Industrial chain node development evaluation method and system based on new quality productivity index

Abstract

This invention discloses a method and system for evaluating the development of industrial chain nodes based on a new quality productivity index, relating to the field of industrial chain node development evaluation technology. The method includes: establishing a node list, an indicator list, and an indicator direction table; collecting raw indicator data and merging it into a raw node indicator table; unifying dimensions and handling missing data to obtain a standardized node indicator table and generating node indicator vectors; acquiring supply and demand relationship data; calculating the supply ratio and substitution difficulty to obtain supply dependence intensity and constructing an industrial chain dependence graph; calculating on-chain criticality scores and node capability scores to generate a node evaluation result table containing pseudo-criticality markers; scoring pseudo-critical nodes using a downgrade scoring model and downgrading them to form a corrected result table. This invention achieves accurate screening and dynamic calibration of pseudo-critical nodes by jointly modeling node capability and on-chain dependence and introducing pseudo-criticality identification and downgrade correction.

Description

Technical Field

[0001] This invention relates to the field of industrial chain node development assessment technology, and more specifically, to a method and system for assessing industrial chain node development based on the new quality productivity index. Background Technology

[0002] As the collaborative division of labor in the industrial chain continues to deepen, when carrying out industrial chain governance, resource allocation and risk management, industry authorities, leading enterprises and financial institutions usually need to conduct a comprehensive assessment of the innovation capabilities, digitalization level, green and low-carbon performance and operational delivery capabilities of industrial chain nodes, and form a ranking of node development levels, a list of key nodes and phased improvement directions based on this.

[0003] In practical implementation, the industrial chain is characterized by diverse nodes, dispersed data sources, and significant differences in statistical standards. Assessment tasks often involve multiple time points, multiple indicator groups, and cross-stage comparisons. To meet comparability requirements, assessment results need to be linked to the supply and demand relationships between nodes to identify key nodes that constrain the stability of the industrial chain. Simultaneously, with the increasing demand for supply chain resilience, factors such as the substitutability of supply between nodes, certification cycles, and switching costs are gradually becoming important constraints influencing the determination of "criticality," and urgently need to be reasonably expressed and calculated within the assessment framework.

[0004] However, existing technologies still have key shortcomings in assessing the development of nodes in the industrial chain and identifying critical nodes. Specifically, the determination of critical nodes relies heavily on scale-based indicators or the proportion of a single contribution, lacking a mechanism to quantify the supply share and the degree of supply substitutability into dependence intensity. This makes it easy to misjudge nodes with a high supply share but strong substitutability as critical nodes, thus making it difficult to identify pseudo-critical nodes. In addition, there is a lack of quantitative downgrading and dynamic calibration processes for identified pseudo-critical nodes, making it difficult to revise the critical node list in a timely manner as supply and demand changes, affecting the consistency of subsequent resource allocation and governance decisions. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of the existing technology and achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the development of industrial chain nodes based on the new quality productivity index, comprising: Based on the set of nodes in the industrial chain, a node list, an indicator list, and an indicator direction table are established. The original data of the corresponding indicators are obtained and merged to form the original node indicator table. The original node indicator table is processed with unified dimensions and missing data to obtain the standardized node indicator table. The scores of each indicator group in the indicator list are summarized according to the weight within the group and concatenated to generate a set of node indicator vectors. Obtain the node list and supply and demand relationship data and form a set of supply and demand relationship records. At the target time point, calculate the supply ratio and substitution difficulty for the upstream node number and the downstream node number. Calculate the supply dependence intensity value based on the supply ratio and substitution difficulty and write it into the supply dependence relationship table. Construct the industrial chain dependence graph based on the supply dependence relationship table. The chain criticality score of each node number at the target time point is calculated based on the chain dependency graph. The node capability score of each node number at the target time point is calculated based on the node indicator vector set. The node development evaluation result is generated based on the chain criticality score and the node capability score, and a node evaluation result table containing pseudo-critical node markers is output. Extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

[0006] Furthermore, the method for obtaining the modified node evaluation result table includes: Filter records marked as true from the node evaluation result table, extract the corresponding node numbers and summarize them to form a pseudo-critical node set; extract the node evaluation results corresponding to the target time point from the pseudo-critical node set to form a pseudo-critical node sub-table; The input feature vector of the pseudo-key node sub-table is constructed based on the pseudo-key downgrade scoring model. Input the input feature vector into the preset pseudo-key downgrade scoring model to obtain the pseudo-key downgrade score; write the node number, target time point and pseudo-key downgrade score into the pseudo-key node record table; For each pseudo-critical node record in the pseudo-critical node record table, read the corresponding comprehensive development score and pseudo-critical downgrade score, and calculate the downgrade coefficient based on the downgrade weight. The downgrade coefficient is equal to the downgrade weight multiplied by the pseudo-critical downgrade score. Calculate the corrected comprehensive development score based on the comprehensive development score and the downgrade coefficient. The corrected comprehensive development score is equal to the comprehensive development score multiplied by 1 minus the downgrade coefficient. Write the corrected comprehensive development score back to the node evaluation result table to form the corrected node evaluation result table, and record the downgrade coefficient and pseudo-critical downgrade score in the corrected node evaluation result table.

[0007] Furthermore, methods for constructing the input feature vector of the pseudo-key node degrading scoring model based on the pseudo-key node sub-table include: For each node number in the pseudo-critical node set at the target time point, the on-chain criticality score, node capability score, comprehensive development score, and criticality support score are extracted as basic features. Difference features are calculated based on preset thresholds. The difference features include criticality difference and support difference. The criticality difference is equal to the criticality score threshold minus the on-chain criticality score, and the support difference is equal to the support threshold minus the criticality support score. The basic features and difference features are concatenated to obtain the input feature vector of the node number at the target time point.

[0008] Furthermore, methods for calculating the on-chain criticality score of each node number at the target time point based on the supply chain dependency graph include: For each node number in the node list, retrieve all directed relationships in the supply chain dependency graph that satisfy the condition that the upstream node number is the current node number, to obtain the first set of directed relationships where the current node number is the upstream node number; for each directed relationship in the first set of directed relationships, read the corresponding supply dependence intensity value, and perform summation on all supply dependence intensity values ​​to obtain the supply influence of the current node number at the target time point; retrieve all directed relationships in the supply chain dependency graph that satisfy the condition that the downstream node number is the current node number, to obtain the second set of directed relationships where the current node number is the downstream node number; for each directed relationship in the second set of directed relationships, read the corresponding supply dependence intensity value, and perform summation on all supply dependence intensity values ​​to obtain the influence of the current node number at the target time point; Based on the supply impact and control of each node number in the node list at the target time point, calculate the original value of the chain criticality of that node number at the target time point. The on-chain criticality score is obtained by standardizing the raw on-chain criticality values.

[0009] Furthermore, the method for calculating the node capability score of each node number at the target time point based on the node indicator vector set includes: For each node number in the node list at the target time point, obtain the innovation input score, digitalization score, green and low-carbon score, and operation delivery score corresponding to the current node number at the target time point, and assign a first weight, a second weight, a third weight, and a fourth weight respectively. Based on the weighted geometric average method, calculate the node capability score by integrating the innovation input score, digitalization score, green and low-carbon score, and operation delivery score.

[0010] Furthermore, the method for generating node development evaluation results based on on-chain criticality scores and node capability scores, and outputting a node evaluation result table containing pseudo-critical node tags, includes: For each node number in the node list, obtain the on-chain criticality score and node capability score at the target time point; perform a fusion calculation on the on-chain criticality score and node capability score based on the preset fusion weight to obtain the comprehensive development score of the node number at the target time point; For each node in the node list, the comprehensive development score at the target time point is assessed, and node numbers with a comprehensive development score greater than or equal to the comprehensive development threshold are identified as the set of potential key candidate nodes. For each node number in the set of potential key candidate nodes, the corresponding on-chain keyness score and node capability score are obtained, and the keyness support score is calculated based on the on-chain keyness score and comprehensive development score. When the on-chain keyness score is less than the keyness score threshold, the node capability score is greater than or equal to the node capability score threshold, and the keyness support score is less than the support score threshold, the pseudo-key node is marked as true; otherwise, the pseudo-key node is marked as false. The node number, target time point, on-chain keyness score, node capability score, comprehensive development score, keyness support score, and pseudo-key node mark are written into the node evaluation result table.

[0011] Furthermore, the method for constructing the supply chain dependency graph includes: Obtain the node list and supply and demand relationship data; the supply and demand relationship data includes upstream node number, downstream node number, supply volume data, number of suppliers, certification cycle, and switching cost information; Based on the node list, node consistency processing is performed on the supply and demand relationship data. The node consistency processing includes node number verification and relationship record filtering. After the node consistency processing is completed, the supply and demand relationship data is constructed into a supply and demand relationship record set. Based on the supply and demand relationship record set, for each group of upstream node numbers and downstream node numbers at the target time point, calculate the supply ratio from upstream node number to downstream node number. The supplier difficulty value is calculated based on the number of suppliers, the certification cycle difficulty value is calculated based on the certification cycle, and the switching cost difficulty value is calculated based on the switching cost. The supplier difficulty value, certification cycle difficulty value, and switching cost difficulty value are weighted and summed to obtain the substitution difficulty. Calculate the supply dependence intensity value based on the supply share and the difficulty of substitution; write the supply dependence intensity value into the supply dependence table; A supply chain dependency graph is constructed based on a node list and a supply dependency table. The supply chain dependency graph includes a vertex set and an edge set. The vertex set consists of node numbers from the node list. The edge set consists of directed relationships from upstream node numbers to downstream node numbers from the supply dependency table. Each edge in the edge set carries the supply dependency strength value at the corresponding time point.

[0012] Furthermore, methods for establishing node lists, indicator lists, and indicator direction tables based on the set of nodes in the industrial chain include: Based on the set of nodes in the industrial chain, a node list and an indicator list are established. The node list includes node number and chain link type, which is used to characterize whether the node belongs to the upstream, midstream, or downstream link of the industrial chain. The indicator list includes innovation input indicator group, digitalization indicator group, green and low-carbon indicator group, and operation and delivery indicator group. The innovation input indicator group includes R&D intensity, number of patents, and proportion of high-value patents. The digitalization indicator group includes equipment networking rate, business system coverage rate, and automation level. The green and low-carbon indicator group includes energy consumption per unit output, carbon emissions per unit output, and environmental compliance records. The operation and delivery indicator group includes on-time delivery rate, quality pass rate, and rework rate. Establish an indicator direction table to synchronize the node list and indicator list; the indicator direction table includes two types of direction markers: larger values ​​are better and smaller values ​​are better.

[0013] Furthermore, the method for summarizing the scores of each indicator group in the indicator list according to the weight within the group and concatenating them to generate a set of node indicator vectors includes: For each of the innovation input indicator group, digitalization indicator group, green and low-carbon indicator group, and operation and delivery indicator group, an intra-group weight table is established. Based on the intra-group weight table, the standardized indicator values ​​within the corresponding indicator group are weighted and summarized to obtain the innovation input score, digitalization score, green and low-carbon score, and operation and delivery score. The innovation input score, digitalization score, green and low-carbon score, and operation and delivery score are concatenated according to the preset field order to obtain the node indicator vector of the node number at the target time point. The node indicator vector generation process is repeated for all nodes in the node list at each time point to obtain the node indicator vector set.

[0014] The industrial chain node development assessment system based on the new quality productivity index is used to implement the aforementioned industrial chain node development assessment method based on the new quality productivity index, including: The node indicator vector generation module establishes a node list, indicator list, and indicator direction table based on the industrial chain node set; it acquires the original indicator data and merges it to form the original node indicator table; it performs unified dimension processing and missing data processing on the original node indicator table to obtain the node standardized indicator table; it summarizes the scores of each indicator group in the indicator list according to the weight within the group and concatenates them to generate a node indicator vector set. The industrial chain relationship construction module obtains the node list and supply and demand relationship data and forms a set of supply and demand relationship records. At the target time point, it calculates the supply ratio and substitution difficulty for upstream node number and downstream node number. Based on the supply ratio and substitution difficulty, it calculates the supply dependence intensity value and writes it into the supply dependence relationship table. Based on the supply dependence relationship table, it constructs the industrial chain dependence graph. The pseudo-critical node identification module calculates the on-chain criticality score of each node number at the target time point based on the industry chain dependency graph, calculates the node capability score of each node number at the target time point based on the node indicator vector set, generates node development evaluation results based on the on-chain criticality score and node capability score, and outputs a node evaluation result table containing pseudo-critical node markers. The pseudo-critical node degradation module is used to extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

[0015] Compared with existing technologies, the industrial chain node development evaluation method and system based on the new quality productivity index proposed in this invention has the following technical effects and advantages: This invention focuses on the development assessment of industrial chain nodes of new quality productivity. It collects, merges and standardizes indicators such as innovation input, digitalization, green and low-carbon development and operation and delivery according to a unified standard, forming a node indicator vector. This allows indicators at different nodes and at different times to be aligned and compared under the same dimension and the same standard of superiority and inferiority, thus providing a consistent input basis for subsequent index calculations.

[0016] This invention further introduces the supply and demand correspondence between nodes, quantifies the supply ratio and the difficulty of substitution into the supply dependence intensity, and constructs an industry chain dependence graph with time series values ​​accordingly, so that the criticality on the chain can simultaneously reflect the supply contribution and the degree of substitutability, avoiding misjudging substitutable nodes as critical nodes based solely on scale or single performance.

[0017] Based on this, the criticality score on the chain is calculated based on the industry chain dependency graph, and then integrated with the node capability score obtained from the node indicator vector to form a comprehensive development score. The candidate nodes for key nodes are screened by the comprehensive development threshold, and the criticality support degree and the threshold are jointly calculated to generate pseudo-key node markers, so as to structurally distinguish pseudo-key nodes that have good capability performance but insufficient key constraints on the chain.

[0018] For marked pseudo-critical nodes, this invention introduces a pseudo-critical node degradation scoring model to output degradation scores, and performs degradation correction on the comprehensive development score based on the degradation scores to form a modified node evaluation result table. This enables the critical node list and ranking results to be updated adaptively as the dependency relationship changes, improving the stability, interpretability and reproducibility of the evaluation conclusions, and thus addressing the core technical problem of difficult-to-identify pseudo-critical nodes.

[0019] In summary, this invention achieves accurate screening and dynamic calibration of pseudo-critical nodes by jointly modeling node capabilities and on-chain dependencies and introducing pseudo-critical node identification and degradation correction. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the industrial chain node development evaluation system based on the new quality productivity index according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of the industrial chain node development evaluation method based on the new quality productivity index in Embodiment 2 of the present invention; Figure 3 This is a flowchart of the method for constructing the industry chain dependency graph in Embodiment 1 of the present invention; Figure 4 This is a flowchart of the method for obtaining the correction node evaluation result table in Embodiment 1 of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be described in detail, clearly, and completely below with reference to the accompanying drawings. It should be particularly noted that the specific embodiments described below are only for better illustrating and explaining the technical solutions of the present invention, and are intended to enable those skilled in the art to better understand and implement the present invention, and should not be construed as limiting the scope of protection of the present invention. Without departing from the spirit and substance of the present invention, those skilled in the art can modify, adjust, or make equivalent substitutions based on the content disclosed in the present invention, and these should all be considered within the scope of protection of the present invention.

[0022] Example 1: Please see Figure 1 As shown, this embodiment discloses an industrial chain node development evaluation system based on the new quality productivity index, including various modules that achieve data transmission through wired and / or wireless connections.

[0023] The node indicator vector generation module establishes a node list, indicator list, and indicator direction table based on the industrial chain node set; it acquires the original indicator data and merges it to form the original node indicator table; it performs unified dimension processing and missing data processing on the original node indicator table to obtain the standardized node indicator table; it summarizes the scores of each indicator group in the indicator list according to the weight within the group and concatenates them to generate a node indicator vector set.

[0024] Methods for establishing node lists, indicator lists, and indicator direction tables based on industry chain node sets include: A node list and an indicator list are established based on a set of industry chain nodes. The node list describes each node in the industry chain node set, and the node record includes the node number and the type of link in the chain. The type of link indicates whether the node belongs to the upstream, midstream, or downstream link. The industry chain node set is the set of evaluation objects, and each industry chain node corresponds to an evaluation unit in the industry chain. The indicator list records the indicator items to be collected and generated. The indicator list includes innovation input indicator groups, digitalization indicator groups, green and low-carbon indicator groups, and operational delivery indicator groups. The innovation input indicator group includes R&D intensity, number of patents, and the proportion of high-value patents; the digitalization indicator group includes equipment networking rate, business system coverage rate, and automation level; the green and low-carbon indicator group includes energy consumption per unit output, carbon emissions per unit output, and environmental compliance records; and the operational delivery indicator group includes on-time delivery rate, quality pass rate, and rework rate.

[0025] To synchronize the node list and the indicator list, an indicator direction table is established. The indicator direction table is used to mark the optimization direction of each indicator item. The indicator direction table includes two types of direction marks: larger values ​​are better and smaller values ​​are better. This is to ensure that different indicator items meet the same good and bad judgment rules after unified processing, and to ensure that the interpretation of each indicator item is consistent in the subsequent processing.

[0026] The methods for obtaining a standardized node indicator table by acquiring raw indicator data and merging it to form a raw node indicator table, and then performing uniform dimension processing and missing data handling on the raw node indicator table, include: The raw indicator data is obtained based on the node list and indicator list. The raw indicator data is used to represent the original indicator values ​​of each node in the industrial chain node set at the target time point. The raw indicator data includes the node number, the time point, and the original indicator value corresponding to each indicator item in the indicator list, so that the raw indicator data can be associated with the node list and indicator list item by item.

[0027] It should be noted that the original data for the indicators comes from data submitted by enterprises, data recorded in business systems, data recorded in equipment operation and quality inspection, and data disclosed in statistical annual reports or public disclosures.

[0028] Based on the original indicator data, a merge process is performed on the node numbers and time points to obtain the original node indicator table. The original node indicator table uses the node number and time point as a composite index. Each row in the original node indicator table records the set of original indicator values ​​for the corresponding node at the target time point, and the set of original indicator values ​​corresponds item-by-item to the indicator items in the indicator list. To ensure that the original node indicator table can be used for subsequent standardization processing, field validation and range validation are performed on the original node indicator table. Field validation confirms that the field set of the original node indicator table is consistent with the indicator list, and range validation determines whether the original indicator values ​​in the original node indicator table fall within a preset reasonable range. When the range validation determines that an indicator's original value is an outlier, the outlier is marked as a missing value to be filled, ensuring that missing values ​​to be filled and missing indicator values ​​are filled using a unified rule in subsequent processing.

[0029] It should be noted that, for the purpose of illustrating the range verification and outlier handling process, the following sample data is provided. The node list is shown in Table 1, and the node records in the node list include the node number and the chain link type.

[0030] Table 1 Example of a node list

[0031] The indicator list selects some indicator items for illustrative purposes. The correspondence between the selected indicator items and the indicator direction table is shown in Table 2.

[0032] Table 2. List of Indicators and Examples of Indicator Directions

[0033] For the indicators listed in Table 2, the preset reasonable ranges include: the preset reasonable range for device network connectivity rate is [0,1], the preset reasonable range for on-time delivery rate is [0,1], and the preset reasonable range for return rate is [0,1]. After obtaining the original data of the indicators based on the node list and indicator list, the original node indicator table is obtained by merging according to the node number and time point. An example of the original node indicator table is shown in Table 3, where the time point is "June 2025".

[0034] Table 3. Examples of Original Representations of Node Indicators

[0035] When performing range verification on Table 3, the preset reasonable range for device connectivity rate is [0,1]. The device connectivity rate of node 001 in June 2025 is 1.20, which exceeds the preset reasonable range. Therefore, the device connectivity rate of node 001 in June 2025 is marked as a missing value to be filled. The preset reasonable range for repair rate is [0,1]. The repair rate of node 002 in June 2025 is -0.03, which exceeds the preset reasonable range. Therefore, the repair rate of node 002 in June 2025 is marked as a missing value to be filled. Through the above marking process, the missing values ​​to be filled and the missing index values ​​in the original node index table are filled using the same rules in subsequent processing.

[0036] To illustrate the execution of the completion rule, example device connectivity rates for node 001 at historical time points are provided: Node 001's device connectivity rate in May 2025 was 0.76, and its device connectivity rate in April 2025 was 0.80. Based on the device connectivity rates at these historical time points, the historical average of the node is calculated. The historical average is (0.76 + 0.80) / 2 = 0.78. Therefore, the device connectivity rate of node 001 in December 2025, which was marked as having a missing value to be completed, is completed to 0.78. Furthermore, if node 002 does not have a corresponding return rate value at a historical time point, resulting in the inability to obtain the node's historical average, then the average value of the same link is calculated within the same time point and the same chain link type. For example, within the upstream link range in June 2025, if the return rate of node 001 is 0.05, then the average value of the same link can be determined as 0.05, and based on this, the return rate of node 002 marked as having missing values ​​to be filled in June 2025 is filled in to 0.05, thereby completing the unified filling process for outliers and missing values.

[0037] A standardized parameter set is constructed based on the original node indicator table and the indicator direction table. This standardized parameter set maps the original indicator values ​​to a preset value range. The standardized parameter set is constructed separately for each indicator item in the indicator list. The standardized parameter set includes a minimum and a maximum indicator value, which are calculated based on a set of node samples within the same chain link type, thereby reducing the impact of differences in caliber between different chain link types on the standardization results. When the minimum and maximum indicator values ​​are equal, the standardized indicator value for the corresponding indicator item is set to a preset neutral value of 0.5 to avoid division by zero in the standardization calculation and to suppress extreme sample perturbations.

[0038] The original node indicator table is standardized based on a standardized parameter set and indicator direction table to obtain a standardized node indicator table. For indicators marked as "the larger the value, the better" in the indicator direction table, a linear mapping is performed based on the original indicator value, the minimum indicator value, and the maximum indicator value to obtain a standardized indicator value. For indicators marked as "the smaller the value, the better" in the indicator direction table, a linear mapping is first performed based on the original indicator value, the minimum indicator value, and the maximum indicator value to obtain an intermediate standardized value. Then, the direction of the intermediate standardized value is reversed to obtain a standardized indicator value, ensuring that the standardized indicator values ​​in the node standardized indicator table satisfy the unified interpretation that "the larger the value, the better the performance". The standardized indicator values ​​are limited to the range of 0 to 1. When the linear mapping result exceeds the range, the excess part is truncated to ensure that the standardized indicator value is within the preset range.

[0039] It should be noted that standardization processing is used to convert the original values ​​of indicators in the original node indicator table into standardized indicator values ​​in the node standardized indicator table. Standardized indicator values ​​are limited to a range of 0 to 1 and adhere to the unified interpretation that "larger values ​​represent better performance." During standardization, for each indicator item in the original node indicator table, the minimum and maximum values ​​are first read from the standardization parameter set, and the original indicator values ​​for the corresponding node number and time point are read from the original node indicator table. When the maximum indicator value is greater than the minimum indicator value, a linear mapping is performed based on the original indicator value, minimum indicator value, and maximum indicator value to obtain an intermediate standardized value. The intermediate standardized value is calculated using the formula: Intermediate Standardized Value = (Original Indicator Value - Minimum Indicator Value) / (Maximum Indicator Value - Minimum Indicator Value). When the indicator direction table marks the target indicator item with the direction of "the larger the value, the better", the intermediate standardized value is determined as the standardized indicator value; when the indicator direction table marks the target indicator item with the direction of "the smaller the value, the better", the intermediate standardized value is reversed to obtain the standardized indicator value. The direction reversal process satisfies that the standardized indicator value = 1 - the intermediate standardized value, so that the indicator item that was originally better the smaller value also satisfies the interpretation of "the larger the value, the better" after standardization.

[0040] The linear mapping result may exceed the range of 0 to 1 due to abnormal fluctuations or boundary values. The standardization process further truncates the standardized index values. The truncation rules are as follows: when the standardized index value is less than 0, the standardized index value is set to 0; when the standardized index value is greater than 1, the standardized index value is set to 1. To avoid the linear mapping denominator being zero due to the maximum and minimum index values ​​being equal, when the maximum and minimum index values ​​are equal, the standardized index value of the corresponding index item is directly set to a preset neutral value of 0.5, and this preset neutral value is written to the node's standardized index table.

[0041] To facilitate understanding of the calculation process of the above standardized index values, the following example is given: When the minimum value of the index corresponding to the device network rate is 0.60 and the maximum value is 0.90, the original value of the index corresponding to the device network rate at the target time point is 0.75, and the direction mark of the device network rate in the index direction table is "the larger the value, the better", the intermediate standardized value = (0.75 - 0.60) / (0.90 - 0.60) = 0.50, and the standardized index value is 0.50; When the minimum value of the index corresponding to the return rate is 0.02 and the maximum value is 0.08, the original value of the index corresponding to the return rate at the target time point is 0.05, and the direction mark of the return rate in the index direction table is "the smaller the value, the better", the intermediate standardized value = (0.05 - 0.02) / (0.08 - 0.02) = 0.50, and the direction is reversed to obtain the standardized index value = 1 - 0.50 = 0.50, thereby achieving a unified interpretation of different directional index items in the node standardized index table.

[0042] To ensure the completeness of the standardized indicator table for nodes, missing data completion is performed to obtain a completed standardized indicator table. The missing data completion process employs a hierarchical rule: when a standardized indicator value for the same indicator exists at a historical time point corresponding to a node number, the historical standardized indicator value sequence is extracted and the node's historical mean is calculated; this historical mean is used to fill in the missing standardized indicator value at the target time point. When the node's historical mean is unavailable, a sample set of standardized indicator values ​​for the same indicator is extracted within the same time point and the same chain link type, and the mean for that link is calculated; this mean is used to fill in the missing standardized indicator value. When the mean for that link is unavailable, the global mean of the indicator is calculated and used to fill in the missing standardized indicator value. Through this hierarchical rule, the completed standardized indicator table meets the input requirements for subsequently generating node indicator vectors without introducing additional data sources.

[0043] The methods for summarizing the scores of each indicator group in the indicator list according to the weight within the group and concatenating them to generate a set of node indicator vectors include: For each of the innovation input, digitalization, green and low-carbon, and operational delivery indicator groups, a weighted table is established. This table represents the contribution ratio of different indicator items within the same indicator group. Based on the weighted table, the standardized indicator values ​​within the corresponding indicator group are weighted and aggregated to obtain the innovation input score, digitalization score, green and low-carbon score, and operational delivery score. These scores are then concatenated according to preset field order to obtain the node indicator vector at the target time point. This node indicator vector generation process is repeated for all nodes in the node list at each time point to obtain a set of node indicator vectors. This set is used for subsequent calculation of the new quality productivity index and evaluation of industrial chain node development.

[0044] It should be noted that when establishing weight tables for the innovation input indicator group, digitalization indicator group, green and low-carbon indicator group, and operation and delivery indicator group, the weight assignment rules for the group weights are established using a preset weight configuration method, which can be either uniform weighting or proportional weighting. When using uniform weighting, the numerical value of each indicator item within the same indicator group is calculated based on the number of items in that group, and the weight of each indicator item within the same group is set to the reciprocal of its numerical value, thus ensuring that the weights of each indicator item within the same group are equal. When using proportional weighting, a corresponding weight coefficient is first set for each indicator item within the same indicator group. This weight coefficient represents the importance of the indicator item within the group. Then, normalization is performed on each weight coefficient within the same indicator group. This normalization process converts the weight coefficients into group weights and satisfies the constraint that the sum of the group weights is 1, thus obtaining the group weights of each indicator item within the same indicator group. The weight records within each indicator group are written into an intra-group weight table. This table uses the indicator group identifier as the index field, the indicator item identifier as the column or key field, and the intra-group weight as the value field, resulting in intra-group weight tables for the Innovation Input Indicator Group, Digitalization Indicator Group, Green and Low-Carbon Indicator Group, and Operational Delivery Indicator Group. These intra-group weight tables are used in subsequent intra-group score calculations to perform weighted summation of the standardized indicator values ​​within the corresponding indicator group.

[0045] The supply chain relationship construction module acquires the node list and supply and demand relationship data and forms a set of supply and demand relationship records. At the target time point, it calculates the supply ratio and substitution difficulty for upstream and downstream node numbers. Based on the supply ratio and substitution difficulty, it calculates the supply dependence intensity value and writes it into the supply dependence relationship table. Based on the supply dependence relationship table, it constructs the supply chain dependence graph.

[0046] like Figure 3 As shown, the method for constructing the industry chain dependency graph includes: Obtain the node list and supply-demand relationship data. The node list provides the node number and link type of each node in the industry chain node set. The supply-demand relationship data characterizes the upstream and downstream supply and demand correspondence between nodes. This data comes from procurement supply lists, bills of materials, or manually maintained upstream and downstream pairing tables. The supply-demand relationship data includes upstream node numbers, downstream node numbers, and supply volume data related to supply ratio calculations, as well as supplier numbers, certification cycles, and switchover cost information related to substitution difficulty calculations. Switchover cost information can be obtained by summing up modification costs, verification costs, and downtime losses.

[0047] Based on the node list, node consistency processing is performed on the supply and demand relationship data. This process includes node number verification and relationship record filtering. Node number verification determines whether the upstream and downstream node numbers in the supply and demand relationship data exist in the node list. Relationship record filtering removes relationship records that fail node number verification and retains those that pass. After node consistency processing, the supply and demand relationship data is constructed into a supply and demand relationship record set, which is used for subsequent dependency strength calculations.

[0048] It should be noted that each record in the supply and demand relationship record set includes at least the upstream node number, downstream node number, time point, and supply quantity data. The supply quantity data represents the quantity or amount of goods supplied by the upstream node number to the downstream node number at the same time point. The supply ratio is defined as the proportion of the supply quantity of the upstream node number to the downstream node number at the target time point to the total supply quantity of the downstream node number at the target time point. Therefore, when the same downstream node number corresponds to multiple upstream node numbers at the same target time point, multiple supply ratios are formed respectively.

[0049] Based on the supply and demand relationship record set, for each pair of upstream and downstream node numbers, the supply ratio of the upstream node number to the downstream node number is calculated at the target time point. Specifically, for the same downstream node number at the same target time point, if the supply and demand relationship data indicates that the downstream node number corresponds to multiple upstream node numbers, multiple supply ratios are calculated separately. Each supply ratio corresponds to the supply contribution of an upstream node number to that downstream node number. The denominators of multiple supply ratios are consistent, which is the total downstream supply volume obtained by the downstream node number from all upstream node numbers at the target time point. Therefore, multiple supply ratios satisfy the constraint of summing to 1 under the condition that the supply and demand relationship data is complete and consistent. For the same upstream node number at the same target time point, if the supply and demand relationship data indicates that the upstream node number supplies to multiple downstream node numbers, multiple supply ratios are calculated separately. Each supply ratio corresponds to the supply contribution of the upstream node number to different downstream node numbers. Since the total downstream supply volume corresponding to different downstream node numbers is different, the multiple supply ratios corresponding to the upstream node number are not required to satisfy the constraint of summing to 1.

[0050] The supplier difficulty value is calculated based on the number of suppliers, the certification cycle difficulty value based on the certification cycle, and the switching cost difficulty value based on the switching cost. A weighted summation is then performed on these three values ​​to obtain the replacement difficulty. The supplier difficulty value is determined according to corresponding preset tiering rules, including single-supplier, few-supplier, and multi-supplier tiers, with different tiers corresponding to different supplier difficulty values. The certification cycle difficulty value is determined according to corresponding preset tiering rules, including long-cycle, medium-cycle, and short-cycle tiers, with different tiers corresponding to different certification cycle difficulty values. The switching cost difficulty value is determined according to corresponding preset tiering rules, including high-cost, medium-cost, and low-cost tiers, with different tiers corresponding to different switching cost difficulty values.

[0051] For example, in the technical solution of this invention, the number of suppliers represents the number of candidate suppliers that can supply downstream nodes with the same type of materials or the same type of capabilities at the target time point. The fewer the number of suppliers, the greater the supplier difficulty value. When the number of suppliers is equal to 1, it is considered a single supplier category, and the supplier difficulty value is 1.0; when the number of suppliers is between 2 and 3, it is considered a small number of suppliers category, and the supplier difficulty value is 0.7; when the number of suppliers is greater than or equal to 4, it is considered a multiple supplier category, and the supplier difficulty value is 0.4. When the number of suppliers is missing, the supplier difficulty value is set to a preset neutral difficulty value, which is 0.5.

[0052] The authentication period characterizes the authentication time required for a downstream node to introduce a new upstream node number or replace an existing upstream node number. A longer authentication period corresponds to a higher authentication difficulty value. When the authentication period is greater than 180 days, it is considered a long period with a difficulty value of 1.0; when the authentication period is between 90 and 180 days, it is considered a medium period with a difficulty value of 0.7; and when the authentication period is less than 90 days, it is considered a short period with a difficulty value of 0.4. If an authentication period is missing, the authentication difficulty value is set to a preset neutral difficulty value of 0.5.

[0053] Switching cost represents the cost required for a downstream node to switch its supply source from its current upstream node to another upstream node. Higher switching costs correspond to greater switching difficulty. For example, the low-cost threshold is set to 500,000 yuan, and the high-cost threshold is set to 2 million yuan. When the switching cost is higher than the high-cost threshold, the switching difficulty value is 1.0; when the switching cost is between the high-cost and low-cost thresholds, the switching difficulty value is 0.7; and when the switching cost is lower than the low-cost threshold, the switching difficulty value is 0.4.

[0054] The calculation method for the substitution difficulty includes: ; The difficulty of substitution is Supplier difficulty value The difficulty value of the certification cycle is The switching cost difficulty value is . , and For the corresponding weighting coefficients, , , , . Used to characterize the proportion of supplier difficulty value to substitution difficulty. Used to characterize the proportion of the certification cycle difficulty value to the substitution difficulty. It is used to characterize the proportion of the switching cost difficulty value to the replacement difficulty.

[0055] For example, in the technical solution of the present invention, it can be Set to 0.5. Set to 0.2, Set to 0.3. The rationale is that the number of suppliers directly determines whether alternative sources exist, and has a more fundamental impact on whether substitution is possible. A relatively higher proportion is taken; the certification cycle reflects the time investment required for substitution, and has a greater impact on how quickly substitution can occur. In general manufacturing, it is important but usually less so than the number of suppliers. A relatively low proportion should be chosen; switching costs reflect the economic and organizational investment required for substitution, affecting the affordability of the substitution, and in actual decision-making, they usually constitute a major constraint along with the number of suppliers. The value is higher than the weight corresponding to the authentication period. Furthermore, the weight parameter... , and It can be stored as a preset weight parameter in the system configuration and adjusted in different industry chain scenarios; for example, it can be increased for industries with strong compliance requirements. Scenarios with stronger funding constraints can improve The weighting adjustment does not change the calculation structure of the substitution difficulty, but only changes the proportion of each difficulty value's contribution to the substitution difficulty.

[0056] It should be noted that, in this invention, substitution difficulty is used to convert the substitutability information in the supply and demand relationship record set into a calculable quantitative parameter. This parameter is then used as an edge attribute in the supply chain dependency graph to participate in dependency strength calculation. This allows dependency strength to characterize both the proportion of supply contribution and the substitutability of the supply source. This addresses the technical problem in this invention where existing technologies, lacking the introduction of inter-node dependency and substitution relationships, misclassify substitutable nodes as critical nodes and struggle to identify pseudo-critical nodes. When calculating the dependency strength from upstream node number to downstream node number, the supply chain relationship construction module uses both substitution difficulty and supply share to form edge weights, enabling the edge weights to simultaneously reflect both the proportion of supply contribution and the ease of substitution. Substitutability difficulty can also be invoked by subsequent modules for further analysis and ranking calculations of dependency relationships.

[0057] The supply dependence intensity value is calculated based on the supply share and substitution difficulty. This value characterizes the degree of supply dependence of a downstream node on an upstream node at a target time point, and is equal to the product of the supply share and substitution difficulty. The supply dependence intensity value is calculated separately for each pair of upstream and downstream node numbers in the supply-demand relationship record set at the target time point, resulting in multiple supply dependence intensity values ​​at the same target time point. Specifically, for the same downstream node number at the same target time point, if that downstream node number corresponds to multiple upstream node numbers, multiple supply shares are calculated separately, and multiple supply dependence intensity values ​​are calculated based on each supply share and the corresponding substitution difficulty. These multiple supply dependence intensity values ​​correspond to the degree of supply dependence of each upstream node number on that downstream node number. For the same upstream node number at the same target time point, if that upstream node number supplies to multiple downstream node numbers, multiple supply dependence intensity values ​​are calculated separately for each downstream node number. Since the total downstream supply and substitution difficulty may differ for different downstream node numbers, the supply dependence intensity values ​​corresponding to different downstream node numbers are not required to satisfy a summation constraint.

[0058] It should be noted that the supply dependence intensity value is calculated using the upstream node number, downstream node number, and time point as a joint index. The supply dependence intensity value is equal to the product of the supply share and the substitution difficulty. The supply share is the proportion of the supply obtained by the downstream node number from the upstream node number at the target time point to the total supply of the downstream node number. The substitution difficulty is the ease with which the downstream node number switches its supply source from the upstream node number to other upstream node numbers.

[0059] The supply dependence intensity value is written into the supply dependence table. The supply dependence table uses upstream node number, downstream node number, and time point as index fields, and the supply dependence intensity value as the value field, ensuring that each pair of upstream and downstream node numbers in the supply and demand relationship record set corresponds to a queryable supply dependence intensity value record at each time point. The dependence table also retains supply share and substitution difficulty as optional fields for subsequent interpretation and verification.

[0060] After the supply dependency table is generated, the supply chain relationship construction module performs consistency processing on the relationship records of the same downstream node number and within the same time range. This consistency processing ensures consistent supply ratio definitions and eliminates aggregation errors. Specifically, it includes: performing a summation check on all supply ratios corresponding to the same downstream node number at the same time point; when the summation of supply ratios deviates from a preset range, normalizing all supply ratios within that range, and recalculating the corresponding supply dependency strength value based on the normalized supply ratios, thus ensuring the comparability of multiple supply dependency strength values ​​corresponding to the same downstream node number at the same time point.

[0061] After completing the consistency processing, the supply chain relationship construction module constructs a supply chain dependency graph based on the node list and the supply dependency table. The supply chain dependency graph includes a vertex set and an edge set. The vertex set consists of node numbers from the node list; the edge set consists of directed relationships from upstream node numbers to downstream node numbers from the supply dependency table; each edge in the edge set carries a supply dependency strength value at the corresponding time point. For data containing multiple time points, the supply chain relationship construction module writes the supply dependency strength values ​​for each time point, so that the supply chain dependency graph forms a sequence of supply dependency strength values ​​that change over time on the same edges.

[0062] The pseudo-critical node identification module calculates the on-chain criticality score of each node number at the target time point based on the industry chain dependency graph, calculates the node capability score of each node number at the target time point based on the node indicator vector set, generates node development evaluation results based on the on-chain criticality score and node capability score, and outputs a node evaluation result table containing pseudo-critical node markers.

[0063] Methods for calculating the on-chain criticality score of each node number at a target time point based on the supply chain dependency graph include: For each node number in the node list, all directed relationships satisfying the condition that the upstream node number is the current node number are retrieved in the supply chain dependency graph, resulting in the first set of directed relationships where the current node number is the upstream node number. For each directed relationship in the first set, the corresponding supply dependency strength value is read, and all supply dependency strength values ​​are summed to obtain the supply influence of the current node number at the target time point. Then, all directed relationships satisfying the condition that the downstream node number is the current node number are retrieved in the supply chain dependency graph, resulting in the second set of directed relationships where the current node number is the downstream node number. For each directed relationship in the second set, the corresponding supply dependency strength value is read, and all supply dependency strength values ​​are summed to obtain the influence of the current node number at the target time point.

[0064] It should be noted that, to facilitate the explanation of the extraction method of the directed relation set and the calculation caliber of supply influence and institutional constraints, the following example is given: Suppose the current node number is node A. In the supply dependency relation table corresponding to the target time point, there are three directed relation records: a directed relation record with upstream node number A and downstream node number B, a directed relation record with upstream node number A and downstream node number C, and a directed relation record with upstream node number E and downstream node number A. When calculating the supply influence of node A, all directed relations that satisfy the upstream node number A are retrieved in the supply chain dependency graph, thus obtaining the first directed relation set with node A as the upstream node number. The first directed relation set includes the directed relations from node A to node B and the directed relations from node A to node C. The supply dependence strength value corresponding to each directed relation in the first directed relation set is summed to obtain the supply influence of node A at the target time point.

[0065] When calculating the dependence of node A, all directed relations that satisfy the downstream node number of node A are retrieved in the supply chain dependency graph, thereby obtaining a second set of directed relations with node A as the downstream node number. The second set of directed relations contains directed relations from node E to node A. The supply dependence strength value corresponding to each directed relation in the second set of directed relations is summed to obtain the dependence of node A at the target time point.

[0066] Through the above examples, the supply impact degree and the system of influence correspond to the summary of outgoing relationships with the current node number as the upstream node number and the summary of incoming relationships with the current node number as the downstream node number, respectively, thereby supporting the on-chain criticality calculation process based on the industry chain dependency graph.

[0067] Based on the supply influence and constraint of each node number in the node list at the target time point, calculate the original value of the on-chain criticality of that node number at the target time point. The original value of the on-chain criticality satisfies: Original value of on-chain criticality = Supply influence weight × Supply influence + Constraint weight × Constraint, where the supply influence weight and constraint weight are preset weight parameters and satisfy the sum of the weights equals 1.

[0068] The method for calculating the original value of the on-chain criticality includes: ; in, Number the current node At the target time point The raw on-chain criticality value is used to characterize the criticality of a node in the supply chain dependency graph. Indicates the current node number At the target time point The supply impact, representing the current node number. The sum of the supply dependence strength values ​​of each downstream node number when used as an upstream node number. Indicates the current node number At the target time point The system indicates the current node number. When used as a downstream node number, it represents the sum of supply dependence strength values ​​from each upstream node number. The supply impact weight is used to characterize the contribution ratio of the supply impact degree to the original value of the criticality on the chain. The constraint weight is used to characterize the contribution ratio of the constraint. For example, in the technical solution of the present invention, the supply influence weight can be set to 0.6, that is, the corresponding constraint weight is 0.4.

[0069] It should be noted that when calculating the original value of on-chain criticality, supply influence weight and constraint weight are preset. In the determination of criticality of nodes in the industrial chain, supply influence directly corresponds to the degree of constraint of the current node number as a supplier on the chain. Therefore, in the context of supply chain governance, a relatively higher supply influence weight can be configured for supply influence. Constraint is used to characterize the dependence of the current node number on upstream supply. Nodes with higher constraints do not necessarily constitute key supply points on the chain. Therefore, a relatively lower constraint weight can be configured for constraints to achieve differentiated integration of the two types of characteristics. The corresponding weight configuration is called in different application scenarios to adapt to the evaluation focus; for example, in the application scenario of ensuring supply security, the supply influence weight is increased to strengthen the integration contribution of supply-side constraint characteristics; in the application scenario of identifying vulnerable links, the supply influence weight is reduced and the constraint weight is increased accordingly to strengthen the integration contribution of dependence characteristics. Through the aforementioned weighting configuration mechanism, the calculation process of the original on-chain criticality value maintains a consistent calculation structure. At the same time, it allows for adjustments to the contribution ratio of supply influence and subjectivity in the fusion calculation based on business concerns, thereby providing a consistent input for subsequent on-chain criticality score calculation and pseudo-critical node tagging generation.

[0070] The on-chain criticality score is obtained by standardizing the raw on-chain criticality values. Specifically, at the target time point, the minimum and maximum criticality values ​​of all node numbers are statistically analyzed to obtain the minimum and maximum criticality values. A linear mapping is then performed on the raw on-chain criticality values ​​based on the minimum and maximum criticality values ​​to obtain the on-chain criticality score. When the corresponding values ​​of the maximum and minimum criticality values ​​are equal, the on-chain criticality score is set to a preset neutral value. The on-chain criticality score is limited to a value range of 0 to 1. If the value exceeds the range, truncation is performed.

[0071] The node capability score is calculated based on the node indicator vector of each node number in the node list at the target time point. The node capability score is calculated separately for each node number in the node list, thus forming multiple node capability scores at the target time point. The node capability score and the on-chain criticality score correspond one-to-one with the same node number and the same target time point, and are used together with the on-chain criticality score to participate in the node development evaluation. The calculation of the node capability score includes: obtaining the innovation input score, digitalization score, green and low-carbon score and operation delivery score corresponding to the current node number at the target time point, configuring the first weight, second weight, third weight and fourth weight respectively, and merging the four scores based on the weighted geometric average method to obtain the node capability score.

[0072] The method for calculating the node capability score includes: ; in, Number the current node At the target time point The node capability score, Number the current node At the target time point The Scores for each indicator group For example, the first is the innovation input score, the second is the digitalization score, the third is the green and low-carbon score, and the fourth is the operational delivery score. For the first The group-level fusion weight of the scores of each indicator group, for example... The group-level fusion weight for innovation input scores is denoted as the first weight. The group-level fusion weight for the digital intelligence score is denoted as the second weight. The group-level fusion weight for green and low-carbon scores is denoted as the third weight. The group-level fusion weight for the operational delivery score is denoted as the fourth weight. These are extremely small constants used to prevent the entire product from becoming 0. For example, the first, second, third, and fourth weights are all set to 0.25.

[0073] In one embodiment of the present invention, the node capability score is calculated not by weighted summation, but by weighted geometric mean. The innovation input score, digitalization score, green and low-carbon score, and operational delivery score corresponding to the node number at the target time point are obtained. A first weight, a second weight, a third weight, and a fourth weight are assigned to each of the four scores, where the first, second, third, and fourth weights are non-negative and their sum equals 1. Through the weighted geometric mean calculation method, the node capability score is obtained from the product structure of the scores within the four groups. This ensures that a lower score in any group has a more significant downward effect on the overall node capability score, thereby suppressing the masking effect of a single high-scoring group on the overall capability assessment.

[0074] For ease of understanding, the following numerical example is provided. Assume the score for the first group is 0.90, the second group is 0.90, the third group is 0.90, and the fourth group is 0.20, with equal weights for all four. When using a weighted summation method, the node capability score is the arithmetic mean of the four scores, and the node capability score is... When using the weighted geometric mean, the node capability score is the fourth root of the four scores, and the node capability score is... Therefore, it can be seen that when there are obvious weak link scores, the weighted geometric mean method gives a lower node capability score than the weighted sum method, making the weak link score have a more significant impact on the final capability assessment.

[0075] The above-described processing corresponds to the technical problem this invention aims to solve. This invention addresses the issue in existing technologies where nodes with outstanding performance in a single indicator but significant weaknesses in other dimensions are overestimated during comprehensive evaluation of industry chain nodes. This leads to the misclassification of substitutable or non-critical nodes as critical nodes and difficulty in identifying pseudo-critical nodes during node classification, resource allocation, and critical node identification. By employing a weighted geometric average to calculate node capability scores, a low score in any indicator group will non-linearly suppress the node capability score, thereby exposing weaknesses early in the comprehensive evaluation stage and reducing the risk of masking due to linear superposition. Based on the joint determination of node capability scores and on-chain criticality scores, this invention can more reliably screen out pseudo-critical nodes that appear strong but lack critical constraints or have highly substitutable dependencies, providing a more consistent input basis for subsequent industry chain node development evaluation and management strategy generation.

[0076] Methods for generating node development evaluation results based on on-chain criticality scores and node capability scores, and outputting a node evaluation result table containing pseudo-critical node tags, include: For each node number in the node list, obtain the on-chain criticality score and node capability score at the target time point. The on-chain criticality score and node capability score correspond one-to-one with the node number and the target time point. Perform a fusion calculation on the on-chain criticality score and node capability score based on a preset fusion weight to obtain the comprehensive development score of the node number at the target time point, thereby forming multiple comprehensive development scores at the target time point.

[0077] The calculation method for the comprehensive development score includes: ; in, Number the current node At the target time point The overall development score, Number the current node At the target time point On-chain criticality score, This is the on-chain keyness fusion weight, with a value ranging from 0 to 1. Weighting of node capabilities and This is used to characterize the contribution ratio of on-chain criticality score and node capability score to the overall development score.

[0078] For example, in one embodiment of the technical solution of the present invention, the on-chain criticality fusion weight is 0.6, and the node capability fusion weight is 0.4. The on-chain criticality fusion weight and the node capability fusion weight are used to appropriately increase the contribution ratio of the on-chain criticality score in the overall development score, so that the overall development score is more sensitive to changes in the industry chain dependence relationship; at the same time, the contribution ratio of the node capability score is retained, so that the overall development score is still affected by the node's own capability level. The fusion weight is stored as a configurable parameter in the system configuration items, and the value of the fusion weight can be adjusted according to the application scenario; for example, in an application scenario that ensures supply security, increasing... To enhance the contribution of on-chain criticality scores and reduce [the impact of these scores] in application scenarios that promote capability enhancement. This improves the contribution of node capability scores, thereby adapting to different evaluation focuses in different scenarios without changing the fusion computing structure.

[0079] For each node in the node list, the comprehensive development score at the target time point is assessed, and node numbers with a comprehensive development score greater than or equal to the comprehensive development threshold are identified as the set of potential key candidate nodes. For each node number in the set of potential key candidate nodes, the corresponding on-chain keyness score and node capability score are obtained, and the keyness support score is calculated based on the on-chain keyness score and comprehensive development score. When the on-chain keyness score is less than the keyness score threshold, the node capability score is greater than or equal to the node capability score threshold, and the keyness support score is less than the support score threshold, the pseudo-key node is marked as true; otherwise, the pseudo-key node is marked as false. The node number, target time point, on-chain keyness score, node capability score, comprehensive development score, keyness support score, and pseudo-key node mark are written into the node evaluation result table.

[0080] The calculation method for the criticality support degree includes: ; in, Number the current node At the target time point The criticality support score is used to characterize the proportion of on-chain criticality contribution to the overall development score. A lower criticality support score indicates that the overall development score is more dominated by node capabilities. The formula for calculating criticality support score does not directly use... The reason is that the overall development score is obtained by weighted integration. When expressing the proportion of the contribution of the key item in the overall development score, the weighted on-chain key score should be used.

[0081] It should be noted that, under the judgment logic of this invention, the threshold parameters are recommended to be consistent with the value range of each score, all configured within the range of 0 to 1, and the distribution statistics of the target time point across all nodes are used as the basis for threshold setting. The comprehensive development threshold is used to generate a set of candidate nodes for potential key nodes, the keyness score threshold is used to constrain nodes with insufficient keyness on the chain, the node capability score threshold is used to filter node numbers with medium to high capabilities, so that the determination of pseudo-key nodes is limited to nodes with decent capability performance but insufficient keyness on the chain, and the support threshold is used to constrain nodes whose comprehensive development score is mainly due to a low proportion of keyness contribution.

[0082] For example, in the technical solution of this invention, the target time point is set to the end of a certain month, and the node list contains 100 node numbers. The on-chain criticality score, node capability score, and comprehensive development score have all been standardized to 0 to 1. The comprehensive development threshold is set to 0.75 to select node numbers with comprehensive development scores in the top 25% as a set of potential key candidate nodes; the criticality score threshold is set to 0.40 to determine node numbers with on-chain criticality scores at a relatively low level; the node capability score threshold is set to 0.60 to determine node numbers with node capability scores at a medium-to-high level; and the support threshold is set to 0.45 to determine node numbers with criticality support at a relatively low level. These thresholds can be used as system configuration items at the target time point with the same caliber.

[0083] The reasons for setting the above thresholds are as follows: The comprehensive development threshold is used to generate a set of potential key candidate nodes. A higher threshold is used because the set of potential key candidate nodes corresponds to the objects most likely to be included in key business focus and resource investment. Screening through the comprehensive development threshold first can narrow the diagnostic scope and avoid meaningless pseudo-key judgments of nodes with low comprehensive levels. The keyness score threshold is used to identify nodes with insufficient on-chain keyness. The keyness score threshold is set at a relatively low level because one of the core characteristics of pseudo-key nodes is "being treated as key but lacking on-chain keyness." Therefore, the keyness score threshold is used to make this necessary condition of insufficient on-chain keyness explicit. The node capability score threshold is used to identify nodes with decent capabilities. The node capability score threshold is set at a medium-to-high level because pseudo-key nodes are not weak nodes, but rather nodes with "decent capability performance but insufficient on-chain constraints." The node capability score threshold can avoid mislabeling generally weak nodes as pseudo-keys. The support threshold is used to identify nodes in the overall development score where the contribution of keyness items is relatively low. The reason for setting the support threshold to a relatively low level is that when the overall development score reaches the candidate threshold for a pseudo-key node but the keyness support is still low, the overall development score is more likely to be boosted by capability items rather than supported by on-chain keyness, which conforms to the pseudo-key structure characteristic of "seemingly important but not on-chain key".

[0084] The above-mentioned judgment scheme is related to the technical problem to be solved by this invention as follows: This invention addresses the problem that existing technologies do not introduce inter-node dependency and substitution relationships, easily misclassify substitutable nodes as key nodes, and struggle to identify pseudo-key nodes. In the preliminary steps, it calculates the supply dependency intensity value and constructs a supply chain dependency graph by using supply ratio and substitution difficulty, enabling the on-chain keyness score to reflect dependency relationships and substitutability. Based on this, this invention, under the premise that the comprehensive development score has already been used to screen potential key candidate nodes, introduces a joint judgment of keyness score threshold, capability score threshold, and support threshold: a high comprehensive development score ensures that the node will indeed enter the key candidate range; low keyness score and low support jointly ensure "insufficient on-chain keyness and lack of keyness support in the comprehensive score"; a not low capability score ensures "not a generally weak node." Thus, "substitutable nodes or non-key nodes that are misjudged as key nodes" are output to the node evaluation result table as pseudo-key nodes, forming an interpretable, configurable, and reproducible diagnostic caliber, supporting the full disclosure of the technical solution of this invention.

[0085] The pseudo-critical node degradation module is used to extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

[0086] like Figure 4 As shown, the method for obtaining the correction node evaluation result table includes: Records marked as true for pseudo-critical nodes are selected from the node evaluation result table, and their corresponding node numbers are extracted and summarized to form a pseudo-critical node set. At the same time, the node evaluation results corresponding to the target time point of the pseudo-critical node set are extracted to form a pseudo-critical node sub-table. Each row of the pseudo-critical node sub-table corresponds one-to-one with a node number and a target time point, which is used as the input sample set for the subsequent pseudo-critical node downgrade scoring model.

[0087] The input feature vector of the pseudo-key node degradation scoring model is constructed based on a pseudo-key node sub-table. For each node number in the pseudo-key node set at the target time point, the on-chain keyness score, node capability score, comprehensive development score, and keyness support level are extracted as basic features. Difference features are calculated based on preset thresholds to enhance discriminative power. The difference features include keyness difference and support difference, where the keyness difference equals the keyness score threshold minus the on-chain keyness score, and the support difference equals the support threshold minus the keyness support level. When the keyness difference or support difference is less than 0, the corresponding difference is set to 0 so that the difference feature only represents the degree of inadequacy below the threshold. The input feature vector of the node number at the target time point is obtained by concatenating the basic features and the difference features.

[0088] The input feature vector is fed into a preset pseudo-keyword degradation scoring model to obtain a pseudo-keyword degradation score. The pseudo-keyword degradation score ranges from 0 to 1, with a higher score indicating a stronger degradation process for that pseudo-keyword node. The pseudo-keyword degradation module writes the node number, target time point, and pseudo-keyword degradation score into a pseudo-keyword node record table, which is used to support the traceability of subsequent degradation processes.

[0089] For each pseudo-critical node record in the pseudo-critical node record table, read the corresponding comprehensive development score and pseudo-critical node downgrade score, and calculate the downgrade coefficient based on the downgrade weight. The downgrade coefficient equals the downgrade weight multiplied by the pseudo-critical node downgrade score; when the downgrade coefficient is greater than 1, the downgrade coefficient is truncated to 1. Calculate the corrected comprehensive development score based on the comprehensive development score and the downgrade coefficient. The corrected comprehensive development score equals the comprehensive development score multiplied by 1 minus the downgrade coefficient; when the corrected comprehensive development score is less than 0, the corrected comprehensive development score is set to 0. Write the corrected comprehensive development score back to the node evaluation result table to form the corrected node evaluation result table, and record the downgrade coefficient and pseudo-critical node downgrade score in the corrected node evaluation result table. This allows for subsequent sorting, grading, or resource allocation based on the corrected comprehensive development score, and also enables explanation of the reasons for the downgrade of pseudo-critical nodes based on the downgrade coefficient and pseudo-critical node downgrade score.

[0090] It should be noted that the pseudo-key node degradation scoring model outputs a pseudo-key node degradation score for each node number in the pseudo-key node set at the target time point. The pseudo-key node degradation score is used to characterize the strength of the degradation processing performed by the pseudo-key node. The training process of the pseudo-key node degradation scoring model is based on constructing a training sample set based on the historical node evaluation result table, and the model parameters are fitted and the model is solidified in a supervised learning manner to ensure that the model output is reproducible and can be directly driven by the node evaluation result table.

[0091] During the training data preparation phase, the pseudo-keyage degradation scoring model training module obtains node evaluation result tables corresponding to multiple historical time points. These tables include node ID, time point, on-chain keyness score, node capability score, comprehensive development score, keyness support level, and pseudo-keyage node markers. The module uses these historical time points as sample time points and selects records from the node evaluation result tables where the pseudo-keyage node marker is true for each sample time point, extracting the corresponding node ID to form a training pseudo-keyage node set. Each record in the training pseudo-keyage node set corresponds one-to-one with a node ID and a sample time point, serving as a training sample. To ensure that the training samples have predictable learning objectives, the module sets a preset prediction window and collects node evaluation results for the same node ID at subsequent time points after the sample time point, based on this preset prediction window, to obtain a sequence of subsequent node evaluation results.

[0092] In the feature construction phase, the pseudo-key downgrade scoring model training module constructs an input feature vector for each training sample. The input feature vector includes basic features and difference features. Basic features consist of the on-chain key score, node capability score, comprehensive development score, and key support score at the sample's time point. Difference features characterize the deviation of the sample from the threshold. Difference features include key difference and support difference. The key difference equals the key score threshold minus the on-chain key score, and the support difference equals the support threshold minus the key support score. When the key difference or support difference is less than 0, the corresponding difference is set to 0. The basic features and difference features are concatenated according to a preset field order to obtain the input feature vector for the training sample.

[0093] In the label construction phase, the pseudo-keyword downgrade scoring model training module constructs supervised learning labels for each training sample. These supervised learning labels reflect the pseudo-keyword degree or downgrade intensity of the pseudo-keyword node within the subsequent prediction window. The supervised learning labels are calculated from the evaluation result sequence of subsequent nodes. Specifically, the pseudo-keyword downgrade scoring model training module extracts the subsequent comprehensive development score sequence from the subsequent node evaluation result sequence and calculates the decline magnitude of the comprehensive development score. The decline magnitude is equal to the comprehensive development score at the sample time point minus the minimum comprehensive development score within the subsequent prediction window; when the decline magnitude is less than 0, it is set to 0. To map the decline magnitude of the comprehensive development score to supervised learning labels within the range of 0 to 1, the pseudo-keyword downgrade scoring model training module performs normalization processing on the decline magnitude of the comprehensive development score. The normalization processing uses a preset upper limit value for the decline magnitude. The label value is obtained by dividing the decline magnitude of the comprehensive development score by the upper limit value, and results exceeding the range of 0 to 1 are truncated, thus obtaining the supervised learning labels for the training samples. By constructing the labels as described above, the supervised learning labels can directly reflect the degree of decline in the overall performance of pseudo-critical nodes within subsequent time windows, which can be used to guide the downgrade scoring model in learning the appropriate downgrade intensity.

[0094] During the model training phase, the pseudo-keyword downgrade scoring model training module divides the training sample set into a training subset and a validation subset. It selects a regression-based machine learning model as the pseudo-keyword downgrade scoring model and fits the input feature vectors of the training subset to the supervised learning labels to obtain model parameters. During training, the validation subset is used to evaluate the error between the model output and the supervised learning labels, and the model parameters are solidified based on the error convergence condition or a preset number of iterations. After model training is complete, the pseudo-keyword downgrade scoring model training module writes the solidified model parameters and feature fields sequentially into the model configuration file. This enables the pseudo-keyword downgrade module to construct model inputs in the same field order and obtain repeatable pseudo-keyword downgrade scoring outputs during runtime.

[0095] To ensure the stability and interpretability of the model output, the pseudo-keyage downgrade scoring model training module performs value range verification and missing value imputation on the input feature vector during the training data preparation stage. Value range verification is used to confirm that the key score, node capability score, comprehensive development score, and key support score are all within the range of 0 to 1. Missing value imputation is used to fill in the missing feature values ​​according to the preset neutral value or the historical average of the same node. At the same time, the pseudo-keyage downgrade scoring model training module records the preset prediction window, key score threshold, node capability score threshold, and support threshold to ensure that the model training caliber is consistent with the online inference caliber, thereby supporting the full disclosure and reproducible implementation of the technical solution of this invention.

[0096] Example 2: Please see Figure 2 As shown, this embodiment provides a method for evaluating the development of industrial chain nodes based on the new quality productivity index, including: Based on the set of nodes in the industrial chain, a node list, an indicator list, and an indicator direction table are established. The original data of the corresponding indicators are obtained and merged to form the original node indicator table. The original node indicator table is processed with unified dimensions and missing data to obtain the standardized node indicator table. The scores of each indicator group in the indicator list are summarized according to the weight within the group and concatenated to generate a set of node indicator vectors. Obtain the node list and supply and demand relationship data and form a set of supply and demand relationship records. At the target time point, calculate the supply ratio and substitution difficulty for the upstream node number and the downstream node number. Calculate the supply dependence intensity value based on the supply ratio and substitution difficulty and write it into the supply dependence relationship table. Construct the industrial chain dependence graph based on the supply dependence relationship table. The chain criticality score of each node number at the target time point is calculated based on the chain dependency graph. The node capability score of each node number at the target time point is calculated based on the node indicator vector set. The node development evaluation result is generated based on the chain criticality score and the node capability score, and a node evaluation result table containing pseudo-critical node markers is output. Extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

[0097] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0098] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the development of industrial chain nodes based on the new quality productivity index, characterized in that: include: Based on the set of nodes in the industrial chain, a node list, an indicator list, and an indicator direction table are established. The original data of the corresponding indicators are obtained and merged to form the original node indicator table. The original node indicator table is processed with unified dimensions and missing data to obtain the standardized node indicator table. The scores of each indicator group in the indicator list are summarized according to the weight within the group and concatenated to generate a set of node indicator vectors. Obtain the node list and supply and demand relationship data and form a set of supply and demand relationship records. At the target time point, calculate the supply ratio and substitution difficulty for the upstream node number and the downstream node number. Calculate the supply dependence intensity value based on the supply ratio and substitution difficulty and write it into the supply dependence relationship table. Construct the industrial chain dependence graph based on the supply dependence relationship table. The chain criticality score of each node number at the target time point is calculated based on the chain dependency graph. The node capability score of each node number at the target time point is calculated based on the node indicator vector set. The node development evaluation result is generated based on the chain criticality score and the node capability score, and a node evaluation result table containing pseudo-critical node markers is output. Extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

2. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, The method for obtaining the evaluation result table of the correction node includes: Filter records marked as true from the node evaluation result table, extract the corresponding node numbers and summarize them to form a pseudo-critical node set; extract the node evaluation results corresponding to the target time point from the pseudo-critical node set to form a pseudo-critical node sub-table; The input feature vector of the pseudo-key node sub-table is constructed based on the pseudo-key downgrade scoring model. Input the input feature vector into the preset pseudo-key downgrade scoring model to obtain the pseudo-key downgrade score; write the node number, target time point and pseudo-key downgrade score into the pseudo-key node record table; For each pseudo-critical node record in the pseudo-critical node record table, read the corresponding comprehensive development score and pseudo-critical downgrade score, and calculate the downgrade coefficient based on the downgrade weight. The downgrade coefficient is equal to the downgrade weight multiplied by the pseudo-critical downgrade score. Calculate the corrected comprehensive development score based on the comprehensive development score and the downgrade coefficient. The corrected comprehensive development score is equal to the comprehensive development score multiplied by 1 minus the downgrade coefficient. Write the corrected comprehensive development score back to the node evaluation result table to form the corrected node evaluation result table, and record the downgrade coefficient and pseudo-critical downgrade score in the corrected node evaluation result table.

3. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 2, characterized in that, Methods for constructing the input feature vector of a pseudo-keypoint downgrade scoring model based on a pseudo-keypoint node sub-table include: For each node number in the pseudo-critical node set at the target time point, the on-chain criticality score, node capability score, comprehensive development score, and criticality support score are extracted as basic features. Difference features are calculated based on preset thresholds. The difference features include criticality difference and support difference. The criticality difference is equal to the criticality score threshold minus the on-chain criticality score, and the support difference is equal to the support threshold minus the criticality support score. The basic features and difference features are concatenated to obtain the input feature vector of the node number at the target time point.

4. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, Methods for calculating the on-chain criticality score of each node number at a target time point based on the supply chain dependency graph include: For each node number in the node list, retrieve all directed relationships in the supply chain dependency graph that satisfy the condition that the upstream node number is the current node number, to obtain the first set of directed relationships where the current node number is the upstream node number; for each directed relationship in the first set of directed relationships, read the corresponding supply dependence intensity value, and perform summation on all supply dependence intensity values ​​to obtain the supply influence of the current node number at the target time point; retrieve all directed relationships in the supply chain dependency graph that satisfy the condition that the downstream node number is the current node number, to obtain the second set of directed relationships where the current node number is the downstream node number; for each directed relationship in the second set of directed relationships, read the corresponding supply dependence intensity value, and perform summation on all supply dependence intensity values ​​to obtain the influence of the current node number at the target time point; Based on the supply impact and control of each node number in the node list at the target time point, calculate the original value of the chain criticality of that node number at the target time point. The on-chain criticality score is obtained by standardizing the raw on-chain criticality values.

5. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, Methods for calculating the node capability score of each node at the target time point based on a set of node indicator vectors include: For each node number in the node list at the target time point, obtain the innovation input score, digitalization score, green and low-carbon score, and operation delivery score corresponding to the current node number at the target time point, and assign a first weight, a second weight, a third weight, and a fourth weight respectively. Based on the weighted geometric average method, calculate the node capability score by integrating the innovation input score, digitalization score, green and low-carbon score, and operation delivery score.

6. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, Methods for generating node development evaluation results based on on-chain criticality scores and node capability scores, and outputting a node evaluation result table containing pseudo-critical node tags, include: For each node number in the node list, obtain the on-chain criticality score and node capability score at the target time point; perform a fusion calculation on the on-chain criticality score and node capability score based on the preset fusion weight to obtain the comprehensive development score of the node number at the target time point; For each node in the node list, the comprehensive development score at the target time point is assessed, and node numbers with a comprehensive development score greater than or equal to the comprehensive development threshold are identified as the set of potential key candidate nodes. For each node number in the set of potential key candidate nodes, the corresponding on-chain keyness score and node capability score are obtained, and the keyness support score is calculated based on the on-chain keyness score and comprehensive development score. When the on-chain keyness score is less than the keyness score threshold, the node capability score is greater than or equal to the node capability score threshold, and the keyness support score is less than the support score threshold, the pseudo-key node is marked as true; otherwise, the pseudo-key node is marked as false. The node number, target time point, on-chain keyness score, node capability score, comprehensive development score, keyness support score, and pseudo-key node mark are written into the node evaluation result table.

7. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, The method for constructing the supply chain dependency graph includes: Obtain the node list and supply and demand relationship data; the supply and demand relationship data includes upstream node number, downstream node number, supply volume data, number of suppliers, certification cycle, and switching cost information; Based on the node list, node consistency processing is performed on the supply and demand relationship data. The node consistency processing includes node number verification and relationship record filtering. After the node consistency processing is completed, the supply and demand relationship data is constructed into a supply and demand relationship record set. Based on the supply and demand relationship record set, for each group of upstream node numbers and downstream node numbers at the target time point, calculate the supply ratio from upstream node number to downstream node number. The supplier difficulty value is calculated based on the number of suppliers, the certification cycle difficulty value is calculated based on the certification cycle, and the switching cost difficulty value is calculated based on the switching cost. The supplier difficulty value, certification cycle difficulty value, and switching cost difficulty value are weighted and summed to obtain the substitution difficulty. Calculate the supply dependence intensity value based on the supply share and the difficulty of substitution; write the supply dependence intensity value into the supply dependence table; A supply chain dependency graph is constructed based on a node list and a supply dependency table. The supply chain dependency graph includes a vertex set and an edge set. The vertex set consists of node numbers from the node list. The edge set consists of directed relationships from upstream node numbers to downstream node numbers from the supply dependency table. Each edge in the edge set carries the supply dependency strength value at the corresponding time point.

8. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 1, characterized in that, Methods for establishing node lists, indicator lists, and indicator direction tables based on industry chain node sets include: Based on the set of nodes in the industrial chain, a node list and an indicator list are established. The node list includes node number and chain link type, which is used to characterize whether the node belongs to the upstream, midstream, or downstream link of the industrial chain. The indicator list includes innovation input indicator group, digitalization indicator group, green and low-carbon indicator group, and operation and delivery indicator group. The innovation input indicator group includes R&D intensity, number of patents, and proportion of high-value patents. The digitalization indicator group includes equipment networking rate, business system coverage rate, and automation level. The green and low-carbon indicator group includes energy consumption per unit output, carbon emissions per unit output, and environmental compliance records. The operation and delivery indicator group includes on-time delivery rate, quality pass rate, and rework rate. Establish an indicator direction table to synchronize the node list and indicator list; the indicator direction table includes two types of direction markers: larger values ​​are better and smaller values ​​are better.

9. The method for evaluating the development of industrial chain nodes based on the new quality productivity index according to claim 8, characterized in that, The methods for summarizing the scores of each indicator group in the indicator list according to the weight within the group and concatenating them to generate a set of node indicator vectors include: For each of the innovation input indicator group, digitalization indicator group, green and low-carbon indicator group, and operation and delivery indicator group, an intra-group weight table is established. Based on the intra-group weight table, the standardized indicator values ​​within the corresponding indicator group are weighted and summarized to obtain the innovation input score, digitalization score, green and low-carbon score, and operation and delivery score. The innovation input score, digitalization score, green and low-carbon score, and operation and delivery score are concatenated according to the preset field order to obtain the node indicator vector of the node number at the target time point. The node indicator vector generation process is repeated for all nodes in the node list at each time point to obtain the node indicator vector set.

10. A supply chain node development evaluation system based on a new quality productivity index, used to implement the supply chain node development evaluation method based on a new quality productivity index as described in any one of claims 1-9, characterized in that, include: The node indicator vector generation module establishes a node list, indicator list, and indicator direction table based on the industrial chain node set; it acquires the original indicator data and merges it to form the original node indicator table; it performs unified dimension processing and missing data processing on the original node indicator table to obtain the node standardized indicator table; it summarizes the scores of each indicator group in the indicator list according to the weight within the group and concatenates them to generate a node indicator vector set. The industrial chain relationship construction module obtains the node list and supply and demand relationship data and forms a set of supply and demand relationship records. At the target time point, it calculates the supply ratio and substitution difficulty for upstream node number and downstream node number. Based on the supply ratio and substitution difficulty, it calculates the supply dependence intensity value and writes it into the supply dependence relationship table. Based on the supply dependence relationship table, it constructs the industrial chain dependence graph. The pseudo-critical node identification module calculates the on-chain criticality score of each node number at the target time point based on the industry chain dependency graph, calculates the node capability score of each node number at the target time point based on the node indicator vector set, generates node development evaluation results based on the on-chain criticality score and node capability score, and outputs a node evaluation result table containing pseudo-critical node markers. The pseudo-critical node degradation module is used to extract a set of pseudo-critical nodes from the node evaluation result table, input the node evaluation results corresponding to the set of pseudo-critical nodes into a preset pseudo-critical degradation scoring model to obtain a pseudo-critical degradation score, and perform degradation processing on the pseudo-critical nodes based on the pseudo-critical degradation score to generate a corrected node evaluation result table.

Images