A family life cycle identification method and system based on dynamic calibration and knowledge inheritance

Abstract

This disclosure provides a method, system, electronic device, storage medium, and program product for identifying family lifecycles based on dynamic calibration and knowledge inheritance. It addresses the problems of lifecycle identification failing to adapt to dynamic consumption changes and the difficulty in knowledge inheritance. The method includes: establishing a set of mapping relationships between family lifecycle stages and corresponding consumption characteristics based on historical family consumption data and stage labels, forming a knowledge base mapping table; acquiring consumption data, matching it with the mapping table, and adjusting the lifecycle stage determination criteria according to dynamically calculated calibration parameters; if the confidence level of the result is lower than a threshold, fusing family background characteristics and consumption characteristics to generate an enhanced feature vector, and correcting the matching result to obtain a corrected stage label; updating the mapping table, and triggering a new round of calibration parameter calculation and determination criterion optimization based on the updated mapping table and real-time monitored consumption data. This disclosure solves the pain points of model rigidity and knowledge dependence on individuals.

Description

Technical Field

[0001] This disclosure relates to the fields of data processing and machine learning technology, and in particular to a family life cycle identification method and system based on dynamic calibration and knowledge inheritance, electronic devices, computer-readable storage media, and computer program products. Background Technology

[0002] In fields such as financial services and precision marketing, accurately identifying family customers at different lifecycle stages (such as the honeymoon period, parenting period, education period, maturity period, and empty nest period) is a crucial prerequisite for building a personalized product and service system. Currently, the mainstream technological approach to achieving this goal relies on static analysis models or rule engines built based on historical consumption data.

[0003] However, existing technical solutions reveal the following fundamental flaws that urgently need to be addressed when faced with the complexity and dynamism of actual business operations: The contradiction lies between the static fixation of models and the dynamic evolution of business. Household consumption patterns continuously and dynamically change with socio-economic trends, the natural evolution of family structures, and the influence of external policies. Once deployed, the models or rules upon which existing technologies rely have fixed internal judgment logic, feature weights, and classification boundaries, lacking effective online adaptive mechanisms. This prevents the system from automatically adjusting to shifting consumption trends, leading to a continuous decline in accuracy and numerous misjudgments over time.

[0004] The contradiction lies between a single analytical dimension and the complexity of the judgment scenarios. In actual business, especially during the transitional phase of the life cycle or when dealing with atypical families, relying solely on consumption data is often insufficient to make highly reliable judgments. Most existing solutions are limited to a single dimension of consumption transaction data, failing to effectively integrate multi-source, heterogeneous background information such as family member composition, economic capacity, and geographical attributes. This limitation in dimensionality makes the system insufficiently robust when dealing with complex and ambiguous judgment scenarios, easily leading to low-confidence outputs or erroneous conclusions.

[0005] There is a contradiction between reliance on tacit experience and the standardization and sustainable transfer of analytical methods. High-quality judgments often rely heavily on the long-term, ineffable "business intuition" and "tacit experience" accumulated by analytical experts. Current technological systems lack effective means to systematically extract, structure, and standardize the reuse of this type of tacit knowledge. This not only results in analytical methods heavily relying on a few core personnel, but also prevents the stable transfer of knowledge within teams and its effective migration between systems, thus hindering the scalable and sustainable development and improvement of analytical capabilities.

[0006] Therefore, the existing technology system has three core problems: static models cannot adapt to dynamic consumption changes, single dimensions are difficult to support the judgment of complex scenarios, and tacit knowledge cannot be standardized and passed on. There is an urgent need for an intelligent analysis method that can achieve adaptive dynamic calibration, multi-dimensional feature fusion, and has the ability to continuously accumulate and pass on knowledge, so as to improve the accuracy, adaptability and maintainability of family life cycle identification. Summary of the Invention

[0007] To address the shortcomings of existing family lifecycle identification models, such as their inability to adapt to dynamic consumption changes, the difficulty of using a single dimension to support complex scenario judgments, and the inability to standardize and pass on tacit knowledge, this disclosure provides a family lifecycle identification method and system, electronic device, computer-readable storage medium, and computer program product based on dynamic calibration and knowledge inheritance. By constructing a dynamically updatable knowledge base mapping table, adaptive identification of family lifecycle stages is achieved. It can automatically calibrate judgment criteria according to changes in consumption trends, improving identification accuracy; it integrates multi-dimensional background features to enhance judgment robustness when data is ambiguous; and it standardizes and structures the analyzed knowledge, forming a inheritable and self-optimizing closed-loop system, effectively solving the industry pain points of rigid static models and knowledge dependence on individuals.

[0008] Firstly, this disclosure provides a family lifecycle identification method based on dynamic calibration and knowledge inheritance, including the following steps: Construct a knowledge base mapping table: Based on historical household consumption data and stage tags, establish a set of mapping relationships between household life cycle stages and corresponding consumption characteristics to form a structured knowledge base mapping table; Dynamic matching and calibration: Obtain the current household consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results to adjust the judgment criteria for the life cycle stage; Feature enhancement and result correction: If the confidence of the matching result is lower than the threshold, the background features and consumption features of the current household in the non-consumption dimension are fused to generate an enhanced feature vector, and the matching result is corrected based on the enhanced feature vector to obtain the corrected stage label; Knowledge base update and closed-loop feedback: The knowledge base mapping table is updated according to the corrected stage labels, and a new round of calibration parameter calculation and judgment standard optimization is triggered based on the updated mapping table and real-time monitored consumption data, forming a closed-loop knowledge control process.

[0009] Furthermore, based on historical household consumption data and stage tags, a set of mapping relationships between household life cycle stages and corresponding consumption characteristics is established, forming a structured knowledge base mapping table, including: Cluster analysis was performed on historical household consumption data to obtain behavioral characteristic vectors for different consumer groups; By combining family structure information, a family life cycle discrimination rule is constructed, and each family in the historical family consumption data is assigned a life cycle stage label; Based on the life cycle stage labels and behavioral feature vectors, an association rule algorithm is used to calculate the support and confidence between each consumption feature and the family stage, and strong association rules are selected to construct a knowledge rule base. Based on the knowledge rule base, high-frequency consumption features corresponding to each lifecycle stage are extracted to generate a structured knowledge base mapping table.

[0010] Furthermore, the step of obtaining the current household consumption data, matching it with the knowledge base mapping table, and dynamically calculating calibration parameters based on the matching results includes: The new household consumption data is initially classified based on the categories and amounts in the consumption data, and then matched with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification. Based on the deviation range, the consumption characteristics of different family groups are analyzed, the dynamic trends of consumption categories and amounts are identified, and the results are compared with the standards of the knowledge base mapping table. The calibration parameters for dynamic adjustment are obtained by calculating the rate of change of consumption structure and time series offset. A user profile model is constructed by analyzing historical consumption patterns. The life cycle stage judgment criteria are adjusted according to the calibration parameters and matched with current consumption data to obtain the matching result of family stage affiliation.

[0011] Furthermore, if the confidence level of the matching result is lower than a threshold, the background features and consumption features of the current household (excluding consumption) are fused to generate an enhanced feature vector. The matching result is then corrected based on this enhanced feature vector to obtain a corrected stage label, including: When the confidence level of the match is lower than the threshold, the current family's composition, income level, and residential area information are obtained as background features. The background features are quantized and vectorized, and then concatenated with the consumption feature vector to form an enhanced feature vector. The enhanced feature vectors are analyzed using a clustering algorithm to determine their respective clusters; Based on the matching relationship between the central features of the clusters and the predefined stage features, the corrected stage labels are determined.

[0012] Furthermore, the step of updating the knowledge base mapping table according to the corrected stage labels, and triggering a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, includes: The knowledge base mapping table is updated by updating the stage tags, a structured analysis template is generated, standard guidelines that new employees can call are obtained, and standardized analysis basis is obtained. Based on the standardized analysis criteria, a feedback mechanism for dynamically adjusting strategies is constructed to continuously monitor real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated. The user profile model is optimized based on the recalculated calibration parameters. Different family structures and consumption patterns are reacquired, and the judgment rules are adaptively adjusted to obtain a universal tag system across scenarios and an updated set of knowledge records.

[0013] Furthermore, the new household consumption data is initially classified based on the category and amount in the consumption data, and matched with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification, including: Obtain the consumption categories and corresponding amounts of new households, and vectorize them according to the proportion of consumption amount of each category to the total consumption amount to obtain the consumption feature vector of new households; A similarity algorithm is used to calculate the similarity value between the new family consumption feature vector and the typical feature vectors of each life cycle stage in the knowledge base mapping table, so as to obtain the preliminary classification result of the new family and the corresponding feature matching degree. If the feature matching degree is lower than a preset threshold, the difference between the new household consumption vector and the corresponding stage feature vector in the knowledge base mapping table is identified, and the category with a difference greater than a preset deviation threshold is selected as the difference category. Calculate the degree of deviation for each differentiated product category, and determine the adjustment amount for the current life cycle stage judgment boundary based on the degree of deviation; Based on the adjustment amount, the boundary values ​​of the feature vectors of the original life cycle stages are corrected. Euclidean distance is used to calculate the distance between the new household consumption feature vector and the corrected feature vectors of the current stage and adjacent stages. The stage with the smallest distance is selected as the calibrated classification result. Based on the difference between the preliminary classification result and the calibrated classification result, the deviation range of the preliminary classification is determined.

[0014] Furthermore, the step of analyzing the consumption characteristics of different family groups based on the deviation range, identifying the dynamic trends of consumption categories and amounts, and comparing them with the standards of the knowledge base mapping table, and obtaining dynamically adjusted calibration parameters by calculating the rate of change in consumption structure and time series offset, includes: Based on the deviation range, families are clustered and grouped, and the consumption categories and corresponding amounts of each group at different time points are extracted; Calculate the changes in consumption amount for each category between adjacent time points, and use the moving average method to obtain the dynamic trend of consumption patterns of family groups; The dynamic change trend of the consumption pattern is compared item by item with the standard consumption pattern trend value corresponding to the life cycle stage in the knowledge base mapping table, and the consumption structure change rate of each category is calculated. Based on the consumption structure change rate sequence, the periodic characteristics of consumption changes in each category are calculated using the autocorrelation function, and the difference between the actual peak consumption time point and the standard peak time point is identified to obtain the time offset. By combining the weighted average method with the change rate weight and time offset weight of each category, the calibration parameters are dynamically adjusted.

[0015] Furthermore, the step of constructing a user profile model based on historical consumption patterns, adjusting the lifecycle stage judgment criteria according to the calibration parameters, and matching it with current consumption data to obtain the matching result of family stage affiliation includes: By extracting category preferences, consumption frequency, and amount distribution features from historical consumption data, a multi-dimensional feature vector containing consumption habits, category preferences, and amount ranges is constructed. The multidimensional feature vectors are processed using a dimensionality reduction algorithm, and the principal components whose cumulative contribution rate exceeds a preset threshold are retained to obtain a set of typical consumption features for each life cycle stage as a user profile model. Based on the rate of change and time offset in the calibration parameters, the adjustment range of the judgment threshold for each life cycle stage is calculated to obtain the adjusted life cycle stage judgment standard. Using the adjusted lifecycle stage judgment criteria and the typical feature set in the user profile model, features are extracted and standardized from the current household's real-time consumption data. The distance between the standardized current features and the typical feature set of each stage is calculated, and the stage with the smallest distance is selected as the matching result for household stage affiliation.

[0016] Furthermore, by updating the knowledge base mapping table through the revised stage tags, generating a structured analysis template, obtaining standard guidelines that new employees can access, and obtaining standardized analysis basis, including: By revising the stage labels, we extract the typical consumption characteristics and judgment rules of families in each stage and update the knowledge base mapping table. Based on the updated knowledge base mapping table, a tree-structured analysis template is constructed, which includes stage categories, feature dimensions, and specific judgment rules. Based on the structured analysis template, judgment conditions and feature thresholds are extracted to form a judgment sequence sorted by priority. A unique identifier is assigned to each judgment sequence to establish an index, thereby obtaining a standardized analysis basis.

[0017] Furthermore, the feedback mechanism for constructing a dynamic adjustment strategy based on the standardized analysis criteria continuously monitors real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated, including: Based on the judgment thresholds and characteristic ranges in the standardized analysis criteria, normal fluctuation ranges for each consumer product category are set, and a monitoring parameter table is established. By periodically collecting real-time consumption data and comparing it with the monitoring parameter table, a threshold-triggered feedback mechanism is constructed. The latest consumption data is obtained at preset time intervals, and the deviation of consumption amount for each category from the benchmark value is calculated. If the absolute value of the deviation exceeds the preset upper and lower limits of fluctuation, it is marked as abnormal fluctuation. Based on the abnormal fluctuation records, the recent consumption data sequence of the family groups that experienced the abnormality is extracted, and a smoothing algorithm is used to obtain the trend value. The ratio of the trend value to the benchmark value is calculated as the adjustment coefficient. The adjustment coefficient is multiplied by the original calibration parameters to obtain the recalculated calibration parameters.

[0018] Furthermore, the user profile model is optimized based on the recalculated calibration parameters, different family structures and consumption patterns are re-acquired, the judgment rules are adaptively adjusted, a cross-scenario universal tagging system is obtained, and an updated knowledge record set is obtained, including: Based on the recalculated calibration parameters, the feature weights of each lifecycle stage in the user profile model are adjusted to obtain an optimized set of feature vectors. By performing cluster analysis on the optimized feature vectors, the features of different family structure types and corresponding consumption patterns are re-extracted; Extract multiple consumption characteristics for each household type across different consumption channels; calculate the stability index of each consumption characteristic across different consumption channels; if the stability index of a certain consumption characteristic exceeds a preset threshold, then include that consumption characteristic in the general feature set. Adjust the scope of application of the original judgment rules based on the general feature set, construct a label mapping relationship that includes channel identifier, family type, core features and applicable conditions, and generate a general label system across scenarios; The general tag system is merged with the knowledge base mapping table to obtain an updated set of knowledge records.

[0019] Secondly, this disclosure provides a family life cycle identification system based on dynamic calibration and knowledge inheritance, the system comprising: The module is designed to establish a set of mapping relationships between family life cycle stages and corresponding consumption characteristics based on historical family consumption data and stage labels, forming a structured knowledge base mapping table; The matching and calibration module is configured to acquire the current household's consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results in order to adjust the judgment criteria for the life cycle stage. The correction module is configured to, if the confidence level of the matching result is lower than a threshold, fuse the background features and consumption features of the current household's non-consumption dimension to generate an enhanced feature vector, and correct the matching result based on the enhanced feature vector to obtain the corrected stage label; The update feedback module is configured to update the knowledge base mapping table according to the corrected stage labels, and trigger a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, forming a closed-loop knowledge control process.

[0020] Thirdly, this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, the one or more computer programs being executed by the at least one processor to enable the at least one processor to perform the aforementioned family lifecycle identification method based on dynamic calibration and knowledge inheritance.

[0021] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the aforementioned family life cycle identification method based on dynamic calibration and knowledge inheritance.

[0022] Fifthly, this disclosure provides a computer program product that includes computer-readable code or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the aforementioned family lifecycle identification method based on dynamic calibration and knowledge inheritance.

[0023] Beneficial effects: This disclosure provides a family lifecycle identification method and system, electronic device, computer-readable storage medium, and computer program product based on dynamic calibration and knowledge inheritance. Through dynamic matching and calibration, calibration parameters are automatically generated and applied based on real-time matching feedback and consumption trend analysis (such as calculating the rate of change in consumption structure and time offset). This allows the judgment criteria to adaptively adjust with changes in socioeconomic conditions and family behavior, fundamentally overcoming the accuracy decay problem caused by the rigidity of rules in traditional static models. Secondly, through "feature enhancement and result correction," when the confidence level is insufficient based solely on consumption data, the system automatically integrates multi-dimensional background information such as family members, income, and region for collaborative analysis, significantly improving the success rate and robustness in challenging scenarios such as lifecycle transition periods or complex family structures. Finally, through "knowledge updating and closed-loop feedback," the corrected knowledge is deposited and updated in the knowledge base in the form of structured templates. This not only realizes the explicit and standardized inheritance of analysts' tacit experience, reducing reliance on talent, but also forms a continuously learning, self-iterable intelligent closed-loop system through real-time monitoring and feedback-triggered recalibration, achieving long-term sustainable evolution of analytical capabilities.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0025] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the embodiments of the present disclosure to explain the disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the detailed description of exemplary embodiments with reference to the accompanying drawings, in which: Figure 1 A flowchart illustrating a family life cycle identification method based on dynamic calibration and knowledge inheritance provided in Embodiment 1 of this disclosure; Figure 2 This is a flowchart illustrating a family life cycle identification method based on dynamic calibration and knowledge inheritance, provided in Embodiment 2 of this disclosure. Figure 3 This is a block diagram of a family life cycle identification system based on dynamic calibration and knowledge inheritance, provided in Embodiment 3 of this disclosure; Figure 4 This is a block diagram of an electronic device provided in Embodiment 4 of this disclosure. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions of this disclosure, exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments of this disclosure to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0027] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0028] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0029] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Words such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0030] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein. Those skilled in the art will understand that the specific order of execution of the steps in the methods described above in the specific embodiments should be determined by their function and possible internal logic.

[0031] The family lifecycle identification method based on dynamic calibration and knowledge inheritance according to embodiments of this disclosure can be executed by electronic devices such as terminal devices or servers. Terminal devices can be in-vehicle devices, user equipment (UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital assistants (PDAs), handheld devices, computing devices, in-vehicle devices, wearable devices, etc. The method can be implemented by a processor calling computer-readable program instructions stored in memory. Alternatively, the method can be executed by a server.

[0032] Example 1

[0033] Figure 1 This is a flowchart illustrating a family lifecycle identification method based on dynamic calibration and knowledge inheritance, provided in Embodiment 1 of this disclosure. (Refer to...) Figure 1 The method includes: Step S100: Construct a knowledge base mapping table: Based on historical household consumption data and stage tags, establish a set of mapping relationships between household life cycle stages and corresponding consumption characteristics to form a structured knowledge base mapping table; Step S200: Dynamic matching and calibration: Obtain the current household consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results to adjust the judgment criteria for the life cycle stage; Step S300: Feature enhancement and result correction: If the confidence of the matching result is lower than the threshold, the background features and consumption features of the current household's non-consumption dimension are fused to generate an enhanced feature vector, and the matching result is corrected based on the enhanced feature vector to obtain the corrected stage label; Step S400: Knowledge base update and closed-loop feedback: Update the knowledge base mapping table according to the corrected stage labels, and trigger a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data to form a closed-loop knowledge control process.

[0034] The purpose of this disclosure is to enable family lifecycle identification to avoid over-reliance on fixed rules and a single data source, and to adapt to the complexity and dynamism of family consumption behavior in order to cope with dynamically changing family data.

[0035] Specifically, in the method described, Step S100 is used to construct a knowledge base mapping table (hereinafter referred to as the mapping table). This step aims to build a structured knowledge base that associates family life cycle stages (such as the honeymoon period, parenting period, education period, maturity period, and empty nest period) with typical consumption behavior characteristics. First, historical family consumption data is collected and organized, including transaction records, product categories, consumption frequency, consumption amount, and family life cycle stage labels determined through manual review or historical business rules. Then, data mining techniques are used to analyze this historical data, extracting consumption pattern characteristics that characterize families at different stages, such as the expenditure proportion of each product category, the consumption intensity of specific categories (such as maternal and infant products, and education and training services), and the cyclical patterns of consumption. Finally, these characteristics are associated with the corresponding life cycle stages and stored in a structured, queryable format (such as a database table or knowledge graph), forming the initial knowledge base mapping table. This table constitutes the benchmark knowledge system for subsequent intelligent identification and analysis.

[0036] Step S200 implements dynamic matching and calibration. When it is necessary to determine the life cycle stage of a new family (i.e., the current family), the system first obtains the family's recent consumption data. This data is transformed into a feature representation comparable to that in the knowledge base mapping table (e.g., calculating the consumption amount percentage vector for each consumer category). Subsequently, the matching degree of this feature representation is calculated with the typical features of each stage pre-stored in the knowledge base mapping table to obtain a preliminary stage determination result and matching degree score. If the matching degree is lower than a preset confidence threshold, it indicates that the current family's consumption pattern deviates significantly from the standard pattern in the existing knowledge base, and the system initiates a dynamic calibration mechanism. This calibration mechanism calculates a set of calibration parameters by analyzing the feature deviation between the current family and the standard pattern, and combining it with the consumption trend analysis of family groups with similar deviations (e.g., analyzing changes in consumption structure over time). Using this set of parameters, the system dynamically adjusts the determination criteria for the life cycle stage (e.g., adjusting the weight or threshold of each feature dimension in the determination), and uses the adjusted criteria to re-match the current family's consumption data, thereby obtaining a more accurate stage assignment result.

[0037] Step S300 involves feature enhancement and result correction. In some cases, even after dynamic calibration, the confidence level of matching results based on pure consumption data remains low (e.g., the family is in a transitional phase between two life cycles). In this situation, the system initiates the feature enhancement process. Specifically, the system retrieves non-consumption-related background information about the family, such as family member composition (age, number), total family income level, and residential location. This background information is quantified and fused with the original consumption feature vector to form a more comprehensive and higher-dimensional enhanced feature vector. Next, the system uses methods such as cluster analysis in machine learning to reclassify the family in the enhanced feature space, identify the most similar family group, and correct the initial matching results based on the typical stage labels of this group, ultimately determining a corrected stage label with higher confidence.

[0038] Step S400 involves updating the knowledge base and implementing closed-loop feedback. To ensure the knowledge base can continuously evolve and adapt to changes in consumption patterns, the system establishes a closed-loop feedback mechanism. Specifically, the system uses the revised stage labels and their corresponding consumption and background features output in step S300 as new valid samples to update the knowledge base mapping table. This update can be incremental, such as adjusting the standard values ​​or weights of features corresponding to specific stages. Simultaneously, based on the updated knowledge base, the system automatically generates standardized analysis rules and operational guidelines, facilitating new employees' rapid mastery of the analysis logic. More importantly, the system continuously monitors the real-time consumption data stream of identified households. Once abnormal fluctuations in their consumption patterns are detected, a new round of calibration parameter calculations and model optimization is automatically triggered. Through this closed loop of "monitoring-feedback-update-optimization," the entire system achieves continuous knowledge accumulation, dynamic adjustment of standards, and self-iteration of recognition capabilities, forming an intelligent analysis process with adaptive and self-learning capabilities.

[0039] This disclosure discloses an embodiment that constructs a family lifecycle intelligent identification system through closed-loop collaboration of four core steps. This system is capable of dynamically adapting to changes, comprehensively utilizing multi-dimensional information, and possessing knowledge inheritance and self-optimization capabilities. The implementation processes in each step (such as algorithms for feature extraction, similarity metrics for matching, and specific methods for clustering) can be implemented in various ways well known to those skilled in the art, and all changes to these specific implementation methods fall within the protection scope of this invention.

[0040] This disclosed embodiment improves the accuracy and adaptability of stage identification. By introducing a "dynamic matching and calibration" step, it can automatically adjust the judgment criteria based on real-time matching feedback and macro consumption trends, effectively solving the core pain point of traditional static models being unable to adapt to the dynamic evolution of household consumption behavior due to rigid rules. For example, when overall social education expenditures advance or inflation of a certain type of consumer goods is significant, the system can automatically calibrate the model by calculating the rate of change in consumption structure and time offset, ensuring that the stage identification of households in the new period remains accurate, thereby significantly improving the model's identification accuracy and timeliness throughout the entire life cycle. Enhancing the robustness of analysis in complex scenarios, the "feature enhancement and result correction" step overcomes the limitations of relying solely on single-dimensional analysis of consumption data. When facing households in the transitional period of their life cycle, with ambiguous consumption patterns or special structures, the system can automatically integrate multi-dimensional background information such as family members, income, and region for collaborative analysis. This multi-source information fusion mechanism provides a "multi-sensor fusion" perspective for analysis, greatly improving the success rate of judgment and the reliability of conclusions in marginal cases and difficult scenarios, thus enhancing the overall robustness of the system. By making tacit knowledge explicit and ensuring its sustainable inheritance, the entire solution constructs a complete closed loop of knowledge generation, application, and accumulation. Particularly in the "knowledge base update and closed-loop feedback" step, the system feeds back verified correction results and rules to the knowledge base in a structured manner (such as tree-structured analysis templates). This not only transforms the "tacit experience" of senior analysts into "explicit assets" that the enterprise can share, query, and access, but also enables new employees to quickly master unified and standardized analytical methods, greatly reducing the business's dependence on core individuals and ensuring the consistency and sustainability of analytical capabilities. The construction of a self-driven, self-optimizing intelligent analysis system forms a complete "perception-decision-learning" closed loop. By monitoring anomalies in consumer data in real time, the system can proactively identify problems and trigger model recalibration and optimization. This means that the system is no longer a static tool requiring frequent manual intervention and retraining, but an intelligent agent with continuous learning and self-iteration capabilities. This self-evolutionary capability ensures that the system can maintain high performance in the long term and continuously deepen its understanding with data accumulation, providing long-term, stable intelligent support for the business. This method automates the entire process from data matching, confidence assessment, feature fusion to knowledge updating, freeing manual labor from tedious and repetitive data comparison and rule maintenance, thus significantly improving operational efficiency. Simultaneously, its output of high-confidence stage labels and structured knowledge can directly serve downstream decision-making systems such as automated marketing, personalized recommendations, and precise risk control, enhancing the overall intelligence and automation level of business decision-making.

[0041] Furthermore, based on historical household consumption data and stage tags, a set of mapping relationships between household life cycle stages and corresponding consumption characteristics is established, forming a structured knowledge base mapping table, including: Cluster analysis was performed on historical household consumption data to obtain behavioral characteristic vectors for different consumer groups; By combining family structure information, a family life cycle discrimination rule is constructed, and each family in the historical family consumption data is assigned a life cycle stage label; Based on the life cycle stage labels and behavioral feature vectors, an association rule algorithm is used to calculate the support and confidence between each consumption feature and the family stage, and strong association rules are selected to construct a knowledge rule base. Based on the knowledge rule base, high-frequency consumption features corresponding to each lifecycle stage are extracted to generate a structured knowledge base mapping table.

[0042] Constructing a knowledge base mapping table requires first extracting data on household consumption frequency, amount, and product categories from historical transaction records and user behavior logs. Household consumption pattern indicators are calculated based on the proportion of each product category and the purchase time interval. K-means clustering is then used to cluster these indicators, yielding behavioral feature vectors for different consumer groups. Based on the product category distribution and frequency characteristics in these behavioral feature vectors, combined with information on family member age distribution, number of children, and length of residence, a family lifecycle discrimination rule is constructed. If a family has children aged 0-6 and the head of household is between 25 and 40 years old, it is classified as a growing family; if the head of household is over 60 years old and has no minor children, it is classified as an empty-nest family. Each family is assigned a corresponding lifecycle stage label according to the discrimination rule. Based on the lifecycle stage labels and behavioral feature vectors, each family's consumption feature value is paired with its lifecycle stage label. The Apriori algorithm is used to calculate the support and confidence between each consumption feature and the family stage. Strong association rules with a confidence score greater than 0.8 are selected to construct a knowledge rule base. Based on the strong association rules in the knowledge rule base, high-frequency consumption features corresponding to each life cycle stage are extracted, and a structured mapping table (i.e., knowledge base mapping table) containing stage identifiers, typical consumption feature sets, high-frequency consumption categories, and consumption amount ranges is generated. The mapping relationship between each life cycle stage and its corresponding consumption features is stored using the stage identifier as the primary key, thus obtaining a mapping table between knowledge and family stage features.

[0043] Specifically, when extracting household consumption data from historical transaction records and user behavior logs, the frequency of consumption reflects the household's purchasing activity, the amount of consumption reflects the household's economic strength, and the category of consumption reveals the structure of the household's living needs.

[0044] For example, a family with infants and toddlers might purchase formula and diapers 15-20 times per month, while families with elderly members living alone primarily focus on health supplements and daily necessities, purchasing only 5-8 times per month. By calculating the proportion of each category's spending in total consumption, if baby products account for more than 30%, it can be preliminarily determined that the family is in the child-rearing stage. Analyzing the purchase time intervals can identify consumption patterns; families that regularly purchase fresh food weekly show significant differences in lifestyle compared to families that make random purchases. When processing consumption pattern indicators, the K-means clustering algorithm first needs to standardize the data to avoid the influence of orders of magnitude differences in consumption amounts on the clustering results. The algorithm iteratively calculates the Euclidean distance from each data point to the cluster center, continuously adjusting the cluster center position until convergence.

[0045] In one possible implementation, the number of clusters is set to five, corresponding to families in the newlywed, parenting, education, maturity, and empty nest stages, respectively. After clustering, each family obtains a multi-dimensional behavioral feature vector, containing its numerical performance across various consumption dimensions. The construction of family lifecycle discrimination rules requires comprehensive consideration of information from multiple dimensions.

[0046] Specifically, besides the age of the head of household and the number of children, the length of residence is also an important factor. Families who have moved into the community less than a year ago are often in a period of adjustment, and their consumption behavior may differ from that of similar families who have been living there for a long time. The discrimination rule adopts a decision tree logical structure, first determining whether there are minor children, then further subdividing according to the age of the children, and finally combining this with the age of the head of household to determine the specific stage. This multi-layered discrimination mechanism improves the accuracy of classification and avoids misjudgments that may be caused by a single indicator. When mining the association rules between consumption characteristics and family stage, the Apriori algorithm calculates the support of itemsets by scanning the database.

[0047] It should be noted that support represents the probability that a certain consumption characteristic and a specific family stage occur simultaneously, while confidence represents the conditional probability that a particular family stage exists given the occurrence of a certain consumption characteristic. The algorithm starts with a single itemset, gradually generates frequent itemsets, and then extracts strong association rules from the frequent itemsets.

[0048] Preferably, the minimum support is set to 0.1 to ensure that the rule has sufficient universality; the confidence threshold of 0.8 ensures the reliability of the rule.

[0049] Furthermore, the step of obtaining the current household consumption data, matching it with the knowledge base mapping table, and dynamically calculating calibration parameters based on the matching results includes: The new household consumption data is initially classified based on the categories and amounts in the consumption data, and then matched with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification. Based on the deviation range, the consumption characteristics of different family groups are analyzed, the dynamic trends of consumption categories and amounts are identified, and the results are compared with the standards of the knowledge base mapping table. The calibration parameters for dynamic adjustment are obtained by calculating the rate of change of consumption structure and time series offset. A user profile model is constructed by analyzing historical consumption patterns. The life cycle stage judgment criteria are adjusted according to the calibration parameters and matched with current consumption data to obtain the matching result of family stage affiliation.

[0050] This embodiment describes how to implement the "dynamic matching and calibration" step. This process aims to process new household consumption data, achieving accurate stage identification through matching, deviation analysis, and parameter calibration. It includes: Preliminary classification and boundary calibration When the consumption data of new family C enters the system, the system first performs preliminary classification. Specifically, the system summarizes family C's consumption records from the past quarter by category, calculates the consumption percentage of each category, and forms a consumption feature vector, such as [Education: 0.38, Maternal and Infant Products: 0.15, Food: 0.25, Entertainment: 0.10, ...]. Subsequently, the system calculates the matching degree between this vector and the "typical consumption feature vector" of each stage in the knowledge base mapping table. Assuming the matching degree with "Education Period" is 0.72 and the matching degree with "Parenting Period" is 0.65, since the highest matching degree of 0.72 is lower than the preset threshold of 0.80, the system determines that the confidence of the preliminary classification result is insufficient and triggers the calibration process. The system analyzes the specific differences between family C's consumption vector and the standard vector of "Education Period" in each dimension, identifies that "the proportion of maternal and infant products" is significantly higher than the standard value (15% vs 5%), thus determining that the current judgment boundary needs to be adjusted, and calculates that the deviation range of the preliminary classification is "a significant positive deviation in the maternal and infant dimension".

[0051] Trend Analysis and Parameter Generation

[0052] Based on the aforementioned deviation range, the system retrieves all recent family groups exhibiting similar positive deviations in the "maternal and infant dimension" (assuming this includes families C, D, E, etc.) and analyzes their consumption data sequences over the past 6 months. Through time-series analysis, the system finds that the group's "education expenditure ratio" shows a steady upward trend, while the "maternal and infant expenditure ratio" declines more slowly than the historical standard pattern. Calculations show that the "education expenditure change rate" is +8% (historical standard is +5%), and the "time offset of maternal and infant expenditure change" lags by 3 months (i.e., declines 3 months later than the standard pattern). Combining these indicators, the system generates a set of dynamic calibration parameters through weighted calculations, such as: {Education weight adjustment factor: 1.1, Maternal and infant tolerance threshold: +0.05, Time compensation: -3 months}. These parameters quantify the difference between current consumption trends and historical standards.

[0053] Model Adjustment and Final Matching

[0054] A user profile model constructed using long-term historical consumption data (this model has extracted core features for each stage through methods such as principal component analysis) is adjusted according to the calibration parameters generated in step two. For example, the threshold for determining the "education period" is adjusted from 0.30 to 0.28, and the upper limit for the "maternal and infant expenditure ratio" is adjusted from 0.10 to 0.15. Finally, the adjusted criteria are used to recalculate the consumption feature vector of family C. After calibration, the matching degree between family C and "education period" increases to 0.88, and the system ultimately outputs the matching result for family stage affiliation as "education period" with a high confidence level.

[0055] This disclosed embodiment achieves precise "perception-response" dynamic optimization through dynamic matching and calibration: the complete feedback loop includes: triggering calibration through a matching degree threshold → analyzing specific deviations → identifying group trends → generating quantitative parameters → adjusting the model → re-matching. This mechanism transforms the system from a "one-off" judgment tool into an intelligent system capable of sensing data changes, responding to trends, and optimizing its own judgments in real time, significantly improving the accuracy of identification in environments with rapidly changing consumer behavior. Furthermore, it upgrades decision-making from "single-point judgment" to "group trend-driven": traditional methods only perform isolated matching on data from individual households. This method generates calibration parameters by analyzing group trends with similar deviations, allowing the system's adjustment decisions to be based on broader and more robust statistical regularities, rather than an overreaction to a single outlier. This greatly enhances the scientific nature and noise resistance of the system's decision-making, avoiding misjudgments caused by individual household consumption anomalies. It also provides the model with the impetus for continuous evolution: by quantifying consumption trends into parameters such as "rate of change" and "time offset" and using them to adjust the model, the system effectively establishes a mechanism that internalizes changes in the external environment (macro-consumption trends) into model parameters. This allows the user profile model to continuously and gradually adapt to overall socio-economic changes, possessing an inherent driving force for continuous evolution and solving the performance degradation problem that is unavoidable in traditional static models. Simultaneously, it balances the timeliness and stability of calibration: by triggering calibration through the condition of "match degree below a threshold," the system achieves on-demand calibration, ensuring timely adjustments when significant deviations occur while avoiding unnecessary and potentially unstable frequent adjustments during normal data fluctuations. This design achieves a good balance between system sensitivity and stability.

[0056] Furthermore, if the confidence level of the matching result is lower than a threshold, the background features and consumption features of the current household (excluding consumption) are fused to generate an enhanced feature vector. The matching result is then corrected based on this enhanced feature vector to obtain a corrected stage label, including: When the confidence level of the match is lower than the threshold, the current family's composition, income level, and residential area information are obtained as background features. The background features are quantized and vectorized, and then concatenated with the consumption feature vector to form an enhanced feature vector. The enhanced feature vectors are analyzed using a clustering algorithm to determine their respective clusters; Based on the matching relationship between the central features of the clusters and the predefined stage features, the corrected stage labels are determined.

[0057] If the confidence level of the matching result is lower than the preset standard, background features (such as family member information, income level, and residential area) of non-consumption dimensions are called to enhance the features, and the enhanced family feature vector is obtained. Then, the consumption features are clustered and analyzed to determine the corrected stage label.

[0058] When calculating the confidence level of the matching results, the difference between the minimum and second-minimum distance values ​​is calculated and then divided by the minimum distance value to obtain the confidence level. If the confidence level is lower than a preset standard threshold, the family's age structure, average monthly income range, and residential area code are extracted from the database as supplementary features. Based on the supplementary features, the family's age is grouped into 0-18 years, 19-60 years, and over 60 years old, and the proportion of each group is calculated as the age structure vector. The average monthly income range is mapped to a consumption capacity level value of 1-10 according to a preset income level table. The residential area code is obtained by querying the regional average consumption level table to obtain the corresponding regional coefficient. These processed supplementary features are concatenated with the original consumption feature vector to obtain the enhanced family feature vector. For the enhanced family feature vector, a hierarchical clustering algorithm is used to calculate the distance matrix between each family feature vector. A clustering tree is formed through a bottom-up merging process. The optimal number of clusters is determined based on the ratio of intra-cluster distance to inter-cluster distance. The center vector of each cluster is extracted and its dominant feature is identified. The dominant feature is matched with predefined life cycle stage features to determine the corrected stage label.

[0059] Specifically, the confidence score reflects the reliability of the classification results.

[0060] Specifically, when a family's distance from the education stage is 1.2 and its distance from the parenting stage is 1.5, the difference of 0.3 divided by the minimum distance of 1.2 yields a confidence level of 0.25. This relatively low confidence level indicates that the family may be in a transitional phase between two life cycles, and it is difficult to accurately determine its affiliation based solely on consumption data. The confidence threshold is typically set around 0.5; values ​​below this indicate the need to incorporate more dimensions of information to aid in the judgment. The introduction of supplementary features provides multi-faceted support for classification.

[0061] In one possible implementation, a family consists of two adults and one 15-year-old teenager, with an age structure vector of [0, 0.67, 0.33], indicating a predominantly middle-aged demographic. A monthly income of 15,000 yuan corresponds to level 7 in a pre-defined income level table, reflecting above-average consumption capacity. Living in a central area of ​​a first-tier city, the family has a regional coefficient of 1.3, indicating a higher-than-average consumption level in the region. This supplementary information enriches the family profile from three dimensions: population structure, economic capacity, and geographical environment. The feature vector concatenation process integrates multi-source information. The original consumption feature vector might contain 10 dimensions, such as the consumption proportion of various categories like food, education, and entertainment. The supplementary features add four new dimensions: three age group proportions and one comprehensive index. The comprehensive index is obtained by multiplying the consumption capacity level by the regional coefficient; for example, level 7 multiplied by the coefficient 1.3 equals 9.1, reflecting the family's relative consumption level in the local area. After concatenation, a 14-dimensional enhanced feature vector is formed, preserving consumption behavior information while incorporating the socio-economic background. Hierarchical clustering algorithms exhibit unique advantages when dealing with augmented features.

[0062] It's important to note that this algorithm first treats each family as an independent cluster, calculating the Euclidean distance between all family pairs to form a distance matrix. The two closest families are merged first, forming a small cluster with two members. The algorithm then continues to find and merge the closest cluster pairs, a process that continues until all families are grouped into one large cluster. Each branch of the clustering tree records the distance at the time of merging. By analyzing the quality of the clustering results obtained from cutting the clustering tree at different heights, the optimal number of clusters is determined. Cluster quality is evaluated based on a balance between internal compactness and external separation. A ratio of 3.125 indicates good clustering performance when the average distance between families within a cluster is 0.8 and the average distance between different cluster centers is 2.5. The center vector of each cluster is obtained by calculating the mean of the feature vectors of all families within that cluster; the dominant features are the dimensions with the most prominent values ​​in the center vector. The correction of stage labels reflects data-driven classification optimization.

[0063] For example, the center vector of a certain cluster shows that education expenditure accounts for 0.35%, middle-aged people account for 0.8%, and consumption capacity level is 8. These dominant characteristics highly match the predefined characteristics of "families in the education period". Therefore, all families in this cluster are labeled as "families in the education period". This label correction based on actual data clustering is more adaptable to the dynamic changes in family consumption patterns than simply relying on preset rules.

[0064] Furthermore, the step of updating the knowledge base mapping table according to the corrected stage labels, and triggering a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, includes: The knowledge base mapping table is updated by updating the stage tags, a structured analysis template is generated, standard guidelines that new employees can call are obtained, and standardized analysis basis is obtained. Based on the standardized analysis criteria, a feedback mechanism for dynamically adjusting strategies is constructed to continuously monitor real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated. The user profile model is optimized based on the recalculated calibration parameters. Different family structures and consumption patterns are reacquired, and the judgment rules are adaptively adjusted to obtain a universal tag system across scenarios and an updated set of knowledge records.

[0065] The knowledge base update and closed-loop feedback process aims to transform the identification results into persistent knowledge and form a self-optimizing closed loop through real-time monitoring. Specifically, it includes: Step 1: Knowledge Accumulation and Structure Once the system determines that family D's final stage label is "Education Period" through multi-feature fusion correction, this result, along with its corresponding consumption feature vector and enhanced background features (such as "having teenage children" and "middle income"), will serve as a validated, high-quality sample. The system first updates the knowledge base mapping table; for example, it fine-tunes the average or distribution range of "online education service consumption ratio" in the "Education Period" feature set to reflect the latest consumption patterns. Simultaneously, the system automatically generates a structured analysis template based on the key rules and feature thresholds used in this determination (such as "total education ratio > 30%" and "monthly frequency of teenage cultural and entertainment activities > 2 times"), according to a hierarchical relationship of "stage-feature-threshold." This template can be directly queried and used by new employees as a standardized analytical basis to guide their judgments on similar families, achieving immediate solidification and inheritance of analytical experience.

[0066] Step Two: Dynamic Monitoring and Feedback Trigger

[0067] Based on the updated knowledge base and standards, the system establishes personalized consumption baselines and normal fluctuation ranges for all families it serves. For example, for families in the "education period," a "normal range for monthly education expenditure fluctuations" is set at ±15%. The system then monitors the subsequent consumption flows of family D and other families in real time. Suppose that after three months, the monitoring finds that family D's education expenditure has plummeted by 40% for two consecutive months, far exceeding the normal fluctuation range, the system immediately marks it as an abnormal fluctuation event. This event automatically triggers a feedback signal indicating that the criteria for judging such families still need to be reassessed to see if they remain applicable.

[0068] Step 3: Recalibration and System Generalization

[0069] Upon receiving feedback signals, the system aggregates family groups that have recently experienced similar abnormal fluctuations. Using the latest consumption data, it re-executes the calibration parameter calculation process (e.g., analyzing new trends and shifts). Using the newly calculated parameters, the system optimizes the user profile model, for example, adjusting the weight of different features in the "education period" determination. After model optimization, the system can rediscover and summarize new combinations of family structures and consumption patterns. Furthermore, the system analyzes the performance of these patterns across different consumption channels (e.g., online platforms, offline stores), extracting core features that consistently appear across all channels, and constructing a cross-scenario universal tagging system (e.g., "stable education-investing family"). Finally, all the new knowledge—the updated mapping table, the optimized model, and the universal tagging system—is integrated into an updated knowledge record set, completing a full knowledge iteration cycle.

[0070] Through knowledge base updates and closed-loop feedback, this disclosed embodiment can achieve a closed-loop knowledge value-added process of "practice-theory-re-practice": a successful (or only successful after correction) specific identification case has its core logic precipitated into a standardized analysis template, transforming individual or single-instance analytical wisdom into reusable organizational assets. Simultaneously, by monitoring subsequent data anomalies and triggering recalibration, new practical data is used to test and correct existing theories (models), driving the continuous evolution of the knowledge base and forming a closed-loop growth flywheel of knowledge value. It constructs system intelligence from "passive response" to "proactive early warning": traditional systems only make judgments when processing new data. This method, by establishing a continuous monitoring mechanism based on standardized criteria, enables the system to proactively discover potential problems. When consumer behavior undergoes drastic changes, the system can proactively issue early warnings and trigger self-checks and optimizations, marking a shift from a passive "data processing tool" to a proactive "state-aware and maintenance intelligent agent." Achieving enhanced generalization capabilities from "scenario adaptation" to "scenario penetration": Through recalibrated analysis of cross-channel feature stability, the system can extract a more essential family consumption profile stripped of specific scenario noise. The resulting cross-scenario universal tagging system not only improves the model's adaptability to new channels (such as newly developed community group-buying businesses) but also allows the understanding of the family lifecycle to transcend the limitations of a single consumption scenario, achieving a higher level of generalization and explanatory power. Ultimately forming an ecosystem with "self-evolution" capabilities: The "update-monitor-feedback-re-optimize" process described in this embodiment is an automated cycle requiring no manual intervention. This transforms the entire system from a static model that gradually ages after deployment into an intelligent ecosystem that continuously learns from its own operational results, adjusts to environmental changes, and evolves relentlessly from data flow. This ensures the long-term effectiveness and vitality of the solution, representing the highest level of technical effectiveness of this invention.

[0071] Furthermore, the new household consumption data is initially classified based on the category and amount in the consumption data, and matched with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification, including: Obtain the consumption categories and corresponding amounts of new households, and vectorize them according to the proportion of consumption amount of each category to the total consumption amount to obtain the consumption feature vector of new households; A similarity algorithm is used to calculate the similarity value between the new family consumption feature vector and the typical feature vectors of each life cycle stage in the knowledge base mapping table, so as to obtain the preliminary classification result of the new family and the corresponding feature matching degree. If the feature matching degree is lower than a preset threshold, the difference between the new household consumption vector and the corresponding stage feature vector in the knowledge base mapping table is identified, and the category with a difference greater than a preset deviation threshold is selected as the difference category. Calculate the degree of deviation for each differentiated product category, and determine the adjustment amount for the current life cycle stage judgment boundary based on the degree of deviation; Based on the adjustment amount, the boundary values ​​of the feature vectors of the original life cycle stages are corrected. Euclidean distance is used to calculate the distance between the new household consumption feature vector and the corrected feature vectors of the current stage and adjacent stages. The stage with the smallest distance is selected as the calibrated classification result. Based on the difference between the preliminary classification result and the calibrated classification result, the deviation range of the preliminary classification is determined.

[0072] In the initial classification and boundary calibration process, new household consumption data are initially classified according to consumer goods category and amount. The feature matching is then performed against the mapping table features to obtain the feature matching degree. If the degree is below a threshold, the lifecycle stage determination boundary is calibrated to determine the deviation range of the initial classification. The specific implementation process is as follows: The process involves acquiring the consumption categories and corresponding amounts of new households, vectorizing them according to the proportion of each category's consumption amount to the total consumption amount, and using a cosine similarity algorithm to calculate the similarity value between the vector and the typical feature vectors of each life cycle stage in a pre-established mapping table. This yields the preliminary classification result and corresponding feature matching degree for the new household. For the feature matching degree, if it is lower than a preset threshold, the differences between each dimension of the new household's consumption vector and the feature vector of the corresponding stage in the mapping table are identified. Categories with an absolute difference greater than a preset deviation threshold are selected as differential categories. The ratio of the actual consumption amount of each differential category to the standard amount in the mapping table is calculated as the degree of deviation. Based on the degree of deviation, the adjustment amount for the current life cycle stage's judgment boundary is determined. The feature vector boundary values ​​of the original life cycle stage are corrected based on this adjustment amount. Euclidean distance is used to calculate the distance between the new household's consumption feature vector and the corrected feature vectors of the current stage and adjacent stages before and after. The stage with the smallest distance is selected as the calibrated classification result. The deviation range of the preliminary classification is determined by calculating the difference between the stage numbers before and after calibration.

[0073] Specifically, the vectorized representation of new household consumption data is the foundation for achieving accurate classification.

[0074] Specifically, when a new family enters the analysis system, their spending on each consumer category is converted into a percentage representation. Assuming a family's total monthly spending is 10,000 yuan, with 3,500 yuan spent on education and training, 2,000 yuan on baby products, 1,500 yuan on daily necessities, and 3,000 yuan on dining and entertainment, the corresponding vector representation would be [0.35, 0.20, 0.15, 0.30]. This proportionalization eliminates the absolute differences in amounts between families with different income levels, allowing families at the same life stage but with different incomes to be correctly identified. The cosine similarity algorithm, when calculating feature matching degree, essentially measures the degree of similarity between two vectors in direction.

[0075] In one possible implementation, the algorithm calculates the cosine of the angle between the new household's consumption vector and the typical feature vectors of each stage stored in the mapping table. The cosine is 1 when the two vectors are in the same direction, -1 when they are in opposite directions, and 0 when they are orthogonal. Through this calculation, the system can quantify the similarity between the new household and each predefined life cycle stage.

[0076] For example, a family with school-aged children, whose high proportion of spending is on education, will show a high degree of similarity to the typical characteristics of "families in the education period". When the feature matching degree is lower than the threshold, the system needs to conduct in-depth analysis of the specific reasons for the low matching.

[0077] It should be noted that the process of identifying discrepancies involves comparing the consumption percentage of each category in the new household with the standard percentage for the corresponding stage in the mapping table. If the difference in percentage for a certain category exceeds 0.1, it is marked as a discrepancy category. The degree of deviation is calculated using the ratio of the actual value to the standard value; this relative measure can more accurately reflect the severity of the deviation.

[0078] For example, if a new family's education expenditure accounts for 0.35%, while the initial matching standard value for a "parenting-age family" is 0.15, the deviation is 2.33, indicating that the family may have already entered the education stage. The boundary adjustments of life cycle stages reflect the system's adaptive capability. Based on the identified differences and their degree of deviation, the system dynamically adjusts the boundary values ​​determined at each stage.

[0079] In one implementation, if multiple new families exhibit a consistent deviation trend within the same category, the system adjusts the feature boundaries of that stage in that dimension accordingly. Euclidean distance calculation provides another metric, considering the cumulative effect of differences across dimensions. By calculating the geometric distance from the new family feature vector to the center point of each stage, the system can comprehensively assess the accuracy of the classification. Determining the deviation range provides an important reference for subsequent classification optimization. By comparing the stage number differences before and after calibration, the potential errors in the initial classification can be quantified.

[0080] Furthermore, the step of analyzing the consumption characteristics of different family groups based on the deviation range, identifying the dynamic trends of consumption categories and amounts, and comparing them with the standards of the knowledge base mapping table, and obtaining dynamically adjusted calibration parameters by calculating the rate of change in consumption structure and time series offset, includes: Based on the deviation range, families are clustered and grouped, and the consumption categories and corresponding amounts of each group at different time points are extracted; Calculate the changes in consumption amount for each category between adjacent time points, and use the moving average method to obtain the dynamic trend of consumption patterns of family groups; The dynamic change trend of the consumption pattern is compared item by item with the standard consumption pattern trend value corresponding to the life cycle stage in the knowledge base mapping table, and the consumption structure change rate of each category is calculated. Based on the consumption structure change rate sequence, the periodic characteristics of consumption changes in each category are calculated using the autocorrelation function, and the difference between the actual peak consumption time point and the standard peak time point is identified to obtain the time offset. By combining the weighted average method with the change rate weight and time offset weight of each category, the calibration parameters are dynamically adjusted.

[0081] The process of analyzing the consumption characteristics of different family groups based on the deviation range, identifying the dynamic trends of consumption categories and amounts to obtain the dynamic trends of consumption patterns, comparing them with historical mapping tables, and calculating the rate of change in consumption structure and time series offset to obtain dynamically adjusted calibration parameters includes: Households are clustered based on their deviation range. Consumption categories and corresponding monetary values ​​for each group at different time points are extracted. The change in consumption amount for each category between adjacent time points is calculated. The arithmetic mean of the changes at the previous three time points is used as the trend value for the current time point. By continuously calculating the trend values ​​at each time point, the dynamic trend of consumption patterns for each household group is obtained. For the trend value sequence of each category in the dynamic trend of consumption patterns, it is compared item by item with the standard consumption pattern trend value for the corresponding life cycle stage in the historical mapping table. The difference between the consumption proportion of each category at the current time point and the historical standard proportion is calculated. The change rate of consumption structure for each category is obtained by dividing the difference by the standard proportion. Based on the consumption structure change rate sequence, the periodic characteristics of consumption changes for each category are calculated using an autocorrelation function. The difference between the time point when the actual consumption change reaches its peak and the corresponding peak time point in the standard pattern is identified, resulting in a time offset. A weighted average method is used to combine the change rate weights of each category and the time offset weights to obtain dynamically adjusted calibration parameters.

[0082] Specifically, family clustering based on deviation ranges highlights the importance of differential analysis.

[0083] Specifically, when the system identifies a deviation range of 0.2-0.3 in the initial classification of certain households, these households with similar deviation characteristics are grouped together. This grouping method makes subsequent trend analysis more accurate because households in similar transitional periods often exhibit similar patterns of changing consumption behavior. The calculation of moving averages plays a role in smoothing fluctuations in identifying consumption trends.

[0084] For example, a family group's education expenditures for three consecutive months were 2000 yuan, 3500 yuan, and 3000 yuan respectively. The arithmetic mean of these three values ​​is 2833 yuan, which is taken as the current trend value. This method can filter out occasional consumption fluctuations, such as concentrated purchases during the back-to-school season, thus revealing a more stable consumption trend. As the time window slides, a new trend value is calculated based on the data from the most recent three months at each new time point, forming a smooth trend curve. Calculating the rate of change in consumption structure requires a deep understanding of the concept of relative change.

[0085] In one possible implementation, if the historical mapping table shows that the standard value for the proportion of food consumption by mature households is 0.25, while the actual proportion for the current group is 0.30, then the difference is 0.05. By dividing the difference of 0.05 by the standard value of 0.25, the structural change rate of this category is 0.2, which represents a 20% increase. This relative measurement method eliminates the influence of differences in the base values ​​of different categories, making the degree of change in each category comparable. The autocorrelation function is used in time series analysis to identify periodic patterns in the data.

[0086] It should be noted that consumer behavior often exhibits seasonal or life-cycle patterns. By calculating the correlation of consumption data across different time intervals, the system can identify cyclical characteristics such as "a surge in education spending every September" or "a decrease in the purchase of baby and maternity products every three months." When the actual consumption peak occurs in the 8th month, while the standard pattern indicates it should occur in the 10th month, the system identifies a two-month time offset. The weighted average method considers the differences in importance across different dimensions when comprehensively calibrating the parameters.

[0087] For example, when determining whether a family is transitioning from the parenting phase to the education phase, the rate of change in education spending might be weighted at 0.4, while the rate of change in maternity and baby products might be weighted at 0.3, with other categories sharing the remaining 0.3 weight. The time offset is also weighted according to the importance of each category. By multiplying the rate of change for each category by its corresponding weight and summing the results, then combining this sum with the weighted result of the time offset, a comprehensive calibration parameter value is obtained. This parameter value directly reflects the degree and direction of the family's consumption pattern deviating from the standard pattern, providing a quantitative basis for subsequent classification adjustments.

[0088] Furthermore, the step of constructing a user profile model based on historical consumption patterns, adjusting the lifecycle stage judgment criteria according to the calibration parameters, and matching it with current consumption data to obtain the matching result of family stage affiliation includes: By extracting category preferences, consumption frequency, and amount distribution features from historical consumption data, a multi-dimensional feature vector containing consumption habits, category preferences, and amount ranges is constructed. The multidimensional feature vectors are processed using a dimensionality reduction algorithm, and the principal components whose cumulative contribution rate exceeds a preset threshold are retained to obtain a set of typical consumption features for each life cycle stage as a user profile model. Based on the rate of change and time offset in the calibration parameters, the adjustment range of the judgment threshold for each life cycle stage is calculated to obtain the adjusted life cycle stage judgment standard. Using the adjusted lifecycle stage judgment criteria and the typical feature set in the user profile model, features are extracted and standardized from the current household's real-time consumption data. The distance between the standardized current features and the typical feature set of each stage is calculated, and the stage with the smallest distance is selected as the matching result for household stage affiliation.

[0089] By extracting category preferences, consumption frequency, and spending distribution features from historical consumption data, a multi-dimensional feature vector containing consumption habits, category preferences, and spending ranges is constructed. Principal component analysis (PCA) is used to reduce the dimensionality of this vector, retaining principal components with a cumulative contribution rate exceeding a preset threshold. This yields a set of typical consumption characteristics for each lifecycle stage, serving as the user profile model. Based on the rate of change and time offset in the calibration parameters, the adjustment range of the judgment threshold for each lifecycle stage is calculated. If the calibration parameters show a positive rate of change for a certain category and a negative time offset, the judgment threshold for the corresponding category in that stage is multiplied by the reciprocal of the calibration parameters to obtain the adjusted lifecycle stage judgment criteria. Using the adjusted judgment criteria and the typical feature set in the user profile model, features are extracted and standardized from the current household's real-time consumption data. The Euclidean distance between the standardized current features and the typical feature sets of each stage is calculated, and the stage with the smallest distance is selected as the matching result for household stage affiliation.

[0090] Specifically, principal component analysis plays a crucial role in dimensionality reduction when constructing user profile models.

[0091] In one possible implementation, the original consumption characteristics may encompass dozens of dimensions, such as consumption data for various food, clothing, education, and healthcare subcategories. By calculating the covariance matrix of these characteristics, the direction of greatest data variation is identified, and principal component analysis (PCA) projects the high-dimensional data onto a few principal components. When the cumulative contribution rate of the three principal components reaches 0.85, it means that these three dimensions have retained 85% of the information in the original data, simplifying computational complexity while preserving the main characteristics of the data. The typical consumption characteristic set for each life cycle stage reflects the uniqueness of different family stages.

[0092] Specifically, the primary component of a child-rearing family's spending is likely to consist mainly of maternity and baby products, children's food, and healthcare, while the secondary component leans towards daily necessities. In contrast, the primary component of a child-rearing family's spending shifts significantly towards education and training, stationery and books, and extracurricular activities. This characteristic set is constructed based on statistical analysis of extensive historical data, with each stage forming a unique "consumption fingerprint." Adjustments to the judgment thresholds using calibration parameters demonstrate dynamic adaptability.

[0093] For example, when the calibration parameters show a change rate of 1.3 in education spending, indicating that the actual growth is 30% faster than the standard model, and the time offset is -2, it means that this growth occurred two months earlier than expected. In this case, the system calculates an adjustment of approximately 0.77, the reciprocal of 1.3, adjusting the original threshold of 0.25 for the proportion of education spending to 0.19. This adjustment allows families that entered their peak education spending period earlier to be more accurately identified as families in their education phase. The accuracy of the threshold adjustment directly affects the accuracy of the classification.

[0094] It's important to note that the adjustment range varies across different categories. The timing of earlier or later consumption behavior often concentrates on specific categories; for example, education spending may be brought forward due to policy changes, while daily living expenses remain relatively stable. By specifically adjusting the judgment thresholds for each category, the system can more flexibly adapt to changing consumption patterns. Standardization eliminates the influence of unit of measurement in feature matching. After standardization, current household consumption data is converted into a distribution with a mean of 0 and a standard deviation of 1 for each feature dimension. This allows for comparisons of different categories with vastly different amounts on the same scale. The Euclidean distance calculation, based on the standardized data, comprehensively reflects the overall differences between current households and typical characteristics of each stage across multiple consumption dimensions. The principle of minimum distance ensures the rationality of the classification.

[0095] In one implementation, a family's distance from the typical characteristics of the nurturing stage is 2.3, its distance from the education stage is 1.5, and its distance from the maturity stage is 3.8. The system selects the education stage, with the smallest distance, as the family's affiliation stage. This geometric distance-based classification method comprehensively considers information from all consumption dimensions, avoids misjudgments that may be caused by a single indicator, and achieves accurate identification of family life cycle stages.

[0096] Furthermore, by updating the knowledge base mapping table through the revised stage tags, generating a structured analysis template, obtaining standard guidelines that new employees can access, and obtaining standardized analysis basis, including: By revising the stage labels, we extract the typical consumption characteristics and judgment rules of families in each stage and update the knowledge base mapping table. Based on the updated knowledge base mapping table, a tree-structured analysis template is constructed, which includes stage categories, feature dimensions, and specific judgment rules. Based on the structured analysis template, judgment conditions and feature thresholds are extracted to form a judgment sequence sorted by priority. A unique identifier is assigned to each judgment sequence to establish an index, thereby obtaining a standardized analysis basis.

[0097] By revising the stage labels, typical consumption characteristics and judgment rules for households at each stage are extracted. The feature data corresponding to the stage in the original mapping table are replaced with the revised feature values, and a timestamp is used to mark the updated version, resulting in an updated knowledge base mapping table. Based on the updated knowledge base mapping table, a three-level tree structure is constructed, with stage categories as the root node, feature dimensions of each stage as second-level nodes, and specific judgment rules as leaf nodes. Data field names, data types, and value ranges are defined in each node to generate a structured analysis template. Based on the tree structure of the structured analysis template, judgment conditions and feature thresholds are extracted by traversing each leaf node. Judgment conditions under the same stage are sorted by priority to form a judgment sequence. By assigning a unique identifier to each judgment sequence and establishing an index, standardized analysis basis that can be queried and invoked is obtained.

[0098] Specifically, the update mechanism of the knowledge base mapping table embodies the concept of dynamic learning.

[0099] Specifically, when the system discovers through cluster analysis that the proportion of education expenditure by families during their school years has increased from 0.25 to 0.35, this revised feature value will replace the old data in the mapping table. The timestamp not only records the update time, such as "2025-06-30-14:35:22", but also retains the ability to trace historical versions. This version control mechanism allows analysts to observe the evolution of household consumption patterns over time, providing a data foundation for predicting future changes. The tree structure organizes the complex mapping relationships.

[0100] In one possible implementation, the root node contains five main lifecycle stages: newlywed period, parenting period, education period, maturity period, and empty nest period. Taking the education period as an example, its second-level nodes expand into multiple feature dimensions: education expenditure ratio, children's age distribution, cultural and entertainment consumption, frequency of electronic product purchases, etc. The leaf nodes under each feature dimension contain specific judgment rules, such as "education expenditure ratio greater than 0.3", "at least one child aged 6-18", "monthly purchase of supplementary teaching materials more than 3 times", etc. The standardized definition of data fields ensures the accurate transmission of information.

[0101] It should be noted that each node contains standardized field definitions. The education expenditure percentage field is defined as a floating-point number, with a value range of 0-1; children's ages are stored as an integer array, with each element representing the age of one child; purchase frequency is defined as an integer, with the unit being "times / month". This strict limitation of data types and ranges avoids ambiguity during data entry and processing. The priority sorting of the judgment sequence follows the principle of from primary to secondary.

[0102] For example, when determining whether a family is in the education stage, the primary criterion is whether the children are within the school-age range—this is the most crucial characteristic. Secondly, it's whether the proportion of education expenditure reaches a threshold, as families with school-age children typically increase their educational spending significantly. Thirdly, it's the purchase frequency of related consumer goods, such as stationery and supplementary teaching materials. This hierarchical determination mechanism improves the accuracy and efficiency of classification. The allocation of unique identifiers enables rapid location and retrieval. Each determination sequence is assigned a structured identifier, such as "EDU-001-CHK," representing the first set of core determination rules for the education stage. By establishing a mapping index between identifiers and determination content, new employees can quickly locate the corresponding set of determination rules after entering query conditions. This indexing mechanism optimizes the process from item-by-item searching to direct location, significantly improving query efficiency. The formation of standardized analysis criteria provides clear guidance for business operations. By integrating determination conditions, threshold parameters, and processing flows into a structured document, new employees do not need to deeply understand complex algorithm principles; they only need to check each item according to the standardized determination sequence to accurately determine the family's life cycle. This standardization not only lowers the technical barrier to analysis but also ensures consistency in the conclusions reached by different analysts.

[0103] Furthermore, the feedback mechanism for constructing a dynamic adjustment strategy based on the standardized analysis criteria continuously monitors real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated, including: Based on the judgment thresholds and characteristic ranges in the standardized analysis criteria, normal fluctuation ranges for each consumer product category are set, and a monitoring parameter table is established. By periodically collecting real-time consumption data and comparing it with the monitoring parameter table, a threshold-triggered feedback mechanism is constructed. The latest consumption data is obtained at preset time intervals, and the deviation of consumption amount for each category from the benchmark value is calculated. If the absolute value of the deviation exceeds the preset upper and lower limits of fluctuation, it is marked as abnormal fluctuation. Based on the abnormal fluctuation records, the recent consumption data sequence of the family groups that experienced the abnormality is extracted, and a smoothing algorithm is used to obtain the trend value. The ratio of the trend value to the benchmark value is calculated as the adjustment coefficient. The adjustment coefficient is multiplied by the original calibration parameters to obtain the recalculated calibration parameters.

[0104] Based on the judgment thresholds and characteristic ranges in the standardized analysis criteria, normal fluctuation ranges for each consumer product category are set, and a monitoring parameter table including category name, benchmark value, and upper and lower fluctuation limits is established. Real-time consumption data is collected periodically and compared with the monitoring parameter table to construct a threshold-triggered feedback mechanism. For this feedback mechanism, the latest consumption data is acquired at preset time intervals, and the difference between the consumption amount of each product category and the benchmark value is calculated and divided by the benchmark value to obtain the deviation. If the absolute value of the deviation exceeds the preset upper and lower fluctuation limits, it is marked as an abnormal fluctuation, and the abnormal product category, deviation value, and occurrence time are recorded. Based on the abnormal fluctuation records, the recent consumption data sequence of the family group experiencing the abnormality is extracted. The sequence is processed using exponential smoothing to obtain a smoothed trend value. The ratio of the trend value to the benchmark value in the original monitoring parameter table is calculated as an adjustment coefficient. The adjustment coefficient is multiplied by the original calibration parameters to automatically obtain the recalculated calibration parameters.

[0105] Specifically, when establishing a consumer goods category monitoring parameter table, the system first needs to determine the baseline value and fluctuation range for each category based on historical consumption data. Taking a family's food consumption as an example, analysis of data from the past 12 months reveals that the family's average monthly food expenditure is 3,000 yuan, with historical fluctuations within a range of plus or minus 20%. Therefore, a baseline value of 3,000 yuan is set, with an upper limit of 3,600 yuan and a lower limit of 2,400 yuan. This standardized analysis based on historical data can effectively identify normal consumption patterns, laying an accurate comparative foundation for subsequent anomaly detection.

[0106] Specifically, the real-time data acquisition and comparison mechanism obtains the latest consumption records through periodic polling. When the system detects that a family's food consumption this month reaches 4200 yuan, it immediately calculates a deviation of 40%, far exceeding the preset fluctuation limit of 20%, triggering an anomaly flag. At this time, the system records anomaly information, including the category identifier, the specific deviation value, and the timestamp of the occurrence, providing a complete anomaly trajectory for subsequent analysis. This threshold-triggered feedback mechanism can promptly capture significant changes in consumption behavior, avoiding analytical biases caused by delayed detection.

[0107] In one possible implementation, when processing abnormal fluctuation data using exponential smoothing, the system extracts the household's food consumption sequence for the most recent six months, including 2800 yuan, 3200 yuan, 3100 yuan, 4200 yuan, 3900 yuan, and 3500 yuan. The smoothed trend value, calculated using the exponential smoothing algorithm, is 3450 yuan, resulting in an adjustment coefficient of 1.15 compared to the original baseline value of 3000 yuan. This trend analysis method can filter out the interference of short-term fluctuations and accurately identify substantial changes in consumption habits.

[0108] For example, once the adjustment factor is calculated, the system automatically applies it to the calibration parameter update process. The original food category benchmark value of 3000 yuan is multiplied by the adjustment factor of 1.15 to obtain a new benchmark value of 3450 yuan, while the upper and lower limits of fluctuation are also adjusted proportionally to 4140 yuan and 2760 yuan, respectively. This adaptive adjustment mechanism ensures that the monitoring parameters can follow the natural evolution of consumption patterns, avoiding false alarms or missed alarms caused by fixed thresholds.

[0109] It's important to note that multi-category collaborative monitoring provides more comprehensive insights into consumer behavior. When food consumption increases while entertainment consumption decreases, the system uses correlation analysis to identify this as a change in consumption structure rather than an overall expenditure anomaly. This cross-category correlation monitoring mechanism helps to accurately understand the root causes of changes in consumer behavior and provides more precise risk assessment results.

[0110] In one implementation, dynamic adjustment of the time window further enhances the system's adaptability. For categories with significant seasonality, such as clothing, the system employs a quarterly monitoring cycle, while for daily necessities like food, monthly monitoring is used. This differentiated monitoring strategy better adapts to the natural fluctuations of different consumer product categories, reduces judgment errors caused by inappropriate monitoring cycles, and improves the accuracy and practicality of anomaly detection.

[0111] Furthermore, the user profile model is optimized based on the recalculated calibration parameters, different family structures and consumption patterns are re-acquired, the judgment rules are adaptively adjusted, a cross-scenario universal tagging system is obtained, and an updated knowledge record set is obtained, including: Based on the recalculated calibration parameters, the feature weights of each lifecycle stage in the user profile model are adjusted to obtain an optimized set of feature vectors. By performing cluster analysis on the optimized feature vectors, the features of different family structure types and corresponding consumption patterns are re-extracted; Extract multiple consumption characteristics for each household type across different consumption channels; calculate the stability index of each consumption characteristic across different consumption channels; if the stability index of a certain consumption characteristic exceeds a preset threshold, then include that consumption characteristic in the general feature set. Adjust the scope of application of the original judgment rules based on the general feature set, construct a label mapping relationship that includes channel identifier, family type, core features and applicable conditions, and generate a general label system across scenarios; The general tag system is merged with the knowledge base mapping table to obtain an updated set of knowledge records.

[0112] During the recalibration and system generalization process, based on the recalculated calibration parameters, the feature weights of each lifecycle stage in the user profile model are adjusted. The calibration parameters are used as correction coefficients and multiplied element-wise with the original feature vectors to obtain an optimized feature vector set. Through cluster analysis of the optimized feature vectors, different family structure types and corresponding consumption pattern features are re-extracted. Based on the family structure type and consumption pattern features, consumption characteristics of each family type in online shopping, offline supermarkets, and community group buying are extracted. The inverse variance of the frequency of the same feature in different channels is calculated as a stability index. If the stability index exceeds a preset threshold, the feature is included in the general feature set, and the scope of application of the original judgment rules is adjusted according to the general feature set. Through the adjusted judgment rules and the general feature set, a tag mapping relationship including channel identifier, family type, core features, and applicable conditions is constructed to generate a cross-scenario general tag system. This tag system is merged with the established knowledge base mapping table to obtain an updated set of knowledge records.

[0113] Specifically, the application of calibration parameters as correction coefficients embodies the core concept of dynamic optimization.

[0114] Specifically, when the system detects a calibration parameter of 1.2 for education expenditure, it means that the actual consumption trend is 20% higher than the original model's expectation. In the element-wise multiplication operation, the weight of the education dimension in the original feature vector is adjusted from 0.25 to 0.30, and other dimensions are also adjusted accordingly based on their respective calibration parameters. This refined weight adjustment allows the user profile model to accurately reflect the latest trends in consumption behavior. Cluster analysis plays a crucial role in re-extracting family structure types.

[0115] For example, the optimized feature vector set, through clustering, may identify a new family type—the "multi-generational family with children living together for education." These families have both educational expenditures for school-aged children and healthcare expenditures for the elderly, forming a unique consumption pattern. Compared to traditional nuclear families, they exhibit a higher propensity to consume in both education and healthcare dimensions; this complex consumption characteristic is difficult to accurately categorize within the original classification system. Extracting cross-channel consumption characteristics reveals the diversity of family consumption behavior.

[0116] In one possible implementation, families in the parenting stage spend 40% of their maternal and infant product consumption on online shopping platforms, 25% on offline supermarkets, and only 10% on community group buying channels. This difference in channel preference reflects the consumption decision-making logic under different shopping scenarios. Young parents tend to compare prices online for standardized products like formula, while preferring to select fresh food in person at offline supermarkets. The calculation method for the stability index ensures the reliability of general characteristics.

[0117] It should be noted that the reciprocal of variance, as a measure of stability, has clear statistical significance. When the frequency of a feature in the three channels is 0.8, 0.75, and 0.85 respectively, its variance is small, and the corresponding reciprocal of variance is large, indicating that the feature is stable across different channels. Conversely, if the frequencies are 0.9, 0.3, and 0.2 respectively, the variance is large, the stability index is low, indicating that the feature has obvious channel dependence and is not suitable as a general feature. Adjusting the scope of application of the judgment rule improves the flexibility of classification. The original rule may stipulate that "education expenditure exceeding 0.3% is considered an education period," but analysis shows that this rule is well applied to online channels, but needs to be adjusted to 0.2 in community group buying channels. This difference stems from the differences in product structure and pricing strategies across different channels. The establishment of a general feature set ensures consistency in the core judgment logic while allowing for fine-tuning of thresholds based on specific scenarios. The construction of label mapping relationships enables the structured integration of knowledge.

[0118] For example, a complete tag mapping might include: channel identifier "online shopping", family type "family in the education period", core characteristics "education expenditure as a percentage of total expenditure, stationery and book purchase frequency of 8 times per month", and applicable conditions "at least one child aged 6-18". This structured expression retains detailed judgment criteria while facilitating rapid retrieval and application. By merging the newly generated tag system with the original knowledge base mapping table, a more comprehensive and adaptable set of knowledge records is formed, providing a comprehensive reference for subsequent family consumption analysis.

[0119] This disclosure achieves three core breakthroughs by constructing a closed-loop knowledge control system of "construction-calibration-correction-update": First, it enables adaptive dynamic calibration of the model through matching degree feedback and trend analysis, continuously ensuring high accuracy in identifying family life cycles; second, it significantly improves robustness in the face of complex transitional families by fusing multi-dimensional background features for enhanced analysis at low confidence levels; and third, it transforms implicit experience into inheritable standardized knowledge by structuring and accumulating the correction results and establishing a monitoring feedback loop, driving the system to continuously self-optimize, thereby forming an intelligent analysis ecosystem with self-learning and self-evolution capabilities. This fundamentally solves the key pain points of traditional methods, such as the rigidity of static models, over-reliance on expert experience, and inability to adapt to long-term changes.

[0120] Example 2

[0121] Embodiment 2 of this disclosure provides a family life cycle identification method based on dynamic calibration and knowledge inheritance, such as... Figure 2 As shown, the method includes: S101 extracts implicit patterns and label construction standards from historical data for analysts in analyzing household consumption behavior, classifies and stores them according to the characteristics of different family life cycle stages, and obtains a mapping table between knowledge and family stage characteristics. S102, the new household consumption data is initially classified according to the category and amount of consumer goods, and the feature matching degree is obtained by feature extraction with the mapping table. If it is lower than the threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification. S103, based on the deviation range, analyze the consumption characteristics of different family groups, identify the dynamic change trends of consumption categories and amounts to obtain the dynamic change trends of consumption patterns, compare with the historical mapping table standard, and obtain the calibration parameters for dynamic adjustment by calculating the rate of change of consumption structure and time series offset. S104: Construct a user profile model through historical consumption patterns, adjust the life cycle stage judgment criteria according to calibration parameters, match it with current consumption data, and obtain the matching result of family stage affiliation. S105. If the confidence level of the matching result is lower than the preset standard, then the family member information, income level and residential area are called to perform feature enhancement, and the enhanced family feature vector is obtained. The consumption feature is then clustered to determine the corrected stage label. S106, update the knowledge base mapping table through the revised stage tags, generate a structured analysis template, obtain standard guidelines that new employees can call, and obtain standardized analysis basis; S107, based on standardized analysis, constructs a feedback mechanism for dynamic adjustment strategies, continuously monitors real-time changes in consumption data, and automatically recalculates calibration parameters if abnormal fluctuations are detected. S108 optimizes the user profile model based on the recalculated calibration parameters, re-acquires different family structures and consumption patterns, adaptively adjusts the judgment rules, obtains a universal tag system across scenarios, and obtains an updated knowledge record set.

[0122] The specific implementation process of this embodiment corresponds to that of Embodiment 1. For details of the implementation of each step, please refer to Embodiment 1, which will not be repeated here.

[0123] The first key technical point of this public embodiment lies in the knowledge mapping mechanism for consumption behavior based on the family life cycle. By deeply mining the implicit patterns and labels of analysts in historical consumption data, a knowledge base of consumption characteristics for different stages of the family life cycle is constructed using feature extraction and classification storage methods. A precise mapping table between knowledge and family stage characteristics is established, breaking through the limitations of static analysis based solely on data from a single point in time in traditional consumption analysis. This realizes the dynamic correlation between consumption behavior and family development stages, providing a standardized knowledge foundation for subsequent consumption pattern identification and stage determination, and ensuring the accuracy and consistency of family consumption behavior analysis.

[0124] The second key technical point of this embodiment is the dynamically calibrated consumption pattern recognition and deviation correction algorithm. By initially classifying new household consumption data by category and amount, and calculating the feature matching degree with the historical mapping table, the calibration mechanism is automatically triggered when the matching degree is lower than a preset threshold. The algorithm analyzes the range of consumption characteristic deviations of different household groups, identifies the dynamic changing trends of consumption categories and amounts, and obtains dynamically adjusted calibration parameters by calculating the rate of change of consumption structure and time series offset. This dynamic calibration method based on deviation analysis effectively solves the problem of inaccurate judgment criteria caused by the evolution of consumption patterns over time, and improves the accuracy of household life cycle stage identification.

[0125] The third key technical point of this embodiment is a multi-feature fusion family profile construction and intelligent matching system. This system constructs a user profile model through historical consumption patterns, dynamically adjusts the life cycle stage judgment criteria according to calibration parameters, and automatically calls up multi-dimensional features such as family member information, income level, and residential area to enhance the matching result when the confidence level is lower than the preset standard. The enhanced family feature vector is constructed and cluster analysis is performed to determine the corrected stage label. This multi-feature fusion matching mechanism breaks through the limitations of a single consumption data dimension, establishes a comprehensive analysis system of consumption behavior and family background information, and significantly improves the accuracy and robustness of family life cycle stage judgment.

[0126] Example 3

[0127] Embodiment 3 of this disclosure provides a family life cycle identification system based on dynamic calibration and knowledge inheritance, such as... Figure 3 As shown, the system includes: Module 11 is configured to establish a set of mapping relationships between family life cycle stages and corresponding consumption characteristics based on historical family consumption data and stage labels, forming a structured knowledge base mapping table; The matching and calibration module 12 is configured to acquire the current household's consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results in order to adjust the judgment criteria for the life cycle stage. The correction module 13 is configured to, if the confidence level of the matching result is lower than the threshold, fuse the background features and consumption features of the current household's non-consumption dimension to generate an enhanced feature vector, and correct the matching result based on the enhanced feature vector to obtain the corrected stage label; The update feedback module 14 is configured to update the knowledge base mapping table according to the corrected stage labels, and trigger a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, forming a closed-loop knowledge control process.

[0128] Furthermore, the construction module 11 is specifically configured as follows: Cluster analysis was performed on historical household consumption data to obtain behavioral characteristic vectors for different consumer groups; By combining family structure information, a family life cycle discrimination rule is constructed, and each family in the historical family consumption data is assigned a life cycle stage label; Based on the life cycle stage labels and behavioral feature vectors, an association rule algorithm is used to calculate the support and confidence between each consumption feature and the family stage, and strong association rules are selected to construct a knowledge rule base. Based on the knowledge rule base, high-frequency consumption features corresponding to each lifecycle stage are extracted to generate a structured knowledge base mapping table.

[0129] Furthermore, the matching and calibration module 12 includes: The preliminary classification and boundary calibration unit is configured to perform preliminary classification of new household consumption data based on the category and amount in the consumption data, and match it with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the preliminary classification. The trend analysis and parameter generation unit is configured to analyze the consumption characteristics of different family groups based on the deviation range, identify the dynamic trends of consumption categories and amounts, compare them with the standards of the knowledge base mapping table, and obtain dynamically adjusted calibration parameters by calculating the rate of change of consumption structure and time series offset. The model adjustment and final matching unit is configured to construct a user profile model through historical consumption patterns, adjust the life cycle stage judgment criteria according to the calibration parameters, and match it with the current consumption data to obtain the matching result of family stage affiliation.

[0130] Furthermore, the correction module 13 is specifically configured as follows: When the confidence level of the match is lower than the threshold, the current family's composition, income level, and residential area information are obtained as background features. The background features are quantized and vectorized, and then concatenated with the consumption feature vector to form an enhanced feature vector. The enhanced feature vectors are analyzed using a clustering algorithm to determine their respective clusters; Based on the matching relationship between the central features of the clusters and the predefined stage features, the corrected stage labels are determined.

[0131] Furthermore, the update feedback module 14 includes: The knowledge accumulation and structuring unit is configured to update the knowledge base mapping table through the modified stage tags, generate a structured analysis template, obtain standard guidelines that new employees can call, and obtain standardized analysis basis. The dynamic monitoring and feedback triggering unit is configured to construct a feedback mechanism for dynamic adjustment strategy based on the standardized analysis criteria, continuously monitor real-time changes in consumption data, and automatically recalculate calibration parameters if abnormal fluctuations are detected. The recalibration and system generalization unit is configured to optimize the user profile model based on the recalculated calibration parameters, reacquire different family structures and consumption patterns, adaptively adjust the judgment rules, obtain a cross-scenario general label system, and obtain an updated knowledge record set.

[0132] Furthermore, the preliminary classification and boundary calibration unit is specifically configured as follows: Obtain the consumption categories and corresponding amounts of new households, and vectorize them according to the proportion of consumption amount of each category to the total consumption amount to obtain the consumption feature vector of new households; A similarity algorithm is used to calculate the similarity value between the new family consumption feature vector and the typical feature vectors of each life cycle stage in the knowledge base mapping table, so as to obtain the preliminary classification result of the new family and the corresponding feature matching degree. If the feature matching degree is lower than a preset threshold, the difference between the new household consumption vector and the corresponding stage feature vector in the knowledge base mapping table is identified, and the category with a difference greater than a preset deviation threshold is selected as the difference category. Calculate the degree of deviation for each differentiated product category, and determine the adjustment amount for the current life cycle stage judgment boundary based on the degree of deviation; Based on the adjustment amount, the boundary values ​​of the feature vectors of the original life cycle stages are corrected. Euclidean distance is used to calculate the distance between the new household consumption feature vector and the corrected feature vectors of the current stage and adjacent stages. The stage with the smallest distance is selected as the calibrated classification result. Based on the difference between the preliminary classification result and the calibrated classification result, the deviation range of the preliminary classification is determined.

[0133] Furthermore, the trend analysis and parameter generation unit is specifically configured as follows: Based on the deviation range, families are clustered and grouped, and the consumption categories and corresponding amounts of each group at different time points are extracted; Calculate the changes in consumption amount for each category between adjacent time points, and use the moving average method to obtain the dynamic trend of consumption patterns of family groups; The dynamic change trend of the consumption pattern is compared item by item with the standard consumption pattern trend value corresponding to the life cycle stage in the knowledge base mapping table, and the consumption structure change rate of each category is calculated. Based on the consumption structure change rate sequence, the periodic characteristics of consumption changes in each category are calculated using the autocorrelation function, and the difference between the actual peak consumption time point and the standard peak time point is identified to obtain the time offset. By combining the weighted average method with the change rate weight and time offset weight of each category, the calibration parameters are dynamically adjusted.

[0134] Furthermore, the model adjustment and final matching unit is specifically configured as follows: By extracting category preferences, consumption frequency, and amount distribution features from historical consumption data, a multi-dimensional feature vector containing consumption habits, category preferences, and amount ranges is constructed. The multidimensional feature vectors are processed using a dimensionality reduction algorithm, and the principal components whose cumulative contribution rate exceeds a preset threshold are retained to obtain a set of typical consumption features for each life cycle stage as a user profile model. Based on the rate of change and time offset in the calibration parameters, the adjustment range of the judgment threshold for each life cycle stage is calculated to obtain the adjusted life cycle stage judgment standard. Using the adjusted lifecycle stage judgment criteria and the typical feature set in the user profile model, features are extracted and standardized from the current household's real-time consumption data. The distance between the standardized current features and the typical feature set of each stage is calculated, and the stage with the smallest distance is selected as the matching result for household stage affiliation.

[0135] Furthermore, the knowledge accumulation and structuring unit is specifically configured as follows: By revising the stage labels, we extract the typical consumption characteristics and judgment rules of families in each stage and update the knowledge base mapping table. Based on the updated knowledge base mapping table, a tree-structured analysis template is constructed, which includes stage categories, feature dimensions, and specific judgment rules. Based on the structured analysis template, judgment conditions and feature thresholds are extracted to form a judgment sequence sorted by priority. A unique identifier is assigned to each judgment sequence to establish an index, thereby obtaining a standardized analysis basis.

[0136] Furthermore, the dynamic monitoring and feedback triggering unit is specifically configured as follows: Based on the judgment thresholds and characteristic ranges in the standardized analysis criteria, normal fluctuation ranges for each consumer product category are set, and a monitoring parameter table is established. By periodically collecting real-time consumption data and comparing it with the monitoring parameter table, a threshold-triggered feedback mechanism is constructed. The latest consumption data is obtained at preset time intervals, and the deviation of consumption amount for each category from the benchmark value is calculated. If the absolute value of the deviation exceeds the preset upper and lower limits of fluctuation, it is marked as abnormal fluctuation. Based on the abnormal fluctuation records, the recent consumption data sequence of the family groups that experienced the abnormality is extracted, and a smoothing algorithm is used to obtain the trend value. The ratio of the trend value to the benchmark value is calculated as the adjustment coefficient. The adjustment coefficient is multiplied by the original calibration parameters to obtain the recalculated calibration parameters.

[0137] Furthermore, the recalibration and system generalization unit is specifically configured as follows: Based on the recalculated calibration parameters, the feature weights of each lifecycle stage in the user profile model are adjusted to obtain an optimized set of feature vectors. By performing cluster analysis on the optimized feature vectors, the features of different family structure types and corresponding consumption patterns are re-extracted; Extract multiple consumption characteristics for each household type across different consumption channels; calculate the stability index of each consumption characteristic across different consumption channels; if the stability index of a certain consumption characteristic exceeds a preset threshold, then include that consumption characteristic in the general feature set. Adjust the scope of application of the original judgment rules based on the general feature set, construct a label mapping relationship that includes channel identifier, family type, core features and applicable conditions, and generate a general label system across scenarios; The general tag system is merged with the knowledge base mapping table to obtain an updated set of knowledge records.

[0138] The family life cycle identification system based on dynamic calibration and knowledge inheritance in this disclosure is used to implement the family life cycle identification method based on dynamic calibration and knowledge inheritance in Embodiment 1 and Embodiment 2. Therefore, the description is relatively simple. For details, please refer to the relevant descriptions in the previous method embodiments, which will not be repeated here.

[0139] Figure 4 This is a block diagram of an electronic device provided in Embodiment 4 of this disclosure.

[0140] Reference Figure 4 This disclosure provides an electronic device comprising: at least one processor 701; at least one memory 702; and one or more I / O interfaces 703 connected between the processor 701 and the memory 702; wherein the memory 702 stores one or more computer programs executable by the at least one processor 701, the one or more computer programs being executed by the at least one processor 701 to enable the at least one processor 701 to execute the aforementioned family lifecycle identification method based on dynamic calibration and knowledge inheritance.

[0141] This disclosure also provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the aforementioned family lifecycle identification method based on dynamic calibration and knowledge inheritance. The computer-readable storage medium may be volatile or non-volatile.

[0142] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described family lifecycle identification method based on dynamic calibration and knowledge inheritance.

[0143] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0144] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0145] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0146] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0147] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0148] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0149] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0150] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0151] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0152] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A family life cycle identification method based on dynamic calibration and knowledge inheritance, characterized in that, Includes the following steps: Construct a knowledge base mapping table: Based on historical household consumption data and stage tags, establish a set of mapping relationships between household life cycle stages and corresponding consumption characteristics to form a structured knowledge base mapping table; Dynamic matching and calibration: Obtain the current household consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results to adjust the judgment criteria for the life cycle stage; Feature enhancement and result correction: If the confidence of the matching result is lower than the threshold, the background features and consumption features of the current household in the non-consumption dimension are fused to generate an enhanced feature vector, and the matching result is corrected based on the enhanced feature vector to obtain the corrected stage label; Knowledge base update and closed-loop feedback: The knowledge base mapping table is updated according to the corrected stage labels, and a new round of calibration parameter calculation and judgment standard optimization is triggered based on the updated mapping table and real-time monitored consumption data, forming a closed-loop knowledge control process.

2. The method according to claim 1, characterized in that, The process involves establishing a set of mapping relationships between family life cycle stages and corresponding consumption characteristics based on historical household consumption data and stage tags, forming a structured knowledge base mapping table, including: Cluster analysis was performed on historical household consumption data to obtain behavioral characteristic vectors for different consumer groups; By combining family structure information, a family life cycle discrimination rule is constructed, and each family in the historical family consumption data is assigned a life cycle stage label; Based on the life cycle stage labels and behavioral feature vectors, an association rule algorithm is used to calculate the support and confidence between each consumption feature and the family stage, and strong association rules are selected to construct a knowledge rule base. Based on the knowledge rule base, high-frequency consumption features corresponding to each lifecycle stage are extracted to generate a structured knowledge base mapping table.

3. The method according to claim 1, characterized in that, The process of obtaining current household consumption data, matching it with the knowledge base mapping table, and dynamically calculating calibration parameters based on the matching results includes: The new household consumption data is initially classified based on the categories and amounts in the consumption data, and then matched with the features in the knowledge base mapping table to obtain the feature matching degree. If the feature matching degree is lower than a preset threshold, the life cycle stage judgment boundary is calibrated to determine the deviation range of the initial classification. Based on the deviation range, the consumption characteristics of different family groups are analyzed, the dynamic trends of consumption categories and amounts are identified, and the results are compared with the standards of the knowledge base mapping table. The calibration parameters for dynamic adjustment are obtained by calculating the rate of change of consumption structure and time series offset. A user profile model is constructed by analyzing historical consumption patterns. The life cycle stage judgment criteria are adjusted according to the calibration parameters and matched with current consumption data to obtain the matching result of family stage affiliation.

4. The method according to claim 1, characterized in that, If the confidence level of the matching result is lower than the threshold, then the background features and consumption features of the current household (excluding consumption) are fused to generate an enhanced feature vector. Based on this enhanced feature vector, the matching result is corrected to obtain a corrected stage label, including: When the confidence level of the match is lower than the threshold, the current family's composition, income level, and residential area information are obtained as background features. The background features are quantized and vectorized, and then concatenated with the consumption feature vector to form an enhanced feature vector. The enhanced feature vectors are analyzed using a clustering algorithm to determine their respective clusters; Based on the matching relationship between the central features of the clusters and the predefined stage features, the corrected stage labels are determined.

5. The method according to claim 1, characterized in that, The step of updating the knowledge base mapping table according to the corrected stage labels, and triggering a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, includes: The knowledge base mapping table is updated by updating the stage tags, a structured analysis template is generated, standard guidelines that new employees can call are obtained, and standardized analysis basis is obtained. Based on the standardized analysis criteria, a feedback mechanism for dynamically adjusting strategies is constructed to continuously monitor real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated. The user profile model is optimized based on the recalculated calibration parameters. Different family structures and consumption patterns are reacquired, and the judgment rules are adaptively adjusted to obtain a universal tag system across scenarios and an updated set of knowledge records.

6. The method according to claim 3, characterized in that, The process involves initially classifying new household consumption data based on product categories and amounts, and then matching these classifications with features in the knowledge base mapping table to obtain a feature matching degree. If the feature matching degree is lower than a preset threshold, the lifecycle stage determination boundary is calibrated to determine the deviation range of the initial classification, including: Obtain the consumption categories and corresponding amounts of new households, and vectorize them according to the proportion of consumption amount of each category to the total consumption amount to obtain the consumption feature vector of new households; A similarity algorithm is used to calculate the similarity value between the new family consumption feature vector and the typical feature vectors of each life cycle stage in the knowledge base mapping table, so as to obtain the preliminary classification result of the new family and the corresponding feature matching degree. If the feature matching degree is lower than a preset threshold, the difference between the new household consumption vector and the corresponding stage feature vector in the knowledge base mapping table is identified, and the category with a difference greater than a preset deviation threshold is selected as the difference category. Calculate the degree of deviation for each differentiated product category, and determine the adjustment amount for the current life cycle stage judgment boundary based on the degree of deviation; Based on the adjustment amount, the boundary values ​​of the feature vectors of the original life cycle stages are corrected. Euclidean distance is used to calculate the distance between the new household consumption feature vector and the corrected feature vectors of the current stage and adjacent stages. The stage with the smallest distance is selected as the calibrated classification result. Based on the difference between the preliminary classification result and the calibrated classification result, the deviation range of the preliminary classification is determined.

7. The method according to claim 3 or 6, characterized in that, The process involves analyzing the consumption characteristics of different family groups based on the deviation range, identifying the dynamic trends in consumption categories and amounts, comparing them with the standards of the knowledge base mapping table, and obtaining dynamically adjusted calibration parameters by calculating the rate of change in consumption structure and time series offset, including: Based on the deviation range, families are clustered and grouped, and the consumption categories and corresponding amounts of each group at different time points are extracted; Calculate the changes in consumption amount for each category between adjacent time points, and use the moving average method to obtain the dynamic trend of consumption patterns of family groups; The dynamic change trend of the consumption pattern is compared item by item with the standard consumption pattern trend value corresponding to the life cycle stage in the knowledge base mapping table, and the consumption structure change rate of each category is calculated. Based on the consumption structure change rate sequence, the periodic characteristics of consumption changes in each category are calculated using the autocorrelation function, and the difference between the actual peak consumption time point and the standard peak time point is identified to obtain the time offset. By combining the weighted average method with the change rate weight and time offset weight of each category, the calibration parameters are dynamically adjusted.

8. The method according to claim 3, characterized in that, The process of constructing a user profile model based on historical consumption patterns, adjusting the lifecycle stage judgment criteria according to the calibration parameters, and matching it with current consumption data to obtain the matching result of family stage affiliation includes: By extracting category preferences, consumption frequency, and amount distribution features from historical consumption data, a multi-dimensional feature vector containing consumption habits, category preferences, and amount ranges is constructed. The multidimensional feature vectors are processed using a dimensionality reduction algorithm, and the principal components whose cumulative contribution rate exceeds a preset threshold are retained to obtain a set of typical consumption features for each life cycle stage as a user profile model. Based on the rate of change and time offset in the calibration parameters, the adjustment range of the judgment threshold for each life cycle stage is calculated to obtain the adjusted life cycle stage judgment standard. Using the adjusted lifecycle stage judgment criteria and the typical feature set in the user profile model, features are extracted and standardized from the current household's real-time consumption data. The distance between the standardized current features and the typical feature set of each stage is calculated, and the stage with the smallest distance is selected as the matching result for household stage affiliation.

9. The method according to claim 5, characterized in that, The process involves updating the knowledge base mapping table using the revised stage tags, generating a structured analysis template, obtaining standard guidelines that new employees can access, and acquiring standardized analysis criteria, including: By revising the stage labels, we extract the typical consumption characteristics and judgment rules of families in each stage and update the knowledge base mapping table. Based on the updated knowledge base mapping table, a tree-structured analysis template is constructed, which includes stage categories, feature dimensions, and specific judgment rules. Based on the structured analysis template, judgment conditions and feature thresholds are extracted to form a judgment sequence sorted by priority. A unique identifier is assigned to each judgment sequence to establish an index, thereby obtaining a standardized analysis basis.

10. The method according to claim 5, characterized in that, The feedback mechanism for constructing a dynamic adjustment strategy based on the standardized analysis criteria continuously monitors real-time changes in consumption data. If abnormal fluctuations are detected, the calibration parameters are automatically recalculated, including: Based on the judgment thresholds and characteristic ranges in the standardized analysis criteria, normal fluctuation ranges for each consumer product category are set, and a monitoring parameter table is established. By periodically collecting real-time consumption data and comparing it with the monitoring parameter table, a threshold-triggered feedback mechanism is constructed. The latest consumption data is obtained at preset time intervals, and the deviation of consumption amount for each category from the benchmark value is calculated. If the absolute value of the deviation exceeds the preset upper and lower limits of fluctuation, it is marked as abnormal fluctuation. Based on the abnormal fluctuation records, the recent consumption data sequence of the family groups that experienced the abnormality is extracted, and a smoothing algorithm is used to obtain the trend value. The ratio of the trend value to the benchmark value is calculated as the adjustment coefficient. The adjustment coefficient is multiplied by the original calibration parameters to obtain the recalculated calibration parameters.

11. The method according to claim 5 or 10, characterized in that, The process involves optimizing the user profile model based on recalculated calibration parameters, re-acquiring different family structures and consumption patterns, adaptively adjusting the judgment rules, obtaining a cross-scenario universal tagging system, and obtaining an updated knowledge record set, including: Based on the recalculated calibration parameters, the feature weights of each lifecycle stage in the user profile model are adjusted to obtain an optimized set of feature vectors. By performing cluster analysis on the optimized feature vectors, the features of different family structure types and corresponding consumption patterns are re-extracted; Extract multiple consumption characteristics for each household type across different consumption channels; calculate the stability index of each consumption characteristic across different consumption channels; if the stability index of a certain consumption characteristic exceeds a preset threshold, then include that consumption characteristic in the general feature set. Adjust the scope of application of the original judgment rules based on the general feature set, construct a label mapping relationship that includes channel identifier, family type, core features and applicable conditions, and generate a general label system across scenarios; The general tag system is merged with the knowledge base mapping table to obtain an updated set of knowledge records.

12. A family life cycle identification system based on dynamic calibration and knowledge inheritance, characterized in that, The system includes: The module is designed to establish a set of mapping relationships between family life cycle stages and corresponding consumption characteristics based on historical family consumption data and stage labels, forming a structured knowledge base mapping table. The matching and calibration module is configured to acquire the current household's consumption data, match it with the knowledge base mapping table, and dynamically calculate calibration parameters based on the feedback of the matching results in order to adjust the judgment criteria for the life cycle stage. The correction module is configured to, if the confidence level of the matching result is lower than a threshold, fuse the background features and consumption features of the current household's non-consumption dimension to generate an enhanced feature vector, and correct the matching result based on the enhanced feature vector to obtain the corrected stage label; The update feedback module is configured to update the knowledge base mapping table according to the corrected stage labels, and trigger a new round of calibration parameter calculation and judgment standard optimization based on the updated mapping table and real-time monitored consumption data, forming a closed-loop knowledge control process.

13. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed by the at least one processor, the one or more of the computer programs being executed by the at least one processor to enable the at least one processor to perform the family life cycle identification method based on dynamic calibration and knowledge inheritance as described in any one of claims 1-11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the family life cycle identification method based on dynamic calibration and knowledge inheritance as described in any one of claims 1-11.

15. A computer program product, characterized in that, Includes computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the family lifecycle identification method based on dynamic calibration and knowledge inheritance as described in any one of claims 1-11.

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