Truck loading system with intelligent vehicle type recognition and dynamic regulation and control functions

Abstract

The invention provides an intelligent vehicle type identification and dynamic regulation and control loading system, and compared with the prior art, the intelligent vehicle type identification and dynamic regulation and control loading system comprises an arrangement module, an identification module, a loading control module, a dynamic regulation and control module and a real-time monitoring platform. The arrangement module intelligently dispatches and matches vehicles according to transportation requirements; the identification module accurately identifies vehicle information through an image identification algorithm; the loading control module controls the loading device to automatically complete material loading through a PLC. The dynamic regulation and control module adjusts the loading speed according to the real-time loading state, and overload or too slow loading is avoided; and the real-time monitoring platform provides transportation task progress monitoring and abnormity alarming, so that the loading process is ensured to be carried out smoothly. According to the system, the utilization efficiency of transportation resources can be effectively improved, the transportation cost is reduced, and the automation and intelligence level of loading operation is improved. Through intelligent scheduling and dynamic regulation and control, it is ensured that the material loading process is more efficient and accurate.

Description

Technical Field

[0001] The present invention relates to the field of vehicle loading and transportation, and in particular to a vehicle loading system with intelligent vehicle type recognition and dynamic regulation. Background Art

[0002] In industrial scenarios of loading bulk materials, due to the wide variety of vehicle models and obvious differences in load capacity, traditional loading methods often find it difficult to balance efficiency and accuracy, resulting in problems of overloading or underloading. This not only affects the timely completion of material transportation tasks, but may also bring about a series of problems such as vehicle safety hazards and waste of resources. For example, overloading may cause the vehicle to overload, increase transportation costs and safety risks, while underloading may result in a high empty vehicle rate and low transportation efficiency. In addition, since the loading process relies on manual judgment, there is a large degree of subjectivity and error rate, which is not suitable for the efficient loading needs of large-scale and diversified materials.

[0003] This experimental team has been browsing and studying a large number of relevant records and materials for a long time. At the same time, it relies on relevant resources and conducts a large number of relevant experiments. After a large number of searches, it is found that the existing prior art such as CN116142829A, CN117963563A, CN113401676B, and CN113401676A disclosed in the prior art, such as an intelligent high-speed unattended shipping assembly line disclosed in the prior art, including a transfer conveyor arranged between a workshop and a transport vehicle, an automatic destacking system and an automatic feeding system installed at the inner end of the transfer conveyor, and an automatic loading system installed at the outer end of the transfer conveyor. The automatic feeding system transfers the material stack to the automatic destacking system and recycles and sorts the carrier carrying the material, and the automatic destacking system automatically disassembles the material stack and transports the material to the inner end of the transfer conveyor one by one. However, the automatic loading and transportation system in the prior art cannot effectively arrange and monitor the existing vehicles in the garage according to the transportation task, resulting in inaccurate vehicle matching, resource allocation and loading process.

[0004] The present invention is made to solve the common problems in the field such as low transportation efficiency, waste of resources, and safety hazards of overloaded operation caused by overloading or underloading; lack of real-time monitoring and dynamic adjustment mechanism, which leads to inaccurate vehicle matching, resource allocation and loading process, etc. Summary of the invention

[0005] The purpose of the present invention is to propose a vehicle loading system with intelligent vehicle type identification and dynamic control in view of the shortcomings existing in the current field.

[0006] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0007] A loading system with intelligent vehicle type recognition and dynamic regulation, the loading system comprising an arrangement module for intelligently dispatching and matching vehicles in a garage according to material transportation requirements, an identification module for identifying and determining the identity of vehicles entering a material loading area, a loading control module for automatically loading vehicles whose identities have been determined using a PLC-controlled loading device, a dynamic regulation module for dynamically adjusting the material loading speed according to the real-time loading status of the vehicle, and a real-time monitoring platform that can support remote management and data analysis while providing an abnormal alarm function, wherein a weight sensor for real-time monitoring of the vehicle's loading weight is provided on the ground of the material loading area.

[0008] Furthermore, the arrangement module implements the following operation steps:

[0009] S101: Vehicle information in the garage, including the vehicle ID used to distinguish each vehicle, the vehicle load weight C max , compartment volume V max , the material transportation arrangement status of the vehicle Si, the age of the vehicle Y age , the vehicle maintenance coefficient M that reflects the vehicle maintenance status and the maximum single transport distance D that the vehicle can complete in the current state max , M∈[0,1], when M=1, it means the vehicle maintenance status is the best, and when M=0, it means the vehicle maintenance status is the worst;

[0010] S102: Acquire the transportation demand information of the transportation task, wherein the transportation demand information includes the total weight G of the materials to be transported. total , the total volume of the material to be transported V total and the total transport distance D of the material to be transported tota ;

[0011] S103: Calculate the actual load weight C of each vehicle respectively ac :

[0012]

[0013] Among them, k is the nonlinear adjustment coefficient reflecting the nonlinear effect of the age of the vehicle on the load capacity, and n is the exponential parameter of age attenuation;

[0014] S104: Screening vehicles: taking vehicles with Si=1 and η≥1 as candidate vehicles,

[0015] Among them, η is the vehicle transportation efficiency factor, and

[0016] S105: Combine candidate vehicles to generate all vehicle combinations N that meet the first condition. The first condition is: C acNn≥G total , while VNn≥V total ,

[0017] Among them, C ac Nn is the sum of the actual load weight of all vehicles in vehicle combination N, VNn is the sum of the compartment volumes of all vehicles in vehicle combination N,

[0018] All vehicle combinations that meet the first condition will be numbered in sequence as N1, N2...Nm, where m is the number of all combinations that meet the condition;

[0019] S106: Calculate the priority index of each combination:

[0020]

[0021] Among them, Nj∈{N1,N2,N3…Nm}, Cost Nj is the sum of the material transportation costs of all vehicles in the jth vehicle combination, Num Nj is the total number of vehicles in the jth vehicle combination, Value Nj is the priority index of the jth vehicle combination, AvgC is the comparison cost, and AvgN is the comparison number;

[0022] S107: sorting the priority indexes of all vehicle combinations, selecting the vehicle combination with the smallest priority index among all vehicle combinations as the final arrangement combination, and selecting the vehicle combination with the second-lowest priority index as the alternative arrangement combination;

[0023] S108: The final arrangement combination and the alternative arrangement combination matched by each transport task are archived into the system database to provide support for subsequent transport task scheduling optimization.

[0024] Furthermore, the identification module includes a camera device for taking pictures of vehicles entering the material loading area, an identification unit for analyzing and processing the pictures taken by the camera device through an image recognition algorithm to identify the license plate information of the vehicle in the picture and the length h, width w and height g of the vehicle compartment, and a verification unit for verifying the actual information of the vehicle based on the picture.

[0025] Furthermore, the checking unit completes the following steps:

[0026] S201: Obtain the vehicle number and vehicle compartment volume V based on the license plate signal max , and the matching transportation tasks;

[0027] S202: Check whether the size of the carriage in the picture matches the recorded carriage volume:

[0028] exist When , the operation instruction is sent to the loading control module to drive the loading device to transport materials to the vehicle. △path is the difference threshold, △path∈(5%, 15%),

[0029] exist When the vehicle entering the logistics loading area is judged as an error vehicle, the matching value Vmatd of the error vehicle and the vehicle with Si=1 in the garage is calculated respectively, and the vehicle with the smallest difference in matching value Vmat from the error vehicle is selected from the garage and replaced with the error vehicle, and the database is uploaded to change the final arrangement combination of the transportation task, where Vmatd=C ac ×w1+V total ×w2+D tota ×w3, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, w1≥w2≥w3.

[0030] Furthermore, the loading control module implements the following steps:

[0031] S301: Receive the operation instruction sent by the recognition module and obtain the actual load capacity C of the vehicle in the material loading area. ac , compartment volume V max and the density ρ of the transported material of the vehicle,

[0032] S302: Calculate the transport loading weight Gend of the vehicle in the material loading area:

[0033] At the upper limit volume V of the vehicle Cac ≤V max When Gend=C ac ,

[0034] On the contrary, at the upper limit volume V of the vehicle Cac >V max When Gend=V max ,in, ρ is the density of the material, and the weight of the material loaded by the loading device on the vehicle is the transport loading weight of the vehicle,

[0035] S303: Start the conveying equipment to transport materials to the vehicle compartment.

[0036] S304: The loading weight of the vehicle is monitored in real time through a weight sensor. When the cumulative loading weight of the vehicle reaches Gac, the operation of the loading device is stopped and a loading completion signal is sent to remind the vehicle to leave the material loading area.

[0037] Furthermore, the dynamic control module implements the following operation steps:

[0038] S401: During the operation of the loading device, the current loading weight Wreal of the vehicle is collected in real time through the weight sensor and continuously updated;

[0039] S402: According to the current real-time loading weight Wreal of the vehicle, dynamically adjust the conveying rate of the loading device to avoid material overloading or slow loading:

[0040]

[0041] Fmax is the preset initial charging rate of the charging device, Fadj is the charging rate after the charging device is adjusted,

[0042] λ is the first control coefficient, which adjusts the speed of change of the loading rate when approaching the target loading weight by controlling the sensitivity of the loading rate change to Gend, λ∈[0.01, 1],

[0043] σ is the second control coefficient. σ adjusts the sensitivity of the loading rate to the real-time loading weight change by controlling the influence of the increase of loading weight on the loading rate change, б∈[0.01, 1].

[0044] The beneficial effects achieved by the present invention are:

[0045] 1. Improve loading accuracy and efficiency: Through intelligent vehicle type recognition technology, the vehicle type and load capacity can be accurately identified to achieve accurate matching with the transportation task, effectively avoid overloading or underloading, and improve loading efficiency and accuracy.

[0046] 2. Optimize resource allocation: Based on the real-time status of the vehicle and transportation needs, dynamically adjust the loading speed and loading process to avoid waste of resources, improve the utilization rate of transportation resources, and reduce empty and overloaded phenomena.

[0047] 3. Enhance system automation and intelligence: Through PLC control of the loading device and dynamic control module, the loading process can be automated and intelligent, reducing manual intervention, improving the overall stability of the system, reducing human operating errors, and ensuring the smooth completion of the loading task. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0049] Figure 1 A modular schematic diagram of the vehicle loading system for intelligent vehicle type recognition and dynamic control of the present invention.

[0050] Figure 2 It is a modular schematic diagram of the checking unit of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail in combination with its embodiments below; it should be pointed out that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present case. For those skilled in the art, after reviewing the following detailed description, other systems, methods and / or features of this embodiment will become apparent. And the terms describing the positional relationship in the accompanying drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0052] Embodiment 1: Combining with the attached Figure 1 and attached Figure 2 The present embodiment constructs a loading system with intelligent vehicle type recognition and dynamic regulation, the loading system includes a scheduling module for intelligently scheduling and matching vehicles in a garage according to material transportation requirements, an identification module for identifying and determining the identity of vehicles entering a material loading area, a loading control module for automatically loading vehicles whose identities have been determined using a PLC-controlled loading device, a dynamic regulation module for dynamically adjusting the material loading speed according to the real-time loading status of the vehicle, and a real-time monitoring platform that can support remote management and data analysis and provide abnormal alarm functions.

[0053] The arrangement module implements the following operation steps:

[0054] S101: Vehicle information in the garage, including the vehicle ID used to distinguish each vehicle, the vehicle load weight C max , compartment volume V max , the material transportation arrangement status of the vehicle Si, the age of the vehicle Y age , the vehicle maintenance coefficient M that reflects the vehicle maintenance status and the maximum single transport distance D that the vehicle can complete in the current state max , M∈[0,1], when M=1, it means the vehicle maintenance status is the best, and when M=0, it means the vehicle maintenance status is the worst;

[0055] S102: Acquire the transportation demand information of the transportation task, wherein the transportation demand information includes the total weight G of the materials to be transported. total , the total volume of the material to be transported V total and the total transport distance D of the material to be transported tota ;

[0056] S103: Calculate the actual load weight C of each vehicle respectively ac :

[0057]

[0058] Among them, k is the nonlinear adjustment coefficient reflecting the nonlinear effect of the age of the vehicle on the load capacity, n is the exponential parameter of age attenuation,

[0059] Among them, k is based on the age of the vehicle Y age The specific value of k, the nonlinear influence coefficient of load capacity, is obtained by technicians through repeated training and optimization of the performance of vehicles of different ages based on historical data and design goals, and through fitting calculation. k describes how the increase in the age of a vehicle affects its maximum load capacity. Technicians calculate the change trend of k by using the historical performance of a large number of vehicles and repeated training data, and adjust it in combination with the actual use of the vehicle and design goals, so as to determine the k value that can accurately reflect the actual change in vehicle load capacity.

[0060] n is used to control the sensitivity of k to the age of the vehicle in the calculation. The value of n is initially determined by the technicians through fitting calculation and model optimization of the historical data of actual loading capacity and vehicle age changes. The optimal value of n is finally determined through repeated training and optimization to ensure that the degree of influence of vehicle age on load capacity is consistent with the actual situation.

[0061] S104: Screening vehicles: taking vehicles with Si=1 and η≥1 as candidate vehicles,

[0062] Among them, η is the vehicle transportation efficiency factor, and

[0063] S105: Combine candidate vehicles to generate all vehicle combinations N that meet the first condition. The first condition is: C ac Nn≥G total , while VNn≥V total ,

[0064] Among them, C ac Nn is the sum of the actual load weight of all vehicles in vehicle combination N, VNn is the sum of the compartment volumes of all vehicles in vehicle combination N,

[0065] All vehicle combinations that meet the first condition will be numbered in sequence as N1, N2...Nm, where m is the number of all combinations that meet the condition;

[0066] S106: Calculate the priority index of each combination:

[0067]

[0068] Among them, Nj∈{N1,N2,N3…Nm}, Cost Njis the sum of the material transportation costs of all vehicles in the jth vehicle combination, Num Nj is the total number of vehicles in the jth vehicle combination, Value Nj is the priority index of the jth vehicle combination, AvgC is the comparison cost, AvgN is the comparison quantity, the material transportation cost is determined according to the fuel consumption of the vehicle and the highway fee, AvgC is determined by technicians in this field through historical experience data and cost records of actual transportation tasks, and is used to reflect the cost performance of the vehicle combination in the transportation task, and is calculated and determined by technicians in this field based on the average number of vehicles in the vehicle combination in the historical experience data, and AvgN is used to reflect the number of vehicles required for a specific transportation task;

[0069] S107: sorting the priority indexes of all vehicle combinations, selecting the vehicle combination with the smallest priority index among all vehicle combinations as the final arrangement combination, and selecting the vehicle combination with the second-lowest priority index as the alternative arrangement combination;

[0070] S108: The final arrangement combination and the alternative arrangement combination matched by each transport task are archived into the system database to provide support for subsequent transport task scheduling optimization.

[0071] The scheduling module matches the vehicles that complete the transportation tasks according to each transportation task. The design of the scheduling module ensures that each transportation task can be completed by the most suitable vehicle combination through intelligent matching and optimized scheduling. By considering multiple factors such as load, volume, transportation efficiency, distance, and combining vehicle priority, task time limit, etc., the utilization efficiency of transportation resources and the accuracy of task execution are greatly improved, which not only reduces unnecessary waste of resources, but also reduces transportation costs and improves the efficiency of the overall transportation system.

[0072] Embodiment 2: Combining with Figure 1 and attached Figure 2 In addition to the contents of the above embodiments, the identification module includes a camera device for taking pictures of vehicles entering the material loading area, an identification unit for analyzing and processing the pictures taken by the camera device through an image recognition algorithm to identify the license plate information of the vehicle in the picture and the length h, width w and height g of the vehicle compartment, and a verification unit for verifying the actual information of the vehicle based on the picture.

[0073] Among them, the image recognition algorithm includes a feature-based license plate recognition algorithm, a deep learning method, and a target detection algorithm and three-dimensional reconstruction technology for vehicle compartment size recognition. The deep learning method includes CNN, YOLO, FasterR-CNN, and CRNN models. The image recognition algorithm can realize a comprehensive analysis of vehicle images, thereby accurately identifying key information such as the license plate and the length, width, and height of the vehicle compartment.

[0074] The checking unit completes the following steps:

[0075] S201: Obtain the vehicle number and vehicle compartment volume V based on the license plate signal max , and the matching transportation tasks;

[0076] S202: Check whether the size of the carriage in the picture matches the recorded carriage volume:

[0077] exist When , the operation instruction is sent to the loading control module to drive the loading device to transport materials to the vehicle. △path is the difference threshold, △path∈(5%, 15%),

[0078] exist When the vehicle entering the logistics loading area is judged as an error vehicle, the matching value Vmatd of the error vehicle and the vehicle with Si=1 in the garage is calculated respectively, and the vehicle with the smallest difference in matching value Vmat from the error vehicle is selected from the garage and replaced with the error vehicle, and the database is uploaded to change the final arrangement combination of the transportation task, where Vmatd=C ac ×w1+V total ×w2+D tota ×w3, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, w1≥w2≥w3.

[0079] By combining image processing technology and deep learning algorithms, the recognition module can extract key information such as the vehicle's license plate information and compartment size with high precision, ensure the accuracy of the data, and avoid scheduling problems caused by recognition errors. Its automated processing flow significantly reduces manual intervention, improves operational efficiency, and speeds up vehicle entry and loading. In addition, the module has a flexible exception handling mechanism, which selects the most suitable replacement vehicle through similarity calculation and comprehensive matching to ensure the smooth completion of the task. At the same time, the recognition module provides precise support for subsequent vehicle scheduling and resource allocation, effectively optimizes the loading task process, reduces the situation of empty or overloaded vehicles, and significantly improves overall transportation efficiency and resource utilization.

[0080] Embodiment 3: Combining with Figure 1 and attached Figure 2 In addition to the contents of the above embodiments, the loading control module controls the loading device through PLC to quantitatively transport materials of the transport loading weight of the vehicle into the vehicle compartment after the identification module completes the identification. The loading device is a belt conveyor or a screw conveyor that transports materials from the material storage area to the vehicle compartment. A weight sensor for real-time monitoring of the vehicle's loading weight is provided on the ground of the material loading area.

[0081] The loading control module implements the following steps:

[0082] S301: Receive the operation instruction sent by the recognition module and obtain the actual load capacity C of the vehicle in the material loading area. ac , compartment volume V max and the density ρ of the transported material of the vehicle;

[0083] S302: Calculate the transport loading weight Gend of the vehicle in the material loading area:

[0084] At the upper limit volume V of the vehicle Cac ≤V max When Gend=C ac ,

[0085] On the contrary, at the upper limit volume V of the vehicle Cac >V max When Gend=V max ,in, ρ is the density of the material,

[0086] S303: starting the conveying equipment to transfer materials to the carriage of the vehicle;

[0087] S304: The loading weight of the vehicle is monitored in real time through a weight sensor. When the cumulative loading weight of the vehicle reaches Gac, the operation of the loading device is stopped and a loading completion signal is sent to remind the vehicle to leave the material loading area.

[0088] The dynamic control module implements the following operation steps:

[0089] S401: During the operation of the loading device, the current loading weight Wreal of the vehicle is collected in real time through the weight sensor and continuously updated;

[0090] S402: According to the current real-time loading weight Wreal of the vehicle, dynamically adjust the conveying rate of the loading device to avoid material overloading or slow loading:

[0091]

[0092] Wherein, Fmax is the preset initial charging rate of the charging device, Fadj is the charging rate after the charging device is adjusted,

[0093] λ is the first control coefficient. By controlling the sensitivity of the loading rate change to Gend, the loading rate change speed when approaching the target loading weight is adjusted. Specifically, a larger λ value will cause the loading rate to slow down rapidly when approaching the target weight, while a smaller λ value will cause the rate to slow down more gradually. This is suitable for scenarios that require a smoother loading rate change. λ∈[0.01, 1],

[0094] σ is the second control coefficient. σ adjusts the sensitivity of the loading rate to the real-time loading weight change by controlling the impact of the increase in loading weight on the loading rate change. Specifically, a larger σ value will make the loading rate respond to weight changes more quickly, which is suitable for scenarios that require rapid rate adjustment. A smaller σ value makes the rate adjustment smoother, thereby avoiding sudden changes in the loading process, б∈[0.01, 1].

[0095] The real-time monitoring platform implements the following operation steps:

[0096] S501: storing key data of each transport task, including the loading start time, remaining material loading weight, and numbers of loaded and unloaded vehicles of each transport task;

[0097] S502: Monitor the execution time Ttask of each transport task. If the transport task exceeds the preset maximum time threshold Tmax and is not completed, a timeout alarm will be automatically triggered. The alarm will be issued through a visual interface, sound prompt, SMS or email to remind the operator to respond in time.

[0098] S503: All triggered alarm information will be recorded and stored in the system database for subsequent viewing and analysis. The alarm information includes the time when the alarm is triggered, the transportation task related to the alarm, the vehicle number of the transportation task related to the alarm, and the material loading status.

[0099] The present invention provides intelligent regulation and efficient monitoring for the material loading process through the collaborative work of the loading control module, the dynamic regulation module and the real-time monitoring platform. Through precise loading control, dynamic rate adjustment and real-time task monitoring, the system can ensure that the loading process is efficient, safe and smooth. The alarm function of the real-time monitoring platform provides an immediate response to abnormal situations, reduces potential safety hazards and operation delays, and improves the overall transportation efficiency and management level.

[0100] Although the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the present invention. That is, the methods, systems and devices discussed above are examples. Various configurations may appropriately omit, replace or add various processes or components. For example, in alternative configurations, the method may be performed in an order different from that described, and / or various components may be added, omitted and / or combined. Moreover, the features described with respect to certain configurations may be combined in various other configurations, such as different aspects and elements of the configuration may be combined in a similar manner. In addition, as technology develops, the elements therein may be updated, i.e., many elements are examples and do not limit the scope of the present disclosure or claims. And it should be understood that after reading the contents of the record of the present invention, the technician may make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A vehicle loading system with intelligent vehicle type recognition and dynamic control, characterized in that: The loading system includes an arrangement module for intelligently dispatching and matching vehicles in the garage according to the transportation needs of materials, an identification module for identifying and determining the identities of vehicles entering the material loading area, a loading control module for automatically loading vehicles whose identities have been determined using a PLC-controlled loading device, a dynamic regulation module for dynamically adjusting the material loading speed according to the real-time loading status of the vehicle, and a real-time monitoring platform that can support remote management and data analysis while providing abnormal alarm functions, wherein a weight sensor for real-time monitoring of the vehicle's loading weight is provided on the ground of the material loading area.

2. The intelligent vehicle type identification and dynamic control loading system as claimed in claim 1, characterized in that: The arrangement module implements the following operation steps: S101: Obtain vehicle information in the garage, the vehicle information including the vehicle ID used to distinguish each vehicle, the vehicle load weight C max , compartment volume V max , the material transportation arrangement status of the vehicle Si, the age of the vehicle Y age , the vehicle maintenance coefficient M that reflects the vehicle maintenance status and the maximum single transport distance D that the vehicle can complete in the current state max , M∈[0,1], when M=1, it means the vehicle maintenance status is the best, and when M=0, it means the vehicle maintenance status is the worst; S102: Acquire the transportation demand information of the transportation task, wherein the transportation demand information includes the total weight G of the materials to be transported. total , the total volume of the material to be transported V total and the total transport distance D of the material to be transported total ; S103: Calculate the actual load weight C of each vehicle respectively ac : Among them, k is the nonlinear adjustment coefficient reflecting the nonlinear effect of the age of the vehicle on the load capacity, and n is the exponential parameter of age attenuation; S104: Screening vehicles: taking vehicles with Si=1 and η≥1 as candidate vehicles, Among them, η is the vehicle transportation efficiency factor, and S105: Combine candidate vehicles to generate all vehicle combinations N that meet the first condition. The first condition is: C ac Nn≥G total , while VNn≥V total , Among them, C ac Nn is the sum of the actual load weight of all vehicles in vehicle combination N, VNn is the sum of the compartment volumes of all vehicles in vehicle combination N, All vehicle combinations that meet the first condition are numbered in sequence as N1, N2...Nm, where m is the number of all combinations that meet the condition; S106: Calculate the priority index Value of each combination Nj : Among them, Nj∈{N1,N2,N3…Nm}, Cost Nj is the sum of the material transportation costs of all vehicles in the jth vehicle combination, Num Nj is the total number of vehicles in the jth vehicle combination, Value Nj is the priority index of the jth vehicle combination, AvgC is the comparison cost, and AvgN is the comparison number; S107: sorting the priority indexes of all vehicle combinations, selecting the vehicle combination with the smallest priority index among all vehicle combinations as the final arrangement combination, and selecting the vehicle combination with the second-lowest priority index as the alternative arrangement combination; S108: The final arrangement combination and the alternative arrangement combination matched by each transport task are archived into the system database to provide support for subsequent transport task scheduling optimization.

3. The intelligent vehicle type identification and dynamic control loading system as claimed in claim 2, characterized in that: The recognition module includes a camera device for taking pictures of vehicles entering the material loading area, an identification unit for analyzing and processing the pictures taken by the camera device through an image recognition algorithm to identify the license plate information of the vehicle in the picture and the length h, width w and height g of the vehicle compartment, and a verification unit for verifying the actual information of the vehicle based on the picture.

4. The intelligent vehicle type identification and dynamic control loading system as claimed in claim 3, characterized in that: The checking unit completes the following steps: S201: Obtain the vehicle number and vehicle compartment volume V based on the license plate signal max , and the matching transportation tasks; S202: Check whether the size of the carriage in the picture matches the recorded carriage volume: exist When , the operation instruction is sent to the loading control module to drive the loading device to transport materials to the vehicle. △path is the difference threshold, △path∈(5%, 15%), exist When the vehicle entering the logistics loading area is judged as an error vehicle, the matching value Vmatd of the error vehicle and the vehicle with Si=1 in the garage is calculated respectively, and the vehicle with the smallest difference in matching value Vmat from the error vehicle is selected from the garage and replaced with the error vehicle, and the database is uploaded to change the final arrangement combination of the transportation task, where Vmatd=C ac ×w1+V total ×w2+D tota ×w3, w1, w2, w3 are weight coefficients, and w1+w2+w3=1, w1≥w2≥w3.

5. The intelligent vehicle type identification and dynamic control loading system as claimed in claim 4, characterized in that: The loading control module implements the following steps: S301: Receive the operation instruction sent by the recognition module and obtain the actual load capacity C of the vehicle in the material loading area. ac , compartment volume V max and the density ρ of the transported material of the vehicle, S302: Calculate the transport loading weight Gend of the vehicle in the material loading area: At the upper limit volume V of the vehicle Cac ≤V max When Gend=C ac , On the contrary, at the upper limit volume V of the vehicle Cac >V max When Gend=V max ,in, ρ is the density of the material, and the weight of the material loaded by the loading device on the vehicle is the transport loading weight of the vehicle, S303: Start the conveying equipment to transport materials to the vehicle compartment. S304: The loading weight of the vehicle is monitored in real time through a weight sensor. When the cumulative loading weight of the vehicle reaches Gac, the operation of the loading device is stopped and a loading completion signal is sent to remind the vehicle to leave the material loading area.

6. The vehicle loading system with intelligent vehicle type identification and dynamic control as claimed in claim 5, characterized in that: The dynamic control module implements the following operation steps: S401: During the operation of the loading device, the current loading weight Wreal of the vehicle is collected in real time through the weight sensor and continuously updated; S402: According to the current real-time loading weight Wreal of the vehicle, dynamically adjust the conveying rate of the loading device to avoid material overloading or slow loading: Fmax is the preset initial charging rate of the charging device, Fadj is the charging rate after the charging device is adjusted, λ is the first control coefficient, which adjusts the speed of change of the loading rate when approaching the target loading weight by controlling the sensitivity of the loading rate change to Gend, λ∈[0.01, 1], σ is the second control coefficient. σ adjusts the sensitivity of the loading rate to the real-time loading weight change by controlling the influence of the increase of loading weight on the loading rate change, б∈[0.01, 1].

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