Multi-dimensional data fusion-based love and marriage individual positioning method and system

Abstract

The invention relates to the technical field of big data intelligent decision, and provides a love and marriage individual positioning method and system based on multi-dimensional data fusion, and the system comprises a multi-modal input module, a dynamic data collection middle platform, a data fusion processing module, an SWOT analysis matrix generator and a visual feedback module. According to the method, the data capture and analysis capability of a big data model is utilized, multi-dimensional dynamic weight fusion is carried out on regional economic indexes, environmental factors and personalized basic data of users, and the users are helped to objectively know the love and marriage environment where the users are located and carry out relative positioning through quantitative evaluation and SWOT analysis; and meanwhile, the advantages, disadvantages, opportunities and threat items of the user in the love and marriage environment are presented by virtue of visual analysis, and corresponding strategies are provided, so that the love and marriage anxiety of the user can be effectively relieved.

Description

Technical Field

[0001] The present application relates to the field of big data intelligent decision-making technology, and specifically to a method and system for personal positioning of love and marriage based on multi-dimensional data fusion. Background Art

[0002] In recent years, my country's marriage and dating market has undergone significant structural changes. With rapid economic and social development, the economic pressures facing people of marriageable age continue to grow. The rising costs of marriage and childcare have made marriage a "high-cost option" for young people, requiring careful consideration. Data from the National Bureau of Statistics shows that the number of marriage registrations in my country has been declining for several consecutive years, while the number of single people has increased dramatically. At the same time, social attitudes toward marriage and dating have undergone profound shifts, with more and more young people placing personal fulfillment, equal emotional exchange, and improved quality of life at the core of their marriage and dating decisions.

[0003] In the digital age, online social platforms and short video content have a dual impact on marriage and love perceptions: on the one hand, the diverse dissemination of information provides young people with a broader perspective on mate selection, further strengthening their personalized needs for matching interests and values. On the other hand, the mismatch between the "idealized partner" image created in virtual social interactions and the resources in the real marriage market exacerbates cognitive confusion and psychological pressure among those of marriageable age. This, in turn, leads to varying degrees of marriage anxiety among many young people of marriageable age, and they resist marriage due to concerns about financial burdens and family relationships. The heated online discussion of negative marriage cases has further amplified these social sentiments, even fostering gender antagonism in some groups.

[0004] Currently, young people of marriageable age lack a systematic assessment of their comprehensive personal qualities and market positioning when considering marriage and dating. Specifically, the lack of a scientific, quantitative assessment system makes it difficult to objectively reflect their competitiveness in the marriage and dating market. Second, they lack the ability to integrate external environmental data, making it impossible to conduct dynamic analysis based on public data such as regional economic levels, marriage and dating policies, and social culture. Third, influenced by their own subjective perceptions and those of their surroundings, it is difficult for them to objectively and comprehensively understand their strengths, weaknesses, opportunities, and threats in the marriage and dating landscape. Consequently, the current marriageable population suffers from ambiguous criteria and biased positioning in their mate selection process. This can lead to missed opportunities due to over-idealization, while insufficient self-awareness can reduce their confidence in marriage and dating, leading to emotional distress, missed opportunities, and hindered growth. This, in turn, impacts social issues such as marriage rates, consumption and economic structures, and marriage and dating values. Summary of the Invention

[0005] This application provides a marriage and love personal positioning method and system based on multi-dimensional data fusion, aiming to solve the common problems of vague standards and positioning deviation in the current marriageable population during the mate selection process.

[0006] To achieve the above objectives, this application provides a method for personal positioning of love and marriage based on multi-dimensional data fusion. The specific steps are as follows:

[0007] S1. receiving input of basic data through an interactive user terminal;

[0008] S2. Calling public platform APIs, software crawler systems, and data lakes to collect public data sets on regional economic indicators, marriage market data, and cultural values;

[0009] S3. Organize and establish a hierarchical structure model of basic data and public data and a criterion-level judgment matrix;

[0010] S4. Use AHP to calculate and assign weights to the criterion layer judgment matrix;

[0011] S5. Generate a SWOT analysis matrix to quantitatively assess the user's personal strengths, opportunities, weaknesses, and risks in the marriage and love environment;

[0012] S6. Use visualization tools to generate user personal positioning radar maps, object portrait heat maps, and risk warning dashboards.

[0013] Furthermore, in step S1, the basic data includes necessary parameters and personalized parameters;

[0014] Necessary parameters include region, gender, age, health status, education, occupation, income, expenditure, material foundation, family background, fertility intention, values, core needs, communication skills, and clarity of self-awareness;

[0015] Personalization parameters include height, weight, zodiac sign, constellation, hobbies, and MBTI.

[0016] Furthermore, in step S2, the public platform includes the official websites of the local statistics bureau, housing and construction bureau, human resources and social security bureau, civil affairs bureau, and health commission, which are used to obtain data on local salary levels, regional occupational distribution, the number of people of marriageable age and the ratio of men to women, and regional average height;

[0017] The crawler system collects regional career preferences, regional marriage and love public opinion, and regional housing price data for recruitment, matchmaking, and house rental software;

[0018] The data lake includes public data lakes of local forums, community organizations, and marriage and dating platforms, which collect regional marriage and dating cultural information and marriage and dating service resource data.

[0019] Furthermore, in step S3, the target layer of the hierarchical model is: comprehensive assessment of marriage feasibility;

[0020] The criteria layers of the hierarchical model include: material and economics, family and social relations, culture and values, physiology and health, personal freedom, personality traits, and long-term risk prediction.

[0021] Furthermore, the key judgment logic of the criterion layer judgment matrix is:

[0022] Culture and values: Mismatched values ​​may lead to fundamental conflicts in marriage. Based on the core influence of “value consistency” on marital stability in sociological research, this is given the highest relative importance.

[0023] Personality traits: Personality compatibility directly determines the quality of daily interactions. Psychological research shows that insufficient emotional management skills are one of the main causes of marital breakdown, second only to values.

[0024] Material and economics: The economic foundation is the material guarantee for the continuation of marriage, but overemphasizing material things may neglect the emotional core, making it less important than values ​​and personality, and more important than family relationships and health;

[0025] Family and social relations / physiology and health: Family support affects early marital adaptation, and health is the foundation of long-term life, but both are auxiliary factors and are given equal importance;

[0026] Personal freedom: Modern marriages place more emphasis on individual development, but excessive pursuit of freedom may squeeze out the space for marital collaboration, making it less important.

[0027] Long-term risk prediction: Career stability, health risks, etc. affect the ability of marriage to resist risks, but they are "future-oriented" factors and are slightly less important than immediate factors.

[0028] Furthermore, in step S4, based on the criterion layer judgment matrix, the weight distribution result is:

[0029] Culture and values ​​28.5%, personality traits 22.3%, material and economy 15.2%, long-term risk prediction 10.8%, family and social relations 8.7%, physiology and health 7.9%, and personal freedom 6.6%.

[0030] Furthermore, in step S5, when quantitatively evaluating the user's personal strengths, opportunities, weaknesses and risks, combined with weight distribution, focus on high-weight core layers such as culture and values, and personality traits, while taking into account medium-weight support layers such as material and economics, long-term risk prediction, family and social relationships, and avoid excessive interference from low-weight auxiliary layers such as physiology and health, and personal freedom.

[0031] In addition, to achieve the above purpose, the present application also provides a marriage and love personal positioning system based on multi-dimensional data fusion, which is used to implement the above-mentioned marriage and love personal positioning method based on multi-dimensional data fusion, and the system includes:

[0032] Multimodal input module, which is equipped with an integrated structured form and a visual input unit for users to input basic data of love and marriage positioning;

[0033] Dynamic data collection middle platform, which integrates public platform API, software crawler system and data lake, collects public data information of the user's area based on the basic data of marriage and love positioning;

[0034] Data fusion processing module: The data fusion processing module deploys standardized components and a weight distribution engine. The standardized components integrate basic marriage and love positioning data with public data across dimensions, allowing the weight distribution engine to weight data items.

[0035] SWOT analysis matrix generator, which uses the data processed by the data fusion processing module to construct a matrix of strengths, weaknesses, opportunities and threats to provide matching analysis for users;

[0036] Visual feedback module, the visual feedback module visualizes the analysis results of the SWOT analysis matrix generator and provides users with feedback on personal positioning, virtual object portraits and risk warnings.

[0037] Furthermore, users can accurately input the region, gender, age, health status, education, occupation, income, expenditure, material foundation, family background, fertility intention, height, weight, zodiac sign, constellation, hobbies and MBTI in the basic data of marriage and love positioning through an integrated structured form;

[0038] The values, core needs, communication skills, and self-awareness clarity in the basic data of marriage and love positioning can be used to assist users in generating data through visual input units in the form of core questions and answers, scenario simulations, and evaluation tools.

[0039] Furthermore, the marriage and love personal positioning system based on multi-dimensional data fusion also includes a privacy protection module;

[0040] The privacy protection module includes a data desensitization unit for interval processing of some basic data input by users; a federated learning gateway that uses homomorphic encryption to transmit model gradients and only retains the original data locally; and a forgetting right unit that allows users to dynamically delete behavioral trajectory data for a specific time period.

[0041] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0042] Accurately locate and eliminate cognitive bias. This application integrates user personal data and regional environmental data, and uses a dynamic weighting algorithm to analyze and evaluate the user's marriage and love positioning, effectively solving the problem of self-perception distortion caused by subjective cognition.

[0043] Scientific decision-making and targeted strategies. Based on the SWOT matrix analysis with weight distribution, it quantifies and breaks down multi-dimensional data from four dimensions: strengths, weaknesses, opportunities, and threats, generates visual analysis results, and provides users with targeted love and marriage strategies.

[0044] Intelligent recommendations alleviate relationship anxiety. Based on integrated data and SWOT analysis strategies, a virtual mate selection profile that meets the user's objective conditions is generated, identifying the user's potential core needs and making differentiated recommendations based on regional environmental factors.

[0045] Closed-loop feedback boosts confidence in love and marriage. Based on multi-dimensional analysis, the system provides feedback including suggestions for seizing opportunities and mitigating risks, forming a dynamic closed loop of "assessment-suggestion-optimization-reassessment," helping users boost their confidence in love and marriage through predictable improvement plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 A schematic diagram of a method flow for a love and marriage personal positioning method based on multi-dimensional data fusion provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of the system modules of a love and marriage personal positioning system based on multi-dimensional data fusion provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0050] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used in this specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0051] In order to solve the common problems of ambiguous standards and positioning deviation in the process of mate selection among people of marriageable age, this application embodiment provides a marriage and love personal positioning method based on multi-dimensional data fusion. Figure 1 , the method comprises the following steps:

[0052] S1. Receive input of basic data through an interactive user terminal.

[0053] In step S1, the front-end can use a responsive form component to support adaptive input on both mobile and PC devices. It can also configure data classification and validation mechanisms, such as automatically identifying marriageable age groups based on age and determining whether a user is "entering the marriageable stage, prime candidates for marriage, or primarily married." Alternatively, it can perform logical validation on income and occupation types. By receiving basic user input, a personalized user data platform is constructed, providing internal variables for subsequent data analysis.

[0054] Basic data includes both essential parameters and personalized parameters. Essential parameters focus on "real-life survival foundation" and "core compatibility." Fundamental conflicts between these parameters can destabilize the foundation of a marriage. Personalized parameters, centered around "emotional preferences" and "cultural and entertainment labels," can enhance relationship compatibility and romantic experience, but should not be considered decisive.

[0055] Specifically, the necessary parameters include core compatibility conditions and the basic conditions for survival and development in reality. Core compatibility conditions include age, which influences fertility plans and the synchronization of life stages; educational background, which generally reflects cognitive level, value formation, communication patterns, and other related factors; health status, which includes physical and mental health and directly affects the ability to take responsibility in marriage and long-term quality of life; family background, which includes the cultural traditions, parenting methods, economic status, and degree of intervention in children's marriages of the original family, which may cause intergenerational conflicts and is an important hidden factor in relationship stability; fertility intentions, including childrearing intentions, division of childrearing labor, and childrearing philosophy, which are core issues in marriage; values, which include the consistency of value cognition, which is the underlying logic of long-term harmonious relationships; communication skills, which include the ability to effectively express needs, listen, and negotiate, which is key to protecting marital conflicts; and self-awareness, which includes a clear understanding of one's core needs, which is related to one's sense of happiness in the relationship.

[0056] The basic conditions for real survival and development involve economic-related material conditions such as monthly / annual income, average monthly expenditure, savings, houses and cars - which directly affect the material foundation of marriage, quality of life and risk resistance, and are key supporting factors; region - involves living costs, family support, career development opportunities, etc., and regional differences are important environmental variables that affect other parameters; occupation - reflects social roles, income stability, time allocation patterns and social resources, and may also affect the division of family responsibilities.

[0057] Specifically, personalized parameters involve external surface features such as height and weight - which belong to aesthetic preferences for appearance and are related to attractiveness, but are not the core of relationships; zodiac signs and constellations - personality labels based on culture or entertainment, with no scientific basis, mostly social topics or psychological implications; hobbies - interests and hobbies reflect common topics and lifestyles, which can increase the fun of getting along, but are not necessary; MBTI - can assist in understanding personality tendencies, but needs to be combined with actual behavioral observations.

[0058] S2. Call the public platform API, software crawler system and data lake to collect public data sets of regional economic indicators, marriage market data, and cultural values.

[0059] In step S2, the public data set mainly involves public data information of the user's area, such as core economic indicators, population structure and marriage market supply and demand, cultural customs and social concepts, policies and public services.

[0060] Specifically, core economic indicators include regional salary levels, used to assess economic competitiveness and match the marriage market's expectations of material well-being; and regional housing prices, which influence housing demand and marriage costs. Population structure and marriage market supply and demand include the number of eligible individuals and the male-to-female ratio in a region, used to assess competition; and occupational distribution, which reflects the matching of career preferences and resources in the marriage market. Cultural customs and social attitudes include regional bride price levels, which influence marriage costs and family relationships; marriage and romance public opinion, which provides insights into shifts in local social attitudes; and regional average height, which influences appearance preferences and social confidence. Policies and public services include regional marriage and childbearing policies, which influence marriage and childbearing decisions; and marriage and romance service resources that can expand social opportunities.

[0061] Specifically, the public platform includes the official websites of local statistics bureaus, housing and construction bureaus, human resources and social security bureaus, civil affairs bureaus, health commissions, etc., which are used to obtain local salary levels, regional occupational distribution, the number of people of marriageable age and the ratio of men to women, and regional average height data; the crawler system collects regional occupational preferences, regional marriage and love public opinion, and regional housing price data from software such as recruitment, matchmaking, and house rental; the data lake includes public data lakes of local forums, community organizations, marriage and love platforms, etc., which collect regional marriage and love cultural information and marriage and love service resource data.

[0062] Furthermore, it is also possible to extend the collection of information on the local education level distribution, which to a certain extent affects the value matching; the divorce rate and marital stability in the region, to assist in assessing marriage risks; and the activity level of related social circles. Active social resources will affect the efficiency of marriage matching.

[0063] S3. Organize and establish a hierarchical structure model of basic data and public data and a criterion-level judgment matrix.

[0064] In step S3, based on the direct impact on the core stability and practical feasibility of the marriage relationship, the basic data input by the user and the collected public data are integrated and sorted into multi-dimensional data, including commonality merging and element separation, to obtain seven superordinate factors, specifically:

[0065] Material and economic, including economic foundation, family economic background, income and expenditure;

[0066] Family and social relationships, including family support, family values, and family networks;

[0067] Culture and values, including educational background, consumption attitudes, fertility attitudes, and values;

[0068] Physiology and health, including appearance, body shape, age, and health status;

[0069] Personal freedom, including lifestyle and career planning;

[0070] Personality traits, including personality compatibility and emotional management skills;

[0071] Long-term risk prediction, including occupational stability, age-related disease risks, and environmental variables.

[0072] A hierarchical model for users is established, with "comprehensive assessment of marriage feasibility" as the target layer and seven higher-level factors as the criteria layer. Complex multi-dimensional marriage and love assessment data is organized into a computable structured model.

[0073] Furthermore, a pairwise judgment matrix was used to construct a criterion-level judgment matrix. The pairwise judgment matrix uses a 1-to-9 scale, with 1 = equally important, 3 = slightly important, 5 = significantly important, 7 = strongly important, and 9 = extremely important, with 2 / 4 / 6 / 8 being intermediate values. The relative importance of each element in the criterion level was determined through logical deduction and social science research consensus, as shown in Table 1.

[0074] Table 1 Criteria layer judgment matrix

[0075]

[0076] Among them, the importance of the criteria layer is ranked as follows: first - culture and values, second - personality traits, third - material and economy, fourth - family and social relations / physiology and health, fifth - personal freedom, and sixth - long-term risk prediction.

[0077] The key judgment logic is that the essence of marriage is long-term cooperation, and mismatches in values ​​may lead to fundamental conflicts. Referring to the core influence of "value consistency" on marital stability in sociological research, culture and values ​​are given the first relative importance; personality compatibility directly determines the quality of daily interactions. Psychological research shows that insufficient emotional management ability is one of the main causes of marital breakdown, second only to values ​​in importance; economic foundation is the material guarantee for the continuation of marriage, but over-emphasis on material may ignore the emotional core, so its importance is lower than values ​​and personality, and higher than family relationships and health; family support affects the initial adaptation to marriage, and health is the foundation of long-term life, but both are auxiliary factors and of medium importance; modern marriage pays more attention to individual development, but excessive pursuit of freedom may squeeze the space for marital cooperation, so personal freedom is less important; career stability, health risks, etc. affect the ability of marriage to resist risks, but they are "future-oriented" factors and are slightly less important than immediate factors.

[0078] S4. Use AHP to calculate and assign weights to the criterion layer judgment matrix.

[0079] In step S4, the weight calculation uses the arithmetic mean method to calculate the normalized mean of each row of the criterion layer judgment matrix, and undergoes a consistency test. The final weight distribution results are shown in Table 2.

[0080] Table 2 Weight distribution results

[0081]

[0082] Furthermore, based on the final weight distribution results, the superordinate factors affecting marriage decisions were analyzed: first, the priority of values ​​and personality is in line with the definition that the essence of marriage is an "emotional-cognitive community". The core factors determine the stability of long-term cooperation, avoiding the misunderstanding of "materialism" or "superficial matching"; and it is necessary to affirm the necessity of material foundation, but not exaggerate its role. At the same time, it incorporates long-term risk assessment to reflect rational decision-making considerations of future uncertainties; the weights of family support and personal freedom are moderate, reflecting the dual needs of modern marriage for "intergenerational independence" and "individual development"; finally, weakening short-term attraction factors such as appearance and strengthening long-term survival factors such as health are in line with the trend of marriage shifting from "passion-oriented" to "responsibility-oriented".

[0083] S5. Generate a SWOT analysis matrix to quantitatively evaluate the user's personal strengths, opportunities, weaknesses and risks in the marriage and love environment.

[0084] In step S5, the SWOT matrix is designed in combination with AHP weight distribution and marriage evaluation factors. The internal positive factors-strength, internal negative factors-weakness, external favorable factors-opportunity, and external unfavorable factors-threat of the user are extracted for each weight factor. The SWOT matrix is cross-analyzed, and the evaluation result is converted into a feasible strategic analysis. The SWOT matrix is shown in Table 3.

[0085] Table 3 SWOT matrix diagram

[0086] Advantage(S) Disadvantage (W) Opportunity (O) SO Strategy WO Strategy Threat (T) ST Strategy WT Strategy

[0087] Specifically, in generating the strategy in combination with AHP weight, attention is focused on the high-weight core layer of culture and values, personality characteristics, and the like, the medium-weight support layer of material and economy, long-term risk prediction, family and social relations, and the like is taken into account, and the low-weight auxiliary layer of physiology and health, personal freedom, and the like is avoided from interfering with the core decision excessively. The specific examples of the SWOT matrix project of the user are shown in Tables 4 and 5.

[0088] Table 4 SWOT matrix framework for user marriage condition evaluation-internal factors

[0089]

[0090]

[0091] Table 5 SWOT matrix framework for user marriage condition evaluation-external factors

[0092]

[0093] S6, a visual tool is used to generate a user personal positioning radar chart, an object image heat map, and a risk prompt dashboard.

[0094] In step S6, the SO and WO strategies, based on the SWOT matrix, generate a radar chart for the user's personal positioning. By comparing the user's absolute strengths and relative weaknesses with the regional average, the user can be identified, avoiding either blind confidence or excessive anxiety when self-analyzing and positioning. A heat map of complementary and compatible objects is created based on the user's strengths, weaknesses, and weighted matching. This visually matches user adaptation factors according to weights, preventing the neglect of core conflicts due to minor factors. The ST and WT strategies, based on the SWOT matrix, generate a risk warning dashboard by highlighting key weaknesses and risk items, transforming the qualitative descriptions in the SWOT matrix into quantifiable risk indicators. Color / icon alerts are provided to help users prioritize high-impact issues. The synergy between the personal positioning radar chart, object profile heat map, and risk warning dashboard allows users to move from "fuzzy perception" to "data-driven" decision-making, making in-depth judgments around high-weighted dimensions and replacing subjective judgments with indicators such as the "economic safety line" and the "value conflict index," thereby reducing decision-making bias.

[0095] Therefore, the present invention provides a method for locating individuals in love and marriage based on multi-dimensional data fusion, which has the following technical effects:

[0096] Multidimensional data integration builds an objective evaluation benchmark. By integrating user baseline data with multi-source heterogeneous data such as regional economic indicators and cultural values, a hierarchical model with seven dimensional criteria layers, including material economy and social relations, is established. This effectively overcomes the limitations of traditional relationship evaluations, which are dominated by subjective experience. Using the AHP (Analytical Hierarchy Process) to construct a pairwise judgment matrix for multidimensional data, this eliminates evaluation distortions caused by individual cognitive biases and provides a verifiable objective benchmark for relationship feasibility analysis.

[0097] Dynamic weighting enables precise strategic positioning. This approach uses a normalized arithmetic mean method to weight criteria-level elements, dynamically adjusting the evaluation system based on scenarios such as material foundation and cultural compatibility. Compared to fixed-weight models, this method improves the accuracy of identifying strengths within the SWOT analysis matrix and aligns risk thresholds with actual regional market conditions, helping users accurately identify their competitive advantages and areas for improvement in the dating market.

[0098] Quantified decision support generates executable strategic paths. By converting abstract factors such as personality traits and long-term risks into calculable parameters, combined with the traffic light warning mechanism in the risk warning dashboard, complex relationship decisions can be broken down into actionable phased goals.

[0099] Visual dynamic feedback forms a positive promotion closed loop. Based on the radar chart and heat map visualization comparison, users can intuitively perceive the matching probability changes brought by condition adjustment. Combined with the "strength conversion suggestion-risk mitigation plan" double path generated by SWOT analysis, a strong correlation between evaluation results and behavior improvement is established, breaking the vicious cycle of "high anxiety-low action".

[0100] As an implementation solution, please refer to Figure 2 The application also provides a multi-source data fusion dynamic commodity pricing system, which is used to implement the multi-source data fusion dynamic commodity pricing method in the foregoing embodiments. The system comprises:

[0101] The multi-modal input module is an interactive functional module for user to input basic data. The multi-modal input module is configured with an integrated structured form and a visual input unit. The integrated structured form is a form integrating basic items required by system analysis. The basic items involved in the integrated structured form are part of the items for which the user can accurately provide fixed or similar parameters. The visual input unit has data input components of interactive auxiliary tools such as core question and answer tools, scenario simulation tools, and evaluation tools, which are mainly aimed at some abstract concepts or part of items that are easily disturbed by user's subjective cognition. By receiving the basic data of the user, the system can build a corresponding user portrait, which is convenient for subsequent data integration and analysis.

[0102] Among them, the data of region, gender, age, health status, education, occupation, income, expenditure, material basis, family background, fertility intention, height, weight, zodiac, constellation, hobby and MBTI can be accurately input by the user through the integrated structured form; the data of values, core needs, communication ability, and self-cognition clarity can be generated by the user with the help of the visual input unit using core question and answer, scenario simulation, and evaluation tools.

[0103] For example, when the visual input unit is used to input data of user values, it can analyze the values through a value coordinate axis. Specifically, a number of core value labels are designed, such as "freedom and independence", "career achievement", "material security", "emotional resonance", "family stability", and "social contribution", etc. The user can sort and put them into a two-dimensional coordinate system according to their subjective importance and analyze to generate corresponding basic data; and / or use the STAR-R model to backtrack important decisions. Specifically, the user inputs some past key decisions, such as career choice, major consumption, relationship turning point, etc. S scenario-T trigger point-A action-R result-R reflection record, and according to the record, the corresponding basic data is generated by attribution analysis.

[0104] The dynamic data collection middle platform integrates public platform APIs such as local statistics bureaus, human resources and social security bureaus, civil affairs bureaus, and health commissions; related software crawler systems such as recruitment, matchmaking, and house rentals; and data lakes of local forums, community organizations, and marriage and dating platforms. It collects corresponding core economic indicators, population structure and marriage and dating market supply and demand, cultural customs and social concepts, policies and public services, and other public data information based on the basic data input by users.

[0105] Specifically, the collected public data can be cleansed to address heterogeneous data. For example, this can be achieved by using the calendar year as the statistical period to unify the temporal dimension, matching the city code of the user's IP address to unify the spatial dimension, and using the IQR quartile method to filter extreme data and address outliers. Public data sets serve as external environmental variables for the system's analysis of user relationship and marriage personalization, helping to address the problem of traditional subjective assessments being detached from the actual impact of local conditions.

[0106] The data fusion processing module deploys standardized components and weight distribution engines to perform cross-dimensional integration and processing on the data from the multimodal input module and the dynamic data acquisition middle platform.

[0107] Exemplarily, the normalization component may be used to perform a Z-score normalization method, and the weight allocation engine may perform weight allocation based on an AHP (Analytical Hierarchy Process).

[0108] Z-score normalization, also known as standard deviation normalization, is a data normalization method. Since different variables may have different units and magnitudes, normalization converts them into dimensionless values, allowing for comparison and analysis of different variables on the same scale. The normalized data has a mean of 0 and a standard deviation of 1, following a standard normal distribution, making it easier to visualize and understand the data.

[0109] The AHP weight distribution is a method of decomposing a complex problem into multiple levels, determining the relative importance of each factor by establishing a hierarchical model, constructing a judgment matrix, performing single-level sorting and total-level sorting, etc. Among them, the establishment of hierarchical model is to decompose the factors involved in the problem into several levels from top to bottom according to different attributes. The top layer is the target layer, which is usually the goal of decision-making or the result to be achieved. The middle layer is the criterion layer, which is the various criteria or factors affecting the realization of the target. The bottom layer is the scheme layer, which is the various feasible schemes or measures to achieve the target. The judgment matrix is constructed by comparing the relative importance of each element in the same level with respect to the element in the upper level. The 1-9 scale method is used to represent the comparison results with numerical values, forming the judgment matrix. The single-level sorting is to calculate the characteristic vector and the maximum eigenvalue of the judgment matrix, and to determine the relative importance of each element with respect to the element in the upper level by the characteristic vector, i.e. the weight vector. The total-level sorting is to calculate the combined weight of each element in the scheme layer with respect to the target layer, i.e. the total-level sorting. The weights of each level are integrated to obtain the total weight of each scheme with respect to the target, thereby providing a basis for decision-making.

[0110] The SWOT analysis matrix generator constructs the advantage, disadvantage, opportunity and threat matrix using the data processed by the data fusion processing module, and matches the analysis for the user.

[0111] Among them, the SWOT matrix is a tool commonly used for strategic analysis of enterprises, which helps enterprises to develop corresponding development strategies by comprehensively analyzing the internal strengths-S, weaknesses-W, external opportunities-O and threats-T of enterprises.

[0112] Similarly, by taking the specific content of each weighted element as the classification factor and specific item of the SWOT matrix, the SWOT matrix can be used to analyze the marriage strategy of the user personally. Specifically, based on the specific conditions of the user, the user can analyze the internal factors that the user himself has and can make him in a favorable position in the marriage market competition, i.e. the advantage item; the factors that exist in the user himself and may limit his development or make him in an unfavorable position in the marriage competition, i.e. the disadvantage item; the factors and trends that exist in the external environment of the region where the user lives and are favorable to the user's marriage promotion, i.e. the opportunity point; the factors and challenges that may have an adverse effect on the user's marriage development in the overall environment of the region where the user lives, i.e. the threat point.

[0113] Specifically, the SWOT matrix construction lists the user's strengths, weaknesses, opportunities and threats in a four-quadrant matrix, and through the combination analysis of different factors, draws corresponding strategic recommendations, such as the advantage opportunity strategy - SO, which uses the user's own advantages to seize regional environmental opportunities; the disadvantage opportunity strategy - WO, which makes up for the user's own disadvantages by utilizing the opportunities brought by regional differences; the advantage threat strategy - ST, which relies on the user's own advantages to deal with the general threats in the region; the disadvantage threat strategy - WT, which considers how to overcome the user's own disadvantages and avoid external threats in the environment.

[0114] Visual feedback module, the visual feedback module visualizes the analysis results of the SWOT analysis matrix generator and provides users with feedback on personal positioning, virtual object portraits and risk warnings.

[0115] Specifically, the visual feedback module includes a personal positioning radar chart that displays the user's scores in each dimension and the regional average. By comparing with the regional average, it identifies the user's absolute advantages and relative shortcomings, avoiding blind confidence or excessive anxiety when the user self-analyzes and positions himself; an object portrait heat map that sorts weighted factors by weight matching, visually matches user adaptation factors by weight distribution, and prevents the neglect of core contradictions due to minor factors; and a risk warning dashboard that differentially highlights key disadvantages and risk items, converts the qualitative descriptions in the SWOT matrix into quantifiable risk indicators, and uses color / icon warnings to help users prioritize high-impact issues.

[0116] In particular, the marriage and love personal positioning system based on multi-dimensional data fusion also includes a privacy protection module.

[0117] Among them, the privacy protection module includes a data desensitization unit for interval processing of some basic data input by users; a federated learning gateway that uses homomorphic encryption to transmit model gradients and only retains the original data locally; and a forgetting right unit that allows users to dynamically delete behavioral trajectory data for a specific time period.

[0118] Furthermore, the marriage and love personal positioning system based on multi-dimensional data fusion provided by this application has the following advantages: utilizing the data capture and analysis capabilities of the big data model, regional economic indicators, environmental factors and users' personalized basic data are dynamically weighted in multiple dimensions, and through quantitative evaluation and SWOT analysis, it helps users to objectively understand their own marriage and love environment and relative positioning; at the same time, with the help of visual analysis, it presents the user's strengths, weaknesses, opportunities and threats in the marriage and love environment and provides corresponding strategies to effectively alleviate the user's marriage and love anxiety.

[0119] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for personal love and marriage positioning based on multi-dimensional data fusion, characterized by: The method comprises the following steps: S1. receiving input of basic data through an interactive user terminal; S2. Calling public platform APIs, software crawler systems, and data lakes to collect public data sets on regional economic indicators, marriage market data, and cultural values; S3. Organize and establish a hierarchical structure model of basic data and public data and a criterion-level judgment matrix; S4. Use AHP to calculate and assign weights to the criterion layer judgment matrix; S5. Generate a SWOT analysis matrix to quantitatively assess the user's personal strengths, opportunities, weaknesses, and risks in the marriage and love environment; S6. Use visualization tools to generate user personal positioning radar maps, object portrait heat maps, and risk warning dashboards.

2. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 1 is characterized in that: In step S1, the basic data includes necessary parameters and personalized parameters; The necessary parameters include region, gender, age, health status, education, occupation, income, expenditure, material foundation, family background, fertility intention, values, core needs, communication skills, and clarity of self-awareness; The personalized parameters include height, weight, zodiac sign, constellation, hobbies, and MBTI.

3. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 1 is characterized in that: In step S2, the public platform includes the official websites of the local statistics bureau, housing and construction bureau, human resources and social security bureau, civil affairs bureau, and health commission, which are used to obtain data on local salary levels, regional occupational distribution, the number of people of marriageable age and the ratio of men to women, and regional average height; The crawler system collects regional occupational preferences, regional marriage and love public opinion, and regional housing price data for recruitment, matchmaking, and house rental software; The data lake includes public data lakes of local forums, community organizations, and marriage and love platforms, which collect regional marriage and love cultural information and marriage and love service resource data.

4. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 1 is characterized in that: In step S3, The target layers of the hierarchical model are: comprehensive assessment of marriage feasibility; The criteria layers of the hierarchical model include: material and economy, family and social relations, culture and values, physiology and health, personal freedom, personality traits, and long-term risk prediction.

5. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 4 is characterized in that: The key judgment logic of the criterion layer judgment matrix is: Culture and values: Mismatched values ​​can lead to fundamental conflicts in marriages. Based on the core impact of "value consistency" on marital stability in sociological research, this is given the highest relative importance. Personality traits: Personality compatibility directly determines the quality of daily interactions. Psychological research shows that insufficient emotional management skills are one of the main causes of marital breakdown, second only to values. Material and economics: The economic foundation is the material guarantee for the continuation of marriage, but overemphasizing material things may neglect the emotional core, making it less important than values ​​and personality, and more important than family relationships and health; Family and social relations / physiology and health: Family support affects early marital adaptation, and health is the foundation of long-term life, but both are auxiliary factors and are given equal importance; Personal freedom: Modern marriages place more emphasis on individual development, but excessive pursuit of freedom may squeeze out the space for marital collaboration, making it less important. Long-term risk prediction: Career stability and health risks will affect the risk resistance of marriage, but they are "future-oriented" factors and are slightly less important than immediate factors.

6. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 5 is characterized in that: In step S4, based on the criterion layer judgment matrix, the weight distribution result is: Culture and values ​​28.5%, personality traits 22.3%, material and economy 15.2%, long-term risk prediction 10.8%, family and social relations 8.7%, physiology and health 7.9%, and personal freedom 6.6%.

7. The method for personal love and marriage positioning based on multi-dimensional data fusion according to claim 6 is characterized in that: In step S5, when quantitatively evaluating the user's personal strengths, opportunities, weaknesses, and risks, combined with weight distribution, focus on high-weight core layers such as culture and values, and personality traits, while taking into account medium-weight support layers such as material and economics, long-term risk prediction, family and social relationships, and avoid excessive interference from low-weight auxiliary layers such as physiology and health, and personal freedom.

8. A love and marriage personal positioning system based on multi-dimensional data fusion, characterized by: A method for locating individuals in love and marriage based on multi-dimensional data fusion according to any one of claims 1 to 7, the system comprising: A multimodal input module, wherein the multimodal input module is configured with an integrated structured form and a visual input unit for users to input basic data of love and marriage positioning; Dynamic data collection middle platform, which integrates public platform API, software crawler system and data lake, and collects public data information of the user's area based on the basic data of marriage and love positioning; A data fusion processing module deploys a standardization component and a weight distribution engine. The standardization component integrates the basic marriage and love positioning data with the public data across dimensions, so that the weight distribution engine can weight the data items. A SWOT analysis matrix generator, which uses the data processed by the data fusion processing module to construct a strengths, weaknesses, opportunities and threats matrix to provide matching analysis for users; A visual feedback module visualizes the analysis results of the SWOT analysis matrix generator and provides users with feedback on personal positioning, virtual object portraits and risk warnings.

9. The love and marriage personal positioning system based on multi-dimensional data fusion according to claim 8 is characterized in that: The region, gender, age, health status, education, occupation, income, expenditure, material foundation, family background, fertility intention, height, weight, zodiac sign, constellation, hobbies and MBTI in the basic data of marriage and love positioning can be accurately input by the user through the integrated structured form; The values, core needs, communication skills, and self-awareness clarity in the basic data of marriage and love positioning can use the visual input unit to assist users in generating data in the form of core questions and answers, scenario simulations, and evaluation tools.

10. The love and marriage personal positioning system based on multi-dimensional data fusion according to claim 8, characterized in that: Also includes a privacy protection module; The privacy protection module includes a data desensitization unit for interval processing of some basic data input by users; a federated learning gateway that uses homomorphic encryption to transmit model gradients and only retains the original data locally; and a forgetting right unit that allows users to dynamically delete behavioral trajectory data for a specific time period.

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