A business customer big data recommendation method and system based on artificial intelligence

Abstract

The application provides a business customer big data recommendation method and system based on artificial intelligence, which comprises the following steps: acquiring resource information, demand information and environment information for business customer recommendation; identifying the discordance existing in the resource information, demand information and environment information based on the resource information, demand information and environment information; analyzing the real concerns of all parties behind the discordance to infer the value ranking of all parties; forming a new scheme concept according to the inferred value ranking, wherein the scheme concept discovers a new balance point by changing the definition of the problem or the dimension of solving the problem; forming an operable recommendation scheme based on the balance point; and performing multi-dimensional evaluation on the recommendation scheme to determine whether the recommendation scheme meets the preset standard. The application effectively solves the problems in the prior art, such as the disconnection between the recommendation scheme and the actual landing, the lack of understanding of high-level business intentions and the difficulty in handling multiple conflicts.

Description

Technical Field

[0001] This application relates to the fields of artificial intelligence and big data processing technology, and more specifically, to a business customer big data recommendation method and system based on artificial intelligence. Background Technology

[0002] Enterprises can conduct big data analysis on massive amounts of customer behavior, transaction records and market information, and use intelligent computer programs to uncover potential customer needs, thereby enabling personalized recommendations for products or services.

[0003] However, existing commercial customer recommendation systems still suffer from several key bottlenecks. First, most recommendation methods focus on matching customer needs, failing to effectively combine the enterprise's own resource investment and technical implementation capabilities for dynamic constraints and optimization. This often leads to feasibility challenges in practical implementation. Second, the systems often lack a deep understanding and alignment with high-level business intentions, making it difficult to adapt to rapidly changing market environments and resulting in rigid recommendation strategies. Third, when multiple conflicts arise between customer needs, enterprise resources, business goals, and environmental factors, existing technologies lack effective coordination and trade-off mechanisms, significantly limiting the practicality, adaptability, and operability of the recommendation results. Summary of the Invention

[0004] This application discloses a business customer big data recommendation method and system based on artificial intelligence, which aims to address the shortcomings of existing business customer recommendation systems in combining enterprise resources, understanding senior management business intentions, and handling multiple conflicts.

[0005] Firstly, this application discloses a business customer big data recommendation method based on artificial intelligence, including:

[0006] Obtain resource information, demand information, and environmental information for use in recommending business clients;

[0007] Based on resource information, demand information, and environmental information, identify the inconsistencies that exist.

[0008] To address the inconsistencies, analyze the true priorities of each party to infer their value hierarchy.

[0009] Based on the inferred value ranking, new solution ideas are formed. These solutions aim to find a new balance point by changing the definition of the problem or the dimensions of problem-solving.

[0010] Based on the equilibrium point, an actionable recommendation scheme is formed;

[0011] The recommended solutions are evaluated in multiple dimensions to determine whether they meet the preset criteria.

[0012] Secondly, this application also discloses an artificial intelligence-based big data recommendation system for commercial customers, which includes:

[0013] The information acquisition module is used to acquire resource information, demand information, and environmental information for business customer recommendations;

[0014] The inconsistency identification module is used to identify inconsistencies based on resource information, demand information, and environmental information.

[0015] The value analysis module is used to analyze the true values ​​of each party behind discord, in order to infer the value ranking of each party.

[0016] The solution conception module is used to generate new solution concepts based on the inferred value ranking. Solution concepts can discover new balance points by changing the definition of the problem or the dimensions of problem-solving.

[0017] The solution generation module is used to generate actionable recommended solutions based on the equilibrium point.

[0018] The solution evaluation module is used to perform multi-dimensional evaluation of recommended solutions to determine whether they meet preset criteria.

[0019] This application can effectively integrate multi-source information, identify potential conflicts, and discover new balance points through value ranking and innovative solution concepts, thereby generating more feasible and adaptable recommended solutions. It solves the problems of existing technologies, such as the disconnect between recommended solutions and actual implementation, lack of understanding of high-level business intentions, and difficulty in handling multiple conflicts. Attached Figure Description

[0020] Figure 1 This application provides a flowchart illustrating a business customer big data recommendation method based on artificial intelligence.

[0021] Figure 2 This is a schematic diagram of the structure of a business customer big data recommendation system based on artificial intelligence, provided for this application. Detailed Implementation

[0022] The technical solutions in this application will now be clearly and completely described in conjunction with the accompanying drawings.

[0023] This application proposes a business customer big data recommendation method based on artificial intelligence, such as... Figure 1 As shown, it includes the following steps:

[0024] Obtain resource information, demand information, and environmental information for use in recommending business clients;

[0025] Based on resource information, demand information, and environmental information, identify the inconsistencies that exist.

[0026] To address the inconsistencies, analyze the true priorities of each party to infer their value hierarchy.

[0027] Based on the inferred value ranking, new solution ideas are formed. These solutions aim to find a new balance point by changing the definition of the problem or the dimensions of problem-solving.

[0028] Based on the equilibrium point, an actionable recommendation scheme is formed;

[0029] The recommended solutions are evaluated in multiple dimensions to determine whether they meet the preset criteria.

[0030] Resource information refers to all internal and external resources that a company can access when making business customer recommendations, including but not limited to product inventory, service capabilities, technical support, human resources, marketing budget, supply chain status, and partnerships. This information forms the basis for assessing the feasibility of a recommendation plan.

[0031] Demand information refers to the specific requirements, preferences, pain points, and potential business growth points of business customers for products or services. This information can be obtained through customer interviews, market research, historical transaction data, and behavioral log analysis.

[0032] Environmental information refers to external factors that influence business decisions, including market trends, industry policies, competitor activities, macroeconomic conditions, technological developments, and socio-cultural context. This information helps in understanding the macroeconomic context and potential risks of a recommended solution.

[0033] Inconsistencies refer to contradictions, conflicts, or mismatches between resource information, demand information, and environmental information. For example, customer needs may not match the company's existing product capabilities, or market trends may deviate from the company's strategic direction.

[0034] What is truly valued refers to the factors that all parties (such as businesses, customers, and competitors) genuinely focus on and prioritize when facing disharmony. This may go beyond superficial needs or resource constraints, involving deeper value orientations and strategic goals.

[0035] Value prioritization refers to arranging different value elements according to what each party truly values. This prioritization is key to developing new solutions and finding a balance.

[0036] Solution conception refers to redefining the problem or solving it from different dimensions through innovative thinking after identifying value priorities, in order to find new solutions.

[0037] The equilibrium point refers to the state in which the interests and constraints of all parties are effectively coordinated and satisfied in a new solution concept, so that the recommended solution can not only meet customer needs, but also conform to corporate resources and strategies, and adapt to the external environment.

[0038] A recommended solution refers to specific, actionable business recommendations based on a balance point, including product mix, service model, pricing strategy, and marketing campaigns.

[0039] Multidimensional evaluation refers to a comprehensive assessment of a recommended solution from multiple perspectives, including but not limited to financial benefits, customer satisfaction, market competitiveness, resource consumption, risk level, and alignment with corporate strategy.

[0040] The scheme proposed in this application will be further elaborated here.

[0041] First, it's necessary to acquire resource information, demand information, and environmental information for business customer recommendations. This information forms the foundation of the entire recommendation process. For example, resource information can include a company's product catalog, service capabilities list, technology stack, sales team size, and marketing budget. This data can be integrated through the company's internal ERP, CRM, financial, and human resources systems. Demand information can be extracted from customers' historical purchase records, service orders, online behavioral data, survey results, and social media comments. This typically requires using Natural Language Processing (NLP) techniques to analyze unstructured text and data mining techniques to identify patterns in structured data. Environmental information can come from market research reports, industry news, publicly available competitor information, macroeconomic indicators, and policy and regulatory databases. This data can be obtained through web crawlers, API calls, and third-party data service providers.

[0042] Secondly, identifying inconsistencies is the starting point for discovering problems and seeking solutions. For example, when customer demand focuses on a service that a company does not yet possess or is insufficiently capable of providing, there is an inconsistency between demand and resources. Similarly, when the market environment shows a high demand for a certain emerging technology, but the company's internal strategy has not yet responded, there is an inconsistency between environment and strategy. This identification can be done by building multidimensional data models and utilizing rule engines or machine learning algorithms. For example, a series of predefined conflict rules can be set; if customer demand for product A exceeds the company's maximum production capacity for product A, then an inconsistency exists. More complex identification can be achieved by training classification models, taking various combinations of information as input, and outputting whether an inconsistency exists and its type.

[0043] Secondly, for the identified inconsistencies, analyze the true priorities of each party to infer their value ranking. This step aims to gain a deeper understanding of the nature of the conflict, rather than merely scratching the surface. For example, when customers complain about high product prices, their true priorities may not be just the price itself, but rather value for money, return on investment, or long-term operating costs. Companies may value profit margins, market share, or brand image. Competitors may value market leadership or technological innovation advantages. This analysis can be achieved by building knowledge graphs, causal reasoning models, or using deep learning models to learn from a large number of historical cases. For example, by analyzing the sentiment and keywords in customer feedback texts, combined with their historical behavioral data, we can infer their preferences for core values ​​such as efficiency, cost, and quality.

[0044] Next, based on the inferred value ranking, new solution concepts are formed. These concepts aim to discover new equilibrium points by changing the definition of the problem or the dimensions of problem-solving. This step is the core innovation of this method, aiming to break out of existing frameworks and seek breakthrough solutions. For example, if a traditional solution addresses the problem by reducing product prices, a new concept might shift to improving the customer's overall return on investment through value-added services, thus changing the definition of the problem. Or, if an existing solution addresses the problem by improving product performance, a new concept might shift to providing a more comprehensive solution through ecosystem collaboration, thus changing the dimensions of problem-solving. This concept formation can leverage generative artificial intelligence models (such as large language models), inputting value rankings and inconsistencies to generate various innovative solution drafts.

[0045] Then, based on the discovered equilibrium point, an actionable recommendation scheme is formed. Once a new equilibrium point is found, it needs to be translated into specific, actionable business recommendations. For example, if the equilibrium point lies in meeting customers' needs for high cost-effectiveness by providing customized services, then the recommendation scheme might include launching customized service packages for specific industry customers and providing flexible payment options. These schemes need to detail product characteristics, service processes, pricing strategies, marketing channels, and expected results. The scheme can be formed by expert systems using rule-based reasoning based on the equilibrium point, or by optimization algorithms under a series of constraints (such as resource limitations and cost budgets).

[0046] Finally, evaluation is a crucial step in ensuring the quality and feasibility of the recommended solutions. Multidimensional evaluation can include financial assessment (such as return on investment and profit contribution), market assessment (such as market share growth and customer satisfaction), risk assessment (such as technological risk, market risk, and compliance risk), and strategic alignment assessment. Preset criteria can include minimum return on investment, customer satisfaction targets, and market share targets. Evaluation can be conducted through simulation testing using model building or by obtaining real-world data through small-scale pilot projects. For example, A / B testing can be used to apply different recommended solutions to different customer groups and collect feedback data for comparative analysis.

[0047] This method, when formulating solution ideas, doesn't simply patch things up within an existing framework. Instead, it discovers new balance points by changing the definition of the problem or the dimensions of problem-solving. For example, in traditional recommendation systems, when customer needs don't match enterprise resources, the usual solution is to adjust products or find alternatives. This method, however, might find an innovative solution that satisfies deep customer needs, fully utilizes existing enterprise resources, and adapts to the external environment by redefining customer value or introducing new dimensions of solution such as ecosystem collaboration. This out-of-the-box thinking allows this method to generate more innovative and groundbreaking recommendation solutions, effectively avoiding the problems of poor feasibility and rigid strategies in existing technologies.

[0048] In some embodiments, the steps of forming new solution ideas based on the inferred value ranking described above, and discovering new equilibrium points by changing the definition of the problem or the dimensions of problem-solving, include:

[0049] Construct a simulated sandbox environment that includes representations of competitor behavior patterns, customer group decision-making logic, and internal strategic directives of the enterprise;

[0050] When changes in the market environment, customer behavior, or internal corporate strategy are detected, a value priority portfolio is generated.

[0051] Input the value priority combination into the simulation sandbox environment for pre-playing, in order to simulate the market reaction and business results that the value priority combination may bring;

[0052] By comparing the results of value priority combinations in a simulated sandbox and combining them with internal corporate strategic directives, the value ranking logic can be identified.

[0053] Based on the identified value ranking logic, the value reconstruction rules are adjusted to form new solution ideas. These solutions aim to discover new equilibrium points by changing the definition of the problem or the dimensions of problem-solving.

[0054] Specifically, building a simulation sandbox environment refers to creating a virtual, controllable simulation platform designed to simulate real-world business market environments. This simulation sandbox environment is configured to include representations of competitor behavior patterns, customer decision-making logic, and internal strategic directives. Competitor behavior patterns can be understood as digital models of competitors' potential pricing strategies, product launches, and marketing activities under different market scenarios. Customer decision-making logic refers to modeling the target customer group's purchasing decision-making process, preference changes, and feedback mechanisms when faced with different products or services. The representation of internal strategic directives encompasses the digital expression of the company's long-term goals, resource constraints, core competencies, and priorities—its internal guiding principles. These representations can be built based on historical data, expert experience, machine learning models, and other methods.

[0055] The system is triggered to generate a value priority set when changes in the market environment, customer behavior, or internal corporate strategy are detected. Changes in the market environment can include fluctuations in macroeconomic indicators, adjustments in industry policies, and technological innovations; changes in customer behavior can be reflected in shifts in purchasing habits, demand preferences, and feedback channels; changes in internal corporate strategy may involve new business objectives, adjustments in resource allocation, or organizational restructuring. A value priority set refers to a hypothetical scenario that reorders or combines the value demands of different stakeholders (such as customers, the company, and competitors) under specific changing circumstances.

[0056] Subsequently, the value priority combination is input into a simulated sandbox environment for pre-running. The pre-running process involves running the value priority combination in a virtual sandbox environment to simulate the potential ripple effects and business outcomes in the real market. For example, it can simulate the impact of a new pricing strategy on customer purchase intentions, or the impact of new product features on competitors' market share.

[0057] Based on this, the value prioritization combination is compared with the pre-simulation results in the sandbox, and combined with the company's internal strategic directives to identify the value ranking logic. Pre-simulation results can include simulated market share changes, customer satisfaction index, profit growth rate, and other indicators. By comparing these simulation results with the company's internal strategic directives, it is possible to assess whether the value prioritization combination aligns with the company's long-term development goals and resource constraints. Identifying the value ranking logic refers to extracting from the pre-simulation results which value elements are most critical to achieving business objectives in the current context, and the interactions between them.

[0058] Ultimately, based on the identified value ranking logic, the value reconstruction rules are adjusted to form new solution concepts. Value reconstruction rules refer to the principles guiding how to redefine a problem or expand the dimensions of problem-solving. By adjusting these rules, new, more innovative, and feasible solution concepts can be systematically generated. These conceptions discover new equilibrium points by changing the definition of the problem or the dimensions of problem-solving. For example, when it is found that customers have significantly increased their value ranking for convenience, the rules can be adjusted to shift the focus of the solution concept from feature richness to process simplification, thereby finding new market entry points and equilibrium points.

[0059] This application's solution provides a safe and controllable experimental platform for generating solution concepts by constructing a simulated sandbox environment that includes competitor behavior patterns, customer group decision-making logic, and internal strategic directives. When market conditions, customer behavior, or internal corporate strategies change, the system can generate multiple hypothetical value priority combinations. These combinations are input into the simulated sandbox for pre-deployment, allowing for a quantitative assessment of the potential market reactions and business outcomes of different solution concepts before actual deployment. By comparing the pre-deployment results and combining them with internal strategic directives, the most effective value prioritization logic in the current context can be identified. Based on this, the system can dynamically adjust the value reconstruction rules, enabling the resulting solution concepts to more accurately respond to external changes and internal strategies, thereby systematically changing the definition of the problem or the dimensions of problem-solving to discover a better balance. This iterative and verification mechanism effectively avoids the uncertainties and risks associated with generating solution concepts directly based on experience or intuition in traditional methods.

[0060] In some embodiments, the step of performing a multidimensional evaluation of the recommended solution to determine whether the recommended solution meets the preset criteria includes:

[0061] Obtain the expected results and actual feedback data of the recommended solutions. The actual feedback data includes customer contract data, service ticket data, financial investment data, market response, customer evaluation, competitor dynamics, and user behavior data.

[0062] The deviation between the expected results and the actual feedback data is calculated. When the deviation of any key indicator exceeds the preset significant deviation threshold, a calibration trigger signal is generated. The calibration trigger signal includes the name, direction, degree of deviation of the indicator, as well as the scheme identifier and time range involved.

[0063] Based on the calibration trigger signal, the calibration of the knowledge system of the context analysis engine is triggered, including:

[0064] When the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction, the latest market text data related to the core value concept is re-analyzed, the semantic context feature set inside the context analysis engine is updated, and the concept association weight table is adjusted to associate it more with words and phrases that frequently appear in the latest data.

[0065] When the calibration trigger signal indicates that the customer's actual emphasis on indirect financial factors does not match the forecast, the latest customer behavior log is reviewed, the implicit value mapping rules of customer behavior patterns within the context analysis engine are adjusted, and the weight of relevant implicit value in the customer's decision-making logic is increased.

[0066] When the calibration trigger signal indicates that the competitor's actual market response or product strategy does not match the forecast, analyze the latest competitor product launch announcements, marketing activities and industry analysis reports, update the competitor behavior pattern parameters inside the context analysis engine, and adjust the competitor strategy parameter table.

[0067] When the calibration trigger signal indicates that the actual matching degree between the recommended solution and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the latest unstructured text of the corporate internal strategic documents, senior management meeting minutes and internal communication records are re-analyzed, and the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine are adjusted to transform them into actionable design principles or solution preference weights.

[0068] Manage the frequency of knowledge system calibration, and pause new calibration triggers within a preset time after a knowledge system calibration is completed;

[0069] After the knowledge system is calibrated, a portion of the latest market data and historical data that were not used for calibration are used to conduct backtesting tests or small-scale simulations on the new knowledge system. Only knowledge updates that pass the preset verification standards will be formally adopted and integrated into the context analysis engine.

[0070] Specifically, when obtaining the expected results and actual feedback data of the recommendation solution, the expected results can refer to various business indicators predicted based on historical data and models during the solution design phase, such as the expected customer signing rate, the improvement in service efficiency, and the increase in financial revenue. Actual feedback data covers a wide range of information, including customer signing data, service ticket data, financial investment data, market response, customer evaluations, competitor activities, and user behavior data. This data comprehensively reflects the performance of the recommendation solution in actual application.

[0071] The calculation of the deviation between expected results and actual feedback data refers to quantifying the difference by comparing the predicted and actual values ​​of various key indicators. For example, it can calculate the percentage deviation between the actual and expected value of the signing rate, or the absolute deviation between the actual average and expected average value of service order processing time. When the deviation of any key indicator, such as the actual signing rate being significantly lower than the expected value, or the customer satisfaction score dropping significantly, exceeding a preset significant deviation threshold, the system will automatically generate a calibration trigger signal. This calibration trigger signal is not a simple alarm, but contains detailed contextual information, such as the name of the indicator that deviated (e.g., customer signing rate), the direction of the deviation (e.g., lower than expected), the degree of deviation (e.g., 20% deviation), the identifier of the recommended solution involved, and the time range in which the deviation occurred, so as to conduct accurate knowledge system calibration subsequently.

[0072] Furthermore, based on the calibration trigger signal, the knowledge system of the context analysis engine will be calibrated. The context analysis engine is a core component of this method, and its knowledge system encompasses a deep understanding of customers, the market, competitors, and internal corporate strategies. The calibration process involves targeted knowledge updates for different types of deviation signals.

[0073] Specifically, when the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction—for example, the system initially predicted that the customer valued cost-effectiveness, but actual feedback shows that the customer is more concerned about service quality or innovation—the system will re-analyze the latest market text data related to that core value concept, such as industry reports, customer interview records, and social media discussions. Through natural language processing technology, the semantic context feature set within the context analysis engine will be updated, and the concept association weight table will be adjusted to associate it more closely with frequently occurring words and phrases in the latest data, thereby more accurately capturing the customer's true understanding of the core value.

[0074] When a calibration trigger signal indicates that a customer's actual emphasis on non-financial factors differs from predictions—for example, the system initially assumed a customer valued brand reputation only moderately, but actual behavior logs (such as the time spent on brand profile pages when selecting suppliers, and click-through rates on brand-related news) show a much higher level of attention—the latest customer behavior logs will be re-examined. Through behavioral pattern analysis, the implicit value mapping rules of customer behavior patterns within the contextual analysis engine are adjusted, increasing the weight of relevant implicit values ​​in the customer's decision-making logic. This allows the system to more accurately predict customer preferences regarding non-financial factors.

[0075] When a calibration trigger signal indicates that a competitor's actual market response or product strategy deviates from predictions—for example, if the system initially predicted a competitor would employ a price war strategy but actually observed the launch of a differentiated high-end product—the system will analyze the latest competitor product launch announcements, marketing activities, and industry analysis reports. This information will be used to update the competitor behavior pattern parameters within the context analysis engine, such as adjusting their product launch cycle, pricing strategy range, and marketing preference intensity, and refining the competitor strategy parameter table to more accurately predict future competitor actions.

[0076] When the calibration trigger signal indicates that the actual alignment between the recommended solution and the company's senior management strategy does not match the prediction, or when the senior management strategy itself undergoes new evolution—for example, when senior management decides to shift from prioritizing market share to prioritizing profit margin—the unstructured text of the latest internal strategic documents, senior management meeting minutes, and internal communication records will be re-analyzed. Through text mining and semantic analysis, the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine will be adjusted, transforming them into actionable design principles or solution preference weights to ensure that the recommended solution always aligns with the company's latest strategic objectives.

[0077] In addition, once a knowledge system calibration is completed, the system will pause new calibration triggers for a preset time, such as setting a cooling-off period, to ensure the stability and effectiveness of the calibration.

[0078] Finally, only knowledge updates that pass the preset verification criteria will be formally adopted and integrated into the context analysis engine, thereby ensuring the quality and reliability of knowledge updates.

[0079] This application first obtains the expected results and actual feedback data of the recommended scheme, and calculates the degree of deviation between the two, thereby enabling timely and objective identification of problems in the practical application of the recommended scheme. When the deviation exceeds a preset threshold, the system can generate a calibration trigger signal containing detailed contextual information, which makes subsequent knowledge system calibration highly targeted.

[0080] Furthermore, this solution refines the knowledge system of the contextual analysis engine based on different types of calibration trigger signals. For example, when a customer's understanding of core value concepts changes, the system can update the semantic contextual feature set and concept association weight table by analyzing the latest market text data, thus enabling the contextual analysis engine to more accurately grasp the customer's true needs. When a customer's emphasis on indirect financial factors changes, the system can adjust the implicit value mapping rules by reviewing customer behavior logs, allowing the recommended solutions to better align with the customer's deep preferences. When competitors' strategies change, the system can update the competitor's behavior pattern parameters and strategy parameter tables by analyzing multi-source data, enabling the recommended solutions to more effectively respond to market competition. When the company's senior management strategy evolves, the system can adjust the solution generation preference guidance rules by analyzing internal unstructured text, ensuring that the recommended solutions always remain consistent with the company's strategy.

[0081] In some embodiments, the steps described above, when the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction, to reanalyze the latest market text data related to the core value concept, update the semantic context feature set within the context analysis engine, and adjust the concept association weight table to associate it more closely with frequently occurring words and phrases in the latest data, include:

[0082] Acquire market textual data related to the core value concept;

[0083] The market text data is segmented into words, and a set of words related to the core value concept is identified.

[0084] Calculate the semantic association strength between each word in the word set and the core value concept, and extract semantic context features;

[0085] Regularly perform difference analysis between the historical semantic context feature set and the latest extracted semantic context feature set to identify newly added, disappeared, or changed semantic context features;

[0086] Based on the results of the difference analysis, dynamically adjust the concept association weight table;

[0087] By combining customer group tag information, market text data is segmented, semantic context features are extracted for different customer groups, and their respective concept association weight tables are calculated and maintained.

[0088] The association strength of semantic context features is subjected to time decay processing.

[0089] Acquiring market textual data related to core value concepts refers to collecting textual information related to specific core value concepts (such as efficiency improvement, cost optimization, and innovation capabilities) from public or internal data sources through various methods such as web crawling, API interfaces, or data import. These data sources can include news reports, industry analysis reports, social media discussions, customer reviews, forum posts, and internal sales communication records, aiming to comprehensively capture the latest market and customer perceptions and discussion trends regarding these concepts.

[0090] Segmenting market text data and identifying a set of words related to the core value concept involves using Natural Language Processing (NLP) technology to accurately segment the massive amounts of text data and remove common stop words. Based on this, words or phrases closely related to the core value concept are selected using a pre-defined keyword list, topic models (such as Latent Dirichlet Allocation (LDA), or word vector similarity calculations, thus forming a dynamically updated set of words.

[0091] Calculating the semantic association strength between each word in a word set and the core value concept, and extracting semantic contextual features, refers to using advanced semantic analysis methods such as word frequency-inverse document frequency (TF-IDF), co-occurrence network analysis, topic models, or deep learning word embeddings (such as Word2Vec and BERT) to quantify the semantic relevance between each word or phrase in the word set and the core value concept. Semantic contextual features can be understood as these highly associated words and their combination patterns in specific contexts, which together constitute a deeper understanding of the core value concept.

[0092] Regularly performing differential analysis between historical and newly extracted semantic context feature sets to identify newly added, disappeared, or altered semantic context features involves periodically comparing semantic context feature sets extracted over different time periods. This can be achieved using metrics such as Jaccard similarity, cosine similarity, or KL divergence to detect which words or phrases are newly emerging, which are no longer popular, and which words have experienced significant changes in their association with core value concepts. This differential analysis helps to promptly identify evolutions in market and customer perception.

[0093] Dynamically adjusting the concept association weight table based on the difference analysis results means updating the concept association weight table maintained internally by the context analysis engine in real time based on the results of the difference analysis. For example, for newly added semantic context features, an initial weight is assigned to them; for features with significantly increased association strength, their weight is increased; for features with significantly weakened or disappeared association strength, their weight is decreased or removed, to ensure that the weight table can accurately reflect the market's latest understanding and importance of core value concepts.

[0094] By combining customer group tagging information to segment market text data and extracting semantic contextual features for different customer groups, and calculating and maintaining their respective concept association weight tables, this approach takes into account that different customer groups (e.g., customers from different industries, sizes, and regions) may have different understandings and emphases on the same core value concept. Therefore, during data acquisition and analysis, market text data is categorized based on customer tags such as industry, size, and region. Then, for each segmented customer group, the aforementioned semantic contextual feature extraction and concept association weight table adjustment processes are performed independently, thereby forming a personalized value understanding model for a specific customer group.

[0095] Applying time decay to the association strength of semantic context features involves incorporating a time factor when calculating and adjusting the association strength. For example, recently emerging market text data and semantic context features are given higher weights, while the weights of older data and features gradually decrease over time. This mechanism ensures that the context analysis engine always focuses on the latest and most relevant market dynamics and customer perceptions, avoiding interference from outdated information in value judgments.

[0096] This application continuously acquires and analyzes the latest market text data to capture changes in customers' understanding of core value concepts in real time. Through word segmentation, semantic association strength calculation, and difference analysis, it can accurately identify which words and phrases have increased or decreased relevance to core value concepts, and even reveal new association patterns. Furthermore, by combining customer group tag information for segmentation, it ensures personalized capture of value perceptions for different customer groups. The time decay processing mechanism ensures the dynamism and timeliness of the knowledge system, enabling the contextual analysis engine to always understand customers' core value needs based on the latest market context, thereby effectively correcting deviations caused by discrepancies between customers' understanding and predictions of core value concepts.

[0097] In some embodiments, when a calibration trigger signal indicates that a customer's actual emphasis on indirect financial factors does not match the forecast, the steps of re-examining the latest customer behavior logs, adjusting the implicit value mapping rules of customer behavior patterns within the context analysis engine, and increasing the weight of relevant implicit value in the customer's decision-making logic include:

[0098] Extract sequences of behavioral events related to indirect financial factors from customer behavior logs;

[0099] Contextual analysis is performed on the extracted behavioral event sequences to identify the association patterns between the behavioral event sequences and indirect financial factors;

[0100] Weights are assigned to different indirect financial factors in the sequence of behavioral events.

[0101] The identified correlation patterns and set weights are applied to customer behavior logs to calculate customer scores on their emphasis on various indirect financial factors.

[0102] Based on the importance score, adjust the implicit value mapping rules of customer behavior patterns within the context analysis engine to increase the weight of high-scoring implicit values ​​in customer decision-making logic.

[0103] Specifically, extracting behavioral event sequences related to non-financial factors from customer behavior logs involves using pre-defined keywords, behavioral pattern recognition rules, or machine learning models to filter out specific behavioral events related to non-financial factors such as brand image, user experience, social responsibility, after-sales service, and product innovation from raw customer interaction records, browsing history, purchase paths, and service requests. For example, customer discussions about the company's brand image on social media, feedback on user experience in product forums, and evaluations of after-sales support in service tickets can all be considered relevant behavioral events.

[0104] Contextual analysis of extracted behavioral event sequences to identify patterns of association between these sequences and indirect financial factors can be understood as using Natural Language Processing (NLP), sequence pattern mining algorithms, or graph neural networks to analyze the temporal order, adjacent events, event intensity, and underlying sentiment of these behavioral events. This reveals the deep connection between customer behavior and specific indirect financial factors. For example, a customer's repeated visits to product review pages and reading of user reviews before a purchase may indicate that the customer highly values ​​the product's user experience and reputation.

[0105] In practical applications, weights are assigned to behavioral event sequences based on different indirect financial factors. Specifically, this means assigning different importance coefficients to different behavioral event sequences based on expert experience, historical data analysis, or A / B testing results. For example, the behavior of a customer proactively posting positive reviews on social media may have a higher weight than simply browsing a product introduction page, because the former better reflects the customer's strong approval of a particular indirect financial factor.

[0106] Furthermore, the identified correlation patterns and assigned weights are applied to customer behavior logs to calculate a score indicating the customer's level of importance placed on various indirect financial factors. The aim is to quantify the degree of importance a customer places on each indirect financial factor. This can be achieved by combining the weight of each behavioral event sequence with its frequency or intensity, and summing these together to obtain a total score for each indirect financial factor. For example, a customer's score indicating their emphasis on environmental protection can be calculated by statistically summing their weighted participation in environmental activities, purchase of environmentally friendly products, and attention to environmental news.

[0107] Based on the importance score, the implicit value mapping rules of customer behavior patterns within the context analysis engine are adjusted to increase the weight of high-scoring implicit values ​​in the customer's decision-making logic. The aim is to enable the context analysis engine to more accurately reflect the customer's true decision-making preferences. Specifically, the higher the score of indirect financial factors, the greater their influence in the customer's decision-making model. Therefore, when generating recommended solutions, those that can satisfy the implicit values ​​that the customer highly values ​​will be given priority.

[0108] This application's solution systematically extracts behavioral event sequences related to indirect financial factors from customer behavior logs and performs in-depth contextual analysis to accurately identify the correlation patterns between customer behavior and implicit value. By assigning weights to different behavioral event sequences and combining these correlation patterns, a quantitative score can be calculated on the customer's emphasis on various indirect financial factors. It is precisely because of this refined quantification process that the contextual analysis engine can dynamically and accurately adjust its internal customer behavior pattern implicit value mapping rules based on these scores, thereby increasing the weight of high-scoring implicit values ​​in the customer's decision-making logic.

[0109] In some embodiments, when the calibration trigger signal indicates that a competitor's actual market response or product strategy does not match the forecast, the steps of analyzing the latest competitor product launch announcements, marketing activities, and industry analysis reports, updating the competitor behavior pattern parameters within the context analysis engine, and adjusting the competitor strategy parameter table include:

[0110] Acquire multi-source heterogeneous data from competitors, including product launch announcements, marketing campaign reports, industry analysis reports, public discussions on social media, and related patent application documents;

[0111] Standardize the acquired multi-source heterogeneous data to unify the data format and structure;

[0112] Key information is extracted from the standardized data, including product characteristics, price adjustments, marketing efforts, and technological innovation directions.

[0113] The extracted key information is compared with existing competitor behavior pattern parameters and strategy parameter tables to identify newly added, modified, or deleted competitor behavior patterns and strategy elements.

[0114] Based on the comparison results, update the competitor behavior pattern parameters, including adjusting the product launch cycle, pricing strategy range, and marketing preference intensity of specific competitors.

[0115] Based on the comparison results, the competitor strategy parameter table was adjusted, including updating their possible response plans and priorities in different market scenarios.

[0116] After the update is completed, manage the frequency of updates, including pausing frequent updates to the same competitor within a preset time period.

[0117] Specifically, acquiring multi-source heterogeneous data on competitors refers to collecting relevant information about competitors from different sources and in different formats through various channels and technologies. This data is not limited to traditional product launch announcements, marketing campaign reports, and industry analysis reports, but also includes public discussions on social media, such as user reviews of competitors' products or services, analyses of key opinion leaders (KOLs), and relevant patent applications submitted by competitors. These documents can reveal their future technological development directions and potential product strategies. The acquisition of multi-source heterogeneous data aims to build a comprehensive and multi-dimensional competitive intelligence view, avoiding information bias or lag that may result from a single data source.

[0118] Standardizing the acquired multi-source heterogeneous data and unifying the data format and structure aims to eliminate the inconsistency in format caused by the diversity of data sources. For example, it converts text, image, and table data of different formats into structured data that can be used for subsequent analysis, which facilitates subsequent automated processing and analysis.

[0119] Key information is extracted from standardized data, including product characteristics, price adjustments, marketing efforts, and technological innovation directions. This section aims to accurately identify core elements that directly impact business decisions from massive amounts of data. For example, product characteristics may include feature updates, performance improvements, and design changes; price adjustments may refer to promotional activities and changes in pricing strategies; marketing efforts can be quantified as advertising spending, channel coverage, and brand exposure; and technological innovation directions can be identified through patent keywords and R&D investment reports.

[0120] The extracted key information is compared with existing competitor behavior pattern parameters and strategy parameter tables to identify newly added, modified, or deleted competitor behavior patterns and strategy elements. This step is the basis for dynamic updates, discovering the latest changes in competitor behavior through comparison, such as whether they have launched new products, adjusted pricing strategies, changed marketing focuses, or made new breakthroughs in specific technology areas.

[0121] Based on the comparison results, update the competitor behavior parameters, including adjusting the product launch cycle, pricing strategy range, and marketing preference intensity of specific competitors. For example, if a competitor's product launch cycle is found to be significantly shortened, its launch cycle parameters should be adjusted; if it frequently engages in price wars, the low-price range of its pricing strategy should be expanded; if it invests heavily in a particular social media platform, its marketing preference intensity on that platform should be increased.

[0122] Based on the comparison results, adjust the competitor strategy parameter table, including updating their possible responses and priorities under different market scenarios. For example, when market demand declines, competitors may resort to price reductions or launch new products to stimulate demand; when facing new entrants, they may adopt defensive pricing or strengthen brand promotion. These responses and their priorities should be dynamically adjusted based on the latest intelligence.

[0123] After an update is completed, manage the update frequency, including pausing frequent updates to the same competitor within a preset time period. This aims to avoid system instability or resource waste caused by overly frequent updates, while ensuring the effectiveness and stability of updates. For example, it can be set that after a major update, the next update to that competitor will be at least 24 hours later, unless an extremely important unforeseen event occurs.

[0124] This application's solution, by introducing a multi-source heterogeneous data acquisition mechanism and performing standardized data processing and key information extraction, can more comprehensively and timely capture the dynamic changes of competitors. It is precisely due to the accurate identification of this key information and the effective comparison with existing model parameters that the context analysis engine can identify subtle changes in competitors' behavioral patterns and strategic elements. Through dynamic adjustments to the competitor behavioral pattern parameters and strategy parameter tables, this solution ensures that the competitive intelligence within the context analysis engine remains up-to-date and highly accurate, thus providing a more solid and reliable decision-making foundation for subsequent solution design and recommendations.

[0125] In some embodiments, when the calibration trigger signal indicates that the actual match between the recommended solution and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the steps of re-analyzing the latest unstructured text of corporate internal strategic documents, senior management meeting minutes, and internal communication records, and adjusting the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine to transform them into actionable design principles or solution preference weights include:

[0126] Obtain the latest unstructured text of corporate internal strategic documents, high-level meeting minutes, and internal communication records;

[0127] The unstructured text was preprocessed, and the core strategic themes in the text were identified.

[0128] By combining a pre-defined strategic intent dictionary and an expert knowledge base, semantic disambiguation is performed on the expressions of the above core strategic themes to obtain clear strategic intent.

[0129] Analyze the above unstructured text to identify the connections between different strategic intentions in order to uncover potential internal conflicts;

[0130] Based on the pre-set conflict resolution rules, determine the dominant strategic intent in the aforementioned internal conflicts;

[0131] Based on the aforementioned clear strategic intent and the aforementioned dominant strategic intent, the scheme generation preference guidance rules under the strategic intent constraints within the context analysis engine are adjusted and transformed into operable design principles or scheme preference weights.

[0132] Specifically, acquiring the latest unstructured texts of internal corporate strategy documents, high-level meeting minutes, and internal communication records refers to the system continuously collecting various unstructured data sources within the company, such as official strategic reports, quarterly or annual performance reports, executive meeting minutes, internal memos, email communications, and project charters. These texts typically contain strategic directions, goals, and decision-making information from senior management.

[0133] The preprocessing of the unstructured text and the identification of core strategic themes within it can be understood as cleaning and standardizing the acquired raw text. This includes operations such as word segmentation, stop word removal, lemmatization or stemming, and named entity recognition. The identification of core strategic themes can employ Natural Language Processing (NLP) techniques, such as topic models (e.g., Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), keyword extraction algorithms, or text summarization algorithms, aiming to extract key areas and concerns of corporate strategy from large amounts of text.

[0134] In practical applications, by combining a pre-defined strategic intent dictionary and an expert knowledge base, semantic disambiguation is performed on the expressions of the aforementioned core strategic themes to arrive at clear strategic intentions. The aim is to resolve the potential ambiguity and polysemy in strategic language. The strategic intent dictionary can predefine the company's various strategic goals, specific objectives, and their multiple expressions in different contexts. The expert knowledge base can contain rules or mapping relationships constructed by domain experts to associate specific phrases or concepts with clear strategic intentions. For example, the expression "market share growth" can be clearly resolved through semantic disambiguation to the strategic intention of expanding market influence.

[0135] Furthermore, analyzing the aforementioned unstructured text helps identify the connections between different strategic intentions, thereby uncovering potential internal conflicts. The aim is to reveal possible synergistic or competitive relationships between strategic goals within the organization. This can be achieved through co-occurrence analysis, semantic similarity calculation, or graph-based analysis methods. Potential internal conflicts may manifest as two strategic intentions competing for the same limited resources, targeting mutually exclusive market segments, or implying conflicting operational priorities, such as the potential conflict between rapid market expansion and strict cost control.

[0136] Based on this, and according to pre-defined conflict resolution rules, the dominant strategic intent in the aforementioned internal conflicts is determined. The purpose is to provide a clear priority assessment mechanism when strategic conflicts are detected. These conflict resolution rules can be pre-defined guidelines, such as determining the dominant strategic intent based on strategic hierarchy (corporate-level strategy takes precedence over departmental-level strategy), resource allocation priority, time frame, or specific business objectives. For example, if short-term profitability conflicts with long-term market penetration, the rules can specify which strategic intent has higher priority in the current market environment.

[0137] Ultimately, based on the explicit and dominant strategic intent, the context analysis engine adjusts the solution generation preference guidance rules constrained by this strategic intent, transforming them into actionable design principles or solution preference weights. The aim is to translate abstract strategic intent into concrete, actionable guidelines. This can include design principles, such as all recommended solutions prioritizing customer value; or solution preference weights, for example, if innovative product development is the dominant strategic intent, recommended solutions involving new product features or market entry strategies will receive higher weights; conversely, if cost optimization is the dominant strategic intent, solutions emphasizing efficiency improvement or resource conservation will be favored. These design principles and preference weights directly guide the context analysis engine in generating and ranking potential recommended solutions.

[0138] This application's solution, through systematic processing of unstructured strategic text, effectively addresses the challenge of accurately extracting and transforming actionable guiding rules from ambiguous, polysemous, and potentially conflicting senior management strategies. By acquiring and preprocessing the latest internal strategic texts, this application lays the foundation for subsequent in-depth analysis. By combining a strategic intent dictionary and an expert knowledge base for semantic disambiguation, this application ensures that the identified strategic intents are clear and explicit, thus avoiding the problem of recommended solutions not aligning with corporate goals due to strategic misunderstanding biases. Furthermore, by analyzing the correlations between different strategic intents and identifying potential conflicts, and determining the dominant strategic intent based on pre-defined conflict resolution rules, this application effectively handles potential strategic inconsistencies within the enterprise, ensuring that the final solution generation preference guidance rules are unified and prioritized. Therefore, when generating recommended solutions, the contextual analysis engine can accurately reflect the true strategic intent of senior management, avoiding the problem of recommended solutions not aligning with corporate goals due to strategic misunderstanding biases.

[0139] In some embodiments, the steps described above—re-analyzing the latest unstructured texts such as corporate strategic documents, high-level meeting minutes, and internal communication records, and adjusting the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine to transform them into actionable design principles or solution preference weights—include:

[0140] Continuously acquire unstructured texts of internal corporate strategic documents, high-level meeting minutes, and internal communication records;

[0141] Perform thematic analysis on the acquired text to identify the strategic intentions contained therein;

[0142] Construct a strategic intent evolution map, map the identified strategic intents onto the map, and record the time and context in which the strategic intents appear;

[0143] By analyzing the evolution map of strategic intentions, the strength of the association and the degree of conflict between the old and new strategic intentions are identified. The strength of the association and the degree of conflict are quantified by the co-occurrence frequency, semantic distance and time series change trend of strategic intentions on the map.

[0144] Based on the strength of association and the degree of conflict, the policy rules guiding the generation of solutions under the constraints of strategic intent within the context analysis engine are dynamically adjusted, transforming them into actionable design principles or solution preference weights. These adjustments include:

[0145] When a new strategic intent is identified, an initial preference weight is assigned to it and integrated into the solution generation preference guidance rule;

[0146] When a change in the correlation strength of strategic intent is identified, its weight in the scheme generation preference guidance rule is adjusted;

[0147] When conflicts are identified between strategic intentions, the preference weights of the relevant strategic intentions are adjusted according to the degree of conflict and the pre-set conflict resolution rules in order to determine the dominant strategic intention.

[0148] When outdated strategic intentions are identified, their weight in the policy guidance rules for solution generation is reduced or they are removed.

[0149] Specifically, continuously acquiring unstructured text from internal strategic documents, high-level meeting minutes, and internal communication records refers to the system's uninterrupted collection of various strategy-related documents generated within the enterprise, such as annual strategic plans, quarterly business reviews, departmental work reports, high-level email correspondence, and internal forum discussions, through automated interfaces or manual input. This text forms the foundational data for understanding the enterprise's strategic intent and its evolution. Thematic analysis of the acquired text to identify the strategic intents it contains can be performed using Natural Language Processing (NLP) techniques, such as topic modeling (LDA, NMF), text clustering, or deep learning models, to automatically extract and summarize core strategic themes from massive amounts of unstructured text, such as market expansion, cost optimization, technological innovation, and customer experience improvement. Furthermore, constructing a strategic intent evolution graph involves using the identified strategic intents as nodes and establishing relationships between them based on their occurrence at different times and in different contexts. The evolution graph can be a dynamic knowledge graph, where each strategic intent node is accompanied by a timestamp and contextual information from the specific document or communication record in which it appears. By analyzing the evolution graph of strategic intentions, the system identifies the strength of association and degree of conflict between old and new strategic intentions, specifically using graph analysis algorithms and semantic similarity calculation methods. For example, the co-occurrence frequency of strategic intentions on the graph can measure their synergy or association; semantic distance (e.g., calculated through word vectors or sentence vectors) can reflect their conceptual proximity or difference; and the changing trends over time can reveal the rise, decline, or transformation of strategic intentions. These quantitative indicators provide data support for subsequent dynamic adjustments. Based on the strength of association and degree of conflict, the system dynamically adjusts the solution generation preference guidance rules under the constraints of strategic intentions within the context analysis engine, transforming them into actionable design principles or solution preference weights, including various adjustment strategies. When a new strategic intention is identified, such as ESG sustainable development, the system assigns it an initial preference weight and incorporates it into the solution generation preference guidance rules to ensure that the new strategy is considered. When the strength of association of strategic intentions changes, such as a significant increase in the association between market expansion and technological innovation, their weights in the solution generation preference guidance rules are adjusted accordingly to reflect this new synergistic relationship. When a conflict is identified between strategic intentions, such as short-term profit maximization versus long-term R&D investment, the system adjusts the preference weights of the relevant strategic intentions based on preset conflict resolution rules (e.g., priority of high-level directives, priority of core business, etc.) and the quantified degree of conflict, to determine the current dominant strategic intention. Furthermore, when outdated strategic intentions are identified, such as a traditional business model transformation that is no longer a core strategy after completion, the system reduces its weight in the solution generation preference guidance rules or removes it altogether to avoid unnecessary constraints on the generation of new solutions.

[0150] This application, by continuously monitoring unstructured text within an enterprise and constructing a strategic intent evolution map, can dynamically and comprehensively capture subtle changes and long-term trends in the enterprise's senior management strategy. Traditional solutions may only focus on the latest strategic documents, making it difficult to detect the gradual evolution of strategic intent or potential conflicts. This solution, however, by quantifying the co-occurrence frequency, semantic distance, and time-series changes of strategic intent, can more accurately identify the strength of the association and the degree of conflict between old and new strategic intents. For example, when a strategic intent appears significantly more frequently in recent documents and is semantically closer to the existing dominant strategic intent, it indicates an increased association strength, and its weight should be increased accordingly. Conversely, if a strategic intent is not mentioned for a long time or its semantic distance from the current core strategy widens, it may mean that it is outdated and its weight should be reduced. This dynamic analysis mechanism based on the evolution map enables the contextual analysis engine to more accurately understand the true intent and priority of the enterprise's strategy, thereby providing preference guidance that is more in line with the enterprise's current and future strategic direction when generating solutions.

[0151] In some embodiments, when a conflict is identified between strategic intentions, the step of adjusting the preference weights of the relevant strategic intentions according to the degree of conflict and preset conflict resolution rules to determine the dominant strategic intention includes:

[0152] Continuously obtain strategic intent statements from different levels and departments within the company;

[0153] The strategic intent statement text is subject-specifically identified and intent-analyzed to clarify the specific content and source level of each strategic intent;

[0154] Construct a strategic intent conflict matrix to quantify the degree of conflict between different strategic intents. The degree of conflict is quantified by analyzing the co-occurrence frequency, semantic distance, and competitive relationship of strategic intents in the company's internal strategic documents.

[0155] Obtain the priority statements of senior management regarding different strategic intentions. These priority statements are quantified by analyzing the degree of emphasis on strategic intentions, the direction of resource allocation, and the clarity of decision-making instructions in senior management meeting minutes and internal communication records.

[0156] Based on the strategic intent conflict matrix and the priority statements of senior management regarding different strategic intents, the preference weights for strategic intents are dynamically adjusted. These adjustments include:

[0157] When there is a conflict between strategic intentions, the preference weight of the dominant strategic intention is increased and the preference weight of the non-dominant strategic intention is decreased, based on the degree and priority of the conflict.

[0158] When there is a synergistic relationship between strategic intentions, the preference weight of the relevant strategic intentions is increased jointly according to the degree of synergy;

[0159] When the source of strategic intent is at a higher level, it is assigned a higher initial preference weight;

[0160] When the strategic intent is highly relevant to the company's core business, it should be given a higher initial preference weight.

[0161] Based on the adjusted preference weights, the dominant strategic intent is determined and transformed into actionable design principles or solution preference weights.

[0162] Continuously acquiring strategic intent statements from different levels and departments within the enterprise refers to the system's ongoing collection of unstructured text data, such as strategic documents, reports, meeting minutes, and internal communication records, from various departments and management levels within the enterprise. The purpose is to ensure a comprehensive and real-time understanding of the enterprise's overall strategic intent.

[0163] The process of identifying and parsing thematic statements and intentions within strategic intent statements to clarify the specific content and source level of each strategic intent can be understood as using Natural Language Processing (NLP) technology to perform deep analysis on the collected text, identifying the core strategic themes expressed in the text, and further analyzing the specific strategic intentions implied by these themes. Simultaneously, the system identifies the department or level of origin of these strategic intentions (e.g., whether they come from high-level decisions, business department planning, or functional department goals), which helps in prioritizing subsequent conflict resolution. Its purpose is to transform unstructured textual information into structured, analyzable strategic intent data.

[0164] A strategic intent conflict matrix is ​​constructed to quantify the degree of conflict between different strategic intents. This degree of conflict is quantified by analyzing the co-occurrence frequency, semantic distance, and competitive relationship of strategic intents in internal strategic documents. Specifically, the strategic intent conflict matrix is ​​a two-dimensional table used to represent the relationships between different strategic intents. The quantification of conflict degree can be achieved in several ways: for example, if two strategic intents frequently appear together in internal documents, but the context is mostly negative or represents resource competition, a high co-occurrence frequency may indicate conflict; semantic distance is measured by calculating the similarity between keywords or concept vectors of strategic intents, with a larger distance generally indicating a greater conflict; the competitive relationship of shared resources directly assesses whether different strategic intents need to compete for limited resources such as budget, manpower, time, or market share. Its purpose is to provide a quantitative basis for conflict identification and resolution.

[0165] This system obtains priority statements from senior management regarding different strategic intentions. These priority statements are quantified by analyzing the degree of emphasis on strategic intentions, resource allocation, and the clarity of decision-making instructions in senior management meeting minutes and internal communication records. Specifically, the system assesses the degree of emphasis by analyzing the frequency, length, and tone of mention of specific strategic intentions in senior management meeting minutes; it determines the direction of resource allocation by analyzing budget allocations and project initiation information; and it evaluates the clarity of decision-making instructions by analyzing specific directives and requirements issued by senior management. The aim is to obtain authoritative guidance on strategic intentions from the highest decision-making level of the enterprise, serving as an important basis for resolving conflicts.

[0166] Based on the strategic intent conflict matrix and the priority statements of senior management regarding different strategic intents, the preference weights of strategic intents are dynamically adjusted. These adjustments include: when strategic intents conflict, increasing the preference weight of the dominant strategic intent and decreasing the preference weight of non-dominant strategic intents based on the degree of conflict and priority statements; when strategic intents are synergistic, jointly increasing the preference weights of related strategic intents based on the degree of synergy; assigning higher initial preference weights to strategic intents with higher origin levels; and assigning higher initial preference weights to strategic intents with high relevance to the company's core business. Specifically, when the system identifies a conflict between two or more strategic intents, it comprehensively considers their quantified conflict degree in the conflict matrix and senior management's priority statements. For example, if a strategic intent is explicitly emphasized by senior management and receives resource allocation, its preference weight will be increased even if it conflicts with another strategic intent, while the weight of the other intent will decrease accordingly. Conversely, if strategic intents have synergistic effects, such as improving customer satisfaction and increasing customer repurchase rates being typically synergistic, their preference weights will increase together. Furthermore, strategic intentions from senior management (such as the board of directors and CEO) receive a higher initial weight, while strategic intentions closely related to the company's core business (such as major revenue sources and core competencies) are also given a higher initial weight. The aim is to achieve a refined and dynamic adjustment of the weighting of strategic intention preferences through multi-dimensional considerations.

[0167] Based on the adjusted preference weights, the dominant strategic intent is determined and transformed into actionable design principles or solution preference weights. Specifically, after the dynamic adjustment of the preference weights of all strategic intents, the system identifies the strategic intent with the highest weight as the dominant strategic intent in the current context. This dominant strategic intent, along with other adjusted preference weights, will be further transformed into specific, actionable design principles (e.g., all new product development must prioritize environmental factors) or directly used as preference weight inputs to the solution generation module to guide the generation of recommended solutions. The aim is to transform abstract strategic intents into concrete action guidelines, ensuring that recommended solutions are highly aligned with the company's strategy.

[0168] This application's solution continuously acquires strategic intent statements from different levels and departments within an enterprise, performs thematic identification and intent analysis, thereby comprehensively understanding the various strategic intents within the enterprise and their source levels. Based on this, by constructing a strategic intent conflict matrix to quantify the degree of conflict between different strategic intents, and combining this with the priority statements of different strategic intents by senior management, this solution can systematically assess and identify the complex relationships between strategic intents. It is precisely this multi-dimensional, quantitative analysis that allows for the dynamic and precise adjustment of the preference weights of strategic intents when conflicts exist, based on factors such as the degree of conflict, senior management priority, source level, and relevance to core business. By increasing the preference weight of dominant strategic intents and decreasing the preference weight of non-dominant strategic intents, this solution ensures that the final recommended scheme more accurately reflects the true strategic intent and priorities of senior management, effectively solving the problem that it is difficult to accurately determine the dominant direction based solely on conflict identification.

[0169] Based on the same inventive concept, this application also discloses an artificial intelligence-based big data recommendation system for commercial customers, such as... Figure 2 As shown, the system includes:

[0170] Information acquisition module 1 is used to acquire resource information, demand information, and environmental information for business customer recommendations;

[0171] The inconsistency identification module 2 is used to identify inconsistencies based on resource information, demand information, and environmental information.

[0172] Value Analysis Module 3 is used to analyze the true values ​​of each party behind the discrepancies, in order to infer the value ranking of each party.

[0173] The solution concept module 4 is used to form new solution concepts based on the inferred value ranking. The solution concept discovers a new balance point by changing the definition of the problem or the dimensions of the problem-solving.

[0174] Solution generation module 5 is used to generate an operable recommended solution based on the equilibrium point;

[0175] Solution evaluation module 6 is used to conduct multi-dimensional evaluation of recommended solutions to determine whether the recommended solutions meet the preset standards.

[0176] Clearly, the system provided in this application, through the close collaboration of the aforementioned modules, can significantly improve the practicality, adaptability, and operability of the recommendation results, providing strong support for enterprises to succeed in a complex and ever-changing business environment.

[0177] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application.

Claims

1. A business customer big data recommendation method based on artificial intelligence, characterized in that, include: Obtain resource information, demand information, and environmental information for use in recommending business clients; Based on the resource information, demand information, and environmental information, identify any inconsistencies, where inconsistencies refer to contradictions, conflicts, or mismatches among the resource information, demand information, and environmental information. To address the aforementioned inconsistencies, we analyze the true priorities of each party to infer their value ranking. The true priorities refer to the factors that each party genuinely focuses on and prioritizes at a deeper level when facing inconsistencies. These parties include businesses, customers, and competitors. Based on the value ranking derived from the inference, a new solution concept is formed. The solution concept discovers a new balance point by changing the definition of the problem or the dimensions of problem-solving. Based on the aforementioned balance point, an operable recommendation scheme is formulated; The recommended solution is evaluated in multiple dimensions to determine whether it meets preset criteria, including: Obtain the expected results and actual feedback data of the recommended scheme; Calculate the degree of deviation between the expected results and the actual feedback data, and generate a calibration trigger signal when the deviation of any key indicator exceeds a preset significant deviation threshold; Based on the calibration trigger signal, the calibration of the knowledge system of the context analysis engine is triggered, including: When the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction, the latest market text data related to the core value concept is re-analyzed, the semantic context feature set inside the context analysis engine is updated, and the concept association weight table is adjusted to associate it more with words and phrases that frequently appear in the latest data. When the calibration trigger signal indicates that the customer's actual emphasis on indirect financial factors does not match the prediction, the latest customer behavior log is reviewed again, the implicit value mapping rules of customer behavior patterns inside the context analysis engine are adjusted, and the weight of relevant implicit value in the customer's decision-making logic is increased. When the calibration trigger signal indicates that the competitor's actual market response or product strategy does not match the forecast, the latest competitor product release announcements, marketing activities and industry analysis reports are analyzed to update the competitor behavior pattern parameters inside the context analysis engine and adjust the competitor strategy parameter table. When the calibration trigger signal indicates that the actual matching degree between the recommended scheme and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the unstructured text of the latest corporate internal strategic documents, senior management meeting minutes and internal communication records is re-analyzed, and the scheme generation preference guidance rules under the strategic intent constraints within the context analysis engine are adjusted to transform them into actionable design principles or scheme preference weights.

2. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, The step of forming a new solution concept based on the value ranking derived from the inference, wherein the solution concept discovers a new balance point by changing the definition of the problem or the dimensions of problem-solving, includes: Construct a simulated sandbox environment, which includes representations of competitor behavior patterns, customer group decision-making logic, and internal enterprise strategic instructions; When changes in the market environment, customer behavior, or internal corporate strategy are detected, a value priority portfolio is generated. The value priority combination is input into the simulation sandbox environment for pre-playing to simulate the market reaction and business results that the value priority combination may bring. By comparing the results of the value priority combination in the simulated sandbox and combining them with the internal strategic instructions of the enterprise, the value ranking logic is identified. Based on the identified value ranking logic, the value reconstruction rules are adjusted to form a new solution concept. The solution concept discovers a new balance point by changing the definition of the problem or the dimensions of problem-solving.

3. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, When the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction, the steps of re-analyzing the latest market text data related to the core value concept, updating the semantic context feature set inside the context analysis engine, and adjusting the concept association weight table to associate it more with frequently occurring words and phrases in the latest data include: Acquire market text data related to the core value concept; The market text data is segmented into words, and a set of words related to the core value concept is identified. Calculate the semantic association strength between each word in the word set and the core value concept, and extract semantic context features; Regularly perform difference analysis between the historical semantic context feature set and the latest extracted semantic context feature set to identify newly added, disappeared, or changed semantic context features; Based on the results of the difference analysis, dynamically adjust the concept association weight table; The market text data is segmented by combining customer group tag information, and semantic context features are extracted for different customer groups. The concept association weight table for each group is calculated and maintained. The association strength of the semantic context features is subjected to time decay processing.

4. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, When the calibration trigger signal indicates that the customer's actual emphasis on indirect financial factors does not match the prediction, the steps to re-examine the latest customer behavior logs, adjust the implicit value mapping rules of customer behavior patterns within the context analysis engine, and increase the weight of relevant implicit value in the customer's decision-making logic include: Extract sequences of behavioral events related to indirect financial factors from customer behavior logs; Contextual analysis is performed on the extracted behavioral event sequences to identify the association patterns between the behavioral event sequences and the indirect financial factors; Weights are assigned to the sequence of behavioral events for different indirect financial factors. The identified association patterns and the set weights are applied to the customer behavior log to calculate the customer's score on the importance they place on each of the aforementioned indirect financial factors. Based on the importance score, the implicit value mapping rules of customer behavior patterns within the context analysis engine are adjusted to increase the weight of the implicit value with a high score in the customer decision-making logic.

5. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, When the calibration trigger signal indicates that the competitor's actual market response or product strategy does not match the forecast, the steps of analyzing the latest competitor product release announcements, marketing activities, and industry analysis reports, updating the competitor behavior pattern parameters within the context analysis engine, and adjusting the competitor strategy parameter table include: Acquire multi-source heterogeneous data from competitors; The acquired multi-source heterogeneous data is standardized to unify the data format and structure; Extract key information from the standardized data; The extracted key information is compared with existing competitor behavior pattern parameters and strategy parameter tables to identify newly added, modified, or deleted competitor behavior patterns and strategy elements. Update the competitor behavior pattern parameters based on the comparison results; Based on the comparison results, adjust the competitor strategy parameter table.

6. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, When the calibration trigger signal indicates that the actual match between the recommended solution and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the steps of re-analyzing the latest unstructured text of corporate internal strategic documents, senior management meeting minutes, and internal communication records, and adjusting the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine to transform them into actionable design principles or solution preference weights include: Obtain the latest unstructured text of corporate internal strategic documents, high-level meeting minutes, and internal communication records; The unstructured text is preprocessed, and the core strategic themes in the text are identified; By combining a pre-defined strategic intent dictionary and an expert knowledge base, semantic disambiguation is performed on the expression of the core strategic theme to obtain a clear strategic intent; Analyze the unstructured text to identify the connections between different strategic intentions in order to uncover potential internal conflicts; Based on preset conflict resolution rules, determine the dominant strategic intent in the internal conflict; Based on the stated strategic intent and the dominant strategic intent, the scheme generation preference guidance rules under the strategic intent constraints within the context analysis engine are adjusted and transformed into operable design principles or scheme preference weights.

7. The method for recommending business customers based on big data using artificial intelligence according to claim 1, characterized in that, When the calibration trigger signal indicates that the actual match between the recommended solution and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the steps of re-analyzing the latest unstructured text of corporate internal strategic documents, senior management meeting minutes, and internal communication records, and adjusting the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine to transform them into actionable design principles or solution preference weights include: Continuously acquire unstructured texts of internal corporate strategic documents, high-level meeting minutes, and internal communication records; Perform thematic analysis on the acquired text to identify the strategic intentions contained therein; Construct a strategic intent evolution map, map the identified strategic intents onto the strategic intent evolution map, and record the time and context in which the strategic intents appear; By analyzing the strategic intent evolution map, the strength of the association and the degree of conflict between the old and new strategic intents are identified. The strength of the association and the degree of conflict are quantified by the co-occurrence frequency, semantic distance and time series change trend of the strategic intents on the map. Based on the correlation strength and the degree of conflict, the solution generation preference guidance rules under the strategic intent constraints within the context analysis engine are dynamically adjusted, transforming them into actionable design principles or solution preference weights. The adjustment includes: When a new strategic intent is identified, an initial preference weight is assigned to it and integrated into the scheme generation preference guidance rule; When a change in the correlation strength of strategic intent is identified, its weight in the scheme generation preference guidance rule is adjusted; When a conflict is identified between strategic intentions, the preference weights of the relevant strategic intentions are adjusted according to the degree of conflict and the preset conflict resolution rules in order to determine the dominant strategic intention. When outdated strategic intentions are identified, their weight in the scheme generation preference guidance rules is reduced or they are removed.

8. A business customer big data recommendation system based on artificial intelligence, characterized in that, The system includes: The information acquisition module is used to acquire resource information, demand information, and environmental information for business customer recommendations; The inconsistency identification module is used to identify inconsistencies in the resource information, demand information, and environmental information. Inconsistencies refer to contradictions, conflicts, or mismatches between the resource information, demand information, and environmental information. The value analysis module is used to analyze the true priorities of each party in the aforementioned inconsistencies in order to infer the value ranking of each party. The true priorities refer to the factors that each party truly focuses on and prioritizes at a deeper level when facing inconsistencies. These parties include enterprises, customers, or competitors. The solution conception module is used to form new solution concepts based on the value ranking obtained from the inference. The solution concepts can discover new balance points by changing the definition of the problem or the dimensions of the problem-solving. The solution generation module is used to generate an operable recommended solution based on the balance point; The solution evaluation module is used to perform multi-dimensional evaluation of the recommended solution to determine whether the recommended solution meets preset criteria, including: Obtain the expected results and actual feedback data of the recommended scheme; Calculate the degree of deviation between the expected results and the actual feedback data, and generate a calibration trigger signal when the deviation of any key indicator exceeds a preset significant deviation threshold; Based on the calibration trigger signal, the calibration of the knowledge system of the context analysis engine is triggered, including: When the calibration trigger signal indicates that the customer's actual understanding of the core value concept does not match the prediction, the latest market text data related to the core value concept is re-analyzed, the semantic context feature set inside the context analysis engine is updated, and the concept association weight table is adjusted to associate it more with words and phrases that frequently appear in the latest data. When the calibration trigger signal indicates that the customer's actual emphasis on indirect financial factors does not match the prediction, the latest customer behavior log is reviewed again, the implicit value mapping rules of customer behavior patterns inside the context analysis engine are adjusted, and the weight of relevant implicit value in the customer's decision-making logic is increased. When the calibration trigger signal indicates that the competitor's actual market response or product strategy does not match the forecast, the latest competitor product release announcements, marketing activities and industry analysis reports are analyzed to update the competitor behavior pattern parameters inside the context analysis engine and adjust the competitor strategy parameter table. When the calibration trigger signal indicates that the actual matching degree between the recommended scheme and the corporate senior management strategy does not match the prediction, or when the corporate senior management strategy itself undergoes new evolution, the unstructured text of the latest corporate internal strategic documents, senior management meeting minutes and internal communication records is re-analyzed, and the scheme generation preference guidance rules under the strategic intent constraints within the context analysis engine are adjusted to transform them into actionable design principles or scheme preference weights.

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