061] In this embodiment, based on the analysis result of personnel violation activities of each vehicle, its metering sub-violation identification result is determined.

[0062] In this embodiment, the metering sub-violation identification results of all vehicles are summarized to determine the overall metering violation identification result, which is used to judge whether there is a systematic violation problem in the batch vehicle metering.

[0063] In this embodiment, the on / off scale video of light vehicles records the video data of the on and off scale process of the empty vehicle on the weighing equipment, which is used to analyze the operation of light vehicles and the number of personnel on the vehicle.

[0064] In this embodiment, the on / off scale video of heavy vehicles records the video data of the on and off scale process of the fully loaded vehicle on the weighing equipment, which is used to analyze the operation of heavy vehicles and the number of personnel on the vehicle.

[0065] In this embodiment, metering fraud is an act of interfering with the metering result through violation behaviors (such as artificially changing the weight when getting on and off the vehicle).

[0066] In this embodiment, metering violation refers to abnormal activities of personnel during the metering process (such as unauthorized getting on and off the vehicle, etc.).

[0067] In this embodiment, successful metering indicates that there are no deviations, no frauds, and no violations during the metering process of the vehicle, and the metering result is true and valid.

[0068] The beneficial effects of the above technical solutions: By performing metering fraud identification and metering violation identification on all coal transportation vehicles with the weighing status marked as non-metering abnormal, the depth and breadth of metering anomaly detection can be improved, and the accuracy, efficiency, and transparency of metering management can be enhanced.

[0069] Embodiment 8: The embodiment of the present invention provides a full-process management system for coal transportation metering. The control module includes: Collection module: Real-time collect the metering process data of coal transportation vehicles and record the weighing data of coal transportation vehicles in real time; Analysis module: Analyze the metering process data and weighing data, conduct metering weight deviation identification and metering process deviation identification, and determine the metering deviation identification result; Identification module: Conduct metering fraud identification and metering violation identification on all coal transportation vehicles with the weighing status marked as non-metering abnormal, and determine the metering fraud identification result and metering violation identification result; Generation module: Generate a metering report based on the metering deviation identification result, metering fraud identification result, metering violation identification result, and the weighing status marks of all coal transportation vehicles.

[0070] Advantages of the above technical solution: By analyzing the real-time collected or obtained metering process data and weighing data, conduct metering weight deviation identification, metering process deviation identification, metering fraud identification, and metering violation identification, determine the metering deviation identification result, metering fraud identification result, and metering violation identification result, and generate a metering report. It can improve the transparency and traceability of data, strengthen the intelligence and refinement level of metering management, enhance the depth and breadth of metering anomaly detection, and improve the accuracy, efficiency, and transparency of metering management.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0072] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be realized by means of software plus a necessary general hardware platform, and of course, it can also be realized by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A full-process management method for coal transportation metering, characterized in that Including: 101: Real-time collect the metering process data of coal transportation vehicles and record the weighing data of coal transportation vehicles in real time; 102: Analyze the metering process data and weighing data, conduct metering weight deviation identification and metering process deviation identification, and determine the metering deviation identification result; 103: Conduct metering fraud identification and metering violation identification for all coal transportation vehicles with the weighing status marked as non-metering abnormal, and determine the metering fraud identification result and metering violation identification result; 104: Generate a metering report based on the metering deviation identification result, metering fraud identification result, metering violation identification result, and the weighing status marks of all coal transportation vehicles.

2. The full-process management method for coal transportation metering according to claim 1, characterized in that Real-time collect the metering process data of coal transportation vehicles, including: Determine all key areas for the weighbridge to collect metering process data, and determine the installation positions of multiple cameras in each key area; Install camera groups at all camera installation positions in all key areas, and collect the metering sub-process data of each coal transportation vehicle based on the camera groups; Determine the metering process data based on all metering sub-process data.

3. The full-process management method for coal transportation metering according to claim 2, wherein The light vehicle on-the-scale video, light vehicle off-the-scale video, heavy vehicle on-the-scale video, and heavy vehicle off-the-scale video of the coal transportation vehicle corresponding to the metering sub-process data; The weighing data includes the vehicle identification, heavy vehicle weighing time, vehicle gross weight, coal information, vehicle tare weight, light vehicle weighing time, and weighing status of all coal transportation vehicles.

4. The full-process management method for coal transportation metering according to claim 3, characterized in that Analyze the metering process data and weighing data, conduct metering weight deviation identification and metering process deviation identification, including: Based on the vehicle identification, vehicle gross weight, and vehicle tare weight of all coal transportation vehicles in the weighing data, determine the net weight of each coal transportation vehicle; Judge whether the net weight of each coal transportation vehicle is within the preset transportation weight deviation range, mark the weighing status of the coal transportation vehicle with the net weight not within the preset transportation weight deviation range as metering abnormal, and determine the abnormal label as metering weight deviation; Conduct metering process deviation identification for coal transportation vehicles with the net weight within the preset transportation weight deviation range based on the metering process data.

5. The full-process management method for coal transportation metering according to claim 4, characterized in that Conduct metering process deviation identification for coal transportation vehicles with the net weight within the preset transportation weight deviation range based on the metering process data, including: Based on the metering sub-process data of coal transportation vehicles with the net weight within the preset transportation weight deviation range, calculate the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle; Judge whether the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle are within the process preset deviation range; If either the tare weight fitting process deviation or the gross weight fitting process deviation is not within the process preset deviation range, mark the weighing status of the corresponding coal transportation vehicle as metering abnormal, and determine the abnormal label as metering process deviation; Based on the tare weight fitting process deviation and gross weight fitting process deviation of each coal transportation vehicle, determine the metering sub-deviation identification result of each coal transportation vehicle; Determine the metering deviation identification result based on the metering sub-deviation identification results of all coal transportation vehicles.

6. The full-process management method for coal transportation metering according to claim 5, wherein Calculate the tare weight fitting process deviation and gross weight fitting process deviation for each coal transportation vehicle based on the metering sub-process data of coal transportation vehicles with vehicle net weights within the preset transportation weight deviation range, including: ； ； ； ； ； ； Among them, represents the tare weight fitting process deviation of the coal transportation vehicle, represents the gross weight fitting process deviation of the coal transportation vehicle, D1 represents the first sub-fitting process deviation of the coal transportation vehicle, D2 represents the second sub-fitting process deviation of the coal transportation vehicle, and D3 represents the third sub-fitting process deviation of the coal transportation vehicle, represents the tare weight of the vehicle, represents the gross weight of the vehicle, represents the acceleration due to gravity, and N1 represents the number of weight sensors, represents the weight of the first sub-fitting process deviation, represents the weight of the second sub-fitting process deviation, represents the weight of the third sub-fitting process deviation, represents the weight of the first sub-fitting process deviation and the second sub-fitting process deviation, represents the offset sensitivity coefficient, represents the maximum horizontal coordinate of the weighing area, represents the minimum horizontal coordinate of the weighing area, 、 、 respectively represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the vehicle's center of gravity, 、 、 respectively represent the horizontal coordinate, vertical coordinate, and perpendicular coordinate of the i-th sensor, represents the position vector of the i-th sensor, represents the modulus of the position vector of the i-th sensor, represents the position vector of the j-th sensor, represents the modulus of the position vector of the j-th sensor, represents the first angular difference sensitivity coefficient, represents the first maximum allowable angular difference, represents the overall horizontal tilt angle of the vehicle, represents the overall vertical tilt angle of the vehicle, represents the height difference based on the tilt angle in the horizontal direction, represents the height difference based on the tilt angle in the vertical direction, represents the reference height of the vehicle, represents the second angular difference sensitivity coefficient, represents the second maximum allowable angular difference, represents the maximum vertical coordinate of the weighing area, represents the minimum vertical coordinate of the weighing area, represents the theoretical height of the i-th sensor, represents the angular difference between the i-th sensor and the j-th sensor.

7. A whole-process management method for coal transportation metering according to claim 1, characterized in that Perform metering fraud identification and metering violation identification on all coal transportation vehicles with the weighing status marked as non-metering abnormal, including: Determine the number of people on the light vehicle based on the light vehicle on-board video and light vehicle off-board video in the metering process data; Determine the number of people on the heavy vehicle based on the heavy vehicle on-board video and heavy vehicle off-board video in the metering process data; Judge whether the number of people on the light vehicle is the same as the number of people on the heavy vehicle. Mark the weighing status of the coal transportation vehicle with inconsistent numbers of people on the light vehicle and the heavy vehicle as metering abnormal, and determine the abnormal label as metering fraud; Determine the metering sub-fraud identification result for each coal transportation vehicle based on the number of people on the light vehicle and the number of people on the heavy vehicle of each coal transportation vehicle; Determine the metering fraud identification result based on the metering sub-fraud identification results of all coal transportation vehicles; Analyze the metering process data to judge whether there are any personnel violation activities. Mark the weighing status of the coal transportation vehicle with personnel violation activities as metering abnormal, and determine the abnormal label as metering violation. Record the personnel violation activities of the coal transportation vehicle without personnel violation activities as none; Determine the metering sub-violation identification result for each coal transportation vehicle based on the personnel violation activities of each coal transportation vehicle; Determine the metering violation identification result based on the metering sub-violation identification results of all coal transportation vehicles; For all coal transportation vehicles with the weighing status marked as non-metering abnormal after metering deviation identification, metering fraud identification, and metering violation identification, mark the weighing status of each coal transportation vehicle as metering successful.

8. A whole-process management system for coal transportation and metering, characterized in that, Including: Acquisition module: Real-time acquire the metering process data of coal transportation vehicles and real-time record the weighing data of coal transportation vehicles; Analysis module: Analyze the metering process data and weighing data, perform metering weight deviation identification and metering process deviation identification, and determine the metering deviation identification result; Identification module: Perform metering fraud identification and metering violation identification on all coal transportation vehicles with the weighing status marked as non-metering abnormal, and determine the metering fraud identification result and metering violation identification result; Generation module: Generate a metering report based on the metering deviation identification result, metering fraud identification result, metering violation identification result, and the weighing status marks of all coal transportation vehicles.

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