An all-industry-chain lithium product trade security evaluation method

Abstract

The application provides a full-industry-chain lithium product trade security evaluation method, and relates to the technical field of international trade security evaluation. The method comprises the following steps: based on price change information and trade scale change information in the trade state sequence of the industry chain, combining the structure characteristic parameters, performing dynamic risk conduction analysis on the conduction process of trade risks between different trade subjects and different industry chain links, and generating risk conduction representation results. The full-industry-chain lithium product trade security evaluation method constructs a lithium product full-industry-chain division framework, forms an industry chain trade state sequence based on multi-source data cleaning and standardization, provides accurate basic data for subsequent risk evaluation, comprehensively monitors and analyzes the trade states of each industry chain link, and realizes dynamic tracking and risk identification of the full chain of lithium product trade.

Description

Technical Field

[0001] This invention relates to the field of international trade security assessment technology, specifically a method for assessing the trade security of lithium products across the entire industry chain. Background Technology

[0002] Existing technologies have two main limitations: First, existing evaluations often focus on a single link in the industrial chain or only use traditional supply and demand indicators for analysis, failing to conduct a systematic assessment from the perspective of the entire chain, from upstream lithium salts and midstream lithium battery materials to downstream lithium batteries, making it difficult to reflect the interconnected impact of safety at each link. Second, existing methods are mostly based on historical data for static assessment, lacking effective integration of trade network structure characteristics and unable to respond in real time to dynamic risks such as price fluctuations, resulting in a significant lag in assessment results.

[0003] The most critical flaw in existing technologies lies in their inability to meet the demands of real-time risk warnings due to their static assessment models. Specifically, traditional methods rely on periodic aggregation of historical data, which fails to capture and quantify the transmission effects of dynamic risks such as sudden price fluctuations across the entire industry chain. Especially when facing severe market volatility, this lag can lead to missed risk response windows, making it difficult to support pre-emptive warnings and rapid decision-making. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for evaluating the trade security of lithium products across the entire industry chain. The technical problem this invention aims to solve is: how to construct a dynamic risk transmission analysis model based on the trade status sequence of the industry chain to address the issue that static assessment models in existing technologies cannot capture and quantify the transmission effects of sudden risks in real time.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating the trade security of lithium products across the entire industry chain, comprising: S1. Construct a full industry chain segmentation framework for lithium products, obtain multi-source data reflecting the trade activity status of each link in the industry chain based on the full industry chain segmentation framework, and clean and standardize the multi-source data according to a unified time scale to form an industry chain trade status sequence. S2. Based on the trade state sequence of the industrial chain, a structured model is performed on the trade entities in each link of the industrial chain and the relationships between the trade entities to form an industrial chain trade relationship structure. The industrial chain trade relationship structure represents the connection relationship and trade intensity change of the trade entities. Structural feature parameters are extracted from the industrial chain trade relationship structure. The structural feature parameters represent the concentration, connectivity and symmetry of trade relationships. S3. Based on the price change information and trade volume change information in the trade state sequence of the industrial chain, and combined with the structural feature parameters, perform dynamic risk transmission analysis on the transmission process of trade risk between different trade entities and different links in the industrial chain, and generate risk transmission characterization results. S4. The risk transmission characterization results, the price change information, the trade volume change information, and the structural characteristic parameters are fused together to form a comprehensive trade security evaluation result; S5. Based on the comprehensive evaluation results of trade security, determine the trade security status of each link in the industrial chain, and output trade security risk warning information when the trade security status changes abnormally.

[0006] Preferably, the full industry chain segmentation framework is constructed based on the differences in processing levels and industrial functional attributes of lithium products in trade. The full industry chain segmentation framework includes upstream, midstream and downstream segments. The multi-source data includes lithium product import and export trade data based on specific HS codes, global lithium resource reserve distribution data, regional trade agreements and country risk rating data, and international market price data of lithium products.

[0007] Preferably, the interrelationships are obtained through basic trade data in the industrial chain trade state sequence. The interrelationships include import and export direction relationships that characterize trade flow, trade volume weight relationships that characterize trade intensity, node degree relationships that characterize network connection structure, and reciprocity relationships that characterize trade symmetry. The structured modeling includes taking the trading entities participating in lithium product trade as structural nodes, taking the actual import and export trade records between the trading entities as node connections, and taking the trade scale and trade direction corresponding to the import and export trade records as connection attributes to form the industrial chain trade relationship structure.

[0008] Preferably, the steps of the dynamic risk transmission analysis are as follows: S31. Identify risk sources based on the trade state sequence of the industrial chain and the structural characteristic parameters. The risk sources include specific trading entities or links in the industrial chain that cause price mutations, trade volume mutations, or trade network structure mutations. S32. Based on the aforementioned industrial chain trade relationship structure, simulate the transmission path of trade risks from the source of risk to trade entities and links in the industrial chain; S33. Combining the price change information and trade volume change information in the trade status sequence of the industrial chain, quantify the transmission strength of trade risk along the transmission path and generate the risk transmission characterization result.

[0009] Preferably, the identification of the risk source includes monitoring the changes in the price change information, the changes in the trade volume information, and the changes in the structural characteristic parameters, comparing the changes with a preset mutation threshold, and determining that the trade entity or industrial chain link corresponding to the changes is the risk source when the changes exceed the mutation threshold.

[0010] Preferably, the simulation of the transmission path is based on the connection strength and direction between trade entities in the industrial chain trade relationship structure, as well as the supply and demand relationship between different links in the industrial chain.

[0011] Preferably, the quantification of trade risk includes determining the risk value of the risk source based on the price mutation and trade volume mutation, defining an attenuation function of trade risk on the transmission path according to the connection weight in the industrial chain trade relationship structure and the supply and demand relationship between the links in the industrial chain, and calculating the intensity attenuation of the risk value during the propagation process along the transmission path using the attenuation function to generate a risk transmission characterization result.

[0012] Preferably, the determination of trade security status includes comparing the comprehensive evaluation result of trade security with preset static security thresholds and dynamic change rate thresholds. When the comprehensive evaluation result is lower than the static security level threshold or exceeds the dynamic change rate threshold, it is determined that the trade security status has changed abnormally, and trade security risk warning information is output. The trade security risk warning information includes the specific industrial chain link of the security risk, the core risk indicators and values ​​that led to the determination, the trading partner countries or regions strongly associated with the trade risk, and the risk disposal strategy classification identifier.

[0013] This invention provides a method for evaluating the trade security of lithium products across the entire industry chain. It has the following beneficial effects: This method for evaluating the safety of lithium product trade across the entire industry chain constructs a framework for dividing the lithium product industry chain. Based on multi-source data cleaning and standardization, it forms a sequence of trade statuses across the industry chain, providing accurate basic data for subsequent risk assessment. This enables comprehensive monitoring and analysis of the trade status of each link in the industry chain, achieving dynamic tracking and risk identification of the entire lithium product trade chain.

[0014] By employing structured modeling of the supply chain trade relationship structure, and combining information on price changes, trade volume changes, and structural characteristic parameters, the source of trade risk was accurately identified and the transmission path was simulated. This approach can quantify the intensity of risk transmission, provide timely trade security risk warnings, and improve the responsiveness to lithium product trade risks and the effectiveness of decision support. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the division of the entire lithium product industry chain and the construction of a trade status sequence according to the present invention. Figure 2 This is a schematic diagram of the trade relationship structure modeling of the present invention; Figure 3 This is a flowchart of the dynamic risk transmission analysis of the present invention; Figure 4 This is a flowchart of the comprehensive evaluation and risk warning determination process for trade security in this invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0017] like Figure 1-4 As shown, this embodiment of the invention provides a method for evaluating the trade security of lithium products across the entire industry chain, including: S1. Construct a full-chain segmentation framework for lithium products. Based on this framework, acquire multi-source data reflecting the trade activity status of each link in the chain. Clean and standardize the multi-source data according to a unified time scale to form a chain trade status sequence. The full-chain segmentation framework is constructed based on the differences in processing levels and industrial functional attributes of lithium products in trade. The full-chain segmentation framework includes upstream, midstream, and downstream segments. The multi-source data includes lithium product import and export trade data based on specific HS codes, global lithium resource reserve distribution data, regional trade agreements and country risk rating data, and international market price data for lithium products.

[0018] When standardizing data, using Min-Max normalization or Z-score normalization ensures a uniform scale across different data sources, enabling better fusion of multi-source data for subsequent analysis.

[0019] S2. Based on the trade state sequence of the industrial chain, a structured model is performed on the trading entities and their interrelationships in each link of the industrial chain, forming an industrial chain trade relationship structure. This structure represents the connection relationships and changes in trade intensity among trading entities. Structural feature parameters are extracted from this structure, representing the concentration, connectivity, and symmetry of trade relationships. Interrelationships are obtained from basic trade data in the industrial chain trade state sequence. These relationships include import / export direction relationships (representing trade flow), trade volume weight relationships (representing trade intensity), node degree relationships (representing network connection structure), and reciprocity relationships (representing trade symmetry). The structured modeling involves using trading entities participating in lithium product trade as structural nodes, actual import / export trade records between trading entities as node connections, and the trade scale and direction corresponding to these records as connection attributes, thus forming the industrial chain trade relationship structure.

[0020] S3. Based on price and trade volume changes in the industrial chain trade status sequence, and combined with structural characteristic parameters, a dynamic risk transmission analysis is conducted on the transmission process of trade risk among different trading entities and different links in the industrial chain, generating risk transmission characterization results. The steps of the dynamic risk transmission analysis are as follows: S31. Identify risk sources based on the trade status sequence and structural characteristic parameters of the industrial chain. Risk sources include specific trading entities or links in the industrial chain that cause sudden price changes, sudden changes in trade volume, or sudden changes in the structure of the trade network. Identification of risk sources includes monitoring changes in price fluctuations, changes in trade volume, and changes in structural characteristic parameters. The changes are compared with preset mutation thresholds. When the changes exceed the mutation thresholds, the trading entity or link in the industrial chain corresponding to the changes is identified as the risk source.

[0021] A threshold for price and trade volume fluctuations is set by calculating the standard deviation of historical data. Values ​​exceeding a certain standard deviation range are identified as sources of risk. This threshold should be dynamically adjustable, automatically adjusting based on real-time market fluctuations to ensure the system remains adaptable and flexible in the face of different market environments.

[0022] By combining the dynamic risk transmission coefficient, the dynamic transmission effects of price fluctuations, trade volume changes, and trading partner stability are quantified, generating a risk transmission characterization result. The dynamic risk transmission coefficient quantifies the intensity of risk propagation from price and trade fluctuations to different trading entities, and the formula is as follows:

[0023] in, Indicates the magnitude of price change. Indicates the magnitude of change in trade volume. This indicates the stability score of the trading partner. The weights representing price fluctuations Weights representing changes in trade volume The weights representing the stability of trading partners are all calculated using the entropy weight method.

[0024] S32. Based on the industrial chain trade relationship structure, simulate the transmission path of trade risks from the source of risk to trading entities and links in the industrial chain. The simulation of the transmission path is based on the connection strength and direction between trading entities in the industrial chain trade relationship structure, as well as the supply and demand relationship between different links in the industrial chain.

[0025] By calculating the shortest paths between risk sources and each node, the impact of different paths on other parts of the industry chain is assessed. Combining the weights of network connections, the risk transmission capacity of each node is quantified, and the key role of each link in the overall risk propagation is determined.

[0026] S33. By combining price and trade volume change information in the industrial chain trade state sequence, the transmission intensity of trade risk along the transmission path is quantified, generating a risk transmission characterization result. The quantification of trade risk includes determining the risk value of the risk source based on price and trade volume mutations, defining an attenuation function of trade risk along the transmission path based on the connection weights in the industrial chain trade relationship structure and the supply and demand relationships between industrial chain links, and calculating the intensity attenuation of the risk value during its propagation along the transmission path using the attenuation function, thus generating a risk transmission characterization result.

[0027] S4. The risk transmission characteristics, price change information, trade volume change information, and structural characteristic parameters are integrated and processed to form a comprehensive trade security evaluation result.

[0028] S5. Based on the comprehensive trade security evaluation results, determine the trade security status of each link in the industrial chain, and output trade security risk warning information when the trade security status changes abnormally. The determination of trade security status includes comparing the comprehensive trade security evaluation results with preset static security thresholds and dynamic change rate thresholds. When the comprehensive evaluation result is lower than the static security level threshold or exceeds the dynamic change rate threshold, it is determined that the trade security status has changed abnormally, and trade security risk warning information is output. The trade security risk warning information includes the specific industrial chain link of the security risk, the core risk indicators and values ​​that led to the determination, the trading partner countries or regions strongly associated with the trade risk, and the risk disposal strategy classification identifier.

[0029] The system continuously monitors critical trade data and immediately triggers alarms upon detecting abnormal changes. The alarm mechanism is based on threshold settings; when data fluctuations exceed predetermined risk thresholds, the system promptly notifies relevant personnel via email, SMS, and other means. This real-time alarm function improves response speed, enabling users to make decisions and take countermeasures in the shortest possible time, thereby reducing risk. Example

[0030] This embodiment is based on a whole-industry-chain lithium product trade security evaluation method. It constructs a whole-industry-chain segmentation framework for lithium products and evaluates the trade status of the lithium product industry chain based on multi-source data to provide data support for trade security and risk analysis. The specific implementation method is as follows: 1. Construction of the framework for the division of the entire industry chain In this embodiment, the entire lithium product industry chain is divided into three main segments based on processing stages and functional attributes: Upstream segment: Involves the production of lithium salt products, including basic lithium products such as lithium hydroxide, lithium chloride, and lithium carbonate.

[0031] Midstream segment: mainly involves the production of lithium battery materials, including intermediate products such as lithium hexafluorophosphate and lithium manganese oxide.

[0032] Downstream segment: Primarily involves the manufacturing of lithium batteries, including those used in electric vehicles and energy storage devices. Main products include: Since the specific trade volume of lithium ore products is difficult to calculate accurately, unlisted mineral products are usually concentrated in code 25309099. Therefore, the lithium product industry chain is divided into three links for analysis: upstream lithium salt products, midstream lithium battery materials, and downstream lithium batteries.

[0033] Table 1: Framework Table for the Division of the Entire Industry Chain.

[0034]

[0035] 2. Acquisition of multi-source data This embodiment collected data from the following sources to assess the trade status of the lithium product supply chain: Global lithium resource reserves data: Data source: Geological Survey of Country F. According to the report of the Geological Survey of Country F, the distribution of major global lithium resource reserves is as follows: Country E has lithium reserves of 9.3 million tons, accounting for 31% of global lithium resources. Country B has lithium reserves of 7 million tons, ranking second in the world. Country C has lithium reserves of 4 million tons, ranking third in the world.

[0036] Regional trade agreement data: Data source is the World Trade Organization database. According to the World Trade Organization database, the impact of major global regional trade agreements on lithium product trade is as follows: Regional Trade Agreement A: As an important trade agreement in the relevant region, it has promoted the import and export of lithium products within the region.

[0037] Regional Trade Agreement B: It has had a profound impact on lithium product trade among countries F, G, and H.

[0038] Lithium carbonate price data: Data source: International Energy Association (IEA). According to the IEA's 2023 market report, the average market price of lithium carbonate was $70,000 per tonne, reflecting the growth in global demand and fluctuations in lithium market prices.

[0039] Country Risk Value: Data sourced from the relevant stability indicators in the World Bank's Global Governance Index, used to assess the stability risks of countries in lithium product trade. Country A: Stability index is 0.75.

[0040] Country D: The stability index is 0.45, indicating that there is a certain degree of stability risk, which may affect the stability of lithium product trade.

[0041] Lithium product trade data: Data source is the customs authority of country A. According to customs data, the import and export situation of lithium products in country A is as follows: Country A imported 2,500 tons of lithium hydroxide from Country B, with an import value of US$7.8 million.

[0042] Country A exported 240 tons of lithium chloride to Country C, with an export value of US$1.1 million.

[0043] Country A exported 85 tons of lithium carbonate to Country D, with an export value of US$420,000.

[0044] 3. Data cleaning and standardization All collected data underwent the following steps for cleaning and standardization: Import and export trade data cleaning: Deduplicating import and export data provided by customs and correcting abnormal data to ensure data consistency and accuracy.

[0045] Resource reserve data standardization: All lithium ore resource reserve data are uniformly converted to 10,000-ton units and compared globally to ensure data consistency.

[0046] Price data standardization: All market price data are standardized based on the annual average price to eliminate the impact of annual fluctuations on data analysis.

[0047] 4. The formation of the trade status sequence in the industrial chain After data cleaning and standardization, we aggregated trade data from different stages on a quarterly basis to generate a trade status sequence for the lithium product industry chain. Below is some actual data: Data for the first quarter of 2023: Country A imported 600 tons of lithium hydroxide, valued at US$3.78 million, and exported 3,850 tons of lithium battery materials, valued at US$72 million. Data sourced from Country A's customs authorities.

[0048] Country D imported 1,200 tons of lithium battery materials and exported 2,800 tons of lithium-ion batteries, totaling US$200 million. Data sourced from Country D's customs authorities' "2023 Q1 Trade Statistics".

[0049] The above data is used to generate a detailed sequence of trade statuses in the lithium product industry chain, providing data support for subsequent risk assessment, market analysis, and decision-making.

[0050] In summary, by constructing a framework for the entire lithium product industry chain and combining global lithium resource reserves, trade data, market prices, and country risk indicators, this embodiment generates a sequence of trade statuses within the industry chain. After data cleaning and standardization, the results provide a reliable basis for trade risk assessment, market monitoring, and decision-making. Through the integration and analysis of multi-source data, potential trade risks are identified, helping relevant decision-makers accurately grasp the dynamic changes in the lithium product market, improve trade security, and promote the stable development of the lithium product industry chain. Example

[0051] This embodiment is based on a whole-industry-chain lithium product trade security assessment method. Through dynamic risk transmission analysis, it quantifies the risk transmission process between countries in lithium product trade to assess the trade security risks of different countries and links in the industry chain. The specific implementation method is as follows: 1. Background and Data Source This embodiment analyzes the impact of lithium product market price fluctuations, changes in trade volume among countries, and the stability of trading partners on risk transmission, based on actual information from publicly available data sources. Data sources include: Lithium product market price data provided by the International Lithium Association, lithium product import and export trade data provided by the United Nations International Trade Centre, and trade partner stability score data provided by the World Bank.

[0052] 2. Data Source Lithium product market price data: The lithium hydroxide price data comes from the International Lithium Association, covering January 2025 to April 2025, and reflects changes in supply and demand in the international market.

[0053] The price of lithium hydroxide was $100,000 per ton in January 2025, $105,000 per ton in February 2025 (an increase of 5%), $110,000 per ton in March 2025 (an increase of 4.76%), and $100,000 per ton in April 2025 (a decrease of 9.09%).

[0054] Import and export trade data for lithium products: Based on data provided by the United Nations International Trade Centre, import and export data of lithium products from countries A, B, and C were collected: Country A: Imported 50,000 tons of lithium products in January 2025, 55,000 tons of lithium products in February 2025, and 45,000 tons of lithium products in March 2025.

[0055] Country B: Exported 45,000 tons of lithium products in January 2025, 50,000 tons in February 2025, and 50,000 tons in March 2025.

[0056] Trade partner stability data: The stability score is from the World Bank and ranges from -2.5 to +2.5.

[0057] Based on 2025 data, country A has a stability score of 0.6, while country B has a stability score of 1.2.

[0058] 3. Data Acquisition and Preprocessing Price change data: The price fluctuations of lithium hydroxide from January 2025 to April 2025 are as follows: The price is expected to be $100,000 / ton in January 2025, $105,000 / ton in February 2025 (up 5%), $110,000 / ton in March 2025 (up 4.76%), and $100,000 / ton in April 2025 (down 9.09%).

[0059] Trade volume change data: Country A's import data: 50,000 tons in January 2025, 55,000 tons in February 2025 (an increase of 10%), and 45,000 tons in March 2025 (a decrease of 18.18%).

[0060] Country B's export data: 45,000 tons in January 2025, 50,000 tons in February 2025, an increase of 11.11%, and 50,000 tons in March 2025, remaining stable.

[0061] Structural characteristic parameters: The nodal degrees between countries A and B are 10 and 8 respectively, reflecting the close trade relationship between the two countries.

[0062] 4. Risk Source Identification Price mutation identification: The threshold for price abrupt changes is based on the average and standard deviation of market price fluctuations over the past 5 years, with a 10% fluctuation set as the defining standard for price abrupt changes. Therefore, countries A and B are considered as sources of risk for price abrupt changes.

[0063] In April 2025, the price of lithium hydroxide fell from $110,000 / ton to $100,000 / ton, a decrease of 9.09%, exceeding the set threshold of 10% for sudden change.

[0064] Identification of sudden changes in trade volume: The threshold for abrupt changes in trade volume was derived from a statistical analysis of import and export volume fluctuations over the past three years, with a fluctuation of 15% set as the standard for abrupt change. Therefore, Country A was considered the source of the abrupt change in trade volume.

[0065] In March 2025, Country A's lithium imports decreased from 55,000 tons to 45,000 tons, a decrease of 18.18%, exceeding the set threshold of 15%.

[0066] Structural mutation identification: In March 2025, the proportion of import and export trade between country A and country B decreased from 60% to 50%, indicating a change in the trade structure between the two countries, meeting the criteria for structural abrupt change. Therefore, the trade relationship between country A and country B is considered a source of structural abrupt change.

[0067] 5. Calculation of risk transmission coefficient and simulation of transmission path The dynamic risk transmission coefficient quantifies the intensity of risk propagation from price and trade fluctuations across different trading entities. The formula is as follows:

[0068] in, Indicates the magnitude of price change. Indicates the magnitude of change in trade volume. This indicates the stability score of the trading partner. The weights representing price fluctuations Weights representing changes in trade volume The weights representing the stability of trading partners are all calculated using the entropy weight method.

[0069] Weights calculated using the entropy weight method: Weighting of Price Fluctuations: Due to the significant impact of price fluctuations on the market, the entropy weight method calculates a relatively low information entropy, thus assigning it a larger weight. Based on the data, the weight of price fluctuations is 0.4.

[0070] Weighting of Trade Volume Changes: Fluctuations in trade volume reflect changes in market supply and demand, which are highly uncertain and therefore have high information entropy. Based on the entropy weighting method, the weight of trade volume changes is 0.35.

[0071] Weighting of trading partner stability: This stability indicator has a significant impact on the long-term stability of trade, but its information entropy is relatively small compared to short-term fluctuations in prices and trade volume. Therefore, the weighting of trading partner stability is 0.25.

[0072] Substitute the known data into the calculation:

[0073] Result: The risk transmission intensity between country A and country B is 0.01698.

[0074] Risk transmission path simulation: Because of the close trade relationship between country A and country B, the simulated risk is transmitted from country A to other countries through country B.

[0075] When the connection between country C and country A is weak, and the intensity of risk transmission from country A to country B is high, the intensity of transmission to country C is low.

[0076] 6. Calculation of risk transmission attenuation Attenuation function: The attenuation coefficient is based on the analysis of risk transmission data and related literature over the past 5 years. It is usually derived from empirical studies on industry risk transmission, which show that each risk attenuates by 10% during the transmission process.

[0077] Attenuation calculation: Risk value decay from country A to country B:

[0078] The risk value from country B to country C further decreases:

[0079] 7. Integration and Comprehensive Evaluation of Risk Transmission Results Comprehensive analysis: Final risk score: Country A is the source of risk, with a risk score of 0.01698, indicating medium risk. Country B is affected by Country A, with a risk score of 0.015282, also indicating medium risk. Country C is indirectly affected, with a risk score of 0.01375, indicating low risk.

[0080] Risk warning: Country A: Due to sudden changes in lithium product imports and significant price fluctuations, Country A is rated as a medium-risk source, and it is recommended to strengthen monitoring of trade volume and price fluctuations.

[0081] Country B: Due to changes in trade relations with Country A, Country B is rated as a medium-risk source, and market fluctuations with Country A should be monitored.

[0082] Country C: The risk is low, but the indirect risk transmission path between Country C and Country B still needs to be monitored.

[0083] The results indicate that country A faces a higher risk, country B a medium risk, and country C a lower risk. Dynamic risk transmission analysis provides policymakers with a basis for risk control measures targeting different trading entities, optimizes risk management measures for lithium product trade, reduces potential trade security risks, and provides scientific and quantifiable support for lithium product trade security assessment, helping to more accurately identify and respond to market fluctuations and risks.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating the trade security of lithium products across the entire industry chain, characterized in that, include: S1. Construct a full industry chain segmentation framework for lithium products, obtain multi-source data reflecting the trade activity status of each link in the industry chain based on the full industry chain segmentation framework, and clean and standardize the multi-source data according to a unified time scale to form an industry chain trade status sequence. S2. Based on the trade state sequence of the industrial chain, perform structured modeling of the trade entities in each link of the industrial chain and the relationships between the trade entities to form an industrial chain trade relationship structure, and extract structural feature parameters from the industrial chain trade relationship structure; S3. Based on the price change information and trade volume change information in the trade status sequence of the industrial chain, and combined with the structural feature parameters, perform dynamic risk transmission analysis on the transmission process of trade risk among different trade entities and different links in the industrial chain, and generate risk transmission characterization results. S4. The risk transmission characterization results, the price change information, the trade volume change information, and the structural characteristic parameters are fused together to form a comprehensive trade security evaluation result; S5. Based on the comprehensive evaluation results of trade security, determine the trade security status of each link in the industrial chain, and output trade security risk warning information when the trade security status changes abnormally.

2. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 1, characterized in that: The entire industry chain segmentation framework is constructed based on the differences in processing levels and industrial functional attributes of lithium products in trade. The entire industry chain segmentation framework includes upstream, midstream and downstream segments. The multi-source data includes lithium product import and export trade data based on specific HS codes, global lithium resource reserve distribution data, regional trade agreements and country risk rating data, and international market price data of lithium products.

3. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 1, characterized in that: The interrelationships are obtained through basic trade data in the trade state sequence of the industrial chain. The interrelationships include import and export direction relationships that represent trade flow, trade volume weight relationships that represent trade intensity, node degree relationships that represent network connection structure, and reciprocity relationships that represent trade symmetry. The structured modeling includes taking the trading entities participating in lithium product trade as structural nodes, taking the actual import and export trade records between the trading entities as node connections, and taking the trade scale and trade direction corresponding to the import and export trade records as connection attributes to form the trade relationship structure of the industrial chain.

4. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 1, characterized in that: The steps of the dynamic risk transmission analysis are as follows: S31. Identify risk sources based on the trade state sequence of the industrial chain and the structural characteristic parameters. The risk sources include specific trading entities or links in the industrial chain that cause price mutations, trade volume mutations, or trade network structure mutations. S32. Based on the aforementioned industrial chain trade relationship structure, simulate the transmission path of trade risks from the source of risk to trade entities and links in the industrial chain; S33. Combining the price change information and trade volume change information in the trade status sequence of the industrial chain, quantify the transmission strength of trade risk along the transmission path and generate the risk transmission characterization result.

5. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 4, characterized in that: The identification of the risk source includes monitoring the changes in the price change information, the changes in the trade volume information, and the changes in the structural characteristic parameters, comparing the changes with a preset mutation threshold, and determining that the trade entity or industrial chain link corresponding to the changes is the risk source when the changes exceed the mutation threshold.

6. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 4, characterized in that: The simulation of the transmission path is based on the connection strength and direction between trade entities in the industrial chain trade relationship structure, as well as the supply and demand relationship between different links in the industrial chain.

7. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 4, characterized in that: The quantification of trade risk includes determining the risk value of the risk source based on the price mutation and trade volume mutation, defining the attenuation function of trade risk on the transmission path according to the connection weight in the industrial chain trade relationship structure and the supply and demand relationship between the links of the industrial chain, and calculating the intensity attenuation of the risk value during the propagation process along the transmission path to generate risk transmission characterization results.

8. The method for evaluating the trade security of lithium products across the entire industry chain according to claim 1, characterized in that: The determination of the trade security status includes comparing the comprehensive evaluation result of trade security with preset static security thresholds and dynamic change rate thresholds. When the comprehensive evaluation result is lower than the static security level threshold or exceeds the dynamic change rate threshold, it is determined that the trade security status has changed abnormally, and trade security risk warning information is output. The trade security risk warning information includes the specific industrial chain link of the security risk, the core risk indicators and values ​​that led to the determination, the trading partner countries or regions strongly associated with the trade risk, and the risk disposal strategy classification identifier.

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