An intelligent analysis method and system for enterprise digital transformation

Abstract

This invention discloses an intelligent analysis method and system for enterprise digital transformation, relating to the field of enterprise digital transformation technology. The method includes the following specific steps: Perception and preprocessing: Using web data crawling technology, relevant text information of contextual factors is obtained from multiple channels. After part-of-speech tagging and entity recognition through natural language processing, the information is classified. This invention utilizes multiple information collection channels to capture key information such as policy and regulatory updates and market dynamic changes in real time, and constructs a knowledge graph system containing rich enterprise knowledge. It can deeply associate and accurately match new contextual factors with the enterprise's internal business processes, technical architecture, and other knowledge, enabling the system to provide enterprises with more scientific and accurate digital transformation decision-making suggestions, helping enterprises better cope with changes in the external environment, optimize internal management processes, and improve business operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of enterprise digital transformation technology, specifically to an intelligent analysis method and system for enterprise digital transformation. Background Technology

[0002] In today's wave of globalization and informatization, digital transformation has become a key strategy for enterprises to enhance their competitiveness, achieve business innovation and sustainable development. With the rapid development of technologies such as big data, artificial intelligence and cloud computing, enterprises are facing unprecedented opportunities and challenges. Digital transformation can not only help enterprises optimize internal management processes and improve operational efficiency, but also promote product and service innovation and enhance market competitiveness. However, in the process of digital transformation, enterprises need to fully consider the complex factors of the internal and external environment and formulate scientific and reasonable transformation strategies.

[0003] Despite the increasing importance of enterprise digital transformation, traditional digital transformation analysis methods have significant shortcomings in dealing with complex and ever-changing internal and external environments. On the one hand, traditional methods lack a comprehensive understanding and in-depth analysis of situational factors such as policy and regulatory updates, market dynamics, and industry emergencies, making it difficult to capture the potential impact of external environmental changes on enterprise business and digital transformation in a timely and accurate manner. On the other hand, traditional methods lack effective knowledge reasoning mechanisms, making it difficult to deeply correlate and match situational factors with internal business processes, technical architecture, market competition, and other knowledge, thus failing to provide enterprises with comprehensive and accurate digital transformation decision-making advice.

[0004] To address the aforementioned issues, it is necessary to optimize existing intelligent analysis methods for enterprise digital transformation. By leveraging contextual awareness and knowledge reasoning technologies, comprehensive monitoring and in-depth analysis of internal and external environmental contextual factors can be achieved. Therefore, developing an intelligent analysis method and system for enterprise digital transformation that can comprehensively realize the above characteristics is of great significance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent analysis method and system for enterprise digital transformation. It can continuously monitor and accurately analyze internal and external contextual factors such as policies, regulations, and market dynamics by constructing comprehensive information collection channels. Simultaneously, it utilizes knowledge graph technology to build an enterprise knowledge base, transforming core knowledge such as business processes and technical architecture into nodes and edges in the knowledge graph, enabling effective organization and utilization of knowledge. Through semantic matching and reasoning engines, it deeply associates and accurately matches contextual factors with enterprise knowledge, thereby analyzing the direct impact and potential risks of contextual factors on enterprise business and digital transformation. Based on this, and combined with the enterprise's strategic goals and actual situation, it uses intelligent decision-making algorithms to generate digital transformation decision recommendations covering short-term operational measures and long-term strategic planning. Furthermore, this invention establishes an evaluation mechanism based on feedback from actual application effects, enabling continuous optimization of analysis results and decision recommendations, ensuring that enterprises can adapt to the ever-changing environment and achieve sustainable development during the digital transformation process.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On one hand, an intelligent analysis method for enterprise digital transformation, comprising the following specific steps:

[0007] Perception and Preprocessing: Using web data crawling technology, we obtain text information related to contextual factors from multiple channels. After natural language processing, we perform part-of-speech tagging and entity recognition, and then classify the information. We also extract core information related to the enterprise's business through information extraction algorithms based on semantic association mining.

[0008] Knowledge graph construction steps: Collect multi-source data within the enterprise, use knowledge extraction algorithms based on pattern matching and semantic understanding to identify entities, relationships and attributes, integrate knowledge through knowledge fusion algorithms based on entity similarity measurement and conflict resolution, and use graph databases to store and form a knowledge graph system;

[0009] Association matching: Using a deep learning-based semantic feature extraction model, contextual factor information and knowledge in the knowledge graph are transformed into low-dimensional vectors. Vector similarity is calculated using a similarity metric formula improved based on cosine similarity, and association matching relationships are determined based on a set threshold.

[0010] Influence Reasoning: Based on the relationships and professional knowledge, a set of reasoning rules is constructed. Rule-based reasoning is used to determine whether the situational factors, information and knowledge meet the rule conditions to obtain the direct influence. At the same time, a reasoning model is used to analyze the knowledge graph to infer the indirect influence.

[0011] Decision suggestion generation and optimization: Establish a decision suggestion template library. Based on the factors affecting the reasoning results, corporate strategic goals, resource status, and current business situation, select the template with the highest matching degree through a template selection algorithm to generate decision suggestions. Evaluate the effect by collecting business data after implementation, and adjust and optimize the knowledge graph, reasoning rules, and template library accordingly.

[0012] Furthermore, in the perception and preprocessing step, core information related to the enterprise's business is extracted using an information extraction algorithm based on semantic association mining. The algorithm formula is as follows:

[0013]

[0014] Among them, S rel (E1, E2) represents the semantic association strength between two entities E1 and E2. By calculating the semantic association strength between entities, we can determine which information is relevant to the company's business and thus extract the core information. It is the set of semantic association paths between entities E1 and E2. sim(s) is the semantic similarity of path s, which measures the degree of semantic similarity between two entities connected by this path. w(s) is the weight of path s, which reflects the importance of the path in semantic association. E1 and E2 are entities identified from text information.

[0015] Furthermore, in the knowledge graph construction step, multi-source data within the enterprise is collected, including business process documents, technical architecture materials, market competition analysis reports, and financial statement data. Data mining tools and natural language processing technology are used to identify entity objects, relationships between entities, and entity attributes from the aforementioned multi-source data. A method based on similarity calculation and entity alignment is adopted to integrate the knowledge extracted from different data sources, eliminate duplicate knowledge records and conflicts between knowledge, and use graph database storage technology to store the integrated knowledge in the form of a graph structure.

[0016] Furthermore, in the knowledge graph construction step, a method based on similarity calculation and entity alignment is used to integrate knowledge extracted from different data sources, eliminating duplicate knowledge records and conflicts between knowledge. The calculation method is as follows:

[0017] Sim(E1,E2)=β×Sim name (E1,E2)+(1-β)×Sim attr (E1,E2)

[0018] Where Sim(E1,E2) is the overall similarity between two entities E1 and E2, used to determine whether entities extracted from different data sources are the same entity, thereby performing knowledge fusion and eliminating duplication and conflict. β is a weight parameter used to balance the relative importance of entity name similarity and attribute similarity in the overall similarity calculation. name (E1, E2) represents the name similarity between entities E1 and E2. attr (E1, E2) represents the attribute similarity between entities E1 and E2, which are entities extracted from different data sources.

[0019] Furthermore, in the association matching step, for the new contextual factor information obtained in the perception and preprocessing steps, semantic representation technology in natural language processing is used to convert the textual information into a low-dimensional vector form to extract its semantic features. For each entity and relation in the knowledge graph, semantic representation technology is used to convert it into a low-dimensional vector form to form a knowledge vector. Then, a similarity measurement method based on vector space is used to calculate the similarity value between the new contextual factor information vector and the knowledge vector in the knowledge graph. A similarity threshold is set, and contextual factor information with a similarity value greater than the threshold is associated and matched with the corresponding knowledge in the knowledge graph to determine the specific association relationship between contextual factors and enterprise knowledge.

[0020] Furthermore, in the association matching step, a similarity measurement method based on vector space is used to calculate the similarity value between the new scenario factor information vector and the knowledge vector in the knowledge graph. The calculation method is as follows:

[0021]

[0022] Among them, Sim cos-improved (V1, V2) represents the similarity between the improved vectors V1 and V2, used to calculate the similarity between the contextual factor information vector and the knowledge vector in the knowledge graph. V1 and V2 represent the contextual factor information vector and the knowledge vector in the knowledge graph, respectively, and are low-dimensional vector representations obtained through semantic feature extraction. V1·V2 is the dot product of vectors V1 and V2, used to measure the similarity between the two vectors in direction. γ is an adjustment parameter, d is the dimension of the vector, and v 1i and v 2i : Represent the i-th element of vectors V1 and V2 respectively.

[0023] Furthermore, in the influence reasoning step, based on the association between the situational factors and enterprise knowledge determined in the association matching step, and combined with the enterprise's professional knowledge and experience in the business domain, a set of reasoning rules containing causal and conditional relationships is constructed. Using rule-based reasoning technology, new situational factor information and relevant knowledge in the knowledge graph are used as input. Reasoning operations are performed based on the constructed set of reasoning rules to analyze the direct impact of situational factors on various specific aspects of the enterprise's current business, including procurement, production, sales, and after-sales service. Using a graph-based reasoning model, based on the knowledge graph, the indirect impact of situational factors on the enterprise's business and digital transformation at different stages is inferred by analyzing the connection relationships and information transmission between nodes in the graph.

[0024] Furthermore, the decision suggestion generation and optimization uses a template selection algorithm based on multi-factor comprehensive evaluation to select the template with the highest matching degree to generate decision suggestions. The template selection algorithm is as follows:

[0025]

[0026] Among them, Score template (T k ) is template T k The overall score is used to evaluate the applicability of the template in generating decision recommendations. i Factor F i The weights are used to measure the importance of different factors in template selection. factor (F i ,T k ) is factor F i For template T k The score represents the template T. k In satisfying factor F i In terms of capabilities, m is the total number of factors used to evaluate the template, and T is... k This is the k-th template in the decision-making suggestion template library, which includes short-term operational measures templates and long-term strategic planning idea templates. F i These are the factors used to evaluate the template, including the urgency of situational factors, the company's resource constraints, and market trends.

[0027] On the other hand, an intelligent analysis system for enterprise digital transformation includes the following components:

[0028] Context awareness and preprocessing module: Connects multiple information collection channels to continuously and comprehensively monitor various context factors. When new context events occur, it uses information collection technology and natural language processing technology to obtain information and perform detailed classification and in-depth analysis preprocessing.

[0029] Knowledge graph construction module: Collects multi-source data within the enterprise, uses knowledge extraction and knowledge fusion technologies to construct knowledge graphs, transforms enterprise knowledge into nodes and edges in the knowledge graph, and thus builds a knowledge graph system;

[0030] The association matching module performs semantic analysis and feature extraction on new contextual factors, and uses semantic matching technology to deeply associate and accurately match them with various types of knowledge in the knowledge graph. It also determines the association between contextual factors and enterprise knowledge by calculating similarity.

[0031] Impact Reasoning Module: Utilizing the reasoning engine of the knowledge graph, based on the relationships determined by the association matching module, and employing reasoning rules and algorithms, it conducts an in-depth analysis of the direct and indirect impacts of contextual factors on various aspects of the enterprise's current business and different stages of digital transformation.

[0032] Decision Recommendation Generation and Optimization Module: Based on the analysis results of the Influence Reasoning Module, and combined with the company's strategic goals and actual situation, this module uses intelligent decision-making algorithms to generate digital transformation decision recommendations, including short-term specific operational measures and long-term strategic planning ideas. At the same time, this module establishes an evaluation mechanism based on feedback from the actual application effects of the company's digital transformation. As the contextual factors change and the knowledge graph is updated and improved, the analysis results and decision recommendations are continuously optimized.

[0033] Compared with existing technologies, this intelligent analysis method and system for enterprise digital transformation has the following beneficial effects:

[0034] I. This invention utilizes multiple information collection channels to capture key information such as policy and regulatory updates and market dynamics in real time, and constructs a knowledge graph system containing rich enterprise knowledge. It can deeply associate and accurately match new scenario factors with the enterprise's internal business processes, technical architecture, and other knowledge, enabling the system to provide enterprises with more scientific and accurate digital transformation decision-making suggestions, helping enterprises better cope with changes in the external environment, optimize internal management processes, and improve business operation efficiency.

[0035] Second, by establishing an evaluation mechanism based on feedback from the actual application effects of enterprise digital transformation, this invention can collect and analyze the actual effect data of enterprises in the process of implementing decision-making suggestions in real time. Based on this data, the analysis results and decision-making suggestions are evaluated, and the knowledge graph, reasoning rules, and decision-making algorithms are adjusted and optimized according to the evaluation results. This enables the system to continuously adapt to changes in the internal and external environment of enterprises, providing enterprises with more practical and forward-looking digital transformation decision-making suggestions, and helping enterprises achieve sustainable development.

[0036] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0038] Figure 1 A flowchart illustrating an intelligent analysis method for enterprise digital transformation;

[0039] Figure 2 This is a schematic diagram of the structure of an intelligent analysis system for enterprise digital transformation. Detailed Implementation

[0040] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0041] Example 1

[0042] A large manufacturing company, which mainly produces auto parts and whose business covers parts design, production, sales and after-sales service, faces challenges such as frequent updates to automotive industry policies and regulations (such as environmental standards and safety regulations), intense market competition leading to large fluctuations in market demand, and challenges brought about by technological innovation in the industry. The company urgently needs to carry out digital transformation to enhance its competitiveness.

[0043] Every hour, the system gathers policy and regulatory updates (such as updates to environmental standards related to new energy vehicle parts), market demand fluctuations (such as seasonal changes in demand for parts for different vehicle models), and industry technological innovation dynamics (such as the application of new materials in automotive parts) from multiple channels, including government websites (such as the websites of the Ministry of Industry and Information Technology and the Ministry of Environmental Protection), automotive industry news media, and professional market research institutions. The system performs word segmentation, part-of-speech tagging, and entity recognition on the gathered information, categorizing it into policy and regulatory categories, market dynamics, and technological innovation categories. Through information extraction techniques, it identifies core points relevant to the company's production processes, product quality, market share, and other business aspects, such as the specific restrictions imposed by a new environmental policy on pollutant emissions during the company's production process.

[0044] Monthly, we collect internal business process documents (such as component production processes and supply chain management processes), technical architecture data (such as technical parameters of production equipment and product development roadmaps), market competition analysis reports, financial statements, and other data. From this data, we identify entities (such as production workshops, R&D departments, key component products, and major customers), relationships (such as the collaboration between production workshops and R&D departments, and the supply and demand relationship between products and customers), and attributes (such as component specifications and production equipment performance indicators). We then integrate the extracted knowledge using methods based on similarity calculation and entity alignment, eliminating duplication and conflicts. The calculation method is: Sim(E1,E2)=β×Sim name (E1,E2)+(1-β)×Sim attr (E1, E2), where Sim(E1, E2) is the overall similarity between two entities E1 and E2, used to determine whether entities extracted from different data sources are the same entity, thereby performing knowledge fusion and eliminating duplication and conflict. β is a weight parameter used to balance the relative importance of entity name similarity and attribute similarity in the overall similarity calculation. name (E1, E2) represents the name similarity between entities E1 and E2. attr (E1, E2) represents the attribute similarity between entities E1 and E2. E1 and E2 are entities extracted from different data sources. The integrated knowledge is constructed into a knowledge graph using a graph database. For example, "production workshop" is used as a node and connected to the "R&D department" node through a "collaboration relationship" edge, and related collaboration content and frequency attribute information are labeled.

[0045] Semantic representation techniques are used to transform newly acquired contextual factor information (such as new environmental policy information) and knowledge in knowledge graphs (such as knowledge related to enterprise production processes) into low-dimensional vectors. Vector similarity is calculated using a vector space-based similarity metric. This reveals a correlation between the restrictions on pollutant emissions in new environmental policies and emission indicators in a specific stage of the enterprise's production process, thus establishing the relationship between the two. The calculation method is as follows: Among them, Sim cos-improved (V1, V2) represents the similarity between the improved vectors V1 and V2, used to calculate the similarity between the contextual factor information vector and the knowledge vector in the knowledge graph. V1 and V2 represent the contextual factor information vector and the knowledge vector in the knowledge graph, respectively, and are low-dimensional vector representations obtained through semantic feature extraction. V1·V2 is the dot product of vectors V1 and V2, used to measure the similarity between the two vectors in direction. γ is an adjustment parameter, d is the dimension of the vector, and v 1i and v 2i : Represent the i-th element of vectors V1 and V2 respectively.

[0046] Based on the experience and historical data of business experts, a set of reasoning rules is constructed, such as "If environmental policies reduce the emission restrictions on a certain type of pollutant, the company needs to adjust the corresponding treatment process in its production process." The new environmental policy information and production process-related knowledge from the knowledge graph are input to infer the direct impact, that is, the company needs to adjust its existing production processes to meet the new environmental requirements. Using a graph-based reasoning model, the indirect impact is analyzed, such as the adjustment of the production process may lead to changes in the demand for raw material procurement, as well as the need for maintenance and upgrades of production equipment.

[0047] The template library includes templates for short-term operational measures (such as specific steps for adjusting production processes and plans for purchasing new environmental protection equipment) and templates for long-term strategic planning (such as focusing on the R&D of environmentally friendly components and establishing long-term partnerships with environmental technology suppliers). Based on the impact reasoning results, combined with the company's strategic goals (such as becoming a leading environmental protection company in the industry), resource status (existing funds and technical personnel reserves), and current business situation, appropriate templates are selected from the template library to generate decision recommendations. For example, in the short term, new wastewater treatment equipment may be purchased to meet new emission requirements, and in the long term, investment in the R&D of environmentally friendly components may be increased. Production data (such as output and scrap rate) and cost data (such as raw material procurement costs and equipment maintenance costs) are collected quarterly through the company's ERP system to evaluate the effectiveness of the decision recommendations.

[0048] Based on the evaluation results, the production process knowledge in the knowledge graph, the relevant rules in the reasoning rule set, and the templates in the decision suggestion template library are adjusted and optimized. If it is found that the scrap rate increases after the new production process adjustment, the production process-related knowledge and reasoning rules are adjusted, and the specific content of the production process adjustment in the decision suggestion template is updated.

[0049] Example 2

[0050] A financial services company providing various financial services such as banking, securities, and insurance faces challenges including constantly changing financial regulatory policies (such as adjustments to interest rate policies and stricter risk supervision requirements), financial market volatility (such as changes in stock market performance and exchange rate fluctuations), and diversified customer needs (such as personalized wealth management needs and the growth of mobile payment demands). The company needs to improve service quality and risk management capabilities through digital transformation. Hourly, it captures information from relevant online channels regarding updates to financial regulatory policies (such as new capital adequacy requirements), changes in financial market performance (such as stock index fluctuations and bond yield changes), and trends in customer needs (such as higher demands for the convenience of mobile payments). This information is segmented, labeled with parts of speech, and identified as entities, categorized into policy and regulation categories, market dynamics categories, and customer needs categories. Key points relevant to the company's business (such as loan business, investment business, and customer service) are extracted, such as the impact of new capital adequacy requirements on the company's loan amount and investment portfolio.

[0051] Monthly, we collect internal business process documents (such as loan approval processes and customer service processes), technical architecture materials (such as financial transaction system architecture and customer information management system function introductions), market competition analysis reports, financial statements, and other data. We identify entities (such as different business departments, financial products, and customer groups), relationships (such as collaboration relationships between business departments and matching relationships between financial products and customer groups), and attributes (such as the rate of return of financial products, risk level, and credit rating of customers). We integrate the extracted knowledge, eliminate duplication and conflict, and use graph databases to build a knowledge graph. For example, we use "Loan Business Department" as a node and connect it to "Investment Business Department" node through "Business Collaboration Relationship" edges, and label the scope and frequency of collaborative business information.

[0052] New contextual information (such as interest rate policy adjustment information) and knowledge in the knowledge graph (such as knowledge related to corporate loan business) are transformed into low-dimensional vectors. Vector similarity is calculated to determine the relationship between interest rate policy adjustments and knowledge such as loan interest rate setting and loan approval standards in corporate loan business.

[0053] Construct a set of inference rules, such as "If interest rates fall, the demand for loans by enterprises may increase, requiring adjustments to loan approval processes and risk assessment standards." Infer the direct impact, namely that enterprises need to simplify loan approval processes and adjust risk assessment indicators to cope with the potential increase in loan demand. At the same time, infer the indirect impact, such as that the increase in loan demand may affect the enterprise's cash flow management, requiring adjustments to the investment portfolio to ensure sufficient funds, while also increasing the workload and service quality requirements of the customer service department.

[0054] The template library includes templates for short-term operational measures (such as adjusting specific steps in the loan approval process and increasing customer service staff training programs) and templates for long-term strategic planning (such as optimizing portfolio strategies and developing a customer risk assessment model based on big data). Based on the impact reasoning results, combined with the company's strategic goals (such as increasing market share and enhancing risk management capabilities), resource status (existing funds and technical personnel reserves), and current business situation, decision-making recommendations are generated. For example, in the short term, increase loan approval staff to cope with business volume growth, and in the long term, increase investment in big data technology research and development to improve risk management levels.

[0055] Each quarter, data such as the number of loan approvals, customer satisfaction, and cash flow indicators are collected through the company's business information system to evaluate the effectiveness of decision-making recommendations. Based on the evaluation results, the loan business knowledge in the knowledge graph, the relevant rules in the reasoning rule set, and the templates in the decision-making recommendation template library are adjusted and optimized. If it is found that the approval efficiency does not meet expectations after adding loan approval personnel, the relevant knowledge and reasoning rules of the loan approval process are adjusted, and the content on personnel configuration and process optimization in the decision-making recommendation template is updated.

[0056] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An intelligent analysis method for enterprise digital transformation, characterized in that, The method includes the following specific steps: Perception and Preprocessing: Using web data crawling technology, we obtain text information related to contextual factors from multiple channels. After natural language processing, we perform part-of-speech tagging and entity recognition, and then classify the information. We also extract core information related to the enterprise's business through information extraction algorithms based on semantic association mining. Knowledge graph construction steps: Collect multi-source data within the enterprise, use knowledge extraction algorithms based on pattern matching and semantic understanding to identify entities, relationships and attributes, integrate knowledge through knowledge fusion algorithms based on entity similarity measurement and conflict resolution, and use graph databases to store and form a knowledge graph system; Association matching: Using a deep learning-based semantic feature extraction model, contextual factor information and knowledge in the knowledge graph are transformed into low-dimensional vectors. Vector similarity is calculated using a similarity metric formula improved based on cosine similarity, and association matching relationships are determined based on a set threshold. Influence Reasoning: Based on the relationships and professional knowledge, a set of reasoning rules is constructed. Rule-based reasoning is used to determine whether the situational factors, information and knowledge meet the rule conditions to obtain the direct influence. At the same time, a reasoning model is used to analyze the knowledge graph to infer the indirect influence. Decision suggestion generation and optimization: Establish a decision suggestion template library. Based on the factors affecting the reasoning results, corporate strategic goals, resource status, and current business situation, select the template with the highest matching degree through a template selection algorithm to generate decision suggestions. Evaluate the effect by collecting business data after implementation, and adjust and optimize the knowledge graph, reasoning rules, and template library accordingly.

2. The intelligent analysis method for enterprise digital transformation according to claim 1, characterized in that, In the perception and preprocessing step, core information related to the enterprise's business is extracted using an information extraction algorithm based on semantic association mining. The algorithm formula is as follows: Among them, S rel (E1, E2) represents the semantic association strength between two entities E1 and E2. By calculating the semantic association strength between entities, we can determine which information is relevant to the company's business and thus extract the core information. It is the set of semantic association paths between entities E1 and E2. sim(s) is the semantic similarity of path s, which measures the degree of semantic similarity between two entities connected by this path. w(s) is the weight of path s, which reflects the importance of the path in semantic association. E1 and E2 are entities identified from text information.

3. The intelligent analysis method for enterprise digital transformation according to claim 1, characterized in that, In the knowledge graph construction steps, multi-source data from within the enterprise is collected, including business process documents, technical architecture materials, market competition analysis reports, and financial statement data. Data mining tools and natural language processing technology are used to identify entity objects, relationships between entities, and entity attributes from the above multi-source data. A method based on similarity calculation and entity alignment is used to integrate the knowledge extracted from different data sources, eliminating duplicate knowledge records and conflicts between knowledge. Finally, with the help of graph database storage technology, the integrated knowledge is stored in the form of a graph structure, where nodes represent entities and edges represent relationships between entities, thus constructing a knowledge graph system.

4. The intelligent analysis method for enterprise digital transformation according to claim 3, characterized in that, In the knowledge graph construction step, a method based on similarity calculation and entity alignment is used to integrate knowledge extracted from different data sources, eliminating duplicate knowledge records and conflicts between knowledge. The calculation method is as follows: Sim(E1,E2)=β×Sim name (E1,E2)+(1-β)×Sim attr (E1,E2) Where Sim(E1,E2) is the overall similarity between two entities E1 and E2, used to determine whether entities extracted from different data sources are the same entity, thereby performing knowledge fusion and eliminating duplication and conflict. β is a weight parameter used to balance the relative importance of entity name similarity and attribute similarity in the overall similarity calculation. name (E1, E2) represents the name similarity between entities E1 and E2. attr (E1, E2) represents the attribute similarity between entities E1 and E2, which are entities extracted from different data sources.

5. The intelligent analysis method for enterprise digital transformation according to claim 1, characterized in that, In the association matching step, for the new contextual factor information obtained in the perception and preprocessing steps, semantic representation technology in natural language processing is used to transform the textual information into a low-dimensional vector form to extract its semantic features. For each entity and relation in the knowledge graph, semantic representation technology is used to transform it into a low-dimensional vector form to form a knowledge vector. Then, a similarity measurement method based on vector space is used to calculate the similarity value between the new contextual factor information vector and the knowledge vector in the knowledge graph. A similarity threshold is set, and contextual factor information with a similarity value greater than the threshold is associated and matched with the corresponding knowledge in the knowledge graph to determine the specific association relationship between contextual factors and enterprise knowledge.

6. The intelligent analysis method for enterprise digital transformation according to claim 5, characterized in that, In the association matching step, a similarity measurement method based on vector space is used to calculate the similarity value between the new scenario factor information vector and the knowledge vector in the knowledge graph. The calculation method is as follows: Among them, Sim cos-improved (V1, V2) represents the similarity between the improved vectors V1 and V2, used to calculate the similarity between the contextual factor information vector and the knowledge vector in the knowledge graph. V1 and V2 represent the contextual factor information vector and the knowledge vector in the knowledge graph, respectively, and are low-dimensional vector representations obtained through semantic feature extraction. V1·V2 is the dot product of vectors V1 and V2, used to measure the similarity between the two vectors in direction. γ is an adjustment parameter, d is the dimension of the vector, and v 1i and v 2i : Represent the i-th element of vectors V1 and V2 respectively.

7. The intelligent analysis method for enterprise digital transformation according to claim 1, characterized in that, In the influence reasoning step, based on the association between the situational factors and enterprise knowledge determined in the association matching step, and combined with the enterprise's professional knowledge and experience in the business domain, a set of reasoning rules containing causal and conditional relationships is constructed. Using rule-based reasoning technology, new situational factor information and relevant knowledge in the knowledge graph are used as input. Reasoning operations are performed according to the constructed set of reasoning rules to analyze the direct impact of situational factors on various specific links in the enterprise's current business, including procurement, production, sales, and after-sales. Using a graph-based reasoning model, based on the knowledge graph, the indirect impact of situational factors on the enterprise's business and digital transformation at different stages is inferred by analyzing the connection relationships and information transmission between nodes in the graph.

8. The intelligent analysis method for enterprise digital transformation according to claim 1, characterized in that, The decision suggestion generation and optimization process uses a template selection algorithm based on multi-factor comprehensive evaluation to select the template with the highest matching degree to generate decision suggestions. The template selection algorithm is as follows: Among them, Score template (T k ) is template T k The overall score is used to evaluate the applicability of the template in generating decision recommendations. i Factor F i The weights are used to measure the importance of different factors in template selection. factor (F i ,T k ) is factor F i For template T k The score represents the template T. k In satisfying factor F i In terms of capabilities, m is the total number of factors used to evaluate the template, and T is... k This is the k-th template in the decision-making suggestion template library, which includes short-term operational measures templates and long-term strategic planning idea templates. F i These are the factors used to evaluate the template, including the urgency of situational factors, the company's resource constraints, and market trends.

9. An intelligent analysis system for enterprise digital transformation, the system being applicable to the intelligent analysis method for enterprise digital transformation as described in any one of claims 1-8, characterized in that, The system includes the following components: Context awareness and preprocessing module: Connects multiple information collection channels to continuously and comprehensively monitor various context factors. When new context events occur, it uses information collection technology and natural language processing technology to obtain information and perform detailed classification and in-depth analysis preprocessing. Knowledge graph construction module: Collects multi-source data within the enterprise, uses knowledge extraction and knowledge fusion technologies to construct knowledge graphs, transforms enterprise knowledge into nodes and edges in the knowledge graph, and thus builds a knowledge graph system; The association matching module performs semantic analysis and feature extraction on new contextual factors, and uses semantic matching technology to deeply associate and accurately match them with various types of knowledge in the knowledge graph. It also determines the association between contextual factors and enterprise knowledge by calculating similarity. Impact Reasoning Module: Utilizing the reasoning engine of the knowledge graph, based on the relationships determined by the association matching module, and employing reasoning rules and algorithms, it conducts an in-depth analysis of the direct and indirect impacts of contextual factors on various aspects of the enterprise's current business and different stages of digital transformation. Decision Recommendation Generation and Optimization Module: Based on the analysis results of the Influence Reasoning Module, and combined with the company's strategic goals and actual situation, this module uses intelligent decision-making algorithms to generate digital transformation decision recommendations, including short-term specific operational measures and long-term strategic planning ideas. At the same time, this module establishes an evaluation mechanism based on feedback from the actual application effects of the company's digital transformation. As the contextual factors change and the knowledge graph is updated and improved, the analysis results and decision recommendations are continuously optimized.

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