Intelligent full-automatic truck loading management system and method

Abstract

The invention relates to the technical field of automatic loading, in particular to an intelligent full-automatic loading management system and method, and the method comprises the following steps: determining a target vehicle, building a vehicle standard model of the target vehicle, marking a loading region corresponding to the vehicle standard model, and taking a corresponding loading order as monitoring information, carrying out information binding on the monitoring information and a corresponding target vehicle and a vehicle standard model; order information corresponding to the target vehicle is collected; constructing a cargo model based on the order information; on the basis of position information among multiple feature points in the multiple pieces of feature information, the cargo model and the vehicle standard model are overlapped according to the feature information, overlapped distinguishing feature information is determined, a target loading order corresponding to the target vehicle is determined on the basis of the distinguishing feature information, and early warning is performed on the monitoring information which does not meet a preset condition. The highest space utilization rate can be achieved under the condition that delivery is convenient.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic loading, and in particular to an intelligent full-automatic loading management system and method. Background Art

[0002] In the field of logistics, transportation and warehousing management, loading operations are the key step connecting warehouses and transportation links, and their efficiency and accuracy have a vital impact on the overall logistics operation effect. Traditional loading operations mainly rely on manual experience and simple tools, and there are many problems: unscientific loading plans lead to insufficient utilization of vehicle space; unreasonable cargo stacking easily causes damage or loss of cargo; lack of real-time monitoring of the loading process, and abnormal situations are difficult to detect and handle in time.

[0003] With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent loading management systems have gradually become the goal pursued by the industry. However, the existing intelligent loading management systems still have shortcomings in terms of loading plan formulation, cargo and vehicle matching, and loading process monitoring. For example, the cargo and vehicle matching process lacks refined management, and loading plans are often based on simple rules or empirical judgments and lack scientific basis; the real-time monitoring methods during the loading process are limited, making it difficult to fully and accurately obtain cargo status information.

[0004] To this end, we propose an intelligent, fully automatic loading management system and method to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent fully automatic loading management system and method to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an intelligent fully automatic loading management system and method, the method comprising the following steps: S1: Determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; S2: Collecting order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; building a cargo model based on the order information; S3: Based on the position information between the multiple feature points in the multiple feature information, the cargo model and the vehicle standard model are overlapped according to the feature information, the distinguishing feature information after the overlap is determined, and the target loading order corresponding to the target vehicle is determined based on the distinguishing feature information; S4: Load the target vehicle based on the target loading order, build a twin monitoring model, monitor the monitoring information of the target vehicle based on the twin monitoring model, and issue an early warning for the monitoring information that does not meet the preset conditions.

[0007] Preferably, the corresponding vehicle standard model marks the loading area and the corresponding loading order as monitoring information, and the step of information binding the monitoring information with the corresponding target vehicle and the vehicle standard model includes: obtaining the vehicle that needs to be loaded as the target vehicle, collecting the standard shape and corresponding size of the loading space inside the target vehicle to construct a vehicle standard model; dividing the area that needs to be loaded as the loading area based on the vehicle standard model, determining the corresponding loading order for the loading area, and using the loading area and the corresponding loading order as monitoring information; information binding the monitoring information with the corresponding target vehicle and the vehicle standard model.

[0008] Preferably, the step of constructing a cargo model based on the order information includes: obtaining order information of each loading order, wherein the order information includes the type and quantity of cargo; taking the maximum size of the vehicle standard model as the maximum size of the cargo model, classifying and stacking the cargo in the order information, obtaining the type and quantity of cargo in the order information, stacking multiple types of cargo according to the corresponding quantities of cargo, and laminating the stacked cargo to form a stacking contour, obtaining edge lines and corner points of the stacking contour and corner points and edge lines of the loading space, setting feature points on the edge lines and corner points, taking the edge lines and corner points with feature points corresponding to the stacking contour as multiple cargo feature information, taking the edge lines and corner points with feature points corresponding to the loading space as multiple space feature information, obtaining position information between multiple feature points in the multiple feature information, wherein the position information includes the distance and angle of the connecting line between the multiple feature points; and virtually filling the corresponding feature information on the vehicle standard model to obtain the cargo model.

[0009] Preferably, the corresponding feature information is virtually filled on the vehicle standard model, and the step of obtaining the cargo model includes: setting a filling frame corresponding to multiple spatial feature information in the vehicle standard model, performing virtual filling based on the vehicle standard model and the filling frame, setting multiple filling points corresponding to the virtual filling frame, dividing the surface range of the corresponding vehicle standard model according to the filling points, and recording the position information of the surface range of the corresponding vehicle standard model based on the filling points; connecting multiple adjacent filling points and recording the distance and orientation information between the multiple adjacent filling points to obtain the cargo model.

[0010] Preferably, the steps of overlapping the cargo model and the vehicle standard model according to the feature information based on the position information between multiple feature points in the multiple feature information, determining the distinguishing feature information after the overlap, and determining the target loading order corresponding to the target vehicle based on the distinguishing feature information include: establishing a lead relationship between the feature points in the cargo model and the feature points in the loading space, overlapping the cargo model and the vehicle standard model according to the feature information based on the position information between multiple feature points in the multiple feature information to obtain the distinguishing feature information; determining the first cargo model and the second cargo model, and determining the first distinguishing feature information based on the first cargo model and the vehicle standard model; comparing the first distinguishing feature information with the second cargo model, and taking the loading order corresponding to the second cargo model that meets the first distinguishing feature information as the target loading order; and allocating the loading order corresponding to the second cargo model that does not meet the first distinguishing feature information to the next loading vehicle.

[0011] Preferably, the step of determining the first cargo model and the second cargo model includes: obtaining all loading orders to be loaded, preliminarily screening the loading orders based on the order destination to obtain multiple preliminary loading orders, sorting the multiple preliminary loading orders according to the order placement time points of the loading orders, dividing the multiple preliminary loading orders corresponding to the order placement time points, and obtaining preliminary loading orders for multiple time periods; obtaining cargo models corresponding to multiple preliminary loading orders whose order placement time points are in the earliest time period, obtaining multiple preliminary cargo models, extracting the preliminary cargo model with the smallest first distinguishing feature information between it and the vehicle standard model as the first cargo model; and selecting the cargo model that best fits the first distinguishing feature information from other preliminary cargo models in the same time period as the first cargo model as the second cargo model.

[0012] Preferably, the target vehicle is loaded based on the target loading order, and a twin monitoring model is constructed. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and the step of issuing an early warning for the monitoring information that does not meet the preset conditions includes: obtaining the target loading order, and loading it in sequence according to the delivery destination; obtaining the vehicle standard model and the cargo model to construct the twin monitoring model, obtaining the loading area and the corresponding loading order during the loading process in real time and synchronously mapping them in the twin monitoring model to obtain real-time loading information, and issuing an early warning for the real-time loading information that is inconsistent with the monitoring information.

[0013] An intelligent fully automatic loading management system, applied to any of the intelligent fully automatic loading management methods described above, comprising: The first construction module is used to determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; The second building module is used to collect order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; and build a cargo model based on the order information; A comparison module, for aligning the cargo model with the vehicle standard model according to the feature information based on the position information between the multiple feature points in the multiple feature information, determining the distinguishing feature information after the overlap, and determining the target loading order corresponding to the target vehicle based on the distinguishing feature information; The management module is used to load the target vehicle based on the target loading order and build a twin monitoring model. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and an early warning is issued for the monitoring information that does not meet the preset conditions.

[0014] Compared with the prior art, the present invention has the following beneficial effects: By establishing a standard vehicle model and marking the loading area, the vehicle loading space can be managed in a refined manner. The cargo model is built based on the order information, and the cargo model is overlapped with the vehicle standard model based on the feature information to determine the optimal loading plan and improve the utilization of the vehicle space.

[0015] By obtaining the order information of the loading order, including the type, quantity and stacking profile of the goods, an accurate cargo model is constructed. Based on the overlap comparison between the cargo model and the vehicle standard model, the distinguishing feature information is determined, providing a scientific basis for the loading operation. Real-time monitoring and early warning of the loading process are realized: A twin monitoring model is built to monitor the monitoring information during the loading process in real time. Once monitoring information that does not meet the preset conditions is found, the early warning mechanism is immediately triggered, and personnel are organized to handle it in a timely manner, effectively avoiding the risks of cargo damage and loss. Through preliminary screening and sorting of loading orders, the cargo model that best fits the vehicle standard model is extracted as the first cargo model, and the second cargo model that best fits the first distinguishing feature information is selected. The reasonable allocation and processing of loading orders is ensured, and the overall efficiency of loading operations is improved.

[0016] In summary, the present invention significantly improves the efficiency and accuracy of loading operations, enhances the safety and reliability of logistics transportation, and provides new technical support and management ideas for the development of the logistics industry by finely managing vehicle loading space, scientifically formulating loading plans, real-time monitoring of the loading process, and optimizing the loading order processing flow. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the method flow of the present invention; Figure 2 It is a system structure block diagram of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0020] See also Figure 1 to Figure 2 The present invention provides an intelligent fully automatic loading management system and method technical solution: an intelligent fully automatic loading management method, comprising the following steps: S1: Determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; The steps of marking the loading area and the corresponding loading order corresponding to the vehicle standard model as monitoring information, and binding the monitoring information with the corresponding target vehicle and the vehicle standard model include: obtaining a vehicle to be loaded as the target vehicle, collecting the standard shape and corresponding size of the loading space inside the target vehicle to construct the vehicle standard model; dividing the area to be loaded as the loading area based on the vehicle standard model, determining the corresponding loading order corresponding to the loading area, and using the loading area and the corresponding loading order as monitoring information; binding the monitoring information with the corresponding target vehicle and the vehicle standard model; Specifically, the dimensions of the loading space inside the vehicle are obtained to construct a standard vehicle model, the dimensions including length, width, height and dimensions of possible special shape parts; based on the measured dimension information, the outline information of the vehicle's dimension space loading is generated, so as to construct a standard vehicle model based on the outline dimensions, divide the vehicle standard model into regions and determine the loading orders corresponding to each loading region, and use the monitoring information to subsequently determine whether abnormal behavior occurs in the loading process, thereby improving the accuracy of loading; S2: Collecting order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; building a cargo model based on the order information; The step of constructing a cargo model based on order information includes: obtaining order information of each loading order, wherein the order information includes the type and quantity of cargo; taking the maximum size of the vehicle standard model as the maximum size of the cargo model, classifying and stacking the cargo in the order information, obtaining the type and quantity of cargo in the order information, stacking multiple types of cargo according to the corresponding quantities of cargo, and laminating the stacked cargo to form a stacking contour, obtaining edge lines and corner points of the stacking contour and corner points and edge lines of the loading space, setting feature points on the edge lines and corner points, taking the edge lines and corner points with feature points corresponding to the stacking contour as multiple cargo feature information, taking the edge lines and corner points with feature points corresponding to the loading space as multiple space feature information, obtaining position information between multiple feature points in the multiple feature information, wherein the position information includes the distance and angle of the connecting line between the multiple feature points; and virtually filling the corresponding feature information on the vehicle standard model to obtain the cargo model.

[0021] Specifically, the maximum size of the vehicle standard model here refers to the highest size and the widest size in the loading space of the target vehicle. The highest size and the widest size are used as the highest size and the widest size in the order information when stacking, so as to prevent the situation that the goods after stacking cannot be put into the loading space, so that the goods are stacked horizontally after the longitudinal stacking meets the maximum size of the loading space, so as to ensure that the goods can be loaded into the loading space; by screening the loading orders, the orders suitable for the target vehicle can be screened out, and preliminary screening is carried out according to the destination of the loading orders to facilitate subsequent transportation. Screening according to the order time point can ensure the timeliness of delivery of the goods in the order, so that the loading order most suitable for the target vehicle is selected while ensuring timely delivery of the goods and convenient transportation, and then the goods in the loading order are stacked without exceeding the maximum size of the loading space, and a cargo model is constructed according to the contour information after stacking, which is used to subsequently determine how many loading orders can be matched in the loading space, so as to improve the utilization rate of the loading space while facilitating transportation.

[0022] The steps of virtually filling the vehicle standard model corresponding to the characteristic information to obtain the cargo model include: setting a filling frame corresponding to multiple spatial characteristic information in the vehicle standard model, performing virtual filling based on the vehicle standard model and the filling frame, setting multiple filling points corresponding to the virtual filling frame, dividing the surface range of the corresponding vehicle standard model corresponding to the filling points, and recording the position information of the surface range of the corresponding vehicle standard model based on the filling points; connecting multiple adjacent filling points and recording the distance and orientation information between the multiple adjacent filling points to obtain the cargo model; Specifically, in order to identify the outline of the cargo stacking contour and the fit of the stacking with the loading space, the edge lines and corner points of the loading space in the vehicle standard model are first obtained, and feature points are set on the edge lines and corner points. Multiple feature points can be set on the edge lines, and the edge lines and corner points with feature points set are used as feature information. The feature information here is the outline of an outer shape after the cargo is stacked. Then, the outer contour of the cargo stacking can be obtained according to the connection distance and angle between multiple feature points. Through the relationship between the position and angle reflected by the feature points, virtual filling is performed on the standard model to obtain the cargo model after the cargo is stacked in the order information. The filling here is a specific vehicle The loading space of the standard model is fully filled, and multiple filling points are set on the inner surface of the loading space. Each filling point corresponds to the surface range of the vehicle standard model and the position information of the corresponding surface range of the vehicle standard model is recorded through the filling points. Then, the adjacent multiple filling points are connected, and the distance and orientation between the adjacent multiple filling points are recorded, so that a filling point network located on the inner wall of the loading space can be obtained. The position of the stacking contour inside the loading space is determined according to the position of the stacking contour on the filling point network, and the size of the cargo stacking contour is always smaller than the size of the loading space, so as to obtain the cargo model of the loading order, which can ensure that it fits better with the vehicle standard model while meeting the loading space; S3: Based on the position information between the multiple feature points in the multiple feature information, the cargo model and the vehicle standard model are overlapped according to the feature information, the distinguishing feature information after the overlap is determined, and the target loading order corresponding to the target vehicle is determined based on the distinguishing feature information; Based on the position information between multiple feature points in the multiple feature information, the cargo model and the vehicle standard model are overlapped according to the feature information, and the distinguishing feature information after the overlap is determined. The step of determining the target loading order corresponding to the target vehicle based on the distinguishing feature information includes: establishing a lead relationship between the feature points in the cargo model and the feature points in the loading space, and based on the position information between the multiple feature points in the multiple feature information, the cargo model and the vehicle standard model are overlapped according to the feature information to obtain the distinguishing feature information; determining the first cargo model and the second cargo model, and determining the first distinguishing feature information based on the first cargo model and the vehicle standard model; comparing the first distinguishing feature information with the second cargo model, and taking the loading order corresponding to the second cargo model that meets the first distinguishing feature information as the target loading order; and allocating the loading order corresponding to the second cargo model that does not meet the first distinguishing feature information to the next loading vehicle; Specifically, after the cargo model and the vehicle standard model are overlapped, the distinguishing feature information of the overlap between the two is obtained. When selecting the placement orientation of the cargo model for cargo stacking, the side with the most covered filling points is preferentially selected as the main fitting surface. The distinguishing feature information refers to the difference between the cargo model and the vehicle standard model after the most fitting overlap method is selected. For example, the feature points of the vehicle standard model include eight points ABCDEFGH, which constitute a loading interval, among which ABCD is on an upper plane, EFGH is on a lower plane, and ADHE is on a side plane. The feature points of the first cargo model include abcdefgh, among which adhe is on a side plane, and the side coincides with the side of ADHE, and the position information of the plane corresponding to bcgf inside the vehicle standard model is determined. The spatial information between the plane bcgf and the plane BCGF is used as the distinguishing feature information, including the distance information of the line between the feature point b and the feature point B, and the angle information between the line and the ABCD plane. According to the distance information and the angle information, it is determined that the side bcgf on the cargo model is located at the vehicle The position inside the loading space of the standard model of the vehicle is determined, and the space between the side bcgf and the side BCGF is used as the first distinguishing characteristic information, and it is determined whether the distinguishing characteristic information meets the preset condition. The preset condition refers to other loading orders in the same time period and whose destinations also meet the preset requirements. The cargo model corresponding to the stacking contour of the other loading orders is obtained as the second cargo model, and it is compared with the first distinguishing characteristic information to determine whether the distinguishing characteristic information meets the stacking contour of the other loading orders, and the distinguishing characteristic information between the second cargo model and the first distinguishing characteristic information is continuously obtained as the second distinguishing characteristic information, until the second distinguishing characteristic information cannot meet the cargo model corresponding to the stacking of other loading orders, and other loading orders that meet the second distinguishing characteristic information are used as target loading orders, and the loading orders corresponding to the first cargo model and the loading orders corresponding to the multiple second cargo models are all used as target loading orders. One target vehicle can be loaded with the required cargo of multiple order information, and a more suitable loading order can be selected for the target vehicle under the condition of convenient transportation, thereby improving the utilization rate of the loading space of the target vehicle; The step of determining the first cargo model and the second cargo model includes: obtaining all loading orders to be loaded, preliminarily screening the loading orders based on the order destination to obtain multiple preliminary selected loading orders, sorting the multiple preliminary selected loading orders according to the order placement time points of the loading orders, dividing the multiple preliminary selected loading orders corresponding to the order placement time points, and obtaining the preliminary selected loading orders of multiple time periods; obtaining cargo models corresponding to the multiple preliminary selected loading orders whose order placement time points are in the earliest time period, obtaining multiple preliminary selected cargo models, extracting the preliminary selected cargo model with the smallest first distinguishing feature information between the model and the vehicle standard model as the first cargo model; selecting the cargo model that best fits the first distinguishing feature information from other preliminary selected cargo models in the same time period as the first cargo model as the second cargo model; Specifically, all loading orders are first obtained, and then loading orders with similar destinations or on the way are selected as preliminary loading orders, and then corresponding cargo models are respectively constructed, and then the time periods are divided according to the order time points, so as to classify the cargo models in the same time period, first obtain the cargo model in the earliest time period, and then find the cargo model that best fits the vehicle standard model as the first cargo model. The smallest first distinguishing feature information can be understood as the smallest remaining space in the loading space of the target vehicle after the loading order corresponding to the first cargo model is placed, thereby reducing the transportation destination of the target vehicle and facilitating transportation; after determining the first cargo model, the cargo model with the smallest second distinguishing feature information between the first distinguishing feature information is selected from other cargo models in the same time period, and it is used as the second cargo model, so that it better fits the remaining loading space of the target vehicle, and the loading is carried out in order from large loading orders to small loading orders, which can improve the utilization rate of the vehicle's loading space while delivering the least destinations; S4: Load the target vehicle based on the target loading order, build a twin monitoring model, monitor the monitoring information of the target vehicle based on the twin monitoring model, and issue an early warning for the monitoring information that does not meet the preset conditions; The target vehicle is loaded based on the target loading order, and a twin monitoring model is constructed. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and the steps of issuing an early warning for the monitoring information that does not meet the preset conditions include: obtaining the target loading order, and loading it in sequence according to the delivery destination; obtaining the vehicle standard model and the cargo model to construct the twin monitoring model, obtaining the loading area and the corresponding loading order in the loading process in real time and synchronously mapping them in the twin monitoring model to obtain real-time loading information, and issuing an early warning for the real-time loading information that is inconsistent with the monitoring information; Specifically, a twin monitoring model is constructed based on the vehicle standard model and the cargo model of the target loading order, which is used to monitor in real time whether any abnormalities occur when loading the target loading order. In the monitoring and comparison process, real-time loading information is first obtained, including the number of the target loading order and the corresponding loading area, etc., to determine whether the target loading order in the loading is loaded in the corresponding loading area, and compare it with the loading area and the target loading order in the monitoring information. When the actual loading information is consistent with the monitoring information, it means that the loading process is correct. When the loading area or loading order in the actual loading information is inconsistent with the monitoring information, an early warning prompt is issued, which can effectively monitor the loading process and improve the accuracy of loading. It can realize comprehensive monitoring and early warning of the loading process of the target vehicle to ensure the safe transportation of goods. At the same time, by constructing a digital twin model, real-time synchronous mapping of virtual models and physical entities is realized, improving the intelligent management effect of logistics and transportation; An intelligent fully automatic loading management system, applied to any of the intelligent fully automatic loading management methods described above, comprising: The first construction module is used to determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; The second building module is used to collect order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; and build a cargo model based on the order information; A comparison module, for aligning the cargo model with the vehicle standard model according to the feature information based on the position information between the multiple feature points in the multiple feature information, determining the distinguishing feature information after the overlap, and determining the target loading order corresponding to the target vehicle based on the distinguishing feature information; The management module is used to load the target vehicle based on the target loading order and build a twin monitoring model. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and an early warning is issued for the monitoring information that does not meet the preset conditions.

[0023] The present invention realizes the refined management of vehicle loading space by establishing a vehicle standard model and marking the loading area. The cargo model is constructed based on order information, and the cargo model is overlapped with the vehicle standard model according to the feature information, so as to determine the optimal loading plan and improve the utilization rate of vehicle space.

[0024] By obtaining the order information of the loading order, including the type, quantity and stacking profile of the goods, an accurate cargo model is constructed. Based on the overlap comparison between the cargo model and the vehicle standard model, the distinguishing feature information is determined, providing a scientific basis for the loading operation. Real-time monitoring and early warning of the loading process are realized: A twin monitoring model is built to monitor the monitoring information during the loading process in real time. Once monitoring information that does not meet the preset conditions is found, the early warning mechanism is immediately triggered, and personnel are organized to handle it in a timely manner, effectively avoiding the risks of cargo damage and loss. Through preliminary screening and sorting of loading orders, the cargo model that best fits the vehicle standard model is extracted as the first cargo model, and the second cargo model that best fits the first distinguishing feature information is selected. The reasonable allocation and processing of loading orders is ensured, and the overall efficiency of loading operations is improved.

[0025] In summary, the present invention significantly improves the efficiency and accuracy of loading operations, enhances the safety and reliability of logistics transportation, and can achieve the highest space utilization while facilitating delivery by finely managing vehicle loading space, scientifically formulating loading plans, real-time monitoring of the loading process, and optimizing the loading order processing flow.

[0026] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0027] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent and fully automatic loading management method, characterized in that: The following steps are involved: Determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; Collecting order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; building a cargo model based on the order information; Based on the position information between the multiple feature points in the multiple feature information, the cargo model and the vehicle standard model are overlapped according to the feature information, the distinguishing feature information after the overlap is determined, and the target loading order corresponding to the target vehicle is determined based on the distinguishing feature information; The target vehicle is loaded based on the target loading order, and a twin monitoring model is built. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and an early warning is issued for the monitoring information that does not meet the preset conditions.

2. An intelligent and fully automatic loading management method according to claim 1, characterized in that: The corresponding vehicle standard model marks the loading area and the corresponding loading order as monitoring information, and the step of binding the monitoring information with the corresponding target vehicle and the vehicle standard model includes: A vehicle to be loaded is obtained as a target vehicle, and the standard shape and corresponding dimensions of the loading space inside the target vehicle are collected to construct a vehicle standard model; Based on the vehicle standard model, the area that needs to be loaded is divided as the loading area, the corresponding loading order is determined for the loading area, and the loading area and the corresponding loading order are used as monitoring information; Bind the monitoring information with the corresponding target vehicle and vehicle standard model.

3. The intelligent fully automatic loading management method according to claim 1 is characterized by: The step of building a goods model based on order information includes: Obtaining order information of each loading order, wherein the order information includes the type and quantity of goods; using the maximum size of the vehicle standard model as the maximum size of the cargo model; Classify and stack the goods in the order information, obtain the types and quantities of goods in the order information, stack multiple types of goods according to the corresponding quantities of goods, and laminate the stacked goods to form a stacking outline; Obtain edge lines and corner points of a stacking contour and corner points and edge lines of a loading space, set feature points on the edge lines and corner points, use the edge lines and corner points with feature points set corresponding to the stacking contour as a plurality of cargo feature information, and use the edge lines and corner points with feature points set corresponding to the loading space as a plurality of space feature information; Acquire position information between multiple feature points in the multiple feature information, wherein the position information includes the distance and angle of the connecting line between the multiple feature points; The corresponding characteristic information is virtually filled on the vehicle standard model to obtain the cargo model.

4. The intelligent fully automatic loading management method according to claim 3 is characterized by: The step of virtually filling the corresponding characteristic information on the vehicle standard model to obtain the cargo model comprises: A filling frame is set corresponding to multiple spatial feature information in the vehicle standard model, and virtual filling is performed based on the vehicle standard model and the filling frame; A plurality of filling points are set corresponding to the virtual filling frame, the surface range of the corresponding vehicle standard model is divided corresponding to the filling points, and the position information of the surface range of the corresponding vehicle standard model is recorded based on the filling points; The cargo model is obtained by connecting multiple adjacent filling points and recording the distance and orientation information between the multiple adjacent filling points.

5. The intelligent fully automatic loading management method according to claim 4 is characterized in that: The steps of overlapping the cargo model and the vehicle standard model according to the feature information based on the position information between the multiple feature points in the multiple feature information, determining the distinguishing feature information after the overlap, and determining the target loading order corresponding to the target vehicle based on the distinguishing feature information include: Establishing a lead relationship between characteristic points in the cargo model and characteristic points in the loading space, and based on position information between multiple characteristic points in the multiple characteristic information, overlapping the cargo model with the vehicle standard model according to the characteristic information to obtain distinguishing characteristic information; Determine a first cargo model and a second cargo model, and determine first distinguishing feature information based on the first cargo model and the vehicle standard model; Compare the first distinguishing characteristic information with the second cargo model, and use the shipping order corresponding to the second cargo model that meets the first distinguishing characteristic information as the target shipping order; The loading order corresponding to the second cargo model that does not meet the first distinguishing characteristic information is allocated to the next loading vehicle.

6. An intelligent and fully automatic loading management method according to claim 5, characterized in that: The step of determining the first cargo model and the second cargo model comprises: Obtain all loading orders to be loaded, preliminarily screen the loading orders based on the order destination to obtain multiple preliminary loading orders, and sort the multiple preliminary loading orders according to the order placement time of the loading orders; Divide the multiple preliminary loading orders into corresponding order placement time points to obtain the preliminary loading orders in multiple time periods; Obtain the cargo models corresponding to the multiple preliminary loading orders whose order placement time points are in the earliest time period, and obtain multiple preliminary cargo models; Extracting a preliminary selected cargo model having the smallest first distinguishing feature information from a vehicle standard model as a first cargo model; A cargo model that best fits the first distinguishing feature information is selected from other preliminary cargo models that are in the same time period as the first cargo model as the second cargo model.

7. The intelligent fully automatic loading management method according to claim 1 is characterized by: The steps of loading the target vehicle based on the target loading order, building a twin monitoring model, monitoring the monitoring information of the target vehicle based on the twin monitoring model, and issuing an early warning for the monitoring information that does not meet the preset conditions include: Obtain the target loading order and load it in order according to the delivery destination; Obtain the vehicle standard model and cargo model to build a twin monitoring model, obtain the loading area and corresponding loading orders in the loading process in real time, and synchronously map them in the twin monitoring model to obtain real-time loading information; An early warning will be issued for real-time loading information that is inconsistent with the monitoring information.

8. An intelligent fully automatic loading management system, applied to the intelligent fully automatic loading management method according to any one of claims 1 to 7, characterized in that: include: The first construction module is used to determine the target vehicle, establish a vehicle standard model of the target vehicle, mark the loading area and the corresponding loading order of the vehicle standard model as monitoring information, and bind the monitoring information with the corresponding target vehicle and the vehicle standard model; The second building module is used to collect order information corresponding to the target vehicle, wherein the order information includes cargo type information and cargo outline information after stacking; and build a cargo model based on the order information; A comparison module, for aligning the cargo model with the vehicle standard model according to the feature information based on the position information between the multiple feature points in the multiple feature information, determining the distinguishing feature information after the overlap, and determining the target loading order corresponding to the target vehicle based on the distinguishing feature information; The management module is used to load the target vehicle based on the target loading order and build a twin monitoring model. The monitoring information of the target vehicle is monitored based on the twin monitoring model, and an early warning is issued for the monitoring information that does not meet the preset conditions.

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