A resource prediction method and system for a fabric supply chain

Abstract

The resource prediction method and system for a fabric supply chain provided by an embodiment of the present invention determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data; then, based on the predicted resource data, perform weight iterative update on the prediction weight data of the first resource prediction network, and use the first resource prediction network after performing weight iterative update on the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, thereby using it as the resource application reference data for the fabric supply chain execution process to achieve resource prediction for the fabric supply chain execution process. Thus, combining the resource application node statistical data with the resource prediction resource application reference data can improve the prediction accuracy of the resource application reference data.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to a method and system for predicting resources in a fabric supply chain. Background Art

[0002] Currently, a fabric supply chain execution process is provided to guide the execution process of the fabric supply chain. During the execution process, the resource application reference data generated is valuable for resource prediction optimization. Therefore, how to predict the resource application reference data is a technical problem to be solved urgently. Summary of the Invention

[0003] In view of this, the purpose of the embodiments of the present invention is to provide a method and system for predicting resources in a fabric supply chain, so as to improve the prediction accuracy of resource application reference data.

[0004] In a first aspect, an embodiment of the present invention provides a method for predicting resources in a fabric supply chain, which is applied to an online service platform. The method includes:

[0005] Determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data;

[0006] Update the prediction weight data of the first resource prediction network by weight iteration according to the predicted resource data;

[0007] Use the first resource prediction network after updating the prediction weight data by weight iteration to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, and use the first resource application reference data as the resource application reference data of the fabric supply chain execution process.

[0008] Among them, the online service platform includes a fabric supply chain guidance system, the fabric supply chain execution process includes a fabric supply chain guidance process, and the target resource application node information includes the guidance resource application node information of the fabric supply chain guidance process on the fabric supply chain guidance system.

[0009] Among them, the performing resource prediction on the resource application node statistical data corresponding to the target resource application node information and obtaining the predicted resource data of the resource application node statistical data includes:

[0010] Perform resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a preset resource prediction function, and obtain the predicted resource data of the resource application node statistical data; or

[0011] Using a second resource prediction network to perform resource prediction on the resource application node statistical data corresponding to the target resource application node information to obtain predicted resource application reference data, and predicting the predicted resource data of the resource application node statistical data according to the predicted resource application reference data.

[0012] Among them, mapping information between the resource application attributes of the resource application node statistical data and the prediction configuration information of the predicted resource application reference data is pre-configured in the online service platform. The predicting the predicted resource data of the resource application node statistical data according to the predicted resource application reference data includes:

[0013] Performing prediction analysis on the predicted resource application reference data to obtain the prediction configuration information of the predicted resource application reference data;

[0014] Determining the resource application attributes of the resource application node statistical data according to the prediction configuration information of the predicted resource application reference data and the mapping information.

[0015] Among them, after using the first resource prediction network after weight iterative update of the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, it further includes:

[0016] Processing the first resource application reference data based on the predicted resource data to obtain a second resource application reference data, and using the second resource application reference data as the resource application reference data of the fabric supply chain execution process.

[0017] An embodiment of the present invention further provides a resource prediction system for a fabric supply chain, which is applied to an online service platform. The system includes:

[0018] A first prediction module, configured to determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data;

[0019] An update module, configured to perform weight iterative update on the prediction weight data of the first resource prediction network according to the predicted resource data;

[0020] A second prediction module, configured to use the first resource prediction network after weight iterative update of the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain a first resource application reference data, and use the first resource application reference data as the resource application reference data of the fabric supply chain execution process.

[0021] In summary, the resource prediction method and system for the fabric supply chain provided by the embodiments of the present invention determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data; then, according to the predicted resource data, perform weight iteration update on the prediction weight data of the first resource prediction network, and use the first resource prediction network after weight iteration update of the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, so as to use the first resource application reference data as the resource application reference data of the fabric supply chain execution process, and realize the resource prediction of the fabric supply chain execution process. Thus, combining the resource application node statistical data for resource prediction of the resource application reference data can improve the prediction accuracy of the resource application reference data.

[0022] In order to make the above objects, features, and advantages of the embodiments of the present invention more obvious and understandable, the following will be described in detail in combination with the embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 shows a schematic flowchart of the resource prediction method for the fabric supply chain provided by the embodiments of the present invention;

[0025] Figure 2 shows a functional module block diagram of the resource prediction system for the fabric supply chain provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In order to enable the trainees in the technical field of the present invention to better understand the solution of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in combination with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. According to the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0027] The terms "first", "second", "third", etc. (if any) in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented, for example, in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Figure 1 The flowchart shows a resource prediction method for a fabric supply chain provided by an embodiment of the present invention. The resource prediction method for the fabric supply chain can be executed by an online service platform.

[0029] The online service platform may include a processor, such as a central processing unit (CPU), and each processing unit can implement hardware threads. The online service platform may also include any storage medium for storing any kind of information such as code, settings, data, etc. Non-limiting examples include any one or a combination of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical discs, etc. More generally, any storage medium can store information using any technology. Further, any storage medium can provide volatile or non-volatile retention of information. Further, any storage medium can represent a fixed or removable component of the online service platform. In one case, when the processor executes the associated instructions stored in any storage medium or combination of storage media, the online service platform can perform any operation of the associated instructions. The online service platform also includes a drive unit for interacting with any storage medium, such as a hard disk drive unit, an optical disc drive unit, etc.

[0030] The online service platform also includes input / output (I / O) for receiving various inputs (via the input unit) and for providing various outputs (via the output unit). A specific output mechanism may include a presentation device and an associated graphical user interface (GUI). The online service platform may also include a network interface for exchanging data with other devices via a communication unit. A communication bus couples the components described above together.

[0031] The communication unit can be implemented in any way, for example, through a local area network, a wide area network (such as the Internet), a point-to-point connection, etc., or any combination thereof. The communication unit can include any combination of hardwired links, wireless links, routers, gateway functions, name line service platforms, etc. governed by any protocol or combination of protocols.

[0032] The following introduces the detailed steps of the resource prediction method for the fabric supply chain.

[0033] Step 1, determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data.

[0034] Step 2, perform weight iterative update on the prediction weight data of the first resource prediction network according to the predicted resource data;

[0035] Step 3, use the first resource prediction network after performing weight iterative update on the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, and use the first resource application reference data as the resource application reference data of the fabric supply chain execution process.

[0036] In an alternative embodiment, the online service platform includes a fabric supply chain guidance system, the fabric supply chain execution process includes a fabric supply chain guidance process, and the target resource application node information includes the guidance resource application node information of the fabric supply chain guidance process on the fabric supply chain guidance system.

[0037] In an alternative embodiment, the performing resource prediction on the resource application node statistical data corresponding to the target resource application node information and obtaining the predicted resource data of the resource application node statistical data includes:

[0038] Performing resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a preset resource prediction function and obtaining the predicted resource data of the resource application node statistical data; or

[0039] Performing resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a second resource prediction network to obtain predicted resource application reference data, and predicting the predicted resource data of the resource application node statistical data according to the predicted resource application reference data.

[0040] In an alternative embodiment, mapping information between the resource application attributes of the resource application node statistics data and the prediction configuration information of the predicted resource application reference data is pre-configured in the online service platform. The predicting the predicted resource data of the resource application node statistics data based on the predicted resource application reference data includes:

[0041] Performing predictive analysis on the predicted resource application reference data to obtain the prediction configuration information of the predicted resource application reference data;

[0042] Determining the resource application attributes of the resource application node statistics data according to the prediction configuration information of the predicted resource application reference data and the mapping information.

[0043] In an alternative embodiment, after using the first resource prediction network obtained by weight iterative updating of the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, it further includes:

[0044] Processing the first resource application reference data based on the predicted resource data to obtain a second resource application reference data, and using the second resource application reference data as the resource application reference data for the fabric supply chain execution process.

[0045] Figure 2 FIG. shows a functional module diagram of a resource prediction system for a fabric supply chain according to an embodiment of the present invention. The functions implemented by the resource prediction system for the fabric supply chain can correspond to the steps executed by the above method. The resource prediction system for the fabric supply chain can be understood as the above online service platform, or a processor of the online service platform, or can also be understood as a component that realizes the functions of the present invention under the control of the online service platform independent of the above online service platform or processor. The functions of each functional module of the resource prediction system for the fabric supply chain will be described in detail below. The system includes a first prediction module 210, an update module 220, and a second prediction module 230.

[0046] The first prediction module 210 is configured to determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistics data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistics data.

[0047] The update module 220 is configured to perform weight iterative update on the prediction weight data of the first resource prediction network according to the predicted resource data.

[0048] A second prediction module 230, configured to perform resource prediction on the fabric supply chain execution process on the target resource application node information by using a first resource prediction network after weight iterative update of the prediction weight data to obtain first resource application reference data, and use the first resource application reference data as the resource application reference data for the fabric supply chain execution process.

[0049] In an alternative embodiment, the online service platform includes a fabric supply chain guidance system, the fabric supply chain execution process includes a fabric supply chain guidance process, and the target resource application node information includes the guidance resource application node information of the fabric supply chain guidance process on the fabric supply chain guidance system.

[0050] In an alternative embodiment, the first prediction module is specifically configured to:

[0051] Perform resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a preset resource prediction function, and obtain the predicted resource data of the resource application node statistical data; or

[0052] Perform resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a second resource prediction network to obtain predicted resource application reference data, and predict the predicted resource data of the resource application node statistical data according to the predicted resource application reference data.

[0053] In an alternative embodiment, mapping information between the resource application attributes of the resource application node statistical data and the prediction configuration information of the predicted resource application reference data is pre-configured in the online service platform. The first prediction module is specifically configured to:

[0054] Perform prediction analysis on the predicted resource application reference data to obtain the prediction configuration information of the predicted resource application reference data;

[0055] Determine the resource application attributes of the resource application node statistical data according to the prediction configuration information of the predicted resource application reference data and the mapping information.

[0056] In an alternative embodiment, after obtaining the first resource application reference data by performing resource prediction on the fabric supply chain execution process on the target resource application node information by using the first resource prediction network after weight iterative update of the prediction weight data, the second prediction module is further configured to process the first resource application reference data based on the predicted resource data to obtain second resource application reference data, and use the second resource application reference data as the resource application reference data for the fabric supply chain execution process.

[0057] In summary, for the resource prediction method and system of the fabric supply chain provided by the embodiments of the present invention, by determining the target resource application node information of the fabric supply chain execution process, resource prediction is performed on the resource application node statistical data corresponding to the target resource application node information, and the predicted resource data of the resource application node statistical data is obtained; then, according to the predicted resource data, the prediction weight data of the first resource prediction network is iteratively updated, and the fabric supply chain execution process on the target resource application node information is resource predicted by using the first resource prediction network after the prediction weight data is iteratively updated to obtain the first resource application reference data, so that the first resource application reference data is used as the resource application reference data of the fabric supply chain execution process, and the resource prediction of the fabric supply chain execution process is realized. Thus, combining the resource application node statistical data with the resource prediction resource application reference data can improve the prediction accuracy of the resource application reference data.

[0058] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0059] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0060] In addition, in each embodiment of the present invention, the various functional modules may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.

[0061] It is replaceable and can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, online service platform, or data center to another website, computer, online service platform, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as an online service platform or a data center that includes an integrated available medium. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0062] It should be noted that in this document, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article, or device that includes the element.

[0063] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A resource prediction method for a fabric supply chain, applied to an online service platform, characterized in that The method includes: Determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data; Update the prediction weight data of the first resource prediction network iteratively according to the predicted resource data; Use the first resource prediction network after iteratively updating the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, and use the first resource application reference data as the resource application reference data of the fabric supply chain execution process; The online service platform includes a fabric supply chain guidance system, the fabric supply chain execution process includes a fabric supply chain guidance process, and the target resource application node information includes the guidance resource application node information of the fabric supply chain guidance process on the fabric supply chain guidance system; The performing resource prediction on the resource application node statistical data corresponding to the target resource application node information and obtaining the predicted resource data of the resource application node statistical data includes: Performing resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a preset resource prediction function and obtaining the predicted resource data of the resource application node statistical data; or Performing resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a second resource prediction network to obtain predicted resource application reference data, and predicting the predicted resource data of the resource application node statistical data according to the predicted resource application reference data; Mapping information between the resource application attributes of the resource application node statistical data and the prediction configuration information of the predicted resource application reference data is pre-configured in the online service platform. The predicting the predicted resource data of the resource application node statistical data according to the predicted resource application reference data includes: Performing prediction analysis on the predicted resource application reference data to obtain the prediction configuration information of the predicted resource application reference data; Determining the resource application attributes of the resource application node statistical data according to the prediction configuration information of the predicted resource application reference data and the mapping information; After using the first resource prediction network after iteratively updating the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain the first resource application reference data, it further includes: Processing the first resource application reference data based on the predicted resource data to obtain the second resource application reference data, and using the second resource application reference data as the resource application reference data of the fabric supply chain execution process.

2. A resource prediction system for a fabric supply chain, applied to an online service platform, characterized in that, The system includes: A first prediction module, configured to determine the target resource application node information of the fabric supply chain execution process, perform resource prediction on the resource application node statistical data corresponding to the target resource application node information, and obtain the predicted resource data of the resource application node statistical data; An update module, configured to perform weight iterative update on the prediction weight data of the first resource prediction network according to the predicted resource data; A second prediction module, configured to use the first resource prediction network after performing weight iterative update on the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information, and obtain first resource application reference data, and use the first resource application reference data as the resource application reference data of the fabric supply chain execution process; The online service platform includes a fabric supply chain guidance system, the fabric supply chain execution process includes a fabric supply chain guidance process, and the target resource application node information includes the guidance resource application node information of the fabric supply chain guidance process on the fabric supply chain guidance system; The first prediction module is specifically configured to: Perform resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a preset resource prediction function, and obtain the predicted resource data of the resource application node statistical data; or Perform resource prediction on the resource application node statistical data corresponding to the target resource application node information by using a second resource prediction network to obtain predicted resource application reference data, and predict the predicted resource data of the resource application node statistical data according to the predicted resource application reference data; Mapping information between the resource application attributes of the resource application node statistical data and the prediction configuration information of the predicted resource application reference data is pre-configured in the online service platform, and the first prediction module is specifically configured to: Perform prediction analysis on the predicted resource application reference data to obtain the prediction configuration information of the predicted resource application reference data; Determine the resource application attributes of the resource application node statistical data according to the prediction configuration information of the predicted resource application reference data and the mapping information; The second prediction module is further configured to, after using the first resource prediction network after performing weight iterative update on the prediction weight data to perform resource prediction on the fabric supply chain execution process on the target resource application node information to obtain first resource application reference data, process the first resource application reference data based on the predicted resource data to obtain second resource application reference data, and use the second resource application reference data as the resource application reference data of the fabric supply chain execution process.

Images