Industrial chain price signal hub node identification management system and data processing method

Abstract

The application provides an industry chain price signal hub node identification management system and a data processing method, and belongs to the technical field of data processing, and specifically comprises: a pairing processing module responsible for screening edges with price changes on both ends into a computable pairing set; a price transmission analysis module responsible for calculating the Pearson correlation coefficient of the logarithmic yield rate of each edge in the computable pairing set as the price transmission strength of the connecting edge of the varieties on both ends; a screening module responsible for retaining edges with a transmission strength greater than or equal to a threshold θ, and constructing a weighted directed price transmission subgraph with the transmission strength as the weight; and a node screening module responsible for calculating the price in-degree Pin, the price out-degree Pout, the weighted in-degree and the weighted out-degree of each node in the weighted price transmission subgraph, and fusing the Pin, the Pout, the weighted in-degree and the weighted out-degree into a hub score, sorting the hub scores from high to low, and selecting the nodes before a preset order to construct a hub node list, so that the effective identification of the hub nodes is realized.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a management system and data processing method for identifying and managing pivotal nodes of price signals in an industrial chain. Background Technology

[0002] In the commodity supply chain, price signals are transmitted sequentially along the upstream and downstream relationships—rising prices of upstream raw materials drive up processing costs in the midstream, which in turn affects downstream product prices. Accurately identifying the key nodes in the price signal propagation process is crucial for establishing an efficient price monitoring system, reducing information collection costs, and improving the efficiency of price early warning systems.

[0003] Existing technical solutions construct a directed graph representing the upstream and downstream relationships of the industry chain, calculate the out-degree (the number of edges that the node transmits downstream) and in-degree (the number of edges that the node receives from the upstream), and identify nodes with higher degrees as key nodes in the industry chain, thus enabling the identification and processing of pivotal nodes in price changes within the industry chain. However, this approach suffers from the following technical shortcomings: When identifying and analyzing price changes in the supply chain, different commodities have different data sources, which often leads to failures in obtaining price data due to problems with the data source. Therefore, how to verify the availability of data sources and update the identification of commodities to determine the availability of different data sources, thereby reducing acquisition failures and improving the reliability of price data acquisition and processing, has become an urgent technical problem to be solved.

[0004] In summary, to solve the above-mentioned technical problems, this application provides a supply chain price signal hub node identification and management system and data processing method. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, in the first aspect, this application provides a supply chain price signal hub node identification and management system, which specifically includes: The price acquisition and processing module is responsible for determining the monitoring and analysis strategy for the price data of related products based on the similarity of related products in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, it identifies related products for price data verification and comparison analysis in different data sources and uses them as verification target products to verify the degree of correlation between the data source of the target products and the related products of the monitoring and analysis targets, as well as the price data acquisition and processing strategy of the verification target products, and determines the method for acquiring and processing the price data of the monitoring and analysis targets. The pairing processing module obtains all upstream and downstream relationship edges from the upstream and downstream relationship table of the industrial chain of the monitoring and analysis target based on the acquisition processing method, and filters out edges with price changes at both ends and includes them in the computable pairing set. The price transmission analysis module is responsible for extracting the historical price series of the two end products of each edge in the computable pair set, calculating the Pearson correlation coefficient of the logarithmic returns, and using it as the price transmission strength of the connecting edge between the two end products. The filtering module is responsible for retaining edges with a transmission strength greater than or equal to a threshold θ in the full topology graph, and constructing a weighted directed price transmission subgraph with the transmission strength as the weight. The node filtering module is responsible for calculating the price in-degree Pin, price out-degree Pout, weighted in-degree, and weighted out-degree of each node in the weighted price transmission subgraph. It then weights and merges Pin, Pout, weighted in-degree, and weighted out-degree into a hub score, sorts the nodes by score from high to low, and selects the nodes before the preset order to construct a hub node list.

[0006] Furthermore, the calculation formula for the hub score is: hub score = w1 × (Pin + Pout) + w2 × (weighted in-degree + weighted out-degree) + w3 × price penetration, where price penetration = Pin × Pout, and w1, w2, and w3 are preset weight values.

[0007] Furthermore, the method also includes classifying hubs into price signal source type, price signal sink type, and price signal bridge type based on the relative size of Pin and Pout: Price signal source type: Pout >> Pin, price radiates outwards; Price signal pattern: Pin >> Pout, influenced by multiple price movements; Price signal bridge pattern: Pin ≈ Pout, price inflow and outflow equilibrium.

[0008] Furthermore, the price activity rate of each node is calculated as: Price Activity Rate = Number of valid edges in the subgraph / Total number of edges in the topology × 100%. The trend of price activity rate is monitored: when the price activity rate of a node continues to decline and exceeds the preset threshold, a relationship dormancy warning is triggered.

[0009] Furthermore, the criteria for screening calculable pairs include rapid pre-screening based on the labels "upstream has market information" and "this product has market information" in the upstream-downstream relationship table.

[0010] Secondly, this application provides a data processing method applied to the aforementioned industry chain price signal hub node identification and management system, specifically including: Based on the monitoring and analysis targets of the industrial chain, identify the related commodities in the industrial chain of the monitoring and analysis targets, and determine the monitoring and analysis strategy for the price data of the related commodities according to the degree of similarity of the related commodities in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, related commodities for price data verification and comparison analysis in different data sources are identified and used as verification target commodities. Based on the composition data of the verification target commodities in the related commodities of each monitoring and analysis target, the price data acquisition and processing strategy of the verification target commodities is determined. Determine the degree of correlation between the data source of the target product and the related products of the monitoring and analysis target, and determine the method for obtaining and processing the price data of the monitoring and analysis target in combination with the acquisition and processing strategy of the price data of the target product; Based on the acquisition and processing method, the acquisition and processing of the prices of the associated products of the monitoring and analysis target are determined to obtain the acquisition failure processing process. According to the acquisition and processing data of the monitoring and analysis target, and the degree of overlap between the data source of the associated failed products in the acquisition failure processing process and the data source of the verification target products, the update processing method of the verification target products of the monitoring and analysis target is determined.

[0011] Furthermore, the related products in the industrial chain of the monitored and analyzed target are the products in the industrial chain of the monitored and analyzed target.

[0012] Furthermore, the method for determining the monitoring and analysis strategy for the price data of the related commodities is as follows: Based on the degree of similarity of related goods in the industrial chain between the monitoring and analysis targets, determine the amount of overlap of related goods in the industrial chain between the monitoring and analysis targets. The similarity coefficient of the related products in the industrial chain between the monitored and analyzed targets is determined by the number of overlapping products in the industrial chain between the monitored and analyzed targets. Based on the similarity coefficient of related products in the industrial chain between the monitoring and analysis targets, a monitoring and analysis strategy for the price data of the related products is determined.

[0013] The beneficial effects of this invention are as follows: Based on the similarity of related commodities in the industrial chain between the monitoring and analysis targets, a monitoring and analysis strategy for the price data of related commodities is determined. This achieves the reliability of the verification process for the availability of price data sources for related commodities in the industrial chain of different monitoring and analysis targets after the identification and processing of target commodities from the perspective of similarity. Based on the reliability, it determines which commodities to use as verification target commodities, thus laying the foundation for the effective verification of the availability status of different price data sources.

[0014] By acquiring the failure handling process and the degree of overlap between the data source of the failed related products and the price of the verification target product in the failure handling process, the reliability of the acquisition and processing of the price data of the current monitoring and analysis target product is determined, as well as the reference value of the data source of the price of the verification target product. Based on the reference value and the reliability of the acquisition and processing of the price data, the update processing method of the verification target product of the monitoring and analysis target is determined, that is, how to perform the update processing of the verification target product, thereby laying the foundation for further improving the reliability of the acquisition and processing of the price data of the related products of the monitoring and analysis target.

[0015] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0018] Figure 1 This is a framework diagram of a supply chain price signal hub node identification and management system; Figure 2 It is a flowchart of a data processing method; Figure 3 This is a flowchart illustrating the method for determining the monitoring and analysis strategy for related product price data; Figure 4 This is a flowchart illustrating the method for determining the acquisition and processing strategy of price data for the target product. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] Example 1 like Figure 1 As shown, this application provides a supply chain price signal hub node identification and management system, specifically including: The price acquisition and processing module is responsible for determining the monitoring and analysis strategy for the price data of related products based on the similarity of related products in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, it identifies related products for price data verification and comparison analysis in different data sources and uses them as verification target products to verify the degree of correlation between the data source of the target products and the related products of the monitoring and analysis targets, as well as the price data acquisition and processing strategy of the verification target products, and determines the method for acquiring and processing the price data of the monitoring and analysis targets. The pairing processing module obtains all upstream and downstream relationship edges from the upstream and downstream relationship table of the industrial chain of the monitoring and analysis target based on the acquisition processing method, and filters out edges with price changes at both ends and includes them in the computable pairing set. The price transmission analysis module is responsible for extracting the historical price series of the two end products of each edge in the computable pair set, calculating the Pearson correlation coefficient of the logarithmic returns, and using it as the price transmission strength of the connecting edge between the two end products. The filtering module is responsible for retaining edges with a transmission strength greater than or equal to a threshold θ in the full topology graph, and constructing a weighted directed price transmission subgraph with the transmission strength as the weight. The node filtering module is responsible for calculating the price in-degree Pin, price out-degree Pout, weighted in-degree, and weighted out-degree of each node in the weighted price transmission subgraph. It then weights and merges Pin, Pout, weighted in-degree, and weighted out-degree into a hub score, sorts the nodes by score from high to low, and selects the nodes before the preset order to construct a hub node list.

[0021] Furthermore, the calculation formula for the hub score is: hub score = w1 × (Pin + Pout) + w2 × (weighted in-degree + weighted out-degree) + w3 × price penetration, where price penetration = Pin × Pout, and w1, w2, and w3 are preset weight values.

[0022] Specifically, the method also includes classifying hubs into price signal source type, price signal sink type, and price signal bridge type based on the relative size of Pin and Pout: Price signal source type: Pout >> Pin, price radiates outwards; Price signal pattern: Pin >> Pout, influenced by multiple price movements; Price signal bridge pattern: Pin ≈ Pout, price inflow and outflow equilibrium.

[0023] Understandably, the price activity rate of each node is calculated as: Price Activity Rate = Number of valid edges in the subgraph / Total number of edges in the topology × 100%. The trend of price activity rate changes is monitored: when the price activity rate of a node continues to decline and exceeds a preset threshold, a relationship dormancy warning is triggered.

[0024] It should be noted that the criteria for screening calculable pairs include rapid pre-screening based on the labels "upstream has market information" and "this product has market information" in the upstream-downstream relationship table.

[0025] Specifically, the hub score is calculated as follows: Hub score = w1 × (Pin + Pout) + w2 × (weighted in-degree + weighted out-degree) + w3 × price penetration, where price penetration = Pin × Pout, and w1, w2, and w3 are preset weight values.

[0026] Additionally, it should be noted that the method also includes classifying hubs into price signal source type, price signal sink type, and price signal bridge type based on the relative size of Pin and Pout: Price signal source type: Pout >> Pin, price radiates outwards; Price signal pattern: Pin >> Pout, influenced by multiple price movements; Price signal bridge pattern: Pin ≈ Pout, price inflow and outflow equilibrium.

[0027] Specifically, calculate the price activity rate of each node: Price activity rate = number of valid edges in the subgraph / total number of edges in the topology × 100%. Monitor the trend of price activity rate changes: when the price activity rate of a node continues to decline and exceeds the preset threshold, trigger a relationship dormancy warning.

[0028] Understandably, the criteria for screening calculable pairs include rapid pre-screening based on the labels "upstream has market information" and "this product has market information" in the upstream-downstream relationship table.

[0029] Specifically, the logarithmic return = ln(P) t / P t-1 ) = ln(P t ) - ln(P t-1 Pt is the daily average price P on trading day t. t-1 The price In is the daily average of the previous trading day, which is the natural logarithm with a base e. The Pearson correlation coefficient is calculated using the following formula:

[0030] Where: n is the number of common trading days; X_t is the daily logarithmic return of the upstream product on the t-th common trading day; Y_t is the daily logarithmic return of the downstream product on the t-th common trading day; This represents the mean of the daily logarithmic return series of upstream commodities. = (1 / n) × Σ X_t; The mean of the daily logarithmic return series of downstream products. = (1 / n) × Σ Y_t.

[0031] In one possible embodiment, a transmission strength threshold θ = 0.3 is set. From the total of 3,358 topological edges, 300 edges with market data for both sides are selected, and positive correlation coefficients are calculated for 267 of them. Then, effective transmission edges with r ≥ 0.3 are selected, resulting in a weighted price transmission subgraph consisting of 59 nodes.

[0032] The top 5 price signal hubs are: Billet (PID=72): Hub score 3.11. The price signal of billet enters through 1 inbound edge and spreads downstream to rebar, hot-rolled coil, wire rod, etc. through 4 outbound edges, with a penetration of 4. Of the 21 topological relationships of billet, only 5 (23.8%) are active at the price level.

[0033] Hot-rolled coil (PID=831): Hub score 2.56. 2 in, 2 out, penetration 4. Activity rate 26.7% (4 out of 15 relationships activated).

[0034] Benzene (PID=48): Hub score 2.50. 1 in, 3 out. As a basic raw material in the chemical industry chain, its price is rapidly transmitted to downstream products such as styrene and phenol.

[0035] Naphtha (PID=394): Hub score 2.49. 0 in, 4 out, a typical price signal source type hub—as the starting point for refining, its price changes radiate unidirectionally to multiple downstream sectors (gasoline, toluene, propylene, etc.).

[0036] Praseodymium-neodymium alloy (PID=982): Hub score 2.09. 3 in, 0 out, a typical price signal convergence hub—as a rare earth metal product, its price converges multiple price signals from upstream oxide raw materials such as neodymium oxide and praseodymium oxide. Activity rate 100% (all 3 topological relationships are activated at the price level).

[0037] It is worth noting that three of the top five hubs are nodes in the ferrous metals / steel chain (steel billets and hot-rolled coils), which is related to the highly market-oriented pricing and standardized production processes in the steel industry chain—transparent pricing and clear transmission paths make the transmission efficiency of price signals in the steel chain much higher than in other industry chains.

[0038] Based on the hub scores, it is recommended that the top 5 nodes be included in the "Industry Chain Price Signal Sentinel Monitoring Pool".

[0039] These five nodes cover the area with the most concentrated price signal interaction in the entire industry chain. When price anomalies occur at these key nodes: (1) trace the range of downstream affected varieties through out-degree; (2) trace upstream varieties that may be the source of their price changes through in-degree; (3) determine whether there is a risk of transmission breakage by combining the breakage warning (Scheme 2).

[0040] Example 2 like Figure 2 As shown, this application provides a data processing method applied to the aforementioned supply chain price signal hub node identification and management system, specifically including: S1 uses the monitoring and analysis target of the industrial chain to identify related products in the industrial chain of the monitoring and analysis target, and determines the monitoring and analysis strategy for the price data of the related products based on the similarity of the related products in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, S2 identifies related commodities for price data verification and comparison analysis in different data sources, and uses them as verification target commodities. Based on the composition data of the verification target commodities in the related commodities of each monitoring and analysis target, S2 determines the acquisition and processing strategy of the price data of the verification target commodities. S3 determines the degree of correlation between the data source of the target product and the related products of the monitoring and analysis target, and determines the method for obtaining and processing the price data of the monitoring and analysis target in combination with the acquisition and processing strategy of the price data of the target product. S4 determines the acquisition failure process based on the acquisition processing method to determine the acquisition processing of the price of the associated product of the monitoring and analysis target. Based on the acquisition processing data of the monitoring and analysis target and the degree of overlap between the data source of the associated failed product and the data source of the verification target product in the acquisition failure process, the update processing method of the verification target product of the monitoring and analysis target is determined.

[0041] Furthermore, the related products in the industrial chain of the monitored and analyzed target are the products in the industrial chain of the monitored and analyzed target.

[0042] Specifically, such as Figure 3 As shown, the method for determining the monitoring and analysis strategy for the price data of the related commodities is as follows: In this embodiment, a monitoring and analysis strategy for the price data of related products is determined based on the similarity of related products in the industrial chain between the monitoring and analysis targets. This realizes the reliability of the verification process for the availability of price data sources of related products in the industrial chain of different monitoring and analysis targets after the identification of target products from the perspective of similarity. Based on the reliability, it determines which products to use as verification target products, thus laying the foundation for the effective verification of the availability status of different price data sources.

[0043] S11 determines the number of overlapping related goods in the industrial chain between the monitoring and analysis targets based on the degree of similarity of related goods in the industrial chain between the monitoring and analysis targets; S12 determines the similarity coefficient of related products in the industrial chain between the monitored and analyzed targets by analyzing the number of overlapping related products in the industrial chain between the monitored and analyzed targets; S13 determines the monitoring and analysis strategy for the price data of the related products based on the similarity coefficient of the related products in the industrial chain between the monitoring and analysis targets.

[0044] It is understood that the similarity coefficient of related products in the industrial chain between the monitored and analyzed targets is determined based on the proportion of related products in the industrial chain between the monitored and analyzed targets and other monitored and analyzed targets.

[0045] Specifically, based on the similarity coefficient of related products in the industrial chain between the monitoring and analysis targets, a monitoring and analysis strategy for the price data of the related products is determined, including: If the average similarity coefficient of related products in the industrial chain between various monitoring and analysis targets is less than the preset similarity coefficient threshold, the monitoring and analysis strategy for the price data of the related products is to take the related products from the data source with more than one target quantity of price data as the verification target products. If the average similarity coefficient of related products in the industrial chain between various monitoring and analysis targets is not less than the preset similarity coefficient threshold, then the monitoring and analysis strategy for the price data of the related products is to take the related products of the monitoring and analysis targets that have more than one target number of price data sources and belong to the related products of the monitoring and analysis targets that are above the preset number threshold as the verification target products.

[0046] Specifically, such as Figure 4 As shown, the method for determining the acquisition and processing strategy of the price data of the target product for verification is as follows: In this embodiment, based on the composition data of the verification target products in the associated products of each monitoring and analysis target, the reliability of obtaining and processing the price data of the associated products of the monitoring and analysis target is determined. Based on the reliability of obtaining and processing the price data of the associated products of the monitoring and analysis target, the acquisition and processing strategy of the price data of the verification target products is determined, thereby realizing the balanced control of the availability verification of the data source and the difficulty of data acquisition and processing.

[0047] S21 uses the composition data of the verification target products in the associated products of each monitoring and analysis target to determine the composition ratio of the verification target products in the associated products of the monitoring and analysis target. S22 determines the matching weight coefficient for acquiring the price data of the monitoring and analysis target based on the composition ratio; S23 determines the acquisition and processing strategy for the price data of the verification target commodity by matching the acquisition weight coefficients of the price data of each monitoring and analysis target.

[0048] It is understandable that the acquisition and processing strategy for the price data of the verification target commodity is determined by matching the weighting coefficients of the price data acquisition for each monitoring and analysis target, specifically including: Based on the acquisition matching weight coefficient of the monitoring data of each monitoring and analysis target, the monitoring and analysis targets with an acquisition matching weight coefficient not less than the preset matching weight coefficient threshold are determined and used as reliable monitoring targets. Based on the proportion of reliable monitoring targets among the monitoring and analysis targets and the average of the matching weight coefficients for the acquisition of monitoring data of each monitoring and analysis target, a comprehensive reliability value is determined. It is then determined whether the comprehensive reliability value is greater than a preset reliability threshold. If so, the acquisition and processing strategy for the price data of the verification target commodity is determined to be a basic acquisition strategy. If not, the acquisition and processing strategy for the price data of the verification target commodity is determined to be a dynamic acquisition strategy.

[0049] Specifically, the basic acquisition strategy determines the price data acquisition and processing strategy based on the number of monitoring and analysis targets to which the verification target product is located. That is, when the number of monitoring and analysis targets to which the verification target product is located is greater than the preset threshold for the number of monitoring and analysis targets, the basic cycle is used for price data acquisition and processing; otherwise, the second basic cycle is used for price data acquisition and processing.

[0050] It should be noted that the basic period is shorter than the second basic period.

[0051] It is understood that the dynamic acquisition strategy specifically includes: When the number of monitoring and analysis targets for the target product being verified is greater than the preset threshold for the number of monitoring and analysis targets, the basic cycle is used to acquire and process the price data. Otherwise, if the number of failed attempts to acquire the price data for the target product within the most recent preset time period is greater than the preset failure number threshold, the basic cycle is used to acquire and process the price data. If the number of failed attempts to acquire the price data for the target product within the most recent preset time period is not greater than the preset failure number threshold, the second basic cycle is used to acquire and process the price data.

[0052] It is understood that the method for determining the acquisition and processing method of the price data of the monitoring and analysis target is as follows: In this embodiment, based on the correlation between the data source of the target product and the associated product of the monitoring and analysis target, and the acquisition and processing strategy of the price data of the target product, the data source of the associated product of the monitoring and analysis target is determined. The reliability and timeliness of the data source verification processing using the target product are considered. By using the data source of the associated product of the monitoring and analysis target to verify the data source, the method for acquiring and processing the price data of the monitoring and analysis target is determined. This not only reduces the occurrence of price data acquisition and processing when there are anomalies in the data source, but also ensures the timeliness of price data acquisition and processing.

[0053] S31 determines the overlap ratio between the data sources of the related products of the monitoring and analysis target and the data sources of the related products of the monitoring and analysis target based on the degree of correlation between the data sources of the related products of the monitoring and analysis target and the data sources of the related products of the monitoring and analysis target. Based on the overlap ratio between the data sources of the related products of the monitoring and analysis target and the data sources of the related products of the monitoring and analysis target and the data sources of the related products of the monitoring and analysis target, the correlation weight coefficient between the monitoring and analysis product and different verification target products is determined. It is understandable that the above steps include the following situations: Case 1: The correlation weight value of the monitored and analyzed product is determined by the sum of the correlation weight coefficients between the monitored and analyzed product and different verification target products. If the correlation weight value of the monitored and analyzed product is not greater than the preset correlation weight threshold, then the method for obtaining and processing the price data of the monitored and analyzed target is as follows: as long as the number of times the verification target product is obtained in the price data source acquisition and processing process in the most recent preset time period is greater than the preset number of times it is obtained, and there is no acquisition failure, then the price data acquisition and processing of the associated products of the monitored and analyzed target is performed. Case 2: If the correlation weight value of the monitored and analyzed product is greater than the preset correlation weight threshold, and if the average overlap ratio of the data source of the monitored and analyzed target's related products with the data source of the verified target product's related products is greater than the preset overlap ratio threshold, then the method for obtaining and processing the price data of the monitored and analyzed target is determined to be that as long as there is no failure in obtaining the data source of the verified target product's price in the most recent preset time period, then the price data of the monitored and analyzed target's related products will be obtained and processed. Case 3: If the average overlap ratio between the data source of the associated product of the monitored and analyzed target and the data source of the associated product of the verified target is not greater than the preset overlap ratio threshold, proceed to step S32. S32 determines the verification matching weight value of the data source of the target product based on the acquisition and processing strategy of the price data of the target product. It is understandable that in the above steps, it is determined whether the average value of the verification matching weight value of the data source of different verification target products is greater than the preset matching weight threshold. If so, the timeliness of the verification processing of the data source is high. Therefore, the method for obtaining and processing the price data of the monitoring and analysis target is determined to be that as long as there is no failure in obtaining the price data source of the verification target product in the most recent preset time period, the price data of the associated products of the monitoring and analysis target is obtained and processed. Otherwise, the process proceeds to step S33. S33 determines the method for acquiring and processing the price data of the monitoring and analysis target based on the correlation weight coefficient between the monitoring and analysis target and different verification target products, as well as the monitoring matching type of the data source of the verification target products.

[0054] It is understood that the correlation weight coefficient between the monitored and analyzed goods and the verified target goods is determined based on the sum of the overlap ratios between the data sources of the monitored and analyzed target goods and the data sources of the verified target goods.

[0055] Specifically, the verification matching weight value of the data source of the verification target product is determined according to the preset weight value corresponding to the acquisition and processing strategy of the price data of the verification target product. The verification matching weight value of the verification target product in the basic period is greater than the verification matching weight value of the verification target product in the dynamic basic period and the second basic period. The verification matching weight value of the verification target product in the dynamic basic period and the second basic period is greater than the verification matching weight value of the verification target product in the second basic period.

[0056] It is understood that the method for obtaining and processing price data of the monitoring and analysis target is determined based on the correlation weight coefficient between the monitoring and analysis target and different verification target products, as well as the monitoring matching type of the data source of the verification target products. Specifically, this includes: Based on the correlation weight coefficients between the monitoring and analysis target and different verification target products, verification target products with correlation weight coefficients greater than a preset weight coefficient threshold are identified and used as associated verification products of the monitoring and analysis target. It is then determined whether the average verification matching weight value of the associated verification products of the monitoring and analysis target is greater than a preset matching weight threshold. If so, the timeliness of the data source verification process is high. Therefore, the method for obtaining and processing the price data of the monitoring and analysis target is as follows: if there are no acquisition failures in the price data source acquisition process of the verification target product within the most recent preset time period, then the price data acquisition process of the associated products of the monitoring and analysis target is performed. If not, the method for obtaining and processing the price data of the monitoring and analysis target is as follows: if the number of acquisitions of the verification target product in the price data source acquisition process within the most recent preset time period is greater than a preset acquisition number threshold, or if all associated verification products have price data source acquisition processes and there are no acquisition failures, then the price data acquisition process of the associated products of the monitoring and analysis target is performed.

[0057] It should be noted that when there are no retrieval failures, the consistency of the price data obtained from different data sources is determined based on the price data obtained from different data sources. The price data with the highest consistency, i.e., the largest number of data sources with consistent price data, is taken as the result of obtaining the product's price data.

[0058] Specifically, the method for determining the update processing method of the verification target goods of the monitoring and analysis target is as follows: In this embodiment, by acquiring the failure handling process and the degree of overlap between the data source of the failed related products and the price of the verification target product in the failure handling process, the reliability of the acquisition and processing of the price data of the current monitoring and analysis target product is determined, as well as the reference value of the data source of the price of the verification target product. Based on the reference value and the reliability of the acquisition and processing of the price data, the update processing method of the verification target product of the monitoring and analysis target is determined, that is, how to perform the update processing of the verification target product, thereby laying the foundation for further improving the reliability of the acquisition and processing of the price data of the related products of the monitoring and analysis target.

[0059] S41 defines the acquisition process that fails during the price acquisition process of the monitored and analyzed target as the acquisition failure process, and defines the associated products that fail to acquire during the acquisition failure process as the failed associated products. It is understood that the above steps include the following: S411 Based on the proportion of acquisition failure processes in the acquisition process, determine the failure impact coefficient, and determine whether the failure impact coefficient is greater than the preset impact coefficient threshold. If so, determine the update processing method of the verification target product of the monitoring and analysis target as follows: if the average overlap ratio of the data source of the price of the related product of the monitoring and analysis target and other related products is greater than the preset overlap ratio threshold, then use it as the verification target product; otherwise, proceed to step S412. S412 Based on the price acquisition and processing data of the monitored and analyzed target, the acquisition and processing process excluding the acquisition failure process is taken as other acquisition processes. It is determined whether the average interval of adjacent other acquisition processes is less than the preset interval threshold. If so, it is determined that the update processing method of the verification target product of the monitored and analyzed target is no update processing required. If not, proceed to step S42. S42 determines the overlap ratio between the data sources of the prices of the failed related products and the verification target products based on the degree of overlap between the data sources of the failed related products and the verification target products during the failure handling process. It is understandable that in the above steps, the average overlap ratio of the data sources of the prices of different failed associated products and the verification target products is determined to be greater than the preset overlap ratio threshold. If so, the update processing method of the verification target products of the monitoring and analysis target is determined to be no update processing required. If not, proceed to step S43. S43 determines the update processing method for the verification target product of the monitoring and analysis target based on the acquisition and processing data of the price of the monitoring and analysis target and the overlap ratio of the data source of the price of the verification target product of the failed related product in the acquisition failure processing process.

[0060] It is understood that the overlap ratio of the data sources for the prices of the failed associated products and the target products for verification is determined based on the proportion of the number of data sources for the prices of the failed associated products to the number of data sources for the prices of the target products for verification.

[0061] Specifically, based on the acquisition and processing data of the price of the monitored and analyzed target, and the overlap ratio of the data sources of the price of the failed related products in the failure processing process, the update processing method of the verification target product of the monitored and analyzed target is determined, specifically including: The acquisition matching coefficient of the price of the associated product of the monitoring and analysis target is determined by the average of the interval duration of the other adjacent acquisition processes and the average of the overlap ratio of the data sources of the prices of different failed associated products and the verification target product. It is then determined whether the acquisition matching coefficient of the price of the associated product of the monitoring and analysis target is greater than a preset matching coefficient threshold. If so, the update processing method of the verification target product of the monitoring and analysis target is determined to be no update processing required. If not, the update processing method of the verification target product of the monitoring and analysis target is determined to be: if the average overlap ratio of the data sources of the prices of the associated product of the monitoring and analysis target and other associated products is greater than a preset overlap ratio threshold, and there is an acquisition failure process within the most recent preset time period, then it is regarded as the verification target product.

[0062] Furthermore, the shorter the average interval between the adjacent acquisition processes, the higher the average overlap ratio of the data sources for the prices of different failed related products and the target product being verified, and the higher the acquisition matching coefficient of the related products of the monitoring and analysis target. Specifically, this is determined based on the ratio of the preset duration threshold to the average interval between the adjacent acquisition processes and the average of the average overlap ratio of the data sources for the prices of different failed related products and the target product being verified.

[0063] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0064] The foregoing has described specific embodiments of this specification. 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 that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0065] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A supply chain price signal hub node identification and management system, characterized in that, Specifically, it includes: The price acquisition and processing module is responsible for determining the monitoring and analysis strategy for the price data of related products based on the similarity of related products in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, it identifies related products for price data verification and comparison analysis in different data sources and uses them as verification target products to verify the degree of correlation between the data source of the target products and the related products of the monitoring and analysis targets, as well as the price data acquisition and processing strategy of the verification target products, and determines the method for acquiring and processing the price data of the monitoring and analysis targets. The pairing processing module obtains all upstream and downstream relationship edges from the upstream and downstream relationship table of the industrial chain of the monitoring and analysis target based on the acquisition processing method, and filters out edges with price changes at both ends and includes them in the computable pairing set. The price transmission analysis module is responsible for extracting the historical price series of the two end products of each edge in the computable pair set, calculating the Pearson correlation coefficient of the logarithmic returns, and using it as the price transmission strength of the connecting edge between the two end products. The filtering module is responsible for retaining edges with a transmission strength greater than or equal to a threshold θ in the full topology graph, and constructing a weighted directed price transmission subgraph with the transmission strength as the weight. The node filtering module is responsible for calculating the price in-degree Pin, price out-degree Pout, weighted in-degree, and weighted out-degree of each node in the weighted price transmission subgraph. It then weights and merges Pin, Pout, weighted in-degree, and weighted out-degree into a hub score, sorts the nodes by score from high to low, and selects the nodes before the preset order to construct a hub node list.

2. The supply chain price signal hub node identification and management system as described in claim 1, characterized in that, The formula for calculating the hub score is: Hub score = w1 × (Pin + Pout) + w2 × (weighted in-degree + weighted out-degree) + w3 × price penetration, where price penetration = Pin × Pout, and w1, w2, and w3 are preset weight values.

3. The supply chain price signal hub node identification and management system as described in claim 1, characterized in that, The method also includes classifying hubs into price signal source type, price signal sink type, and price signal bridge type based on the relative size of Pin and Pout: Price signal source type: Pout >> Pin, price radiates outwards; Price signal pattern: Pin >> Pout, influenced by multiple price movements; Price signal bridge pattern: Pin ≈ Pout, price inflow and outflow equilibrium.

4. The supply chain price signal hub node identification and management system as described in claim 1, characterized in that, Calculate the price activity rate of each node. Price activity rate = number of valid edges in the subgraph / total number of edges in the topology × 100%. Monitor the trend of price activity rate changes: when the price activity rate of a node continues to decline and exceeds the preset threshold, trigger a relationship dormancy warning.

5. A data processing method, applied to the supply chain price signal hub node identification and management system as described in any one of claims 1-4, characterized in that, Specifically, it includes: Based on the monitoring and analysis targets of the industrial chain, identify the related commodities in the industrial chain of the monitoring and analysis targets, and determine the monitoring and analysis strategy for the price data of the related commodities according to the degree of similarity of the related commodities in the industrial chain between the monitoring and analysis targets. Based on the monitoring and analysis strategy, related commodities for price data verification and comparison analysis in different data sources are identified and used as verification target commodities. Based on the composition data of the verification target commodities in the related commodities of each monitoring and analysis target, the price data acquisition and processing strategy of the verification target commodities is determined. Determine the degree of correlation between the data source of the target product and the related products of the monitoring and analysis target, and determine the method for obtaining and processing the price data of the monitoring and analysis target in combination with the acquisition and processing strategy of the price data of the target product; Based on the acquisition and processing method, the acquisition and processing of the prices of the associated products of the monitoring and analysis target are determined to obtain the acquisition failure processing process. According to the acquisition and processing data of the monitoring and analysis target, and the degree of overlap between the data source of the associated failed products in the acquisition failure processing process and the data source of the verification target products, the update processing method of the verification target products of the monitoring and analysis target is determined.

6. The data processing method as described in claim 5, characterized in that, The related products in the industrial chain of the monitored and analyzed target are the products in the industrial chain of the monitored and analyzed target.

7. The data processing method as described in claim 5, characterized in that, The method for determining the monitoring and analysis strategy for the price data of the related commodities is as follows: Based on the degree of similarity of related goods in the industrial chain between the monitoring and analysis targets, determine the amount of overlap of related goods in the industrial chain between the monitoring and analysis targets. The similarity coefficient of the related products in the industrial chain between the monitored and analyzed targets is determined by the number of overlapping products in the industrial chain between the monitored and analyzed targets. Based on the similarity coefficient of related products in the industrial chain between the monitoring and analysis targets, a monitoring and analysis strategy for the price data of the related products is determined.

8. The data processing method as described in claim 5, characterized in that, The similarity coefficient of related products in the industrial chain between the monitored and analyzed targets is determined based on the proportion of related products in the industrial chain between the monitored and analyzed targets and other monitored and analyzed targets.

9. The data processing method as described in claim 7, characterized in that, Based on the similarity coefficient of related products in the industrial chain between the monitoring and analysis targets, a monitoring and analysis strategy for the price data of the related products is determined, specifically including: If the average similarity coefficient of related products in the industrial chain between various monitoring and analysis targets is less than the preset similarity coefficient threshold, the monitoring and analysis strategy for the price data of the related products is to use related products from data sources with more than one target quantity of price data as the verification target products. If the average similarity coefficient of related products in the industrial chain between various monitoring and analysis targets is not less than a preset similarity coefficient threshold, then the monitoring and analysis strategy for determining the price data of the related products is to take the related products of the monitoring and analysis targets that have more than one target number of price data sources and belong to the related products of the monitoring and analysis targets that are above the preset number threshold as the verification target products.

10. The data processing method as described in claim 5, characterized in that, The method for determining the update processing method of the target commodities for the monitoring and analysis targets is as follows: In the process of acquiring and analyzing the price of the target, if there is a failure to acquire the price, the acquisition process is defined as the acquisition failure process, and the associated products that fail to acquire the price in the acquisition failure process are defined as the failed associated products. Based on the degree of overlap between the data source of the price of the failed associated product and the price of the target product during the failure handling process, the overlap ratio between the data source of the failed associated product and the price of the target product is determined. Based on the acquisition and processing data of the price of the monitored and analyzed target, and the overlap ratio of the data sources of the price of the target product for verification of the failed related products in the failure processing process, the update processing method of the target product for verification of the monitored and analyzed target is determined.

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