A Digital Supply Chain Data Analysis and Processing Method and System

Abstract

The present invention provides a method and system for digital supply chain data analysis and processing, belonging to the technical field of data processing. Specifically, it includes: determining the transportation mileage of raw materials of different qualified enterprises through the locations of the qualified enterprises, and determining the supply delay risk of the qualified enterprises through the transportation mileage of different matching transportation methods. When the supply delay risk of the qualified enterprises meets the requirements, obtaining the change situation between the matching transportation methods during the transportation process of the qualified enterprises and the loss data of different matching transportation methods, and determining the transportation risk and transportation reliable enterprises of the qualified enterprises by combining the transportation mileage of different matching transportation methods and the type of raw materials. Obtaining the historical purchase data of the raw materials, and determining the optimization processing object of the supply chain of the transportation reliable enterprises by combining the transportation risk, supply delay risk and raw material data deviation of different transportation reliable enterprises, ensuring the reliability of the supply of raw materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a method and system for digital supply chain data analysis and processing. Background Art

[0002] The normal operation of textile enterprises involves a variety of raw materials, and the supply reliability of the raw material supply chain is crucial for the normal operation of textile enterprises. Therefore, it has become an urgent technical problem to improve the operation reliability of the supply chain of textile enterprises based on the analysis and processing of supply chain data.

[0003] In order to improve the operation reliability of the supply chain, in the existing technical solutions, the supply chain procurement data of raw materials in different dimensions is determined by combining order data. Specifically, in the invention patent application CN202310905941.9, "Textile Chemical Fiber Supply Chain Management Decision Support System and Method", a similar technical solution is given. However, the following technical defects exist in the existing technical solutions:

[0004] For the supply chain enterprises of raw materials of textile enterprises, due to the differences in distribution locations, there are differences in the transportation mileage and transportation methods of their raw materials, which will not only lead to differences in the timeliness of raw material supply, but also there are certain differences in the quality risks of raw materials during transportation. Therefore, if the above factors cannot be combined to determine the differentiated supply chain optimization method, the supply reliability of the supply chain of textile enterprises cannot be guaranteed.

[0005] In view of the above technical problems, the present invention provides a method and system for digital supply chain data analysis and processing. Summary of the Invention

[0006] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0007] According to one aspect of the present invention, a method for digital supply chain data analysis and processing is provided.

[0008] A method for digital supply chain data analysis and processing specifically includes:

[0009] S1 Obtain the supply chain enterprises of the raw materials of the textile enterprise, use historical supply data to determine the data deviation situations of different supply chain enterprises in different dimensions of the raw materials, and determine the raw material data deviation of different supply chain enterprises and the qualified enterprises in the supply chain enterprises through the data deviation situations;

[0010] S2 Determine the transportation mileage of the raw materials of different qualified enterprises through the locations of the qualified enterprises, and determine the supply delay risk of the qualified enterprises through the transportation mileage of different matching transportation methods;

[0011] S3 When the supply delay risk of the qualified enterprise meets the requirements, obtain the change situation between the matching transportation modes during the transportation process of the qualified enterprise and the loss data of different matching transportation modes, and determine the transportation risk and reliable transportation enterprises of the qualified enterprise by combining the transportation mileage of different matching transportation modes and the type of the raw material.

[0012] S4 Obtain the historical purchase data of the raw material, and determine the optimization processing object of the supply chain of the reliable transportation enterprise by combining the transportation risks, supply delay risks and raw material data deviations of different reliable transportation enterprises.

[0013] The beneficial effects of the present invention are as follows:

[0014] 1. In the present invention, by determining the raw material data deviation of different supply chain enterprises and the qualified enterprises in the supply chain according to the data deviation situation, not only the screening of unqualified enterprises is realized from the perspective of the data deviation situation that meets the data requirements of different dimensions of the raw material, but also the data deviation situations of other supply chain enterprises in different dimensions are considered, avoiding the impact on the safe and reliable production of textile enterprises due to the large deviation between the raw material data and other supply chain enterprises, and improving the determination processing efficiency of the optimization processing object of the supply chain by screening unqualified enterprises.

[0015] 2. In the present invention, based on the historical purchase data, the transportation risks, supply delay risks and raw material data deviations of different reliable transportation enterprises, the optimization processing object of the supply chain of the reliable transportation enterprise is determined. It not only considers the differences in the demand for the number of supply chain enterprises due to the differences in the purchase quantity and purchase batches, but also further combines the differences in the optimization processing priorities of different reliable transportation enterprises due to the differences in transportation risks, supply delay risks and raw material data deviations, so as to realize the differential optimization processing of reliable transportation enterprises and improve the reliability of the supply of raw materials.

[0016] A further technical solution is that the supply chain enterprises of the raw material are determined according to the analysis result of the historical supply data of the raw material of the textile enterprise.

[0017] A further technical solution is that the dimension is determined according to the standard requirements of the raw material in different dimensions. Specifically, the screening requirement dimension is determined by using the standard requirements, and the dimension is determined through the screening requirement dimension.

[0018] A further technical solution is that the data deviation situation includes the data deviation amounts between different supply chain enterprises in different dimensions.

[0019] A further technical solution lies in that the method for determining the raw material data deviation of the supply chain enterprise is as follows:

[0020] Using the data deviation situation to determine the data deviation amounts of different dimensions of the raw materials between the supply chain enterprise and other supply chain enterprises, and using the data deviation amounts of different dimensions of the raw materials with other supply chain enterprises to determine the data deviation enterprises of the supply chain enterprise;

[0021] Based on the number of the data deviation enterprises, determine the raw material data deviation of the supply chain enterprise.

[0022] A further technical solution lies in that based on the number of the data deviation enterprises, determining the raw material data deviation of the supply chain enterprise specifically includes:

[0023] Taking the ratio of the number of the data deviation enterprises to the number of the supply chain enterprise as the raw material data deviation of the supply chain enterprise.

[0024] A further technical solution lies in that when the number of dimensions for which the data deviation amounts between other supply chain enterprises and the supply chain enterprise do not meet the requirements is greater than the preset number of dimensions, then determine the other supply chain enterprises as data deviation enterprises.

[0025] A further technical solution lies in that the method for determining the supply chain optimization processing object of the transportation reliable enterprise is as follows:

[0026] Determine the historical purchase batches of the raw materials through the historical purchase data of the raw materials, and combine the purchase quantities of the raw materials in different historical purchase batches to determine the limited quantity of the supply chain enterprises of the raw materials;

[0027] Based on the weights of the transportation risks, supply delay risks, and raw material data deviations of different transportation reliable enterprises, determine the processing priority values of different transportation reliable enterprises;

[0028] According to the processing priority values and the limited quantity of the supply chain enterprises, determine the supply chain optimization processing object of the transportation reliable enterprise.

[0029] A further technical solution lies in that according to the processing priority values and the limited quantity of the supply chain enterprises, determining the supply chain optimization processing object of the transportation reliable enterprise specifically includes:

[0030] According to the limited quantity of the supply chain enterprises and the number of the transportation reliable enterprises, determine the optimized processing quantity of the transportation reliable enterprise;

[0031] Determine the optimization processing object of the supply chain based on the optimized processing quantity of the transportation reliable enterprise and the processing priority value.

[0032] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned digital supply chain data analysis processing method.

[0033] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are achieved and obtained by the structure specifically pointed out in the specification and the drawings.

[0034] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, the detailed description is as follows. Description of the Drawings

[0035] By referring to the drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious;

[0036] Figure 1 is a flowchart of a digital supply chain data analysis processing method;

[0037] Figure 2 is a flowchart of a method for determining the raw material data deviation of a supply chain enterprise;

[0038] Figure 3 is a flowchart of a method for determining the supply delay risk of a qualified enterprise;

[0039] Figure 4 is a flowchart of a method for determining the transportation risk of a qualified enterprise;

[0040] Figure 5 is a flowchart of a method for determining the optimization processing object of the supply chain of a transportation reliable enterprise;

[0041] Figure 6 is a framework diagram of a computer system. Detailed Embodiments

[0042] To enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0043] For supply chain enterprises of raw materials for textile enterprises, due to differences in the transportation mileage and transportation methods of raw materials among different supply chain enterprises, there will be certain differences in the timeliness of raw material supply and the quality risk of raw materials during transportation. Therefore, it is necessary to optimize supply chain enterprises by combining timeliness and quality risk to ensure the reliability of the supply chain of textile enterprises.

[0044] Specifically, in this application, the supply delay risk is determined according to the transportation mileage of supply chain enterprises, and the transportation risk is determined according to the transportation method and the switching situation between transportation methods. Finally, the determination of the supply chain optimization processing object is realized by combining the types and quantities of raw materials provided by different supply chain enterprises, the supply delay risk, and the transportation risk.

[0045] Raw material data deviation of supply chain enterprises: It is determined according to the proportion of the number of dimensions in which the raw materials of the supply chain enterprise have data deviation. Specifically, when the proportion of the number of dimensions is 0.1, the raw material data deviation of the supply chain enterprise is 0.1.

[0046] Qualified enterprises: Supply chain enterprises with a raw material data deviation less than 0.05 are qualified enterprises.

[0047] Supply delay risk: It is determined according to the sum of the preset delay risks corresponding to the transportation mileage of different matching transportation methods. Specifically, if the preset delay risks are 0.1, 0.2, and 0.3 respectively, the supply delay risk is 0.6.

[0048] Supply delay risk meets the requirements: When the supply delay risk is less than 0.3, it is determined that the supply delay risk meets the requirements.

[0049] Transportation risk of qualified enterprises: It is determined according to the sum of the loss rates of different matching transportation methods in qualified enterprises. If the loss rates are 0.1, 0.05, and 0.2 respectively, the transportation risk of qualified enterprises is 0.35.

[0050] Transportation reliable enterprises: Qualified enterprises with a transportation risk less than 0.3. Embodiment 1

[0051] To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, a digital supply chain data analysis and processing method is provided, which specifically includes:

[0052] S1 Obtain supply chain enterprises of raw materials for textile enterprises, use historical supply data to determine the data deviation situations of different supply chain enterprises in different dimensions of the raw materials, and determine the raw material data deviation of different supply chain enterprises and qualified enterprises in the supply chain enterprises through the data deviation situations;

[0053] Furthermore, the supply chain enterprise of the raw materials is determined according to the analysis result of the historical supply data of the raw materials of the textile enterprise.

[0054] It should be noted that the dimension is determined according to the standard requirements of the raw materials in different dimensions. Specifically, the dimension of the screening requirements is determined by using the standard requirements, and the dimension is determined through the dimension of the screening requirements.

[0055] It can be understood that the data deviation situation includes the data deviation amounts between different supply chain enterprises in different dimensions.

[0056] It should be further noted that as Figure 2 shown, the method for determining the raw material data deviation of the supply chain enterprise is:

[0057] Use the data deviation situation to determine the data deviation amounts of the supply chain enterprise and other supply chain enterprises in different dimensions of the raw materials, and use the data deviation amounts of the raw materials in different dimensions with other supply chain enterprises to determine the data deviation enterprises of the supply chain enterprise;

[0058] Based on the number of the data deviation enterprises, determine the raw material data deviation of the supply chain enterprise.

[0059] It can be understood that determining the raw material data deviation of the supply chain enterprise based on the number of the data deviation enterprises specifically includes:

[0060] Take the ratio of the number of the data deviation enterprises and the number of the supply chain enterprise as the raw material data deviation of the supply chain enterprise.

[0061] Furthermore, when the number of dimensions for which the data deviation amounts between other supply chain enterprises and the supply chain enterprise do not meet the requirements is greater than the preset number of dimensions, then determine the other supply chain enterprises as data deviation enterprises.

[0062] In another embodiment, the method for determining the raw material data deviation of the supply chain enterprise is:

[0063] Use the data deviation situation to determine the data deviation amounts of the supply chain enterprise and other supply chain enterprises in different dimensions of the raw materials, and use the data deviation amounts to determine the dimension deviation enterprises of the supply chain enterprise in different dimensions. Judge whether there are dimensions for which the number of dimension deviation enterprises does not meet the requirements. If so, determine that the supply chain enterprise does not belong to a qualified enterprise. If not, proceed to the next step;

[0064] Determine the comprehensive data deviation of the supply chain enterprise in different dimensions based on the dimensional deviation enterprises of the supply chain enterprise in different dimensions and the data deviation amounts of different dimensional deviation enterprises, and judge whether there are dimensions where the comprehensive data deviation of the supply chain enterprise does not meet the requirements. If so, determine that the supply chain enterprise does not belong to a qualified enterprise. If not, proceed to the next step;

[0065] Determine the number of dimensions where the data deviation amount of the supply chain enterprise and other supply chain enterprises in the raw materials does not meet the requirements based on the data deviation amounts of the supply chain enterprise and other supply chain enterprises in different dimensions, and judge whether there are other supply chain enterprises where the number of dimensions with data deviation amounts not meeting the requirements does not meet the requirements. If so, determine that the supply chain enterprise does not belong to a qualified enterprise. If not, proceed to the next step;

[0066] Based on the data deviation amounts of the supply chain enterprise and other supply chain enterprises in different dimensions, determine the material data deviation of the supply chain enterprise and other supply chain enterprises in the raw materials, and judge whether there are other supply chain enterprises where the material data deviation does not meet the requirements. If so, determine that the supply chain enterprise does not belong to a qualified enterprise. If not, proceed to the next step;

[0067] Perform the raw material data deviation of the supply chain enterprise through the material data deviation of the supply chain enterprise and different other supply chain enterprises in the raw materials.

[0068] It should be noted that when the supply chain enterprise does not belong to a qualified enterprise, the supply chain enterprise is taken as the object for supply chain optimization processing.

[0069] S2 Determine the transportation mileage of the raw materials of different qualified enterprises through the locations of the qualified enterprises, and determine the supply delay risk of the qualified enterprises through the transportation mileage of different matching transportation methods;

[0070] Furthermore, the transportation mileage is determined based on the location of the qualified enterprise and the location of the textile enterprise.

[0071] It can be understood that the matching transportation method is determined according to the transportation method of the qualified enterprise on the transportation route, where the transportation route is determined based on the location of the qualified enterprise and the location of the textile enterprise.

[0072] It should be noted that as Figure 3 shown, the method for determining the supply delay risk of the qualified enterprise is:

[0073] Determine the matching delay risks of different matching transportation modes in different transportation mileage intervals based on the types of different matching transportation modes, and determine the transportation delay risks of different matching transportation modes in combination with the transportation mileage of different matching transportation modes;

[0074] Determine the supply delay risk of the qualified enterprise through the transportation delay risks of different matching transportation modes.

[0075] Furthermore, the value range of the supply delay risk of the qualified enterprise is between 0 and 1. When the supply delay risk of the qualified enterprise is greater than the preset risk, it is determined that the supply delay risk of the qualified enterprise does not meet the requirements.

[0076] It should be further noted that when the supply delay risk of the qualified enterprise does not meet the requirements, the qualified enterprise is taken as the object for supply chain optimization processing.

[0077] In another embodiment, the method for determining the supply delay risk of the qualified enterprise is as follows:

[0078] Determine the basic delay risk of the raw materials of the qualified enterprise through the transportation mileage of the raw materials of the qualified enterprise;

[0079] Determine the transportation mode dispersion degree of the qualified enterprise according to the proportion of the transportation mileage of the matching transportation mode of the qualified enterprise;

[0080] Determine the delay risk compensation amount of the qualified enterprise through the transportation mode dispersion degree of the qualified enterprise, and determine the supply delay risk of the qualified enterprise in combination with the basic delay risk of the raw materials of the qualified enterprise.

[0081] In another embodiment, the method for determining the supply delay risk of the qualified enterprise is as follows:

[0082] S21 Determine the basic delay risk of the raw materials of the qualified enterprise through the transportation mileage of the raw materials of the qualified enterprise, and judge whether the basic delay risk of the raw materials of the qualified enterprise meets the requirements. If so, go to the next step; if not, determine that the supply delay risk of the qualified enterprise does not meet the requirements;

[0083] S22 Judge whether the basic delay risk of the raw materials of the qualified enterprise is within the preset delay risk interval. If so, go to the next step; if not, go to step S25;

[0084] S23 Determine the dispersion degree of the transportation modes of the qualified enterprises according to the proportion of the transportation mileage of the matching transportation modes of the qualified enterprises, and judge whether the dispersion degree of the transportation modes of the qualified enterprises meets the requirements. If so, proceed to the next step; if not, determine that the supply delay risk of the qualified enterprises does not meet the requirements.

[0085] S24 Determine the matching delay risks of different matching transportation modes in different transportation mileage intervals based on different types of matching transportation modes, and determine the transportation delay risks of different matching transportation modes in combination with the transportation mileage of different matching transportation modes. Judge whether there is a matching transportation mode with a transportation delay risk that does not meet the requirements. If so, determine that the supply delay risk of the qualified enterprises does not meet the requirements; if not, proceed to the next step.

[0086] S25 Determine the supply delay risk of the qualified enterprises through the transportation delay risks of different matching transportation modes and the basic delay risk.

[0087] S3 When the supply delay risk of the qualified enterprises meets the requirements, obtain the change situation between the matching transportation modes during the transportation process of the qualified enterprises and the loss data of different matching transportation modes, and determine the transportation risk and transportation reliable enterprises of the qualified enterprises in combination with the transportation mileage of different matching transportation modes and the type of the raw materials.

[0088] It should be further noted that the method for determining the transportation risk of the qualified enterprises is as follows:

[0089] Determine the historical transportation times with loss situations through the loss data of different matching transportation modes, and judge whether the historical transportation times with loss situations of the qualified enterprises are greater than the preset transportation times. If so, determine that the qualified enterprises do not belong to transportation reliable enterprises; if not, proceed to the next step.

[0090] Determine the total loss quantity of the qualified enterprises according to the loss quantities of different historical transportation times with loss situations, and judge whether the total loss quantity of the qualified enterprises meets the requirements. If so, proceed to the next step; if not, determine that the qualified enterprises do not belong to transportation reliable enterprises.

[0091] Determine the historical loss risk of the raw materials of the qualified enterprises through the loss data of different matching transportation modes to determine the historical transportation times with loss situations, and combine the proportion of the historical transportation times with loss situations and the loss quantities of the historical transportation times with loss situations. Judge whether the historical loss risk of the raw materials of the qualified enterprises meets the requirements. If so, proceed to the next step; if not, determine that the qualified enterprises do not belong to transportation reliable enterprises.

[0092] Determine the number of changes in the matching transportation mode during the transportation of the qualified enterprise by using the switching situation of the matching transportation mode during the transportation of the qualified enterprise, and determine the handling and transportation risk of the raw materials of the qualified enterprise through the type of the raw materials and the number of changes;

[0093] Determine the transportation breakage risk of the raw materials of the qualified enterprise based on the transportation mileage of different matching transportation modes and the type of the raw materials;

[0094] Determine the transportation risk of the qualified enterprise by using the transportation breakage risk, historical breakage risk and handling and transportation risk of the raw materials of the qualified enterprise, and determine the transportation reliable enterprise in the qualified enterprise based on the transportation risk.

[0095] Further, determining the transportation reliable enterprise in the qualified enterprise based on the transportation risk specifically includes:

[0096] When the transportation risk of the qualified enterprise is greater than the preset transportation risk threshold, it is determined that the qualified enterprise does not belong to the transportation reliable enterprise.

[0097] It can be understood that when the qualified enterprise does not belong to the transportation reliable enterprise, the qualified enterprise is used as the object for supply chain optimization processing.

[0098] In another embodiment, as Figure 4 shown, the method for determining the transportation risk of the qualified enterprise is:

[0099] Determine the number of changes in the matching transportation mode during the transportation of the qualified enterprise by using the switching situation of the matching transportation mode during the transportation of the qualified enterprise, and determine the handling and transportation risk of the raw materials of the qualified enterprise through the type of the raw materials and the number of changes;

[0100] Determine the historical transportation times with transportation losses by using the transportation loss data of different matching transportation modes, and determine the historical transportation loss risk of the raw materials of the qualified enterprise in combination with the proportion of the historical transportation times with transportation losses and the number of transportation losses in the historical transportation times with transportation losses;

[0101] Determine the transportation breakage risk of the raw materials of the qualified enterprise based on the transportation mileage of different matching transportation modes and the type of the raw materials;

[0102] Determine the transportation risk of the qualified enterprise by using the transportation breakage risk, historical breakage risk and handling and transportation risk of the raw materials of the qualified enterprise, and determine the transportation reliable enterprise in the qualified enterprise based on the transportation risk.

[0103] S4 obtains the historical procurement data of the raw materials, and determines the objects for optimizing the supply chain of the reliable transportation enterprises by combining the transportation risks, supply delay risks and raw material data deviations of different reliable transportation enterprises.

[0104] Further, the historical procurement data includes the historical procurement batches of the raw materials, the procurement quantities of the raw materials in different historical procurement batches, and the interval duration between adjacent historical procurement times.

[0105] It should be noted that, as Figure 5 shown, the method for determining the objects for optimizing the supply chain of the reliable transportation enterprises is as follows:

[0106] Determine the historical procurement batches of the raw materials through the historical procurement data of the raw materials, and determine the limited quantity of the supply chain enterprises of the raw materials by combining the procurement quantities of the raw materials in different historical procurement batches;

[0107] Determine the processing priority values of different reliable transportation enterprises based on the weights and of the transportation risks, supply delay risks and raw material data deviations of different reliable transportation enterprises;

[0108] Determine the objects for optimizing the supply chain of the reliable transportation enterprises according to the processing priority values and the limited quantity of the supply chain enterprises.

[0109] It can be understood that determining the objects for optimizing the supply chain of the reliable transportation enterprises according to the processing priority values and the limited quantity of the supply chain enterprises specifically includes:

[0110] Determine the optimized processing quantity of the reliable transportation enterprises according to the limited quantity of the supply chain enterprises and the quantity of the reliable transportation enterprises;

[0111] Determine the objects for optimizing the supply chain according to the optimized processing quantity of the reliable transportation enterprises and the processing priority values. Embodiment 2

[0112] In the second aspect, as Figure 6 shown, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, wherein: when the processor runs the computer program, it executes the above-mentioned digital supply chain data analysis and processing method.

[0113] Wherein the above-mentioned digital supply chain data analysis and processing method specifically includes:

[0114] Supply chain enterprises that obtain raw materials for textile enterprises use historical supply data to determine the data deviation situations of different supply chain enterprises in different dimensions of the raw materials, and determine the raw material data deviation of different supply chain enterprises and the qualified enterprises in the supply chain enterprises through the data deviation situations;

[0115] Determine the transportation mileage of the raw materials of different qualified enterprises through the locations of the qualified enterprises, and determine the supply delay risks of the qualified enterprises through the transportation mileage of different matching transportation methods;

[0116] Determine the number of changes in the matching transportation methods during the transportation process of the qualified enterprises by using the switching situations of the matching transportation methods of the qualified enterprises during the transportation process, and determine the handling and transportation risks of the raw materials of the qualified enterprises through the type of the raw materials and the number of changes;

[0117] Determine the historical number of transportation times with loss situations through the loss data of different matching transportation methods, and determine the historical loss risks of the raw materials of the qualified enterprises by combining the proportion of the historical number of transportation times with loss situations and the loss quantity of the historical number of transportation times with loss situations. Determine the transportation breakage risks of the raw materials of the qualified enterprises based on the transportation mileage of different matching transportation methods and the type of the raw materials. Determine the transportation risks of the qualified enterprises by using the transportation breakage risks, historical breakage risks, and handling and transportation risks of the raw materials of the qualified enterprises, and determine the transportation reliable enterprises among the qualified enterprises based on the transportation risks;

[0118] Determine the historical purchase batches of the raw materials through the historical purchase data of the raw materials, and determine the limited quantity of the supply chain enterprises of the raw materials by combining the purchase quantities of the raw materials in different historical purchase batches;

[0119] Determine the processing priority values of different transportation reliable enterprises based on the weights and of the transportation risks, supply delay risks, and raw material data deviations of different transportation reliable enterprises, and determine the supply chain optimization processing objects of the transportation reliable enterprises according to the processing priority values and the limited quantity of the supply chain enterprises.

[0120] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0121] The specific embodiments of the present specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0122] The above description is only for one or more embodiments of the present specification and is not intended to limit the present specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of the present specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included within the scope of the claims of the present specification.

Claims

1. A digital supply chain data analysis and processing method, characterized in that: Specifically include: Obtain the supply chain enterprises of the raw materials of the textile enterprises, use the historical supply data to determine the data deviation of different supply chain enterprises in different dimensions of the raw materials, and determine the raw material data deviation of different supply chain enterprises and qualified enterprises in the supply chain enterprises through the data deviation; Determine the transportation mileage of raw materials of different qualified enterprises through the location of qualified enterprises, and determine the supply delay risk of qualified enterprises through the transportation mileage of different matching transportation modes; When the supply delay risk of the qualified enterprise meets the requirements, obtain the change of the matching transportation modes of the qualified enterprise during the transportation process and the transportation loss data of different matching transportation modes, and determine the transportation risk of the qualified enterprise and the reliable transportation enterprise in combination with the transportation mileage of different matching transportation modes and the type of the raw materials; Obtain historical procurement data of the raw materials, and determine supply chain optimization processing objects of reliable transport enterprises in combination with transport risks, supply delay risks and raw material data deviations of different reliable transport enterprises; The raw material data deviation of a supply chain enterprise is determined based on the proportion of the number of dimensions in which the supply chain enterprise has data deviations in the raw materials.

2. The digital supply chain data analysis and processing method according to claim 1, characterized in that: The supply chain enterprise of the raw materials is determined based on the analysis results of the historical supply data of the raw materials of the textile enterprise.

3. The digital supply chain data analysis and processing method according to claim 1, characterized in that: The dimensions are determined according to the standard requirements of the raw materials in different dimensions. Specifically, the standard requirements are used to determine the screening requirement dimensions, and the different dimensions are determined through the screening requirement dimensions.

4. The digital supply chain data analysis and processing method according to claim 1, characterized in that: When the supply chain enterprise is not a qualified enterprise, the supply chain enterprise will be taken as an object of supply chain optimization processing.

5. The digital supply chain data analysis and processing method according to claim 1, characterized in that: The matching transportation mode is determined according to the transportation mode of the qualified enterprise on the transportation route, wherein the transportation route is determined according to the location of the qualified enterprise and the location of the textile enterprise.

6. The digital supply chain data analysis and processing method according to claim 1, characterized in that: The method for determining the supply delay risk of the qualified enterprise is: Determine the matching delay risk of different matching transport modes in different transport mileage intervals based on the types of different matching transport modes, and determine the transportation delay risk of different matching transport modes in combination with the transport mileage of different matching transport modes; The supply delay risk of the qualified enterprise is determined by matching the transportation delay risks of different transportation modes.

7. The digital supply chain data analysis and processing method according to claim 1, characterized in that: The method for determining the supply delay risk of the qualified enterprise is: S21 determines the basic delay risk of the raw materials of the qualified enterprise according to the transportation mileage of the raw materials of the qualified enterprise, and judges whether the basic delay risk of the raw materials of the qualified enterprise meets the requirements. If so, proceed to the next step; if not, it is determined that the supply delay risk of the qualified enterprise does not meet the requirements; S22 determines whether the basic delay risk of the raw materials of the qualified enterprise is within the preset delay risk range, if so, proceeds to the next step, if not, proceeds to step S25; S23 determines the dispersion of the modes of transportation of the qualified enterprise according to the proportion of transportation mileage of the matching modes of transportation of the qualified enterprise, and judges whether the dispersion of the modes of transportation of the qualified enterprise meets the requirements. If so, proceed to the next step; if not, it is determined that the supply delay risk of the qualified enterprise does not meet the requirements; S24 determines the matching delay risks of different matching transportation modes in different transportation mileage intervals based on the types of different matching transportation modes, and determines the transportation delay risks of different matching transportation modes in combination with the transportation mileages of different matching transportation modes, and determines whether there is a matching transportation mode whose transportation delay risk does not meet the requirements. If so, it is determined that the supply delay risk of the qualified enterprise does not meet the requirements. If not, proceed to the next step; S25 determines the supply delay risk of the qualified enterprise by matching the transportation delay risks of different transportation modes and the basic delay risk.

8. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, a digital supply chain data analysis and processing method as described in any one of claims 1-7 is executed.

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