Investment project risk assessment method and device, and storage medium

Abstract

The invention is suitable for the field of investment risk assessment, and provides an investment project risk assessment method and device and a storage medium, and the method comprises the steps: constructing a social network graph and a relationship strength scoring model, analyzing node centrality and community stability in real time, simulating an interference situation, and recognizing a key cooperation relationship and a potential risk. Therefore, comprehensive risk management support is provided for financial health of the startup company. According to the method, through the social network graph and the dynamic relationship strength scoring model, the key cooperation relationship of the startup company is effectively identified and analyzed, the risk is monitored in real time, and deep scene analysis is provided, so that effective investment risk assessment and management are realized under the conditions of short-term financial data fluctuation and financial history scarcity.

Description

Technical Field

[0001] The invention belongs to the field of investment risk assessment, and in particular relates to an investment project risk assessment method, equipment and storage medium. Background Art

[0002] In the rapidly changing technology market, new startups face a high degree of uncertainty and complexity. The rapid evolution of the market environment often makes short-term financial data fluctuate significantly, and traditional risk assessment methods are difficult to apply to these emerging enterprises. Due to the scarcity of historical financial data of early-stage startups, investors often lack reliable basis when assessing their feasibility. In addition, startups' products usually rely on multiple suppliers and technology providers, and the instability of these partnerships will directly affect the actual deliverability of the products. With the acceleration of technology iteration cycles, unstable supply chains and partnerships have also brought additional challenges to financial management and risk assessment. In this context, the field of investment risk assessment urgently needs innovative methods and tools to comprehensively consider the complexity of market dynamics, financial indicators and partnerships, and provide investors with more comprehensive risk insights.

[0003] Startup technology companies often face a rapidly changing market environment and uncertain financial prospects. In a technology market with high iteration cycles and technological innovation, short-term financial data often fluctuates significantly. In addition, early-stage startups have less financial historical data, and traditional risk assessment methods are insufficient in such investments. Moreover, most startup products involve multiple suppliers or technology providers, and these partnerships may be extremely changeable, which in turn affects the actual deliverability of the product. Unstable supply chains and partnerships bring additional complexity to financial management and risk assessment. Summary of the invention

[0004] The purpose of the present invention is to provide an investment project risk assessment method, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a method for assessing investment project risks, the method comprising: Identify all nodes related to the startup and establish edges between nodes based on the nature of the partnership to generate a social network graph; Analyze the centrality of nodes in the network graph to determine the influence and stability of startups in the network, use community detection algorithms to identify communities in the network, evaluate the stability and interdependence of communities, and analyze whether startups rely on core nodes in specific communities; Based on the edge weights between nodes, a relationship strength scoring model is constructed to evaluate the stability and risk of cooperative relationships, and the changes in social networks are monitored in real time to dynamically update the risk assessment model; Simulate the impact of interference in the network on the financial health of the startup, analyze the risk performance under different scenarios, evaluate the impact of changes in different nodes in the network on the overall risk, and identify the most sensitive partnerships for the company; Visualize social networks through graph tools and mark key nodes and potential risks.

[0006] As a further solution of the present invention, the identification of all nodes related to the startup company and the establishment of edges between the nodes according to the nature of the cooperative relationship to generate a social network graph specifically include: Obtain basic information about the startup and identify all node information related to the startup; The node information includes: The startup’s service and technology suppliers, including direct and indirect suppliers; the company’s founders, core team members and their backgrounds; the startup’s customer base; and information about investors who have invested in the startup; Integrate the collected node information, establish a node database, and define attributes for each node; Analyze the cooperation between each node, identify the interactions between nodes, classify different types of relationships, and evaluate the nature and stability of these relationships; Based on the identified node relationships, establish edges between nodes, define edge weights, and set attributes for each edge; Use network graph tools to visualize nodes and edges and generate social network graphs.

[0007] As a further solution of the present invention, the node centrality in the network diagram is analyzed to determine the influence and stability of the startup company in the network, the community detection algorithm is used to identify the community in the network, the stability and interdependence of the community are evaluated, and the startup company is analyzed whether it depends on the core nodes in a specific community, which specifically includes: Select a centrality indicator, calculate the centrality value of each node, and record the results in the node database; Based on the calculated centrality index, evaluate each node’s potential influence on the startup and the entire network, and identify the startup’s core nodes; Analyze the social network graph, identify different communities, evaluate the stability of each community, analyze the dependencies between different communities, and identify the core communities of startups; Analyze whether the startup company relies on core nodes in a specific community, analyze the impact of core nodes on the startup company's operations, and assess potential risks.

[0008] As a further solution of the present invention, the centrality index includes: Degree centrality: measures the number of direct connections of a node, reflecting the direct influence of the node; Betweenness centrality: measures the bridge role of nodes in information flow and identifies key information transmission nodes; Closeness centrality: evaluates the average distance between a node and other nodes, reflecting the speed of influence of the node; Eigenvector centrality: Considers the influence of connected nodes and identifies important nodes in the network.

[0009] As a further solution of the present invention, the relationship strength scoring model is constructed to evaluate the stability and risk of the cooperative relationship, and the changes in the social network are monitored in real time to dynamically update the risk assessment model. Specifically: Define the edge weight calculation method and calculate the node and nodes The edge weight between : ; in, represents the market potential score, represents the resource complementarity score, Represents the team capability score, represents the risk assessment score, and ; Establish a relationship strength scoring model to conduct comprehensive relationship evaluation, specifically: ; Based on the relationship strength score, the stability of each partnership is evaluated and high-risk and low-risk partnerships are identified; Build a real-time monitoring mechanism to track changes in social networks and regularly update node and edge data; According to the changes in real-time monitoring data, the risk assessment model is dynamically adjusted and the warning value is set. When the relationship strength score is lower than the warning value, a risk warning is triggered.

[0010] As a further solution of the present invention, the impact of interference in the simulated network on the financial health of the startup company, analyzing the risk performance under different scenarios, evaluating the impact of changes in different nodes in the network on the overall risk, and identifying the most sensitive partnerships for the company specifically include: Calculate node centrality metrics to identify key nodes that affect network stability and financial health; Identify and simulate possible disruptions, including failure of key nodes, changes in partnerships, and external shocks; Identify key indicators related to financial health, establish dynamic financial models, and analyze the impact of network structure changes on financial health; Random sampling is used to simulate the impact of different scenarios on financial health, analyze the simulation results, and evaluate the impact of changes at different nodes on overall financial health.

[0011] As a further solution of the present invention, the centrality index of the computing node is specifically: Calculate the degree centrality of each node. The higher the degree centrality of each node, the more direct connections the node has with other nodes, and the greater the market influence: ; Representation Node The degree of a node, that is, the number of edges directly connected to the node; Calculate the betweenness centrality of each node, that is, the degree of involvement of each node in the shortest path. The higher the betweenness centrality, the greater the bridge role of the node in information circulation and resource flow: ; Represents any node, which is other partners or customers of the startup company; Indicates that the node Another node other than Represents a slave node To Node The number of all shortest paths; Indicates passing through node The number of shortest paths; Calculate the closeness centrality of each node, that is, the average distance between each node and other nodes in the network. The higher the closeness centrality, the faster the node interacts with other nodes: ; represents all nodes in the social network graph, Representation Node With any node The shortest path length between Calculate the eigenvector centrality of the node, that is, the importance of the node: ; Representation and Node Connected neighbor nodes; represents the maximum eigenvalue corresponding to the network; Calculate the PageRank of a node, that is, the relative importance of the node: ; represents the damping factor; Representation and Node Directly adjacent neighbor nodes; Representation Node The degree.

[0012] As a further solution of the present invention, the dynamic financial model is established to analyze the impact of network structure changes on financial health, specifically: Develop revenue models and analyze changes in customers and sales volume that are directly related to the stability of the company's network and the market environment: ; Indicates at time Total investment; Indicates the number of current partners and customers; Indicates Unit price per customer; Indicates at time From Sales volume per customer; Indicates time growth rate; Develop cost models to analyze the financial pressures of changing suppliers: ; Indicates at time Total cost; represents the quantity of fixed costs; Indicates Item fixed cost; Amount representing variable costs; Indicates at time No. The amount of the variable cost item; Indicates time Other expenses; Develop a cash flow model: ; represents the cash flow at time; Indicates time Total income; Indicates time Total cost; Develop a profit model and combine it with a cash flow model to analyze financial health: ; Indicates at time Profits; Indicates time of tax expenditures.

[0013] Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned investment project risk assessment method.

[0014] Another object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned investment project risk assessment method.

[0015] The beneficial effects of the present invention are: This method effectively addresses the problem of significant fluctuations in short-term financial data for startups in the context of high iteration cycles and frequent technology updates. By building a social network graph, companies can deeply identify and analyze key nodes such as their suppliers, partners, and customers, thereby obtaining a relatively stable view of the relationship network. This view enables companies to more clearly understand market dynamics and changes in partnerships, helping to more accurately predict financial performance.

[0016] In view of the scarcity of historical financial data for early-stage startups, this method provides a new risk assessment framework through node centrality analysis and community detection algorithms. Enterprises no longer rely solely on traditional historical data, but instead evaluate the stability and risk of partnerships by monitoring the strength of relationships in social networks in real time. This method enables companies to effectively conduct risk assessment and management in the absence of rich financial data.

[0017] Considering the complexity of startups’ products often relying on multiple suppliers or technology providers, this method ensures real-time monitoring of changes in partnerships through a dynamically updated relationship strength scoring model. This mechanism can promptly capture the addition of new partners or the breakup of existing relationships, helping companies quickly identify potential risks and reduce financial losses caused by supply chain instability.

[0018] The approach also provides deeper risk insights by simulating network disruptions and analyzing risk performance under different scenarios. This scenario analysis enables companies to identify which partnerships have the greatest impact on their financial health, providing management with effective decision-making basis to help them prioritize high-risk nodes and allocate resources accordingly. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a method for risk assessment of an investment project provided by an embodiment of the present invention; Figure 2A flowchart for identifying all nodes related to a startup company and establishing edges between the nodes according to the nature of the partnership to generate a social network graph provided by an embodiment of the present invention; Figure 3 A flowchart for determining the influence and stability of a startup company in a network, using a community detection algorithm to identify communities in the network, evaluating the stability and interdependence of communities, and analyzing whether the startup company depends on core nodes in a specific community, provided by an embodiment of the present invention; Figure 4 A flowchart of building a relationship strength scoring model and evaluating the stability and risk of a cooperative relationship provided by an embodiment of the present invention; Figure 5 A flowchart for simulating the impact of interference in a network on the financial health of a startup company, analyzing risk performance under different scenarios, and evaluating the impact of changes in different nodes in the network on the overall risk provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0022] Figure 1 A flow chart of a method for risk assessment of an investment project provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes: S100, identifies all nodes related to the startup and creates edges between nodes based on the nature of the partnership to generate a social network graph; like Figure 2 As shown, the identification of all nodes related to the startup company and establishing edges between the nodes according to the nature of the cooperative relationship to generate a social network graph specifically includes: S110, obtaining basic information of the startup company and identifying all node information related to the startup company; The node information includes: The startup’s service and technology suppliers, including direct and indirect suppliers; the company’s founders, core team members and their backgrounds; the startup’s customer base; and information about investors who have invested in the startup; S120, integrating the collected information of various nodes, establishing a node database, and defining attributes for each node; S130, analyzing the cooperative relationship between each node, identifying the interactions between nodes, classifying different relationship types, and evaluating the nature and stability of these relationships; S140, establishing edges between nodes according to the identified node relationships, defining edge weights, and setting attributes for each edge; S150, using a network graph tool to visualize nodes and edges to generate a social network graph.

[0023] In this step, we will obtain basic information about the startup company and collect basic data using public commercial databases, including information such as the company's founding time, industry, main products, financing rounds and financing amounts. We will also structure these data to form standardized company information for subsequent analysis. Next, we will identify all node information related to the startup company, including service and technology suppliers, core team members, customer groups and investor information. In terms of service and technology suppliers, we will identify raw material suppliers, technology service providers, etc. that directly cooperate with the startup company, and also analyze the indirect supply chain required to build the product. In terms of core team members, we will analyze the educational background, work experience, past performance and reputation of the founders and core team members in the industry to form a complete team profile. In addition, we will identify the startup company's main customer groups, including large enterprises, small enterprises and individual consumers, and analyze the customer's industry background and market influence. In terms of investor information, we will collect basic information about investors, including their background, investment portfolio, past investment history and their position in the industry.

[0024] On this basis, a node database is established to integrate all collected node information into the database, and a multi-dimensional data model is constructed to ensure that the data is easy to access and analyze. At the same time, attributes are defined for each node, such as type (enterprise, investor, team member, etc.), industry, geographic location, cooperation history, etc. Next, the cooperative relationship between nodes is analyzed, the cooperative relationship between nodes is identified, and the nature of the cooperation (such as contractual cooperation, strategic cooperation, informal cooperation, etc.) is analyzed, different types of relationships are classified, and their nature and stability are evaluated, including cooperation frequency, historical performance, and contract terms. After that, the edges between nodes are established according to the node relationship, the directionality and strength of the relationship are clarified, and weights are set for each edge. The weights can be quantified based on factors such as the amount of cooperation, the duration of cooperation, and the history of cooperation.

[0025] Finally, by selecting the appropriate network graph tool, the nodes and edges are visualized, and the generated social network graph will intuitively show the position of the startup in the network, the relationship between nodes, and potential risks. The significant advantage of this whole process is its comprehensiveness, covering the detailed information of the startup and its related parties, providing a comprehensive data basis for subsequent analysis. At the same time, the dynamic nature makes the real-time update of the node database and edge weights helpful to reflect the changes in the market environment, thereby ensuring the real-time and accuracy of risk assessment. The visualized social network graph further enhances the intuitiveness, enabling investors to better understand the ecology of startups and identify key nodes and potential risk points. Through in-depth analysis of the relationship between nodes, the vulnerable links of the partnership can be identified, providing a more valuable reference for investment decisions. In addition, the forward-looking nature of establishing a relationship strength scoring model helps to predict potential risks and adjust investment strategies in a timely manner to reduce financial losses. Overall, this method is applicable to various types of startups, can be flexibly adjusted according to industry characteristics and market changes, and has a wide range of applicability, thus providing a systematic framework for risk assessment of entrepreneurial technology companies, improving the efficiency of risk identification and assessment, and providing important strategic basis for decision makers.

[0026] S200, analyzes the node centrality in the network graph, determines the influence and stability of the startup in the network, uses community detection algorithms to identify communities in the network, evaluates the stability and interdependence of communities, and analyzes whether the startup relies on the core nodes in a specific community; like Figure 3 As shown, the method of analyzing the node centrality in the network graph, judging the influence and stability of the startup in the network, using the community detection algorithm to identify the communities in the network, evaluating the stability and interdependence of the communities, and analyzing whether the startup relies on the core nodes in a specific community, specifically includes: S210, selecting a centrality index, calculating the centrality value of each node, and recording the result in a node database; S220, based on the calculated centrality index, evaluates the potential influence of each node on the startup and the entire network, and identifies the core nodes of the startup; S230, analyzing the social network graph, identifying different communities, evaluating the stability of each identified community, analyzing the dependencies between different communities, and identifying the core community of the startup company; S240, analyze whether the startup company relies on the core nodes in a specific community, analyze the impact of the core nodes on the startup company's operations, and assess potential risks.

[0027] In an embodiment of the present invention, the centrality index includes: Degree centrality: measures the number of direct connections of a node, reflecting the direct influence of the node; Betweenness centrality: measures the bridge role of nodes in information flow and identifies key information transmission nodes; Closeness centrality: evaluates the average distance between a node and other nodes, reflecting the speed of influence of the node; Eigenvector centrality: Considers the influence of connected nodes and identifies important nodes in the network.

[0028] In this step, it is necessary to select appropriate centrality indicators in order to comprehensively evaluate the influence of each node in the social network. Common centrality indicators include degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Degree centrality is used to measure the number of direct connections of a node, reflecting the direct influence of the node; betweenness centrality measures the bridge role of the node in the flow of information and identifies key information transmission nodes; closeness centrality evaluates the average distance between the node and other nodes, reflecting the speed of the node's influence in the network; eigenvector centrality considers not only the number of connections of the node itself, but also the influence of the connected nodes, so as to identify more important nodes in the network.

[0029] Next, the centrality value of each node is calculated and the results are recorded in the node database. These centrality indicators can effectively evaluate the potential influence of each node on the startup and the entire network, and identify the core nodes that are critical to the startup. This evaluation process involves in-depth analysis of the social network graph to identify different communities and evaluate the stability of each community. In addition, it is necessary to analyze the dependencies between different communities and identify the core communities of the startup. In this step, special attention is paid to whether the startup relies on the core nodes in a specific community, and the impact of these core nodes on the operation of the startup is deeply analyzed, and the related potential risks are evaluated.

[0030] By comprehensively applying multiple centrality indicators, this step not only improves the accuracy of evaluating node influence, but also reveals potential risk points. For example, a startup company that relies on a core node may face greater operational risks when the node changes. At the same time, identifying the relationship between core communities and communities can help companies optimize resource allocation and improve cooperation efficiency in a complex market environment.

[0031] S300, based on the edge weights between nodes, builds a relationship strength scoring model to evaluate the stability and risk of partnerships, monitors changes in social networks in real time, and dynamically updates the risk assessment model; like Figure 4 As shown, the relationship strength scoring model is constructed to evaluate the stability and risk of the cooperative relationship, and the changes in the social network are monitored in real time to dynamically update the risk assessment model. Specifically: S310, define the calculation method of edge weight and calculate the node and nodes The edge weight between : ; in, represents the market potential score, represents the resource complementarity score, Represents the team capability score, represents the risk assessment score, and ; S320, establish a relationship strength scoring model to conduct a comprehensive relationship evaluation, specifically: ; S330, based on the relationship strength score, evaluates the stability of each partnership and identifies high-risk and low-risk partnerships; S340, builds a real-time monitoring mechanism to track changes in social networks and regularly update node and edge data; S350 dynamically adjusts the risk assessment model according to the changes in real-time monitored data and sets a warning value. When the relationship strength score is lower than the warning value, a risk warning is triggered.

[0032] In this step, a relationship strength scoring model is constructed, the main purpose of which is to evaluate the stability and risk of the partnership between nodes. This formula takes into account the market environment, resource allocation, team capabilities, and potential risks, and provides a comprehensive score for each partnership. This score can effectively evaluate the stability of each partnership and identify high-risk and low-risk partnerships.

[0033] In addition, it also includes the establishment of a monitoring mechanism to track changes in social networks and update risk assessment models in real time. This mechanism relies on the dynamic changes in data to adjust risk scores and set warning values ​​to identify potential risks in a timely manner. By dynamically monitoring social networks and updating scoring models, it can ensure that the assessment of potential risks of startups remains accurate and effective in a rapidly changing market environment.

[0034] By building a relationship strength scoring model, it not only provides a quantitative evaluation standard for the cooperative relationship between nodes, but also dynamically adapts to changes in the market and network environment. This flexibility allows investors to monitor the risk status of startups in real time and respond in a timely manner. At the same time, comprehensive consideration of multi-dimensional factors such as market potential, resource complementarity and team capabilities ensures the comprehensiveness and accuracy of the evaluation. In addition, through the early warning mechanism, potential risks can be identified in advance, helping companies take measures early to reduce financial losses and enhance operational stability.

[0035] S400, simulates the impact of interference in the network on the financial health of the startup, analyzes the risk performance under different scenarios, evaluates the impact of changes in different nodes in the network on the overall risk, and identifies the most sensitive partnerships for the company; like Figure 5 As shown in the figure, the impact of interference in the simulated network on the financial health of the startup company is analyzed, the risk performance under different scenarios is evaluated, the impact of changes in different nodes in the network on the overall risk is evaluated, and the most sensitive partnerships to the company are identified, including: S410, calculates the centrality index of nodes and identifies key nodes that affect network stability and financial health; S420, identify and simulate possible interference situations, including failure of key nodes, changes in cooperative relationships, and external shocks; S430, identify key indicators related to financial health, establish dynamic financial models, and analyze the impact of network structure changes on financial health; S440, randomly sample and simulate the impact of different scenarios on financial health, analyze the simulation results, and evaluate the impact of changes at different nodes on overall financial health.

[0036] In this step, the centrality indicators of each node in the social network are calculated, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. These centrality indicators can help identify key nodes that affect network stability and the financial health of startups. Degree centrality indicates the number of direct connections between nodes, reflecting the direct influence of nodes; betweenness centrality measures the bridge role of nodes in information flow and identifies key information transmission nodes; closeness centrality evaluates the average distance between nodes and other nodes, reflecting the speed of node influence; and PageRank considers the connection quality of nodes to identify important nodes in the network. The calculation results of these indicators will be used in the construction of subsequent models to help identify key nodes.

[0037] Next, identify possible disruptions and simulate them. These include failures at key nodes, such as if individual core team members or key suppliers withdraw, which could lead to disruptions in the operational chain; changes in partnerships, such as deterioration in relationships between partners or the entry of new competitors; and external shocks, including changes in the market environment (such as policy changes, economic fluctuations, etc.). After identifying key nodes and disruptions, build a dynamic financial model to analyze the impact of changes in network structure on financial health. The model is in the form of total revenue, which will take into account the number of partners, the number of customers, and the time growth rate to reflect the company's financial stability under network changes. In addition, key indicators such as operating costs and cash flow need to be calculated to fully reflect the financial situation.

[0038] We simulate different scenarios using random sampling to analyze the impact of changes in different nodes in the network on the overall financial health. This involves simulating different disruptions multiple times and evaluating the financial performance under each scenario to identify the most sensitive partnerships for the company.

[0039] By comprehensively considering node centrality and network structure, the impact on financial health is comprehensively evaluated to ensure the depth and breadth of the evaluation; a dynamic financial model is established to enable it to respond to changes in network structure in real time and enhance the flexibility of risk management; by simulating different scenarios, potential risks can be identified in advance, helping companies to formulate corresponding response strategies and reduce potential financial losses; combining quantitative analysis with empirical simulation, comprehensive information support is provided to investors and management to guide decision-making.

[0040] In the embodiment of the present invention, the centrality index of the computing node is specifically: Calculate the degree centrality of each node. The higher the degree centrality of each node, the more direct connections the node has with other nodes, and the greater the market influence: ; Representation Node The degree of a node, that is, the number of edges directly connected to the node; Calculate the betweenness centrality of each node, that is, the degree of involvement of each node in the shortest path. The higher the betweenness centrality, the greater the bridge role of the node in information circulation and resource flow: ; Represents any node, which is other partners or customers of the startup company; Indicates that the node Another node other than Represents a slave node To Node The number of all shortest paths; Indicates passing through node The number of shortest paths; Calculate the closeness centrality of each node, that is, the average distance between each node and other nodes in the network. The higher the closeness centrality, the faster the node interacts with other nodes: ; represents all nodes in the social network graph, Representation Node With any node The shortest path length between Calculate the eigenvector centrality of the node, that is, the importance of the node: ; Representation and Node Connected neighbor nodes; represents the maximum eigenvalue corresponding to the network; Calculate the PageRank of a node, that is, the relative importance of the node: ; represents the damping factor; Representation and Node Directly adjacent neighbor nodes; Representation Node The degree.

[0041] In the embodiment of the present invention, the dynamic financial model is established to analyze the impact of network structure changes on financial health, specifically: Develop revenue models and analyze changes in customers and sales volume that are directly related to the stability of the company's network and the market environment: ; Indicates at time Total investment; Indicates the number of current partners and customers; Indicates Unit price per customer; Indicates at time From Sales volume per customer; Indicates time growth rate; Develop cost models to analyze the financial pressures of changing suppliers: ; Indicates at time Total cost; represents the quantity of fixed costs; Indicates Item fixed cost; Amount representing variable costs; Indicates at time No. The amount of the variable cost item; Indicates time Other expenses; Develop a cash flow model: ; represents the cash flow at time; Indicates time Total income; Indicates time Total cost; Develop a profit model and combine it with a cash flow model to analyze financial health: ; Indicates at time Profits; Indicates time of tax expenditures.

[0042] S500, visualizes social networks through graph tools and marks key nodes and potential risks.

[0043] The present invention also proposes a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned investment project risk assessment method.

[0044] The present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned investment project risk assessment method.

[0045] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0046] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0047] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0048] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0049] 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 and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for risk assessment of an investment project, characterized in that: The method comprises: Identify all nodes related to the startup and establish edges between nodes based on the nature of the partnership to generate a social network graph; Analyze the centrality of nodes in the network graph to determine the influence and stability of startups in the network, use community detection algorithms to identify communities in the network, evaluate the stability and interdependence of communities, and analyze whether startups rely on core nodes in specific communities; Based on the edge weights between nodes, a relationship strength scoring model is constructed to evaluate the stability and risk of cooperative relationships, and the changes in social networks are monitored in real time to dynamically update the risk assessment model; Simulate the impact of interference in the network on the financial health of the startup, analyze the risk performance under different scenarios, evaluate the impact of changes in different nodes in the network on the overall risk, and identify the most sensitive partnerships for the company; Visualize social networks through graph tools and mark key nodes and potential risks.

2. The method according to claim 1, characterized in that The identification of all nodes related to the startup company and establishing edges between the nodes according to the nature of the partnership to generate a social network graph specifically includes: Obtain basic information about the startup and identify all node information related to the startup; The node information includes: The startup’s service and technology suppliers, including direct and indirect suppliers; the company’s founders, core team members and their backgrounds; the startup’s customer base; and information about investors who have invested in the startup; Integrate the collected node information, establish a node database, and define attributes for each node; Analyze the cooperation between each node, identify the interactions between nodes, classify different types of relationships, and evaluate the nature and stability of these relationships; Based on the identified node relationships, establish edges between nodes, define edge weights, and set attributes for each edge; Use network graph tools to visualize nodes and edges and generate social network graphs.

3. The method according to claim 2, characterized in that The analysis of the node centrality in the network graph, judging the influence and stability of the startup in the network, using the community detection algorithm to identify the community in the network, evaluating the stability and interdependence of the community, and analyzing whether the startup depends on the core nodes in a specific community, specifically includes: Select a centrality indicator, calculate the centrality value of each node, and record the results in the node database; Based on the calculated centrality index, evaluate each node’s potential influence on the startup and the entire network, and identify the startup’s core nodes; Analyze the social network graph, identify different communities, evaluate the stability of each community, analyze the dependencies between different communities, and identify the core communities of startups; Analyze whether the startup company relies on core nodes in a specific community, analyze the impact of core nodes on the startup company's operations, and assess potential risks.

4. The method according to claim 3, characterized in that The centrality indicators include: Degree centrality: measures the number of direct connections of a node, reflecting the direct influence of the node; Betweenness centrality: measures the bridge role of nodes in information flow and identifies key information transmission nodes; Closeness centrality: evaluates the average distance between a node and other nodes, reflecting the speed of influence of the node; Eigenvector centrality: Considers the influence of connected nodes and identifies important nodes in the network.

5. The method according to claim 4, characterized in that The relationship strength scoring model is constructed to evaluate the stability and risk of the partnership, monitor the changes in the social network in real time, and dynamically update the risk assessment model. Specifically: Define the edge weight calculation method and calculate the node and nodes The edge weight between : ； in, represents the market potential score, represents the resource complementarity score, Represents the team capability score, represents the risk assessment score, and ; Establish a relationship strength scoring model to conduct comprehensive relationship evaluation, specifically: ； Based on the relationship strength score, the stability of each partnership is evaluated and high-risk and low-risk partnerships are identified; Build a real-time monitoring mechanism to track changes in social networks and regularly update node and edge data; According to the changes in real-time monitoring data, the risk assessment model is dynamically adjusted and the warning value is set. When the relationship strength score is lower than the warning value, a risk warning is triggered.

6. The method according to claim 5, characterized in that The simulation of the impact of interference in the network on the financial health of the startup company, analyzing the risk performance under different scenarios, evaluating the impact of changes in different nodes in the network on the overall risk, and identifying the most sensitive partnerships for the company, specifically includes: Calculate node centrality metrics to identify key nodes that affect network stability and financial health; Identify and simulate possible disruptions, including failure of key nodes, changes in partnerships, and external shocks; Identify key indicators related to financial health, establish dynamic financial models, and analyze the impact of network structure changes on financial health; Random sampling is used to simulate the impact of different scenarios on financial health, analyze the simulation results, and evaluate the impact of changes at different nodes on overall financial health.

7. The method according to claim 6, characterized in that The centrality index of the computing node is specifically: Calculate the degree centrality of each node. The higher the degree centrality of each node, the more direct connections the node has with other nodes, and the greater the market influence: ； Representation Node The degree of a node, that is, the number of edges directly connected to the node; Calculate the betweenness centrality of each node, that is, the degree of involvement of each node in the shortest path. The higher the betweenness centrality, the greater the bridge role of the node in information circulation and resource flow: ； Represents any node, which is other partners or customers of the startup company; Indicates that the node Another node other than Represents a slave node To Node The number of all shortest paths; Indicates passing through node The number of shortest paths; Calculate the closeness centrality of each node, that is, the average distance between each node and other nodes in the network. The higher the closeness centrality, the faster the node interacts with other nodes: ； represents all nodes in the social network graph, Representation Node With any node The shortest path length between Calculate the eigenvector centrality of the node, that is, the importance of the node: ； Representation and Node Connected neighbor nodes; represents the maximum eigenvalue corresponding to the network; Calculate the PageRank of a node, that is, the relative importance of the node: ； represents the damping factor; Representation and Node Directly adjacent neighbor nodes; Representation Node The degree.

8. The method according to claim 6, characterized in that The dynamic financial model is established to analyze the impact of network structure changes on financial health, specifically: Develop revenue models and analyze changes in customers and sales volume that are directly related to the stability of the company's network and the market environment: ； Indicates at time Total investment; Indicates the number of current partners and customers; Indicates Unit price per customer; Indicates at time From Sales volume per customer; Indicates time growth rate; Develop cost models to analyze the financial pressures of changing suppliers: ； Indicates at time Total cost; represents the quantity of fixed costs; Indicates Item fixed cost; Amount representing variable costs; Indicates at time No. The amount of the variable cost item; Indicates time Other expenses; Develop a cash flow model: ； represents the cash flow at time; Indicates time Total income; Indicates time Total cost; Develop a profit model and combine it with a cash flow model to analyze financial health: ； Indicates at time Profits; Indicates time of tax expenditures.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the investment project risk assessment method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the investment project risk assessment method according to any one of claims 1 to 8.

Images