An artificial intelligence-based financial data analysis and early warning system

Abstract

The application discloses a kind of financial data analysis early warning systems based on artificial intelligence, it is related to financial analysis technical field, system includes: data acquisition module, for obtaining the consumption record data of staff;Behavior pattern analysis module, for the behavior pattern analysis of the consumption record data, identifies historical consumption behavior;Consumption mode library construction module, for according to the historical consumption behavior, constructs consumption mode library;Consumption intention identification module, for identifying current consumption intention, the current consumption intention includes consumption type, budget range and time requirement;Consumption optimization suggestion generation module, for according to the current consumption intention and the consumption mode library, generates consumption optimization suggestion.The application can combine historical consumption behavior and current consumption intention to generate consumption optimization suggestion, to realize financial data analysis early warning, improve cost efficiency, reduce operating cost.

Description

Technical Field

[0001] This invention relates to the field of financial analysis technology, and in particular to a financial data analysis and early warning system based on artificial intelligence. Background Technology

[0002] In the daily operations of modern enterprises and institutions, to continuously reduce operating costs, it is necessary to carefully manage every expense, such as employee business trips, office supply purchases, and labor costs. However, existing systems typically check according to fixed rules and regulations, or perform post-event verification, such as setting budgets and reviewing expense reports. While this approach ensures that employees follow the rules and regulations, it makes it difficult to proactively analyze past expense records to identify effective cost-saving strategies. Therefore, operating cost optimization remains merely at the level of checking compliance, failing to effectively reduce operating costs and resulting in low cost-effectiveness.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose an artificial intelligence-based financial data analysis and early warning system that can generate consumption optimization suggestions by combining historical consumption behavior and current consumption intentions, thereby achieving financial data analysis and early warning, improving cost-effectiveness, and reducing operating costs.

[0005] This invention provides an artificial intelligence-based financial data analysis and early warning system, comprising:

[0006] The data acquisition module is used to acquire employees' consumption records.

[0007] The behavior pattern analysis module is used to perform behavior pattern analysis on the consumption record data and identify historical consumption behavior, which includes business trip destination, accommodation type, types of purchased items, consumption amount and time of occurrence.

[0008] The consumption pattern library construction module is used to construct a consumption pattern library based on the historical consumption behavior.

[0009] The consumption intention recognition module is used to identify the current consumption intention, which includes consumption type, budget range, and time requirement;

[0010] The consumption optimization suggestion generation module is used to generate consumption optimization suggestions based on the current consumption intention and the consumption pattern library.

[0011] The embodiments of this application include at least the following beneficial effects: First, the data acquisition module acquires the consumption record data of employees. Then, the behavior pattern analysis module performs behavior pattern analysis on the consumption record data to identify historical consumption behavior. Based on the historical consumption behavior, the consumption pattern library is constructed through the consumption pattern library construction module. The current consumption intention is identified through the consumption intention recognition module. Finally, based on the current consumption intention and the consumption pattern library, the consumption optimization suggestion generation module generates consumption optimization suggestions. This enables the generation of consumption optimization suggestions by combining historical consumption behavior and current consumption intention, thereby achieving financial data analysis and early warning, improving cost-effectiveness, and reducing operating costs.

[0012] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0014] Figure 1 This is a schematic diagram of the structure of an artificial intelligence-based financial data analysis and early warning system according to an embodiment of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0016] In the context of related technologies, in the daily operations of modern enterprises and institutions, to continuously reduce operating costs, it is necessary to carefully manage every expense, such as employee business trips, office supply purchases, and labor costs. However, existing systems typically check according to fixed rules and regulations, or perform post-event verification, such as setting budgets and reviewing expense reports. While this approach ensures that employees follow the rules and regulations, it is difficult to proactively analyze past expense records to identify effective cost-saving strategies. Therefore, operating cost optimization remains merely at the level of checking compliance, failing to effectively reduce operating costs and resulting in low cost-effectiveness.

[0017] For example, in corporate financial management, the refined control and continuous optimization of daily operating costs, especially employee travel, procurement, and labor costs, are becoming increasingly important. Traditional budget control and expense review mainly rely on established rules and regulations and post-event audits, lacking the ability to conduct in-depth analysis of massive historical expense data, proactively identify cost-effective behavior patterns, and positively guide employees' future decisions. This often leaves cost optimization at the level of passive compliance review, making it difficult to form data-driven, scalable best practices to systematically reduce operating costs.

[0018] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0019] like Figure 1 As shown, this embodiment of the invention provides a financial data analysis and early warning system based on artificial intelligence, including:

[0020] The data acquisition module 101 is used to acquire employees' consumption record data;

[0021] The behavior pattern analysis module 102 is used to perform behavior pattern analysis on consumption record data and identify historical consumption behavior, which includes business trip destination, accommodation type, types of purchased items, consumption amount and time of occurrence.

[0022] The consumption pattern library construction module 103 is used to build a consumption pattern library based on historical consumption behavior.

[0023] The consumption intention recognition module 104 is used to identify the current consumption intention, which includes consumption type, budget range and time requirement;

[0024] The consumption optimization suggestion generation module 105 is used to generate consumption optimization suggestions based on the current consumption intention and consumption pattern library.

[0025] The embodiments of this application can generate consumption optimization suggestions by combining historical consumption behavior and current consumption intentions, so as to realize financial data analysis and early warning, improve cost-effectiveness, and reduce operating costs.

[0026] In some embodiments, employee spending records can be acquired first through a data acquisition module. This module can connect to the company's financial system, expense reimbursement system, travel management system, etc., to intelligently acquire employee spending records. For example, it can synchronize employee-submitted expense reports in real time via an API interface, or periodically import historical transaction data from the company's database in batches. The data acquisition module can be configured to periodically scan specified data sources, such as transaction records from the company's internal ERP system or third-party payment platforms, to ensure the timeliness and completeness of the spending record data. A user interface can also be provided, allowing employees to manually upload scanned copies or photos of spending vouchers, which are then recognized and structured using optical character recognition (OCR) technology. It is understood that spending record data refers to various spending voucher information generated by employees in the course of performing their duties, such as expense reports, invoices, and transaction records. This data contains detailed information such as the time, location, amount, and type of spending.

[0027] Then, the behavior pattern analysis module analyzes the consumption record data to identify historical consumption behaviors, including business trip destination, accommodation type, types of purchased items, consumption amount, and time of occurrence. The behavior pattern analysis module can utilize machine learning algorithms, such as cluster analysis and sequence pattern mining, to discover recurring, regular consumption behavior patterns from massive amounts of consumption records. For example, the system can analyze employees' accommodation preferences under different business trip destinations, seasons, and task types, identifying key elements such as business trip destination, accommodation type, types of purchased items, consumption amount, and time of occurrence. The behavior pattern analysis module can employ unsupervised learning algorithms, such as K-means clustering, to group consumption records with similar characteristics into one category, thereby identifying different historical consumption behavior patterns. It can also utilize association rule mining algorithms to discover potential associations between different consumption behaviors; for example, "purchasing airline tickets" is often accompanied by "booking hotels."

[0028] The consumption pattern library is then constructed based on historical consumption behavior using a consumption pattern library construction module. This module structures and stores the analyzed and refined historical consumption behavior patterns, forming a knowledge base that can be queried and matched. The consumption pattern library can contain data from multiple dimensions, such as consumption preferences in different situations, consumption habits of different employee groups, and service quality of different suppliers. The consumption pattern library can be a relational database containing multiple tables, each storing specific types of consumption pattern data; for example, one table stores "business trip accommodation patterns," and another table stores "office supplies procurement patterns." Alternatively, the consumption pattern library can also be a graph database, where nodes represent various elements of consumption behavior (such as destination and accommodation type), and edges represent the relationships between these elements.

[0029] The consumption intent recognition module identifies current consumption intentions, including consumption type, budget range, and time requirement. When an employee plans to make a purchase, the system obtains their consumption intent information through various methods and parses it into key elements such as consumption type, budget range, and time requirement. For example, an employee can input "I'm going to Shanghai for a business trip next week and need to book a hotel and flight. My budget is approximately 5,000 yuan" in natural language. The system will then identify the consumption type as "business trip," the budget range as "5,000 yuan," and the time requirement as "next week." The consumption intent recognition module can provide structured forms to guide employees in inputting information such as consumption type, budget, and time. Alternatively, Natural Language Processing (NLP) technology can be used to perform semantic analysis on the free text input by employees to identify their consumption intent.

[0030] Finally, the consumption optimization suggestion generation module generates consumption optimization suggestions based on the employee's current consumption intention and the consumption pattern library. This module matches and analyzes the employee's current consumption intention with historical data in the consumption pattern library to provide personalized and instructive consumption recommendations. These suggestions might include recommending more economical hotels, more efficient transportation methods, and more cost-effective suppliers. For example, if an employee plans to travel to Shanghai for business, the system might recommend booking a specific budget hotel in advance, based on the consumption pattern library that "employees traveling to Shanghai for business typically choose budget hotels and can save 15% by booking in advance." The consumption optimization suggestion generation module can utilize recommendation system algorithms, such as collaborative filtering or content-based recommendation, to generate personalized consumption optimization suggestions for employees.

[0031] This embodiment represents a significant improvement over traditional financial management systems. Traditional budget control and expense review rely primarily on manual review and established rules and regulations, which are inefficient and fail to fundamentally address cost optimization issues. This embodiment, by introducing artificial intelligence technology, can automatically identify cost-effective behavioral patterns from massive amounts of historical consumption data and proactively guide employees to adopt better options in future consumption choices based on these patterns. For example, existing systems may only determine whether an expense exceeds the budget, while this embodiment can further analyze whether the expense conforms to historical "best practices" and provide employees with better alternatives. This shift from "compliance review" to "efficiency guidance" enables enterprises to reduce costs and improve efficiency from the source, forming data-driven, scalable, and collective best practices, thereby systematically reducing operating costs.

[0032] Through the above technical solution, this embodiment achieves intelligent management and optimization of employee consumption behavior. The data acquisition module provides basic data for the system, the behavior pattern analysis module extracts valuable patterns from massive amounts of data, the consumption pattern library construction module stores these patterns in a structured manner, the consumption intention recognition module understands the current needs of employees, and finally, the consumption optimization suggestion generation module provides consumption optimization suggestions, enabling enterprises to shift from passive post-event review to proactive pre-event guidance, thereby achieving continuous cost optimization and efficiency improvement.

[0033] In some embodiments, constructing a consumption pattern library based on historical consumption behavior may include, but is not limited to, the following steps:

[0034] Step S201: Obtain information on employees' priority ranking of implicit benefits in historical consumption behavior, feedback information on historical consumption behavior, organizational structure information and task criticality information of employees.

[0035] Step S202: Associate priority ranking information, feedback information, organizational structure information, task criticality information with historical consumption behavior to generate a contextual description profile;

[0036] Step S203: Construct a consumption pattern database based on the context description files.

[0037] In some embodiments, building a consumption pattern database solely based on superficial data of historical consumption behavior (such as business trip destination, accommodation type, types of purchased items, spending amount, and time of occurrence) may fail to fully capture the deeper context and potential value orientations behind employees' consumption decisions. This could result in limitations in reflecting employees' actual consumption needs and corporate management goals, thereby affecting the accuracy and effectiveness of subsequent consumption optimization recommendations.

[0038] To this end, we can first obtain information on employees' prioritization of implicit benefits in their historical consumption behavior, their feedback on historical consumption behavior, and their organizational structure and task criticality. We can collect information on how much employees value non-direct monetary benefits beyond explicit costs when making purchases. For example, these implicit benefits may include time efficiency (such as reduced commuting time), comfort (such as choosing higher-end transportation or accommodation), and strategic value (such as improved customer satisfaction or company image). Prioritization information can be obtained through employee questionnaires, preference settings, or analysis of historical behavior data, aiming to understand how employees weigh various benefits in different situations. Employee feedback on historical consumption behavior refers to employees' subjective evaluations and opinions of their consumption experience after completing a purchase. This can include satisfaction ratings, written reviews, or evaluations of specific services or products. This feedback directly reflects employees' true feelings and potential needs regarding consumption behavior, providing a basis for assessing the actual effectiveness of consumption behavior. Employee organizational structure information specifically refers to information such as employees' departments, positions, and levels within the company. This information helps identify the spending characteristics and authority of employees in different positions or departments. For example, the travel standards for senior managers may differ from those for ordinary employees. Task criticality information refers to the importance, urgency, or contribution of the tasks performed by employees to the company's strategic goals. For example, business trips involving important clients may be assigned a higher task criticality, thus influencing their spending standards and patterns.

[0039] Then, priority ranking information, feedback information, organizational structure information, and task criticality information are linked with historical consumption behavior to generate a contextual description profile. This aims to integrate these multi-dimensional contextual factors into a single profile. The contextual description profile is a comprehensive data structure that not only includes basic elements of historical consumption behavior (such as business trip destination, accommodation type, types of purchased items, amount spent, and time of occurrence), but also incorporates employees' personalized preferences, subjective evaluations, and their organizational and task context. Through this association, each historical consumption behavior can be given a richer and more interpretive contextual background.

[0040] Next, a consumption pattern database is constructed based on the contextual description files. These files, rich in contextual information, can be used to identify deeper and more representative consumption patterns. These patterns are no longer simply aggregates of consumption behaviors, but rather contextualized patterns that combine employees' intentions, preferences, organizational roles, and task requirements. This allows the consumption pattern database to more accurately reflect employees' optimal or typical consumption choices in specific situations.

[0041] This embodiment effectively addresses the limitations of building a consumption pattern database solely based on superficial historical consumption behavior data by introducing employee prioritization information on implicit benefits, employee feedback on historical consumption behavior, employee organizational structure information, and task criticality information. Specifically, prioritization information allows the system to understand employees' trade-offs between non-monetary benefits such as time, comfort, and strategic value in different contexts, thereby capturing the deeper motivations behind consumption decisions. Feedback information provides employees' subjective evaluations of their actual consumption experiences, directly reflecting the actual effects and satisfaction levels of their consumption behavior. Organizational structure information and task criticality information provide important business context and management constraints for consumption behavior, enabling the system to differentiate reasonable consumption differences across different positions or tasks. By associating these multi-dimensional contextual information with historical consumption behavior, the generated contextual description profiles can more comprehensively and accurately depict the true background and potential value of each consumption behavior. Therefore, the consumption pattern database built upon these rich contextual description profiles allows for a deeper understanding of employee consumption intentions and corporate management goals, providing a more solid and accurate data foundation for subsequent consumption optimization suggestions.

[0042] To illustrate this technical solution more clearly, a specific example is used below. Suppose an employee needs to travel for work. The system first obtains the employee's priority ranking information for "time efficiency" and "comfort" through questionnaires or historical data analysis. For example, the employee might prioritize time efficiency over comfort. Simultaneously, the system collects feedback information from the employee's past business trips, such as dissatisfaction with a flight delay or praise for a hotel service. Furthermore, the system also obtains the employee's organizational structure information (e.g., sales manager) and task criticality information (e.g., this business trip is to sign a contract with an important client). When the employee makes a business trip purchase, such as booking a flight and hotel, the system associates the basic data of this purchase (destination, amount, time, etc.) with the priority ranking information, feedback information, organizational structure information, and task criticality information to generate a detailed contextual description profile. This profile might contain: "Employee A, Sales Manager, high task criticality, prefers time efficiency to comfort, previously expressed discomfort with economy class. This business trip destination is Shanghai, with a business class ticket and five-star hotel booked, totaling XX yuan." By analyzing numerous such profiles, the system can identify more complex and contextualized consumption patterns, such as "executives tend to sacrifice some explicit costs for higher time efficiency and comfort during important business trips," and store these patterns in a consumption pattern database. When similar situations occur in the future, the system can provide more precise optimization suggestions based on these contextualized patterns.

[0043] Through the above technical solution, this embodiment can construct a more refined and contextualized consumption pattern database. This database not only records employee consumption behavior but also incorporates employees' personalized preferences, subjective evaluations, and their organizational and task context. This allows the system to more accurately understand the underlying reasons and potential value behind consumption behavior when identifying patterns, avoiding the bias of judging solely based on surface data. Consequently, the generated consumption optimization suggestions will be more targeted, reasonable, and persuasive, better balancing employees' personal needs with corporate management goals, thereby improving the overall effectiveness and user satisfaction of the financial data analysis and early warning system.

[0044] In some embodiments, step S203, constructing a consumption pattern library based on the context description profile, may include, but is not limited to, the following steps:

[0045] Step S301: Based on the context description file, determine the dimension weight corresponding to each benefit dimension in the historical consumption behavior. The benefit dimensions include explicit costs, time efficiency, comfort, and strategic value.

[0046] Step S302: Calculate the contextual value score of historical consumption behavior based on each benefit dimension and its corresponding dimension weight;

[0047] Step S303: Identify historical consumption patterns based on contextual value scores;

[0048] Step S304: Construct a consumption pattern library based on multiple historical consumption patterns.

[0049] In some embodiments, since the consumption pattern library is built solely based on contextual description profiles, it may be difficult to comprehensively and quantitatively assess the actual benefits of different historical consumption behaviors in multiple dimensions such as explicit costs, time efficiency, comfort, and strategic value, thereby affecting the refinement of the consumption pattern library and the accuracy of subsequent consumption optimization suggestions.

[0050] To this end, we can first determine the weight of each benefit dimension in historical consumption behavior based on the contextual description file. Benefit dimensions refer to multiple values ​​or impacts considered when evaluating a consumption behavior. They aim to quantify the comprehensive benefits of consumption behavior from different perspectives. Benefit dimensions include explicit costs, time efficiency, comfort, and strategic value. Explicit costs refer to the economic expenditures directly generated by the consumption behavior, such as airfare and hotel costs; time efficiency refers to the time-saving performance of the consumption behavior, such as choosing a direct flight to shorten commuting time; comfort refers to the degree of physical and mental well-being that the consumption behavior brings to employees, such as choosing a more spacious seat or a better accommodation environment; strategic value refers to the positive impact of the consumption behavior on the company's overall strategic goals or business development, such as facilitating key collaborations by arriving at important meetings on time. Dimension weights refer to the relative importance of each benefit dimension in a specific context. These dimension weights can be dynamically determined based on the information in the contextual description file. For example, for an urgent and important business trip, the contextual description profile might indicate that "time efficiency" and "strategic value" have higher priorities, and therefore the corresponding dimension weights would be set to higher values; while for a routine business trip with a limited budget, the dimension weight of "explicit costs" might be increased. Dimension weights can be determined based on expert experience to ensure that they accurately reflect the value orientation in the current context.

[0051] Then, based on each benefit dimension and its corresponding weight, a contextual value score for historical consumption behavior is calculated. Contextual value score is an indicator that quantifies the comprehensive value of historical consumption behavior in a specific context. It can be obtained by weighting the quantified value corresponding to each benefit dimension with its respective dimension weight. For example, by quantifying explicit costs, time efficiency, comfort, and strategic value into numerical values, multiplying them by their respective dimension weights, and then summing the results, the contextual value score for the consumption behavior can be obtained. This score can intuitively reflect the overall performance of the consumption behavior under a multi-dimensional benefit trade-off.

[0052] Next, based on contextual value scores, historical consumption patterns are identified. Historical consumption patterns refer to a set of consumption behaviors with similar contextual value scores or similar combinations of benefit dimensions. By analyzing the contextual value scores of a large number of historical consumption behaviors, different consumption patterns can be identified. For example, a "high-efficiency, low-cost model" or a "high-comfort, high-strategic-value model" can be identified. These patterns represent the consumption strategies that employees tend to adopt in specific contexts and the underlying value orientations.

[0053] Finally, a consumption pattern library is constructed based on various historical consumption patterns. This library is a collection of identified historical consumption patterns, providing the system with rich, context-evaluated reference data.

[0054] This embodiment introduces benefit dimensions and their corresponding weights, enabling the system to conduct multi-dimensional and quantitative evaluations of historical consumption behavior. The contextual description profile includes employee priority ranking information, organizational structure information, and task criticality information, allowing the system to dynamically determine the weight of each benefit dimension. This ensures that the contextual value score accurately reflects the true value orientation under specific circumstances. By calculating the contextual value score, the system can simplify complex consumption behaviors into comparable numerical values, and then identify historical consumption patterns with inherent logic and value preferences based on these scores. This embodiment effectively solves the problem that contextual description profiles alone are insufficient for comprehensively and quantitatively evaluating the benefits of consumption behavior, making the construction of the consumption pattern database more scientific and refined.

[0055] To illustrate this technical solution more clearly, a specific example is used below. Suppose a sales manager needs to travel to another city to visit an important client. Based on the sales manager's organizational structure information (sales manager), task criticality information (visiting an important client), and their priority of implicit benefits (e.g., prioritizing client relationship maintenance and time efficiency), the system extracts relevant information from the scenario description profile. Based on this information, the system determines the weights of the benefit dimensions of this business trip. For example, strategic value (such as client satisfaction and business expansion potential) and time efficiency (such as on-time arrival and efficient communication) are assigned higher weights, while explicit costs and comfort are assigned relatively lower weights. Subsequently, the system obtains the sales manager's historical consumption behavior data. For example, they may have previously chosen to fly a direct business class flight and stay in a five-star hotel near the client's company. The system quantifies the various benefit dimensions of these historical consumption behaviors: business class and five-star hotels have higher explicit costs; direct flights and hotel locations shorten commuting time, reflecting higher time efficiency; business class and five-star hotels provide a good rest environment, reflecting higher comfort; and a successful visit to the important client facilitates cooperation, reflecting higher strategic value.

[0056] Next, the system calculates the contextual value score of the historical consumption behavior based on preset quantification rules and determined dimension weights. For example, by using a weighted summation method, the quantified explicit costs, time efficiency, comfort, and strategic value are multiplied by their respective dimension weights and summed to obtain a comprehensive contextual value score. Finally, based on this contextual value score, the system identifies the consumption pattern represented by the historical consumption behavior, such as a travel pattern of "high strategic value, high time efficiency, and moderate comfort." Once multiple similar historical consumption patterns are identified, these patterns will be integrated and stored in a consumption pattern library, providing a powerful contextual reference for generating future consumption optimization suggestions.

[0057] Through the above technical solution, this embodiment enables a more refined and multi-dimensional value assessment of historical consumption behavior, thereby constructing a more insightful consumption pattern database. This database not only records the consumption behavior itself but also reveals the underlying value drivers and contextual preferences, significantly improving the quality and practicality of the consumption pattern database. Consequently, the generated consumption optimization suggestions will be able to more accurately match the actual needs of employees and the strategic goals of the enterprise, realizing a shift from simple cost control to comprehensive value optimization, thereby improving the intelligence level and decision support capabilities of financial management.

[0058] In some embodiments, after determining the dimension weights corresponding to each benefit dimension in historical consumption behavior based on the context description file in step S301, the following steps may also be included, but are not limited to:

[0059] Receive strategic adjustment instructions, which include consumer behavior guidelines, the status of the instructions' effectiveness, and the scope of business.

[0060] Analyze the strategic adjustment instructions and identify the benefit dimensions that match the business scope as the primary target dimension;

[0061] If the instruction is valid, the dimension weights corresponding to the first objective dimension will be adjusted according to the consumer behavior norms and context description profile.

[0062] In some embodiments, the strategic direction of an enterprise may be adjusted, causing the pre-determined weights of the benefit dimension to fail to reflect the latest strategic direction in a timely manner, thereby affecting the accuracy of the consumption pattern library and the effectiveness of consumption optimization suggestions.

[0063] Therefore, strategic adjustment instructions can be received first. Strategic adjustment instructions are directives issued by corporate management or relevant departments to guide employee spending behavior in line with the company's overall strategic direction. These instructions include spending behavior guidelines, their validity status, and their scope. The instructions can be received by the system through system interfaces, manual input, or other automated methods. Spending behavior guidelines refer to specific requirements or restrictions on particular spending behaviors, such as travel expense limits or preferences for specific suppliers. The validity status indicates whether the instruction is currently in effect, such as "valid" or "invalid." The scope defines the specific business areas or employee groups to which the instruction applies.

[0064] The strategic adjustment instructions are then parsed to identify the benefit dimensions that match the business scope as the primary target dimension. Upon receiving a strategic adjustment instruction, the system parses it to extract key information such as consumer behavior norms, the effectiveness of the instruction, and the business scope. Based on the parsed business scope, the system identifies the benefit dimensions directly related to that scope and designates them as the primary target dimension. For example, if the business scope involves cost control, "explicit costs" might be identified as the primary target dimension.

[0065] If the instruction is valid, the weights of the dimensions corresponding to the first objective dimension will be adjusted based on the consumer behavior norms and contextual description profile. This can be achieved by increasing or decreasing the weight values, so that this benefit dimension plays a more important role or has its influence weakened in the subsequent contextual value scoring calculation.

[0066] This embodiment introduces strategic adjustment instructions, enabling the system to dynamically respond to changes in corporate strategy. Specifically, when receiving a strategic adjustment instruction containing consumer behavior norms, instruction validity status, and business scope, the system can parse it to identify the benefit dimension related to a specific business scope as the first target dimension. If the instruction is valid, the system will adjust the dimension weights corresponding to the first target dimension in a targeted manner based on the consumer behavior norms explicitly stated in the instruction and existing contextual description files. This ensures that the construction of the consumption pattern library is consistent with the company's latest strategy in real time, avoiding the weight lag problem caused by strategic changes, thereby improving the accuracy of consumption pattern identification and the guidance of subsequent consumption optimization suggestions.

[0067] To illustrate this technical solution more clearly, a specific example is used below. Suppose a company adjusts its core strategic direction to "strictly control travel costs" during a specific period. At this time, the system receives a strategic adjustment instruction, which may include a consumption behavior guideline of "reducing the upper limit of travel expenses by 20%." The instruction's validity status is "valid," and its scope covers "business travel for all employees." After parsing the instruction, the system identifies "explicit costs" as the primary target dimension because it is directly related to travel cost control. Since the instruction is valid, the system, based on the "20% reduction in the upper limit of travel expenses" guideline and the employee's historical contextual description, will increase the weight of the "explicit costs" benefit dimension when calculating the contextual value score. For example, the weight of "explicit costs" can be adjusted from 0.3 to 0.5, while correspondingly reducing the weight of other benefit dimensions (such as comfort and time efficiency) to ensure that in the subsequent consumption pattern identification and optimization suggestion generation process, the system will be more inclined to recommend lower-cost consumption options. In this way, the system can dynamically guide employee consumption behavior to align with the company's latest strategic goals.

[0068] Through the above technical solution, this embodiment can dynamically adjust the weights of the benefit dimension, keeping it in sync with the company's ever-changing strategic goals. This not only enhances the system's flexibility and adaptability but also ensures that the constructed consumption pattern library and generated consumption optimization suggestions more accurately reflect the company's current strategic orientation, thereby effectively improving the decision support capabilities and practical application value of the financial data analysis and early warning system.

[0069] In some embodiments, in step S302, calculating the contextual value score of historical consumption behavior based on each benefit dimension and its corresponding dimension weight may include, but is not limited to, the following steps:

[0070] Obtain reimbursement amount, commuting time reduction, mode of transportation, and customer importance level;

[0071] Based on the reimbursement amount, the explicit costs are quantified to obtain the first quantified value;

[0072] Based on the reduction in commuting time, time efficiency is quantified to obtain a second quantified value;

[0073] Based on the vehicle class, comfort is quantified to obtain a third quantified value;

[0074] Based on the customer importance level, the strategic value is quantified to obtain the fourth quantification value;

[0075] The contextual value score is calculated based on the first, second, third, and fourth quantitative values ​​and the corresponding dimension weights for each benefit dimension.

[0076] In some embodiments, the reimbursement amount, reduction in commuting time, vehicle class, and customer importance level can be obtained first. The reimbursement amount directly reflects the magnitude of explicit costs; the reduction in commuting time measures the improvement in time efficiency; the vehicle class (e.g., economy class, business class, first class, etc.) serves as a quantitative indicator of comfort; and the customer importance level (e.g., ordinary customer, important customer, strategic customer, etc.) reflects the strategic value brought by this consumption behavior. This data can be automatically obtained by the system from travel expense reimbursement systems, schedule management systems, customer relationship management systems, etc., or manually entered by employees when submitting consumption records.

[0077] Then, based on the reimbursement amount, explicit costs are quantified to obtain the first quantified value. Explicit costs typically refer to direct, quantifiable expenditures, such as airfare, hotel, and catering expenses. The reimbursement amount is the most direct manifestation of explicit costs and can be used directly as the first quantified value, or it can be obtained after standardization through a pre-set cost model.

[0078] The time efficiency is then quantified based on the reduction in commute time, resulting in a second quantified value. Time efficiency refers to the time saved through a certain consumption behavior (such as choosing a faster mode of transportation or a closer place to stay). The reduction in commute time can be used directly as the second quantified value, or it can be quantified by converting it into the value of time (such as average hourly wages).

[0079] Comfort is quantified based on the class of transportation, resulting in a third quantified value. Comfort is a relatively subjective indicator, but it can be quantified through objective class classifications. For example, economy class can be assigned a lower comfort quantified value, business class a medium comfort quantified value, and first class a higher comfort quantified value.

[0080] Based on customer importance levels, strategic value is quantified to obtain a fourth quantitative value. Strategic value refers to the positive impact of this consumption behavior on the company's strategic goals or business development. Customer importance level is an important dimension for measuring strategic value; for example, business activities with strategic customers may be assigned a higher strategic value quantitative value.

[0081] Finally, the context value score is calculated based on the first, second, third, and fourth quantitative values ​​and the corresponding dimension weights for each benefit dimension. This means that each quantitative value is weighted and summed with the pre-determined dimension weights to obtain a comprehensive context value score.

[0082] This embodiment acquires specific data such as reimbursement amount, commuting time reduction, vehicle class, and customer importance level, and then correlates and quantifies these data with four benefit dimensions: explicit cost, time efficiency, comfort, and strategic value. This quantification process transforms abstract benefit dimensions into concrete numerical values, objectively presenting the performance of each historical consumption behavior across different benefit dimensions. Subsequently, these quantified values ​​are weighted and fused together with pre-determined dimension weights for each benefit dimension to calculate the comprehensive contextual value score of the historical consumption behavior. This process enables the system to comprehensively and multidimensionally evaluate the value of each consumption behavior, providing a solid data foundation for subsequent identification of historical consumption patterns and the construction of a consumption pattern database.

[0083] Through the above technical solution, this embodiment can achieve accurate and objective calculation of the contextual value score of historical consumption behavior. By introducing specific quantitative indicators such as reimbursement amount, commuting time reduction, vehicle class, and customer importance level, it avoids biases caused by subjective judgment, enabling the accurate quantification of benefit dimensions such as explicit costs, time efficiency, comfort, and strategic value. Therefore, the calculated contextual value score is more convincing and operable, providing a more reliable basis for subsequent consumption pattern identification and consumption optimization suggestions, thereby improving the accuracy and practicality of the entire financial data analysis and early warning system.

[0084] In some embodiments, identifying the current consumption intent may include, but is not limited to, the following steps:

[0085] Obtain the consumption text information entered by the employee;

[0086] The consumption text information is matched with the consumption intent template to identify the consumption type;

[0087] Keyword extraction is performed on the consumption text information to obtain the budget range and time requirements.

[0088] In some embodiments, the system may first obtain consumption text information entered by the employee. The system receives descriptive text entered by the employee through various interactive interfaces, such as mobile applications, web forms, or chatbots, regarding their upcoming consumption activity. This text information may contain natural language descriptions of the employee's consumption purpose, expectations, and limitations.

[0089] The consumption text information is then matched against consumption intent templates to identify the consumption type. A pre-defined consumption intent template library can be used, employing Natural Language Processing (NLP) technology to perform semantic analysis and pattern recognition on the consumption text information input by employees. Consumption intent templates can include common consumption scenarios and corresponding consumption types, such as "business trip," "entertainment," "training," or "purchasing." Through matching, the system can accurately determine the category attribute of the employee's current consumption. The consumption intent template library can be obtained through cluster analysis of a large amount of historical consumption intent data.

[0090] Next, keyword extraction is performed on the consumption text information to obtain the budget range and time requirement. Text analysis algorithms can identify keywords or phrases related to amount and time from the consumption text information entered by employees. For example, an employee might enter "the budget is approximately 5000 yuan" or "hoping to complete it next week," from which the system can extract "5000 yuan" as the budget range and "next week" as the time requirement. The purpose is to extract structured key information from unstructured text data, providing accurate input for generating subsequent consumption optimization suggestions.

[0091] This embodiment guides employees to input consumption text information and uses natural language processing technology to parse this unstructured text, thereby transforming vague consumption intentions into structured consumption types, budget ranges, and time requirements. Specifically, the system first obtains the employee's description of the upcoming consumption activity, which is usually in natural language form. Then, by matching this consumption text information with a preset consumption intention template, the system can identify the employee's consumption type, such as business trip, internal training, or client entertainment. Based on this, the system further extracts keywords from the consumption text information to accurately obtain the employee's budget constraints and time requirements for this consumption. This processing method enables the system to efficiently and accurately extract the key elements constituting the current consumption intention from the employee's natural language description, laying a data foundation for generating subsequent consumption optimization suggestions.

[0092] Through the above technical solution, this embodiment can achieve automated and intelligent identification of employees' current consumption intentions. By directly parsing employees' natural language descriptions, this embodiment significantly improves the convenience and accuracy of consumption intention identification, reduces the operational burden on employees, and minimizes data errors caused by incomplete information or misunderstandings. Therefore, the system can more comprehensively and accurately grasp employees' consumption needs, providing solid data support for generating personalized and contextualized consumption optimization suggestions, thereby enhancing the intelligence level and user experience of the entire financial data analysis and early warning system.

[0093] In some embodiments, generating consumption optimization suggestions based on current consumption intentions and a consumption pattern library may include, but is not limited to, the following steps:

[0094] Step S401: Match the current consumption intention with the consumption pattern library to identify multiple candidate consumption patterns;

[0095] Step S402: Conduct correlation analysis on multiple candidate consumption patterns to identify common consumption scenarios and value orientations;

[0096] Step S403: Based on shared consumption scenarios and value orientations, conduct a comprehensive analysis of multiple candidate consumption patterns to obtain comprehensive analysis results;

[0097] Step S404: Based on the comprehensive analysis results, generate consumption optimization suggestions.

[0098] In some embodiments, the current consumption intent can be matched against a consumption pattern library to identify multiple candidate consumption patterns. Based on information such as the employee's current consumption type, budget range, and time requirements, the system can search and filter historical consumption patterns highly relevant to these intents from the established consumption pattern library. For example, if the current consumption intent is "business trip, budget 5000 yuan, to be completed within three days," the system will identify historical consumption patterns related to "business trip," "similar budget range," and "similar time requirements" as candidates from the consumption pattern library. The purpose is to initially narrow down the scope, focusing on historical experiences most relevant to the current need.

[0099] Then, correlation analysis is performed on multiple candidate consumption patterns to identify common consumption scenarios and value orientations. Further analysis can be conducted on the identified candidate consumption patterns to uncover common characteristics and potential deeper connections between them. For example, by analyzing these candidate patterns, it may be discovered that they all occur in specific business scenarios (common consumption scenarios), or they all reflect preferences for cost control, efficiency priorities, or comfort guarantees (value orientations). This step aims to extract deeper patterns and preferences from surface-level patterns, providing a basis for subsequent comprehensive analysis.

[0100] Then, based on shared consumption scenarios and value orientations, a comprehensive analysis of multiple candidate consumption patterns is conducted to obtain a comprehensive analysis result. Based on the identification of shared scenarios and value orientations, these candidate patterns can be evaluated and compared in a multi-dimensional and systematic manner. For example, by combining shared scenarios (such as "urgent business trips") and value orientations (such as "time efficiency priority"), different candidate patterns (such as "high-speed rail + budget hotel" versus "airplane + express hotel") can be quantitatively evaluated and prioritized across multiple benefit dimensions, including explicit costs, time efficiency, comfort, and strategic value, thus forming a comprehensive analysis result. The purpose is to provide decision support for the final recommendation generation.

[0101] Finally, based on the comprehensive analysis results, consumption optimization suggestions are generated. The system can automatically generate specific and actionable consumption recommendations based on the conclusions of the comprehensive analysis. For example, if the comprehensive analysis indicates that "high-speed rail + budget hotel" performs best in balancing efficiency and cost in a specific context, the system will generate corresponding suggestions, possibly including specific hotel or transportation recommendations. The aim is to transform the complex analysis process into guidance that is easy for users to understand and implement.

[0102] This embodiment effectively addresses the potential issues of generalization and lack of specificity in the consumption optimization suggestion generation process through matching, correlation analysis, and comprehensive analysis. First, matching current consumption intentions with a consumption pattern library ensures that subsequent analysis starts from historical experiences highly relevant to employees' current needs, avoiding interference from irrelevant information. Second, correlation analysis of multiple identified candidate consumption patterns delves into the common consumption contexts and value orientations behind these patterns. This allows the system to go beyond simply replicating surface patterns and understand employees' deeper needs and preferences in specific contexts. This identification of context and value orientations enables subsequent comprehensive analysis to more accurately assess the merits of different consumption patterns. Finally, based on this refined comprehensive analysis, the generated consumption optimization suggestions more accurately align with employees' actual needs and the company's management goals, thereby enhancing the practicality and effectiveness of the suggestions.

[0103] To illustrate the technical solution more clearly, a specific example is used below. Suppose an employee needs to take a three-day business trip to Shanghai, with a budget of 3,000 yuan, and wants to save costs as much as possible while ensuring efficiency. First, the consumption intention recognition module identifies the employee's current consumption intention as: consumption type "business trip", budget range "3,000 yuan", and time requirement "three days". Next, the consumption optimization suggestion generation module matches the current consumption intention with the consumption pattern library. The consumption pattern library may contain the following historical consumption patterns: (1) Pattern A: Business trip to Shanghai, second-class seat on high-speed rail, economy hotel, total cost 2,500 yuan, time spent 6 hours. (2) Pattern B: Business trip to Shanghai, economy class on airplane, express hotel, total cost 3,500 yuan, time spent 4 hours. (3) Pattern C: Business trip to Shanghai, first-class seat on high-speed rail, mid-range hotel, total cost 3,200 yuan, time spent 6 hours. The system identifies Pattern A, Pattern B and Pattern C as candidate consumption patterns.

[0104] Subsequently, the system performs correlation analysis on the three candidate consumption patterns, identifying a common consumption scenario of "business trip to Shanghai" and a common value orientation of "balancing efficiency and cost." Based on this common consumption scenario and value orientation, the system conducts a comprehensive analysis of Pattern A, Pattern B, and Pattern C. For example, under the value orientation of "balancing efficiency and cost," Pattern A is optimal in terms of explicit cost but has moderate time efficiency; Pattern B is optimal in terms of time efficiency but exceeds the budget; Pattern C is moderate in both explicit cost and time efficiency, but still slightly exceeds the budget. The system may further consider employees' historical preferences or company policies for weighted evaluation. Finally, based on the comprehensive analysis results, the system may generate the following consumption optimization suggestion: "It is recommended to choose Pattern A: Take a second-class seat on the high-speed rail to Shanghai and stay in an economy hotel. This option is within the budget and can better balance efficiency and cost. If higher time efficiency is required, Pattern B can be considered, but additional budget needs to be requested." In this way, the system can provide specific, personalized, and decision-based consumption optimization suggestions.

[0105] In another scenario, suppose a company's administrative department needs to purchase A4 printing paper and writing pens as office supplies, with a budget of 5,000 yuan, and wants to save costs as much as possible while meeting office needs. First, the consumption intention recognition module identifies the company's current consumption intention as: consumption type "office supplies purchase", budget range "5,000 yuan". Next, the consumption optimization suggestion generation module matches the current consumption intention with the consumption pattern library. The consumption pattern library may contain the following historical consumption patterns: (1) Pattern D: 2,000 sheets of X brand environmentally friendly printing paper, 100 M brand business writing pens, total cost 6,000 yuan. (2) Pattern E: 2,000 sheets of Y brand economy printing paper, 100 M brand business writing pens, total cost 5,200 yuan. (3) Pattern F: 2,000 sheets of Y brand economy printing paper, 100 N brand practical writing pens, total cost 4,000 yuan. The system identifies Pattern D, Pattern E and Pattern F as candidate consumption patterns.

[0106] Subsequently, the system conducts a correlation analysis on the three candidate consumption patterns, identifying a common consumption scenario of "office supplies procurement" and a common value orientation of "balancing demand and cost." Based on this common consumption scenario and value orientation, the system performs a comprehensive analysis of patterns D, E, and F. For example, when frequently hosting high-end meetings, pattern D better meets the company's business image needs, but the budget exceeds the limit significantly; pattern E still maintains a business image, but still slightly exceeds the budget; pattern F is within the budget, but the business image is insufficient. The system will further consider the company's historical preferences and conduct a weighted evaluation. Finally, based on the comprehensive analysis results, the system may generate the following consumption optimization suggestion: "It is recommended to choose pattern E: purchase 2000 sheets of Y brand economy printer paper and 100 M brand business writing pens. This plan can better showcase the company's business image, balance demand and cost, and has a small budget overrun. If there are higher requirements for the business image, pattern D can be considered, but a larger budget will need to be requested." In this way, the system can provide specific, personalized, and decision-based consumption optimization suggestions.

[0107] Through the above technical solution, this embodiment can generate more personalized, contextualized, and value-driven consumption optimization suggestions. By identifying common consumption scenarios and value orientations, this embodiment enables suggestions to better reflect employees' real needs and preferences under specific conditions, thereby significantly improving the adoption rate and actual optimization effect. Furthermore, through multi-dimensional comprehensive analysis, this embodiment can provide employees with suggestions that offer greater decision-support value, helping them balance multiple objectives such as cost control, efficiency improvement, and comfort assurance while meeting business needs, thereby improving overall financial management efficiency and employee satisfaction.

[0108] In some embodiments, in step S403, a comprehensive analysis is performed on multiple candidate consumption patterns based on shared consumption scenarios and value orientations to obtain a comprehensive analysis result, which may include, but is not limited to, the following steps:

[0109] Step S501: Obtain strategic adjustment information, which includes the core strategic direction;

[0110] Step S502: Take the benefit dimension indicated by the core strategic direction as the second target dimension;

[0111] Step S503: Adjust the dimension weights corresponding to the second target dimension among multiple candidate consumption patterns to increase the dimension weights corresponding to the second target dimension;

[0112] Step S504: Based on the common consumption context and value orientation, conduct a comprehensive analysis of the adjusted candidate consumption patterns to obtain the comprehensive analysis results.

[0113] In some embodiments, an organization's strategic direction may be dynamically adjusted in response to market conditions or internal decisions. If the comprehensive analysis process fails to incorporate this strategic adjustment information in a timely and effective manner, the resulting consumption optimization recommendations may not fully reflect the organization's current core strategic direction, thereby affecting the effectiveness and guidance of the recommendations.

[0114] Therefore, it's essential to first obtain information on strategic adjustments. Strategic adjustment information refers to a set of instructions or data released internally or externally to guide future behavior and decision-making. This information typically includes core strategic directions, such as "cost control," "efficiency improvement," "customer experience optimization," or "market expansion." Core strategic directions refer to the areas or objectives that an organization prioritizes and focuses on developing during a specific period.

[0115] Then, the benefit dimensions indicated by the core strategic direction are used as the second objective dimensions. The second objective dimension refers to the benefit dimension directly indicated or associated with the core strategic direction. For example, if the core strategic direction is "cost control," then the explicit cost dimension might be identified as the second objective dimension; if the core strategic direction is "efficiency improvement," then the time efficiency dimension might be identified as the second objective dimension. This identification process ensures that subsequent analysis and recommendations are closely aligned with the organization's current strategic priorities.

[0116] Next, the weights of the second objective dimension among multiple candidate consumption patterns are adjusted to increase its weight. Before conducting the comprehensive analysis, the importance of the identified second objective dimension in evaluating candidate consumption patterns can be dynamically increased. For example, the weight of the second objective dimension can be multiplied by 1.2 to increase its proportion in the weighted summation. This aims to ensure that consumption patterns highly aligned with the current core strategic direction receive higher evaluation scores and are thus prioritized for recommendation when generating consumption optimization suggestions. Finally, based on shared consumption scenarios and value orientations, the adjusted candidate consumption patterns are comprehensively analyzed to obtain the comprehensive analysis results.

[0117] This embodiment effectively solves the aforementioned problems by introducing strategic adjustment information and transforming its core direction into weight adjustments for specific benefit dimensions. Specifically, when the system acquires strategic adjustment information containing the core strategic direction, it can identify a second target dimension that matches that core strategic direction. Subsequently, by increasing the weight of this second target dimension in the evaluation of multiple candidate consumption patterns, consumption patterns that perform better in the core strategic direction receive higher priority in the comprehensive analysis. This dynamic weight adjustment mechanism ensures that the final consumption optimization recommendations more accurately reflect the organization's current strategic priorities, thereby avoiding a disconnect between recommendations and actual needs caused by strategic changes.

[0118] To illustrate this technical solution more clearly, a specific example is used below. Suppose a company issues a strategic adjustment directive for "comprehensive cost reduction and efficiency improvement" in a certain quarter, with the core strategic directions explicitly indicating "cost control" and "time efficiency improvement." When employees submit their consumption intentions, the system identifies multiple candidate consumption patterns. Before conducting comprehensive analysis, the system obtains the aforementioned strategic adjustment information. Based on the core strategic directions of "cost control" and "time efficiency improvement," the system identifies "explicit costs" and "time efficiency" as the second target dimensions. Subsequently, the system adjusts the dimension weights corresponding to "explicit costs" and "time efficiency" in these candidate consumption patterns; for example, increasing the corresponding dimension weights by 20%. After adjusting the weights, the system then conducts a comprehensive analysis of these adjusted candidate consumption patterns based on shared consumption contexts and value orientations. For example, a travel plan that performs better in terms of "explicit costs" and "time efficiency" (such as choosing economy class, booking a lower-priced hotel, and convenient transportation) will receive a higher overall score and thus be prioritized for recommendation to employees. In this way, the system ensures that the generated consumption optimization suggestions can actively guide employees' consumption behavior and align it with the company's strategic goal of "reducing costs and increasing efficiency".

[0119] Through the above technical solution, this embodiment makes the process of generating consumption optimization suggestions more dynamic and adaptable. This embodiment can respond promptly to organizational strategic adjustments, ensuring that the provided suggestions remain highly consistent with the company's current core strategic direction. Therefore, it not only enhances the practicality and guiding value of consumption optimization suggestions but also helps promote the synergistic development of employee consumption behavior and the organization's overall strategic goals, thereby achieving more efficient and strategically significant resource allocation.

[0120] In some embodiments, in step S504, a comprehensive analysis is performed on the adjusted multiple candidate consumption patterns based on common consumption scenarios and value orientations to obtain a comprehensive analysis result, which may include, but is not limited to, the following steps:

[0121] Step S601: Identify the interrelationships between benefit dimensions;

[0122] Step S602: Adjust the interaction weights between benefit dimensions based on mutual influence, common consumption scenarios, and value orientations;

[0123] Step S603: Calculate the fusion weight corresponding to each candidate consumption mode based on the dimension weight and interaction weight corresponding to the second target dimension;

[0124] Step S604: Based on the fusion weight corresponding to each candidate consumption mode, perform weighted fusion on the adjusted multiple candidate consumption modes to obtain the comprehensive analysis results.

[0125] In some embodiments, complex interrelationships often exist between different benefit dimensions. For example, improving the benefit of one dimension may come at the expense of another, or it may create a synergistic effect with another dimension. If only the weight adjustment of a single objective dimension is focused on, while ignoring these inherent interactions, the comprehensive analysis results may be incomplete or fail to fully reflect the actual value trade-offs, thereby affecting the accuracy of consumption optimization recommendations.

[0126] To this end, we can first identify the interrelationships between benefit dimensions. By analyzing historical consumption data or expert knowledge, we can determine the positive or negative correlations and their strengths among different benefit dimensions (such as explicit costs, time efficiency, comfort, and strategic value). For example, improving comfort may lead to increased explicit costs, but it may also improve employee satisfaction and strategic value (such as in important business occasions).

[0127] Then, based on the interrelationships, shared consumption contexts, and value orientations, the interaction weights between benefit dimensions are adjusted. After identifying the interrelationships between benefit dimensions, the strength of the mutual influence can be quantified and adjusted according to the current shared consumption context (e.g., business trip type, task urgency) and value orientation (e.g., cost control priority, efficiency priority, or customer satisfaction priority), forming interaction weights. These interaction weights reflect the degree to which a change in one benefit dimension affects other benefit dimensions in a specific context.

[0128] Then, based on the dimensional weights and interaction weights corresponding to the second objective dimension, the fusion weight for each candidate consumption model is calculated. The fusion weight is a comprehensive score calculated for each candidate consumption model after comprehensively considering the direct dimensional weights (including the weights of the second objective dimension adjusted according to the strategic adjustment instructions) and the interaction weights of mutual influence between dimensions. This fusion weight can more comprehensively reflect the overall value of each candidate consumption model under the multi-dimensional benefit trade-off.

[0129] Finally, based on the fusion weight corresponding to each candidate consumption mode, the adjusted candidate consumption modes are weighted and fused to obtain a comprehensive analysis result. The calculated fusion weights can be used to weight and sum the benefits of each candidate consumption mode to obtain a final comprehensive analysis result. This result will serve as the basis for generating consumption optimization suggestions.

[0130] This embodiment identifies the interrelationships between benefit dimensions and adjusts the interaction weights accordingly, enabling the system to gain a deeper understanding and quantification of the complex relationships between different benefit dimensions during comprehensive analysis. Therefore, when calculating the fusion weights, not only the independent importance of each dimension is considered, but also the synergistic or restrictive effects between them are incorporated. This refined handling of the intrinsic relationships allows for a more comprehensive, objective, and realistic comprehensive analysis result when weighting and fusing multiple adjusted candidate consumption patterns, thus overcoming the limitations that may arise from relying solely on adjusting the weights of a single dimension.

[0131] To illustrate this technical solution more clearly, a specific example is used below. Suppose an employee needs to make an urgent cross-regional business trip involving negotiations with an important client. First, the strategic adjustment information might set "strategic value" as the second objective dimension and increase its weight. Based on this, the system will identify the interrelationships between benefit dimensions. For example, the system might identify that improving "comfort" (such as choosing business class or a high-star hotel) increases "explicit costs," but simultaneously significantly improves the employee's "time efficiency" (reducing fatigue and improving work performance) and "strategic value" (leaving a good impression on the client and increasing the success rate of negotiations). Next, based on the shared consumption context of this "urgent business trip" and the value orientation of "negotiations with an important client," the system will adjust the interaction weights between benefit dimensions. For example, in the current context, the interaction weight of the positive impact of "comfort" on "strategic value" will be significantly increased, while the interaction weight of the negative impact of "explicit costs" on "strategic value" will be appropriately reduced to reflect the shift in strategic priorities.

[0132] Subsequently, the system calculates the integration weights for different candidate consumption patterns (e.g., economy class + budget hotel, business class + high-end hotel) based on the adjusted weights of the second objective dimension and these interaction weights. For example, a consumption pattern including business class and high-end hotels, despite its higher explicit cost, may have a much higher integration weight than other patterns due to its high scores in the "strategic value" and "comfort" dimensions, as well as the adjusted strong positive interactions between these dimensions. Finally, the system performs a weighted integration of the adjusted candidate consumption patterns based on these integration weights to obtain a comprehensive analysis result. This result will clearly indicate that, in the current context, choosing the business class and high-end hotel pattern, despite its higher cost, is a more optimized choice from the perspective of overall benefits (especially strategic value and time efficiency), thus generating corresponding consumption optimization suggestions.

[0133] Through the above technical solution, this embodiment can provide a more refined and accurate comprehensive analysis result. By fully considering the complex interactions between different benefit dimensions, the generated consumption optimization suggestions will be more in line with actual needs and strategic goals, and can better balance multiple dimensions such as cost, efficiency, comfort, and strategic value, thereby improving the decision support capability and practical application value of the financial data analysis and early warning system.

[0134] In some embodiments, step S601, identifying the interrelationships between benefit dimensions, may include, but is not limited to, the following steps:

[0135] Obtain market environment data;

[0136] Identify information about changes in the market environment based on market environment data;

[0137] Extract the initial influence relationships between multiple benefit dimensions related to the core strategic direction from a pre-defined benefit dimension relationship database;

[0138] Based on information about changes in the market environment, the initial influence relationship is revised to obtain the mutual influence relationship.

[0139] In some embodiments, if the interactions fail to adequately consider the dynamic changes in the external market environment, the identified relationships may deviate from the actual situation. Consequently, subsequent adjustments to the interaction weights between benefit dimensions may not accurately reflect the current market reality, thus affecting the accuracy and effectiveness of the final consumption optimization recommendations.

[0140] Therefore, it's essential to first obtain market environment data. Market environment data refers to external factors that may influence business operations and employee consumption behavior, such as macroeconomic indicators (e.g., inflation rate, GDP growth rate), industry development trends, competitor strategies, policy and regulatory changes, and seasonal factors. This data can be obtained through various means, including public channels, third-party data service providers, or internal market research.

[0141] Then, based on market environment data, identify information about changes in the market environment. This can be achieved through in-depth analysis of the acquired market environment data, using techniques such as time series analysis, trend forecasting, and anomaly detection, to determine how the current market environment has changed relative to historical or expected states. For example, this can identify specific market changes such as economic downturns, intensified industry competition, and the introduction of new policies.

[0142] Next, the initial influence relationships between multiple benefit dimensions related to the core strategic direction are extracted from the pre-defined benefit dimension relationship database. The pre-defined benefit dimension relationship database is a knowledge base storing predefined influence relationships between different benefit dimensions. For example, during an economic upswing, investment in "strategic value" may have a smaller impact on "explicit costs," while during an economic downturn, stricter control of "explicit costs" may be needed to support "strategic value." This database can be built based on expert experience, historical data analysis, or industry best practices. Initial influence relationships refer to the default or baseline influence relationships between benefit dimensions related to the current core strategic direction, extracted from the pre-defined benefit dimension relationship database.

[0143] Finally, based on information about changes in the market environment, the initial impact relationships are revised to obtain the mutual impact relationships. These initial impact relationships can be dynamically adjusted based on the identified changes in the market environment. For example, if the market environment information indicates an economic downturn, it may be necessary to increase the negative impact weight of "explicit costs" on other benefit dimensions, or decrease the positive impact weight of "comfort" on "time efficiency," in order to adapt to the new economic situation.

[0144] This embodiment introduces market environment data and dynamically identifies changes in the market environment based on this data, thereby enabling real-time correction of the initial influence relationships between preset benefit dimensions. This dynamic correction mechanism allows the mutual influence relationships between benefit dimensions to more accurately reflect the current actual external environment, avoiding deviations that may be caused by static relationships. Therefore, when subsequently adjusting the interaction weights between benefit dimensions, a more realistic mutual influence relationship can be used, making the adjustment of interaction weights more reasonable. This improves the accuracy of the fusion weight calculation, ultimately making the comprehensive analysis results more reliable and providing a solid foundation for generating more adaptive and effective consumption optimization suggestions.

[0145] To illustrate this technical solution more clearly, a specific example is used below. Suppose a company is facing a market environment of intensified competition and rising raw material costs. The system first acquires relevant market environment data, such as industry reports and raw material price indices. Based on this data, the system identifies the market environment changes of "intensified market competition" and "rising raw material costs." Subsequently, the system extracts the initial impact relationships between benefit dimensions (such as explicit costs, time efficiency, and strategic value) related to the current core direction of the "cost control" strategy from a pre-defined benefit dimension relationship database. For example, the initial relationship might state that a reduction in "explicit costs" has a slight negative impact on "strategic value." However, given the intensified market competition and rising raw material costs, the system will modify the initial impact relationship between "explicit costs" and "strategic value" based on the identified market environment changes. For example, the weight of the negative impact of a reduction in "explicit costs" on "strategic value" might be increased to reflect that, in the current environment, excessive cost-cutting might have a greater impact on long-term strategic development. Simultaneously, the relationship between "time efficiency" and "explicit costs" might also be modified; for example, under cost pressure, investment in time efficiency might require a more rigorous cost-benefit assessment. Through this dynamic correction, the system can obtain a more accurate understanding of the interrelationships between benefit dimensions that better reflect the current market reality, thus providing more accurate input for subsequent comprehensive analysis of consumption patterns.

[0146] Through the above technical solution, this embodiment overcomes the static problem of identifying the interrelationships between benefit dimensions, enabling the system to dynamically adjust the correlation strength between benefit dimensions according to the constantly changing market environment. This significantly improves the system's adaptability to changes in the external environment, ensuring that the generated consumption optimization suggestions remain effective and instructive under different economic cycles, industry trends, or policy backgrounds, thereby providing enterprises with more accurate and forward-looking financial management and decision support.

[0147] The beneficial effects of implementing the embodiments of the present invention include: First, the data acquisition module acquires the consumption record data of employees. Then, the behavior pattern analysis module performs behavior pattern analysis on the consumption record data to identify historical consumption behavior. Based on the historical consumption behavior, the consumption pattern library is constructed by the consumption pattern library construction module. The current consumption intention is identified by the consumption intention recognition module. Finally, based on the current consumption intention and the consumption pattern library, the consumption optimization suggestion generation module generates consumption optimization suggestions. This enables the generation of consumption optimization suggestions by combining historical consumption behavior and current consumption intention, thereby achieving financial data analysis and early warning, improving cost-effectiveness, and reducing operating costs.

[0148] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A financial data analysis and early warning system based on artificial intelligence, characterized in that, include: The data acquisition module is used to acquire employees' consumption records. The behavior pattern analysis module is used to perform behavior pattern analysis on the consumption record data and identify historical consumption behavior, which includes business trip destination, accommodation type, types of purchased items, consumption amount and time of occurrence. The consumption pattern library construction module is used to construct a consumption pattern library based on the historical consumption behavior. The consumption intention recognition module is used to identify the current consumption intention, which includes consumption type, budget range, and time requirement; The consumption optimization suggestion generation module is used to generate consumption optimization suggestions based on the current consumption intention and the consumption pattern library; The consumption pattern library construction module is also used for: Obtain information on employees' prioritization of implicit benefits in historical consumption behavior, employee feedback on historical consumption behavior, as well as employees' organizational structure and task criticality. The priority ranking information, feedback information, organizational structure information, and task criticality information are associated with the historical consumption behavior to generate a contextual description profile; Based on the aforementioned contextual description files, the consumption pattern library is constructed. The step of constructing the consumption pattern library based on the context description file includes: Based on the aforementioned contextual description file, determine the dimension weight corresponding to each benefit dimension in the historical consumption behavior. The benefit dimensions include explicit cost, time efficiency, comfort, and strategic value. Calculate the contextual value score of the historical consumption behavior based on each benefit dimension and its corresponding weight. Based on the contextual value score, identify historical consumption patterns; The aforementioned consumption pattern library is constructed based on multiple historical consumption patterns; After determining the dimension weight corresponding to each benefit dimension in the historical consumption behavior based on the aforementioned contextual description file, the process further includes: Receive strategic adjustment instructions, which include consumer behavior norms, the status of the instructions' effectiveness, and the scope of business. The strategic adjustment instructions are analyzed, and the benefit dimension that matches the business scope is identified as the first target dimension; If the instruction is valid, then the dimension weights corresponding to the first target dimension are adjusted according to the consumption behavior norms and the context description file. The consumption optimization suggestion generation module is also used for: The current consumption intention is matched with the consumption pattern library to identify multiple candidate consumption patterns; A correlation analysis was performed on the multiple candidate consumption patterns to identify common consumption scenarios and value orientations; Based on the shared consumption context and value orientation, a comprehensive analysis of the multiple candidate consumption patterns is conducted to obtain the comprehensive analysis results. Based on the comprehensive analysis results, the consumption optimization suggestions are generated; The step involves a comprehensive analysis of the multiple candidate consumption patterns based on the shared consumption context and value orientation, yielding a comprehensive analysis result, including: Obtain strategic adjustment information, which includes the core strategic direction; The benefit dimension indicated by the core strategic direction is taken as the second objective dimension; The dimension weights corresponding to the second target dimension among the multiple candidate consumption patterns are adjusted to increase the dimension weights corresponding to the second target dimension. Based on the shared consumption context and value orientation, a comprehensive analysis is conducted on the adjusted candidate consumption patterns to obtain the comprehensive analysis results.

2. The system according to claim 1, characterized in that, The step of calculating the contextual value score of the historical consumption behavior based on each benefit dimension and its corresponding weight includes: Obtain reimbursement amount, commuting time reduction, mode of transportation, and customer importance level; Based on the reimbursement amount, the explicit cost is quantified to obtain a first quantified value; The time efficiency is quantified based on the reduction in commuting time to obtain a second quantified value; The comfort level is quantified based on the vehicle class to obtain a third quantified value; Based on the customer importance level, the strategic value is quantified to obtain a fourth quantified value; The context value score is calculated based on the first quantified value, the second quantified value, the third quantified value, the fourth quantified value, and the dimension weights corresponding to each benefit dimension.

3. The system according to claim 1, characterized in that, The identification of current consumption intent includes: Obtain the consumption text information entered by the employee; The consumption text information is matched with the consumption intent template to identify the consumption type; Keyword extraction is performed on the consumption text information to obtain the budget range and the time requirement.

4. The system according to claim 1, characterized in that, The step involves a comprehensive analysis of the adjusted candidate consumption patterns based on the shared consumption context and value orientation, yielding the comprehensive analysis results, including: Identify the interrelationships between benefit dimensions; Adjust the interaction weights between the benefit dimensions based on the mutual influence relationships, the shared consumption scenarios, and value orientations. Calculate the fusion weight corresponding to each candidate consumption mode based on the dimension weight corresponding to the second target dimension and the interaction weight; Based on the fusion weight corresponding to each candidate consumption pattern, the adjusted multiple candidate consumption patterns are weighted and fused to obtain the comprehensive analysis result.

5. The system according to claim 4, characterized in that, The interrelationships between the dimensions of recognition benefits include: Obtain market environment data; Based on the market environment data, identify information on changes in the market environment; Extract the initial influence relationships between multiple benefit dimensions related to the core strategic direction from a pre-defined benefit dimension relationship database; Based on the market environment change information, the initial influence relationship is corrected to obtain the mutual influence relationship.

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