Data analysis system based on family pedigree

Abstract

The embodiment of the application discloses a kind of data analysis systems based on family map, the system includes: front desk interaction module;The front desk interaction module includes family characteristic analysis module and user question and answer module;The family characteristic analysis module is used to generate the family characteristic analysis result of user according to the data information of multiple generations of family members input by user;The family characteristic analysis result is used to indicate the influence degree of different dimensions of family characteristics to user;The user question and answer module is used to recommend at least one question and answer intelligent agent for user according to user input session information;And based on the session information, using the first question and answer intelligent agent selected by user, generate session response for the session information, and associated with the family characteristic analysis result;Wherein, the user question and answer module is built-in with multiple different question and answer intelligent agents, and the knowledge base and response style of different question and answer intelligent agents are different.

Description

Technical Field

[0001] This application relates to the field of data analysis, specifically to a data analysis system based on family tree mapping. Background Technology

[0002] Family tree-based data analysis systems typically display information such as blood relations, marital relationships, names, genders, and birth and death dates of family members in a hierarchical tree structure based on user-inputted family member data. This information is then presented to the user through a visual interface. Family tree-based data systems are widely used in fields such as psychology, sociology, and genetics.

[0003] However, in the field of data analysis, especially in family tree-based data analysis systems, there are generally technical problems such as limited data processing capabilities, low levels of intelligence, and poor functional synergy. Specifically, most family tree-based data analysis systems can only visualize the family relationship structure and cannot perform multi-dimensional feature extraction and quantitative analysis of multi-generational family member data. They also lack the ability to automatically analyze deep family characteristics and identify the degree of influence. Furthermore, they fail to integrate data analysis with user interaction; user interaction and family data analysis are disconnected, resulting in low data utilization.

[0004] This results in low data analysis and utilization efficiency and poor user interaction capabilities for family tree-based data analysis systems. Summary of the Invention

[0005] To address the aforementioned technical problems, embodiments of this application provide a data analysis system based on family tree mapping.

[0006] In a first aspect, embodiments of this application provide a data analysis system based on family tree mapping, the system comprising:

[0007] Front-end interaction module; The front-end interaction module includes a family characteristic analysis module, a user question and answer module, a user growth analysis module, and a virtual character dialogue module; The family feature analysis module is used to generate family feature analysis results for the user based on the data information of multi-generational family members input by the user; the family feature analysis results are used to indicate the degree of influence of family features in different dimensions on the user; The user question-answering module is used to recommend at least one question-answering agent to the user based on the user's input conversation information; and based on the conversation information, using the first question-answering agent selected by the user, to generate a conversation response that is related to the conversation information and the family feature analysis results; wherein, the user question-answering module has multiple different question-answering agents built in, and the different question-answering agents have different preset knowledge bases and response styles; The user growth analysis module is used to generate user growth analysis results based on the user's self-assessment data; and to generate user growth records based on the growth analysis results. The virtual character dialogue module is used to configure a virtual character intelligent agent based on the intelligent agent character tags input by the user; and to generate a dialogue response for the dialogue content input by the user using the virtual character intelligent agent.

[0008] In some embodiments, the family feature analysis module includes a family map construction submodule and a family multi-dimensional feature analysis submodule; The family graph construction submodule is used to generate a user's family graph based on the data information of multi-generational family members input by the user; wherein, each generation of family members includes at least one family member, and the data information includes the corresponding family member's identity attribute information, family relationship information, life event information, and behavioral information; The family multi-dimensional feature analysis submodule is used to generate multi-dimensional family feature analysis results for the user based on the data information of multi-generational family members input by the user. The multi-dimensional family feature analysis results include at least one of the following: family structure features, family relationship features, family event features, family influence features, family dynamic features, and family pattern features. Specifically, the family structure features indicate the number of family generations and the number of family members per generation; the family relationship features indicate the type of relationship between different family members; the family event features indicate the life events that occur to different family members; the family influence features indicate the degree of influence of the life events that occur to family members on the user, wherein the more times the same life event occurs, the greater the influence on the user; the family dynamic features indicate the degree of influence of the types of roles that different family members play in the family on the user, wherein the more family members with the same role type, the greater the influence on the user; and the family pattern features indicate the degree of influence of the behavioral patterns of different family members on the user, wherein the more family members with the same behavioral pattern, the greater the influence on the user.

[0009] In some embodiments, the family characteristic analysis module further includes a family risk assessment submodule; The family risk assessment submodule is used to determine the user's family risk score based on the data information of multi-generational family members input by the user, as well as the preset basic scores and preset weights of the data types to which different data information belongs. The user's family risk level is determined based on the family risk score and the preset mapping relationship between risk score and risk level.

[0010] In some embodiments, the family feature analysis module further includes a custom family member relationship analysis submodule; The custom family member relationship analysis submodule is used to generate a relationship evaluation between the user and each of the first family members based on the spatial location relationship between the user and at least one first family member set by the user in the interactive interface.

[0011] In some embodiments, the custom family member relationship analysis submodule is specifically used for: Based on the family member identifiers of each first family member, determine the preset distance and preset facing angle between the user and each first family member, as well as the preset scaling factor of the node symbol of each first family member; and obtain the position parameters of the user and each first family member configured by the user in the interactive interface, the actual scaling parameters and rotation parameters of the node symbol corresponding to each first family member. Based on the position parameters of the user and each of the first family members, determine the actual distance between the user and each of the first family members; and based on the rotation parameters of the node symbols corresponding to each of the first family members, determine the actual facing angle between the user and each of the first family members. Based on the difference between the preset distance and the actual distance between the user and each member of the first family, the difference between the preset facing angle and the actual facing angle, and the difference between the preset scaling factor and the actual scaling factor of each member of the first family, an evaluation of the relationship between the user and each member of the first family is generated.

[0012] In some embodiments, the user question-answering module includes a question-answering agent recommendation submodule and a question-answering agent response submodule; The question-answering agent recommendation submodule is used to determine the conversation scenario based on the user's input conversation information, and to determine at least one question-answering agent that matches the conversation scenario; The question-answering agent response submodule is used to call the first question-answering agent selected by the user to generate a conversation response associated with the family feature analysis results.

[0013] In some embodiments, the question-answering agent response submodule is specifically used for: Invoke the first question-and-answer agent selected by the user; Using the first question-answering agent, relevant knowledge of the conversation scenario corresponding to the first question-answering agent is injected into the conversation format through the preset conversation format of the first question-answering agent to generate an initial conversation response; The family feature analysis results are injected into the initial session response to generate an intermediate session response; The intermediate session response is compressed into a session response for the session information according to a preset compression rule.

[0014] In some embodiments, the user growth analysis module is specifically used for: Obtain self-assessment data for different growth dimensions input by the user, and determine the user's growth score for different growth dimensions based on the self-assessment data and the preset scoring rules for each growth dimension. A user's growth record is generated based on the user's historical growth scores across different growth dimensions; the growth record is used to indicate changes in the user's growth status across different growth dimensions.

[0015] In some embodiments, the virtual character dialogue module is specifically used for: The system obtains user-inputted intelligent character tags, which include at least family member identifiers and at least one of the following: tone of voice, conversational atmosphere, and frequently used phrases. Based on the agent role tags, configure the agent parameters of the preset virtual agent to generate the virtual agent; Based on the dialogue content input by the user, the virtual character intelligent agent generates dialogue responses.

[0016] In some embodiments, the system further includes a backend management module, which includes a user authentication submodule and a conversation content review submodule; The user authentication submodule is used to authenticate the user's identity; The dialogue content review submodule is used to perform compliance verification on the conversation responses generated by the user question and answer module and the dialogue replies generated by the virtual character dialogue module.

[0017] The family tree-based data analysis system provided in this application, by setting up a front-end interaction module integrating a family feature analysis module, a user question-and-answer module, a user growth analysis module, and a virtual character dialogue module, can effectively solve the technical problems of low data analysis and utilization efficiency and poor user interaction in related technologies. It also has the following beneficial effects: First, this application processes data information of multi-generational family members through the family feature analysis module, enabling multi-dimensional family feature extraction and quantitative analysis, effectively improving the analysis depth and data utilization rate of family tree data, and fundamentally improving the system's data analysis efficiency; Second, this application… This application utilizes a user question-and-answer module, combined with family characteristic analysis results, to match users with corresponding question-and-answer agents and generate conversational responses. This achieves deep collaboration between family data analysis and user interaction, resolving the disconnect between user interaction and data analysis, and significantly improving user interaction adaptability and data utilization efficiency. Furthermore, this application analyzes self-assessment data on growth and generates growth records through a user growth assessment module, further expanding the data application dimensions of the family tree-based data analysis system and improving the overall efficiency of data utilization. Finally, this application uses a virtual character dialogue module to generate virtual character agents based on user configuration and complete dialogue interactions, further enhancing the user's interactive experience.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are merely embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort, and this application can be applied to other similar scenarios based on the provided drawings.

[0020] Figure 1 This is a schematic diagram of a data analysis method based on family tree diagrams provided in an embodiment of this application. Detailed Implementation

[0021] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. The described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0022] It should be noted that the terms "system," "device," "unit," and / or "module" used in this application are methods of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they can be replaced by other expressions.

[0023] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature.

[0024] Figure 1 This is a schematic diagram illustrating the structure of a family tree-based data analysis method provided in an embodiment of this application. Figure 1 As shown, the system includes a front-end interaction module 101; The front-end interaction module 101 includes a family characteristic analysis module 1011, a user question and answer module 1012, a user growth analysis module 1013, and a virtual character dialogue module 1014. The family feature analysis module 1011 is used to generate the user's family feature analysis results based on the data information of multi-generational family members input by the user; the family feature analysis results are used to indicate the degree of influence of family features of different dimensions on the user; The user question-answering module 1012 is used to recommend at least one question-answering agent to the user based on the user's input conversation information; and based on the conversation information, using the first question-answering agent selected by the user, to generate a conversation response that is related to the conversation information and the family feature analysis result; wherein, the user question-answering module 1012 has multiple different question-answering agents built in, and the different question-answering agents have different preset knowledge bases and response styles. The user growth analysis module 1013 is used to generate user growth analysis results based on the user's self-assessment data on growth; and to generate user growth records based on the growth analysis results. The virtual character dialogue module 1014 is used to configure a virtual character intelligent agent according to the intelligent agent character tag input by the user; and to generate a dialogue response for the dialogue content input by the user using the virtual character intelligent agent.

[0025] In some embodiments, the family feature analysis module 1011 includes a family map construction submodule, a family multi-dimensional feature analysis submodule, a family risk assessment submodule, and a custom family member relationship analysis submodule.

[0026] In some embodiments, the family tree construction submodule is used to generate a user's family tree based on the data information of multi-generational family members input by the user; Each generation of family members includes at least one family member, and the data information includes the corresponding family member's identity attribute information, family relationship information, life event information, and behavioral information.

[0027] The identity attribute information includes at least one of the following: name, gender, age (or time of death if deceased), nationality, ethnicity, educational background, occupation, religious belief, etc.

[0028] The family relationship information includes at least one of the following: whether the person was adopted or fostered, the frequency of separation from parents, and the relationship with parents (close, distant, etc.).

[0029] The life event information includes at least one of the following: life milestones, war, famine, life changes, legal events (whether imprisonment), illness, etc.

[0030] The behavioral information includes at least one of the following: cause of death, drug dependence, etc.

[0031] In some embodiments, the family tree construction submodule generates the user's family tree based on the user-input data of multi-generational family members and using preset family tree rendering rules. For example, different family members are rendered as node symbols of the same size, and the generational level and position of the node symbols corresponding to each family member in the family tree are determined according to preset node symbol arrangement rules; at the same time, data belonging to identity attribute information in the data information is rendered as node shape symbols; data belonging to behavioral information in the data information is rendered as color fills and / or additional markers of node shape symbols; data belonging to life event information in the data information is rendered as text labels of node shape symbols; and data belonging to family relationships in the data information is rendered as the line shapes between node shape symbols.

[0032] Specifically, for example, when a family member is male, the corresponding node's shape symbol is rendered as a square; when a family member is female, the corresponding node's shape symbol is rendered as a circle; when a family member's disease information includes a cardiovascular disease marker, the upper left quadrant of the node's shape symbol is filled with red; when a family member's disease information includes a mental illness marker, the upper right quadrant is filled with purple; when a family member's disease information includes a cancer marker, the lower left quadrant is filled with blue; when a family member's disease information includes a hereditary disease marker, the lower right quadrant is filled with green, and so on.

[0033] The family multi-dimensional feature analysis submodule is used to generate multi-dimensional family feature analysis results for users based on the data information of multi-generational family members input by the user; the multi-dimensional family feature analysis results include at least one of the following: family structure features, family relationship features, family event features, family influence features, family dynamic features, and family pattern features.

[0034] The family structure features are used to indicate the number of family generations and the number of family members in each generation; For example, the family multi-dimensional feature analysis submodule counts the number of family generations and the number of family members in each generation based on the identity attributes of family members. Specifically, for example, a user's family members have a total of four generations, namely the user (person in question), parents, grandparents, and great-grandparents. Among them, there are four people in the person in question, namely the user, the user's older brother, older sister, and younger brother; and three people in the parents' generation, namely the user's father, mother, and uncle.

[0035] In some embodiments, family structure characteristics can also be used to indicate the age difference between family members of different generations, the stability of marriages between family members who are spouses, and whether there is population mobility within the family.

[0036] The family relationship feature is used to indicate the type of relationship between different family members; For example, the family multi-dimensional feature analysis submodule can statistically analyze the relationship types between different family members based on the family relationship information of family members; specifically, for example, the relationship between the user and the father is indifferent, the relationship between the user and the mother is intimate, and the relationship between the user and the spouse is dependent, etc.

[0037] The family event feature is used to indicate life events that occur to different family members; the family influence feature is used to indicate the degree of influence of life events that occur to family members on users, wherein the more times the same life event occurs, the greater the degree of influence on users; For example, the family multi-dimensional feature analysis submodule statistically analyzes the life events that occurred to different family members based on their life event information; specifically, for instance, the user experienced a major famine, the user's grandfather experienced a major famine, and the user's uncle has cancer. Since two family members experienced major famines, the impact of the major famines on the user is considered significant.

[0038] The family dynamics feature is used to indicate the degree of influence of the role types of different family members in the family on the user. The more family members of the same role type, the greater the degree of influence on the user. For example, the family multi-dimensional feature analysis submodule can statistically analyze the role types of different family members based on their life event information. Specifically, if a user's grandfather and great-grandfather both participated in wars and were heroes, then the heroes would have a greater influence on the user.

[0039] The family pattern feature is used to indicate the degree of influence of the behavioral patterns of different family members on the user. The more family members with the same behavioral pattern, the greater the influence on the user.

[0040] For example, the family multi-dimensional feature analysis submodule can statistically analyze the behavioral patterns of different family members based on their behavioral information; specifically, if a user's grandfather and great-grandfather both had alcohol dependence, then alcohol dependence would have a significant impact on the user.

[0041] It is understood that the data and analysis results of family members disclosed above only show a portion of the data and analysis results. In actual applications, other data and analysis results can be added as needed, and this application does not limit this.

[0042] In some embodiments, the results of multi-dimensional family characteristic analysis can be displayed on the user interface in the form of text boxes; for example, each text box carries the analysis results of one dimension of family characteristics, making it convenient for users to view by dimension. Specifically, the user interface sequentially displays text boxes for family structure characteristics, family relationship characteristics, family event characteristics, family influence characteristics, family dynamic characteristics, and family pattern characteristics. Each text box independently displays the analysis content of the corresponding dimension, and the dimension name is marked at the top of the text box, enabling users to clearly distinguish and view the degree of influence of different family characteristics on themselves, and realizing the intuitive and structured presentation of the analysis results.

[0043] The family risk assessment submodule is used to determine the user's family risk score based on the data information of multi-generational family members input by the user, as well as the preset basic scores and preset weights of the data types to which different data information belongs. The user's family risk level is determined based on the family risk score and the preset mapping relationship between risk score and risk level.

[0044] The data types include all data types in the data information except for identity attribute information. For example, the family relationship information includes whether the person was adopted or fostered, the frequency of separation from parents, and the relationship with parents; the life event information includes life milestones, war, famine, life changes, legal events (whether imprisoned), and diseases; and the behavioral information includes the cause of death and drug dependence.

[0045] Different data types correspond to different preset base scores. For example, mental illnesses have a higher base score than ordinary physical illnesses, traumatic life events have a higher base score than general life events, and high-conflict relationships have a higher base score than general estrangement relationships.

[0046] The preset weights are used to characterize the contribution of different data types to family risk. For example, data types that appear repeatedly across generations have a higher weight than data types that appear only once, and data related to close relatives have a higher weight than data related to distant relatives. The family risk assessment submodule performs a weighted sum based on the basic score and corresponding weight of each data point to obtain the user's family risk score.

[0047] In some embodiments, the preset mapping relationship between risk scores and risk levels includes: when the family risk score is lower than a first threshold, the corresponding family risk level is low risk; when the family risk score is between the first threshold and a second threshold, the corresponding family risk level is medium risk; and when the family risk score is higher than the second threshold, the corresponding family risk level is high risk.

[0048] For example, if two or more generations of family members exhibit mental health-related behaviors, and close relatives have high-conflict relationships and drug dependence behaviors, then the weighted family risk score is higher than the second threshold, and the family risk level is determined to be high risk.

[0049] In some embodiments, the custom family member relationship analysis submodule is used to generate a relationship evaluation between the user and each of the first family members based on the spatial location relationship between the user and at least one first family member set by the user in the interactive interface.

[0050] The custom family member relationship analysis submodule is specifically used for: Based on the family member identifiers of each first family member, determine the preset distance and preset facing angle between the user and each first family member, as well as the preset scaling factor of the node symbol of each first family member; and obtain the position parameters of the user and each first family member configured by the user in the interactive interface, the actual scaling parameters and rotation parameters of the node symbol corresponding to each first family member. The family member identifier is used to distinguish the identities of different first family members, including but not limited to father, mother, spouse, children, brothers and sisters, etc. The preset distance, preset facing angle, and preset scaling factor are all set based on the conventional kinship relationship of the corresponding family member identifier. For example, the preset distance between the user and the parents is less than the preset distance with distant relatives, the preset facing angle between the user and the spouse is positive relative (around 0°), and the preset scaling factor of the user's own node symbol is greater than that of other family members.

[0051] The position parameters include the planar coordinates (x-axis coordinates and y-axis coordinates) of the user and each first family member in the interactive interface, used to calculate the distance between them (such as Euclidean distance); the actual scaling parameter is used to characterize the display size of the first family member node symbol in the interface, reflecting the user's level of attention to the family member; the rotation parameter is used to characterize the orientation angle (0°-360°) of the node symbol, used to determine the orientation relationship between the first family member and the user.

[0052] Based on the position parameters of the user and each of the first family members, determine the actual distance between the user and each of the first family members; and based on the rotation parameters of the node symbols corresponding to each of the first family members, determine the actual facing angle between the user and each of the first family members. The actual distance is calculated using planar coordinates, specifically the Euclidean distance between the user's coordinates and the coordinates of the first family member. The actual facing angle is calculated using rotation parameters. Based on the positive orientation of the user node symbol, the angle between the orientation of the first family member node symbol and the reference direction is determined. An angle within ±60° is considered a positive orientation, while an angle outside this range is considered a deviation or lateral orientation.

[0053] Based on the difference between the preset distance and the actual distance between the user and each member of the first family, the difference between the preset facing angle and the actual facing angle, and the difference between the preset scaling factor and the actual scaling factor of each member of the first family, an evaluation of the relationship between the user and each member of the first family is generated.

[0054] Each difference corresponds to a different evaluation weight. The difference between the actual distance and the preset distance has the highest weight, followed by the difference in facing angle, and the difference in scaling factor has the lowest weight. For example, if the actual distance is less than the preset distance, the actual facing angle is positive, and the actual scaling factor is greater than the preset scaling factor, the relationship is evaluated as "close and valued". If the actual distance is greater than the preset distance, the actual facing angle is negative, and the actual scaling factor is less than the preset scaling factor, the relationship is evaluated as "distancing and neglected". If only a single difference exceeds the reasonable range, the relationship is evaluated as "average and slightly biased".

[0055] In some embodiments, users can adjust the position of the node symbols corresponding to the first family members through drag-and-drop operations in the interactive interface, thereby adjusting the spatial relationship between the first family members and the user. After the user completes the drag-and-drop operation, the system automatically records the new position parameters of the node symbols corresponding to the first family members and updates the relationship evaluation results synchronously. For example, if the user drags the node symbol representing "father" to a position closer to its own node symbol and sets the display size of the node symbol to be larger than the default size, the system will update the relationship evaluation to "the user has a close relationship with the father and attaches great importance to him" based on this operation.

[0056] In some embodiments, users can manually input their feelings about a family member's relationship (such as "close", "neutral", "distant") via the "Relationship Evaluation" button in the interactive interface. The system combines the user's input description of feelings with the adjustment of node symbol positions to generate a relationship evaluation that better reflects the user's actual feelings, ensuring the accuracy and personalization of the relationship evaluation.

[0057] Meanwhile, the interactive interface includes a "Relationship Evaluation Modification" entry, allowing users to modify their description of their relationship feelings towards a family member at any time. The system updates the relationship evaluation results in real time and simultaneously adjusts the rendering format of the node symbols of that family member in the family tree (such as darkening the node symbol color and adding special marks), achieving synchronous mapping between user operations and node symbol changes.

[0058] In some embodiments, the user question-answering module 1012 includes a question-answering agent recommendation submodule and a question-answering agent response submodule; The question-answering agent recommendation submodule is used to determine the conversation scenario based on the user's input conversation information, and to determine at least one question-answering agent that matches the conversation scenario; The question-answering agent response submodule is used to call the first question-answering agent selected by the user to generate a conversation response associated with the family feature analysis results.

[0059] The question-answering agent response submodule is specifically used for: Invoke the first question-and-answer agent selected by the user; The invocation operation includes starting the running program of the first question-answering agent, loading the agent's preset knowledge base, response style parameters and session processing logic, and ensuring that the first question-answering agent can complete the generation of session response based on its own configuration.

[0060] Using the first question-answering agent, relevant knowledge of the conversation scenario corresponding to the first question-answering agent is injected into the conversation format through the preset conversation format of the first question-answering agent to generate an initial conversation response; The preset conversation format is independently configured by each question-answering agent, including the sentence structure, tone, and content layout of the reply. For example, the conversation format for a question-answering agent in an emotional expression scenario is "empathic guidance + question response + reassurance suggestions," while the conversation format for an agent in a rational analysis scenario is "problem breakdown + knowledge interpretation + logical deduction." The relevant knowledge of the conversation scenario refers to professional knowledge, guidance strategies, and coping methods that match the current conversation scenario. For example, relevant knowledge for a family relationship counseling scenario includes communication skills for relatives and methods for resolving conflicts, while relevant knowledge for a health anxiety scenario includes the hereditary patterns of family diseases and daily protection suggestions. The relevant knowledge is injected into the conversation format, that is, the knowledge content is filled into the corresponding positions according to the preset sentence structure to form an initial conversation reply that conforms to the agent's style and is scenario-specific.

[0061] The family feature analysis results are injected into the initial session response to generate an intermediate session response; The injection of family characteristic analysis results refers to extracting family structure characteristics, family pattern characteristics, family influence characteristics, and other content related to the current conversation information and integrating them into the initial conversation response. This ensures that the response content is tailored to the user's family background, avoiding generic and untargeted replies. For example, if the user's conversation information is "Why do I always avoid family communication?", the initial conversation response would be "Avoidance of communication is usually related to personal psychological state and communication habits. You can try to gradually express your own thoughts proactively." After injecting family characteristic analysis results, such as "Multiple generations of family members exhibit a pattern of avoiding communication, and this pattern has a high degree of influence on you," the intermediate conversation response would be "Avoidance of communication is usually related to personal psychological state and communication habits. Considering your family characteristics, multiple generations of family members exhibit a pattern of avoiding communication, and this pattern has a high degree of influence on you. You can try to gradually express your own thoughts proactively and gradually break the established pattern."

[0062] The intermediate session response is compressed into a session response for the session information according to a preset compression rule.

[0063] The preset compression rules include three core requirements: simplifying redundant expressions, retaining core information, and controlling response length. Specifically, this means deleting repetitive knowledge interpretations, retaining suggestions and family characteristic-related content directly related to the conversation, and controlling the response length within a preset range (e.g., within 200 characters) to ensure that the conversation response is concise and accurate, containing both contextual knowledge and family characteristic-related information, while also facilitating quick reading and comprehension by the user. For example, the intermediate conversation response could be compressed into "Avoidance of communication is related to personal status and communication habits. Considering your family characteristics, multiple generations of members exhibit an avoidance of communication pattern that has a significant impact on you. It is recommended to gradually take the initiative to express yourself and break the established pattern," as the final conversation response.

[0064] In some embodiments, the user growth analysis module 1013 is specifically used for: Obtain self-assessment data for different growth dimensions input by the user, and determine the user's growth score for different growth dimensions based on the self-assessment data and the preset scoring rules for each growth dimension. The growth dimensions include, but are not limited to: self-awareness, emotion management, intimate relationships, family boundaries, responsibility awareness, stress coping, and self-integration. The self-assessment data includes users' scores for their current status in each dimension, subjective descriptions, and selections of behavior frequency or degree levels. The preset scoring rules include quantifying, converting, and weighting the self-assessment results of each dimension, transforming qualitative descriptions into unified numerical growth scores to intuitively reflect users' development levels in the corresponding dimensions.

[0065] A user's growth record is generated based on the user's historical growth scores across different growth dimensions; the growth record is used to indicate changes in the user's growth status across different growth dimensions.

[0066] The historical records include multiple growth scores submitted by the user at different time points. The user growth analysis module 1013 generates trend-based and time-series growth records by longitudinally comparing multiple scores in the same dimension. This is used to reflect the user's improvement, fluctuation, or decline in each growth dimension, and to achieve continuous tracking and visualization of the personal growth process.

[0067] In some embodiments, the virtual character dialogue module 1014 is specifically used for: The system obtains user-inputted intelligent character tags, which include at least family member identifiers and at least one of the following: tone of voice, conversational atmosphere, and frequently used phrases. The family member identifiers include, but are not limited to, roles such as father, mother, grandfather, grandmother, spouse, children, and siblings; the tone of voice includes gentle, serious, friendly, reserved, encouraging, and stern; the conversational atmosphere includes soothing, nostalgic, healing, rational, and relaxed; and the commonly used phrases are user-defined character-specific expressions.

[0068] Based on the agent role tags, configure the agent parameters of the preset virtual agent to generate the virtual agent; The intelligent agent parameters include dialogue style parameters, language habit parameters, emotional tendency parameters, and response logic parameters. The virtual character dialogue module 1014 assigns parameter values ​​and makes personalized adjustments to the preset general virtual character model based on the identity, tone, atmosphere, and common phrases in the intelligent character tags, generating a customized virtual character intelligent agent that perfectly matches the tags.

[0069] Based on the dialogue content input by the user, the virtual character intelligent agent generates dialogue responses.

[0070] The dialogue responses are tailored to the identity characteristics, tone, and common expression habits of the configured character, while maintaining logical coherence and content that matches the user's input, thus achieving an immersive and personalized dialogue interaction with the customized virtual family character.

[0071] In some embodiments, the system further includes a backend management module 102, which includes a user authentication submodule and a conversation content review submodule. The user authentication submodule is used to authenticate the user's identity; The authentication process includes verifying user accounts, passwords, mobile phone verification codes, biometric features, or third-party authorization information. Only after verification can users log in to the system and use the various functions of the front-end interaction module 101, preventing unauthorized access and ensuring user data security and system usage rights.

[0072] The dialogue content review submodule is used to perform compliance verification on the conversation response generated by the user question and answer module 1012 and the dialogue reply generated by the virtual character dialogue module 1014.

[0073] The compliance verification includes detecting sensitive information in the response content, identifying illegal expressions, and investigating inappropriate emotional guidance. If illegal content is detected, the dialogue content review submodule will intercept, filter, or replace the response to ensure that the system output content complies with laws and regulations, public order and good morals, and platform norms, and to avoid generating inappropriate or risky content.

[0074] It is understood that the background management module 102 may also include a user data management submodule, a system log management submodule, and an intelligent agent configuration submodule, etc.

[0075] The user data management submodule is used to uniformly store, back up, update, and delete user-entered multi-generational family member data, family characteristic analysis results, growth self-assessment data, growth records, and interactive dialogue records.

[0076] Among its features, it supports regular automatic and manual backups of user data to prevent data loss; it allows users to modify, supplement, and delete their own family data while recording data modification history; and it encrypts and stores sensitive user data to protect user privacy and security, with the corresponding data only accessible to the user and authorized administrators.

[0077] The system log management submodule is used to record all system operations, including user login / logout records, data entry / modification records, agent call records, audit records, and system operation anomaly records.

[0078] The log information includes operation time, operation subject, operation content, operation result and exception details. Administrators can query, filter and export logs by time, operation type, operation subject and other conditions, which facilitates system operation and maintenance to troubleshoot problems, trace operation behavior and ensure stable system operation.

[0079] The intelligent agent configuration submodule is used by the administrator to configure and maintain the multiple question-and-answer intelligent agents built into the user question-and-answer module 1012 and the preset virtual role intelligent agents of the virtual role dialogue module 1014.

[0080] Among these features, users can add, modify, or delete preset knowledge bases, response styles, and conversation formats for question-and-answer agents. They can also adjust the basic parameters, tone templates, and commonly used language libraries of virtual character agents. Furthermore, the system supports optimizing the response logic of agents to ensure the accuracy and adaptability of the agent's output content and improve the user interaction experience.

[0081] The family tree-based data analysis system provided in this application, by setting up a front-end interaction module 101 integrating a family feature analysis module 1011, a user question-and-answer module 1012, a user growth analysis module 1013, and a virtual character dialogue module 1014, can effectively solve the technical problems of low data analysis and utilization efficiency and poor user interaction in related technologies, and has the following beneficial effects: First, this application processes the data information of multi-generational family members through the family feature analysis module 1011, which can realize multi-dimensional family feature extraction and quantitative analysis, effectively improve the analysis depth and data utilization of family map data, and fundamentally improve the data analysis efficiency of the system. Secondly, this application uses the user question-and-answer module 1012 to match the user with the corresponding question-and-answer agent and generate a conversation response by combining the family feature analysis results. This achieves deep collaboration between family data analysis and user interaction, solves the problem of the separation between user interaction and data analysis, and significantly improves the adaptability of user interaction and the efficiency of data utilization. Furthermore, this application analyzes the self-assessment data of growth through the user growth assessment module and generates growth records, which further expands the data application dimensions of the family tree-based data analysis system and improves the efficiency of comprehensive data utilization. Furthermore, this application enhances the user's interactive experience by generating a virtual character intelligent agent based on user configuration and completing dialogue interaction through the virtual character dialogue module 1014.

[0082] The above embodiments of this application describe a family tree-based data analysis system. This system may include hardware and software modules, implementing the aforementioned functions in the form of hardware, software modules, or a combination of both. Each of the above functions can be executed using hardware, software modules, or a combination of both.

[0083] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A data analysis system based on family tree, characterized in that, The system includes: a front-end interaction module; The front-end interaction module includes a family characteristic analysis module, a user question and answer module, a user growth analysis module, and a virtual character dialogue module; The family feature analysis module is used to generate family feature analysis results for the user based on the data information of multi-generational family members input by the user; the family feature analysis results are used to indicate the degree of influence of family features in different dimensions on the user; The user question-answering module is used to recommend at least one question-answering agent to the user based on the user's input conversation information; and based on the conversation information, using the first question-answering agent selected by the user, to generate a conversation response that is related to the conversation information and the family feature analysis results; wherein, the user question-answering module has multiple different question-answering agents built in, and the different question-answering agents have different preset knowledge bases and response styles; The user growth analysis module is used to generate user growth analysis results based on the user's self-assessment data; and to generate user growth records based on the growth analysis results. The virtual character dialogue module is used to configure a virtual character intelligent agent based on the intelligent agent character tags input by the user; and to generate a dialogue response for the dialogue content input by the user using the virtual character intelligent agent.

2. The system according to claim 1, characterized in that, The family feature analysis module includes a family map construction submodule and a family multi-dimensional feature analysis submodule; The family graph construction submodule is used to generate a user's family graph based on the data information of multi-generational family members input by the user; wherein, each generation of family members includes at least one family member, and the data information includes the corresponding family member's identity attribute information, family relationship information, life event information, and behavioral information; The family multi-dimensional feature analysis submodule is used to generate multi-dimensional family feature analysis results for the user based on the data information of multi-generational family members input by the user. The multi-dimensional family feature analysis results include at least one of the following: family structure features, family relationship features, family event features, family influence features, family dynamic features, and family pattern features. Specifically, the family structure features indicate the number of family generations and the number of family members per generation; the family relationship features indicate the type of relationship between different family members; the family event features indicate the life events that occur to different family members; the family influence features indicate the degree of influence of the life events that occur to family members on the user, wherein the more times the same life event occurs, the greater the influence on the user; the family dynamic features indicate the degree of influence of the types of roles that different family members play in the family on the user, wherein the more family members with the same role type, the greater the influence on the user; and the family pattern features indicate the degree of influence of the behavioral patterns of different family members on the user, wherein the more family members with the same behavioral pattern, the greater the influence on the user.

3. The system according to claim 2, characterized in that, The family characteristic analysis module also includes a family risk assessment sub-module; The family risk assessment submodule is used to determine the user's family risk score based on the data information of multi-generational family members input by the user, as well as the preset basic scores and preset weights of the data types to which different data information belongs. The user's family risk level is determined based on the family risk score and the preset mapping relationship between risk score and risk level.

4. The system according to claim 2, characterized in that, The family feature analysis module also includes a custom family member relationship analysis submodule; The custom family member relationship analysis submodule is used to generate a relationship evaluation between the user and each of the first family members based on the spatial location relationship between the user and at least one first family member set by the user in the interactive interface.

5. The system according to claim 4, characterized in that, The custom family member relationship analysis submodule is specifically used for: Based on the family member identifiers of each first family member, determine the preset distance and preset facing angle between the user and each first family member, as well as the preset scaling factor of the node symbol of each first family member; And, obtain the position parameters of the user and each member of the first family configured by the user in the interactive interface, the actual scaling parameters and rotation parameters of the node symbols corresponding to each member of the first family; Based on the position parameters of the user and each of the first family members, determine the actual distance between the user and each of the first family members; and based on the rotation parameters of the node symbols corresponding to each of the first family members, determine the actual facing angle between the user and each of the first family members. Based on the difference between the preset distance and the actual distance between the user and each member of the first family, the difference between the preset facing angle and the actual facing angle, and the difference between the preset scaling factor and the actual scaling factor of each member of the first family, an evaluation of the relationship between the user and each member of the first family is generated.

6. The system according to claim 1, characterized in that, The user question-and-answer module includes a question-and-answer agent recommendation submodule and a question-and-answer agent response submodule; The question-answering agent recommendation submodule is used to determine the conversation scenario based on the user's input conversation information, and to determine at least one question-answering agent that matches the conversation scenario; The question-answering agent response submodule is used to call the first question-answering agent selected by the user to generate a conversation response associated with the family feature analysis results.

7. The system according to claim 6, characterized in that, The question-answering agent response submodule is specifically used for: Invoke the first question-and-answer agent selected by the user; Using the first question-answering agent, relevant knowledge of the conversation scenario corresponding to the first question-answering agent is injected into the conversation format through the preset conversation format of the first question-answering agent to generate an initial conversation response; The family feature analysis results are injected into the initial session response to generate an intermediate session response; The intermediate session response is compressed into a session response for the session information according to a preset compression rule.

8. The system according to claim 1, characterized in that, The user growth analysis module is specifically used for: Obtain self-assessment data for different growth dimensions input by the user, and determine the user's growth score for different growth dimensions based on the self-assessment data and the preset scoring rules for each growth dimension. A user's growth record is generated based on the user's historical growth scores across different growth dimensions; the growth record is used to indicate changes in the user's growth status across different growth dimensions.

9. The system according to claim 1, characterized in that, The virtual character dialogue module is specifically used for: The system obtains user-inputted intelligent character tags, which include at least family member identifiers and at least one of the following: tone of voice, conversational atmosphere, and frequently used phrases. Based on the agent role tags, configure the agent parameters of the preset virtual agent to generate the virtual agent; Based on the dialogue content input by the user, the virtual character intelligent agent generates dialogue responses.

10. The system according to claim 1, characterized in that, The system also includes a backend management module, which includes a user authentication submodule and a conversation content review submodule. The user authentication submodule is used to authenticate the user's identity; The dialogue content review submodule is used to perform compliance verification on the conversation responses generated by the user question and answer module and the dialogue replies generated by the virtual character dialogue module.

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