Supply chain whole-process data optimization acquisition management method and system

Abstract

The invention discloses a supply chain whole-process data optimization acquisition management method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring supply chain full-process data; on the basis of the full-process data, obtaining an implosion degree value of each product; obtaining a shared urgent degree value of each product based on the popular degree value of each product; and based on the sharing urgency degree value of each product, obtaining a data uplink optimization sequence of each product. According to the application, firstly, the popular degree value of the product is evaluated according to the sales volume of the product, and the sharing urgent degree of the data corresponding to the product is analyzed on the basis of the popular degree value. Compared with a traditional method of carrying out consistent uplink sharing on all real-time supply chain data, the method can adjust the uplink priority of the product data in combination with the sharing urgent degree of the data corresponding to the product, thereby reducing the probability of occurrence of phenomena of important data lag and poor sharing performance when blockchain computing resources are insufficient.

Description

Technical Field

[0001] This application relates to the field of data processing technology, specifically a method and system for optimizing and managing data collection throughout the entire supply chain. Background Technology

[0002] A supply chain refers to a functional network structure that revolves around a core enterprise, connecting suppliers, manufacturers, distributors, and end users, from initial components to intermediate and final products, and finally delivering the products to consumers through a sales network. Blockchain-managed real-time data sharing mechanisms help enterprises grasp supply and demand information from other participants in the supply chain, enabling them to adjust production, procurement, inventory management, and decision-making in a timely manner, thereby optimizing supply chain management and reducing costs.

[0003] Traditional blockchain technology often shares all real-time data on the chain in a consistent manner when sharing data from various supply chain nodes. However, in real-world scenarios, the computational cost of sharing all real-time data is too high due to the limitations of blockchain performance, resulting in low sharing efficiency. Furthermore, the process of sharing data on the chain does not consider the product competitiveness reflected in the supply chain data or the correlation between data, leading to low data priority adaptation. This can result in the poor performance of delayed sharing of important data when blockchain computing resources are strained, further reducing the efficiency of blockchain sharing technology in supply chain management. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for optimizing and managing data collection throughout the entire supply chain, so as to solve the technical problem of low priority matching of product data on the blockchain in the prior art.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] Firstly, this application proposes a technical solution for a supply chain end-to-end data optimization collection and management method, which includes:

[0007] Acquire end-to-end supply chain data; the end-to-end data includes time-domain data of any product at any node in the supply chain; the time-domain data includes at least the sales volume, purchase volume, and inventory of the corresponding product.

[0008] Based on the full-process data, the popularity score of each product is obtained; the popularity score is used to reflect at least the magnitude of the sales volume of the corresponding product.

[0009] Based on the popularity score of each product, the urgency score of sharing for each product is obtained; the urgency score of sharing is at least used to reflect the urgency of uploading the corresponding product's data to the blockchain.

[0010] Based on the urgency of sharing among various products, the optimal order for uploading data to the blockchain for each product is determined.

[0011] Based on the preferred order of data uploading to the blockchain for each product, the data for each product is uploaded and updated.

[0012] As a specific solution in this application, the step of obtaining the popularity value of each product based on the full-process data includes:

[0013] Based on the full-process data, a first supply chain node is obtained; the first supply chain node is any supply chain node in the full-process data for which no product has obtained a best-selling status value.

[0014] Based on the first supply chain node, a first product and multiple second products are obtained; the first product is any product in the first supply chain node for which no best-selling value has been obtained; the product type of the second product is the same as that of the first product.

[0015] Based on the first product, a first average amplitude is obtained; the first average amplitude is the average of the amplitudes of each sales volume of the first product within a preset time period;

[0016] Based on each second product, obtain the average second amplitude; the average second amplitude is the average of the sales volume amplitudes corresponding to each second product within a preset time period;

[0017] Based on the first average amplitude and the second average amplitude, the popularity value of the first product is obtained.

[0018] As a specific solution in this application, obtaining the popularity value of the first product based on the first average amplitude and the second average amplitude includes:

[0019] Based on the first product, a sales volume curve is obtained; the horizontal axis of the sales volume curve is time, and the vertical axis is the sales volume of the first product.

[0020] The sales volume curve is divided into two segments to obtain the first curve segment and the second curve segment; the first curve segment is in the first time sequence, and the second curve segment is in the second time sequence.

[0021] Based on the first curve segment, obtain the first average slope;

[0022] Based on the second curve segment, obtain the second average slope;

[0023] Based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, the popularity value of the first product is obtained.

[0024] As a specific solution in this application, after obtaining the popularity value of the first product, the method further includes:

[0025] Based on the sales volume curve, obtain the fitted straight line;

[0026] Based on the fitted line, the slope of the line is obtained;

[0027] Based on the slope of the line, the popularity value of the first product is corrected.

[0028] As a specific solution in this application, the step of obtaining the sharing urgency value of each product based on its popularity value includes:

[0029] Based on the full-process data, a second supply chain node is obtained; the second supply chain node is any supply chain node in the full-process data for which any product has not obtained a sharing urgency value.

[0030] Based on the second supply chain node, a third product and multiple fourth products are obtained; the third product is any product in the second supply chain node that has not obtained a shared urgency value; the fourth product is any product in the second supply chain node other than the third product.

[0031] Based on the third product and multiple fourth products, obtain the correlation coefficients corresponding one-to-one with each fourth product; the correlation coefficients are the Pearson correlation coefficients formed between the sales volume of the third product and the sales volume of the corresponding fourth product.

[0032] Based on the popularity score and various correlation coefficients of the third product, the internal weight of the third product is obtained;

[0033] Based on the internal weights, the urgency value of sharing the third product is obtained.

[0034] As a specific solution in the technical solution of this application, the step of obtaining the sharing urgency value of the third product based on the internal weight includes:

[0035] Based on the third product, multiple third supply chain nodes are obtained; the third supply chain node is any upstream or downstream supply chain node related to the third product in the full-process data;

[0036] Based on each third supply chain node, a fourth supply chain node is obtained; the fourth supply chain node is any supply chain node among the third supply chain nodes for which the first average distance to the third product has not been obtained.

[0037] Based on the fourth supply chain node, multiple fifth products are obtained; the fifth product is any product in the fourth supply chain node.

[0038] Based on the third product and each of the fifth products, obtain the dynamic time warping distance corresponding to each of the fifth products; if the fourth supply chain node is located upstream of the third product, the input data for obtaining the dynamic time warping distance is the sales volume of the third product and the inventory of the corresponding fifth product; if the fourth supply chain node is located downstream of the third product, the input data for obtaining the dynamic time warping distance is the sales volume of the third product and the purchase volume of the corresponding fifth product.

[0039] Based on each dynamic time warp distance, a first average distance is obtained; the first average distance is the average of each dynamic time warp distance.

[0040] Based on each first average distance, a second average distance is obtained; the second average distance is the average of each first average distance; each first average distance corresponds one-to-one with each third supply chain node.

[0041] The external weights are obtained based on the internal weights and the second average distance;

[0042] Based on the external weights, the urgency value of sharing the third product is obtained.

[0043] As a specific solution in this application, after obtaining the external weights based on the internal weights and the second average distance, the method further includes:

[0044] Based on each third supply chain node, obtain the reverse correlation node; the reverse correlation node is the supply chain node in each third supply chain whose first average distance does not satisfy the propagation characteristic.

[0045] Based on each inverse correlation node, obtain the absolute value of the difference corresponding to each inverse correlation node; the absolute value of the difference is the absolute value of the difference between the first average distance corresponding to the inverse correlation node and the average value of the first average distances corresponding to the two adjacent supply chain nodes of the inverse correlation node.

[0046] The correction coefficient is obtained based on the absolute value of each difference;

[0047] The external weights are corrected based on the correction coefficient.

[0048] As a specific solution in this application, obtaining the sharing urgency value of the third product based on the external weight includes:

[0049] Based on the third product, multiple sixth products are obtained; the sixth product is a product in the full-process data whose dynamic time warp distance from the third product is greater than a preset value.

[0050] Based on each sixth product, obtain the first quantity; the first quantity is the quantity of each sixth product.

[0051] Based on the first quantity and the external weight, the sharing urgency value of the third product is obtained.

[0052] As a specific solution in this application, the step of obtaining the preferred order for data uploading to the blockchain based on the sharing urgency value of each product includes:

[0053] Based on each product, obtain the seventh product; the seventh product is any product among the products whose data uplink optimization order has not been obtained;

[0054] Based on the seventh product, a first urgency value and a second urgency value are obtained; the first urgency value is the shared urgency value corresponding to the seventh product; the second urgency value is the average of the shared urgency values ​​corresponding to all products other than the seventh product.

[0055] Based on the first urgency value and the second urgency value, the preferred order for uploading the data of the seventh product to the blockchain is obtained.

[0056] Secondly, this application proposes a technical solution for a supply chain end-to-end data optimization and collection management system, which includes:

[0057] The data acquisition module is used to acquire data from the entire supply chain process; the entire supply chain data includes time-domain data of any product at any node in the supply chain; the time-domain data includes at least the sales volume, purchase volume, and inventory of the corresponding product.

[0058] The processing module is used to obtain the popularity index of each product based on the full-process data; the popularity index is used to reflect at least the magnitude of the sales volume of the corresponding product.

[0059] Furthermore, based on the popularity score of each product, the urgency score for sharing each product is obtained; the urgency score for sharing is used to reflect at least the urgency of uploading the corresponding product's data to the blockchain;

[0060] Furthermore, based on the urgency of sharing among various products, the optimal order for uploading data to the blockchain for each product is determined;

[0061] Furthermore, based on the preferred order of data upload for each product, the data for each product is uploaded and updated.

[0062] As a specific solution in the technical solution of this application, the processing module is further configured to obtain a first supply chain node based on the full-process data; the first supply chain node is any supply chain node in the full-process data where any product has not obtained a best-selling status value;

[0063] Furthermore, based on the first supply chain node, a first product and multiple second products are obtained; the first product is any product in the first supply chain node for which no best-selling value has been obtained; the product type of the second product is the same as that of the first product.

[0064] Furthermore, based on the first product, a first average amplitude is obtained; the first average amplitude is the average of the amplitudes of each sales volume of the first product within a preset time period;

[0065] Furthermore, based on each second product, a second average amplitude is obtained; the second average amplitude is the average of the sales volume amplitudes corresponding to each second product within a preset time period.

[0066] Furthermore, based on the first average amplitude value and the second average amplitude value, the popularity value of the first product is obtained.

[0067] As a specific solution in the technical solution of this application, the processing module is further configured to obtain a sales volume curve based on the first product; the horizontal axis of the sales volume curve is time, and the vertical axis is the sales volume of the first product.

[0068] Furthermore, the sales volume curve is divided into two segments to obtain a first curve segment and a second curve segment; the first curve segment is in the first time sequence, and the second curve segment is in the second time sequence.

[0069] And, based on the first curve segment, obtain the first average slope;

[0070] And, based on the second curve segment, obtain the second average slope;

[0071] Furthermore, based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, the popularity value of the first product is obtained.

[0072] As a specific solution in the technical solution of this application, the processing module is further used to obtain a fitted straight line based on the sales volume curve;

[0073] And, based on the fitted line, the slope of the line is obtained;

[0074] Furthermore, the popularity score of the first product is corrected based on the slope of the straight line.

[0075] As a specific solution in the technical solution of this application, the processing module is further configured to obtain a second supply chain node based on the full-process data; the second supply chain node is any supply chain node in the full-process data where any product has not obtained a sharing urgency value;

[0076] Furthermore, based on the second supply chain node, a third product and multiple fourth products are obtained; the third product is any product in the second supply chain node that has not obtained a shared urgency value; the fourth product is any product in the second supply chain node other than the third product.

[0077] Furthermore, based on the third product and multiple fourth products, a correlation coefficient corresponding to each fourth product is obtained; the correlation coefficient is the Pearson correlation coefficient formed between the sales volume of the third product and the sales volume of the corresponding fourth product.

[0078] Furthermore, based on the popularity score and various correlation coefficients of the third product, the internal weight of the third product is obtained;

[0079] Furthermore, based on the internal weights, the sharing urgency value of the third product is obtained.

[0080] As a specific solution in the technical solution of this application, the processing module is further configured to obtain multiple third supply chain nodes based on the third product; the third supply chain node is any upstream or downstream supply chain node related to the third product in the full-process data;

[0081] And, based on each third supply chain node, a fourth supply chain node is obtained; the fourth supply chain node is any supply chain node among the third supply chain nodes that has not obtained the first average distance to the third product;

[0082] Furthermore, based on the fourth supply chain node, multiple fifth products are obtained; the fifth product is any product in the fourth supply chain node;

[0083] Furthermore, based on the third product and each of the fifth products, a dynamic time-warped distance corresponding to each of the fifth products is obtained; if the fourth supply chain node is located upstream of the third product, the input data for obtaining the dynamic time-warped distance is the sales volume of the third product and the inventory of the corresponding fifth product; if the fourth supply chain node is located downstream of the third product, the input data for obtaining the dynamic time-warped distance is the sales volume of the third product and the purchase volume of the corresponding fifth product.

[0084] Furthermore, based on each dynamic time warp distance, a first average distance is obtained; the first average distance is the average of each dynamic time warp distance.

[0085] Furthermore, based on each first average distance, a second average distance is obtained; the second average distance is the average of each first average distance; each first average distance corresponds one-to-one with each third supply chain node.

[0086] And, based on the internal weights and the second average distance, the external weights are obtained;

[0087] Furthermore, based on the external weights, the sharing urgency value of the third product is obtained.

[0088] As a specific solution in the technical solution of this application, the processing module is further used to obtain reverse-related nodes based on each third supply chain node; the reverse-related nodes are supply chain nodes in each third supply chain node whose corresponding first average distance does not satisfy the propagation characteristic;

[0089] Furthermore, based on each inverse correlation node, the absolute value of the difference corresponding to each inverse correlation node is obtained; the absolute value of the difference is the absolute value of the difference between the first average distance corresponding to the inverse correlation node and the average value of the first average distances corresponding to the two adjacent supply chain nodes of the inverse correlation node.

[0090] Furthermore, correction coefficients are obtained based on the absolute values ​​of each difference;

[0091] Furthermore, the external weights are corrected based on the correction coefficient.

[0092] As a specific solution in the technical solution of this application, the processing module is further configured to obtain multiple sixth products based on the third product; the sixth product is a product in the full-process data whose dynamic time regularization distance from the third product is greater than a preset value;

[0093] And, based on each sixth product, obtain a first quantity; the first quantity is the quantity of each sixth product;

[0094] Furthermore, based on the first quantity and the external weight, the sharing urgency value of the third product is obtained.

[0095] As a specific solution in the technical solution of this application, the processing module is further configured to obtain a seventh product based on each product; the seventh product is any product among the products whose data uplink priority order has not been obtained;

[0096] Furthermore, based on the seventh product, a first urgency value and a second urgency value are obtained; the first urgency value is the shared urgency value corresponding to the seventh product; the second urgency value is the average of the shared urgency values ​​corresponding to all products other than the seventh product.

[0097] Furthermore, based on the first urgency value and the second urgency value, the preferred order for uploading the data of the seventh product to the blockchain is obtained.

[0098] Compared with the prior art, the beneficial effects of this application are:

[0099] This application first assesses the product's "hot-selling" potential based on its sales volume, and then analyzes the urgency of sharing corresponding product data based on this potential. Compared to the traditional approach of consistently sharing all real-time supply chain data on-chain, this application can adjust the on-chain priority of product data based on the urgency of sharing corresponding product data. This reduces the probability of important data lag and poor sharing performance when blockchain computing resources are strained, thus optimizing the management efficiency of blockchain sharing technology for the entire supply chain. Attached Figure Description

[0100] Figure 1 This is a flowchart illustrating a supply chain end-to-end data optimization and collection management method proposed in an embodiment of this application.

[0101] Figure 2 This is a schematic diagram of the structure of a supply chain end-to-end data optimization collection and management system proposed in an embodiment of this application;

[0102] Figure 3 This is a schematic diagram illustrating the first average distance between a blockbuster product and various supply chain nodes as proposed in the embodiments of this application. Detailed Implementation

[0103] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0104] The terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. For example, the first product and the second product mentioned below are different products. It should be understood that such names can be used interchangeably where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, 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 that includes a series of steps or modules is not necessarily limited to those steps or modules explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. The division of modules in the embodiments of this application is merely a logical division. In actual applications, there may be other division methods. For example, multiple modules may be combined into or integrated into another system, or some features may be ignored or not performed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interface, and the indirect coupling or communication connection between modules may be electrical or other similar forms. None of these are limited in the embodiments of this application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed among multiple circuit modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of this application.

[0105] To address the technical problem of low priority matching of product data upload in existing supply chains, this application proposes an embodiment of a supply chain end-to-end data optimization collection and management method. For example... Figure 1 As shown, the supply chain end-to-end data optimization collection and management method includes steps S100 to S500.

[0106] Step S100: Obtain full supply chain process data.

[0107] It's important to understand that a supply chain encompasses the entire process from raw material procurement to the end consumer. A complete supply chain should include at least several nodes, such as suppliers, manufacturers, distributors, and retailers. Blockchain technology enables real-time data sharing among these nodes to avoid information silos, thus facilitating data retrieval and traceability. Traditional blockchain technology, when sharing information, indiscriminately uploads real-time data from all supply chain nodes without prioritization. This approach fails to consider the product capabilities reflected in the supply chain data and the relationships between data points, leading to reduced efficiency in supply chain management.

[0108] In this embodiment, the end-to-end data includes time-domain data of any product at any supply chain node in the supply chain. The time-domain data includes at least the sales volume, purchase volume, and inventory of the corresponding product. It is important to understand that for each individual supply chain node in the currently analyzed supply chain, products can be distinguished according to a product classification system. For example, in an apparel supply chain network, products could be shirts or shorts, etc.

[0109] It is important to clarify that the following text uses the apparel supply chain as an example to illustrate the proposed method for optimizing and managing the entire supply chain data process. This does not mean that the proposed method is only applicable to the apparel supply chain. It should be understood that the proposed method is applicable to all supply chain application scenarios, such as mobile phone supply chains or aircraft supply chains.

[0110] In this embodiment, there is no limitation on the duration of the entire process data; for example, the duration of the entire process data can be 7 days or 30 days. To avoid the inability to accurately determine the popularity of each product due to a short duration of the entire process data, and to avoid a large amount of subsequent data processing due to an excessively long duration of the entire process data, in one embodiment of this application, the duration of the entire process data can be 15 to 20 days.

[0111] Step S200: Based on the full-process data, obtain the popularity value of each product.

[0112] It's important to understand that since the entire supply chain is a product-centric network topology, the product's strength reflects crucial information in the supply chain data. For example, for relatively popular products with strong product capabilities, the demand for after-sales and inquiry services is greater. Therefore, the supply chain should focus more on recording relevant data for these products to facilitate traceability and service inquiries at each stage of the supply chain. For popular products, due to their large shipment volume, the amplitude of various data types is higher than that of similar data for other products. Based on this, in this embodiment, the popularity level value is used to at least reflect the magnitude of the corresponding product's sales volume.

[0113] In the embodiments of this application, any reasonable method can be used to obtain the popularity value of each product based on the full-process data. For example, the sales volume of a product can be directly used as the popularity value of that product. In a specific embodiment of this application, step S200, obtaining the popularity value of each product based on the full-process data, includes steps S210 to S250.

[0114] Step S210: Based on the full-process data, obtain the first supply chain node.

[0115] In this embodiment, the first supply chain node is any supply chain node in the full-process data where no product has obtained a "hot-selling" value.

[0116] Step S220: Based on the first supply chain node, obtain the first product and multiple second products.

[0117] In this embodiment, the first product is any product in the first supply chain node for which no "hot-selling" value has been obtained. That is, in this embodiment, the method for obtaining the "hot-selling" value of any product can be the same as the method for obtaining the "hot-selling" value of the first product. The product type of the second product is the same as the product type of the first product.

[0118] In the embodiments of this application, products of the same type can be set as needed. For example, shirts of different colors can be set as products of the same type; short-sleeved shirts and long-sleeved shirts can be set as products of the same type; shirts with different patterns can also be set as products of the same type, etc.

[0119] Step S230: Based on the first product, obtain the first amplitude mean.

[0120] As mentioned above, for a certain product (e.g., product number one), if the recent sales volume of the product is relatively large, then the product has more potential to become a best-selling product. In order to determine the recent actual sales volume of a certain product (e.g., product number one), in this embodiment, the first average value is the average of the sales volume values ​​of the first product within a preset time period.

[0121] In this embodiment, the length of the preset time period is not limited; that is, the length of the preset time period can be set according to needs. It should be noted that if the preset time period is set too short or too long, it will not reflect the recent actual sales volume of the first product. After multiple trials and verifications, the inventors found that the length of the preset time period can be set to 24 to 48 hours.

[0122] It is important to understand that the first average value is the average of the sales volume amplitudes of the first product within a preset time period. In the computer field, obtaining the average of multiple values ​​(i.e., the sales volume amplitudes) (i.e., the first average value) is a mature technology, which will not be elaborated here.

[0123] Step S240: Obtain the second amplitude mean based on each second product.

[0124] In this embodiment, the second average value is the average of the sales volume amplitudes corresponding to each second product within a preset time period.

[0125] Step S250: Based on the first average amplitude and the second average amplitude, obtain the popularity value of the first product.

[0126] It's important to understand that for products with the potential to become bestsellers, their sales volume will inevitably exceed that of similar products in the initial stages. In other words, in this embodiment, compared to the second average amplitude value, the larger the first average amplitude value, the greater the potential for the first product to become a bestseller. Therefore, the bestseller potential value of the first product can be the magnitude of the difference between the first average amplitude value and the second average amplitude value; for example, the bestseller potential value of the first product can be the difference or ratio between the first average amplitude value and the second average amplitude value.

[0127] It is important to note that popular products, due to their high popularity and unique product features, experience a significant short-term increase in order volume (equivalent to sales volume) at the consumer end. Furthermore, as sales volume continues to rise, their exposure will further increase, thus creating a positive feedback loop that further boosts sales. In other words, if a product shows signs of becoming a bestseller, its sales volume will gradually increase. Based on this, step S250, based on the first average amplitude and the second average amplitude, obtains the bestseller status value of the first product, including steps S251 to S255.

[0128] Step S251: Based on the first product, obtain the sales volume curve.

[0129] In this embodiment, the horizontal axis of the sales volume curve represents time, and the vertical axis represents the sales volume of the first product. In the computer field, establishing a corresponding curve (i.e., a sales volume curve) based on time-series data (i.e., sales volume) is a mature technology, and will not be elaborated upon here.

[0130] Step S252: Divide the sales volume curve into two segments to obtain the first curve segment and the second curve segment.

[0131] In this embodiment, the first curve segment occurs before the second curve segment. In this embodiment, the sales volume curve can be divided into two segments (i.e., the first curve segment and the second curve segment) in any reasonable way. For example, the midpoint of the sales volume curve can be used as the boundary to divide the sales volume curve into two segments; alternatively, a target coordinate point can be obtained from the sales volume curve. This target coordinate point is located on the sales volume curve, and it is the coordinate point whose slope is closest to the average slope of the sales volume curve. Therefore, the target coordinate point can be used as the boundary to divide the sales volume curve into two segments.

[0132] Step S253: Based on the first curve segment, obtain the first average slope.

[0133] It should be clear that obtaining the average slope (i.e., the first average slope) of a certain curve (i.e., the first curve segment) is a mature technique, which will not be elaborated here.

[0134] Step S254: Based on the second curve segment, obtain the second average slope.

[0135] It should be clear that obtaining the average slope (i.e., the second average slope) of a certain curve (i.e., the second curve segment) is a mature technique, which will not be elaborated here.

[0136] Step S255: Based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, obtain the popularity value of the first product.

[0137] In embodiments of this application, the popularity score of the first product can be obtained based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope using any reasonable method. For example, in one embodiment of this application, step S255: the calculation formula for obtaining the popularity score of the first product based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope is as follows:

[0138]

[0139] Where A1 represents the popularity value of the first product; B1 represents the first average amplitude; B2 represents the second average amplitude; k1 represents the first average slope; k2 represents the second average slope; a represents the zero-prevention coefficient, which can be a positive number infinitely close to 0. The zero-prevention coefficient a can be set according to the requirements, for example, the zero-prevention coefficient a can be 0.01 or 0.001, etc.; || represents the absolute value.

[0140] In this embodiment, the larger the blockbuster value A1 is, the greater the sales volume of the first product is likely to surge, meaning the first product has the potential to become a blockbuster; conversely, the smaller the value A1 is, the less likely the first product is to become a blockbuster.

[0141] In another embodiment of this application, step S255: Based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, the calculation formula for the popularity value of the first product is as follows:

[0142]

[0143] Wherein, A1 represents the popularity value of the first product; B1 represents the first average amplitude; B2 represents the second average amplitude; k1 represents the first average slope; k2 represents the second average slope; a represents the zero-prevention coefficient, which can be a positive number infinitely close to 0. The zero-prevention coefficient a can be set according to the requirements, for example, the zero-prevention coefficient a can be 0.01 or 0.001, etc.

[0144] In this embodiment, the larger the blockbuster value A1 is, the greater the sales volume of the first product is likely to surge, meaning the first product has the potential to become a blockbuster; conversely, the smaller the value A1 is, the less likely the first product is to become a blockbuster.

[0145] It is important to understand that the greater the increase in the sales volume of the first product, the greater the potential of the first product to become a blockbuster. Based on this, in one embodiment of this application, after obtaining the blockbuster value of the first product in step S250, the method further includes steps S260 to S280.

[0146] Step S260: Obtain a fitted straight line based on the sales volume curve.

[0147] It is important to understand that obtaining the fitted line corresponding to a certain curve (i.e., the sales volume curve) is a mature technology, which will not be elaborated here.

[0148] Step S270: Based on the fitted straight line, obtain the slope of the straight line.

[0149] It should be clear that obtaining the slope of a line (i.e., the slope of a fitted line) is a mature technique, which will not be elaborated here.

[0150] Step S280: Based on the slope of the straight line, correct the popularity value of the first product.

[0151] In this embodiment, step S280, based on the slope of the straight line, uses the following formula to correct the popularity value of the first product:

[0152] A2 = k3 × A1

[0153] Where A2 represents the modified popularity score; k3 represents the slope of the line; and A1 represents the original popularity score. In this embodiment, the larger the slope k3, the greater the potential for the first product to become a bestseller, meaning the modified popularity score is also greater.

[0154] Step S300: Based on the popularity value of each product, obtain the urgency value of sharing each product.

[0155] As the background technology shows, in the process of ensuring consistent on-chain data across the entire supply chain during traditional blockchain information sharing, the relationships between supply chain nodes are often hidden. If the blockchain transmission network performance is poor, there will be transmission lags for some strongly correlated data, which will seriously affect the sharing of important data. Based on this, in this embodiment, the sharing urgency value is used to reflect at least the degree of urgency for on-chaining the data of the corresponding product.

[0156] It's easy to understand that the higher the "popularity" value of the first product, the greater its potential to become a bestseller, and thus the greater the urgency for data related to that product to be uploaded to the blockchain. Based on this, in the embodiments of this application, the popularity value of each product can be directly used as the shared urgency value for all products. In other words, in this embodiment, if a product has a higher popularity value (i.e., a higher shared urgency value), then the data corresponding to that product should be prioritized for uploading to the blockchain.

[0157] It's important to note that within the same supply chain node, different products can have competitive or mutually beneficial relationships. For example, if the sales volume of a short-sleeved shirt printed with a certain cartoon character increases significantly, the order volume of other popular short-sleeved shirts in the same supply chain (e.g., shirts featuring characters unrelated to the original character) will compete with that shirt in terms of sales volume. Conversely, if other less popular short-sleeved shirts exist in the supply chain (e.g., shirts featuring characters related to the original character), these less popular shirts will receive more exposure due to the popularity of the higher-selling shirts in the same store, creating a mutually beneficial relationship between them. For two competing products, a change in the current product's popularity level will cause an inverse change in the other product's popularity level; conversely, for products with a mutually beneficial relationship, a change in the current product's popularity level will cause a unidirectional change in the other product's popularity level. Based on this, step S300, which obtains the sharing urgency value of each product based on the popularity value of each product, may include steps S310 to S350.

[0158] Step S310: Based on the full-process data, obtain the second supply chain node.

[0159] In this embodiment, the second supply chain node is any supply chain node in the full-process data where no product has obtained a sharing urgency value.

[0160] Step S320: Based on the second supply chain node, obtain the third product and multiple fourth products.

[0161] In this embodiment, the third product is any product in the second supply chain node that has not obtained a sharing urgency value. That is, in this embodiment, the method for obtaining the sharing urgency value of any product can be the same as the method for obtaining the sharing urgency value of the third product. The fourth product is any product in the second supply chain node other than the third product.

[0162] Step S330: Based on the third product and multiple fourth products, obtain the correlation coefficients that correspond one-to-one with each of the fourth products.

[0163] In this embodiment, the correlation coefficient is the Pearson correlation coefficient formed between the sales volume of the third product and the corresponding sales volume of the fourth product. It should be noted that obtaining the Pearson correlation coefficient between two sets of data (i.e., the sales volume of the third product and the sales volume of the fourth product) is a mature technique and will not be elaborated upon here.

[0164] Step S340: Based on the popularity value and various correlation coefficients of the third product, obtain the internal weight of the third product.

[0165] In this embodiment, step S340, based on the popularity value of the third product and various correlation coefficients, obtains the following formula for calculating the internal weight of the third product:

[0166]

[0167] Where C represents the internal weight of the third product; X represents the number of correlation coefficients; P i represents the value of the i-th correlation coefficient; || represents the absolute value; soft() represents the weighting function, which makes the sum of the internal weights of each product in the second supply chain node equal to 1.

[0168] In this embodiment, if the absolute value of the correlation coefficient between the third product and the other products in the second supply chain node (i.e., the fourth products) is larger (i.e., the greater the degree of negative or positive correlation), then the internal weight of the third product is also larger.

[0169] Step S350: Based on the internal weight, obtain the sharing urgency value of the third product.

[0170] In the embodiments of this application, the sharing urgency value of the third product can be obtained based on the internal weight using any reasonable method. For example, the sharing urgency value of the third product can be the product of the internal weight corresponding to the third product and the corresponding best-selling value.

[0171] It should be noted that product data has a transmission effect in different supply chain nodes. For example, the larger the product sales volume in a distributor, the higher the demand for production efficiency of the upstream manufacturer, and thus the higher the demand for supply from the upstream raw material supplier. Moreover, this correlation is a positive correlation. Based on this, step S350, based on the internal weight, obtains the sharing urgency value of the third product, which may include steps S351 to S358.

[0172] Step S351: Based on the third product, obtain multiple third supply chain nodes.

[0173] In this embodiment, the third supply chain node is any upstream or downstream supply chain node related to the third product in the full-process data.

[0174] Step S352: Based on each third supply chain node, obtain the fourth supply chain node.

[0175] In this embodiment, the fourth supply chain node is any supply chain node among the various third supply chain nodes that has not obtained the first average distance (see below) to the third product. That is, in this embodiment, the method for obtaining the first average distance between any third supply chain node and the third product is the same as the method for obtaining the first average distance between the fourth supply chain node and the third product.

[0176] Step S353: Based on the fourth supply chain node, obtain multiple fifth products.

[0177] In this embodiment, the fifth product is any product in the fourth supply chain node.

[0178] Step S354: Based on the third product and each of the fifth products, obtain the dynamic time warping distance corresponding to each of the fifth products.

[0179] In this embodiment, if the fourth supply chain node is located upstream of the third product, the input data for obtaining the dynamic time-normalized distance is the sales volume of the third product and the corresponding inventory of the fifth product. If the fourth supply chain node is located downstream of the third product, the input data for obtaining the dynamic time-normalized distance is the sales volume of the third product and the corresponding purchase volume of the fifth product.

[0180] It's important to understand that Dynamic Time Warping (DTW) is a classic algorithm for measuring the similarity between two time series (especially those of different lengths or with inconsistent temporal rhythms). Its core idea is to find the optimal alignment by "stretching" or "compressing" local segments of the time series, thereby calculating the minimum cumulative distance between them. This overcomes the limitations of traditional distance metrics (e.g., Euclidean distance) when dealing with unequal-length or temporally distorted sequences. In other words, in this embodiment, obtaining the DTW from the time-domain data of the third and fifth products is a mature technology and will not be elaborated upon here.

[0181] Step S355: Obtain the first average distance based on each dynamic time warp distance.

[0182] In this embodiment, the first average distance is the average of each dynamic time warp distance. It should be clear that obtaining the average of multiple values ​​(i.e., each dynamic time warp distance) (i.e., the first average distance) is a mature technology, and will not be elaborated here.

[0183] Step S356: Obtain the second average distance based on each first average distance.

[0184] In this embodiment, the second average distance is the average of all the first average distances, and each first average distance corresponds one-to-one with each third supply chain node. It should be clear that obtaining the average of multiple values ​​(i.e., the first average distances) (i.e., the second average distance) is a mature technology, and will not be elaborated here.

[0185] Step S357: Obtain the external weights based on the internal weights and the second average distance.

[0186] In this embodiment, the external weight can be obtained in any reasonable way based on the internal weight and the second average distance. For example, the external weight can be the sum or product of the internal weight and the second average distance.

[0187] In a specific embodiment of this application, step S357: Based on the internal weights and the second average distance, the calculation formula for the external weights is as follows:

[0188] D = soft(C × E)

[0189] Where D represents the external weight of the third product; C represents the internal weight of the third product; E represents the second average distance; and soft() represents the weighting function, which makes the sum of the internal weights of each product in the second supply chain node equal to 1.

[0190] In this embodiment, a larger second average distance indicates a greater correlation between the third product and its upstream and downstream supply chain nodes, meaning a greater external weight for the third product. This also indicates a greater urgency to upload the third product's relevant data to the blockchain.

[0191] It's important to note that due to the inherent lag in data transmission through the supply chain, when a product suddenly becomes a hit, for example, if supply and demand can be met at the intermediate stage, retailers are most sensitive to this surge in demand, which then propagates sequentially to distributors, manufacturers, and material suppliers. However, if supply and demand cannot be met at the intermediate stage, such as a manufacturer's reduced production efficiency, the impact of this surge in demand, while initially transmitted normally from retailers to distributors and even to the manufacturer, will gradually decrease as it spreads outwards, affecting the supply and demand relationships at both ends of the supply chain. In other words, after a product (e.g., product 3) suddenly becomes a hit, its first average distance from each supply chain node will gradually decrease from the node with the largest distance towards both ends. The closer the product's first average distance from each node conforms to this characteristic, the more reliable the external weights obtained; conversely, the less reliable the external weights, the less reliable they are.

[0192] To enable those skilled in the art to clearly understand this feature, such as Figure 3 As shown, Figure 3 This is a diagram illustrating the average distance between a blockbuster product and various nodes in the supply chain. For example... Figure 3 As shown, there are a total of 7 supply chain nodes associated with this product, namely supply chain node 1 to supply chain node 7. The first average distance between supply chain node 1 and the product is 0.56, supply chain node 2 is 0.3, supply chain node 3 is 0.6, supply chain node 4 is 0.85, supply chain node 5 is 0.7, supply chain node 6 is 0.56, and supply chain node 7 is 0.6. As mentioned earlier, since supply chain node 4 has the largest first average distance to the product (0.85), if the first average distances of the product conform to the spreading trend described above, the spread will proceed forward from supply chain node 4 (i.e., from supply chain node 4 to supply chain node 1), with each first average distance decreasing sequentially; similarly, the spread will proceed backward from supply chain node 4 (i.e., from supply chain node 4 to supply chain node 7), with each first average distance also decreasing sequentially.

[0193] It is important to note that during the traversal from the supply chain node with the largest average distance to both sides, if a single node does not conform to the above-described propagation pattern, then that supply chain node is defined as a reverse-related node. For example, ... Figure 3 As shown, since the first average distance corresponding to supply chain node 3 is 0.6 and the first average distance corresponding to supply chain node 1 is 0.56, the first average distance corresponding to supply chain node 2 should be greater than 0.56 and less than 0.6 to conform to the above-mentioned spread rule. However, the first average distance corresponding to supply chain node 2 is 0.3, therefore supply chain node 2 is a reverse-linked node. Similarly, since the first average distance corresponding to supply chain node 5 is 0.7 and the first average distance corresponding to supply chain node 7 is 0.6, the first average distance corresponding to supply chain node 6 should be greater than 0.6 and less than 0.7 to conform to the above-mentioned spread rule. However, the first average distance corresponding to supply chain node 6 is 0.78, therefore supply chain node 6 is a reverse-linked node.

[0194] As mentioned above, the normal behavior of the spread characteristic of the first average distance of each supply chain node should be that the first average distance starts from the supply chain node with the largest first average distance and spreads to both sides of it, with the first average distance decreasing sequentially. However, as can be seen from the definition of the reverse-linked node, the first average distance of the reverse-linked node does not satisfy this spread characteristic. Moreover, the greater the difference between the first average distance corresponding to the reverse-linked node and the average of the first average distances corresponding to its two adjacent supply chain nodes, the higher the degree of violation of the spread rule of the reverse-linked node. Therefore, by comprehensively considering all the reverse-linked nodes in the supply chain, the degree of conformity of the spread characteristic of this part of the supply chain data can be reflected. Consequently, the data that conforms to this characteristic more should have a larger external weight. Based on this, in one embodiment of this application, after obtaining the external weight based on the internal weight and the second average distance in step S357, the method further includes steps S357a to S357d.

[0195] Step S357a: Based on each third supply chain node, obtain the reverse related nodes.

[0196] In this embodiment, the reverse-association node is the supply chain node whose first average distance among the various third supply chain nodes does not satisfy the propagation characteristic.

[0197] Step S357b: Based on each inverse correlation node, obtain the absolute value of the difference that corresponds one-to-one with each inverse correlation node.

[0198] In this embodiment, the absolute value of the difference is the absolute value of the difference between the first average distance corresponding to the reverse-related node and the average value of the first average distances corresponding to the two adjacent supply chain nodes of the reverse-related node.

[0199] Step S357c: Obtain the correction coefficient based on the absolute value of each difference.

[0200] In the embodiments of this application, any reasonable method can be used to obtain the correction coefficient based on the absolute value of each difference. For example, the correction coefficient can be the reciprocal of the absolute value of each difference. In a specific embodiment of this application, step S357c: the calculation formula for obtaining the correction coefficient based on the absolute value of each difference is as follows:

[0201]

[0202] Where F represents the correction coefficient corresponding to the third product; Y represents the number of inversely related nodes in each third supply chain node; Q n This represents the absolute value of the difference corresponding to the nth inverse correlation node; 'a' represents the zero-prevention coefficient, which can be a positive number infinitely close to 0. The zero-prevention coefficient 'a' can be set according to the requirements, for example, it can be 0.01 or 0.001; 'norm()' represents the normalization function, which is used to map the values ​​in parentheses to the range [0, 1].

[0203] Step S357d: Based on the correction coefficient, correct the external weights.

[0204] In this embodiment, step S357d, the calculation formula for correcting the external weights based on the correction coefficient is as follows:

[0205] D'=D×F

[0206] Where D' represents the corrected external weight; D represents the original external weight; and F represents the correction coefficient corresponding to the third product.

[0207] Step S358: Based on the external weights, obtain the sharing urgency value of the third product.

[0208] In this embodiment, the greater the external weight, the greater the urgency for the data corresponding to the third product to be uploaded to the blockchain. In other words, in this embodiment, the external weight corresponding to the third product can be directly used as the sharing urgency value of the third product.

[0209] It is important to note that some data in a supply chain node may have strong correlations with data from multiple other supply chain nodes. For example, different garments manufactured by a manufacturer may use the same raw materials. When extracting data for the same product across all supply chain nodes, some data may be used in the extraction process for different products. This portion of product data is recorded as cross-application data, and this cross-application data has more important information. Based on this, in one embodiment of this application, step S358, based on the external weight, obtains the sharing urgency value of the third product, including steps S358a to S358c.

[0210] Step S358a: Based on the third product, obtain a plurality of sixth products.

[0211] In this embodiment, the sixth product is the product in the full-process data whose dynamic time warp distance from the third product is greater than a preset value.

[0212] Step S358b: Obtain the first quantity based on each sixth product.

[0213] In this embodiment, the first quantity is the quantity of each sixth product;

[0214] Step S358c: Based on the first quantity and the external weight, obtain the sharing urgency value of the third product.

[0215] In embodiments of this application, the sharing urgency value of the third product can be obtained based on the first quantity and the external weight in any reasonable manner. For example, in one embodiment of this application, step S358c: the calculation formula for obtaining the sharing urgency value of the third product based on the first quantity and the external weight is as follows:

[0216] G = norm(R × D')

[0217] Where G represents the urgency of sharing the third product; R represents the first quantity; D' represents the corrected external weight; norm() represents the normalization function, used to map the values ​​in parentheses to the range [0, 1].

[0218] In another embodiment of this application, step S358c: Based on the first quantity and the external weight, the calculation formula for obtaining the sharing urgency value of the third product is as follows:

[0219]

[0220] Where G represents the urgency value of sharing the third product; R represents the first quantity; R maxD' represents the maximum value of the first quantity corresponding to each product in the second supply chain node; D' represents the corrected external weight; norm() represents the normalization function, which is used to map the values ​​in parentheses to the range [0, 1].

[0221] Step S400: Based on the urgency value of sharing for each product, obtain the preferred order for uploading data to the blockchain for each product.

[0222] In this embodiment, the sharing urgency value corresponding to each product can be directly used as the preferred order for data uploading to the blockchain for each product. That is, the higher the sharing urgency value of a product, the higher the preferred order for data uploading to the blockchain for that product.

[0223] In a specific embodiment of this application, step S400, based on the sharing urgency value of each product, obtains the preferred order of data uploading to the blockchain for each product, including steps S410 to S430.

[0224] Step S410: Based on each product, obtain the seventh product.

[0225] In this embodiment, the seventh product is any product among all products for which the preferred order for data uploading to the blockchain has not been obtained. That is to say, in this embodiment, the method for obtaining the preferred order for data uploading to the blockchain of any product can be the same as the method for obtaining the preferred order for data uploading to the blockchain of the seventh product.

[0226] Step S420: Based on the seventh product, obtain the first urgency value and the second urgency value.

[0227] In this embodiment, the first urgency value is the shared urgency value corresponding to the seventh product. The second urgency value is the average of the shared urgency values ​​corresponding to all products other than the seventh product.

[0228] Step S430: Based on the first urgency value and the second urgency value, obtain the preferred order for uploading the data of the seventh product to the blockchain.

[0229] Specifically, in step S430, based on the first urgency value and the second urgency value, the calculation formula for the preferred order of data uplink of the seventh product is as follows:

[0230] H = norm(J1 - J2)

[0231] Where H represents the preferred order of data uploading for the seventh product; J1 represents the first urgency value; J2 represents the second urgency value; norm() represents the normalization function, used to map the values ​​in parentheses to the range [0, 1].

[0232] In this embodiment, the higher the preferred order for uploading the data of the seventh product to the blockchain, the greater the urgency of uploading the data of the seventh product to the blockchain.

[0233] Step S500: Based on the preferred order of data uploading to the blockchain for each product, upload and update the data for each product.

[0234] As mentioned earlier, the higher the product's priority order for on-chain data upload, the greater the urgency of uploading the product's data to the blockchain. In other words, in this embodiment, data corresponding to products with higher priority order for on-chain data upload is uploaded first.

[0235] It is important to understand that the embodiment of the supply chain end-to-end data optimization collection and management method proposed in this application first assesses the product's "hot-selling" value based on its sales volume, and then analyzes the urgency of sharing the corresponding product data based on this value. Compared to the traditional approach of consistently uploading and sharing all real-time supply chain data onto the blockchain, this application can adjust the priority of uploading product data to the blockchain based on the urgency of sharing the corresponding product data. This reduces the probability of important data lag and poor sharing performance when blockchain computing resources are strained, thereby optimizing the management efficiency of blockchain sharing technology for the entire supply chain.

[0236] Having introduced the supply chain end-to-end data optimization collection and management method proposed in the embodiments of this application, the following describes an embodiment of a supply chain end-to-end data optimization collection and management system proposed in this application. For example... Figure 2 As shown, the supply chain end-to-end data optimization and collection management system 10 includes:

[0237] The data acquisition module 11 is used to acquire data from the entire supply chain process; the data from the entire process includes time-domain data of any product at any node in the supply chain; the time-domain data includes at least the sales volume, purchase volume and inventory of the corresponding product.

[0238] Processing module 12 is used to obtain the popularity index of each product based on the full-process data; the popularity index is used to reflect at least the magnitude of the sales volume of the corresponding product;

[0239] Furthermore, based on the popularity score of each product, the urgency score for sharing each product is obtained; the urgency score for sharing is used to reflect at least the urgency of uploading the corresponding product's data to the blockchain;

[0240] Furthermore, based on the urgency of sharing among various products, the optimal order for uploading data to the blockchain for each product is determined;

[0241] Furthermore, based on the preferred order of data upload for each product, the data for each product is uploaded and updated.

[0242] As a specific embodiment of this application, the processing module 12 is further configured to obtain a first supply chain node based on the full-process data; the first supply chain node is any supply chain node in the full-process data where any product has not obtained a best-selling status value;

[0243] Furthermore, based on the first supply chain node, a first product and multiple second products are obtained; the first product is any product in the first supply chain node for which no best-selling value has been obtained; the product type of the second product is the same as that of the first product.

[0244] Furthermore, based on the first product, a first average amplitude is obtained; the first average amplitude is the average of the amplitudes of each sales volume of the first product within a preset time period;

[0245] Furthermore, based on each second product, a second average amplitude is obtained; the second average amplitude is the average of the sales volume amplitudes corresponding to each second product within a preset time period.

[0246] Furthermore, based on the first average amplitude value and the second average amplitude value, the popularity value of the first product is obtained.

[0247] As a specific embodiment of this application, the processing module 12 is further configured to obtain a sales volume curve based on the first product; the horizontal axis of the sales volume curve is time, and the vertical axis is the sales volume of the first product;

[0248] Furthermore, the sales volume curve is divided into two segments to obtain a first curve segment and a second curve segment; the first curve segment is in the first time sequence, and the second curve segment is in the second time sequence.

[0249] And, based on the first curve segment, obtain the first average slope;

[0250] And, based on the second curve segment, obtain the second average slope;

[0251] Furthermore, based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, the popularity value of the first product is obtained.

[0252] As a specific embodiment of this application, the processing module 12 is further configured to obtain a fitted straight line based on the sales volume curve;

[0253] And, based on the fitted line, the slope of the line is obtained;

[0254] Furthermore, the popularity score of the first product is corrected based on the slope of the straight line.

[0255] As a specific embodiment of this application, the processing module 12 is further configured to obtain a second supply chain node based on the full-process data; the second supply chain node is any supply chain node in the full-process data where any product has not obtained a sharing urgency value;

[0256] Furthermore, based on the second supply chain node, a third product and multiple fourth products are obtained; the third product is any product in the second supply chain node that has not obtained a shared urgency value; the fourth product is any product in the second supply chain node other than the third product.

[0257] Furthermore, based on the third product and multiple fourth products, a correlation coefficient corresponding to each fourth product is obtained; the correlation coefficient is the Pearson correlation coefficient formed between the sales volume of the third product and the sales volume of the corresponding fourth product.

[0258] Furthermore, based on the popularity score and various correlation coefficients of the third product, the internal weight of the third product is obtained;

[0259] Furthermore, based on the internal weights, the sharing urgency value of the third product is obtained.

[0260] As a specific embodiment of this application, the processing module 12 is further configured to obtain multiple third supply chain nodes based on the third product; the third supply chain node is any upstream or downstream supply chain node related to the third product in the full-process data;

[0261] And, based on each third supply chain node, a fourth supply chain node is obtained; the fourth supply chain node is any supply chain node among the third supply chain nodes that has not obtained the first average distance to the third product;

[0262] Furthermore, based on the fourth supply chain node, multiple fifth products are obtained; the fifth product is any product in the fourth supply chain node;

[0263] Furthermore, based on the third product and each of the fifth products, a dynamic time-warped distance corresponding to each of the fifth products is obtained; if the fourth supply chain node is located upstream of the third product, the input data for obtaining the dynamic time-warped distance is the sales volume of the third product and the inventory of the corresponding fifth product; if the fourth supply chain node is located downstream of the third product, the input data for obtaining the dynamic time-warped distance is the sales volume of the third product and the purchase volume of the corresponding fifth product.

[0264] Furthermore, based on each dynamic time warp distance, a first average distance is obtained; the first average distance is the average of each dynamic time warp distance.

[0265] Furthermore, based on each first average distance, a second average distance is obtained; the second average distance is the average of each first average distance; each first average distance corresponds one-to-one with each third supply chain node.

[0266] And, based on the internal weights and the second average distance, the external weights are obtained;

[0267] Furthermore, based on the external weights, the sharing urgency value of the third product is obtained.

[0268] As a specific embodiment of this application, the processing module 12 is further configured to obtain reverse-related nodes based on each third supply chain node; the reverse-related nodes are supply chain nodes in each third supply chain node whose corresponding first average distance does not satisfy the propagation characteristic;

[0269] Furthermore, based on each inverse correlation node, the absolute value of the difference corresponding to each inverse correlation node is obtained; the absolute value of the difference is the absolute value of the difference between the first average distance corresponding to the inverse correlation node and the average value of the first average distances corresponding to the two adjacent supply chain nodes of the inverse correlation node.

[0270] Furthermore, correction coefficients are obtained based on the absolute values ​​of each difference;

[0271] Furthermore, the external weights are corrected based on the correction coefficient.

[0272] As a specific embodiment of this application, the processing module 12 is further configured to obtain multiple sixth products based on the third product; the sixth product is a product in the full-process data whose dynamic time warp distance from the third product is greater than a preset value;

[0273] And, based on each sixth product, obtain a first quantity; the first quantity is the quantity of each sixth product;

[0274] Furthermore, based on the first quantity and the external weight, the sharing urgency value of the third product is obtained.

[0275] As a specific embodiment of this application, the processing module 12 is further configured to obtain a seventh product based on each product; the seventh product is any product among the products whose preferred order of data uplink has not been obtained;

[0276] Furthermore, based on the seventh product, a first urgency value and a second urgency value are obtained; the first urgency value is the shared urgency value corresponding to the seventh product; the second urgency value is the average of the shared urgency values ​​corresponding to all products other than the seventh product.

[0277] Furthermore, based on the first urgency value and the second urgency value, the preferred order for uploading the data of the seventh product to the blockchain is obtained.

[0278] It is important to understand that the embodiment of the supply chain end-to-end data optimization and collection management system proposed in this application first assesses the product's popularity level based on its sales volume, and then analyzes the urgency of sharing the corresponding product data based on this popularity level. Compared to the traditional approach of consistently uploading and sharing all real-time supply chain data onto the blockchain, this application can adjust the priority of uploading product data to the blockchain based on the urgency of sharing the corresponding product data. This reduces the probability of important data lag and poor sharing performance when blockchain computing resources are strained, thereby optimizing the management efficiency of blockchain sharing technology for the entire supply chain.

[0279] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0280] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the methods, apparatuses, and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0281] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between devices or modules may be electrical, mechanical, or other forms.

[0282] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0283] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0284] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0285] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video optical disc), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0286] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles of this application.

Claims

1. A method for optimizing and managing data collection throughout the entire supply chain, characterized in that, include: Acquire end-to-end supply chain data; the end-to-end data includes time-domain data of any product at any node in the supply chain. The time-domain data includes at least the sales volume, purchase volume, and inventory of the corresponding product; Based on the full-process data, the popularity score of each product is obtained; the popularity score is used to reflect at least the magnitude of the sales volume of the corresponding product. Based on the popularity score of each product, the urgency score of sharing for each product is obtained; the urgency score of sharing is at least used to reflect the urgency of uploading the corresponding product's data to the blockchain. Based on the urgency of sharing among various products, the optimal order for uploading data to the blockchain for each product is determined. Based on the preferred order of data uploading to the blockchain for each product, the data for each product is uploaded and updated.

2. The supply chain end-to-end data optimization collection and management method according to claim 1, characterized in that, The process of obtaining the popularity score of each product based on the full-process data includes: Based on the full-process data, a first supply chain node is obtained; the first supply chain node is any supply chain node in the full-process data for which no product has obtained a best-selling status value. Based on the first supply chain node, a first product and multiple second products are obtained; the first product is any product in the first supply chain node for which no best-selling value has been obtained; the product type of the second product is the same as that of the first product. Based on the first product, a first average amplitude is obtained; the first average amplitude is the average of the amplitudes of each sales volume of the first product within a preset time period; Based on each second product, obtain the average second amplitude; the average second amplitude is the average of the sales volume amplitudes corresponding to each second product within a preset time period; Based on the first average amplitude and the second average amplitude, the popularity value of the first product is obtained.

3. The supply chain end-to-end data optimization collection and management method according to claim 2, characterized in that, The step of obtaining the popularity score of the first product based on the first average amplitude and the second average amplitude includes: Based on the first product, a sales volume curve is obtained; the horizontal axis of the sales volume curve is time, and the vertical axis is the sales volume of the first product. The sales volume curve is divided into two segments to obtain the first curve segment and the second curve segment; the first curve segment is in the first time sequence, and the second curve segment is in the second time sequence. Based on the first curve segment, obtain the first average slope; Based on the second curve segment, obtain the second average slope; Based on the first average amplitude, the second average amplitude, the first average slope, and the second average slope, the popularity value of the first product is obtained.

4. The supply chain end-to-end data optimization collection and management method according to claim 3, characterized in that, After obtaining the popularity score of the first product, the method further includes: Based on the sales volume curve, obtain the fitted straight line; Based on the fitted line, the slope of the line is obtained; Based on the slope of the line, the popularity value of the first product is corrected.

5. The supply chain end-to-end data optimization collection and management method according to any one of claims 1 to 4, characterized in that, The process of obtaining the sharing urgency value of each product based on its popularity score includes: Based on the full-process data, a second supply chain node is obtained; the second supply chain node is any supply chain node in the full-process data for which any product has not obtained a sharing urgency value. Based on the second supply chain node, a third product and multiple fourth products are obtained; the third product is any product in the second supply chain node that has not obtained a shared urgency value; the fourth product is any product in the second supply chain node other than the third product. Based on the third product and multiple fourth products, obtain the correlation coefficients corresponding one-to-one with each fourth product; the correlation coefficients are the Pearson correlation coefficients formed between the sales volume of the third product and the sales volume of the corresponding fourth product. Based on the popularity score and various correlation coefficients of the third product, the internal weight of the third product is obtained; Based on the internal weights, the urgency value of sharing the third product is obtained.

6. The supply chain end-to-end data optimization collection and management method according to claim 5, characterized in that, The step of obtaining the sharing urgency value of the third product based on the internal weight includes: Based on the third product, multiple third supply chain nodes are obtained; the third supply chain node is any upstream or downstream supply chain node related to the third product in the full-process data; Based on each third supply chain node, a fourth supply chain node is obtained; the fourth supply chain node is any supply chain node among the third supply chain nodes for which the first average distance to the third product has not been obtained. Based on the fourth supply chain node, multiple fifth products are obtained; the fifth product is any product in the fourth supply chain node. Based on the third product and each of the fifth products, obtain the dynamic time warping distance corresponding to each of the fifth products; if the fourth supply chain node is located upstream of the third product, the input data for obtaining the dynamic time warping distance is the sales volume of the third product and the inventory of the corresponding fifth product; if the fourth supply chain node is located downstream of the third product, the input data for obtaining the dynamic time warping distance is the sales volume of the third product and the purchase volume of the corresponding fifth product. Based on each dynamic time warp distance, a first average distance is obtained; the first average distance is the average of each dynamic time warp distance. Based on each first average distance, a second average distance is obtained; the second average distance is the average of each first average distance; each first average distance corresponds one-to-one with each third supply chain node. The external weights are obtained based on the internal weights and the second average distance; Based on the external weights, the urgency value of sharing the third product is obtained.

7. The supply chain end-to-end data optimization collection and management method according to claim 6, characterized in that, After obtaining the external weights based on the internal weights and the second average distance, the method further includes: Based on each third supply chain node, obtain the reverse correlation node; the reverse correlation node is the supply chain node in each third supply chain whose first average distance does not satisfy the propagation characteristic. Based on each inverse correlation node, obtain the absolute value of the difference corresponding to each inverse correlation node; the absolute value of the difference is the absolute value of the difference between the first average distance corresponding to the inverse correlation node and the average value of the first average distances corresponding to the two adjacent supply chain nodes of the inverse correlation node. The correction coefficient is obtained based on the absolute value of each difference; The external weights are corrected based on the correction coefficient.

8. The supply chain end-to-end data optimization collection and management method according to claim 6, characterized in that, The step of obtaining the sharing urgency value of the third product based on the external weights includes: Based on the third product, multiple sixth products are obtained; the sixth product is a product in the full-process data whose dynamic time warp distance from the third product is greater than a preset value. Based on each sixth product, obtain the first quantity; the first quantity is the quantity of each sixth product. Based on the first quantity and the external weight, the sharing urgency value of the third product is obtained.

9. The supply chain end-to-end data optimization collection and management method according to claim 8, characterized in that, The process of obtaining the preferred order for uploading data to the blockchain for each product based on the urgency value of sharing among each product includes: Based on each product, obtain the seventh product; the seventh product is any product among the products whose data uplink optimization order has not been obtained; Based on the seventh product, a first urgency value and a second urgency value are obtained; the first urgency value is the shared urgency value corresponding to the seventh product; the second urgency value is the average of the shared urgency values ​​corresponding to all products other than the seventh product. Based on the first urgency value and the second urgency value, the preferred order for uploading the data of the seventh product to the blockchain is obtained.

10. A supply chain end-to-end data optimization and collection management system, characterized in that, include: The data acquisition module is used to acquire data from the entire supply chain process; the data from the entire process includes time-domain data of any product at any node in the supply chain. The time-domain data includes at least the sales volume, purchase volume, and inventory of the corresponding product; The processing module is used to obtain the popularity index of each product based on the full-process data; the popularity index is used to reflect at least the magnitude of the sales volume of the corresponding product. Furthermore, based on the popularity score of each product, the urgency score for sharing each product is obtained; the urgency score for sharing is used to reflect at least the urgency of uploading the corresponding product's data to the blockchain; Furthermore, based on the urgency value of sharing for each product, the preferred order for uploading data to the blockchain for each product is obtained; and based on the preferred order for uploading data to the blockchain for each product, the data for each product is uploaded and updated.

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