A loading device and method for finished asphalt materials

Abstract

This invention discloses an asphalt loading device and method, comprising a movable base and a support seat mounted on the base for supporting a truck bed. A weighing sensor is provided between the base and the support seat. When loading asphalt, the truck bed is first driven onto the support seat, and the base moves to the outlet of the asphalt mixing plant. Asphalt enters the truck bed through the outlet, thus achieving automatic asphalt loading. A valve is provided at the outlet of the asphalt mixing plant to control the start and stop of asphalt loading. This eliminates the need for manual truck driving, preventing respiratory diseases, pneumoconiosis, and other occupational diseases, and protecting the driver's health.

Description

Technical Field

[0001] This invention relates to the field of asphalt loading and unloading, specifically to an asphalt finished material loading device and loading method. Background Technology

[0002] Asphalt mixing plants are an indispensable and crucial component of road construction, directly impacting the quality and service life of pavement. Their production efficiency and the uniformity of the mixture are decisive for the overall project outcome. Ensuring the stability of the mixture's quality during loading and unloading is a core objective in this field. However, current operating methods have revealed significant shortcomings in practical applications. Traditional manual unloading methods often rely on the operator's experience, making it difficult to precisely control the unloading location and quantity. This results in uneven distribution of the mixture when loaded onto vehicles, affecting the smoothness and durability of subsequent construction. A deeper problem lies in the lack of dynamic monitoring and adjustment capabilities during the loading process, hindering the timely detection and correction of potential quality issues.

[0003] During loading, the high-temperature asphalt mixture releases a large amount of harmful fumes (such as benzo[a]pyrene and sulfides). Prolonged exposure to this environment can easily lead to respiratory illnesses, pneumoconiosis, and other occupational diseases for drivers. Furthermore, because the vehicle's position is fixed during unloading, the mixture tends to accumulate unevenly within the truck bed, creating localized overloads or gaps. This uneven loading and segregation directly results in quality variations during paving, causing irreversible damage to road construction. Summary of the Invention

[0004] The purpose of this invention is to solve the above-mentioned problems and provide an asphalt finished material loading device and loading method, which can realize the automatic loading of asphalt mixture, prevent damage to the driver's body, prevent segregation, and ensure the paving quality of the road surface.

[0005] The technical solution adopted by this invention to solve its technical problem is:

[0006] A loading device for finished asphalt materials includes a movable base and a support seat mounted on the base for supporting the truck bed. A weighing sensor is provided between the base and the support seat.

[0007] Furthermore, the support base is provided with a fixing block for positioning the front wheel and a positioning mechanism for positioning the rear wheel.

[0008] Furthermore, the positioning mechanism includes a positioning plate, a rotating rod disposed within the support base and rotating within the support base, and a hydraulic cylinder for driving the rotating rod to rotate. The support base has a groove, and the rotating rod and hydraulic cylinder are disposed within the groove. Two rotating rods are arranged side by side. A support shaft is disposed within the groove, passing through the middle position of the rotating rod and rotatably connected to the middle of the rotating rod. The front ends of the two rotating rods are connected as a whole by a connecting rod. The piston rod end of the hydraulic cylinder is provided with a sleeve that cooperates with the connecting rod. A vertical part is provided at one end of the outer side of the rotating rod, and the upper end of the vertical part is fixedly connected to the positioning plate.

[0009] Furthermore, it also includes a track for supporting the base, and the lower end of the base is provided with rollers that cooperate with the track;

[0010] It also includes a drive mechanism for moving the base, the drive mechanism including a chain and a motor for driving the chain, the chain being connected to the lower end of the base via a mounting plate.

[0011] Furthermore, a transition plate is inclined at the left end of the support base, and a support frame is provided between the transition plate and the support base.

[0012] Furthermore, a method for loading finished asphalt material onto trucks, applied to the aforementioned apparatus, includes the following steps:

[0013] S101 Acquires initial data on the distribution of the mixture inside the truck bed through a sensor array and constructs a real-time mapping map; S102 Compares the real-time mapping map with a preset uniform distribution target to determine the unloading position offset; S103 Generates an adjustment command based on the unloading position offset and transmits it to the drive system to complete the vehicle position fine-tuning; S104 Acquires an image of the adjusted mixture distribution state through an image data acquisition device and determines whether a preset uniformity threshold has been reached; S105 If the preset uniformity threshold has not been reached, the offset calculation and position fine-tuning are repeatedly executed until the preset uniformity threshold is met;

[0014] S106 continuously collects dynamic data streams of the mixture inside the truck bed to determine if there are any signs of particle separation or abnormal accumulation.

[0015] S107 If signs of separation are detected, the quality hazard warning mechanism will be activated.

[0016] S108, based on feedback instructions, jointly controls the unloading rate and position, updates the distribution status of the mixture in real time, and determines that abnormal areas have been corrected.

[0017] Furthermore, step S105 mainly includes:

[0018] If the mixture distribution image does not reach the preset uniformity threshold, the offset adjustment parameters are analyzed, a new adjustment command is generated, the new adjustment command is transmitted to the drive system to trigger position fine-tuning, and updated distribution data is obtained. Based on the updated distribution data, it is determined whether it meets the preset uniformity threshold, and the process is repeated until the target state is reached.

[0019] Furthermore, step S108 mainly includes:

[0020] Real-time data of unloading is acquired through a sensor network, and preliminary processing is performed to obtain an evaluation result of the initial distribution state.

[0021] Based on the evaluation results of the initial distribution state, the unloading rate and unloading position are jointly controlled to determine the distribution change trend after control.

[0022] In response to the aforementioned distribution change trend, the data model of the mixture distribution state is updated in real time, and anomaly detection is performed on the distribution state to determine whether there are abnormal areas.

[0023] If the abnormal area is detected, the unloading rate and position are adjusted again by the adjustment command to obtain the adjusted distribution status data;

[0024] Based on the adjusted distribution data, the correction signals of the abnormal areas are analyzed to obtain the verification results of the area confirmation;

[0025] Based on the verification results confirmed by the region, update the database records for status monitoring to determine whether the overall distribution status has reached stability;

[0026] If the overall distribution state does not reach stability, the control and detection process is repeated to obtain the latest distribution state data and determine the final correction signal and the confirmation result of the region.

[0027] The beneficial effects of this invention are:

[0028] 1. This invention includes a movable base and a support seat mounted on the base for supporting the truck bed. A weighing sensor is provided between the base and the support seat. When loading finished asphalt, the truck bed is first driven onto the support seat, and the base moves to the discharge port of the asphalt mixing plant. The asphalt enters the truck bed through the discharge port, thus realizing automatic asphalt loading. A valve is provided at the discharge port of the asphalt mixing plant to control the start and stop of asphalt loading. No manual driving of the truck is required, preventing respiratory diseases, pneumoconiosis, and other occupational diseases, and protecting the driver's health.

[0029] 2. During the loading of asphalt mixtures, the weight of the loaded asphalt mixtures should be monitored in real time to ensure accurate weight measurement. Traditional weighing methods involve first measuring the weight of the empty truck, and then weighing it after the truck bed is filled. However, this method is time-consuming, requires a dedicated person to be responsible for weighing, and the temperature of the asphalt mixture in the truck bed can change, affecting the quality and leading to inaccurate weight measurements.

[0030] 3. This invention acquires vehicle position and material pile distribution within the truck bed by deploying a sensor array in the unloading area, generating a real-time mixture distribution mapping map. Based on a preset uniformity target, it analyzes local overload or empty areas, determines the unloading position offset, and triggers the vehicle dynamic positioning module for fine-tuning. After adjustment, the distribution state is scanned again to determine if a uniformity threshold has been reached; if not, position fine-tuning is performed cyclically. Simultaneously, the dynamic data of the mixture is monitored in real time, separation phenomena are detected and warnings are issued, and the unloading rate and position are jointly controlled based on feedback instructions. This invention achieves uniformity and stability in the mixture unloading process through dynamic feedback control, effectively avoiding separation phenomena and improving unloading quality and efficiency. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of the present invention;

[0032] Figure 2 This is a schematic diagram of the internal structure of the present invention. Figure 1 ;

[0033] Figure 3 This is a schematic diagram of the internal structure of the present invention. Figure 2 ;

[0034] Figure 4 This is a flowchart of the present invention;

[0035] Figure 5 This is a diagram of the mixture stacking state detection system of the present invention;

[0036] Figure 6 This is a flowchart for judging the uniformity of mixture distribution in this invention;

[0037] Figure 7 This is a flowchart illustrating the cyclic fine-tuning process of the carriage position in this invention.

[0038] Figure 8 A flowchart illustrating the feedback instructions for targeted adjustments in this invention;

[0039] Figure 9 This is a flowchart of the abnormal area correction process of the present invention.

[0040] In the diagram: base 1, support 2, load cell 3, fixing block 4, positioning plate 5, rotating rod 6, hydraulic cylinder 7, support shaft 8, connecting rod 9, sleeve 10, track 11, roller 12, chain 13, motor 14, transition plate 15, support frame 16. Detailed Implementation

[0041] like Figure 1 and Figure 3 As shown, an asphalt loading device includes a movable base 1 and a support seat 2 mounted on the base 1 for supporting a truck bed. A weighing sensor 3 is installed between the base 1 and the support seat 2. When loading asphalt, the truck bed is first driven onto the support seat 2, and the base 1 moves to the outlet position of the asphalt mixing plant. The asphalt enters the truck bed through the outlet, thus achieving automatic asphalt loading. A valve is installed at the outlet of the asphalt mixing plant to control the start and stop of asphalt loading. This eliminates the need for manual truck driving, preventing respiratory diseases, pneumoconiosis, and other occupational diseases, and protecting the driver's health.

[0042] During the loading of asphalt mixtures onto trucks, the weight of the loaded asphalt mixtures should be monitored in real time to ensure accurate weight measurement. Traditional weighing methods involve first measuring the weight of the empty truck, and then weighing it after the truck bed is filled. However, this method is time-consuming, requires a dedicated person to be responsible for weighing, and the temperature of the asphalt mixture in the truck bed can change, affecting the quality and leading to inaccurate weight measurements.

[0043] like Figure 1 As shown, the support base 2 is provided with a fixing block 4 for positioning the front wheels and a positioning mechanism for positioning the rear wheels. The fixing block 4 and the positioning mechanism are used to position the carriage to ensure that the carriage moves synchronously when the support base 2 moves.

[0044] like Figure 2As shown, the positioning mechanism includes a positioning plate 5, a rotating rod 6 disposed within the support base 2 and rotating within the support base 2, and a hydraulic cylinder 7 for driving the rotating rod 6 to rotate. The support base 2 has a groove, within which the rotating rod 6 and hydraulic cylinder 7 are disposed. Two rotating rods 6 are arranged side-by-side. A support shaft 8, passing through the middle of the rotating rod 6 and rotatably connected to it, is disposed within the groove. The front ends of the two rotating rods 6 are connected as a single unit by a connecting rod 9. The piston rod end of the hydraulic cylinder 7 has a sleeve 10 that cooperates with the connecting rod 9. One end of the rotating rod 6 has a vertical section, the upper end of which is fixedly connected to the positioning plate 5. After the carriage moves onto the support base 2, the front wheels of the carriage contact the positioning plate 5. Then, the piston rod of the hydraulic cylinder 7 extends, and under the action of the sleeve 10 and the connecting rod 9, the rotating rod 6 rotates, causing the positioning plate 5 to swing upwards and towards the rear wheels of the carriage, thereby achieving the positioning of the carriage. By adjusting the extension length of the piston rod of the hydraulic cylinder 7, the swing angle of the rotating rod 6 can be adjusted, allowing the positioning plate 5 to position different rear wheel positions. By setting a vertical part, it is ensured that when the positioning plate 5 swings upward, the rotating rod 6 will not interfere with the support base 2.

[0045] like Figure 1 As shown, it also includes a track 11 for supporting the base 1, and a roller 12 that cooperates with the track 11 is provided at the lower end of the base 1; a groove is provided on the ground and the track is placed in the groove.

[0046] like Figure 1 As shown, it also includes a drive mechanism for moving the base 1. The drive mechanism includes a chain 13 and a motor 14 for driving the chain 13. Support frames are symmetrically provided at the bottom of the groove. Each support frame is provided with a sprocket that cooperates with the chain. The motor 14 is mounted on one side of the support frame and drives the corresponding sprocket to rotate. The chain 13 is connected to the lower end of the base 1 through a mounting plate.

[0047] like Figure 3 As shown, a transition plate 15 is inclined at the left end of the support base 2, and a support frame 16 is provided between the transition plate 15 and the support base 2. The transition plate 15 facilitates the truck to enter the support base 2, and the support frame 16 can increase the strength of the transition plate 15.

[0048] like Figure 4 As shown, a method for loading finished asphalt material onto a truck, applied to the aforementioned device, includes the following steps:

[0049] S101 Acquires initial data on the distribution of the mixture inside the truck bed through a sensor array and constructs a real-time mapping map; S102 Compares the real-time mapping map with a preset uniform distribution target to determine the unloading position offset; S103 Generates an adjustment command based on the unloading position offset and transmits it to the drive system to complete the vehicle position fine-tuning; S104 Acquires an image of the adjusted mixture distribution state through an image data acquisition device and determines whether a preset uniformity threshold has been reached; S105 If the preset uniformity threshold has not been reached, the offset calculation and position fine-tuning are repeatedly executed until the preset uniformity threshold is met;

[0050] S106 continuously collects dynamic data streams of the mixture inside the truck bed to determine if there are any signs of particle separation or abnormal accumulation.

[0051] S107 If signs of separation are detected, the quality hazard warning mechanism will be activated.

[0052] S108, based on feedback instructions, jointly controls the unloading rate and position, updates the distribution status of the mixture in real time, and determines that abnormal areas have been corrected.

[0053] like Figure 5 As shown, step S101 mainly includes: acquiring initial data streams of vehicle location and mixture accumulation within the truck bed using a sensor array deployed in the unloading area, thus obtaining a raw data set of mixture accumulation. Based on the raw data set, image processing technology is used to extract features of the mixture accumulation height and distribution range, determining a preliminary scan result of the accumulation state. If the accumulation height in the preliminary scan result exceeds a preset threshold range, outliers are removed using a data filtering method to obtain corrected distribution data. For the corrected distribution data, a convolutional neural network model is used to perform in-depth analysis of the mixture distribution range to determine the uniformity characteristics of the distribution state. Based on the uniformity characteristics, the correlation data between the distribution state and the real-time mapping is obtained, constructing a dynamic mapping map of the mixture accumulation.

[0054] When deploying a sensor array in the unloading area, a combination of LiDAR and cameras is used to collect initial data streams of vehicle location and material accumulation within the truck bed in real time. When constructing a dynamic mapping map, the material accumulation state can be presented in a three-dimensional visualization based on uniformity characteristics and the correlation data from the real-time mapping.

[0055] Step S102 mainly includes: acquiring the distribution data of the mixture inside the truck bed through a real-time mapping map; segmenting the distribution data using image processing technology to obtain the distribution characteristics of local areas; comparing the distribution characteristics with a preset uniform distribution target to determine the coordinate range of local overload areas and local empty areas; analyzing the distribution deviation of the local overload areas and local empty areas using a support vector machine algorithm for the coordinate range to obtain a numerical representation of the degree of deviation; extracting key parameters from the numerical representation of the degree of deviation, calculating the difference value with the uniform distribution target, determining the adjustment direction of the unloading position point, and obtaining preliminary offset data; if the preliminary offset data exceeds a preset threshold range, correcting the offset data using an iterative optimization method to obtain the final adjustment offset result.

[0056] Assuming the freight car is 10 meters long and 2 meters wide, the mapping map shows that the mixture accumulation height reaches 1.2 meters in one area, while in another area it is only 0.3 meters. This uneven distribution intuitively reflects potential problems. For the segmentation of the distribution data, image processing techniques can be used to divide the freight car area into multiple small grids, each grid measuring 0.5 meters × 0.5 meters. After segmentation, the system analyzes the mixture height and density within each grid to obtain the distribution characteristics of the local area. If a grid's height is significantly higher than the surrounding grids, it may be marked as a local overload area, while areas with excessively low heights are marked as local void areas.

[0057] If the initial offset data exceeds a preset threshold, such as a maximum allowable offset of 0.3 meters but a calculated result of 0.5 meters, iterative optimization is required for correction. The system will gradually adjust the offset, reducing it by 0.1 meters each time, and reassess the distribution characteristics until the offset meets the threshold range, ultimately yielding the adjusted result. This method ensures the rationality and stability of the adjustment.

[0058] Throughout the process, each step is closely integrated, forming a complete closed-loop system from data acquisition to final adjustment. Whether it's extracting distribution characteristics or analyzing deviations, everything revolves around optimizing the distribution of the mixture. This systematic approach effectively improves the management efficiency of the unloading area and reduces resource waste.

[0059] Step S103 mainly includes: acquiring the offset value between the vehicle and the target position through data collection at the unloading position, and determining the deviation corresponding to the offset value. Based on the offset value, activating the dynamic positioning function, using the vehicle module to perform real-time position calculation, and obtaining preliminary adjustment requirement data. For the adjustment requirement data, generating a corresponding adjustment command, and sending the adjustment command to the drive system through a transmission process. If the transmission of the adjustment command is interrupted, the transmission process is restarted until successful. After receiving the adjustment command from the drive system, performing a position fine-tuning operation, acquiring real-time feedback data during the fine-tuning process, and determining whether the vehicle is at the target position. Based on the real-time feedback data, continuously updating the accuracy of the position adjustment; if the feedback data exceeds a preset threshold range, recalculating the offset value through dynamic positioning, and generating a new adjustment command. By acquiring a confirmation signal, verifying whether the position adjustment has reached the expected state, acquiring the final signal data, and determining whether the vehicle position is consistent with the unloading position.

[0060] When the dynamic positioning function is activated, the vehicle's built-in positioning system, combined with external reference points, can be used to calculate the vehicle's position in real time. For the generation and transmission of adjustment commands, the commands are sent to the drive system via a wireless communication module. Upon receiving the commands, the drive system gradually adjusts the vehicle's position while simultaneously acquiring real-time feedback data through sensors. If the feedback data exceeds a preset threshold, such as a sudden increase in offset to 0.6 meters (potentially due to external interference or equipment error), the system will recalculate the offset and generate new adjustment commands using the dynamic positioning function. This cyclical adjustment method can handle unexpected situations in complex environments.

[0061] Each of these steps is closely integrated with the needs of adjusting the distribution of the mixture in historical business areas, ensuring that the vehicle unloading position is consistent with the target, thus laying the foundation for subsequent uniform distribution. This multi-stage collaborative approach not only improves operational efficiency but also effectively reduces local overload or gaps.

[0062] like Figure 6 As shown, step S104 mainly includes: scanning the distribution of the mixture inside the carriage using an image acquisition device to obtain initial distribution state image data; using image preprocessing technology to denoise and enhance the initial distribution state image data to obtain a processed distribution image; based on the processed distribution image, using a pre-established feature extraction model to extract key feature parameters of the mixture distribution to determine the distribution feature dataset; and comparing the distribution feature dataset with a preset uniformity threshold.

[0063] Assuming the original image shows a dark and difficult-to-distinguish mixture, enhancement improves the contrast between the dark areas of the accumulated regions and the light areas of the voids, thus improving the accuracy of subsequent feature extraction. During the comparison of the distribution feature dataset with a preset uniformity threshold, the uniformity threshold can be set to a difference in accumulated height not exceeding 0.2 meters and a difference in distribution area not exceeding 10%. Taking the above data as an example, the height difference between the left and right sides is 0.5 meters, and the area difference is 20%, significantly exceeding the threshold range, indicating that the mixture distribution is uneven and requires further adjustment. This comparison method can quickly determine whether the distribution meets the unloading requirements, avoiding problems caused by uneven distribution leading to poor unloading results.

[0064] like Figure 7 As shown, step S105 mainly includes: if the preliminary distribution state characteristics do not reach the preset uniformity threshold, then an offset analysis is performed on the preliminary distribution state characteristics to determine the offset adjustment parameters. An adjustment command is generated based on the offset adjustment parameters, and the adjustment command is transmitted to the drive system to trigger a position fine-tuning operation, obtaining the fine-tuned distribution state update data. The distribution state update data is analyzed a second time to determine whether it meets the uniformity threshold. If it does not meet the uniformity threshold, the offset is recalculated and a new adjustment command is generated. The offset calculation and position fine-tuning process is executed cyclically, obtaining state monitoring data after each cycle until the uniformity threshold is met. Based on the state monitoring data, a support vector machine algorithm is used to classify the distribution state and determine the stability of the final distribution state. By judging the stability of the final distribution state, system feedback information is obtained, the state changes after each adjustment are recorded, and complete adjustment process data is generated.

[0065] Regarding the topic of offset analysis, suppose the mixture distribution inside the truck bed shows excessive accumulation on the left side. Image data analysis reveals that the density value of the left area reaches 60%, while the right side is only 40%, significantly deviating from the uniformity threshold of 50%. In this case, offset analysis would calculate the adjustment parameters required to move to the right, possibly a specific instruction to offset the drive system 0.5 meters to the right.

[0066] Regarding the generation and transmission of adjustment instructions to the drive system, these instructions are not merely simple movement commands; they may also include refined parameters such as speed and direction. For example, if the instruction requires the drive system to move 0.5 meters to the right at a speed of 0.2 meters per second, such a refined design ensures a smooth adjustment process, preventing new uneven distribution of the mixture due to rapid movement. After the instruction is transmitted, the system monitors position changes in real time to ensure accurate execution.

[0067] In the acquisition and secondary analysis of the distribution status update data after fine-tuning, assuming that the density on the left side drops to 52% and the density on the right side rises to 48% after fine-tuning, although this is close to the uniformity threshold, it is still not fully met. The secondary analysis will further identify the remaining deviation areas and determine whether further fine-tuning is needed.

[0068] Regarding the assessment of the stability of the final distribution state and the acquisition of system feedback information, it is assumed that the system recorded state change data during three adjustment processes, including the offset and density changes for each step. This data is not only used for feedback in the current task but also provides a reference for subsequent similar tasks. Once the complete adjustment process data is generated, it can serve as the basis for system optimization, improving the efficiency of future tasks. Through the above multi-faceted analysis and examples, from offset analysis to final data recording, each step closely revolves around adjusting the uniformity of the mixture distribution within the carriage, gradually advancing the achievement of the task objective. These methods can effectively improve adjustment accuracy in practical applications, ensuring that the mixture distribution meets the expected standards and providing reliable support for related operations.

[0069] Step S107 mainly includes: acquiring dynamic data streams of mixed materials from inside the carriage using sensor devices, collecting multi-dimensional information, and obtaining preliminary material state data. Based on the preliminary material state data, feature extraction processing is performed on separation phenomena and accumulation anomalies, and comparison is conducted using a pre-established feature library to determine key feature parameters. If the key feature parameters exceed a preset threshold range, an anomaly judgment process is triggered, and the dynamic data stream is segmented and analyzed through an information processing stage to obtain anomaly fluctuation intervals. Based on the anomaly fluctuation intervals, a support vector machine algorithm is used to classify and identify particle separation and accumulation anomalies, and to determine the distribution of anomaly types. Through the classification and identification results, the correspondence between anomaly types and their positions inside the carriage is obtained, and the core area where the anomaly occurs is determined. For the anomaly distribution in the core area, the dynamic data changes in that area are continuously monitored to obtain real-time updated material state information and determine whether the anomaly persists. If the anomaly persists, the updated material state information is analyzed in depth through an information processing stage to obtain a prediction result of the anomaly development trend.

[0070] like Figure 8As shown, step S107 mainly includes: if the prediction result shows signs of separation, a quality hazard warning mechanism is triggered to generate location data of the abnormal area and determine the specific location information of the abnormality. Based on the location data, an automated transmission module sends the relevant information to the control center to obtain processed feedback instruction data. If the feedback instruction data contains control parameters for targeted adjustment, the information processing module parses the instruction content to determine whether the adjustment parameters meet a preset threshold range. Using the parsed adjustment parameters, the production equipment is updated using a pre-established decision model to obtain real-time feedback information on the equipment's operating status. Based on the feedback information on the equipment's operating status, a secondary verification is performed on the potential risks of quality hazards to determine whether the production process has returned to a stable state. If the secondary verification result shows that an abnormal area still exists, the location data is regenerated through a cyclic monitoring module and transmitted to the control center to obtain new feedback instruction data.

[0071] Suppose that during the transportation of mixed materials inside the truck, the system detects that the particle density in certain areas is abnormally high, reaching the critical value of 500 grams per cubic meter, exceeding the normal range of 300 grams. In this case, the system will automatically trigger a quality hazard warning mechanism, generating location data for the abnormal area, specifying a particular corner inside the truck, such as the rear left side.

[0072] Regarding the transmission of positioning data and the acquisition of feedback commands, it can be envisioned that after receiving abnormal location information, the control center, combining historical data and the current production status, generates a set of adjustment parameters, such as reducing the transport speed to 2 meters per minute or adjusting the vibration frequency of the carriage to 5 times per second. In the parameter update and equipment operation status feedback phase, assuming the production equipment adjusts its operating parameters according to the decision model, the system will collect equipment status data in real time. For example, if the motor operating power stabilizes at around 75%, it indicates that the equipment has adapted to the new parameters. Subsequently, a secondary verification is conducted to address potential quality risks. By continuously monitoring the material distribution density, it is confirmed whether the particle density in the abnormal area has fallen back to the normal range of 300 grams per cubic meter. If the data returns to normal, it indicates that the production process has stabilized; otherwise, cyclical monitoring is required. If the secondary verification still shows the existence of the abnormal area, such as the density remaining at 450 grams per cubic meter, the system will regenerate positioning data and transmit it to the control center to obtain new feedback commands. This cyclical mechanism ensures that abnormal issues are continuously monitored and resolved until the production status fully stabilizes. This approach effectively improves the response speed to problems in the production process and ensures the uniformity and consistency of material transportation.

[0073] like Figure 9As shown, step S108 mainly includes: acquiring real-time data of unloading rate and unloading position through a sensor network, performing preliminary processing in conjunction with feedback commands to obtain an evaluation result of the initial distribution state. Based on the evaluation result of the initial distribution state, a dynamic adjustment mechanism is used to jointly regulate the unloading rate and unloading position to determine the distribution change trend after regulation. For the distribution change trend, the data model of the mixture distribution state is updated in real time, and the support vector machine algorithm is used to detect anomalies in the distribution state to determine whether there are abnormal regions. If an abnormal region is detected, the unloading rate and position are further regulated through adjustment commands to obtain the adjusted distribution state data and determine the degree of correction of the abnormal region. Based on the adjusted distribution state data, the correction signal of the abnormal region is analyzed, and combined with the state monitoring mechanism, a verification result of region confirmation is obtained. Based on the verification result of region confirmation, the database record of state monitoring is updated, and a preset threshold is used for comparison to determine whether the overall distribution state has reached stability. If the overall distribution state has not reached stability, the regulation and detection process is executed cyclically to obtain the latest distribution state data and determine the final correction signal and the region confirmation result.

[0074] Based on the initial distribution assessment results, the dynamic adjustment mechanism can jointly regulate the unloading rate and position according to real-time data. Assuming that analysis reveals insufficient rate due to low equipment operating power and uneven position distribution due to unloading angle deviation, the power can be increased to 90% of the rated value, while the unloading angle is adjusted from 30 degrees to 35 degrees. After adjustment, the distribution trend shows that the coverage rate has increased to 75% and the rate has reached 48 tons, indicating that the adjustment direction is correct. When using the support vector machine algorithm to detect anomalies in the distribution, areas with coverage below 70% or rate fluctuations exceeding 10% can be marked as anomalies. After detecting anomalies, a secondary adjustment of the unloading rate and position via adjustment commands is a crucial step. Assuming that for the aforementioned anomaly areas, the rate is further increased to 52 tons and the angle adjusted to 38 degrees, the coverage rate increases to 78%, indicating a good degree of correction. Combined with the status monitoring mechanism, analysis of the correction signals confirms that the anomaly areas have been largely restored.

[0075] The adjusted distribution status data still needs to be updated in the status monitoring database based on the verification results confirmed by the region. Assuming the preset coverage stability threshold is 75%, and the current data has met the standard, the system will record this adjustment as a successful case for future reference. If the overall distribution status has not reached stability, for example, if the coverage drops back to 72%, the adjustment and detection process needs to be repeated until the data stabilizes above the threshold.

Claims

1. A method for loading finished asphalt material onto trucks, characterized in that, The vehicle loading device includes a movable base (1) and a support seat (2) for supporting the vehicle body, which is set on the base (1). A weighing sensor (3) is provided between the base (1) and the support seat (2). The support seat (2) is provided with a fixing block (4) for positioning the front wheels and a positioning mechanism for positioning the rear wheels. The positioning mechanism includes a positioning plate (5), a rotating rod (6) disposed in the support base (2) and rotating within the support base (2), and a hydraulic cylinder (7) for driving the rotating rod (6) to rotate. The support base (2) has a groove, the rotating rod (6) and the hydraulic cylinder (7) are disposed in the groove, two rotating rods (6) are arranged side by side, and a support shaft (8) is provided in the groove, passing through the middle position of the rotating rod (6) and rotatably connected to the middle of the rotating rod (6). The front ends of the two rotating rods (6) are connected as a whole by a connecting rod (9). The piston rod end of the hydraulic cylinder (7) is provided with a sleeve (10) that cooperates with the connecting rod (9). One end of the outer side of the rotating rod (6) is provided with a vertical part, and the upper end of the vertical part is fixedly connected to the positioning plate (5). It also includes a track (11) for supporting the base (1), and the lower end of the base (1) is provided with a roller (12) that cooperates with the track (11). It also includes a drive mechanism for driving the base (1) to move, the drive mechanism including a chain (13) and a motor (14) for driving the chain (13) to rotate, the chain (13) being connected to the lower end of the base (1) via a mounting plate; The left end of the support base (2) is provided with a transition plate (15) at an inclination, and a support frame (16) is provided between the transition plate (15) and the support base (2). The method includes the following steps: S101 Acquires initial data on the distribution of the mixture inside the truck bed through a sensor array and constructs a real-time mapping map; S102 Compares the real-time mapping map with a preset uniform distribution target to determine the unloading position offset; S103 Generates an adjustment command based on the unloading position offset and transmits it to the drive system to complete the vehicle position fine-tuning; S104 Acquires an image of the adjusted mixture distribution state through an image data acquisition device and determines whether a preset uniformity threshold has been reached; S105 If the preset uniformity threshold has not been reached, the offset calculation and position fine-tuning are repeatedly executed until the preset uniformity threshold is met; S106 continuously collects dynamic data streams of the mixture inside the truck bed to determine if there are any signs of particle separation or abnormal accumulation. S107 If signs of separation are detected, the quality hazard warning mechanism will be activated. S108, based on feedback instructions, jointly controls the unloading rate and position, updates the distribution status of the mixture in real time, and determines that abnormal areas have been corrected.

2. The method for loading finished asphalt material as described in claim 1, characterized in that, Step S105 mainly includes: if the mixture distribution state image does not reach the preset uniformity threshold, then analyze the offset adjustment parameters and generate a new adjustment command; transmit the new adjustment command to the drive system to trigger position fine-tuning and obtain updated distribution state data; determine whether the updated distribution state data meets the preset uniformity threshold, and repeat the process until the target state is reached.

3. The method for loading finished asphalt material as described in claim 1, characterized in that, Step S108 mainly includes: acquiring real-time data of unloading through a sensor network, performing preliminary processing, and obtaining an evaluation result of the initial distribution state; based on the evaluation result of the initial distribution state, jointly controlling the unloading rate and unloading position, and determining the distribution change trend after control. The data model of the mixture distribution state is updated in real time to detect anomalies in the distribution state and determine whether there are abnormal areas. If the abnormal area is detected, the unloading rate and position are adjusted again by the adjustment command to obtain the adjusted distribution status data; Based on the adjusted distribution data, the correction signals of the abnormal areas are analyzed to obtain the verification results of the area confirmation; Based on the verification results confirmed by the region, update the database records for status monitoring to determine whether the overall distribution status has reached stability; If the overall distribution state does not reach stability, the control and detection process is repeated to obtain the latest distribution state data and determine the final correction signal and the confirmation result of the region.

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