Intrinsic emotion analysis system based on psychological information big data

Abstract

The invention relates to the technical field of internal data analysis, in particular to an internal emotion analysis system based on psychological information big data. The system comprises an emotion theme modeling module, a dynamic emotion driving factor analysis module, an internal emotion abnormal trend identification module and an abnormal emotion strategy adjustment module. The emotion driving factor analysis module is used for acquiring psychological research data to perform emotion theme modeling and dynamic emotion driving factor analysis to obtain dynamic emotion driving factor data; performing emotion transition state analysis of different emotion stages based on the dynamic emotion driving factor data to obtain emotion transition state data; and internal emotion abnormal trend recognition is performed on the emotion transition state data, a big data internal emotion analysis model is constructed, an internal emotion abnormal analysis model is obtained, abnormal internal emotion strategy adjustment making is performed, and abnormal emotion strategy adjustment data is obtained. According to the method, the internal emotion analysis technology is optimized, so that the emotion analysis is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of intrinsic data analysis, and in particular to an intrinsic emotion analysis system based on psychology information big data. Background Art

[0002] Intrinsic emotions refer to the emotional experience hidden within an individual, which is influenced and regulated by many factors. Research based on big data of psychological information has revealed the diverse backgrounds of intrinsic emotions, including individual psychological characteristics, social environment, life experience and other aspects. In terms of psychological characteristics, factors such as individual personality traits, emotion regulation ability, cognitive style and so on have an important impact on intrinsic emotions. For example, optimistic people are more likely to experience positive inner emotions, while individuals with low emotional stability are more likely to be affected by external events and produce negative emotions. Social environment is also one of the important backgrounds of intrinsic emotions. Factors such as family, work, and social relationships will have an impact on individual emotional experience. For example, individuals with harmonious and supportive families tend to have a greater sense of happiness and satisfaction. Research based on big data of psychological information not only reveals the complex factors behind intrinsic emotions, but also provides an important reference for understanding and intervening in individual emotional experiences. However, a traditional intrinsic emotion analysis system based on big data of psychological information is often based on static data for analysis, which makes it difficult to capture the dynamic changes of emotions and cannot accurately analyze content emotions. Summary of the invention

[0003] Based on this, it is necessary to provide an intrinsic emotion analysis system based on psychological information big data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, an intrinsic emotion analysis system based on psychology information big data includes the following modules:

[0005] The emotional topic modeling module is used to obtain psychology research data; perform semantic analysis on the psychology research data to obtain psychology semantic data; perform emotional topic modeling on the psychology research data based on the psychology semantic data to obtain emotional topic modeling data;

[0006] The dynamic emotion driving factor analysis module is used to perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data; perform local emotion structure feature analysis on the dynamic emotion segmentation data to obtain local emotion structure feature data; perform dynamic emotion driving factor analysis based on the local emotion structure feature data to obtain dynamic emotion driving factor data;

[0007] The module for identifying the abnormal trend of internal emotions is used to analyze the local emotion stage evolution of the local emotion structure feature data based on the dynamic emotion driving factor data to obtain the local emotion stage evolution data; analyze the emotion transition state of different emotion stages on the local emotion stage evolution data to obtain the emotion transition state data; identify the abnormal trend of internal emotions on the emotion transition state data to obtain the abnormal trend of internal emotions;

[0008] The abnormal emotion strategy adjustment module is used to use the long short-term memory network to construct a big data intrinsic emotion analysis model for the intrinsic emotion abnormal trend data, and obtain the intrinsic emotion abnormal analysis model; according to the intrinsic emotion abnormal analysis model, abnormal intrinsic emotion strategy adjustment is formulated to obtain abnormal emotion strategy adjustment data.

[0009] By acquiring psychology research data and performing semantic analysis, the present invention can deeply understand the psychological state, emotional experience, and behavioral patterns of individuals or groups. Semantic analysis can help extract key information, including emotional vocabulary, behavioral descriptions, and psychological state, and provide data support for subsequent emotional topic modeling. Emotional topic modeling through psychological semantic data can systematically understand the association and transformation between different emotions, and reveal the emotional patterns and psychological mechanisms hidden behind the data. Emotional topic modeling helps to discover and understand the diversity and complexity of human emotional experience, and provide in-depth insights for psychological research. Dynamic emotion segmentation processing can divide psychological semantic data into different emotional fragments or stages, revealing the temporal changes and dynamic evolution of emotions. Local emotion structure feature analysis can deeply explore the microscopic features of emotion fragments or stages, such as emotion intensity, emotion type, emotion duration, etc., so as to understand the internal mechanism of emotions in more detail. According to dynamic emotion segmentation data and local emotion structure feature data, the drivers that affect emotion changes can be identified and analyzed. Factors include external events, internal psychological processes, and social interactions. The analysis of dynamic emotion driving factors helps to reveal the causes and regulatory mechanisms of emotions, and provides a theoretical basis and practical guidance for emotion management and psychological intervention. The analysis of the phased evolution of local emotions can identify the changing trends of individual emotions at different stages, including the rise and fall and stability of emotions. Through the analysis of emotional transition states, the transformation relationship between different emotions can be revealed, including sudden changes in emotions and smooth transition states. The identification of abnormal trends in internal emotions can discover abnormal patterns of individual emotional changes, including abnormal frequency, amplitude, and duration, thereby helping to identify possible mental health problems or emotional regulation difficulties. The internal emotion abnormality analysis model is constructed using the long and short-term memory network, which can learn and model a large amount of internal emotion abnormality trend data, realize intelligent identification and analysis of abnormal emotions, and formulate abnormal emotion strategy adjustment plans based on the internal emotion abnormality analysis model, including personalized emotion management suggestions, emotion regulation skills, etc., to help individuals better understand and deal with their own emotional problems.

[0010] Preferably, the emotional topic modeling module includes the following functions:

[0011] Access to psychological research data;

[0012] Conduct semantic analysis on psychology research data to obtain psychology semantic data; conduct semantic tendency analysis on psychology semantic data to obtain semantic tendency data;

[0013] Emotional topic modeling is performed on the psychological research data according to the psychological semantic data and the semantic tendency data to obtain the emotional topic modeling data.

[0014] By acquiring psychology research data and performing semantic analysis, the present invention can deeply understand the content and information contained in the data, including emotional expression, behavior description, and cognitive process. Semantic tendency analysis further deepens the understanding of the data, helps capture the emotional tendency, attitude and emotional color in the language, and makes the data more informative and emotionally expressive. Emotional topic modeling based on psychological semantic data and semantic tendency data helps to discover potential emotional themes and emotional patterns in the data. Through emotional topic modeling, the types, distribution and correlations of different emotions in the data can be systematically analyzed to reveal the emotional structure and psychological characteristics behind the data. Emotional topic modeling data can provide in-depth insights for research in the field of psychology, helping researchers understand the emotional experience, mental health status, and social interaction of individuals or groups. Furthermore, these data can also be applied to actual scenarios, such as sentiment analysis, user experience research, mental health assessment and other fields, to provide support for decision making and practical applications. The application of technologies such as semantic analysis and emotional topic modeling helps to improve the processing efficiency and accuracy of psychology research data, reduce the workload and subjective bias of manual analysis, and these technologies can also provide a more reliable foundation for data-driven decision-making and application, thereby improving work efficiency and decision quality.

[0015] Preferably, the dynamic emotion driving factor analysis module includes the following functions:

[0016] Perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data;

[0017] Marking the emotion transition points of the dynamic emotion segmentation data to obtain emotion transition point data; performing emotion transition pattern recognition on the emotion transition point data to obtain emotion transition pattern recognition data;

[0018] Perform local emotion structure feature analysis on the dynamic emotion segmentation data according to the emotion transition pattern recognition data to obtain local emotion structure feature data;

[0019] Dynamic emotion driving factor analysis is performed based on local emotion structure feature data and emotion transition pattern recognition data to obtain dynamic emotion driving factor data.

[0020] The dynamic emotion segmentation processing of the present invention can divide psychological semantic data into different emotion fragments, realize fine-grained analysis of emotion changes, and make emotion changes more clearly visible. The marking of emotion transition point data and emotion transition pattern recognition further deepen the understanding of emotion changes, reveal the critical points and patterns of emotion changes, and help grasp the key moments and laws of emotion changes. Through local emotion structure feature analysis and emotion transition pattern recognition, the local emotion features and transition patterns in the dynamic emotion segmentation data can be deeply mined, providing a rich data foundation for subsequent emotion driving factor analysis. The dynamic emotion driving factor analysis can systematically identify factors that affect emotion changes, including external events, internal psychological processes, and social interactions, thereby revealing the causes and regulatory mechanisms of emotion changes; through the analysis of dynamic emotion driving factors, it can help individuals better understand the fluctuations and changes of their emotions and enhance their ability to manage and regulate emotions. The results of the emotion driving factor analysis can also provide a basis for individuals to formulate personalized emotion management strategies, helping individuals to more effectively cope with various emotional states and challenges.

[0021] Preferably, the performing local emotion structure feature analysis on the dynamic emotion segmentation data comprises:

[0022] Performing dynamic emotion time series analysis on the dynamic emotion segmentation data to obtain dynamic emotion time series data; reconstructing the emotion pattern phase space of the dynamic emotion time series data according to the emotion transition pattern recognition data to obtain emotion pattern phase space reconstruction data;

[0023] Perform nonlinear spatial correlation analysis on the emotion pattern phase space reconstruction data to obtain nonlinear spatial correlation data;

[0024] According to the nonlinear spatial correlation data, the Poincare section analysis is performed on the emotional pattern phase space reconstruction data to obtain the nonlinear phase space Poincare section; the spatial trajectory periodicity analysis is performed on the nonlinear phase space Poincare section to obtain the spatial trajectory periodicity data;

[0025] Based on the bifurcation theory technology, the trajectory structure analysis is performed on the periodicity number of the space trajectory to obtain the space trajectory structure data;

[0026] The local emotion structure feature data is obtained by performing a local emotion structure feature analysis based on the spatial trajectory structure data and the spatial trajectory periodicity data.

[0027] Through dynamic emotion time series analysis, the present invention can deeply understand the temporal changes of emotions, including the fluctuation cycle, duration, and amplitude of emotions, which is helpful to identify the regularity and trend of emotion changes and provide basic data for subsequent analysis. Emotional pattern phase space reconstruction can map dynamic emotion time series data to high-dimensional phase space, revealing the multidimensional characteristics of emotion changes. Linear space association analysis can discover the nonlinear relationship between emotions and reveal the interaction and influence between emotions. Poincare cross-section analysis can capture the periodic structure of emotion changes in phase space, helping to understand the stability and periodicity of emotion changes. Spatial trajectory periodicity analysis further analyzes the periodic characteristics of emotion trajectories in phase space, which is helpful to identify the stable cycle and trend of emotion changes. Trajectory structure analysis based on bifurcation theory technology can reveal bifurcation phenomena and critical points in the process of emotion changes, helping to understand the nonlinear dynamics of emotion changes. The final local emotion structure feature analysis comprehensively considers the temporal characteristics, phase space characteristics and dynamic characteristics of emotions, extracts the key features of local emotion changes, and provides an in-depth understanding and basis for the analysis of emotion driving factors.

[0028] Preferably, the dynamic emotion driving factor analysis includes:

[0029] Performing pattern-context mapping on the emotion transition pattern recognition data to obtain emotion pattern-context data;

[0030] Performing situation distribution mapping on the emotion pattern situation data according to the local emotion structure feature data to obtain the local emotion structure situation distribution data;

[0031] Performing structural situational distribution logistic regression analysis on the local emotion structure situational distribution data to obtain situational distribution logistic regression data; performing logical linear factor analysis on the local emotion structure situational distribution data based on the situational distribution logistic regression data to obtain situational emotion logical factor data;

[0032] Based on the recurrent neural network, the logical situation perception model is constructed for the situation emotion logic factor data to obtain the logical situation perception model;

[0033] According to the logical situation perception model, the pre-logical factor prediction of the situation emotion logical factor data is performed to obtain the pre-logical factor data;

[0034] Dynamic emotion driving factor analysis is performed based on the pre-logical factor data and the situational emotion logical factor data to obtain dynamic emotion driving factor data.

[0035] The mapping of emotion pattern context data of the present invention will help to understand the performance and changes of different emotion patterns in different contexts. The mapping of local emotion structure context distribution data can further reveal the distribution and distribution rules of emotions in various contexts, providing a basis for subsequent analysis. Structural context distribution logistic regression analysis and logistic linear factor analysis can identify the correlation and influencing factors between context and emotion, which is helpful to understand the changing rules and driving factors of emotions in different contexts. These analysis results provide quantification and modeling of the relationship between emotion and context, and provide data support for subsequent analysis of emotion driving factors. The logical context perception model constructed based on recurrent neural network can model and predict the complex relationship between context and emotion, and improve the understanding and perception of context factors. The prediction of pre-logical factor data can help predict the emotional state that may appear in future contexts, and provide early warning and guidance for emotion management and regulation. The final dynamic emotion driving factor data will comprehensively consider the complex relationship between emotion, context and other factors, which will help to deeply understand the internal mechanism and dynamic evolution process of emotion change.

[0036] Preferably, the intrinsic emotion abnormal trend identification module includes the following functions:

[0037] Based on the dynamic emotion driving factor data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data;

[0038] Evaluate the emotion volatility of the local emotion phase evolution data to obtain emotion volatility evaluation data;

[0039] According to the emotion volatility evaluation data, the emotion transition state analysis of the local emotion phase evolution data at different emotion phases is performed to obtain the emotion transition state data;

[0040] The abnormal inner emotion trend is identified on the emotion transition state data to obtain the abnormal inner emotion trend data.

[0041] The present invention can identify the different stages and stage characteristics of emotional changes by performing local emotional stage evolution analysis on local emotional structure feature data, which helps to understand the time series pattern of emotional changes and reveal the important nodes and trends in the emotional evolution process. The acquisition of emotional volatility assessment data can quantify the degree and frequency of emotional changes and help identify the stability and regularity of emotional fluctuations, which helps to judge the stability of emotional changes and the ability to regulate emotions, and provides an important reference for subsequent analysis; by analyzing the emotional transition state data, the transition patterns and trends between different emotional stages can be revealed, including sudden changes in emotions, smooth transitions, etc., which helps to discover abnormal emotional transition patterns and warn of existing mental health problems or emotional regulation difficulties. The final intrinsic emotional abnormal trend data will comprehensively consider the results of local emotional stage evolution, emotional volatility assessment and emotional transition state analysis, and identify the emergence of abnormal emotional trends, which helps to early detect and intervene in individual mental health problems or emotional management difficulties.

[0042] Preferably, performing local emotion stage evolution analysis on local emotion structure feature data includes:

[0043] Based on the dynamic emotion driving factor data, the emotion driving factor emotion relationship adjacency table is constructed for the local emotion structure feature data to obtain the emotion driving relationship adjacency table;

[0044] Perform the same adjacency table structure depth traversal on the emotion-driven relationship adjacency table to obtain the adjacency table structure depth data;

[0045] According to the adjacency list structure depth data, the complexity of the relationships between different nodes in the emotion-driven relationship adjacency list is evaluated to obtain the table node relationship complexity data;

[0046] According to the table node relationship complexity data, the emotion-driven relationship adjacency table is divided into adjacency tables to obtain the emotion-driven relationship division adjacency table;

[0047] Performing local implicit numerical calculation on the adjacency table of emotion-driven relationship partitioning to obtain local relationship implicit numerical data; performing adaptive step-size control on the adjacency table of emotion-driven relationship partitioning according to the local relationship implicit numerical data to obtain adjacency table step-size control data;

[0048] According to the local relationship implicit numerical data and the step size control data of the adjacency table, the local error control Euler-Kromer numerical calculation is performed on the adjacency table of the emotion-driven relationship partition to obtain the local error control relationship numerical data;

[0049] According to the local error control relationship numerical data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data.

[0050] By constructing an emotion-driven relationship adjacency table, the present invention can clearly present the driving relationship and influence degree between different emotions, which is helpful to understand the driving factors of emotion changes and their effects in different emotion stages. By performing a structural depth traversal of the adjacency table, the relationship depth and path between different emotion nodes can be discovered. By evaluating the complexity of the relationship between different nodes, the complexity and key nodes of the emotion-driven relationship can be identified. Dividing the emotion-driven relationship adjacency table into different sub-tables is helpful to analyze the relationship patterns and characteristics between different sub-tables. Through adaptive step size control, the calculation step size in the analysis process can be effectively adjusted to improve the calculation efficiency and accuracy. Using the Euler-Cromer numerical calculation method, the local emotion-driven relationship can be accurately numerically simulated and calculated. Through local error control, the calculation error can be effectively controlled to ensure the accuracy and reliability of the analysis results. The final local emotion stage evolution data will comprehensively consider the complexity and dynamic changes of the emotion-driven relationship, revealing the evolution characteristics and laws of emotions at different stages, which is helpful to understand the process and mechanism of emotion change and provide a basis for further emotion management and intervention.

[0051] Preferably, performing emotional transition state analysis on local emotional phase evolution data includes:

[0052] Drawing a fluctuation curve for the emotion fluctuation assessment data to obtain an emotion fluctuation curve; extracting an increasing trend and a decreasing trend of the emotion fluctuation curve to obtain an increasing trend data set and a decreasing trend data set;

[0053] Performing increasing rate calculation and decreasing rate calculation on the curve increasing trend data set and the curve decreasing trend data set respectively, and obtaining curve increasing rate data and curve decreasing rate data respectively;

[0054] According to the curve increasing rate data and the curve decreasing rate data, the local emotion stage evolution data is processed by local emotion manifold learning mapping to obtain local emotion manifold learning data;

[0055] The local structure complexity of the local emotion manifold learning data is calculated by using the manifold learning data point structure analysis algorithm to obtain the local data point complexity data; the local emotion manifold learning data is processed with local complexity constraints according to the local data point complexity data to obtain the local data point complexity constraint data;

[0056] According to the complexity constraint data of the local data points, the local emotion manifold learning data is processed by complexity constraint local linear embedding to obtain local constraint linear embedding data;

[0057] According to the local constrained linear embedding data, the emotion transition state of the local emotion stage evolution data in different emotion stages is analyzed to obtain the emotion transition state data.

[0058] The present invention draws the mood fluctuation curve and can intuitively display the trend and fluctuation degree of mood changes. Extracting the increasing and decreasing trends of the curve can help identify the trend and law of mood changes and reveal the possibility of mood transition. Calculating the increasing and decreasing rates can help quantify the speed of mood changes and further analyze the speed and stability of mood changes, which is helpful to discover the emergence and trend of mood transition states. Through manifold learning and complexity calculation, mood data can be mapped to a low-dimensional space to reveal the structural characteristics and complexity of mood changes, which is helpful to understand the implicit structure and dynamic characteristics of mood changes. Complexity constraints and local linear embedding can extract the key features of local mood changes and control the complexity of data, which is helpful to reduce data dimensions, highlight the important features of mood transitions, and provide a clearer data basis for subsequent analysis. The final mood transition state data will comprehensively consider mood fluctuations, trends, rates and the results of manifold learning, and comprehensively analyze the state and trend of mood changes, which is helpful to identify the emergence and trend of mood transitions and provide an important reference for individual mood management and psychological intervention.

[0059] Preferably, the local structure complexity of the local emotion manifold learning data is calculated using a manifold learning data point structure analysis algorithm, wherein the manifold learning data point structure analysis algorithm is as follows:

[0060]

[0061] Where C represents the complexity result value of the local data point, α represents the nonlinear correlation coefficient of the local emotion manifold learning data, σ represents the data volume coefficient of the local emotion manifold learning data, x represents the estimated value of the data calculation difficulty of the local emotion manifold learning data, μ represents the dimensionality coefficient of the local emotion manifold learning data, t represents the maximum calculation time value, b represents the weight coefficient of the neighborhood data points in the local emotion manifold learning data, and R represents the error adjustment value of the manifold learning data point structure analysis algorithm.

[0062] The present invention constructs a manifold learning data point structure analysis algorithm, and provides a method for comprehensively analyzing the complexity of the local sentiment manifold learning data structure by considering factors such as nonlinear relationships, data volume, dimension, time series, neighborhood weights, and error adjustment. It can capture the complex characteristics of the data set and help reveal the correlation and implicit patterns between data points. The nonlinear correlation coefficient α of the local sentiment manifold learning data, this parameter represents the strength of the nonlinear relationship between data points. A larger α value will enhance the nonlinear relationship between data points and help capture more complex data structures, which is very beneficial for processing data sets with highly nonlinear characteristics; the data volume coefficient σ of the local sentiment manifold learning data, this parameter represents the data volume size of the data set, a larger σ value will increase the distribution range of the data points, and help cover a wider range of data features. When processing large-scale data sets, a larger σ value can provide a more comprehensive data structure analysis; the data calculation difficulty estimate x of the local sentiment manifold learning data, this parameter represents the calculation difficulty estimate of the data point, a larger x value means that the calculation difficulty of the data point is higher and requires more Complex models are used for modeling and analysis. This parameter can help determine the appropriate analysis method for different data points; the dimensionality coefficient μ of the local sentiment manifold learning data, this parameter represents the dimensionality coefficient of the data set. A larger μ value will increase the dimension of the data set, that is, the number of features of the data points. For high-dimensional data sets, a larger μ value can better capture the correlation between data features; the maximum calculation time value t, this parameter represents the maximum calculation time value of the algorithm. Increasing the t value will extend the calculation time of the algorithm, enabling it to analyze data in a longer time range, which is very beneficial for processing time series data or situations where data evolution needs to be observed for a long time; the weight coefficient b of the neighborhood data points in the local sentiment manifold learning data, this parameter represents the weight coefficient of the neighborhood data points. A larger b value will increase the contribution of the neighborhood data points to the complexity of the local data points, which helps to more accurately describe the local relationship between the data points; the error adjustment value R of the manifold learning data point structure analysis algorithm, this parameter represents the error adjustment value of the manifold learning data point structure analysis algorithm. By adjusting the R value, the results of the algorithm can be fine-tuned to obtain a better fitting effect.

[0063] Preferably, performing complexity local constraint processing on the local emotion manifold learning data includes:

[0064] Extract data points from local emotion manifold learning data according to local data point complexity data to obtain local emotion manifold learning data points; calculate local density of data points from local emotion manifold learning data points to obtain local density data of data points;

[0065] According to the local density data of the data points, the nearest neighbor data point relationship is established for the local emotion manifold learning data to obtain the nearest neighbor data point relationship data;

[0066] Based on the nearest neighbor data point relationship data, the structural relationship normal distribution analysis is performed on the local emotion manifold learning data points to obtain the data point relationship normal distribution data; the discrete degree of the data point relationship normal distribution data is calculated to obtain the relationship distribution discrete degree data;

[0067] According to the relationship distribution discrete degree data, the data point relationship normal distribution data and the neighbor data point relationship data, the local emotion manifold learning data points are subjected to structural connectivity distribution constraints to obtain structural connectivity distribution constraint data;

[0068] According to the structural connectivity distribution constraint data, the local emotion manifold learning data points are processed with local complexity constraints to obtain the local data point complexity constraint data.

[0069] The present invention can focus on local data points and reduce data dimension and complexity by extracting local emotion manifold learning data points and calculating local density. The calculation of local density helps to determine the density around each data point, thereby highlighting key emotion change points. Establishing nearest neighbor data point relationships and performing structural relationship analysis can reveal the interactions and connections between data points, which helps to understand the structural characteristics and degree of association between data points. Through relationship normal distribution analysis and discrete degree calculation, the distribution and stability of structural relationships between data points can be evaluated, which helps to identify the stability and reliability of relationships between data points and provide a basis for subsequent analysis. Complexity constraint processing is performed based on structural connectivity distribution constraints, which can control the degree of connectivity and complexity between data points, which helps to extract key local emotion change characteristics and reduce data noise and interference, making the analysis results more accurate and reliable.

[0070] The beneficial effects of the present invention are as follows: the present invention provides an intrinsic emotion analysis system based on psychological information big data, which is an optimization processing of the traditional intrinsic emotion analysis system based on psychological information big data, and solves the problem that the traditional intrinsic emotion analysis system based on psychological information big data is based on static data for analysis, which makes it difficult to capture the dynamic changes of emotions and cannot accurately analyze the emotions of the content. The present invention can well capture the dynamic changes of emotions and improve the accuracy of the analysis of the emotions of the content. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a module flow diagram of an intrinsic emotion analysis system based on psychology information big data;

[0072] Figure 2 for Figure 1 Schematic diagram of the functional flow of the dynamic emotion driving factor analysis module;

[0073] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0074] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve this, please refer to Figure 1 to Figure 2 , an intrinsic emotion analysis system based on psychology information big data, including the following modules:

[0078] The emotional topic modeling module is used to obtain psychology research data; perform semantic analysis on the psychology research data to obtain psychology semantic data; perform emotional topic modeling on the psychology research data based on the psychology semantic data to obtain emotional topic modeling data;

[0079] The dynamic emotion driving factor analysis module is used to perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data; perform local emotion structure feature analysis on the dynamic emotion segmentation data to obtain local emotion structure feature data; perform dynamic emotion driving factor analysis based on the local emotion structure feature data to obtain dynamic emotion driving factor data;

[0080] The module for identifying the abnormal trend of internal emotions is used to analyze the local emotion stage evolution of the local emotion structure feature data based on the dynamic emotion driving factor data to obtain the local emotion stage evolution data; analyze the emotion transition state of different emotion stages on the local emotion stage evolution data to obtain the emotion transition state data; identify the abnormal trend of internal emotions on the emotion transition state data to obtain the abnormal trend of internal emotions;

[0081] The abnormal emotion strategy adjustment module is used to use the long short-term memory network to construct a big data intrinsic emotion analysis model for the intrinsic emotion abnormal trend data, and obtain the intrinsic emotion abnormal analysis model; according to the intrinsic emotion abnormal analysis model, abnormal intrinsic emotion strategy adjustment is formulated to obtain abnormal emotion strategy adjustment data.

[0082] In the embodiment of the present invention, reference Figure 1 The above is a schematic diagram of the module flow of an intrinsic emotion analysis system based on psychology information big data of the present invention. In this example, the intrinsic emotion analysis system based on psychology information big data includes:

[0083] S1: emotional topic modeling module, used to obtain psychology research data; perform semantic analysis on the psychology research data to obtain psychology semantic data; perform emotional topic modeling on the psychology research data according to the psychology semantic data to obtain emotional topic modeling data;

[0084] In an embodiment of the present invention, data sources in various forms including various research documents, experimental data, and questionnaires in the field of psychology are collected, and natural language processing (NLP) technology is used to perform semantic analysis on the psychology research data, and the text data is converted into structured semantic information, including part-of-speech tagging, entity recognition, syntactic analysis and other processing steps, so as to better understand the meaning and content of the text data, and extract semantic information related to psychology from the results of the semantic analysis, such as emotions, psychological states, psychological process keywords and themes. Based on the psychological semantic data, a topic modeling algorithm (such as Latent Dirichlet Allocation, LDA) is used to perform emotional topic modeling on the psychology research data. These models can identify hidden themes and emotional tendencies in text data, decompose text data into a series of emotion-related topics, and convert the results of emotional topic modeling into a data set, in which each document or text data corresponds to the distribution of emotional topics, and each emotional topic is associated with a set of words that describe the emotions or emotional states represented by the topic.

[0085] S2: Dynamic emotion driving factor analysis module, used to perform dynamic emotion segmentation processing on psychological semantic data according to emotion topic modeling data to obtain dynamic emotion segmentation data; perform local emotion structure feature analysis on dynamic emotion segmentation data to obtain local emotion structure feature data; perform dynamic emotion driving factor analysis based on local emotion structure feature data to obtain dynamic emotion driving factor data;

[0086] In an embodiment of the present invention, based on the emotion topic modeling data, dynamic emotion segmentation processing is performed on the psychological semantic data, and the semantic data is segmented into different time periods or emotion segments using a time series analysis method or a window sliding technology. Local emotion structure feature analysis is performed on the dynamic emotion segmentation data to analyze the characteristics of each emotion segment, including the intensity, duration, transition frequency, etc. of the emotion, as well as the relationship between emotions. Based on the local emotion structure feature data, a dynamic emotion driving factor analysis is performed, and statistical analysis, machine learning, or deep learning methods are used to identify the key factors and driving mechanisms that affect emotion changes.

[0087] S3: Intrinsic emotion abnormal trend identification module, used to perform local emotion stage evolution analysis on local emotion structure feature data based on dynamic emotion driving factor data to obtain local emotion stage evolution data; perform emotion transition state analysis at different emotion stages on local emotion stage evolution data to obtain emotion transition state data; perform intrinsic emotion abnormal trend identification on emotion transition state data to obtain intrinsic emotion abnormal trend data;

[0088] In an embodiment of the present invention, based on dynamic emotion driving factor data, a local emotion stage evolution analysis is performed on the local emotion structure feature data, and time series analysis or other related methods are used to identify the evolution trend and change law of the local emotion structure in different time periods. The local emotion stage evolution data is subjected to an emotion transition state analysis at different emotion stages, and the transition pattern, frequency and trend of emotions between different stages are analyzed to identify the occurrence and change of emotion transition states, wherein the emotion transition state analysis refers to the transition from one emotion state to another. Based on the emotion transition state data, the abnormal trend of intrinsic emotions is identified, and the abnormal emotion change patterns and trends are identified using statistical analysis, machine learning or pattern recognition methods.

[0089] S4: Abnormal emotion strategy adjustment module, used to use the long short-term memory network to construct a big data intrinsic emotion analysis model for the intrinsic emotion abnormal trend data, and obtain the intrinsic emotion abnormal analysis model; according to the intrinsic emotion abnormal analysis model, abnormal intrinsic emotion strategy adjustment is formulated to obtain abnormal emotion strategy adjustment data.

[0090] In an embodiment of the present invention, a long short-term memory network (LSTM) or other deep learning models suitable for processing time series data are used to construct a big data intrinsic emotion analysis model for intrinsic emotion abnormal trend data, the abnormal emotion trend data is used as input, the model is trained to learn the changing patterns and trends of intrinsic emotions, and an abnormal intrinsic emotion strategy adjustment plan is formulated based on the constructed intrinsic emotion abnormal analysis model. According to the output results of the model and the identification of abnormal emotions, targeted emotion regulation strategies are designed to help individuals cope with abnormal emotions, and the formulated abnormal intrinsic emotion strategy adjustment plan is converted into a data format to form abnormal emotion strategy adjustment data, including specific methods of emotion regulation, time arrangements, and behavioral guidance content.

[0091] By acquiring psychology research data and performing semantic analysis, the present invention can deeply understand the psychological state, emotional experience, and behavioral patterns of individuals or groups. Semantic analysis can help extract key information, including emotional vocabulary, behavioral descriptions, and psychological state, and provide data support for subsequent emotional topic modeling. Emotional topic modeling through psychological semantic data can systematically understand the association and transformation between different emotions, and reveal the emotional patterns and psychological mechanisms hidden behind the data. Emotional topic modeling helps to discover and understand the diversity and complexity of human emotional experience, and provide in-depth insights for psychological research. Dynamic emotion segmentation processing can divide psychological semantic data into different emotional fragments or stages, revealing the temporal changes and dynamic evolution of emotions. Local emotion structure feature analysis can deeply explore the microscopic features of emotion fragments or stages, such as emotion intensity, emotion type, emotion duration, etc., so as to understand the internal mechanism of emotions in more detail. According to dynamic emotion segmentation data and local emotion structure feature data, the drivers that affect emotion changes can be identified and analyzed. Factors include external events, internal psychological processes, and social interactions. The analysis of dynamic emotion driving factors helps to reveal the causes and regulatory mechanisms of emotions, and provides a theoretical basis and practical guidance for emotion management and psychological intervention. The analysis of the phased evolution of local emotions can identify the changing trends of individual emotions at different stages, including the rise and fall and stability of emotions. Through the analysis of emotional transition states, the transformation relationship between different emotions can be revealed, including sudden changes in emotions and smooth transition states. The identification of abnormal trends in internal emotions can discover abnormal patterns of individual emotional changes, including abnormal frequency, amplitude, and duration, thereby helping to identify possible mental health problems or emotional regulation difficulties. The internal emotion abnormality analysis model is constructed using the long and short-term memory network, which can learn and model a large amount of internal emotion abnormality trend data, realize intelligent identification and analysis of abnormal emotions, and formulate abnormal emotion strategy adjustment plans based on the internal emotion abnormality analysis model, including personalized emotion management suggestions, emotion regulation skills, etc., to help individuals better understand and deal with their own emotional problems.

[0092] Preferably, the emotional topic modeling module includes the following functions:

[0093] Access to psychological research data;

[0094] Conduct semantic analysis on psychology research data to obtain psychology semantic data; conduct semantic tendency analysis on psychology semantic data to obtain semantic tendency data;

[0095] Emotional topic modeling is performed on the psychological research data according to the psychological semantic data and the semantic tendency data to obtain the emotional topic modeling data.

[0096] In an embodiment of the present invention, data sources such as research literature, experimental data, and questionnaires in the field of psychology are collected, and natural language processing (NLP) technology is used to perform semantic analysis on the psychology research data, and text data is converted into structured semantic information, including part-of-speech tagging, entity recognition, syntactic analysis and other processing steps, so as to better understand the meaning and content of the text data, and sentiment analysis or tendency analysis is performed on the psychology semantic data obtained through semantic analysis. The sentiment analysis technology is used to identify the sentiment tendency in the text, including positive, negative or neutral sentiments, and the semantic data and semantic tendency data are combined to perform emotional topic modeling on the psychology research data. A topic modeling algorithm, such as Latent Dirichlet Allocation (LDA), can be used to decompose the text data into a series of emotion-related topics, and the results of the emotional topic modeling are converted into a data set, wherein each document or text data corresponds to the distribution of an emotional topic, and each emotional topic is associated with a set of words that describe the emotion or emotional state represented by the topic.

[0097] By acquiring psychology research data and performing semantic analysis, the present invention can deeply understand the content and information contained in the data, including emotional expression, behavior description, and cognitive process. Semantic tendency analysis further deepens the understanding of the data, helps capture the emotional tendency, attitude and emotional color in the language, and makes the data more informative and emotionally expressive. Emotional topic modeling based on psychological semantic data and semantic tendency data helps to discover potential emotional themes and emotional patterns in the data. Through emotional topic modeling, the types, distribution and correlations of different emotions in the data can be systematically analyzed to reveal the emotional structure and psychological characteristics behind the data. Emotional topic modeling data can provide in-depth insights for research in the field of psychology, helping researchers understand the emotional experience, mental health status, and social interaction of individuals or groups. Furthermore, these data can also be applied to actual scenarios, such as sentiment analysis, user experience research, mental health assessment and other fields, to provide support for decision making and practical applications. The application of technologies such as semantic analysis and emotional topic modeling helps to improve the processing efficiency and accuracy of psychology research data, reduce the workload and subjective bias of manual analysis, and these technologies can also provide a more reliable foundation for data-driven decision-making and application, thereby improving work efficiency and decision quality.

[0098] Preferably, the dynamic emotion driving factor analysis module includes the following functions:

[0099] Perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data;

[0100] Marking the emotion transition points of the dynamic emotion segmentation data to obtain emotion transition point data; performing emotion transition pattern recognition on the emotion transition point data to obtain emotion transition pattern recognition data;

[0101] Perform local emotion structure feature analysis on the dynamic emotion segmentation data according to the emotion transition pattern recognition data to obtain local emotion structure feature data;

[0102] Dynamic emotion driving factor analysis is performed based on local emotion structure feature data and emotion transition pattern recognition data to obtain dynamic emotion driving factor data.

[0103] As an example of the present invention, refer to Figure 2 As shown, Figure 1 The function flow diagram of the dynamic emotion driving factor analysis module in this example includes:

[0104] S201: performing dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data;

[0105] In an embodiment of the present invention, the emotion topic modeling data obtained by the emotion topic modeling module is used as input, and time series analysis or window sliding technology is used to segment the psychological semantic data into different time periods or emotion fragments, and the emotion transition point, that is, the location of the emotion switching, is determined in the time series to obtain dynamic emotion segmentation data.

[0106] S202: marking the emotion transition points of the dynamic emotion segmentation data to obtain emotion transition point data; performing emotion transition pattern recognition on the emotion transition point data to obtain emotion transition pattern recognition data;

[0107] In an embodiment of the present invention, dynamic emotion segmentation data is traversed, sample by sample is analyzed, and predefined emotion transition rules are used to detect emotion transition points in the samples. The determination of emotion transition points can be based on changes in emotion intensity, changes in emotion polarity, or emotion fluctuation indicators. For the discovered emotion transition points, the timestamps or sample indexes of their occurrence are recorded, and machine learning or statistical analysis methods are used to perform pattern recognition on the marked emotion transition point data. The emotion transition point data is used as input features. The goal is to identify different emotion transition patterns. Common emotion transition patterns include gradual changes, mutations, and periodic changes. Clustering, classification, or time series analysis techniques can be used for pattern recognition to discover potential emotion transition patterns in the data.

[0108] S203: performing local emotion structure feature analysis on the dynamic emotion segmentation data according to the emotion transition pattern recognition data to obtain local emotion structure feature data;

[0109] In an embodiment of the present invention, emotion transition pattern recognition data is obtained, including different identified emotion transition patterns and their corresponding time periods or sample indexes, dynamic emotion segmentation data is prepared to ensure that each time period or sample has a corresponding emotion tag or sentiment value, and the dynamic emotion segmentation data is divided into different local emotion structures according to the emotion transition pattern recognition data. For each local emotion structure, emotion feature analysis is performed, including the intensity, polarity, and duration of the emotion. The average emotion intensity, emotion change amplitude, and emotion stability of each local emotion structure can be calculated, and the emotion features obtained for each local emotion structure are integrated to form local emotion structure feature data, and each local emotion structure feature data includes the emotion feature information within the time period or sample.

[0110] S204: Perform dynamic emotion driving factor analysis based on the local emotion structure feature data and the emotion transition pattern recognition data to obtain dynamic emotion driving factor data.

[0111] In an embodiment of the present invention, local emotion structure feature data and emotion transition pattern recognition data are obtained to ensure data consistency and integrity. According to the emotion transition pattern recognition data, the dynamic emotion segmentation data is divided into different local emotion structures. For each local emotion structure, the emotion driving factors therein are analyzed using the local emotion structure feature data. Correlation analysis, factor analysis, machine learning and other methods can be used to identify the driving factors affecting the local emotion structure. The identified dynamic emotion driving factors are integrated into a data set. Each sample represents a local emotion structure and its corresponding driving factor. Each driving factor can be a factor that affects emotion changes, such as a situation, an external event, and an individual characteristic. The dynamic emotion driving factor data is statistically analyzed to explore the characteristics of the driving factors under different emotion transition modes. The analysis results may include information such as the main driving factors, the weights or the degree of influence of the driving factors under different emotion transition modes, and the dynamic emotion driving factor data is obtained.

[0112] The dynamic emotion segmentation processing of the present invention can divide psychological semantic data into different emotion fragments, realize fine-grained analysis of emotion changes, and make emotion changes more clearly visible. The marking of emotion transition point data and emotion transition pattern recognition further deepen the understanding of emotion changes, reveal the critical points and patterns of emotion changes, and help grasp the key moments and laws of emotion changes. Through local emotion structure feature analysis and emotion transition pattern recognition, the local emotion features and transition patterns in the dynamic emotion segmentation data can be deeply mined, providing a rich data foundation for subsequent emotion driving factor analysis. The dynamic emotion driving factor analysis can systematically identify factors that affect emotion changes, including external events, internal psychological processes, and social interactions, thereby revealing the causes and regulatory mechanisms of emotion changes; through the analysis of dynamic emotion driving factors, it can help individuals better understand the fluctuations and changes of their emotions and enhance their ability to manage and regulate emotions. The results of the emotion driving factor analysis can also provide a basis for individuals to formulate personalized emotion management strategies, helping individuals to more effectively cope with various emotional states and challenges.

[0113] Preferably, the performing local emotion structure feature analysis on the dynamic emotion segmentation data comprises:

[0114] Performing dynamic emotion time series analysis on the dynamic emotion segmentation data to obtain dynamic emotion time series data; reconstructing the emotion pattern phase space of the dynamic emotion time series data according to the emotion transition pattern recognition data to obtain emotion pattern phase space reconstruction data;

[0115] Perform nonlinear spatial correlation analysis on the emotion pattern phase space reconstruction data to obtain nonlinear spatial correlation data;

[0116] According to the nonlinear spatial correlation data, the Poincare section analysis is performed on the emotional pattern phase space reconstruction data to obtain the nonlinear phase space Poincare section; the spatial trajectory periodicity analysis is performed on the nonlinear phase space Poincare section to obtain the spatial trajectory periodicity data;

[0117] Based on the bifurcation theory technology, the trajectory structure analysis is performed on the periodicity number of the space trajectory to obtain the space trajectory structure data;

[0118] The local emotion structure feature data is obtained by performing a local emotion structure feature analysis based on the spatial trajectory structure data and the spatial trajectory periodicity data.

[0119] In an embodiment of the present invention, the time series data points are determined by segmenting the data through dynamic emotions, and time series analysis techniques such as sliding windows and smoothing are applied to capture the changing trend of emotions over time. Statistical methods or machine learning techniques such as ARIMA models and LSTM neural networks are used to model and predict the time series data so as to understand the dynamic changes of emotions. The emotion time series data is converted into emotion pattern phase space data, and an appropriate phase space reconstruction technique such as delayed coordinate method or time delay embedding is used to retain the dynamic features in the data, and appropriate delay time and embedding dimension are determined to ensure that the reconstructed phase space can accurately reflect the dynamic characteristics of the original data. Nonlinear spatial correlation analysis methods such as cross-correlation analysis of phase space reconstruction or maximum Lyapunov exponent calculation are used to measure the degree of correlation between emotion patterns, and appropriate correlation metrics are determined to evaluate the correlation between different emotion patterns, and possible nonlinear correlation structures are found to analyze the emotion pattern phase space. Poincare section analysis is performed on the reconstructed data to capture the important geometric features of the phase space trajectory. By drawing Poincare sections in the phase space, the intersection points and intersection structures of the phase space trajectory can be visualized, which helps to understand the dynamic interactive relationship between emotional patterns. Periodic analysis is performed on the Poincare section data to detect possible periodic changes between emotional patterns. Spectral analysis or periodicity detection algorithms, such as Fourier transform or autocorrelation function, are applied to identify periodic components in the spatial trajectory. Based on the bifurcation theory technology, the spatial trajectory periodic data is analyzed to reveal the bifurcation structure between emotional patterns. By analyzing the bifurcation points and bifurcation paths of the trajectories, the transition patterns and evolution trends between emotional patterns can be deeply understood. According to the spatial trajectory structure data and the spatial trajectory periodicity data, the local emotional structure characteristics are analyzed; the local features between emotional patterns, such as local maximum points, local minimum points, etc., and the correlation between these feature points are determined.

[0120] Through dynamic emotion time series analysis, the present invention can deeply understand the temporal changes of emotions, including the fluctuation cycle, duration, and amplitude of emotions, which is helpful to identify the regularity and trend of emotion changes and provide basic data for subsequent analysis. Emotional pattern phase space reconstruction can map dynamic emotion time series data to high-dimensional phase space, revealing the multidimensional characteristics of emotion changes. Linear space association analysis can discover the nonlinear relationship between emotions and reveal the interaction and influence between emotions. Poincare cross-section analysis can capture the periodic structure of emotion changes in phase space, helping to understand the stability and periodicity of emotion changes. Spatial trajectory periodicity analysis further analyzes the periodic characteristics of emotion trajectories in phase space, which is helpful to identify the stable cycle and trend of emotion changes. Trajectory structure analysis based on bifurcation theory technology can reveal bifurcation phenomena and critical points in the process of emotion changes, helping to understand the nonlinear dynamics of emotion changes. The final local emotion structure feature analysis comprehensively considers the temporal characteristics, phase space characteristics and dynamic characteristics of emotions, extracts the key features of local emotion changes, and provides an in-depth understanding and basis for the analysis of emotion driving factors.

[0121] Preferably, the dynamic emotion driving factor analysis includes:

[0122] Performing pattern-context mapping on the emotion transition pattern recognition data to obtain emotion pattern-context data;

[0123] Performing situation distribution mapping on the emotion pattern situation data according to the local emotion structure feature data to obtain the local emotion structure situation distribution data;

[0124] Performing structural situational distribution logistic regression analysis on the local emotion structure situational distribution data to obtain situational distribution logistic regression data; performing logical linear factor analysis on the local emotion structure situational distribution data based on the situational distribution logistic regression data to obtain situational emotion logical factor data;

[0125] Based on the recurrent neural network, the logical situation perception model is constructed for the situation emotion logic factor data to obtain the logical situation perception model;

[0126] According to the logical situation perception model, the pre-logical factor prediction of the situation emotion logical factor data is performed to obtain the pre-logical factor data;

[0127] Dynamic emotion driving factor analysis is performed based on the pre-logical factor data and the situational emotion logical factor data to obtain dynamic emotion driving factor data.

[0128] In the embodiment of the present invention, the emotion transition pattern recognition data is used to map the emotion pattern to a specific situation or background to form emotion pattern situation data. The situation types or situation characteristics corresponding to different emotion patterns are identified through data mining or pattern recognition technology. Based on the local emotion structure feature data, the situation distribution is mapped to the emotion pattern situation data. The correlation between the local emotion structure characteristics and the situation distribution is analyzed to identify the distribution characteristics of the local emotion structure in different situations. Logistic regression analysis is performed on the local emotion structure situation distribution data. The distribution law of the local emotion structure in different situations is explored by using a logistic regression model. The situation factors that affect the local emotion structure are identified. Logical linear factor analysis is performed on the local emotion structure situation distribution data based on the situation distribution logistic regression data. The emotion structure is determined by factor analysis. The system uses situational emotional logic factors to reveal the potential correlation and causal relationship between situations and emotional patterns. Based on recurrent neural networks or other appropriate models, a logical situational awareness model is constructed. The situational emotional logic factor data is used to train the model to learn the complex relationship between situations and emotions, and a situational awareness model is established. According to the logical situational awareness model, the situational emotional logic factor data is predicted by pre-logical factors. The trained model is used to predict the emotional logic factors in future or unknown situations, thereby revealing the impact of situational changes on emotions. Combined with the pre-logical factor data and the situational emotional logic factor data, a dynamic emotion driving factor analysis is conducted to analyze the driving effect of situational changes on emotional patterns, identify the emotional change patterns and driving factors in different situations, and thus deeply understand the dynamic characteristics of emotions and the causal relationship behind them.

[0129] The mapping of emotion pattern context data of the present invention will help to understand the performance and changes of different emotion patterns in different contexts. The mapping of local emotion structure context distribution data can further reveal the distribution and distribution rules of emotions in various contexts, providing a basis for subsequent analysis. Structural context distribution logistic regression analysis and logistic linear factor analysis can identify the correlation and influencing factors between context and emotion, which is helpful to understand the changing rules and driving factors of emotions in different contexts. These analysis results provide quantification and modeling of the relationship between emotion and context, and provide data support for subsequent analysis of emotion driving factors. The logical context perception model constructed based on recurrent neural network can model and predict the complex relationship between context and emotion, and improve the understanding and perception of context factors. The prediction of pre-logical factor data can help predict the emotional state that may appear in future contexts, and provide early warning and guidance for emotion management and regulation. The final dynamic emotion driving factor data will comprehensively consider the complex relationship between emotion, context and other factors, which will help to deeply understand the internal mechanism and dynamic evolution process of emotion change.

[0130] Preferably, the intrinsic emotion abnormal trend identification module includes the following functions:

[0131] Based on the dynamic emotion driving factor data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data;

[0132] Evaluate the emotion volatility of the local emotion phase evolution data to obtain emotion volatility evaluation data;

[0133] According to the emotion volatility evaluation data, the emotion transition state analysis of the local emotion phase evolution data at different emotion phases is performed to obtain the emotion transition state data;

[0134] The abnormal inner emotion trend is identified on the emotion transition state data to obtain the abnormal inner emotion trend data.

[0135] In an embodiment of the present invention, dynamic emotion driving factor data is used to analyze local emotion structure feature data, and by identifying and analyzing the changing trend of the local emotion structure, the emotion state in different time periods is determined. Based on the dynamic emotion driving factor data, a stage evolution analysis is performed on the local emotion structure feature data, the emotion structure feature data is grouped by time period, and the evolution process of the emotion structure in each time period is analyzed to obtain local emotion stage evolution data, and an emotion volatility assessment is performed on the local emotion stage evolution data. A statistical method or indicator is used to assess the degree of emotion fluctuation in different stages to quantify the amplitude and frequency of emotion changes and obtain emotion volatility assessment data. Based on the emotion volatility assessment data, an emotion transition state analysis is performed on the local emotion stage evolution data, and the transition process and state between different emotion stages are analyzed to identify the transition of emotion from one state to another and obtain emotion transition state data. Based on the emotion volatility assessment data and the emotion transition state data, an abnormal trend of intrinsic emotion is identified, and an abnormal emotion fluctuation pattern or transition state is identified using a machine learning or statistical analysis method, thereby determining the abnormal trend of intrinsic emotion and obtaining abnormal trend data of intrinsic emotion.

[0136] The present invention can identify the different stages and stage characteristics of emotional changes by performing local emotional stage evolution analysis on local emotional structure feature data, which helps to understand the time series pattern of emotional changes and reveal the important nodes and trends in the emotional evolution process. The acquisition of emotional volatility assessment data can quantify the degree and frequency of emotional changes and help identify the stability and regularity of emotional fluctuations, which helps to judge the stability of emotional changes and the ability to regulate emotions, and provides an important reference for subsequent analysis; by analyzing the emotional transition state data, the transition patterns and trends between different emotional stages can be revealed, including sudden changes in emotions, smooth transitions, etc., which helps to discover abnormal emotional transition patterns and warn of existing mental health problems or emotional regulation difficulties. The final intrinsic emotional abnormal trend data will comprehensively consider the results of local emotional stage evolution, emotional volatility assessment and emotional transition state analysis, and identify the emergence of abnormal emotional trends, which helps to early detect and intervene in individual mental health problems or emotional management difficulties.

[0137] Preferably, performing local emotion stage evolution analysis on local emotion structure feature data includes:

[0138] Based on the dynamic emotion driving factor data, the emotion driving factor emotion relationship adjacency table is constructed for the local emotion structure feature data to obtain the emotion driving relationship adjacency table;

[0139] Perform the same adjacency table structure depth traversal on the emotion-driven relationship adjacency table to obtain the adjacency table structure depth data;

[0140] According to the adjacency list structure depth data, the complexity of the relationships between different nodes in the emotion-driven relationship adjacency list is evaluated to obtain the table node relationship complexity data;

[0141] According to the table node relationship complexity data, the emotion-driven relationship adjacency table is divided into adjacency tables to obtain the emotion-driven relationship division adjacency table;

[0142] Performing local implicit numerical calculation on the adjacency table of emotion-driven relationship partitioning to obtain local relationship implicit numerical data; performing adaptive step-size control on the adjacency table of emotion-driven relationship partitioning according to the local relationship implicit numerical data to obtain adjacency table step-size control data;

[0143] According to the local relationship implicit numerical data and the step size control data of the adjacency table, the local error control Euler-Kromer numerical calculation is performed on the adjacency table of the emotion-driven relationship partition to obtain the local error control relationship numerical data;

[0144] According to the local error control relationship numerical data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data.

[0145] In the embodiment of the present invention, based on the dynamic emotion driving factor data, an emotion driving relationship adjacency table is constructed, and the relationship between the emotion driving factors is expressed in the form of an adjacency table for subsequent analysis. The constructed emotion driving relationship adjacency table is traversed in depth first to obtain the adjacency table structure depth data. The deep traversal can help understand the relationship depth and structure between the emotion factors. According to the adjacency table structure depth data, the relationship complexity between different nodes is evaluated. The degree, clustering coefficient and other indicators in graph theory can be used to evaluate the relationship complexity between nodes. According to the complexity data of the relationship between nodes, the emotion driving relationship adjacency table is divided. The division can be performed according to the complexity threshold or other standards. For subsequent analysis and calculation, the divided emotion driving relationship adjacency table is locally implicitly numerically calculated, and the numerical calculation method can be used to derive And calculate the implicit relationship between emotional factors to better understand the evolution process of emotions. According to the implicit numerical data of local relationships, the adjacency list of emotion-driven relationship partition is adaptively controlled by step size control. The step size control can adjust the step size of the calculation according to the calculation results to improve the calculation efficiency and accuracy. Combined with the local relationship implicit numerical data and the step size control data, the adjacency list of emotion-driven relationship partition is subjected to local error control Euler-Cromer numerical calculation. Through the numerical calculation method, the emotion evolution process is simulated and analyzed to obtain the local error control relationship numerical data. Based on the local error control relationship numerical data, the local emotion structure feature data is analyzed for the local emotion stage evolution. By analyzing the relationship changes between local emotion factors, the emotion evolution at different stages is obtained, thereby obtaining the local emotion stage evolution data.

[0146] By constructing an emotion-driven relationship adjacency table, the present invention can clearly present the driving relationship and influence degree between different emotions, which is helpful to understand the driving factors of emotion changes and their effects in different emotion stages. By performing a structural depth traversal of the adjacency table, the relationship depth and path between different emotion nodes can be discovered. By evaluating the complexity of the relationship between different nodes, the complexity and key nodes of the emotion-driven relationship can be identified. Dividing the emotion-driven relationship adjacency table into different sub-tables is helpful to analyze the relationship patterns and characteristics between different sub-tables. Through adaptive step size control, the calculation step size in the analysis process can be effectively adjusted to improve the calculation efficiency and accuracy. Using the Euler-Cromer numerical calculation method, the local emotion-driven relationship can be accurately numerically simulated and calculated. Through local error control, the calculation error can be effectively controlled to ensure the accuracy and reliability of the analysis results. The final local emotion stage evolution data will comprehensively consider the complexity and dynamic changes of the emotion-driven relationship, revealing the evolution characteristics and laws of emotions at different stages, which is helpful to understand the process and mechanism of emotion change and provide a basis for further emotion management and intervention.

[0147] Preferably, performing emotional transition state analysis on local emotional phase evolution data includes:

[0148] Drawing a fluctuation curve for the emotion fluctuation assessment data to obtain an emotion fluctuation curve; extracting an increasing trend and a decreasing trend of the emotion fluctuation curve to obtain an increasing trend data set and a decreasing trend data set;

[0149] Performing increasing rate calculation and decreasing rate calculation on the curve increasing trend data set and the curve decreasing trend data set respectively, and obtaining curve increasing rate data and curve decreasing rate data respectively;

[0150] According to the curve increasing rate data and the curve decreasing rate data, the local emotion stage evolution data is processed by local emotion manifold learning mapping to obtain local emotion manifold learning data;

[0151] The local structure complexity of the local emotion manifold learning data is calculated by using the manifold learning data point structure analysis algorithm to obtain the local data point complexity data; the local emotion manifold learning data is processed with local complexity constraints according to the local data point complexity data to obtain the local data point complexity constraint data;

[0152] According to the complexity constraint data of the local data points, the local emotion manifold learning data is processed by complexity constraint local linear embedding to obtain local constraint linear embedding data;

[0153] According to the local constrained linear embedding data, the emotion transition state of the local emotion stage evolution data in different emotion stages is analyzed to obtain the emotion transition state data.

[0154] In an embodiment of the present invention, the emotion volatility assessment data is plotted into a fluctuation curve graph in chronological order. The time can be divided into hours, days or other suitable units. A suitable time interval is selected according to the time span of the data. By observing the fluctuation curve, the increasing segment and the decreasing segment of the curve are identified. The increasing segment indicates that the emotion is in an enhanced state, while the decreasing segment indicates that the emotion tends to be stable or weakened. The increasing segment data and the decreasing segment data on the curve are extracted to form a curve increasing trend data set and a curve decreasing trend data set respectively. The rate of the curve increasing trend data set and the curve decreasing trend data set are calculated respectively. The speed of change of the curve is calculated by linear regression or difference method. The curve increasing rate data and the curve decreasing rate data are used to perform local emotion manifold learning mapping processing. A manifold learning algorithm, such as Locally Linear Embedding (LLE) or t-distributed stochastic neighbor embedding (t-SNE), etc., can be used to map high-dimensional emotion data into low-dimensional space; a manifold learning data point structure analysis algorithm, such as Locally Linear Embedding (LLE) or t-distributed stochastic neighbor embedding (t-SNE), which calculates the structural complexity of each data point in the local sentiment manifold learning data. This can include local density-based methods, such as Local Reachability Density (LRD) or Local Outlier Factor (LOF). Based on the local data point complexity data, the local emotion manifold learning data is processed with complexity constraints. Data points with higher or lower complexity can be retained, or other constraint strategies can be formulated according to specific application requirements. The local data point complexity constraint data is used to perform complexity constrained local linear embedding processing on the local emotion manifold learning data. The local linear embedding algorithm can map high-dimensional data to low-dimensional space by maintaining the local linear relationship between data points, while considering complexity constraints to retain the structural characteristics of data points. Based on the local constrained linear embedding data, the local emotion stage evolution data is analyzed for the emotion transition state of different emotion stages. Clustering algorithms, such as k-means clustering or DBSCAN, can be used to perform cluster analysis on the local constrained linear embedding data to identify data point clusters at different emotion stages. For further analysis of the emotion transition state, time series analysis methods, such as state transition models or event-driven models, can be considered to capture the transition and change patterns of emotions between different stages.

[0155] The present invention draws the mood fluctuation curve and can intuitively display the trend and fluctuation degree of mood changes. Extracting the increasing and decreasing trends of the curve can help identify the trend and law of mood changes and reveal the possibility of mood transition. Calculating the increasing and decreasing rates can help quantify the speed of mood changes and further analyze the speed and stability of mood changes, which is helpful to discover the emergence and trend of mood transition states. Through manifold learning and complexity calculation, mood data can be mapped to a low-dimensional space to reveal the structural characteristics and complexity of mood changes, which is helpful to understand the implicit structure and dynamic characteristics of mood changes. Complexity constraints and local linear embedding can extract the key features of local mood changes and control the complexity of data, which is helpful to reduce data dimensions, highlight the important features of mood transitions, and provide a clearer data basis for subsequent analysis. The final mood transition state data will comprehensively consider mood fluctuations, trends, rates and the results of manifold learning, and comprehensively analyze the state and trend of mood changes, which is helpful to identify the emergence and trend of mood transitions and provide an important reference for individual mood management and psychological intervention.

[0156] Preferably, the local structure complexity of the local emotion manifold learning data is calculated using a manifold learning data point structure analysis algorithm, wherein the manifold learning data point structure analysis algorithm is as follows:

[0157]

[0158] Where C represents the complexity result value of the local data point, α represents the nonlinear correlation coefficient of the local emotion manifold learning data, σ represents the data volume coefficient of the local emotion manifold learning data, x represents the estimated value of the data calculation difficulty of the local emotion manifold learning data, μ represents the dimensionality coefficient of the local emotion manifold learning data, t represents the maximum calculation time value, b represents the weight coefficient of the neighborhood data points in the local emotion manifold learning data, and R represents the error adjustment value of the manifold learning data point structure analysis algorithm.

[0159] The present invention constructs a manifold learning data point structure analysis algorithm, and provides a method for comprehensively analyzing the complexity of the local sentiment manifold learning data structure by considering factors such as nonlinear relationships, data volume, dimension, time series, neighborhood weights, and error adjustment. It can capture the complex characteristics of the data set and help reveal the correlation and implicit patterns between data points. The nonlinear correlation coefficient α of the local sentiment manifold learning data, this parameter represents the strength of the nonlinear relationship between data points. A larger α value will enhance the nonlinear relationship between data points and help capture more complex data structures, which is very beneficial for processing data sets with highly nonlinear characteristics; the data volume coefficient σ of the local sentiment manifold learning data, this parameter represents the data volume size of the data set, a larger σ value will increase the distribution range of the data points, and help cover a wider range of data features. When processing large-scale data sets, a larger σ value can provide a more comprehensive data structure analysis; the data calculation difficulty estimate x of the local sentiment manifold learning data, this parameter represents the calculation difficulty estimate of the data point, a larger x value means that the calculation difficulty of the data point is higher and requires more Complex models are used for modeling and analysis. This parameter can help determine the appropriate analysis method for different data points; the dimensionality coefficient μ of the local sentiment manifold learning data, this parameter represents the dimensionality coefficient of the data set. A larger μ value will increase the dimension of the data set, that is, the number of features of the data points. For high-dimensional data sets, a larger μ value can better capture the correlation between data features; the maximum calculation time value t, this parameter represents the maximum calculation time value of the algorithm. Increasing the t value will extend the calculation time of the algorithm, enabling it to analyze data in a longer time range, which is very beneficial for processing time series data or situations where data evolution needs to be observed for a long time; the weight coefficient b of the neighborhood data points in the local sentiment manifold learning data, this parameter represents the weight coefficient of the neighborhood data points. A larger b value will increase the contribution of the neighborhood data points to the complexity of the local data points, which helps to more accurately describe the local relationship between the data points; the error adjustment value R of the manifold learning data point structure analysis algorithm, this parameter represents the error adjustment value of the manifold learning data point structure analysis algorithm. By adjusting the R value, the results of the algorithm can be fine-tuned to obtain a better fitting effect.

[0160] Preferably, performing complexity local constraint processing on the local emotion manifold learning data includes:

[0161] Extract data points from local emotion manifold learning data according to local data point complexity data to obtain local emotion manifold learning data points; calculate local density of data points from local emotion manifold learning data points to obtain local density data of data points;

[0162] According to the local density data of the data points, the nearest neighbor data point relationship is established for the local emotion manifold learning data to obtain the nearest neighbor data point relationship data;

[0163] Based on the nearest neighbor data point relationship data, the structural relationship normal distribution analysis is performed on the local emotion manifold learning data points to obtain the data point relationship normal distribution data; the discrete degree of the data point relationship normal distribution data is calculated to obtain the relationship distribution discrete degree data;

[0164] According to the relationship distribution discrete degree data, the data point relationship normal distribution data and the neighbor data point relationship data, the local emotion manifold learning data points are subjected to structural connectivity distribution constraints to obtain structural connectivity distribution constraint data;

[0165] According to the structural connectivity distribution constraint data, the local emotion manifold learning data points are processed with local complexity constraints to obtain the local data point complexity constraint data.

[0166] In the embodiment of the present invention, the local data point complexity data is used to extract data points from the local emotion manifold learning data to obtain the local emotion manifold learning data points, and the local density of the extracted local emotion manifold learning data points is calculated. The local density data of each data point is calculated using a density estimation method, such as the K nearest neighbor method or the kernel density estimation method, and the nearest neighbor data point relationship is established based on the local density data of the data point. For each data point, its nearest K neighbors are determined, and these neighbors will become part of its local relationship. The structural relationship normal distribution analysis of the local emotion manifold learning data points is performed using the nearest neighbor data point relationship data. Statistical methods, such as normal distribution fitting, can be used to capture the data point relationships. The normal property of the system is used to calculate the degree of discreteness of the normal distribution data of the data point relationship. The standard deviation or other discreteness indicators of the normal distribution of the data point relationship can be calculated to quantify the distribution shape of the data point relationship. Combined with the discreteness data of the relationship distribution, the normal distribution data of the data point relationship and the neighboring data point relationship data, the structural connectivity distribution constraints are applied to the local sentiment manifold learning data points. Constraints can be formulated, such as keeping the data point relationship distribution within a certain normal range or adjusting the connection density between data points. According to the structural connectivity distribution constraint data, the local sentiment manifold learning data points are processed with local complexity constraints. According to the constraints, the position or connection relationship of the data points is adjusted to meet the local complexity constraints.

[0167] The present invention can focus on local data points and reduce data dimension and complexity by extracting local emotion manifold learning data points and calculating local density. The calculation of local density helps to determine the density around each data point, thereby highlighting key emotion change points. Establishing nearest neighbor data point relationships and performing structural relationship analysis can reveal the interactions and connections between data points, which helps to understand the structural characteristics and degree of association between data points. Through relationship normal distribution analysis and discrete degree calculation, the distribution and stability of structural relationships between data points can be evaluated, which helps to identify the stability and reliability of relationships between data points and provide a basis for subsequent analysis. Complexity constraint processing is performed based on structural connectivity distribution constraints, which can control the degree of connectivity and complexity between data points, which helps to extract key local emotion change characteristics and reduce data noise and interference, making the analysis results more accurate and reliable.

[0168] The beneficial effects of the present invention are as follows: the present invention provides an intrinsic emotion analysis system based on psychological information big data, which is an optimization processing of the traditional intrinsic emotion analysis system based on psychological information big data, and solves the problem that the traditional intrinsic emotion analysis system based on psychological information big data is based on static data for analysis, which makes it difficult to capture the dynamic changes of emotions and cannot accurately analyze the emotions of the content. The present invention can well capture the dynamic changes of emotions and improve the accuracy of the analysis of the emotions of the content.

[0169] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0170] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those 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 invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intrinsic emotion analysis system based on psychology information big data, characterized in that: Includes the following modules: The emotional topic modeling module is used to obtain psychology research data; perform semantic analysis on the psychology research data to obtain psychology semantic data; perform emotional topic modeling on the psychology research data based on the psychology semantic data to obtain emotional topic modeling data; The dynamic emotion driving factor analysis module is used to perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data; perform local emotion structure feature analysis on the dynamic emotion segmentation data to obtain local emotion structure feature data; perform dynamic emotion driving factor analysis based on the local emotion structure feature data to obtain dynamic emotion driving factor data; The module for identifying the abnormal trend of internal emotions is used to analyze the local emotion stage evolution of the local emotion structure feature data based on the dynamic emotion driving factor data to obtain the local emotion stage evolution data; analyze the emotion transition state of different emotion stages on the local emotion stage evolution data to obtain the emotion transition state data; identify the abnormal trend of internal emotions on the emotion transition state data to obtain the abnormal trend of internal emotions; The abnormal emotion strategy adjustment module is used to use the long short-term memory network to construct a big data intrinsic emotion analysis model for the intrinsic emotion abnormal trend data, and obtain the intrinsic emotion abnormal analysis model; according to the intrinsic emotion abnormal analysis model, abnormal intrinsic emotion strategy adjustment is formulated to obtain abnormal emotion strategy adjustment data.

2. The intrinsic emotion analysis system based on psychology information big data according to claim 1 is characterized in that: The sentiment topic modeling module includes the following functions: Access to psychological research data; Conduct semantic analysis on psychology research data to obtain psychology semantic data; conduct semantic tendency analysis on psychology semantic data to obtain semantic tendency data; Emotional topic modeling is performed on the psychological research data according to the psychological semantic data and the semantic tendency data to obtain the emotional topic modeling data.

3. The intrinsic emotion analysis system based on psychology information big data according to claim 1 is characterized in that: The Dynamic Emotion Driver Analysis module includes the following features: Perform dynamic emotion segmentation processing on the psychological semantic data according to the emotion topic modeling data to obtain dynamic emotion segmentation data; Marking the emotion transition points of the dynamic emotion segmentation data to obtain emotion transition point data; performing emotion transition pattern recognition on the emotion transition point data to obtain emotion transition pattern recognition data; Perform local emotion structure feature analysis on the dynamic emotion segmentation data according to the emotion transition pattern recognition data to obtain local emotion structure feature data; Dynamic emotion driving factor analysis is performed based on local emotion structure feature data and emotion transition pattern recognition data to obtain dynamic emotion driving factor data.

4. The intrinsic emotion analysis system based on psychology information big data according to claim 3 is characterized in that: The local emotion structure feature analysis of the dynamic emotion segmentation data includes: Performing dynamic emotion time series analysis on the dynamic emotion segmentation data to obtain dynamic emotion time series data; reconstructing the emotion pattern phase space of the dynamic emotion time series data according to the emotion transition pattern recognition data to obtain emotion pattern phase space reconstruction data; Perform nonlinear spatial correlation analysis on the emotion pattern phase space reconstruction data to obtain nonlinear spatial correlation data; According to the nonlinear spatial correlation data, the Poincare section analysis is performed on the emotional pattern phase space reconstruction data to obtain the nonlinear phase space Poincare section; the spatial trajectory periodicity analysis is performed on the nonlinear phase space Poincare section to obtain the spatial trajectory periodicity data; Based on the bifurcation theory technology, the trajectory structure analysis is performed on the periodicity number of the space trajectory to obtain the space trajectory structure data; The local emotion structure feature data is obtained by performing a local emotion structure feature analysis based on the spatial trajectory structure data and the spatial trajectory periodicity data.

5. The intrinsic emotion analysis system based on psychology information big data according to claim 3 is characterized in that: The dynamic sentiment driver analysis includes: Performing pattern-context mapping on the emotion transition pattern recognition data to obtain emotion pattern-context data; Performing situation distribution mapping on the emotion pattern situation data according to the local emotion structure feature data to obtain the local emotion structure situation distribution data; Performing structural situational distribution logistic regression analysis on the local emotion structure situational distribution data to obtain situational distribution logistic regression data; performing logical linear factor analysis on the local emotion structure situational distribution data based on the situational distribution logistic regression data to obtain situational emotion logical factor data; Based on the recurrent neural network, the logical situation perception model is constructed for the situation emotion logic factor data to obtain the logical situation perception model; According to the logical situation perception model, the pre-logical factor prediction of the situation emotion logical factor data is performed to obtain the pre-logical factor data; Dynamic emotion driving factor analysis is performed based on the pre-logical factor data and the situational emotion logical factor data to obtain dynamic emotion driving factor data.

6. The intrinsic emotion analysis system based on psychology information big data according to claim 1 is characterized in that: The module for identifying abnormal trends of internal emotions includes the following functions: Based on the dynamic emotion driving factor data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data; Evaluate the emotion volatility of the local emotion phase evolution data to obtain emotion volatility evaluation data; According to the emotion volatility evaluation data, the emotion transition state analysis of the local emotion phase evolution data at different emotion phases is performed to obtain the emotion transition state data; The abnormal inner emotion trend is identified on the emotion transition state data to obtain the abnormal inner emotion trend data.

7. The intrinsic emotion analysis system based on psychology information big data according to claim 6 is characterized in that: The local emotion stage evolution analysis of the local emotion structure feature data includes: Based on the dynamic emotion driving factor data, the emotion driving factor emotion relationship adjacency table is constructed for the local emotion structure feature data to obtain the emotion driving relationship adjacency table; Perform the same adjacency table structure depth traversal on the emotion-driven relationship adjacency table to obtain the adjacency table structure depth data; According to the adjacency list structure depth data, the complexity of the relationships between different nodes in the emotion-driven relationship adjacency list is evaluated to obtain the table node relationship complexity data; According to the table node relationship complexity data, the emotion-driven relationship adjacency table is divided into adjacency tables to obtain the emotion-driven relationship division adjacency table; Performing local implicit numerical calculation on the adjacency table of emotion-driven relationship partitioning to obtain local relationship implicit numerical data; performing adaptive step-size control on the adjacency table of emotion-driven relationship partitioning according to the local relationship implicit numerical data to obtain adjacency table step-size control data; According to the local relationship implicit numerical data and the step size control data of the adjacency table, the local error control Euler-Kromer numerical calculation is performed on the adjacency table of the emotion-driven relationship partition to obtain the local error control relationship numerical data; According to the local error control relationship numerical data, the local emotion structure feature data is analyzed for the local emotion stage evolution to obtain the local emotion stage evolution data.

8. The intrinsic emotion analysis system based on psychology information big data according to claim 6 is characterized in that: Emotional transition state analysis of local emotional phase evolution data includes: Drawing a fluctuation curve for the emotion fluctuation assessment data to obtain an emotion fluctuation curve; extracting an increasing trend and a decreasing trend of the emotion fluctuation curve to obtain an increasing trend data set and a decreasing trend data set; Performing increasing rate calculation and decreasing rate calculation on the curve increasing trend data set and the curve decreasing trend data set respectively, and obtaining curve increasing rate data and curve decreasing rate data respectively; Performing local emotion manifold learning mapping processing on the local emotion phase evolution data according to the curve increasing rate data and the curve decreasing rate data, and obtaining the local emotion manifold learning data; The local structure complexity of the local emotion manifold learning data is calculated by using the manifold learning data point structure analysis algorithm to obtain the local data point complexity data; the local emotion manifold learning data is processed with local complexity constraints according to the local data point complexity data to obtain the local data point complexity constraint data; Perform complexity-constrained local linear embedding processing on the local emotion manifold learning data according to the complexity constraint data of the local data points to obtain local constrained linear embedding data; According to the local constrained linear embedding data, the emotion transition state of the local emotion stage evolution data in different emotion stages is analyzed to obtain the emotion transition state data.

9. The intrinsic emotion analysis system based on psychology information big data according to claim 8 is characterized in that: The local structure complexity of the local emotion manifold learning data is calculated using the manifold learning data point structure analysis algorithm, where the manifold learning data point structure analysis algorithm is as follows: Where C represents the complexity result value of the local data point, α represents the nonlinear correlation coefficient of the local emotion manifold learning data, σ represents the data volume coefficient of the local emotion manifold learning data, x represents the estimated value of the data calculation difficulty of the local emotion manifold learning data, μ represents the dimensionality coefficient of the local emotion manifold learning data, t represents the maximum calculation time value, b represents the weight coefficient of the neighborhood data points in the local emotion manifold learning data, and R represents the error adjustment value of the manifold learning data point structure analysis algorithm.

10. The inner emotion analysis system based on psychology information big data according to claim 8, characterized in that: The complexity local constraint processing of local emotion manifold learning data includes: Extract data points from local emotion manifold learning data according to local data point complexity data to obtain local emotion manifold learning data points; calculate local density of data points from local emotion manifold learning data points to obtain local density data of data points; According to the local density data of the data points, the nearest neighbor data point relationship is established for the local emotion manifold learning data to obtain the nearest neighbor data point relationship data; Based on the nearest neighbor data point relationship data, the structural relationship normal distribution analysis is performed on the local emotion manifold learning data points to obtain the data point relationship normal distribution data; the discrete degree of the data point relationship normal distribution data is calculated to obtain the relationship distribution discrete degree data; According to the relationship distribution discrete degree data, the data point relationship normal distribution data and the neighbor data point relationship data, the local emotion manifold learning data points are subjected to structural connectivity distribution constraints to obtain structural connectivity distribution constraint data; According to the structural connectivity distribution constraint data, the local emotion manifold learning data points are processed with local complexity constraints to obtain the local data point complexity constraint data.

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