Warehousing application integrated management platform system based on AI cloud computing

Abstract

The invention provides a storage application comprehensive management platform system based on AI cloud computing, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, determining a target storage scheme relationship network; secondly, extracting at least one first member link taking a specified storage article member as a starting relationship network member from the target storage scheme relationship network, and extracting at least one second member link taking a storage scheme member reflected by the undetermined storage scheme as the starting relationship network member; then, mining a relationship network member semantic vector corresponding to a specified storage article member, and a relationship network member semantic vector corresponding to each undetermined storage scheme; and finally, determining a target storage scheme based on the designated storage article members and the relationship network member semantic vector corresponding to each undetermined storage scheme. On the basis of the content, the problem that in the prior art, the reliability of warehouse management is relatively low can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a warehousing application integrated management platform system based on AI cloud computing. Background Art

[0002] Cloud computing is a technology that provides computing resources through the Internet, allowing users to access and use shared computing resources on demand, including servers, storage, databases, networks, software and analytical tools. It has changed the traditional IT infrastructure model, enabling enterprises and individuals to use technical resources at a lower cost, in a more flexible and efficient way. Among them, the application of cloud computing in warehousing can improve the convenience of warehousing monitoring, but for warehousing management, such as how to store items (i.e. determine the warehousing plan), there is still a relatively low reliability problem. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a warehouse application comprehensive management platform system based on AI cloud computing to improve the problem of relatively low reliability of warehouse management in the prior art.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: A warehousing application integrated management platform system based on AI cloud computing includes a plurality of warehousing front-end devices and a back-end cloud computing device in communication with the plurality of warehousing front-end devices, wherein the back-end cloud computing device is used to execute a warehousing application integrated management method based on AI cloud computing, wherein the warehousing application integrated management method based on AI cloud computing includes: Determine a target storage solution relationship network, and determine member description data corresponding to each relationship network member in the target storage solution relationship network, wherein the target storage solution relationship network includes multiple relationship network members, and relationship line segments are configured in the target storage solution relationship network for two relationship network members that are determined to have a relevant relationship, the multiple relationship network members include multiple storage item members reflecting storage items and multiple storage solution members reflecting pending storage solutions, and the multiple storage item members include designated storage item members reflecting designated storage items, the member description data corresponding to the storage item members are formed based on the storage front-end device collecting information on the storage items, and the member description data corresponding to the storage solution members are used to describe the storage solution; Extracting at least one first member link having the designated storage item member as a starting network member from the target storage solution relationship network, and extracting at least one second member link having the storage solution member reflected by the pending storage solution as a starting network member; Loading member description data corresponding to each first member link corresponding to the designated storage item member and each network member on the first member link, so that the data mining network mines out the network member semantic vector corresponding to the designated storage item member, and loading member description data corresponding to each second member link corresponding to each pending storage plan and each network member on the second member link, so that the data mining network mines out the network member semantic vector corresponding to each pending storage plan; Based on the relationship network member semantic vector corresponding to the designated storage item member and the relationship network member semantic vector corresponding to each of the pending storage plans, a target storage plan corresponding to the designated storage item is determined from multiple pending storage plans, wherein the target storage plan serves as the basis for storing the designated storage item.

[0005] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the step of determining the target warehousing solution corresponding to the designated warehousing item from a plurality of the pending warehousing solutions based on the relationship network member semantic vector corresponding to the designated warehousing item member and the relationship network member semantic vector corresponding to each of the pending warehousing solutions comprises: When all the relationship network members connected by the relationship line segments of the designated storage item member belong to the storage item members, each first member link of the designated storage item is marked as a first target member link, and the relationship network member semantic vector corresponding to the designated storage item member is converted by using a semantic conversion network, and the corresponding relationship network member conversion vector is output, and for each of the pending storage solutions, based on the relationship network member conversion vector and the relationship network member semantic vector corresponding to the pending storage solution, the storage matching parameters of the designated storage item and the pending storage solution are determined; When the relationship network members connected by the designated storage item member through the relationship line segment include storage solution members and storage item members, each first member link of the designated storage item is marked as including a first target member link and a second target member link, and for each pending storage solution, based on the relationship network member semantic vectors corresponding to the designated storage item and the pending storage solution, the storage matching parameters between the designated storage item and the pending storage solution are determined; Based on the storage matching parameters corresponding to each of the pending storage solutions, a target storage solution corresponding to the designated storage items is analyzed in each of the pending storage solutions.

[0006] In some preferred embodiments, in the above-mentioned warehouse application integrated management platform system based on AI cloud computing, the warehouse application integrated management method based on AI cloud computing also includes: Determine a training storage solution relationship network, and determine member description data corresponding to each relationship network member in the training storage solution relationship network, wherein the training storage solution relationship network includes a plurality of relationship network members, and relationship line segments are configured in the storage solution relationship network for two relationship network members that are determined to have a relevant relationship, the plurality of relationship network members include a plurality of storage item members reflecting storage items and a plurality of storage solution members reflecting pending storage solutions, the plurality of storage item members include at least one target storage item member, and the target storage item member is connected to at least one storage solution member and at least one other storage item member through relationship line segments respectively; In the training storage solution relationship network, at least one training relationship network member is determined, and for each training relationship network member, at least one member link that uses the training relationship network member as a starting relationship network member is extracted in the training storage solution relationship network, wherein a member link that connects relationship network members that are all storage item members is marked as a first target member link, and a member link that connects relationship network members that are all storage solution members is marked as a second target member link, the connecting relationship network members refer to relationship network members that have relationship segments with the starting relationship network members, the at least one training relationship network member includes at least one target storage item member, and each member link of the target storage item member includes at least one first target member link and at least one second target member link; Based on the member description data corresponding to the relationship network member on each member link of each training relationship network member, the candidate data mining network and the candidate semantic conversion network are updated at least once, and the corresponding data mining network and semantic conversion network are output, wherein the target update error of the update operation includes a first local update error corresponding to each target storage item member, and the first local update error is used to reflect the difference between the semantic vector output by the corresponding target storage item member in each update operation and the relationship network member conversion vector output by using the candidate semantic conversion network based on the first semantic vector corresponding to the corresponding target storage item member in each update operation. The output semantic vector belongs to the relationship network member semantic The candidate data mining network is used to load the member description data corresponding to the network member on each member link of the training network member, and mine the network member semantic vector corresponding to the training network member, and when each member link of the training network member includes at least one first target member link, the network member semantic vector includes the first semantic vector mined based on the member description data corresponding to the network member on each first target member link, and when each member link of the training network member includes at least one second target member link, the network member semantic vector includes the second semantic vector mined based on the member description data corresponding to the network member on each second target member link.

[0007] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the at least one training relationship network member also includes a warehousing solution member, and the target update error also includes a second local update error corresponding to each target storage item member, wherein in each update operation, the second local update error corresponding to each target storage item member is determined based on the following steps: In the training storage solution relationship network, storage solution members connected to the target storage item member through relationship line segments are determined and marked as related storage solution members, and storage solution members not connected to the target storage item member through relationship line segments are determined in the training storage solution relationship network and marked as non-related storage solution members; Polling each relevant storage solution member and each non-relevant storage solution member to form a corresponding plurality of storage solution member combinations, wherein each of the storage solution member combinations includes a relevant storage solution member and a non-relevant storage solution member; For each storage solution member combination, based on the relationship network member semantic vectors of the target storage item member and the related storage solution members in the storage solution member combination in the current update operation, determine the first storage matching parameter of the target storage item member and the related storage solution member, and based on the relationship network member semantic vectors of the target storage item member and the non-related storage solution members in the storage solution member combination in the current update operation, determine the second storage matching parameter of the target storage item member and the non-related storage solution member, and, based on the difference information between the first storage matching parameter and the second storage matching parameter, determine the storage solution matching sub-error of the target storage item member based on the storage solution member combination; Based on the storage solution matching sub-errors of the target storage item member for all storage solution member combinations, a second local update error of the target storage item member is determined.

[0008] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the target update error also includes a third local update error corresponding to each training relationship network member belonging to the warehouse item member, wherein the third local update error is determined based on the following steps: For each update operation, determine the set center semantic vector of the set center of each vector cluster set in the current update operation and the relationship network member semantic vector of each storage item member in the current update operation, wherein the set center semantic vector of the set center of each vector cluster set when clustering for the first time belongs to the pre-configured reference vector; For each storage item member, based on the relationship network member semantic vector of the storage item member in the current update operation and the collection center semantic vector of each collection center, determine the storage matching parameters that the storage item member has with each collection center respectively; Based on the storage matching parameters of each storage item member and each collection center, the probability of each storage item member being assigned to each vector clustering set is determined; For each storage item member, the third local update error of the storage item member is determined based on the difference information between the storage matching parameters respectively possessed by the storage item member and each collection center and the allocation probability of the storage item member being allocated to the corresponding vector clustering set.

[0009] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the step of determining the probability of each storage item member being assigned to each vector clustering set based on the storage matching parameters respectively possessed by each storage item member and each collection center includes: For each collection center, calculate the sum of the storage matching parameters of each training storage item member corresponding to the collection center, and obtain the clustering representation parameters of the vector clustering set corresponding to the collection center; For each storage item member, based on the storage matching parameters between the storage item member and a collection center, and in combination with the cluster representation parameters of the vector cluster set corresponding to the collection center, determine the collection contribution of the storage item member in the vector cluster set; For each storage item member, the ratio between the set contribution of the storage item member in a vector cluster set and the set contribution of the storage item member in all vector cluster sets is calculated to obtain the allocation probability of the storage item member to the vector cluster set.

[0010] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, each update operation further includes: For each storage item member, the set center of the storage matching parameter with the maximum value corresponding to the storage item member is determined as the target set center, and the vector cluster set corresponding to the target set center is marked, so that the vector cluster set is marked as the vector cluster set corresponding to the storage item member in the next update operation; For each vector cluster set corresponding to the next update operation, based on the target update error, the set center semantic vector of the set center corresponding to the vector cluster set is adjusted.

[0011] In some preferred embodiments, in the above-mentioned warehouse application integrated management platform system based on AI cloud computing, the first local update error corresponding to the target warehouse item member is determined based on the following steps: Based on the vector difference between the output semantic vectors of each collection center and the target stored item member in the current update operation, the collection center semantic vectors of each collection center are aggregated and calculated, and the corresponding first aggregated semantic vector is output, and based on the first aggregated semantic vector, a semantic enhancement operation is performed on the output semantic vector, and the output semantic vector after the semantic enhancement operation is output, and the output semantic vector after the semantic enhancement operation is marked so that it is marked as the storage item enhancement vector of the target stored item member in the current update operation; Based on the vector difference between the first semantic vectors of each collection center and the target storage item member in the current update operation, the collection center semantic vector of each collection center is aggregated and calculated, and the corresponding second aggregate semantic vector is output, and the first semantic vector is semantically enhanced based on the second aggregate semantic vector, and the first semantic vector after the semantic enhancement operation is output, and the first semantic vector after the semantic enhancement operation is converted using the candidate semantic conversion network, and the relationship network member conversion vector of the target storage item member in the current update operation is output; Based on the vector difference between the storage item reinforcement vector and the relationship network member conversion vector of the target storage item member in the current update operation, a first local update error corresponding to the target storage item member is determined.

[0012] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the multiple storage item members include at least one non-related storage item member, and the non-related storage item member refers to a storage item member that has no relationship line segment with a storage solution member; At least one training relationship network member also includes a non-related storage item member, the member links of the non-related storage item member all belong to the first target member link, and the relationship network member semantic vector of the non-related storage item member belongs to the first semantic vector.

[0013] In some preferred embodiments, in the above-mentioned warehousing application integrated management platform system based on AI cloud computing, the first semantic vector or the second semantic vector corresponding to each of the training relationship network members is determined based on the following steps: In each member link of the training relationship network member belonging to the initial relationship network member, each comparison member link is determined, wherein the comparison member link belongs to the first target member link or the second target member link; For each comparison member link, starting from the penultimate network member with the least correlation in the comparison member link, based on the member transfer vectors of each network member with less correlation connected to the network member and the member description data corresponding to the network member, the member transfer vectors are sequentially transferred to the starting network member to determine the member transfer vector of the starting network member on the comparison member link, wherein the member transfer vector of the network member with the least correlation is formed based on the member description data corresponding to the network member with the least correlation; The member transfer vectors of the training relationship network members on each comparison member link are subjected to an associative focusing fusion operation, and a semantic vector corresponding to the comparison member link of the training relationship network member is output, wherein when the comparison member link belongs to the first target member link, the semantic vector corresponding to the comparison member link belongs to the first semantic vector, and when the comparison member link belongs to the second target member link, the semantic vector corresponding to the comparison member link belongs to the second semantic vector.

[0014] The AI ​​cloud computing-based warehousing application integrated management platform system provided by the embodiment of the present invention first determines the target warehousing solution relationship network; secondly, extracts at least one first member link that uses the designated storage item member as the starting network member from the target warehousing solution relationship network, and extracts at least one second member link that uses the storage solution member reflected by the pending warehousing solution as the starting network member; then, mines out the network member semantic vector corresponding to the designated storage item member and the network member semantic vector corresponding to each pending warehousing solution; finally, determines the target warehousing solution based on the designated storage item member and the network member semantic vector corresponding to each pending warehousing solution. Based on the above content, since the network member semantic vector corresponding to the designated storage item member not only includes the semantic information of the member description data corresponding to the designated storage item member itself, but also includes the semantic information of the member description data corresponding to the network member on the first member link, and the network member semantic vector corresponding to the pending storage plan not only includes the semantic information of the member description data corresponding to the pending storage plan itself, but also includes the semantic information of the member description data corresponding to the network member on the second member link, the mined network member semantic vector has better semantic representation ability and richer semantic information, so that the solution determination based on the network member semantic vector can be more reliable, that is, it is guaranteed that the target storage plan has a higher reliability, thereby improving the problem of relatively low reliability of storage management in the prior art.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A structural block diagram of the warehousing application comprehensive management platform system based on AI cloud computing provided in an embodiment of the present invention.

[0017] Figure 2 A flowchart of the steps included in the comprehensive management method for warehousing applications based on AI cloud computing provided in an embodiment of the present invention.

[0018] Figure 3A schematic diagram of a target storage solution relationship network provided in an embodiment of the present invention.

[0019] Figure 4 A schematic diagram of a related process of a first local update error provided by an embodiment of the present invention.

[0020] Figure 5 A schematic diagram of a formation process of a member transfer vector provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, an embodiment of the present invention provides a warehousing application integrated management platform system based on AI cloud computing. The warehousing application integrated management platform system based on AI cloud computing may include multiple warehousing front-end devices (such as terminal devices) and back-end cloud computing devices (such as servers) that are communicatively connected to the multiple warehousing front-end devices.

[0024] In detail, the backend cloud computing device may include a memory and a processor. The memory and the processor are electrically connected directly or indirectly to achieve data transmission or interaction. For example, they may be electrically connected to each other through one or more communication buses or signal lines. The memory may store at least one software function module (computer program) that may exist in the form of software or firmware. The processor may be used to execute the executable computer program stored in the memory, thereby implementing the integrated management method for warehousing applications based on AI cloud computing provided in an embodiment of the present invention (as described later).

[0025] Optionally, the memory may be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may 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, discrete hardware components.

[0026] Combination Figure 2 The embodiment of the present invention also provides a method for integrated management of warehouse applications based on AI cloud computing, which can be applied to the above-mentioned back-end cloud computing device. The method steps defined in the process related to the method for integrated management of warehouse applications based on AI cloud computing can be implemented by the back-end cloud computing device. Figure 2 The specific process shown is explained in detail.

[0027] Step S110, determining a target storage solution relationship network, and determining member description data corresponding to each relationship network member in the target storage solution relationship network.

[0028] In an embodiment of the present invention, the backend cloud computing device can determine the target storage solution relationship network, and determine the member description data corresponding to each network member in the target storage solution relationship network. Figure 3The target storage solution relationship network includes multiple relationship network members, and relationship segments are configured for two relationship network members that are determined to have a relevant relationship in the target storage solution relationship network. The multiple relationship network members include multiple storage item members reflecting storage items and multiple storage solution members reflecting pending storage solutions, and the multiple storage item members include designated storage item members reflecting designated storage items. The member description data corresponding to the storage item members are formed based on the information collection of the corresponding storage items by the storage front-end device (for example, the storage front-end device includes an image collection device such as a camera, which is used to collect images of the storage items to obtain corresponding images as corresponding member description data, and can also obtain information such as the object type or product description of the storage items by object recognition on the images). The member description data corresponding to the storage solution members are used to describe the corresponding storage solution (for example, including a solution on how to store, for example, how to transport the storage items to what location for storage, and what storage conditions are required, and the limitations of conditions such as light, temperature, and humidity). In addition, the relationship line segments between storage item members may mean that the corresponding storage items are related to each other, such as belonging to the same category of items (such as fragile items). And the relationship line segments between storage item members and storage plan members may mean that the corresponding storage items are related to the storage plan, such as the storage items are stored based on the storage plan. The designated storage item members may refer to the storage item members corresponding to the storage items for which the storage plan has not yet been determined, such as Figure 3 The storage item member i in may belong to the same category of items as the storage item member h, and is represented by a relationship line segment.

[0029] Step S120, extracting at least one first member link with the designated storage item member as the starting network member from the target storage solution relationship network, and extracting at least one second member link with the storage solution member reflected by the pending storage solution as the starting network member.

[0030] In an embodiment of the present invention, the backend cloud computing device can extract at least one first member link that uses the designated storage item member as a starting network member from the target storage solution network, and extract at least one second member link that uses the storage solution member reflected by the pending storage solution as a starting network member. Figure 3 The storage item member i in the storage item is used as the designated storage item member, and the first member links "storage item member i, storage item member h, storage plan member 2, storage item member g", "storage item member i, storage item member f, storage item member c, storage plan member 1, storage item member a" and so on can be determined. Figure 3 The storage plan member e in is used as the starting network member, and the second member link "storage item member e, storage plan member 2, storage item member g" can be determined.

[0031] Step S130, loading each first member link corresponding to the designated storage item member and each member description data corresponding to the network member on the first member link, so that the data mining network mines out the network member semantic vector corresponding to the designated storage item member, and, loading each second member link corresponding to each of the pending storage plans and each member description data corresponding to the network member on the second member link, so that the data mining network mines out the network member semantic vector corresponding to each of the pending storage plans.

[0032] In an embodiment of the present invention, the backend cloud computing device can load the member description data corresponding to each first member link corresponding to the designated storage item member and each network member on the first member link, so that the data mining network mines out the network member semantic vector corresponding to the designated storage item member (which can be used to characterize the semantic information of the corresponding member description data), and loads the member description data corresponding to each second member link corresponding to each pending storage plan and each network member on the second member link, so that the data mining network mines out the network member semantic vector corresponding to each pending storage plan (which can be used to characterize the semantic information of the corresponding member description data). In addition, the data mining network can be a neural network formed through training and updating, and the updating process is described below.

[0033] Step S140, based on the relationship network member semantic vector corresponding to the designated storage item member and the relationship network member semantic vector corresponding to each of the pending storage solutions, determine a target storage solution corresponding to the designated storage item from a plurality of the pending storage solutions.

[0034] In an embodiment of the present invention, the backend cloud computing device can determine a target storage solution corresponding to the designated storage item from among multiple pending storage solutions based on the semantic vector of the relationship network member corresponding to the designated storage item member and the semantic vector of the relationship network member corresponding to each pending storage solution, such as determining the pending storage solution with the smallest distance (or the largest similarity) between the semantic vectors as the target storage solution. The target storage solution serves as the basis for storing the designated storage item.

[0035] Based on the above content, since the network member semantic vector corresponding to the designated storage item member not only includes the semantic information of the member description data corresponding to the designated storage item member itself, but also includes the semantic information of the member description data corresponding to the network member on the first member link, and the network member semantic vector corresponding to the pending storage plan not only includes the semantic information of the member description data corresponding to the pending storage plan itself, but also includes the semantic information of the member description data corresponding to the network member on the second member link, the mined network member semantic vector has better semantic representation ability and richer semantic information, so that the solution determination based on the network member semantic vector can be more reliable, that is, it is guaranteed that the target storage plan has a higher reliability, thereby improving the problem of relatively low reliability of storage management in the prior art.

[0036] It should be noted that the specific method of determining the target storage solution corresponding to the designated storage item is not limited. For example, in an alternative implementation, in order to improve the reliability of the determined target storage solution, the above-mentioned step S140 may include: First, when all the relationship network members connected by the designated storage item member through the relationship line segment belong to the storage item member (that is, all the relationship network members directly connected by the designated storage item member through the relationship line segment belong to the storage item member, and the specific application scenario may be that the storage items corresponding to the designated storage item member have the same type of storage items, but there is no possible matching storage solution, which needs to be further determined), each first member link of the designated storage item is marked as a first target member link, and the semantic conversion network is used to convert the semantic vector of the relationship network member corresponding to the designated storage item member, and the corresponding relationship network member conversion vector is output, and for each of the pending storage solutions, based on the relationship network member conversion vector and the relationship network member semantic vector corresponding to the pending storage solution, the storage matching parameters of the designated storage item and the pending storage solution are determined, such as calculating the cosine similarity between the semantic vectors; Secondly, when the relationship network members connected by the designated storage item member through the relationship line segment include storage solution members and storage item members (a specific application scenario may be that the storage items corresponding to the designated storage item member have the same type of storage items, and some possible matching storage solutions are configured manually or in other words, the storage solutions corresponding to the same type of storage items may also be used as possible matching storage solutions), each first member link of the designated storage item is marked as including a first target member link and a second target member link, and for each pending storage solution, based on the semantic vectors of the relationship network members corresponding to the designated storage item and the pending storage solution, the storage matching parameters of the designated storage item and the pending storage solution are determined; Finally, based on the storage matching parameters corresponding to each of the pending storage plans, the target storage plan corresponding to the designated storage items can be analyzed in each of the pending storage plans. For example, the pending storage plan with the maximum storage matching parameter can be determined as the target storage plan corresponding to the designated storage items.

[0037] On the basis of the above embodiments, in order to enable the data mining network to reliably mine the corresponding semantic vectors and enable the semantic conversion network to have reliable semantic conversion capabilities, the comprehensive management method of warehousing applications based on AI cloud computing may also include steps for updating and training the data mining network and the semantic conversion network, such as step S150, step S160 and step S170, and the specific contents of each step are described as follows.

[0038] Step S150, determining a training warehousing solution relationship network, and determining member description data corresponding to each relationship network member in the training warehousing solution relationship network.

[0039] In an embodiment of the present invention, the backend cloud computing device can determine a training storage solution relationship network (as described above), and determine member description data corresponding to each relationship network member in the training storage solution relationship network. The training storage solution relationship network includes multiple relationship network members, and relationship line segments are configured in the storage solution relationship network for two relationship network members that are determined to have a relevant relationship. The multiple relationship network members include multiple storage item members reflecting storage items and multiple storage solution members reflecting pending storage solutions. The multiple storage item members include at least one target storage item member, and the target storage item member is connected to at least one storage solution member and at least one other storage item member through relationship line segments.

[0040] Step S160, in the training storage solution relationship network, at least one training relationship network member is determined, and for each training relationship network member, at least one member link that uses the training relationship network member as a starting relationship network member is extracted in the training storage solution relationship network.

[0041] In an embodiment of the present invention, the backend cloud computing device can determine at least one training network member in the training storage solution network, and for each training network member, extract at least one member link that uses the training network member as the starting network member in the training storage solution network. Among them, the member link that connects network members that are all storage item members is marked as the first target member link, and the member link that connects network members that are all storage solution members is marked as the second target member link. The connecting network member refers to a network member that has a relationship line segment with the starting network member, and the at least one training network member includes at least one target storage item member, and each member link of the target storage item member includes at least one first target member link and at least one second target member link.

[0042] Step S170, based on the member description data corresponding to the relationship network member on each member link of each training relationship network member, the candidate data mining network and the candidate semantic conversion network are updated at least once, and the corresponding data mining network and semantic conversion network are output.

[0043] In an embodiment of the present invention, the backend cloud computing device can perform at least one update operation on the candidate data mining network and the candidate semantic conversion network based on the member description data corresponding to the network member on each member link of each training network member, and output the corresponding data mining network and semantic conversion network. The target update error of the update operation includes the first local update error corresponding to each target storage item member, and the first local update error is used to reflect the difference between the semantic vector output by the corresponding target storage item member in each update operation and the network member conversion vector output by using the candidate semantic conversion network based on the first semantic vector corresponding to the corresponding target storage item member in each update operation. The output semantic vector belongs to the semantic vector of the relationship network member or the second semantic vector, and the candidate data mining network is used to load the member description data corresponding to the relationship network member on each member link of the training relationship network member, and mine the relationship network member semantic vector corresponding to the training relationship network member, and when each member link of the training relationship network member includes at least one first target member link, the relationship network member semantic vector includes the first semantic vector mined based on the member description data corresponding to the relationship network member on each first target member link, and when each member link of the training relationship network member includes at least one second target member link, the relationship network member semantic vector includes the second semantic vector mined based on the member description data corresponding to the relationship network member on each second target member link. It should be noted that the end condition of the at least one update operation may be that the number of update operations reaches a preset number, or that the current target update error converges, which can be configured according to actual needs and is not specifically limited here.

[0044] In order to facilitate the understanding of the determination process of the first local update error, Figure 4 As shown, the data corresponding to the first target member link and the second target member link can be loaded into the candidate data mining network respectively, and then, the mining is performed in the candidate data mining network respectively, and the first semantic vector and the second semantic vector are output, wherein the first semantic vector and the second semantic vector can be fused by splicing, mean calculation, etc. to form the corresponding relationship network member semantic vector, and then, on the one hand, the first semantic vector can be loaded into the candidate semantic conversion network, and the corresponding relationship network member conversion vector can be output, and on the other hand, the second semantic vector can be used as the output semantic vector, or the relationship network member semantic vector can be used as the output semantic vector, and finally, the difference between the relationship network member conversion vector and the output semantic vector can be calculated to obtain the first local error. Then, the network parameters of the candidate data mining network and the candidate semantic conversion network can also be updated and adjusted based on the first local error.

[0045] It is understandable that, in an alternative implementation, in order to further improve the reliability of the network formed by training, the at least one training relationship network member also includes a storage solution member, and the target update error also includes a second local update error corresponding to each target storage item member, wherein, in each update operation, the second local update error corresponding to each target storage item member can be determined based on the following steps: First, in the training storage solution relationship network, storage solution members connected to the target storage item member through relationship segments are determined and marked as related storage solution members, and in the training storage solution relationship network, storage solution members not connected to the target storage item member through relationship segments are determined and marked as non-related storage solution members; Secondly, polling each relevant storage solution member and each non-relevant storage solution member to form a corresponding plurality of storage solution member combinations, wherein each of the storage solution member combinations includes a relevant storage solution member and a non-relevant storage solution member; Then, for each storage solution member combination, based on the relationship network member semantic vectors of the target storage item member and the related storage solution members in the storage solution member combination in the current update operation, determine the first storage matching parameter (such as the cosine similarity between the corresponding relationship network member semantic vectors) of the target storage item member and the related storage solution members, and based on the relationship network member semantic vectors of the target storage item member and the non-related storage solution members in the storage solution member combination in the current update operation, determine the second storage matching parameter of the target storage item member and the non-related storage solution members. Parameters (such as the cosine similarity between the semantic vectors of the corresponding relationship network members), and, based on the difference information between the first storage matching parameter and the second storage matching parameter, determine the storage solution matching sub-error of the target storage item member based on the storage solution member combination (for example, the difference between the first storage matching parameter and the second storage matching parameter can be calculated, and then the difference can be activated by an activation function to obtain a corresponding activation value, and the activation value can be logarithmically processed to obtain a corresponding logarithmic value, and finally, the logarithmic value can be negatively processed to obtain a corresponding storage solution matching sub-error); Finally, based on the storage plan matching sub-error of the target storage item member to all storage plan member combinations, the second local update error of the target storage item member is determined. For example, the sum of the storage plan matching sub-errors can be calculated and the sum can be used as the second local update error of the target storage item member.

[0046] It is understandable that, in an alternative implementation, in order to further improve the reliability of the network formed by training, the target update error also includes a third local update error corresponding to each training relationship network member belonging to the storage item member, wherein the third local update error can be determined based on the following steps: First, for each update operation, determine the set center semantic vector of the set center of each vector cluster set in the current update operation and the relationship network member semantic vector of each storage item member in the current update operation, where the set center semantic vector of the set center of each vector cluster set when clustering for the first time belongs to a pre-configured reference vector (such as a randomly generated vector, which can be updated accordingly in the subsequent processing process); Secondly, for each storage item member, based on the relationship network member semantic vector of the storage item member in the current update operation and the collection center semantic vector of each collection center, the storage matching parameters of the storage item member and each collection center are determined. For example, the cosine similarity between the relationship network member semantic vector and the collection center semantic vector can be calculated; Then, based on the storage matching parameters of each storage item member and each collection center, the probability of each storage item member being assigned to each vector cluster set is determined, that is, the probability of the storage item member being assigned to the corresponding vector cluster set is determined; Finally, for each storage item member, the third local update error of the storage item member is determined based on the difference information between the storage matching parameters respectively possessed by the storage item member and each collection center, and the allocation probability of the storage item member being assigned to the corresponding vector clustering set. In this way, the third local update error can be used to reflect the accuracy of the clustering result. Specifically, the ratio between the allocation probability and the storage matching parameter can be calculated, and then the logarithm operation can be performed on the ratio, as well as the product of the allocation probability and the result of the logarithm operation can be calculated. Finally, the corresponding products can be summed to obtain the corresponding third local update error.

[0047] It is to be understood that, in the above steps, the specific manner of determining the allocation probability is not limited. For example, in an alternative implementation, the allocation probability may be determined based on the following steps: First, for each collection center, the sum of the storage matching parameters of each training storage item member corresponding to the collection center is calculated to obtain the cluster representation parameters of the vector cluster set corresponding to the collection center; Secondly, for each storage item member, based on the storage matching parameters between the storage item member and a collection center, and in combination with the cluster characterization parameters of the vector clustering set corresponding to the collection center, the collection contribution of the storage item member in the vector clustering set is determined; illustratively, the ratio between the square of the storage matching parameter and the cluster characterization parameter can be calculated to obtain the collection contribution. In addition, the collection center refers to the center formed by the cluster, which can also be called the cluster center; Then, for each storage item member, the ratio between the collection contribution of the storage item member in a vector cluster set and the collection contribution (sum value) of the storage item member in all vector cluster sets is calculated to obtain the allocation probability of the storage item member to the vector cluster set.

[0048] It is understandable that in the above steps, the specific manner of performing the update operation is not limited. For example, in an alternative implementation, in order to ensure the reliability of the update operation, each update operation may further include the following steps: First, for each storage item member, the set center of the storage matching parameter with the maximum value corresponding to the storage item member is determined as the target set center, and the vector cluster set corresponding to the target set center is marked, so that the vector cluster set is marked as the vector cluster set corresponding to the storage item member in the next update operation; that is, for each storage item member, by counting the storage matching parameters of each set center corresponding to the storage item member, the set center with the maximum value of the storage matching parameter is taken as the target set center, and the vector cluster set corresponding to the target set center is further taken as the vector cluster set to which the storage item member belongs in the next update operation. For example, if there are 3 vector cluster sets, and the storage matching parameters of the storage item member and the set centers of the 3 vector cluster sets are 98%, 70% and 60% respectively, then the vector cluster set corresponding to the storage matching parameter 98% with the maximum value is taken as the vector cluster set of the storage item member in the next update operation; Secondly, for each vector cluster set corresponding to the next update operation, based on the target update error, the set center semantic vector of the set center corresponding to the vector cluster set is adjusted. That is to say, since the initial set center semantic vector is pre-set, the set center semantic vector needs to be adjusted accordingly in each subsequent update operation to achieve the update. That is to say, the set center semantic vector can be used as a network parameter of the candidate data mining network, and is updated as the candidate data mining network is updated. The specific update process will not be described here one by one, that is, the network parameters are updated and adjusted in the direction of reducing the target update error.

[0049] It can be understood that, in the above steps, the specific method of determining the first local update error is not limited. For example, in an alternative implementation, in order to improve the reliability of the determined first local update error, the first local update error corresponding to the target storage item member can be determined based on the following steps: First, based on the vector difference between the output semantic vectors of each collection center and the target stored item member in the current update operation, the collection center semantic vectors of each collection center are aggregated and calculated to output the corresponding first aggregate semantic vector, and a semantic enhancement operation is performed on the output semantic vector based on the first aggregate semantic vector to output the output semantic vector after the semantic enhancement operation, and the output semantic vector after the semantic enhancement operation is marked so that it is marked as the stored item enhancement vector of the target stored item member in the current update operation; illustratively, the output semantic vector and the collection center semantic vector can be compared. The transposed vectors are multiplied, and then the result of the multiplication is divided by 0.5 power of the number of dimensions of the output semantic vector to obtain a weighted parameter, and then the weighted parameter is multiplied by the set center semantic vector, and the multiplication result is normalized to obtain a first aggregated semantic vector. It should be noted that the number of dimensions of the output semantic vector is equal to the number of dimensions of the set center semantic vector; in addition, the first aggregated semantic vector and the output semantic vector can be multiplied to implement a semantic enhancement operation on the output semantic vector, so that the output semantic vector after the semantic enhancement operation also carries the semantic information carried by the first aggregated semantic vector; Secondly, based on the vector difference between the first semantic vectors of each collection center and the target storage item member in the current update operation, the collection center semantic vectors of each collection center are aggregated and calculated to output the corresponding second aggregate semantic vector, and the first semantic vector is semantically enhanced based on the second aggregate semantic vector to output the first semantic vector after the semantic enhancement operation, and the first semantic vector after the semantic enhancement operation is converted using the candidate semantic conversion network to output the relationship network member conversion vector of the target storage item member in the current update operation. It should be noted that the candidate semantic conversion network may include a parameter matrix, and the first semantic vector after the semantic enhancement operation may be multiplied by the parameter matrix to implement the conversion operation and obtain the corresponding relationship network member conversion vector; Then, based on the vector difference between the storage item reinforcement vector and the relationship network member conversion vector of the target storage item member in the current update operation, the first local update error corresponding to the target storage item member is determined. For example, the vector distance between the storage item reinforcement vector and the relationship network member conversion vector can be used as the first local update error. Based on this, the first local update error not only takes into account the error existing in the conversion output of the first semantic vector of the target storage item member, but also takes into account the clustering loss existing in the clustering of the target storage item members. The semantic vector of the relationship network member obtained thereby can also express more information.

[0050] It is understandable that, in an alternative embodiment, the multiple stored item members include at least one non-related stored item member, and the non-related stored item member refers to a stored item member that has no relationship line segment with a storage solution member. At least one training relationship network member also includes a non-related stored item member, and the member links of the non-related stored item member all belong to the first target member link, and the relationship network member semantic vector of the non-related stored item member belongs to the first semantic vector. Based on this, the first semantic vector or the second semantic vector corresponding to each of the training relationship network members can be determined based on the following steps: First, in each member link of the training relationship network member belonging to the initial relationship network member, each comparison member link is determined, wherein the comparison member links belong to the first target member link or the second target member link, that is, they are all first target member links or all second target member links; Secondly, for each comparison member link, taking the second-to-last network member with the least correlation in the comparison member link as the starting point (correlation may refer to the number of network members spaced from the starting network member on the corresponding comparison member link, the larger the number, the smaller the correlation, for example, the second-to-last network member with the least correlation refers to the network member with the second largest number of network members spaced from the starting network member), based on the member transfer vector of each network member with a smaller correlation connected to the network member (i.e., the network member with the largest number of network members spaced from the starting network member) and the member description data corresponding to the network member, sequentially transfer to the starting network member to determine the starting network member on the comparison member link. The member transfer vector of the network member with the smallest correlation is formed based on the member description data mining corresponding to the network member with the smallest correlation. Specifically, if the member description belongs to the storage item image, the storage item image can be convolved by the convolution model included in the candidate data mining network to obtain the corresponding convolution vector as the corresponding member transfer vector; if the member description data belongs to the storage plan text, the storage plan text can be word embedded by the word embedding model included in the candidate data mining network to obtain the corresponding word embedding vector as the corresponding member transfer vector. For example, the storage plan text is "storage conditions include storage temperatures below 0 degrees..." In this way, "storage conditions" can be embedded as: “0.12, −0.45, 0.23, 0.56, −0.11, 0.34, −0.25, 0.67, −0.18, 0.39, 0.44, −0.30, 0.29, 0.10, −0.55, 0.26, 0.12, 0.04, 0.49, −0.14, 0.58, −0.39, 0.33, 0 .50, −0.21, 0.19, 0.11, −0.60, 0.47, 0.23, −0.01, 0.35, 0.18, −0.22, 0.15, 0.09, −0.28, 0.45, 0.07, −0.36, 0.53, −0.08, 0.40, 0.22, 0.01, −0.27…”; You can embed "include" as: “0.05, 0.11, −0.10, 0.30, 0.21, −0.15, 0.09, 0.22, 0.18, −0.12, 0.25, −0.19, 0.30, −0.23, 0.16, 0.21, 0.05, 0.03, −0.10, 0.12, 0.40, −0.07, 0.25, 0.04, 0.14, 0.05, −0.18, 0.20, 0.11, −0.12, 0.08, 0.03, 0.01, 0.14, 0.03, 0.09, −0.04, 0.22, 0.06, 0.15, 0.02, −0.10, 0.25, 0.09, −0.12…”; "Below 0 degrees" can be embedded as: “0.40, 0.12, −0.11, 0.20, 0.03, −0.30, 0.50, −0.09, 0.31, 0.25, −0.05, 0.09, 0.22, 0.14, −0.01, 0.18, 0.29, −0.15, 0.24, 0.35, 0.02, −0.17, 0.05, 0.03, 0.11, 0.10, 0.15, −0.08, 0.22, 0.12, −0.13, 0.07, 0.10, 0.14, 0.08, 0.20, 0.25, 0.17, −0.19, 0.21, 0.05, −0.03, 0.16, 0.09, −0.25…”; The "storage temperature" can be embedded as: “0.60, 0.20, −0.15, 0.25, 0.30, −0.20, 0.15, 0.40, −0.12, 0.35, 0.02, 0.10, 0.21, 0.15, −0.05, 0.14, 0.18, 0.09, −0.11, 0.24, 0.30, −0.08, 0.20, 0.12, 0.03, −0.10, 0.14, 0.19, 0.25, −0.01, 0.18, 0.11, 0.05, 0.30, 0.02, −0.16, 0.13, 0.01, 0.10, 0.17, −0.12, 0.05, 0.21, 0.07, −0.09…”; Then, the member transfer vectors of the training relationship network members on each comparison member link are subjected to an association focus fusion operation, and the semantic vector corresponding to the comparison member link of the training relationship network member is output, wherein when the comparison member link belongs to the first target member link, the semantic vector corresponding to the comparison member link belongs to the first semantic vector, and when the comparison member link belongs to the second target member link, the semantic vector corresponding to the comparison member link belongs to the second semantic vector; wherein the specific processing process of the association focus fusion operation may include: first, the member transfer vectors of the training relationship network members on each comparison member link are combined in pairs, each combination includes a first vector and a second vector, then, the first vector and the second vector are multiplied to obtain a corresponding similarity parameter, and then the second vector is weighted based on the similarity parameter to obtain a corresponding association vector, and finally, the association vectors corresponding to each combination are superimposed, spliced ​​or averaged to obtain a corresponding semantic vector to achieve association focus fusion.

[0051] It is understood that, in the above steps, the specific manner of sequentially delivering to the initial network members is not limited. For example, in an alternative implementation, the information may be sequentially delivered to the initial network members based on the following steps: First, for any network member in the current transfer stage, based on the member description data corresponding to the network member, the member representation vector corresponding to the network member is mined, such as the convolution vector and embedding vector mentioned above; Secondly, for any network member in the current transfer stage, mark each network member with a smaller correlation having a relationship line segment with the network member, so that it is marked as a comparison network member (such as a network member farther from the starting network member on the comparison member link), and determine the member transfer vector corresponding to the comparison network member; Then, for any network member in the current transfer stage, based on the member representation vector corresponding to the network member and the member transfer vector corresponding to each of the comparison network members, determine the member association parameter between the network member and each of the comparison network members, such as the cosine similarity between the member representation vector and the member transfer vector; Finally, for any network member in the current transfer stage, based on the member association parameters corresponding to each of the compared network members, the member transfer vector corresponding to each of the compared network members is weighted and valued, and the vector obtained by the weight and value calculation and the member characterization vector corresponding to the network member are fused to output the member transfer vector corresponding to the network member; illustratively, the member association parameter can be used as a weight coefficient to perform a weighted sum calculation on each member transfer vector, and the result of the weighted sum calculation can be superimposed on the member characterization vector, and then normalized to obtain the corresponding member transfer vector.

[0052] In order to facilitate the understanding of the above transmission process, combined with Figure 5 , network member 1 is used as the starting network member, and the corresponding network member link also includes network member 2, network member 3, network member 4 and network member 5. First, for the branch of network members 2 and network members 3, the member characterization vector of network member 3 can be used as the corresponding member transfer vector, and then, based on the member transfer vector corresponding to network member 3 and the member characterization vector of network member 2, the member transfer vector corresponding to network member 2 is determined, and, based on the member transfer vector corresponding to network member 2 and the member characterization vector of network member 1, the member transfer vector of network member 1 on this branch is determined. For the branch of network members 4 and network members 5, the member characterization vector of network member 5 can be used as the corresponding member transfer vector, and then, based on the member transfer vector corresponding to network member 5 and the member characterization vector of network member 4, the member transfer vector corresponding to network member 4 is determined, and, based on the member transfer vector corresponding to network member 4 and the member characterization vector of network member 1, the member transfer vector of network member 1 on this branch is determined. Then, the two member transfer vectors of the relationship network member 1 on the two branches can be subjected to an associative focus fusion operation, and the semantic vector corresponding to the comparison member link of the training relationship network member can be output, that is, the first semantic vector or the second semantic vector can be obtained.

[0053] In addition, the target warehousing solution relationship network can be formed on the basis of the training warehousing solution relationship network. For example, a designated storage item member is added to the training warehousing solution relationship network to obtain the corresponding target warehousing solution relationship network. Then, the target warehousing solution relationship network is updated based on the final target warehousing solution to form a new target warehousing solution relationship network. In this way, through the continuous application and updating of the target warehousing solution relationship network, the information included in the target warehousing solution relationship network can be gradually enriched to ensure its reliability.

[0054] In summary, the warehousing application comprehensive management platform system based on AI cloud computing provided by the present invention first determines the target warehousing solution relationship network; secondly, from the target warehousing solution relationship network, extracts at least one first member link that takes the designated warehousing item member as the starting network member, and extracts at least one second member link that takes the warehousing solution member reflected by the pending warehousing solution as the starting network member; then, mines out the relationship network member semantic vector corresponding to the designated warehousing item member, and mines out the relationship network member semantic vector corresponding to each pending warehousing solution; finally, based on the relationship network member semantic vector corresponding to the designated warehousing item member and each pending warehousing solution, determines the target warehousing solution. Based on the above content, since the network member semantic vector corresponding to the designated storage item member not only includes the semantic information of the member description data corresponding to the designated storage item member itself, but also includes the semantic information of the member description data corresponding to the network member on the first member link, and the network member semantic vector corresponding to the pending storage plan not only includes the semantic information of the member description data corresponding to the pending storage plan itself, but also includes the semantic information of the member description data corresponding to the network member on the second member link, the mined network member semantic vector has better semantic representation ability and richer semantic information, so that the solution determination based on the network member semantic vector can be more reliable, that is, it is guaranteed that the target storage plan has a higher reliability, thereby improving the problem of relatively low reliability of storage management in the prior art.

[0055] In several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the apparatus, method and computer program product according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0056] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0057] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0058] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A warehousing application integrated management platform system based on AI cloud computing, characterized in that: The system comprises a plurality of warehouse front-end devices and a back-end cloud computing device in communication with the plurality of warehouse front-end devices, wherein the back-end cloud computing device is used to execute a warehouse application comprehensive management method based on AI cloud computing, wherein the warehouse application comprehensive management method based on AI cloud computing comprises: Determine a target storage solution relationship network, and determine member description data corresponding to each relationship network member in the target storage solution relationship network, wherein the target storage solution relationship network includes multiple relationship network members, and relationship line segments are configured in the target storage solution relationship network for two relationship network members that are determined to have a relevant relationship, the multiple relationship network members include multiple storage item members reflecting storage items and multiple storage solution members reflecting pending storage solutions, and the multiple storage item members include designated storage item members reflecting designated storage items, the member description data corresponding to the storage item members are formed based on the storage front-end device collecting information on the storage items, and the member description data corresponding to the storage solution members are used to describe the storage solution; Extracting at least one first member link having the designated storage item member as a starting network member from the target storage solution relationship network, and extracting at least one second member link having the storage solution member reflected by the pending storage solution as a starting network member; Loading member description data corresponding to each first member link corresponding to the designated storage item member and each network member on the first member link, so that the data mining network mines out the network member semantic vector corresponding to the designated storage item member, and loading member description data corresponding to each second member link corresponding to each pending storage plan and each network member on the second member link, so that the data mining network mines out the network member semantic vector corresponding to each pending storage plan; Based on the relationship network member semantic vector corresponding to the designated storage item member and the relationship network member semantic vector corresponding to each of the pending storage plans, a target storage plan corresponding to the designated storage item is determined from multiple pending storage plans, wherein the target storage plan serves as the basis for storing the designated storage item.

2. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 1, characterized in that: The step of determining a target storage solution corresponding to the designated storage item from among a plurality of the pending storage solutions based on the relationship network member semantic vector corresponding to the designated storage item member and the relationship network member semantic vector corresponding to each of the pending storage solutions comprises: When all the relationship network members connected by the relationship line segments of the designated storage item member belong to the storage item members, each first member link of the designated storage item is marked as a first target member link, and the relationship network member semantic vector corresponding to the designated storage item member is converted by using a semantic conversion network, and the corresponding relationship network member conversion vector is output, and for each of the pending storage solutions, based on the relationship network member conversion vector and the relationship network member semantic vector corresponding to the pending storage solution, the storage matching parameters of the designated storage item and the pending storage solution are determined; When the relationship network members connected by the designated storage item member through the relationship line segment include storage solution members and storage item members, each first member link of the designated storage item is marked as including a first target member link and a second target member link, and for each pending storage solution, based on the relationship network member semantic vectors corresponding to the designated storage item and the pending storage solution, the storage matching parameters between the designated storage item and the pending storage solution are determined; Based on the storage matching parameters corresponding to each of the pending storage solutions, a target storage solution corresponding to the designated storage items is analyzed in each of the pending storage solutions.

3. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 2, characterized in that: The AI ​​cloud computing-based warehousing application comprehensive management method also includes: Determine a training storage solution relationship network, and determine member description data corresponding to each relationship network member in the training storage solution relationship network, wherein the training storage solution relationship network includes a plurality of relationship network members, and relationship line segments are configured in the storage solution relationship network for two relationship network members that are determined to have a relevant relationship, the plurality of relationship network members include a plurality of storage item members reflecting storage items and a plurality of storage solution members reflecting pending storage solutions, the plurality of storage item members include at least one target storage item member, and the target storage item member is connected to at least one storage solution member and at least one other storage item member through relationship line segments respectively; In the training storage solution relationship network, at least one training relationship network member is determined, and for each training relationship network member, at least one member link that uses the training relationship network member as a starting relationship network member is extracted in the training storage solution relationship network, wherein a member link that connects relationship network members that are all storage item members is marked as a first target member link, and a member link that connects relationship network members that are all storage solution members is marked as a second target member link, the connecting relationship network members refer to relationship network members that have relationship segments with the starting relationship network members, the at least one training relationship network member includes at least one target storage item member, and each member link of the target storage item member includes at least one first target member link and at least one second target member link; Based on the member description data corresponding to the relationship network member on each member link of each training relationship network member, the candidate data mining network and the candidate semantic conversion network are updated at least once, and the corresponding data mining network and semantic conversion network are output, wherein the target update error of the update operation includes a first local update error corresponding to each target storage item member, and the first local update error is used to reflect the difference between the semantic vector output by the corresponding target storage item member in each update operation and the relationship network member conversion vector output by using the candidate semantic conversion network based on the first semantic vector corresponding to the corresponding target storage item member in each update operation. The output semantic vector belongs to the relationship network member semantic The candidate data mining network is used to load the member description data corresponding to the network member on each member link of the training network member, and mine the network member semantic vector corresponding to the training network member, and when each member link of the training network member includes at least one first target member link, the network member semantic vector includes the first semantic vector mined based on the member description data corresponding to the network member on each first target member link, and when each member link of the training network member includes at least one second target member link, the network member semantic vector includes the second semantic vector mined based on the member description data corresponding to the network member on each second target member link.

4. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 3, characterized in that: The at least one training relationship network member further includes a storage solution member, and the target update error further includes a second local update error corresponding to each target storage item member, wherein in each update operation, the second local update error corresponding to each target storage item member is determined based on the following steps: In the training storage solution relationship network, storage solution members connected to the target storage item member through relationship line segments are determined and marked as related storage solution members, and storage solution members not connected to the target storage item member through relationship line segments are determined in the training storage solution relationship network and marked as non-related storage solution members; Polling each relevant storage solution member and each non-relevant storage solution member to form a corresponding plurality of storage solution member combinations, wherein each of the storage solution member combinations includes a relevant storage solution member and a non-relevant storage solution member; For each storage solution member combination, based on the relationship network member semantic vectors of the target storage item member and the related storage solution members in the storage solution member combination in the current update operation, determine the first storage matching parameter of the target storage item member and the related storage solution member, and based on the relationship network member semantic vectors of the target storage item member and the non-related storage solution members in the storage solution member combination in the current update operation, determine the second storage matching parameter of the target storage item member and the non-related storage solution member, and, based on the difference information between the first storage matching parameter and the second storage matching parameter, determine the storage solution matching sub-error of the target storage item member based on the storage solution member combination; Based on the storage solution matching sub-errors of the target storage item member for all storage solution member combinations, a second local update error of the target storage item member is determined.

5. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 3, characterized in that: The target update error also includes a third local update error corresponding to each training relationship network member belonging to the storage item member, wherein the third local update error is determined based on the following steps: For each update operation, determine the set center semantic vector of the set center of each vector cluster set in the current update operation and the relationship network member semantic vector of each storage item member in the current update operation, wherein the set center semantic vector of the set center of each vector cluster set when clustering for the first time belongs to the pre-configured reference vector; For each storage item member, based on the relationship network member semantic vector of the storage item member in the current update operation and the collection center semantic vector of each collection center, determine the storage matching parameters that the storage item member has with each collection center respectively; Based on the storage matching parameters of each storage item member and each collection center, the probability of each storage item member being assigned to each vector clustering set is determined; For each storage item member, the third local update error of the storage item member is determined based on the difference information between the storage matching parameters respectively possessed by the storage item member and each collection center and the allocation probability of the storage item member being allocated to the corresponding vector clustering set.

6. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 5, characterized in that: The step of determining the probability of each storage item member being assigned to each vector clustering set based on the storage matching parameters respectively possessed by each storage item member and each collection center comprises: For each collection center, calculate the sum of the storage matching parameters of each training storage item member corresponding to the collection center, and obtain the clustering representation parameters of the vector clustering set corresponding to the collection center; For each storage item member, based on the storage matching parameters between the storage item member and a collection center, and in combination with the cluster representation parameters of the vector cluster set corresponding to the collection center, determine the collection contribution of the storage item member in the vector cluster set; For each storage item member, the ratio between the set contribution of the storage item member in a vector cluster set and the set contribution of the storage item member in all vector cluster sets is calculated to obtain the allocation probability of the storage item member to the vector cluster set.

7. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 5, characterized in that: Each update operation also includes: For each storage item member, the set center of the storage matching parameter with the maximum value corresponding to the storage item member is determined as the target set center, and the vector cluster set corresponding to the target set center is marked, so that the vector cluster set is marked as the vector cluster set corresponding to the storage item member in the next update operation; For each vector cluster set corresponding to the next update operation, based on the target update error, the set center semantic vector of the set center corresponding to the vector cluster set is adjusted.

8. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 5, characterized in that: The first local update error corresponding to the target storage item member is determined based on the following steps: Based on the vector difference between the output semantic vectors of each collection center and the target stored item member in the current update operation, the collection center semantic vectors of each collection center are aggregated and calculated, and the corresponding first aggregated semantic vector is output, and based on the first aggregated semantic vector, a semantic enhancement operation is performed on the output semantic vector, and the output semantic vector after the semantic enhancement operation is output, and the output semantic vector after the semantic enhancement operation is marked so that it is marked as the storage item enhancement vector of the target stored item member in the current update operation; Based on the vector difference between the first semantic vectors of each collection center and the target storage item member in the current update operation, the collection center semantic vector of each collection center is aggregated and calculated, and the corresponding second aggregate semantic vector is output, and the first semantic vector is semantically enhanced based on the second aggregate semantic vector, and the first semantic vector after the semantic enhancement operation is output, and the first semantic vector after the semantic enhancement operation is converted using the candidate semantic conversion network, and the relationship network member conversion vector of the target storage item member in the current update operation is output; Based on the vector difference between the storage item reinforcement vector and the relationship network member conversion vector of the target storage item member in the current update operation, a first local update error corresponding to the target storage item member is determined.

9. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 3, characterized in that: The plurality of storage item members include at least one non-related storage item member, wherein the non-related storage item member refers to a storage item member having no relationship line segment with a storage solution member; At least one training relationship network member also includes a non-related storage item member, the member links of the non-related storage item member all belong to the first target member link, and the relationship network member semantic vector of the non-related storage item member belongs to the first semantic vector.

10. The AI ​​cloud computing-based warehousing application integrated management platform system according to claim 9, characterized in that: The first semantic vector or the second semantic vector corresponding to each of the training relationship network members is determined based on the following steps: In each member link of the training relationship network member belonging to the initial relationship network member, each comparison member link is determined, wherein the comparison member link belongs to the first target member link or the second target member link; For each comparison member link, starting from the penultimate network member with the least correlation in the comparison member link, based on the member transfer vectors of each network member with less correlation connected to the network member and the member description data corresponding to the network member, the member transfer vectors are sequentially transferred to the starting network member to determine the member transfer vector of the starting network member on the comparison member link, wherein the member transfer vector of the network member with the least correlation is formed based on the member description data corresponding to the network member with the least correlation; The member transfer vectors of the training relationship network members on each comparison member link are subjected to an associative focusing fusion operation, and a semantic vector corresponding to the comparison member link of the training relationship network member is output, wherein when the comparison member link belongs to the first target member link, the semantic vector corresponding to the comparison member link belongs to the first semantic vector, and when the comparison member link belongs to the second target member link, the semantic vector corresponding to the comparison member link belongs to the second semantic vector.

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