Modular pcb magazine 3d storage system and method

Abstract

The application relates to the technical field of warehouse management, in particular to a modular PCB material box three-dimensional storage system and method, which comprises a to-be-warehoused acquisition module, a warehouse state monitoring module, a sequence generation module, a matching degree analysis module, a screening module and a sending module. The to-be-warehoused acquisition module is used for acquiring product information of multiple material boxes carried by an AGV intelligent carrier to obtain multiple to-be-warehoused product information. The warehouse state monitoring module is used for acquiring a storage position list in a current warehouse in real time. The sequence generation module is used for generating multiple warehousing sequences based on the multiple to-be-warehoused product information. The matching degree analysis module is used for analyzing the highest matching degree of each warehousing sequence and each storage cabinet in a storable state. The screening module is used for screening a warehousing sequence corresponding to the matching degree meeting a preset condition as a final warehousing sequence. The sending module is used for sending the final warehousing sequence to the AGV intelligent carrier. The AGV intelligent carrier warehouses multiple material boxes corresponding to the multiple to-be-warehoused product information according to the received warehousing sequence. The application has the effect of improving the warehousing efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of warehouse management, and in particular to a modular PCB bin three-dimensional storage system and method. Background Technology

[0002] In the current PCB bin storage management system, intelligent AGV (Automated Guided Vehicle) transport equipment serves as the core operating unit. Guided by system commands, it can autonomously navigate to the target shelf to accurately pick up bins, significantly improving material outbound efficiency. However, a key bottleneck has emerged in actual operation: due to the discrete storage characteristics of PCB bins of different specifications, a single outbound task often requires the AGV to move back and forth between multiple shelf locations. Especially in scenarios with large warehouse space spans, this multi-point picking mode leads to an exponential increase in the complexity of the AGV's operating trajectory, forming a key obstacle that restricts the improvement of overall outbound efficiency.

[0003] To address this issue, existing industry solutions primarily focus on two technical directions: first, shortening AGV travel distances by improving path optimization algorithms (such as introducing dynamic programming or reinforcement learning models); and second, scaling up by increasing the number of transport devices. However, practical experience shows that the former's algorithmic efficiency significantly declines when dealing with highly dynamic order demands and unstructured warehouse layouts, while the latter faces the dilemma of diminishing marginal returns—the non-linear increase in equipment purchase costs, operational complexity, and system coordination difficulties may lead to uncontrolled overall operating costs. This dual pressure of technical bottlenecks and cost constraints urgently requires more groundbreaking solutions. Summary of the Invention

[0004] To improve outbound efficiency, this application provides a modular PCB material box three-dimensional storage system and method.

[0005] The above-mentioned objective of this application is achieved through the following technical solution:

[0006] A modular PCB bin-based three-dimensional storage system includes:

[0007] The "Waiting to be Entered into the Warehouse" module is used to obtain product information of multiple material boxes carried by the AGV intelligent transport vehicle to be entered into the warehouse, thus obtaining multiple product information to be entered into the warehouse.

[0008] The warehouse status monitoring module is used to obtain the list of storage locations in the current warehouse in real time. The list of storage locations contains the location information of each location, including the cabinet where the location is located, the location coordinates, the location status, and the product information in the warehouse.

[0009] The sequence generation module is used to generate multiple inbound sequences based on information about multiple products to be inbound.

[0010] The matching degree analysis module is used to analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. The storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle.

[0011] The filtering module is used to filter out the sequences that meet the preset conditions and have a matching degree corresponding to the final sequence to be stored in the database.

[0012] The sending module is used to send the final inbound sequence to the AGV intelligent transport vehicle to be inbound;

[0013] The AGV intelligent transport vehicle puts multiple bins corresponding to the information of products to be put into storage into storage according to the received storage sequence.

[0014] In a preferred example, this application can be further configured as: a matching degree analysis module, including:

[0015] The sub-matching degree calculation submodule is used to calculate the sub-matching degree between the product information to be put into storage and the storage cabinets in each storage state according to the order of the inbound sequence, and write the storage cabinet with the highest sub-matching degree with the product information to be put into storage into the inbound sequence and associate it with the corresponding product information to be put into storage.

[0016] The calculation submodule is used to take the sum of the highest sub-matching degrees of each product information to be entered into the warehouse sequence as the highest matching degree.

[0017] In a preferred example, this application can be further configured as: a sub-matching degree calculation submodule, including:

[0018] The first set generation unit is used to form a set of product information of the storage cabinet in the storage state by taking the product information in each compartment of the storage cabinet in the storage state and the corresponding compartment coordinates as an element if there is no other product information to be stored before the sequence position of the product information to be stored.

[0019] The second set generation unit is used to match virtual warehouse coordinates for the product information to be received before the sequence position of the product information to be received if there are other product information to be received before the sequence position of the product information to be received. The virtual warehouse coordinates are the warehouse coordinates of the empty warehouse in the storage cabinet with the highest sub-matching degree with the product information to be received before the sequence position of the product information to be received. The unit also uses the product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates as one element, and the product information to be received before the sequence position of the product information to be received and the corresponding warehouse coordinates as one element to form the product information set of the storage cabinet with the storage status.

[0020] The matching degree calculation unit is used to analyze the matching degree between the product information to be put into storage and the product information set of each storage cabinet in each storage state based on the historical outbound record dataset of AGV intelligent transport vehicles in the database. This matching degree is used as the sub-matching degree between the product information to be put into storage and each storage cabinet in each storage state.

[0021] In a preferred embodiment, this application can be further configured as: a compatibility calculation unit, including:

[0022] The parameter acquisition subunit is used to obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; and the number m of historical outbound records containing the information of the products to be received in the historical outbound record dataset.

[0023] The subset generation sub-unit is used to take 'a' elements from the product information set of the storage cabinet in the storage state to obtain a product information subset. Iterate through and take 'a' elements to obtain multiple product information subsets. Any two product information subsets satisfy the following: one of the product information subsets contains an element whose product information or storage location coordinates are different from one of the elements in the other product information subset. The initial value of 'a' is 1.

[0024] Remove sub-units to remove warehouse coordinates from each element of the product information subset to obtain the processed subset;

[0025] The statistics subunit is used to count the occurrence frequency F(a) of each processing subset. The occurrence frequency is obtained by counting as follows: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1.

[0026] The sub-fitness calculation sub-unit is used to calculate Q(a)·F(a) / m to obtain the sub-fitness P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state. Q(a) is a weight value with respect to a. The larger the value of a, the larger the value of Q(a).

[0027] Return to the sub-unit, which is used to assign a+1 to a if a+1 is less than n, and return to the subset generation module;

[0028] The matching degree calculation subunit is used to calculate the matching degree between the sum of P(1) to P(a) as the product information to be put into storage and the product information set of the storage cabinet in the storage state.

[0029] The second objective of this invention is achieved through the following technical solution:

[0030] A modular PCB material box three-dimensional storage method includes:

[0031] The product information of multiple material boxes carried by the AGV intelligent transport vehicle to be put into the warehouse is obtained to obtain multiple product information to be put into the warehouse.

[0032] The system can obtain a list of storage locations in the current warehouse in real time. The list of storage locations includes information about each location, such as the cabinet where the location is located, the location coordinates, the location status, and the product information inside the warehouse.

[0033] Multiple inbound sequences are generated based on information about multiple products to be inbound;

[0034] Analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. The storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle.

[0035] Select the sequences that meet the preset conditions and have the corresponding matching degree as the final sequences to be stored in the database;

[0036] The final inbound sequence is sent to the AGV intelligent transport vehicle to be inbound.

[0037] In a preferred example, this application can be further configured to: analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state, including:

[0038] According to the order of the inbound sequence, calculate the sub-matching degree between the information of the product to be inbound and the storage cabinets of each storage status, and write the storage cabinet with the highest sub-matching degree with the information of the product to be inbound into the inbound sequence and associate it with the corresponding information of the product to be inbound.

[0039] The highest matching degree is the sum of the highest sub-matching degrees of each product information to be added to the warehouse in the warehouse sequence.

[0040] In a preferred embodiment, this application can be further configured to: calculate the sub-matching degree between the product information to be received and the storage cabinets of each storage status, including:

[0041] If there are no other products to be stored before the sequence number of the products to be stored, then the product information in each compartment of the storage cabinet in the storage state and the corresponding compartment coordinates are taken as an element to form the product information set of the storage cabinet in the storage state.

[0042] If there are other products to be received before the sequence number of the products to be received, then virtual warehouse coordinates are matched for the products to be received before the sequence number of the products to be received. The virtual warehouse coordinates are the coordinates of the empty warehouse in the storage cabinet with the highest sub-match degree with the products to be received before the sequence number of the products to be received. The product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates are taken as one element, and the products to be received before the sequence number of the products to be received and the corresponding warehouse coordinates are taken as one element to form the product information set of the storage cabinet with the storage status.

[0043] The matching degree between the information of products to be put into storage and the product information set of each storage cabinet in each storage state is analyzed based on the historical outbound records of AGV intelligent transport vehicles in the database. This matching degree is used as the sub-matching degree between the information of products to be put into storage and the storage cabinet in each storage state.

[0044] In a preferred embodiment, this application can be further configured to: analyze the compatibility between the information of products to be received and the set of product information for each storage cabinet in a given storage state, based on the historical outbound records dataset of AGV intelligent transport vehicles in the database, including:

[0045] S4131. Obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; the number m of historical outbound records containing the information of the products to be received in the historical outbound record dataset;

[0046] S4132. Take a elements from the product information set of the storage cabinets in the storage state to obtain a product information subset. Iterate through the storage cabinets and take a elements to obtain multiple product information subsets. Any two product information subsets satisfy the following: one of the product information subsets contains an element whose product information or storage location coordinates are different from one of the elements in the other product information subset. The initial value of a is 1.

[0047] S4133. Remove the warehouse coordinates from each element of the product information subset to obtain the processed subset;

[0048] S4134. Count the occurrence frequency F(a) of each processing subset. The occurrence frequency is obtained by counting as follows: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1.

[0049] S4135. Calculate Q(a)·F(a) / m to obtain the sub-cooperation degree P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state. Q(a) is the weight value of a. The larger the value of a, the larger the value of Q(a).

[0050] S4136. If a+1 is less than n, then assign a+1 to a and return to S4132.

[0051] S4137. Calculate the sum of P(1) to P(a) as the degree of fit between the product information to be put into storage and the product information set of the storage cabinet in the storage state.

[0052] In summary, this application includes at least one of the following beneficial technical effects:

[0053] 1. During subsequent outbound operations, by comprehensively considering factors such as shelf layout, PCB bin specifications, and outbound requirements, the AGV vehicle's picking path is intelligently optimized and dynamically adjusted. Overall, more bins can be retrieved from one storage cabinet, thereby effectively reducing the AGV vehicle's travel distance and time, and improving outbound efficiency.

[0054] 2. Adding the Q(a) weight value can better reflect the related outbound situation, improve the accuracy of calculation, and improve subsequent outbound efficiency;

[0055] 3. By including the information of products to be put into storage that are ranked higher in the order of their rank within the calculation scope of the information of products to be put into storage that are ranked lower in the order of their rank, the accuracy of the calculation can be further improved. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the connection of each module of the modular PCB bin three-dimensional storage system in one embodiment of this application;

[0057] Figure 2 This is a flowchart illustrating the implementation of a modular PCB bin three-dimensional storage method in one embodiment of this application. Detailed Implementation

[0058] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0059] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this disclosure.

[0060] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0061] Figure 1 This is a schematic diagram of the module connections of a modular PCB bin three-dimensional storage system according to an embodiment of this application. The modular PCB bin three-dimensional storage system includes a waiting-to-be-entered module, a warehouse status monitoring module, a sequence generation module, a matching degree analysis module, a filtering module, and a sending module. The waiting-to-be-entered module, warehouse status monitoring module, sequence generation module, matching degree analysis module, filtering module, and sending module are connected for communication. Specifically, wireless communication is preferred, such as LoRa technology, Wi-Fi / IEEE 802.11 protocol, ZigBee / 802.15.4 protocol, Thread / IEEE802.15.4, Z-Wave protocol, etc.

[0062] Understandably, the warehouse has multiple storage cabinets. AGV intelligent transport vehicles carry several boxes and automatically navigate to the corresponding storage cabinets for loading and unloading. The warehouse status monitoring module includes identifiers installed in each storage location to scan barcodes or QR codes on the outer wall of the boxes, obtaining storage location information. This information includes the cabinet where the storage location is located, its coordinates, status, and product information. The storage cabinet and the identifier within the storage location are pre-associated. The storage location coordinates include the horizontal and vertical axes, and the status is "present" or "absent." Product information includes product name, model, and dimensions. This product information can be set by management personnel, such as its intended use, without specific limitations. After packing the PCB boards, the packing personnel write the corresponding product information into the barcode or QR code.

[0063] The system has a historical outbound record dataset, which stores the historical outbound records of AGV intelligent transport vehicles. The historical outbound records contain product information of multiple bins carried by the AGV intelligent transport vehicle in a single outbound trip, as well as the outbound timestamp.

[0064] The pending warehousing acquisition module is used to acquire product information of multiple bins carried by the AGV intelligent transport vehicle to obtain multiple pending warehousing product information; the pending warehousing acquisition module can be multiple barcode scanners installed on the AGV intelligent vehicle, which scan the barcodes or QR codes on the bins to identify the product information of the bins and transmit it to the system as multiple pending warehousing product information.

[0065] The sequence generation module is used to generate multiple inbound sequences based on multiple product information to be inbound; specifically, it randomly arranges multiple product information to be inbound and iterates through all arrangements to obtain multiple inbound sequences.

[0066] The matching degree analysis module is used to analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. A storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle.

[0067] Specifically, it includes a sub-matching degree calculation submodule and a calculation submodule. The sub-matching degree calculation submodule is used to calculate the sub-matching degree between the product information to be put into storage and the storage cabinets of each storage status according to the order of the inbound sequence, and write the storage cabinet with the highest sub-matching degree with the product information to be put into storage into the inbound sequence and associate it with the corresponding product information to be put into storage.

[0068] It is understandable that calculating the sub-matching degree between the information of a product to be put into storage and the storage cabinets in each storage state means that the sub-matching degree is calculated once for each piece of information of a product to be put into storage and each storage cabinet in each storage state, and then the sub-matching degree is calculated for the next piece of information of a product to be put into storage.

[0069] The sum of the highest sub-matching degrees of each product information to be added to the warehouse in the inbound sequence is used as the highest matching degree.

[0070] The sub-matching degree calculation submodule specifically includes: a first set generation unit, a second set generation unit, and a matching degree calculation unit. The first set generation unit is used to form a set of product information for a storage cabinet in a storable state by taking the product information in each compartment of the storage cabinet and the corresponding compartment coordinates as an element if there is no other product information to be stored before the sequence position of the product information to be stored.

[0071] The second set generation unit is used to match virtual warehouse coordinates for the product information to be received before the sequence position of the product information to be received if there are other product information to be received before the sequence position of the product information to be received. The virtual warehouse coordinates are the coordinates of the empty warehouse in the storage cabinet with the highest sub-match degree with the product information to be received before the sequence position of the product information to be received. The product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates are taken as one element, and the product information to be received before the sequence position of the product information to be received and the corresponding warehouse coordinates are taken as one element to form the product information set of the storage cabinet with the storage status.

[0072] It is understandable that by including the information of products to be put into storage before the order of the product in the calculation scope of the information of products to be put into storage after the order of the product in ...

[0073] The matching degree calculation unit is used to analyze the matching degree between the product information to be put into storage and the product information set of storage cabinets in each storage state based on the historical outbound record dataset of AGV intelligent transport vehicles in the database. This matching degree is used as the sub-matching degree between the product information to be put into storage and each storage cabinet in each storage state.

[0074] The compatibility calculation unit specifically includes a parameter acquisition subunit, a subset generation subunit, a removal subunit, a statistics subunit, a subcompatibility calculation subunit, a return subunit, and a compatibility calculation subunit.

[0075] Among them, the parameter acquisition subunit is used to obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; and the number m of historical outbound records containing information on products to be received in the historical outbound record dataset.

[0076] In one embodiment, the number of fully loaded containers n is 4, and the number m of historical outbound records of the products to be put into storage varies with the different and updated historical outbound record datasets.

[0077] The subset generation subunit is used to take 'a' elements from the product information set of the storage cabinet in the storage state to obtain a product information subset, and then iterate through the set to take 'a' elements to obtain multiple product information subsets.

[0078] The traversal refers to iterating through all distinct subsets of product information. Distinct subsets are defined as any two subsets satisfying the following condition: one subset contains an element whose product information or warehouse coordinates differ from an element in the other subset. The initial value of 'a' is 1. In other words, identical product information but different warehouse coordinates also constitute distinct subsets. This incorporates the number of identical product information items in the storage cabinet, serving as one of the standards for improving coordination. It's understandable that if identical product information exists, after one item is shipped out, another identical item may still be shipped out along with the item to be received, thus also receiving weight.

[0079] The sub-unit removal function is used to remove warehouse coordinates from each element of the product information subset to obtain the processed subset.

[0080] The statistical subunit is used to count the occurrence frequency F(a) of each processing subset. The occurrence frequency is obtained by counting as follows: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1.

[0081] The sub-fitness calculation sub-unit is used to calculate Q(a)·F(a) / m to obtain the sub-fitness P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state.

[0082] It is worth noting that Q(a) is a weight value for a; the larger the value of a, the larger the value of Q(a). In one embodiment, Q(a) and a can be directly proportional or equal. For example, when a is 2, Q(a) is also 2; when a is 4, Q(a) is also 4. Alternatively, when a is 1, Q(a) can be 100%; when a is 2, Q(a) can be 130%; when a is 3, Q(a) can be 160%; and when a is 4, Q(a) can be 180%, increasing in a stepwise manner. This can also be a non-linear increase, primarily reflecting that a higher weight value for items shipped in the same batch increases the probability of them being shipped together in subsequent shipments, thus improving shipping efficiency. Including the weight value of Q(a) further reflects the related shipping situation, improves the accuracy of the calculation, and increases subsequent shipping efficiency.

[0083] The returned subunit is used to assign a+1 to a if a+1 is less than n, and then return to the subset generation module; in this way, the values ​​of Q(1) to Q(a) and P(1) to P(a) can be calculated.

[0084] The matching degree calculation subunit is used to calculate the sum of P(1) to P(a) as the matching degree between the product information to be put into storage and the product information set of the storage cabinet in the storage state.

[0085] In this way, the matching degree between a product information to be put into storage and a set of product information of a storage cabinet in a storage state can be calculated. By repeating the above method, the matching degree between a product information to be put into storage and the set of product information of each storage cabinet in a storage state can be obtained.

[0086] After obtaining the matching degree between a product information to be put into storage and the product information set of each storage cabinet in a storage state, the sub-matching degree between the product information to be put into storage and the product information set of each storage cabinet in a storage state is obtained. Then, the highest sub-matching degree of the product information to be put into storage, and the corresponding storage cabinet, can be obtained and written into the storage sequence. Then, the calculation submodule takes the sum of the highest sub-matching degrees of each product information to be put into storage in the storage sequence as the highest matching degree of the storage sequence.

[0087] The filtering module is used to filter out the entry sequence that meets the preset conditions and the corresponding matching degree as the final entry sequence; specifically, the entry sequence with the highest matching degree among multiple entry sequences is the one that meets the preset conditions, and this entry sequence is used as the final entry sequence, and the information of each product to be entered in the final entry sequence is written into the corresponding storage cabinet.

[0088] The sending module sends the final inbound sequence to the AGV intelligent transport vehicle waiting to be inbound. The AGV intelligent transport vehicle puts the material boxes corresponding to multiple products to be inbound into the corresponding storage cabinets according to the received inbound sequence, thus realizing the inbound process. In the subsequent outbound process, by comprehensively considering factors such as shelf layout, PCB material box specifications, and outbound requirements, the AGV vehicle's picking path is intelligently optimized and dynamically adjusted. Overall, more material boxes can be picked up from one storage cabinet, thereby effectively reducing the AGV vehicle's travel distance and time and improving outbound efficiency.

[0089] This application also provides a modular PCB material box three-dimensional storage method, as shown in the reference. Figure 2 ,include:

[0090] S1. Obtain product information for multiple material boxes carried by the AGV intelligent transport vehicle to be put into storage.

[0091] S2. Obtain the list of storage locations in the current warehouse in real time. The list of storage locations includes the location information of each location, including the cabinet where the location is located, the location coordinates, the location status, and the product information in the warehouse.

[0092] S3. Generate multiple inbound sequences based on multiple product information to be inbound.

[0093] S4. Analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. A storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle.

[0094] S5. Select the sequences that meet the preset conditions and have the corresponding matching degree as the final sequences to be stored in the database.

[0095] S6. Send the final inbound sequence to the AGV intelligent transport vehicle to be inbound.

[0096] S4 includes:

[0097] S41. According to the order of the inbound sequence, calculate the sub-matching degree between the product information to be inbound and the storage cabinets in each storage state, and write the storage cabinet with the highest sub-matching degree with the product information to be inbound into the inbound sequence and associate it with the corresponding product information to be inbound.

[0098] S42. The sum of the highest sub-matching degrees of each product information to be entered into the warehouse in the warehouse sequence is taken as the highest matching degree.

[0099] S41 includes:

[0100] S411. If there are no other products to be stored before the sequence number of the products to be stored, then the product information in each compartment of the storage cabinet in the storage state and the corresponding compartment coordinates are taken as an element to form the product information set of the storage cabinet in the storage state.

[0101] S412. If there are other products to be received before the sequence number of the products to be received, then virtual warehouse coordinates are matched for the products to be received before the sequence number of the products to be received. The virtual warehouse coordinates are the coordinates of the empty warehouse in the storage cabinet with the highest sub-matching degree with the products to be received before the sequence number of the products to be received. The product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates are taken as one element, and the products to be received before the sequence number of the products to be received and the corresponding warehouse coordinates are taken as one element to form the product information set of the storage cabinet with the storage status.

[0102] S413. Analyze the matching degree between the information of products to be put into storage and the product information set of each storage cabinet in each storage state based on the historical outbound record dataset of AGV intelligent transport vehicles in the database. This is used as the sub-matching degree between the information of products to be put into storage and each storage cabinet in each storage state.

[0103] S413 includes:

[0104] S4131. Obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; the number m of historical outbound records containing information on products to be received in the historical outbound record dataset.

[0105] S4132. Take a elements from the product information set of the storage cabinets in the storage state to obtain a product information subset. Iterate through the storage cabinets and take a elements to obtain multiple product information subsets. Any two product information subsets satisfy the following: one of the product information subsets contains an element whose product information or storage location coordinates are different from one of the elements in the other product information subset. The initial value of a is 1.

[0106] S4133. Remove the warehouse coordinates from each element of the product information subset to obtain the processed subset.

[0107] S4134. Count the frequency of occurrence F(a) of each processing subset. The frequency of occurrence is obtained by the following method: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1.

[0108] S4135. Calculate Q(a)·F(a) / m to obtain the sub-cooperation degree P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state. Q(a) is the weight value of a. The larger the value of a, the larger the value of Q(a).

[0109] S4136. If a+1 is less than n, then assign a+1 to a and return to S4132.

[0110] S4137. Calculate the sum of P(1) to P(a) as the degree of fit between the product information to be put into storage and the product information set of the storage cabinet in the storage state.

[0111] For specific limitations regarding the modular PCB bin three-dimensional storage method, please refer to the limitations of the modular PCB bin three-dimensional storage system mentioned above, which will not be repeated here. Each step of the above-described modular PCB bin three-dimensional storage method can be implemented entirely or partially through software, hardware, or a combination thereof.

[0112] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0114] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0115] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0116] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0117] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A modular PCB material bin three-dimensional storage system, characterized in that, include: The "Waiting to be Entered into the Warehouse" module is used to obtain product information of multiple material boxes carried by the AGV intelligent transport vehicle waiting to be entered into the warehouse, and obtain multiple product information waiting to be entered into the warehouse. The warehouse status monitoring module is used to obtain the list of storage locations in the current warehouse in real time. The list of storage locations contains the location information of each location, including the cabinet where the location is located, the location coordinates, the location status, and the product information inside the warehouse. The sequence generation module is used to generate multiple inbound sequences based on information about multiple products to be inbound. The matching degree analysis module is used to analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. The storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle. The filtering module is used to filter out the sequences that meet the preset conditions and have a matching degree corresponding to the final sequence to be stored in the database. The sending module is used to send the final inbound sequence to the AGV intelligent transport vehicle to be inbound; The AGV intelligent transport vehicle puts multiple bins corresponding to the information of products to be put into storage into storage according to the received storage sequence; The matching degree analysis module includes: The sub-matching degree calculation submodule is used to calculate the sub-matching degree between the product information to be put into storage and the storage cabinets in each storage state according to the order of the inbound sequence, and write the storage cabinet with the highest sub-matching degree with the product information to be put into storage into the inbound sequence and associate it with the corresponding product information to be put into storage. The calculation submodule is used to take the sum of the highest sub-matching degrees of each product information to be added to the warehouse in the inbound sequence as the highest matching degree; the sub-matching degree calculation submodule includes: The first set generation unit is used to form a set of product information of the storage cabinet in the storage state by taking the product information in each compartment of the storage cabinet in the storage state and the corresponding compartment coordinates as an element if there is no other product information to be stored before the sequence position of the product information to be stored. The second set generation unit is used to match virtual warehouse coordinates for the product information to be received before the sequence position of the product information to be received if there are other product information to be received before the sequence position of the product information to be received. The virtual warehouse coordinates are the warehouse coordinates of the empty warehouse in the storage cabinet with the highest sub-matching degree with the product information to be received before the sequence position of the product information to be received. The unit also uses the product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates as one element, and the product information to be received before the sequence position of the product information to be received and the corresponding warehouse coordinates as one element to form the product information set of the storage cabinet with the storage status. The matching degree calculation unit is used to analyze the matching degree between the product information to be put into storage and the product information set of each storage cabinet in each storage state based on the historical outbound record dataset of AGV intelligent transport vehicles in the database. This matching degree is used as the sub-matching degree between the product information to be put into storage and each storage cabinet in each storage state.

2. The modular PCB material bin three-dimensional storage system as described in claim 1, characterized in that, The compatibility calculation unit includes: The parameter acquisition subunit is used to obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; and the number m of historical outbound records containing the information of the products to be received in the historical outbound record dataset. The subset generation sub-unit is used to take 'a' elements from the product information set of the storage cabinet in the storage state to obtain a product information subset. Iterate through and take 'a' elements to obtain multiple product information subsets. Any two product information subsets satisfy the following: one of the product information subsets contains an element whose product information or storage location coordinates are different from one of the elements in the other product information subset. The initial value of 'a' is 1. Remove sub-units to remove warehouse coordinates from each element of the product information subset to obtain the processed subset; The statistics subunit is used to count the occurrence frequency F(a) of each processing subset. The occurrence frequency is obtained by counting as follows: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1. The sub-fitness calculation sub-unit is used to calculate Q(a)·F(a) / m to obtain the sub-fitness P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state. Q(a) is a weight value with respect to a. The larger the value of a, the larger the value of Q(a). Return to the sub-unit, which is used to assign a+1 to a if a+1 is less than n, and return to the subset to generate the sub-unit; The matching degree calculation subunit is used to calculate the matching degree between the sum of P(1) to P(a) as the product information to be put into storage and the product information set of the storage cabinet in the storage state.

3. A modular PCB bin three-dimensional storage method based on the modular PCB bin three-dimensional storage system according to any one of claims 1-2, characterized in that, include: The product information of multiple material boxes carried by the AGV intelligent transport vehicle to be put into the warehouse is obtained to obtain multiple product information to be put into the warehouse. The system can obtain a list of storage locations in the current warehouse in real time. The list of storage locations includes information about each location, such as the cabinet where the location is located, the location coordinates, the location status, and the product information inside the warehouse. Multiple inbound sequences are generated based on information about multiple products to be inbound; Analyze the highest matching degree between each inbound sequence and each storage cabinet in a storable state. The storage cabinet in a storable state refers to a storage cabinet whose number of empty storage locations is not less than the number of material boxes carried by the AGV intelligent transport vehicle. Select the sequences that meet the preset conditions and have the corresponding matching degree as the final sequences to be stored in the database; The final inbound sequence is sent to the AGV intelligent transport vehicle to be inbound; wherein, the highest matching degree between each inbound sequence and each storage cabinet in each storage state is analyzed, including: According to the order of the inbound sequence, calculate the sub-matching degree between the information of the product to be inbound and the storage cabinets of each storage status, and write the storage cabinet with the highest sub-matching degree with the information of the product to be inbound into the inbound sequence and associate it with the corresponding information of the product to be inbound. The sum of the highest sub-matching degrees of each product information to be stored in the storage sequence is taken as the highest matching degree; wherein, calculating the sub-matching degree between the product information to be stored and each storage cabinet in a storable state includes: If there are no other products to be stored before the sequence number of the products to be stored, then the product information in each compartment of the storage cabinet in the storage state and the corresponding compartment coordinates are taken as an element to form the product information set of the storage cabinet in the storage state. If there are other products to be received before the sequence number of the products to be received, then virtual warehouse coordinates are matched for the products to be received before the sequence number of the products to be received. The virtual warehouse coordinates are the coordinates of the empty warehouse in the storage cabinet with the highest sub-match degree with the products to be received before the sequence number of the products to be received. The product information in each warehouse of the storage cabinet with the storage status and the corresponding warehouse coordinates are taken as one element, and the products to be received before the sequence number of the products to be received and the corresponding warehouse coordinates are taken as one element to form the product information set of the storage cabinet with the storage status. The matching degree between the information of products to be put into storage and the product information set of each storage cabinet in each storage state is analyzed based on the historical outbound records of AGV intelligent transport vehicles in the database. This matching degree is used as the sub-matching degree between the information of products to be put into storage and the storage cabinet in each storage state.

4. The modular PCB material box three-dimensional storage method as described in claim 3, characterized in that, Based on the historical outbound records of AGV intelligent transport vehicles in the database, we analyze the degree of matching between the information of products to be received and the product information sets of storage cabinets in various storage states, including: S4131. Obtain the number of fully loaded boxes n of the AGV intelligent transport vehicle used for outbound delivery; the number m of historical outbound records containing the information of the products to be received in the historical outbound record dataset; S4132. Take a elements from the product information set of the storage cabinets in the storage state to obtain a product information subset. Iterate through the storage cabinets and take a elements to obtain multiple product information subsets. Any two product information subsets satisfy the following: one of the product information subsets contains an element whose product information or storage location coordinates are different from one of the elements in the other product information subset. The initial value of a is 1. S4133. Remove the warehouse coordinates from each element of the product information subset to obtain the processed subset; S4134. Count the occurrence frequency F(a) of each processing subset. The occurrence frequency is obtained by counting as follows: if the processing subset and the information of the product to be put into the warehouse coexist in a historical outbound record, then the count is 1. S4135. Calculate Q(a)·F(a) / m to obtain the sub-cooperation degree P(a) between the product information to be put into storage and the product information set of the storage cabinet in the storage state. Q(a) is the weight value of a. The larger the value of a, the larger the value of Q(a). S4136. If a+1 is less than n, then assign a+1 to a and return to S4132. S4137. Calculate the sum of P(1) to P(a) as the degree of fit between the product information to be put into storage and the product information set of the storage cabinet in the storage state.

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