A campus express sorting method and device, electronic equipment and medium

Abstract

The application provides a campus express sorting method and device, electronic equipment and medium, the method comprises the following steps: obtaining the storage location specification data of the campus express center, and generating the storage location volume constraint data and the package classification threshold data according to the storage location specification data; obtaining the package attribute data of the to-be-sorted package, and performing classification processing on the to-be-sorted package according to the package attribute data and the package classification threshold data to obtain a package classification result; based on the package classification result and the storage location volume constraint data, performing combination matching processing on a plurality of to-be-sorted packages to obtain a target package combination; according to the target package combination, a target storage location is determined and a package combination shelving plan corresponding to the target storage location is generated. The campus express sorting method can realize package classification and combination matching processing under different storage location specification conditions, improve the adaptation between packages and storage locations, and can improve the utilization level of storage location space and the package processing efficiency of the campus express center.

Description

Technical Field

[0001] This application belongs to the field of campus logistics sorting technology, and relates to a campus express sorting method, device, electronic equipment and medium. Background Technology

[0002] With the development of e-commerce and campus logistics services, the volume of express delivery business in universities continues to grow, and campus express delivery centers have gradually become an important infrastructure for parcel collection and delivery on campuses. Existing campus express delivery centers typically need to uniformly receive, classify, store, and centrally manage parcels of different sizes and types, and complete parcel circulation through shelves, storage locations, sorting equipment, and handling equipment. To improve parcel handling capacity, some campus express delivery centers have begun to introduce automatic measuring equipment, self-service parcel pickup equipment, and robotic handling equipment to automate and intelligently manage the parcel processing flow.

[0003] However, existing campus parcel sorting solutions generally employ fixed storage location specifications and fixed classification rules for parcel management, lacking the ability to dynamically adapt storage location utilization to different specifications. When storage location specifications change, classification rules and storage strategies are difficult to adjust synchronously, resulting in a low degree of matching between parcels of different sizes and storage space. Furthermore, for parcels that have already been sorted, they are typically shelved individually or in fixed combinations, lacking a combination matching mechanism based on storage space constraints. This makes it difficult to fully utilize storage space, thus affecting the overall sorting and storage efficiency of the campus parcel center. Summary of the Invention

[0004] This application provides a campus express delivery sorting method, device, electronic device and medium to solve the problems of insufficient adaptability between storage location specifications and parcel classification rules, lack of dynamic optimization mechanism for parcel combination storage methods and low storage space utilization in the prior art.

[0005] Firstly, this application provides a campus express delivery sorting method, including:

[0006] Obtain the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data;

[0007] Obtain the package attribute data of the packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result;

[0008] Based on the package classification results and the cargo space volume constraint data, multiple packages to be sorted are combined and matched to obtain the target package combination.

[0009] Based on the target package combination, determine the target storage location and generate a package combination shelving plan corresponding to the target storage location.

[0010] In one implementation of the first aspect, the method further includes:

[0011] A robot scheduling task is generated based on the package combination shelving plan, and the robot scheduling task is sent to the robot to perform the shelving operation;

[0012] The system receives the task execution results from the robot and updates the shelf status of each package in the target package combination and the occupancy status of the target storage space based on the task execution results.

[0013] In one implementation of the first aspect, generating cargo space volume constraint data and parcel classification threshold data based on the cargo space specification data includes:

[0014] Calculate the rated volume of the storage location based on the storage location length, width, and height in the storage location specification data;

[0015] Calculate the cargo space volume constraint data based on the rated volume of the cargo space and the preset safety factor;

[0016] Based on the storage location length, storage location width, and storage location height, respectively generate package length classification thresholds, package width classification thresholds, and package height classification thresholds, which are used as the package classification threshold data.

[0017] In one implementation of the first aspect, the step of classifying the packages to be sorted based on the package attribute data and the package classification threshold data to obtain a package classification result includes:

[0018] The package length, package width, and package height in the package attribute data are compared with the package classification threshold data respectively;

[0019] When the package length, package width, and package height all do not exceed the corresponding package classification threshold data, the package to be sorted is identified as a small package;

[0020] If any one of the package length, package width, or package height exceeds the corresponding package classification threshold data, the package to be sorted will be identified as a large package.

[0021] In one implementation of the first aspect, after determining the package to be sorted as a small package, the process includes:

[0022] The thickness data of the small package is compared with a preset thickness threshold to determine the shape category of the small package;

[0023] Based on the matching results of the package size data of the small package with multiple preset size ranges, the package volume level of the small package is determined;

[0024] Based on the package shape category and the package volume level, a detailed classification result for the small package is generated.

[0025] In one implementation of the first aspect, the step of performing combination matching processing on multiple parcels to be sorted based on the parcel classification results and the cargo location volume constraint data to obtain a target parcel combination includes:

[0026] Candidate packages for combination matching are selected based on the package classification results;

[0027] The candidate packages are sorted according to their volume, and a combination of candidate packages is generated based on the sorting results.

[0028] The volume of the candidate package combination is verified based on the cargo space volume constraint data.

[0029] Based on the volume verification results, the packages in the candidate package combination are added or removed to obtain the target package combination.

[0030] In one implementation of the first aspect, determining the target storage location and generating a parcel combination shelving plan corresponding to the target storage location includes:

[0031] The target cargo location is determined using the target package combination;

[0032] Based on the package size data of each package in the target package combination and the storage location specification data of the target storage location, determine the placement order of each package in the target storage location;

[0033] The arrangement position of each package in the target storage location is determined based on the placement order.

[0034] Based on the target package combination, the placement order, and the arrangement position, a package combination shelving plan corresponding to the target storage location is generated.

[0035] Secondly, this application provides a campus express delivery sorting device. The device includes:

[0036] The data processing module is used to acquire the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data;

[0037] The package classification module is used to acquire package attribute data of packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result.

[0038] The combination matching module is used to perform combination matching processing on multiple parcels to be sorted based on the parcel classification results and the cargo location volume constraint data to obtain a target parcel combination;

[0039] The planning and scheduling module is used to determine the target storage location and generate a shelf plan for the package combination corresponding to the target storage location based on the target package combination.

[0040] Thirdly, this application provides an electronic device. The electronic device includes: a memory for storing a computer program; and a processor for executing the computer program stored in the memory to cause the electronic device to perform the campus express delivery sorting method as described in any one of the first aspects.

[0041] Fourthly, this application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the campus express delivery sorting method described in any one of the first aspects.

[0042] As described above, the campus express sorting method, apparatus, electronic device, and medium of this application have the following beneficial effects: First, the storage location specification data of the campus express center is obtained, and storage location volume constraint data and package classification threshold data are generated based on the storage location specification data; then, the package attribute data of the packages to be sorted is obtained, and the packages to be sorted are classified according to the package attribute data and package classification threshold data to obtain the package classification results; further, based on the package classification results and storage location volume constraint data, multiple packages to be classified are combined and matched to obtain the target package combination corresponding to the target storage location; finally, a package combination shelving plan is generated based on the target package combination. This application can realize the adaptation of package classification rules to different storage locations, and realize package combination matching oriented to storage location space constraints, improve the utilization level of storage location resources, improve the campus express center's ability to sort and store packages of different sizes, and enhance the package processing efficiency and storage location management capabilities in the campus scenario. Attached Figure Description

[0043] Figure 1 The diagram shown illustrates an application scenario of the campus express sorting method described in this application embodiment.

[0044] Figure 2 The diagram shown is a flowchart illustrating the campus express delivery sorting method described in this application embodiment.

[0045] Figure 3 This is another flowchart illustrating the campus express delivery sorting method described in an embodiment of this application.

[0046] Figure 4This is another flowchart illustrating the campus express delivery sorting method described in the embodiments of this application.

[0047] Figure 5 The diagram shown is a structural schematic of the campus express sorting device described in the embodiments of this application.

[0048] Figure 6 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application.

[0049] Component designation explanation

[0050] 100 Campus express sorting equipment 110 Data processing module 120 Parcel sorting module 130 Combination matching module 140 Planning and Scheduling Module 200 electronic devices 210 memory 220 processor 230 monitor S100~S400 step S101~S103 step S500~S600 step Detailed Implementation

[0051] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0052] Considering the insufficient compatibility between storage location specifications and parcel classification rules in existing technologies, and the lack of dynamic optimization mechanisms for parcel combination storage methods, this application provides a campus express parcel sorting method. It generates storage location volume constraint data and parcel classification threshold data through storage location specification data, and performs combination matching processing on multiple parcels to be sorted based on parcel classification results and storage location volume constraint data to generate a parcel combination shelving plan.

[0053] The technical solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings. This will enable those skilled in the art to understand and implement the methods of this embodiment without creative effort.

[0054] Figure 1 This diagram illustrates an application scenario of the campus express delivery sorting method as described in this application. Figure 1 As shown, the application environment includes a campus express delivery center management system, robots, and a storage location system. The campus express delivery center management system communicates with both the robots and the storage location system. The storage location system provides storage location specification data and storage location status data; the robots perform package handling and shelving operations; and the campus express delivery center management system performs package sorting, package combination matching, and package combination shelving plan generation.

[0055] Figure 2 The flowchart shown is a process for sorting campus parcels, as described in an embodiment of this application. Figure 2As shown in the embodiments of this application, the campus express delivery sorting method includes the following steps:

[0056] Step S100: Obtain the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data.

[0057] The storage location specification data represents the spatial dimensions of the storage locations in the campus express delivery center, including storage location length (L), storage location width (W), and storage location height (H). The parcel classification threshold data is used to classify arriving parcels by size, and the storage location volume constraint data represents the volume range that the current storage location can accommodate for combined parcel storage.

[0058] As an example, during the deployment phase of the campus express delivery center, the management system can input the length, width, and height parameters of the corresponding storage locations based on the on-site storage location configuration. For instance, the basic storage location parameters can be configured as L=500mm, W=380mm, H=400mm; the expanded storage location parameters can be configured as L=600mm, W=400mm, H=450mm; and the reduced storage location parameters can be configured as L=450mm, W=350mm, H=380mm. After obtaining the storage location specification data, the system generates corresponding storage location volume constraint data and package classification threshold data, which serve as the basis for subsequent package classification and package combination matching processes.

[0059] In one implementation of this embodiment, such as Figure 3 As shown, step S100 includes the following sub-steps:

[0060] S101. Calculate the rated volume of the storage location based on the storage location length, width, and height in the storage location specification data.

[0061] The rated volume of a storage location is used to characterize the theoretical capacity of a single storage location. For example, the system reads the storage location length L, width W, and height H from the storage location specification data, and calculates the rated volume of the storage location according to the following formula:

[0062]

[0063] in, This indicates the rated volume of the storage space.

[0064] For example, when the dimensions of the storage location are 500mm × 380mm × 400mm, the corresponding rated volume of the storage location is:

[0065]

[0066] That is, 76000cm³.

[0067] In other implementations, the storage location specifications can also adopt different configurations such as 600mm×400mm×450mm or 450mm×350mm×380mm. The system can calculate the rated volume of the corresponding storage location based on the length, width and height of the storage location, thereby adapting to the storage location deployment needs of different campus express centers.

[0068] S102, calculate the cargo space volume constraint data based on the rated volume of the cargo space and the preset safety factor.

[0069] The cargo space volume constraint data represents the maximum available volume range allowed for parcel combination matching. The preset safety factor is used to reserve volume margins for pickup gaps and equipment installation space.

[0070] As an example, the system obtains the rated volume of the storage location and calculates the corresponding storage location volume constraint data based on a preset safety factor. In this embodiment, the preset safety factor corresponds to 95%, therefore, the storage location volume constraint data is calculated as follows:

[0071]

[0072] in, This indicates the cargo space volume constraint data.

[0073] For example, when the rated volume of the storage location is 76000 cm³, the corresponding volume constraint data for the storage location is as follows:

[0074]

[0075] In other implementations, the safety factor can also be set to other values ​​between 90% and 98% according to actual operational needs, to correspond to the space reservation requirements of different storage locations.

[0076] S103. Generate parcel length classification threshold, parcel width classification threshold, and parcel height classification threshold based on the parcel location length, parcel location width, and parcel location height, respectively, as parcel classification threshold data.

[0077] Exemplary, the system acquires the storage location length L, storage location width W, and storage location height H respectively, and generates classification thresholds for the corresponding dimensions according to a preset ratio. In this embodiment, 80% of the corresponding storage location size is used as the classification benchmark, and therefore, the thresholds are calculated as follows:

[0078]

[0079]

[0080]

[0081] in, This indicates the threshold for classifying package length. This indicates the threshold for package width classification. This indicates the threshold for classifying package height.

[0082] For example, when the dimensions of the storage location are 500mm × 380mm × 400mm, we can obtain: =400mm =304mm =320mm;

[0083] Correspondingly, when the storage location specifications are adjusted to 600mm×400mm×450mm, the corresponding parcel length classification threshold is 480mm, the parcel width classification threshold is 320mm, and the parcel height classification threshold is 360mm.

[0084] When the storage location specifications are adjusted to 450mm×350mm×380mm, the corresponding parcel length classification threshold is 360mm, the parcel width classification threshold is 280mm, and the parcel height classification threshold is 304mm.

[0085] Step S200: Obtain the package attribute data of the packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result.

[0086] The package attribute data represents the dimensional characteristics of the packages to be sorted, including package length, width, height, and thickness. The package classification result represents the category to which the package belongs. For example, when a package enters the measurement and labeling conveyor line, the system automatically collects the package's length, width, height, and thickness information using a volume recognition device and generates corresponding package attribute data. Then, using package classification threshold data, the system performs classification processing on the packages to determine their respective categories.

[0087] In one implementation of this embodiment, step S200 includes the following sub-steps:

[0088] S201, compare the package length, package width, and package height in the package attribute data with the package classification threshold data respectively.

[0089] As an example, the package length, width, and height of the package to be sorted are obtained and compared with the package length classification threshold, package width classification threshold, and package height classification threshold, respectively, to obtain the comparison results. These comparison results are used to determine whether the package to be sorted meets the classification criteria for small packages.

[0090] For example, when the current storage location dimensions are 500mm × 380mm × 400mm, the corresponding parcel length classification threshold is 400mm, the parcel width classification threshold is 304mm, and the parcel height classification threshold is 320mm. If the dimensions of the parcel to be sorted are 350mm × 250mm × 200mm, then the parcel length, width, and height do not exceed the corresponding classification thresholds; if the dimensions of the parcel to be sorted are 450mm × 350mm × 300mm, then the parcel length and width exceed the corresponding classification thresholds.

[0091] S202: When the package length, package width, and package height all do not exceed the corresponding package classification threshold data, the package to be sorted is identified as a small package.

[0092] Among them, small parcels are used to represent parcels that meet the small parcel classification conditions of the storage location. For example, the system makes a judgment based on the comparison results of the steps. When the parcel length, parcel width, and parcel height do not exceed the corresponding classification threshold, the parcel to be sorted is identified as a small parcel and enters the subsequent sub-classification processing flow.

[0093] Specifically, step S202 may further include:

[0094] The thickness data of small packages is compared with the preset thickness threshold to determine the shape category of the small packages.

[0095] The morphology category represents the packaging form of the small package. Exemplarily, the system acquires the thickness data corresponding to the small package and compares it with a preset thickness threshold. In this embodiment, the preset thickness threshold is 50mm. When the thickness of the small package is greater than 50mm, it can be identified as a cardboard box package; when the thickness of the small package is less than or equal to 50mm, it can be identified as a thin bag package.

[0096] Based on the matching results of the package size data of small parcels with multiple preset size ranges, the package volume level of small parcels is determined.

[0097] The preset size range is dynamically determined based on the parcel classification threshold data.

[0098] As an example, the system matches the length, width, and height of small packages with multiple preset size ranges and determines the corresponding package volume level based on the matching results.

[0099] For cardboard box parcels, they can be divided into three levels: small items to large cardboard boxes, small items to medium cardboard boxes, and small items to small cardboard boxes.

[0100] The size range corresponding to small items to large cardboard boxes is:

[0101] ;

[0102] That is, the length is located at arrive between;

[0103] Width at arrive between;

[0104] The height is located at arrive between.

[0105] When all three conditions are met, the item is classified as: small item - large carton;

[0106] The size range corresponding to small to medium-sized cartons is:

[0107] ;

[0108] The size range for small items - small cardboard boxes is:

[0109] .

[0110] For thin-bag parcels, they can be divided into three levels: small parcels in large thin bags, small parcels in medium thin bags, and small parcels in small thin bags.

[0111] The size range corresponding to small items - large thin bags is:

[0112] ;

[0113] The size range corresponding to small-sized bags is:

[0114] ;

[0115] The size range corresponding to small items and thin bags is:

[0116] .

[0117] Based on the package shape and size category, generate detailed classification results for small packages.

[0118] As an example, the system combines the determined shape category with the package volume level to generate the corresponding sub-classification results for small packages.

[0119] For example, when a small package is 40mm thick and 300mm×220mm in size, and the corresponding size meets the requirements of a medium-thin bag, it is identified as a "small package - medium-thin bag".

[0120] When a small package is thicker than 50mm and its dimensions meet the requirements of a medium-sized carton, it is classified as a "small package - medium-sized carton".

[0121] S203: If any of the package length, package width, or package height exceeds the corresponding package classification threshold data, the package to be sorted will be identified as a large package.

[0122] Among them, large packages represent packages to be sorted that have at least one size dimension exceeding the corresponding classification threshold.

[0123] As an example, the system makes a judgment based on the comparison results. When any one of the package length, package width, and package height exceeds the corresponding classification threshold, the corresponding package to be sorted is identified as a large package, and the corresponding large package classification result is generated.

[0124] For example, when the current storage location dimensions are 600mm × 400mm × 450mm, the corresponding parcel length classification threshold is 480mm, the parcel width classification threshold is 320mm, and the parcel height classification threshold is 360mm. If a parcel to be sorted measures 500mm × 330mm × 370mm, its length, width, and height all exceed the corresponding classification thresholds, therefore the system classifies it as a large parcel.

[0125] Step S300: Based on the parcel classification results and cargo location volume constraints, multiple parcels to be sorted are combined and matched to obtain the target parcel combination.

[0126] The target package combination represents the package combination result that meets the storage conditions of the current storage location. The combination matching process is used to select suitable package combinations for co-storage in the same storage location from multiple packages to be sorted, based on the package classification results and storage location volume constraints. For example, the system acquires the package classification results and storage location volume constraints, and performs combination matching processing on multiple packages to be sorted according to preset combination rules to generate a target package combination corresponding to the target storage location.

[0127] In one implementation of this embodiment, step S300 includes the following sub-steps:

[0128] S301, Based on the parcel classification results, filter candidate parcels to participate in the combination matching.

[0129] As an example, the system filters candidate packages for combination matching based on the package classification results. In this embodiment, the package classification results include large package classification results and small package classification results. The small package classification results further include small package-large cardboard box, small package-medium cardboard box, small package-small cardboard box, small package-large thin bag, small package-medium thin bag, and small package-small thin bag.

[0130] For example, candidate packages can be categorized into cardboard box packages and thin bag packages based on their package form; at the same time, candidate packages can be further classified according to their package volume level in order to perform combination matching processing.

[0131] For packages that have been identified as small items to large cardboard boxes, small items to medium cardboard boxes, and small items to small cardboard boxes, a set of candidate cardboard box packages can be formed.

[0132] For packages that have been identified as small items in large thin bags, small items in medium thin bags, and small items in small thin bags, a set of candidate packages in thin bag category can be formed.

[0133] S302, sort the candidate parcels according to their volume, and generate a combination of candidate parcels based on the sorting results.

[0134] Among them, the candidate package combination represents the package combination result after the initial combination.

[0135] As an example, the system obtains the package volume corresponding to the candidate packages and sorts them in descending order of package volume; then, packages are selected in sequence according to the sorting results to form a candidate package combination.

[0136] Specifically, the system prioritizes larger candidate packages as the basis for combination and gradually adds other candidate packages to form candidate package combinations. In this embodiment, the combination matching follows the volume complementarity principle of "large + small, medium + medium", that is, larger packages are used first to occupy the main space of the cargo space, and smaller packages are used to fill the remaining space.

[0137] For example, when a candidate package contains a large cardboard box, a medium cardboard box, and a small thin bag, the system prioritizes selecting the large cardboard box to form an initial combination, and then gradually adds the medium cardboard box or the small thin bag to form the corresponding candidate package combination.

[0138] S303, perform volume verification on candidate parcel combinations based on cargo space volume constraint data.

[0139] The volume check is used to determine whether the candidate package combination meets the volume constraints of the current storage location. For example, the system calculates the volume of each package in the candidate package combination and then compares the total volume of the candidate package combination with the storage location's volume constraints.

[0140] Specifically, when the total volume of the candidate package combination does not exceed the storage space volume constraint data, the current candidate package combination is determined to meet the volume constraint conditions; when the total volume of the candidate package combination exceeds the storage space volume constraint data, the current candidate package combination is determined to not meet the volume constraint conditions, and the subsequent combination adjustment process begins.

[0141] For example, when the dimensions of the storage location are 500mm × 380mm × 400mm, the corresponding storage location volume constraint is 72200cm³. If the total volume of the current candidate package combination is 28060cm³, the volume constraint condition is met; if the total volume of the current candidate package combination exceeds 72200cm³, it is determined that the volume constraint condition is not met.

[0142] S304. Based on the volume verification results, add or remove packages in the candidate package combination to obtain the target package combination.

[0143] As an example, the system counts the volume of each package in the candidate package combination and calculates the total volume of the candidate package combination; then the total volume of the candidate package combination is compared with the storage space volume constraint data.

[0144] If the total volume of the candidate package combination does not exceed the storage space volume constraint data, the current candidate package combination is determined to meet the volume constraint conditions; if the total volume of the candidate package combination exceeds the storage space volume constraint data, the current candidate package combination is determined to not meet the volume constraint conditions.

[0145] For example, when the dimensions of the storage location are 500mm × 380mm × 400mm, the corresponding storage location volume constraint is 72200cm³. If the total volume of the current candidate package combination is 28060cm³, the volume constraint condition is met; if the total volume of the current candidate package combination exceeds 72200cm³, it is determined that the volume constraint condition is not met.

[0146] Step S400: Based on the target package combination, determine the target storage location and generate a package combination shelving plan corresponding to the target storage location.

[0147] The target storage location represents the storage space resource that matches the target package combination; the package combination shelving plan represents the storage method of the target package combination in the target storage location, including target storage location information, package placement order, and package layout position. For example, the system acquires the target package combination and, in conjunction with storage location specification data, performs storage location matching and spatial arrangement processing on the target package combination to generate a corresponding package combination shelving plan, which can then be used by the robot to perform the shelving task.

[0148] In one implementation of this embodiment, step S400 includes the following sub-steps:

[0149] S401, Determine the target cargo location based on the target package combination.

[0150] For example, the system obtains the total volume of the target package combination and the size information of the component packages, and filters candidate storage locations that meet the storage conditions from the current storage location resources; then, based on the matching results between the target package combination and the candidate storage locations, the corresponding target storage location is determined.

[0151] For example, when the target package combination consists of 1 medium-sized cardboard box and 4 medium-sized thin bags, and the total volume is 28060cm³, the system can match it with the currently available storage location; if the storage location corresponding to a certain storage location has a volume constraint of 72200cm³, and the storage location size meets the storage requirements of each package in the combination, then the storage location is determined as the target storage location.

[0152] S402, based on the parcel size data of each parcel in the target parcel combination and the storage location specification data of the target storage location, determine the placement order of each parcel in the target storage location.

[0153] The placement order represents the order in which each package enters the target storage location.

[0154] As an example, the system obtains the package size data of each package in the target package combination and the storage location specification data of the target storage location, and determines the corresponding placement order based on the size of each package.

[0155] In other words, the system prioritizes larger packages as the base for placement, and then sequentially determines smaller packages so that subsequent packages can utilize the remaining space. For example, for a target package combination consisting of large cardboard boxes and thin bags, the placement order of the large cardboard boxes can be determined first, followed by the placement order of the thin bags; for a target package combination consisting of multiple cardboard boxes, the larger cardboard boxes can be determined first, followed by the smaller cardboard boxes.

[0156] For example, in a target package consisting of one medium-sized cardboard box and four medium-sized thin bags, the medium-sized cardboard box can be determined as the first item to be placed, and then the placement order of the four medium-sized thin bags can be determined in turn.

[0157] S403, determine the arrangement position of each package in the target storage location according to the placement order.

[0158] The arrangement position represents the specific storage area of ​​each package within the target storage location. For example, the system determines the arrangement position of each package within the target storage location based on the placement order, combined with the length, width, and height of the storage location.

[0159] For example, the system first determines the area occupied by the first object in the target storage location based on its size data; then it determines the placement of the next package based on the remaining space and continuously updates the remaining available space until the placement of all packages in the target package combination is determined.

[0160] For example, for a target parcel combination consisting of one medium-sized carton and four medium-sized thin bags, the medium-sized carton can be placed in the main area of ​​the target storage location, and the four medium-sized thin bags can be placed in the remaining space around the medium-sized carton.

[0161] For a target package consisting of two large cardboard boxes and two medium-thin bags, the two large cardboard boxes can be placed in the main area of ​​the target storage location, and the two medium-thin bags can be placed in the remaining space area formed by the large cardboard boxes.

[0162] S404 generates a parcel combination shelving plan corresponding to the target storage location based on the target parcel combination, placement order, and layout position.

[0163] As an example, the system determines the arrangement of each package in the target storage location based on the placement order, combined with the length, width, and height of the storage location.

[0164] Specifically, the system first determines the area occupied by the first object in the target storage location based on its size data; then it determines the placement of the next package based on the remaining space and continuously updates the remaining available space until the placement of all packages in the target package combination is determined.

[0165] For example, for a target package consisting of one medium-sized cardboard box and four medium-sized thin bags, the medium-sized cardboard box can be placed in the main area of ​​the target storage location, and the four medium-sized thin bags can be placed in the remaining space around the medium-sized cardboard box.

[0166] For a target package consisting of two large cardboard boxes and two medium-thin bags, the two large cardboard boxes can be placed in the main area of ​​the target storage location, and the two medium-thin bags can be placed in the remaining space area formed by the large cardboard boxes.

[0167] In one implementation of this embodiment, such as Figure 4 As shown in the embodiment of this application, the campus express delivery sorting method further includes the following steps:

[0168] Step S500: Generate a robot scheduling task based on the package combination shelving plan, and send the robot scheduling task to the robot to perform the shelving operation.

[0169] The robot scheduling task represents the task data required for the robot to perform the parcel shelving operation. For example, the system generates a corresponding robot scheduling task based on the target parcel combination, target storage location, placement order, and arrangement position in the parcel combination shelving plan. The robot scheduling task includes data such as parcel information to be shelved, target storage location information, parcel placement order, and parcel arrangement position.

[0170] For example, for small parcels, the system can send robot scheduling tasks to the cluster inbound robot and the automated warehouse inbound robot. The cluster inbound robot will then transport the target parcels to the loading platform of the inbound system, and the automated warehouse inbound robot will place the target parcels into the target storage location according to the parcel combination and shelving plan.

[0171] For large packages, the system generates a large-item handling scheduling task based on the package attribute data and large-item storage location information, and sends the task to the SLAM-AMR robot. After receiving the large-item handling scheduling task, the SLAM-AMR robot moves the corresponding large package to the designated large-item storage location and reports the task execution result back to the system after completing the shelving.

[0172] Step S600: Receive the task execution results from the robot, and update the shelving status of each package in the target package combination and the occupancy status of the target storage location based on the task execution results.

[0173] The task execution result represents the robot's feedback on the robot's scheduled tasks. For example, after completing package transfer, package delivery, or large item shelving and binding, the robot sends the corresponding task execution result back to the system. Upon receiving the result, the system updates the shelving status of each package in the target package combination and the occupancy status of the target storage location based on the feedback.

[0174] When the task execution result indicates that the package has been successfully put on the shelf, the system updates the shelf status of the corresponding package in the target package combination to "put on shelf" and updates the occupancy status of the target storage location to "occupied".

[0175] When the task execution result indicates an abnormality in the shelving process, the system will re-determine the available storage location or regenerate the corresponding robot scheduling task based on the abnormality feedback.

[0176] For example, when the robot reports that a target storage location is already occupied, the system releases the current target storage location matching result and re-matches an available storage location;

[0177] When the robot reports that the target package combination has been successfully delivered to the target storage location, the system updates the status of the target storage location to occupied and saves the correspondence between the packages and the target storage location.

[0178] In one implementation of this embodiment, the campus express delivery sorting method provided in this application embodiment can be specifically described through the following example:

[0179] The campus express delivery center is equipped with dedicated storage spaces for large items. The dimensions of each storage space are: length (L) 1200mm, width (W) 800mm, and height (H) 1000mm. After obtaining the storage space specifications, the system calculates the rated volume of the storage space as 1200 × 800 × 1000 = 960,000,000 mm³, or 960,000 cm³. Based on a preset safety factor of 95%, the system calculates the volume constraint data, resulting in 960,000 × 95% = 912,000 cm³. The system can also generate corresponding length, width, and height classification thresholds based on the storage space's length, width, and height. The length classification threshold is 1200 × 80% = 960mm, the width classification threshold is 800 × 80% = 640mm, and the height classification threshold is 1000 × 80% = 800mm.

[0180] After a package enters the measurement and labeling conveyor line, the system acquires its attribute data. One package is a suitcase with dimensions of 850mm × 550mm × 350mm and a weight of 12kg. The system compares the suitcase's length, width, and height with the corresponding length, width, and height classification thresholds for the large item storage location. Since 850mm ≤ 960mm, 550mm ≤ 640mm, and 350mm ≤ 800mm, the system determines that the suitcase meets the storage conditions for the large item storage location and generates the corresponding large item package classification result and large item package label data.

[0181] Based on the classification results of large packages, their attribute data, and the status data of their designated storage locations, the system determines the target large package storage location and generates a corresponding robot scheduling task. The robot scheduling task includes the large package identifier, target large package storage location information, package size data, and handling task information. The suitcase is assigned to storage location 05 on the 3rd floor of the large package area, and the corresponding robot scheduling task is sent to the SLAM-AMR robot.

[0182] After receiving a robot dispatch task, the SLAM-AMR robot moves to the end of the measurement and labeling conveyor line to retrieve the corresponding large package and transports it to the target large item storage location. Upon arrival, the SLAM-AMR robot confirms the target large item storage location information by scanning the location's QR code and places the suitcase there. After placement, the SLAM-AMR robot scans the package identifier and the storage location identifier to generate a binding result between the package and the target large item storage location, and then reports the task execution result back to the system.

[0183] After receiving the task execution results from the SLAM-AMR robot, the system updates the shelving status of the large package and the occupancy status of the target large item storage location based on the task execution results. When the task execution result indicates successful shelving, the system updates the shelving status of the large package to "Shelved" and the occupancy status of storage location 05 on the 3rd floor of the large item area to "Occupied." At the same time, the system generates corresponding pickup information based on the package identifier and the target large item storage location information for pickup verification.

[0184] In one implementation of this embodiment, the campus express delivery sorting method provided in this application embodiment can be specifically described through the following other example:

[0185] In this embodiment, the campus express delivery center is equipped with basic storage locations, with a storage location length L of 500mm, a storage location width W of 380mm, and a storage location height H of 400mm.

[0186] After obtaining the basic storage location's specifications, the system calculates the rated volume of the storage location based on its length, width, and height, resulting in a rated volume of 500 × 380 × 400 = 76,000,000 mm³, or 76,000 cm³. The system further calculates the storage location's volume constraint data based on the rated volume and a preset safety factor. When the preset safety factor is 95%, the storage location's volume constraint data is 76,000 × 95% = 72,200 cm³.

[0187] The system generates parcel length, width, and height classification thresholds based on the storage location's length, width, and height, respectively. Using 80% of the storage location's specifications as the classification benchmark, the parcel length threshold is 500 × 80% = 400 mm, the parcel width threshold is 380 × 80% = 304 mm, and the parcel height threshold is 400 × 80% = 320 mm. The system uses these three thresholds as the parcel classification threshold data.

[0188] After packages enter the measurement and labeling conveyor line, the system acquires their attribute data. For packages measuring 350mm × 250mm × 200mm, the system compares their length, width, and height with corresponding package classification thresholds. Since 350mm ≤ 400mm, 250mm ≤ 304mm, and 200mm ≤ 320mm, the system classifies the package as a small package. For packages measuring 450mm × 350mm × 300mm, because their length exceeds 400mm and their width exceeds 304mm, the system classifies them as large packages.

[0189] For packages identified as small parcels awaiting sorting, the system further determines their shape category based on thickness data. If a small parcel measures 300mm × 220mm and is 40mm thick, since the thickness does not exceed 50mm, the system classifies it as a thin-bag parcel. Subsequently, the system determines its volume category based on the parcel's dimensions and the matching results of multiple preset size ranges. Because the parcel's dimensions meet the size range corresponding to medium-thin bags, the system generates a detailed classification result for the parcel as Small Parcel – Medium-Thin Bag. If another small parcel measures 350mm × 250mm × 200mm, the system classifies it as Small Parcel – Medium Carton based on its dimensions.

[0190] After obtaining the parcel classification results, the system performs combination matching processing on multiple parcels to be sorted based on the parcel classification results and the storage location volume constraints. The system filters candidate parcels for combination matching based on the parcel classification results, sorts the candidate parcels according to their volume, and generates candidate parcel combinations. For example, a candidate parcel combination consisting of one medium-sized cardboard box and four medium-sized thin bags is generated, and the volume of this candidate parcel combination is verified according to the storage location volume constraints. The total volume of this candidate parcel combination is approximately 17500cm³ + 4 × 2640cm³ = 28060cm³, which does not exceed 72200cm³, therefore the system determines this candidate parcel combination as the target parcel combination.

[0191] The system determines the target storage location based on the target package combination and generates a package combination shelving plan corresponding to the target storage location. Specifically, the system determines the placement order of each package within the target storage location based on the package size data and the storage location specifications, and then determines the arrangement position of each package within the target storage location based on the placement order. In this embodiment, the system prioritizes the placement of medium-sized cardboard boxes, followed by four medium-sized thin bags. Correspondingly, the system can place the medium-sized cardboard boxes in the main area of ​​the target storage location and arrange the four medium-sized thin bags in the remaining space around the medium-sized cardboard boxes. Subsequently, the system generates a package combination shelving plan corresponding to the target storage location based on the target package combination, placement order, and arrangement position.

[0192] After generating a parcel combination shelving plan, the system generates robot scheduling tasks based on the plan and sends these tasks to the robots responsible for performing the shelving operations. For the target parcel combination, the system can send the corresponding shelving task to the cluster inbound robot and the automated storage and retrieval system (AS / RS) inbound robot. The cluster inbound robot then transfers the target parcel combination to the inbound system's loading platform, and the AS / RS inbound robot places the target parcel combination into the target storage location according to the shelving plan. After completing the shelving operation, the robot reports the task execution result to the system. The system updates the shelving status of each parcel in the target parcel combination based on the task execution result and updates the occupancy status of the target storage location to "occupied."

[0193] The scope of protection of the campus express sorting method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0194] This application also provides a campus express sorting device, which can implement the campus express sorting method described in this application. However, the implementation device of the campus express sorting method described in this application includes, but is not limited to, the structure of the campus express sorting device listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0195] like Figure 5 As shown, this embodiment provides a campus express delivery sorting device. Exemplarily, the device 100 includes:

[0196] The data processing module 110 is used to acquire the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data.

[0197] The package classification module 120 is used to acquire package attribute data of packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result.

[0198] The combination matching module 130 is used to perform combination matching processing on multiple parcels to be sorted based on the parcel classification results and the cargo location volume constraint data to obtain a target parcel combination;

[0199] The planning and scheduling module 140 is used to determine the target storage location and generate a parcel combination shelving plan corresponding to the target storage location based on the target parcel combination.

[0200] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0201] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0202] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0203] This application also provides an electronic device. Figure 6 The diagram shown is a structural schematic of the electronic device described in an embodiment of this application. Figure 6 As shown, in this embodiment, the electronic device 200 includes a memory 210 and a processor 220.

[0204] The memory 210 is used to store computer programs; preferably, the memory 210 includes various media that can store program code, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0205] Specifically, memory 210 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory. Electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. Memory 210 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application. It is understood that memory 210 may be volatile memory or non-volatile memory, or both. Non-volatile memory may be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memories.

[0206] The processor 220 is connected to the memory 210 and is used to execute the computer program stored in the memory 210 so that the electronic device 200 performs the method described in any embodiment of this application.

[0207] Specifically, the processor 220 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0208] Specifically, in this embodiment, the electronic device 200 may further include a display 230. The display 230 is communicatively connected to the memory 210 and the processor 220, and is used to display the methods described in the embodiments of this application.

[0209] This application also provides a computer-readable storage medium. When executed by a processor, the program implements the methods described in any embodiment of this application. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0210] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0211] The descriptions of the processes or structures corresponding to the above-mentioned figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0212] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A campus express delivery sorting method, characterized in that, include: Obtain the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data; Obtain the package attribute data of the packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result; Based on the package classification results and the cargo space volume constraint data, multiple packages to be sorted are combined and matched to obtain the target package combination. Based on the target package combination, determine the target storage location and generate a package combination shelving plan corresponding to the target storage location.

2. The campus express delivery sorting method according to claim 1, characterized in that, The method further includes: A robot scheduling task is generated based on the package combination shelving plan, and the robot scheduling task is sent to the robot to perform the shelving operation; The robot receives the task execution results and updates the shelf status of each package in the target package combination and the occupancy status of the target storage space based on the task execution results.

3. The campus express delivery sorting method according to claim 1, characterized in that, The step of generating storage space volume constraint data and parcel classification threshold data based on the storage space specification data includes: Calculate the rated volume of the storage location based on the storage location length, width, and height in the storage location specification data; Calculate the cargo space volume constraint data based on the rated volume of the cargo space and the preset safety factor; Based on the storage location length, storage location width, and storage location height, respectively generate package length classification thresholds, package width classification thresholds, and package height classification thresholds, which are used as the package classification threshold data.

4. The campus express delivery sorting method according to claim 1, characterized in that, The step of classifying the packages to be sorted based on the package attribute data and the package classification threshold data to obtain package classification results includes: The package length, package width, and package height in the package attribute data are compared with the package classification threshold data respectively; When the package length, package width, and package height all do not exceed the corresponding package classification threshold data, the package to be sorted is identified as a small package; If any one of the package length, package width, or package height exceeds the corresponding package classification threshold data, the package to be sorted will be identified as a large package.

5. The campus express delivery sorting method according to claim 4, characterized in that, After determining that the package to be sorted is a small package, the following steps are included: The thickness data of the small package is compared with a preset thickness threshold to determine the shape category of the small package; Based on the matching results of the package size data of the small package with multiple preset size ranges, the package volume level of the small package is determined; Based on the package shape category and the package volume level, a detailed classification result for the small package is generated.

6. The campus express delivery sorting method according to claim 1, characterized in that, The process of combining and matching multiple packages to be sorted based on the package classification results and the cargo space volume constraints to obtain a target package combination includes: Candidate packages for combination matching are selected based on the package classification results; The candidate packages are sorted according to their volume, and a combination of candidate packages is generated based on the sorting results. The volume of the candidate package combination is verified based on the cargo space volume constraint data. Based on the volume verification results, the packages in the candidate package combination are added or removed to obtain the target package combination.

7. The campus express delivery sorting method according to claim 1, characterized in that, The process of determining the target storage location and generating a parcel combination shelving plan corresponding to the target storage location includes: The target cargo location is determined using the target package combination; Based on the package size data of each package in the target package combination and the storage location specification data of the target storage location, determine the placement order of each package in the target storage location; The arrangement position of each package in the target storage location is determined based on the placement order. Based on the target package combination, the placement order, and the arrangement position, a package combination shelving plan corresponding to the target storage location is generated.

8. A campus express delivery sorting device, characterized in that, include: The data processing module is used to acquire the storage location specification data of the campus express delivery center, and generate storage location volume constraint data and package classification threshold data based on the storage location specification data. The package classification module is used to acquire package attribute data of packages to be sorted, and classify the packages to be sorted according to the package attribute data and the package classification threshold data to obtain the package classification result. The combination matching module is used to perform combination matching processing on multiple parcels to be sorted based on the parcel classification results and the cargo location volume constraint data to obtain a target parcel combination; The planning and scheduling module is used to determine the target storage location and generate a shelf plan for the package combination corresponding to the target storage location based on the target package combination.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing a computer program stored in the memory to cause the electronic device to perform the campus parcel sorting method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the campus express delivery sorting method according to any one of claims 1 to 7.

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