Drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization

Abstract

The invention discloses a drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization. The system comprises a sensing data acquisition module, an intelligent situation identification preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, a self-adaptive intelligent prediction module and a multi-source data dynamic evaluation module. All the modules work cooperatively, and perceptual acquisition, intelligent preprocessing and depth feature extraction of multi-dimensional drawing projection data are achieved, and efficient quantitative analysis, intelligent prediction and dynamic evaluation are achieved. Through the combination of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm, the drawing projection data can be subjected to multi-level and refined analysis, an accurate and automatic talent screening tool is provided, the accuracy and the intelligent level of evaluation are remarkably improved, and the method has a wide application prospect.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence and talent data analysis, and specifically relates to a drawing projection test application system and method combining a hierarchical quantum evolutionary algorithm with an improved particle swarm algorithm. Background Art

[0002] In talent selection and psychological assessment, drawing projective tests, as a widely used assessment method, can reflect the inner psychological state and behavioral tendencies of the test subjects by analyzing their drawing characteristics. However, traditional drawing projective test analysis methods usually rely on the subjective judgment of experts, and have problems such as low data processing efficiency, inconsistent results, and difficulty in achieving large-scale automated assessment. In addition, current computer-assisted assessment technology shows insufficient ability to handle data complexity and dynamics when facing the processing of multimodal data, especially in large-scale data processing and high-dimensional data analysis, where traditional methods cannot effectively cope.

[0003] With the development of artificial intelligence and machine learning technologies, although some intelligent assessment systems have been applied to psychological assessment and talent screening, these systems usually rely on a single algorithm or fixed model and lack the ability to dynamically adapt to different individuals and situations, resulting in insufficient accuracy and personalization of the results. At the same time, when dealing with complex, high-dimensional, and multi-source data, existing systems often face problems such as slow algorithm convergence, poor global search capabilities, and insufficient data feature extraction, which cannot meet the needs of efficient and accurate talent selection. Summary of the invention

[0004] The purpose of the present invention is to provide an application system and method of a drawing projective test in talent selection based on a combination of a hierarchical quantum evolutionary algorithm and an improved particle swarm algorithm, aiming to solve the problems of low data analysis efficiency, insufficient classification accuracy, and lack of personalized evaluation in traditional psychological assessment and talent selection systems. Traditional drawing projective test analysis methods mainly rely on subjective judgment, lack of systematic and quantitative analysis, and are difficult to achieve efficient and accurate evaluation in large-scale talent screening. The present invention realizes the automation, quantitative analysis of individual drawing characteristics and psychological state evaluation in talent selection through the collaborative work of multiple modules, thereby significantly improving the objectivity, accuracy and efficiency of the evaluation.

[0005] In order to cope with these technical bottlenecks, the present invention introduces an innovative technical solution combining a hierarchical quantum evolutionary algorithm and an improved particle swarm algorithm, aiming to improve the processing speed and analysis accuracy of painting projection data through multi-level feature optimization and dynamic intelligent prediction. The hierarchical quantum evolutionary algorithm can perform detailed processing on painting features at different levels to achieve efficient feature extraction and optimization; the improved particle swarm algorithm provides faster convergence speed and stronger global search capabilities in classification prediction. This combination not only breaks through the limitations of traditional psychological assessment systems, but also provides a new solution for large-scale, multi-dimensional, personalized talent selection and psychological assessment.

[0006] Through the present invention, the system can effectively cope with the complex data scenarios faced in the talent screening process, realize the comprehensive evaluation of the candidate's psychological state, behavioral characteristics and potential development, improve the objectivity, accuracy and intelligence level of the evaluation, and open up a new technical path for future talent management, career matching and mental health screening.

[0007] To achieve the above purpose, the present invention provides the following technical solutions: a painting projection test application system based on a combination of a hierarchical quantum evolutionary algorithm and an improved particle swarm algorithm, the system includes a perception data acquisition module, an intelligent context recognition preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, an adaptive intelligent prediction module, and a multi-source data dynamic evaluation module. The system combines the hierarchical quantum evolutionary algorithm with the improved particle swarm algorithm, uses feature extraction and intelligent prediction mechanisms to analyze the painting projection data, and generates a personalized evaluation report.

[0008] The sensory data acquisition module collects the behavioral data, painting images and physiological status data of the individual during the painting process through a multimodal sensory device, and transmits the data to the system storage unit. The module includes a data acquisition unit and a data transmission unit, which can ensure the high dimension, diversity and integrity of the data, support wired and wireless transmission, and ensure the real-time and stability of data transmission.

[0009] The intelligent context recognition preprocessing module performs standardized preprocessing on the collected painting data, including image denoising, image enhancement and context recognition. The preprocessing module dynamically adjusts the preprocessing parameters according to the individual real-time context through a self-learning model to ensure the consistency and accuracy of the data in different contexts and provide high-quality data input for subsequent feature extraction.

[0010] The multi-dimensional feature collaborative extraction module extracts multiple quantitative features of the painting image, as well as behavioral data and emotional data from the processed data through a hierarchical quantum evolutionary algorithm. The module performs multi-level feature extraction through edge detection, morphological processing and quantum evolutionary optimization algorithm to ensure the comprehensiveness and accuracy of data features, and normalizes the extracted features.

[0011] The distributed intelligent quantitative analysis module quantifies the extracted features and generates feature vectors for subsequent analysis. Based on edge computing technology, the module performs real-time data processing and quantitative analysis through distributed nodes to ensure the efficient operation of the system in large-scale data processing.

[0012] The adaptive intelligent prediction module performs multi-category classification and prediction based on quantized feature vectors through an improved particle swarm algorithm. The algorithm improves the global search capability and local convergence speed of the classifier by dynamically adjusting weights and speed parameters, ensuring the accuracy and efficiency of the model when processing high-dimensional, multimodal data, and can make adaptive adjustments according to data changes.

[0013] Furthermore, it also includes a multi-source data dynamic evaluation module, which generates a personalized evaluation report by combining historical data, real-time collected data and contextual information. The evaluation report includes individual psychological state analysis, ability matching analysis and future development potential evaluation, providing forward-looking auxiliary decision-making suggestions for talent selection.

[0014] A method of a painting projection talent selection test system based on a combination of hierarchical quantum evolution and particle swarm algorithm of the present invention comprises the following steps:

[0015] S1. Data collection: The perception data collection module uses high-precision sensors and multimodal perception devices to collect multimodal data of individuals in the painting process in real time, including painting images, behavioral data, and physiological state data. The collected data is encrypted and transmitted and stored in the system's storage unit in real time, providing a solid foundation for subsequent data processing.

[0016] S2. Data preprocessing: The collected painting data is standardized and preprocessed through the intelligent context recognition preprocessing module. The preprocessing process includes image denoising, image enhancement and context recognition. The noise in the data is eliminated through an adaptive denoising algorithm to enhance the clarity and contrast of the image.

[0017] S3. Feature extraction: Through the multi-dimensional feature collaborative extraction module, the hierarchical quantum evolutionary algorithm is used to extract multiple quantitative features from the preprocessed painting data. The extracted features include the strength of the painting strokes, the complexity of the line structure, behavioral characteristics, and emotional characteristics. The hierarchical quantum evolutionary algorithm achieves refined feature extraction of complex painting data through multi-level feature optimization.

[0018] S4. Quantitative analysis: After feature extraction is completed, the extracted features are quantified through the distributed intelligent quantitative analysis module. This module uses edge computing technology to run quantitative analysis simultaneously on multiple distributed computing nodes to generate feature vectors for subsequent classification and prediction.

[0019] S5. Classification and prediction: Based on the quantized feature vector, the system uses the improved particle swarm algorithm for classification and prediction through the adaptive intelligent prediction module. The improved particle swarm algorithm optimizes the classification model by dynamically adjusting the weight and speed of particles, ensuring that the classifier can converge quickly and improve classification accuracy.

[0020] S6. Evaluation and report generation: The classification and prediction results are comprehensively evaluated through the multi-source data dynamic evaluation module, and a personalized evaluation report is generated. The evaluation report includes an analysis of the individual's psychological state, ability matching, and prediction of future development potential. Based on the fusion analysis of historical data and real-time data, it provides forward-looking auxiliary decision-making suggestions. The evaluation report is detailed, including graphical analysis results and text descriptions, intuitively displaying the individual's evaluation results, and providing accurate support for decision-making in talent selection.

[0021] Further, the step S1 comprises:

[0022] S1. Data collection: The system uses high-precision sensors and multimodal sensing devices through the perception data collection module to collect multimodal data of individuals in the painting process in real time. These data include painting image data, behavior data, and physiological state data.

[0023] Data collection is the basic step of the system, which directly determines the quality of input data of subsequent modules. The collected data will be used for analysis by the hierarchical quantum evolutionary algorithm in the feature extraction module, and as input data for classification and prediction in the improved particle swarm algorithm. The design of the system ensures the accuracy and real-time performance of data collection, providing a solid data foundation for the analysis of subsequent steps.

[0024] Multimodal data in data collection: Multimodal data refers to multiple different types of sensory data collected by the system at the same time. In this invention, the system collects three main types of multimodal data:

[0025] Painting image data: The painting image is captured in real time through a high-resolution camera to ensure that the smoothness of the lines, the use of colors, and the details of the graphic structure in the painting are accurately recorded. These image data are stored as a two-dimensional pixel matrix I (x, y), where x and y represent the pixel coordinates of the image, and the pixel value represents the color and grayscale information. This data serves as the main input for subsequent image feature extraction to help identify the geometric features and color composition in the painting.

[0026] Behavioral data: Through a digital tablet equipped with a pressure sensor, the system records the individual's drawing behavior data in real time, including drawing speed, pen pressure, and hand movement trajectory. These data reflect the individual's behavioral characteristics and serve as an important basis for analyzing psychological state and ability performance. The time series of behavioral data is expressed as P(t), where t represents time and P(t) represents the pen pressure value at time point t. Changes in pen pressure and drawing speed provide direct information for identifying individual psychological characteristics.

[0027] Physiological state data: The system collects physiological signals of individuals during the painting process through heart rate sensors and galvanic skin response sensors, including heart rate HR(t) and galvanic skin response GSR(t). These data reflect the emotional state of the individual. During the collection process, heart rate HR(t) indicates the change of heart rate over time, and galvanic skin response GSR(t) reflects the change of skin conductivity, which is usually closely related to emotional fluctuations. Through these physiological data, the system evaluates the individual's psychological state more comprehensively.

[0028] Data transmission and storage

[0029] In order to ensure data security and real-time performance, all collected data is transmitted through encryption. During the transmission process, the AES symmetric encryption algorithm is used to protect the integrity of the data. The transmitted data is stored in the storage unit of the system to provide support for subsequent processing. The data is stored in the form of matrix and time series, so that the data can be effectively called for further feature extraction and analysis. The encryption formula is as follows:

[0030] C=E k (M)

[0031] Among them: C is the encrypted ciphertext; E k is an encryption function, which is encrypted using the key k; M is the original multimodal data, including painting images, behavioral data, and physiological data.

[0032] Parameters and working modes of acquisition equipment

[0033] The device of the present invention is equipped with high-precision sensors and multi-modal sensing capabilities to ensure the accuracy and comprehensiveness of data collection.

[0034] High-resolution camera: The camera has a resolution of 1080p and a frame rate of 30fps to ensure that every detail of the image can be accurately captured during the painting process.

[0035] Digital tablet or pressure-sensitive tablet: The sensor resolution of the pressure-sensitive tablet can detect changes in pen pressure of 0.1 mm. Through high-frequency data acquisition, the pressure-sensitive data and the painting image are processed synchronously to ensure the consistency of the behavioral data and the image data.

[0036] Heart rate sensor and galvanic skin response sensor: The sampling frequency of physiological data is 1kHz to ensure high accuracy and continuity of physiological signals.

[0037] The collected multimodal data are modeled as multidimensional time series data. Through these multidimensional time series data, the system performs feature extraction, especially when analyzing individual behavioral characteristics and physiological responses, which can provide high-quality data support for subsequent quantitative analysis and prediction models.

[0038] The description is as follows:

[0039] D(t)={I(x,y),P(t),HR(t),GSR(t)}

[0040] Where: I(x,y) is the pixel matrix of the painting image; P(t) represents the change of pen pressure over time during the painting process; HR(t) is the heart rate data, reflecting the individual's emotional fluctuations; GSR(t) is the skin electrical response data, representing the individual's emotional response.

[0041] Further, the step S2 is specifically as follows:

[0042] S2. Data Preprocessing

[0043] Data preprocessing is a key step in this patented system. By combining intelligent context-aware technology, the system can adjust denoising and enhancement parameters in real time based on individual physiological and behavioral data. The collected painting data is standardized. This dynamic and adaptive processing mechanism greatly improves the data quality, ensures the clarity, stability and accuracy of the data, and provides high-quality input for the subsequent hierarchical quantum evolutionary algorithm and improved particle swarm algorithm, thereby improving the classification and prediction accuracy of the entire system. This step includes the following core processing links: image denoising, image enhancement, and context recognition and adaptive processing.

[0044] Image denoising: In the process of collecting painting image data, noise is inevitable from environmental factors, device resolution, and interference during transmission. In order to improve data quality, the system uses an adaptive denoising algorithm to dynamically adjust according to the noise intensity in the image to ensure that the detailed information in the image is retained while eliminating the noise. Image denoising is modeled as an optimization problem of removing noise n(x,y) and maintaining the original image signal I(x,y):

[0045] I clean (x,y)=I(x,y)-n(x,y)

[0046] Where: I clean (x, y) is the denoised image; I(x, y) is the original image collected; n(x, y) represents the distribution of noise.

[0047] In order to further improve the denoising effect, the system uses median filtering to remove random noise and ensure clear edges. The formula is as follows:

[0048] I clean (x,y)=median(I(x+i,y+j)),

[0049] Where: N is the neighborhood window, and the noise is smoothed by calculating the median of the pixel neighborhood to avoid blurring of details. median() represents the median operation

[0050] Through this adaptive denoising process, the system ensures image clarity and provides a higher quality data foundation for subsequent analysis and feature extraction.

[0051] Image enhancement: After denoising, the system performs image enhancement processing on the image to improve the contrast, brightness and sharpness of the image in order to better capture details. Image enhancement uses histogram equalization and Laplace operator enhancement.

[0052] Histogram equalization: The purpose of histogram equalization is to stretch the grayscale range of the image and improve the contrast. The formula is as follows:

[0053]

[0054] Where: I min and I max Respectively represent the minimum and maximum grayscale values ​​in the image; I enhanced (x,y) is the enhanced image. Through this processing, the system significantly improves the contrast between light and dark in the image, making the details more prominent.

[0055] Laplace operator enhancement: The Laplace operator is used to sharpen the edges of an image, making the shape features in the image more obvious. Its formula is:

[0056] I sharpened (x,y)=I clean (x,y)+α·ΔI clean (x,y)

[0057] Among them: α is the sharpening strength parameter; Δ is the Laplacian operator, which is used to detect and enhance the edges in the image. Through this operator, the details and edges of the image will be more prominent, ensuring that the shape and structural features of the painting can be accurately extracted in the subsequent steps.

[0058] Context recognition and adaptive preprocessing: In the present invention, the context recognition module dynamically adjusts the preprocessing parameters by analyzing the individual's physiological state and behavioral data to ensure the effectiveness of image processing under different emotional states. The module adaptively adjusts the denoising and enhancement parameters to ensure the stability of the data under different emotional states. The emotional state E(t) is modeled by the following formula:

[0059] E(t)=f(HR(t),GSR(t),P(t))

[0060] Among them: HR(t) is the heart rate data; GSR(t) is the galvanic skin response; P(t) is the pen pressure behavior data during the painting process; f is the emotion perception function, which converts these physiological and behavioral data into the individual's emotional state.

[0061] When the system detects an individual's emotional fluctuations, it adjusts the denoising and enhancement parameters based on the fluctuations. When the heart rate and skin galvanic response increase significantly, indicating that the individual is in an anxious state, the system will strengthen the denoising process; conversely, when the emotion is stable, the system will weaken the denoising and enhance the image sharpness.

[0062] Further, the step S3 comprises:

[0063] S3. Feature extraction

[0064] In the feature extraction step of the present invention, the system uses a multi-dimensional feature collaborative extraction module to extract multiple quantitative features from the preprocessed painting data using a hierarchical quantum evolutionary algorithm. These features include the strength of the painting strokes, the complexity of the line structure, behavioral characteristics, and emotional characteristics. The hierarchical quantum evolutionary algorithm achieves refined extraction of complex painting data through multi-level feature optimization, ensuring that the system accurately analyzes individual behavior and psychological state.

[0065] The task of the multi-dimensional feature collaborative extraction module is to extract key features that can reflect the individual's painting behavior from the preprocessed data, covering the quantitative features of images, behaviors and emotions. By collaboratively extracting these multi-dimensional features, the system can comprehensively analyze the individual's psychological and behavioral characteristics.

[0066] The main features extracted include: Stroke force: reflects the degree of force used by the individual when painting, reflecting the control of painting. Line structure complexity: reflects the smoothness and detail of the painting lines. Behavioral characteristics: painting speed, pen pressure changes, hand trajectory, indicating the individual's painting mode. Emotional characteristics: emotional state derived from physiological and behavioral data, assisting in analyzing individual emotions.

[0067] In the present invention, feature extraction is modeled as an optimization problem, that is, finding the optimal solution among all feature sets. Let X be the painting dataset, f(X) be the fitness function, and the goal of feature extraction is to maximize the function:

[0068] maxf(X)=max(f(T 笔画力度 ,T 线条复杂度 ,T 行为特征 ,T 情绪特征 ))

[0069] Where: T 笔画力度 Indicates the stroke strength characteristics; T 线条复杂度 Indicates the complexity of the line; T 行为特征 Indicates the characteristics of painting behavior; T 情绪特征 Represents the emotional features derived by the emotion recognition module.

[0070] In the feature optimization process, the hierarchical quantum evolutionary algorithm adjusts the weight of each feature through the action of quantum rotating gate, and gradually searches for the optimal feature combination that maximizes the fitness function f(X).

[0071] The hierarchical quantum evolutionary algorithm is the core algorithm used by the present invention to optimize complex features. Through multi-level search and optimization, the importance of different features is quantified hierarchically to capture details in the painting data. The quantum evolutionary algorithm can process high-dimensional and multi-modal data through the superposition state and parallel search capabilities of quantum bits.

[0072] The hierarchical quantum evolutionary algorithm contains the following main steps:

[0073] Initialize the quantum population: The individuals in the quantum population represent potential solutions to the painting features. Each solution is represented by a superposition state of quantum bits, and the state formula is:

[0074] |ψ>=α|0>+β|1>

[0075] Where: |ψ> represents the state of the quantum bit; α and β are complex coefficients, satisfying: |α| 2 +|β| 2 =1Through this quantum state representation, the system can explore multiple feature vectors in parallel and improve the efficiency of feature extraction.

[0076] Quantum revolving door update: The feature search process is updated through a quantum revolving door, and the formula is as follows:

[0077] θ (t+1) =θ (t) +Δθ

[0078] Where: θ (t) is the rotation angle in the current algebra; Δθ is the update step calculated according to the fitness function. Through the action of the quantum rotating gate, the probability distribution of the characteristic solution will be gradually optimized and close to the optimal solution.

[0079] Hierarchical search and local optimization: The hierarchical quantum evolutionary algorithm adopts a hierarchical structure, first performing a global search to extract the main features in the painting data. Then, at each level, it optimizes the local detail features to capture more refined features.

[0080] In the feature extraction of painting data, the hierarchical quantum evolution algorithm uses the quantum evolution algorithm to perform a global search to find the main features in the painting. Then, local optimization is performed in each layer of features to extract more detailed features, such as the tiny details of stroke changes, behavioral features, and emotional features. Multi-level feature optimization can avoid falling into the local optimal solution and ensure that the features finally extracted by the system are globally optimal.

[0081] Further, the step S4 is specifically as follows:

[0082] S4. Quantitative Analysis

[0083] In the quantitative analysis step of the present invention, the system performs quantitative processing on the extracted multidimensional features through a distributed intelligent quantitative analysis module. The main goal of the quantitative processing is to standardize and normalize the extracted multidimensional features into feature vectors for subsequent classification and prediction tasks. In order to improve processing efficiency, the system introduces edge computing technology to distribute computing tasks to multiple distributed computing nodes for parallel operation, thereby reducing computing bottlenecks and improving overall performance.

[0084] The quantitative analysis module in the present invention efficiently completes the processing of complex data through distributed computing nodes, providing reliable feature vector input for subsequent classification and prediction. The edge computing architecture of the system enhances the overall processing capability, ensuring that it can still maintain efficient operation when processing high-dimensional features.

[0085] Through the quantitative analysis steps of standardization, normalization and weight calculation, the system can optimize the extracted multi-dimensional data into easy-to-process feature vectors, improving the accuracy of classification and prediction. Quantitative analysis lays a solid data processing foundation for the entire patent system, making subsequent intelligent decision-making more accurate and stable.

[0086] The main function of quantitative analysis is to standardize and normalize different types of multidimensional features, and to ensure the comparability and importance of features through feature weight calculation. The features processed by quantitative analysis will be converted into feature vector form, providing unified and accurate data input for subsequent classification and prediction modules.

[0087] The main quantitative features include: Stroke force: reflects the strength of the individual's painting. Line structure complexity: measures the complexity, smoothness and detail of the painting lines. Behavioral characteristics: drawing speed, pen pressure changes, hand trajectory. Emotional characteristics: emotional state derived from behavioral and physiological data.

[0088] The extracted features are converted into feature vectors, expressed as:

[0089] V=[v1,v2,v3,…,v n ]

[0090] Among them, v i Indicates that the i-th quantitative feature n is the total number of features extracted.

[0091] Introduction of edge computing technology: In order to improve processing efficiency, the system adopts edge computing technology, which distributes quantitative analysis tasks to multiple computing nodes. The advantage of edge computing is that it can improve data processing speed, especially for large-scale, high-dimensional data processing.

[0092] There are M computing nodes in the system, each of which is responsible for the quantization of a part of the features. The quantization task is expressed as:

[0093]

[0094] Where: V j is the feature vector processed by the jth computing node; w i is the weight of feature i; n j is the number of features processed by the jth node. Through edge computing, each node completes quantitative analysis independently, and the central system integrates the results of all nodes to finally generate a global feature vector V.

[0095] Mathematical model of quantitative analysis: The quantitative analysis process mainly includes standardization, normalization and weight calculation. Each step relies on a mathematical model to ensure that different features are compared and processed at the same scale.

[0096] Standardization: The purpose of standardization is to eliminate the impact of unit differences between different features so that all feature values ​​are within the same numerical range. The formula for standardization is:

[0097]

[0098] Where: x i is the original eigenvalue; μ i is the mean of feature i; σ i is the standard deviation of feature i. After standardization, feature x i The mean of ′ is 0 and the standard deviation is 1, which facilitates the comparison between different features.

[0099] Normalization: Normalization is to map the standardized feature values ​​to the interval [0,1] to enhance the comparability between different features. The normalization formula is:

[0100]

[0101] Where: x min and x max They are the features x i The minimum and maximum values ​​of x i ″ is the normalized feature value. Normalization ensures that all features are in the same numerical range, which is convenient for subsequent analysis.

[0102] Weight calculation: The system uses information gain to calculate the weight of each feature, which reflects the importance of the feature to classification and prediction. The weight calculation formula is:

[0103]

[0104] Where: w i is the weight of feature i; information gain (T i ) is the contribution of feature i to the classification.

[0105] After weight calculation, the system calculates each normalized eigenvalue x i ″ is weighted, and the formula is:

[0106] v i =w i ·x i ″

[0107] Among them, v i is the final quantized eigenvalue, w i is the weight of the feature, x i ″ is the normalized eigenvalue.

[0108] Workflow of distributed quantitative analysis

[0109] The entire quantitative analysis process is completed through the following steps:

[0110] Feature distribution: The system distributes features to various computing nodes to ensure parallel processing.

[0111] Standardization and normalization: Each node performs standardization and normalization on the features it is responsible for to ensure that the value range of all features is consistent. Ensure that different features are compared at the same scale.

[0112] Weight calculation and feature integration: The computing nodes calculate the weights of the features they are responsible for and perform weighted processing on these features. Give each feature a relative importance in classification and prediction tasks

[0113] Eigenvector generation: After all nodes complete the calculation, the central system integrates the quantization results of each node to generate a global eigenvector V.

[0114] Further, the step S5 comprises:

[0115] S5. Classification and prediction steps

[0116] In the classification and prediction step of the present invention, the system uses an improved particle swarm algorithm to classify and predict the quantized feature vectors through an adaptive intelligent prediction module. The improved particle swarm algorithm optimizes the classification model by dynamically adjusting the weight and speed of the particles to ensure that the classifier converges quickly and improves the classification accuracy. The core goal of this step is to effectively classify and predict the input feature data through the optimized classification model, especially the accurate prediction of individual behavior and emotional state.

[0117] Particle swarm algorithm is an optimization algorithm based on swarm intelligence. It simulates the dynamic behavior of bird flocks and fish swarms in nature and seeks the optimal solution through cooperation and competition between individuals. In the present invention, the improved particle swarm algorithm is used to optimize the classification model. Compared with the traditional particle swarm algorithm, the improved particle swarm algorithm improves the convergence speed and classification accuracy by dynamically adjusting the speed and inertia weight of particles, and is particularly suitable for classification tasks of high-dimensional complex data.

[0118] Working principle of improved particle swarm algorithm

[0119] Particle initialization: Each particle in the particle swarm algorithm represents a potential classification model solution. In the classification task of the present invention, the position of the particle P i It corresponds to the parameters of the classification model, and the particle speed Vi determines the direction and amplitude of the next parameter update.

[0120] The particle initialization formula is as follows:

[0121] P i (0) = P min +(P max -P min )·rand()

[0122] V i (0) = V min +(V max -V min )·rand()

[0123] Where: P i (0) is the initial position of the ith particle, i.e., the parameter value of the classification model; V i (0) is the initial velocity of the ith particle; P min and P max are the minimum and maximum values ​​of the particle position respectively; V min and V maxare the minimum and maximum values ​​of the particle speed respectively; rand() is a random number between [0,1].

[0124] Particle update formula: The speed and position of the particle are updated according to its individual optimal solution and the global optimal solution. The speed and position update formula are as follows:

[0125] V i (t+1)=ω·V i (t)+c1·r1·(P best,i -P i (t))+c2·r2·(P global -P i (t))

[0126] P i (t+1)=P i +V i (t+1)

[0127] Where: V i (t) is the velocity of the ith particle in the tth generation; P i (t) is the position of the ith particle in the tth generation; P best,i is the individual optimal position of the particle; P global is the global optimal position of the group; w is the inertia weight, which is used to control the speed change of the particles; c1 and c2 are acceleration constants, which respectively adjust the influence of the individual optimal position and the global optimal position on the speed; r1 and r2 are random factors to ensure the diversity of the particle swarm algorithm.

[0128] Dynamically adjust the inertia weight: In order to improve the classification accuracy and speed up the convergence of the algorithm, the present invention adopts a strategy of dynamically adjusting the inertia weight. The inertia weight w gradually decreases with the increase of the number of iterations. This design enhances the exploration ability of particles in the early stage and ensures that particles are concentrated on the optimal solution in the later stage. The adjustment formula of the inertia weight is as follows:

[0129]

[0130] Where: w max and w min are the maximum and minimum values ​​of the inertia weight respectively; t is the current iteration number; t max This dynamic adjustment strategy ensures that the algorithm can conduct a wider search in the early stage, and focus on the convergence of the optimal solution with a smaller weight in the later stage, thereby optimizing the classification effect.

[0131] Optimization goal of classification and prediction: In the classification model of the present invention, the optimization goal of the improved particle swarm algorithm is to minimize the classification error rate. By continuously adjusting the position and speed of the particles, the classification model can gradually reduce the classification error and improve the accuracy of classification. The optimization objective function is expressed as:

[0132]

[0133] Where: E is the classification error; N is the total number of samples; y i is the true label of the i-th sample; is the predicted label of the i-th sample; Is the loss function, the cross entropy loss function.

[0134] Classification and prediction workflow

[0135] Feature vector input: The feature vector V obtained from the quantitative analysis module is input into the classification model.

[0136] Particle initialization: Randomly generate the initial position and velocity of particles, each particle represents a solution to the classification model.

[0137] Particle update: Each particle updates its speed and position according to its individual optimal position and group optimal position.

[0138] Model optimization: By dynamically adjusting the inertia weight and acceleration constant, the classification error is continuously reduced and the classification model is optimized.

[0139] Classification and prediction: The final optimized classification model is used to classify and predict the input features.

[0140] Further, the step S6 is specifically as follows: S6. Evaluation and report generation

[0141] In the evaluation and report generation steps of the present invention, the system uses a multi-source data dynamic evaluation module to comprehensively evaluate the results of classification and prediction, and generates a personalized evaluation report. This evaluation report is not only a summary of the classification and prediction results, but also combines the fusion analysis of historical data and real-time data to provide comprehensive predictions and suggestions for the individual's psychological state, ability matching and future development potential. The evaluation report provides forward-looking support for the user's decision-making. The content is detailed and includes graphical analysis results and text descriptions to help users understand the individual's evaluation results more intuitively. This module improves the intelligence level of the system through dynamic evaluation and report generation functions, ensuring that efficient decision-making support is provided to users. Provide accurate decision support for talent selection scenarios.

[0142] By combining classification with prediction results, historical data with real-time data, the system can accurately identify the strengths and weaknesses of individuals and provide accurate guidance for future development, greatly enhancing the decision-making efficiency and reliability of talent assessment and capability development. This technology enables the application of the present invention in complex environments to have a high level of competitiveness.

[0143] The core function of dynamic evaluation of multi-source data: The core function of the evaluation and report generation module is to comprehensively evaluate the performance of individuals and generate personalized evaluation reports through the integration analysis of classification and prediction results, historical data and real-time data. The module dynamically analyzes the multi-dimensional characteristics of individuals, including psychological state, ability performance and future development potential.

[0144] The core assessment content includes:

[0145] Psychological state analysis: Based on the individual's emotional and behavioral characteristics, dynamically evaluate their psychological state, anxiety, and changes in stress psychological factors.

[0146] Competency matching assessment: Combine the individual's behavioral characteristics with their ability characteristics to analyze the degree of match between their current abilities and positions or tasks.

[0147] Prediction of future development potential: Predict an individual’s development potential in different tasks or situations through a fusion analysis of historical performance and future trends.

[0148] Mathematical model and logic of data evaluation: The multi-source data dynamic evaluation module combines historical data and real-time data, compares the multi-dimensional characteristics of individuals with classification and prediction results, and uses mathematical models to evaluate the comprehensive performance of individuals.

[0149] Comprehensive evaluation model: The system uses a multidimensional feature vector V = [v1, v2, v3, …, v n ] represents the individual characteristic data, and generates the prediction result C = [c1, c2, c3, ..., c n ], the goal of the evaluation model is to score the individual's performance S based on these data. The evaluation scoring formula is:

[0150]

[0151] Where: w i Represents feature v i The weight of reflects its importance to the evaluation results; i is the characteristic value of the individual; c j represents one of the classification and prediction results; α and β are adjustment parameters used to balance the impact of historical data and real-time data. This formula combines the individual historical characteristics v i And the classification result c jA composite score S is generated to evaluate the overall performance of the individual.

[0152] Fusion of historical and real-time data: In order to more accurately evaluate the performance of individuals, the system adopts a fusion analysis model that combines historical data with real-time data through a weighted strategy. The fusion analysis formula is as follows:

[0153] F=γ·H+(1-γ)·R

[0154] Among them: F represents the final fusion evaluation result; H represents the evaluation result based on historical data; R represents the evaluation result based on real-time data; γ is the fusion weight, which controls the proportion of historical data to real-time data and can be dynamically adjusted according to different application scenarios. Through fusion analysis, the system can flexibly integrate the individual's historical performance and current performance, thereby generating more personalized and accurate evaluation results.

[0155] Core process of report generation

[0156] Data aggregation: The system aggregates classification and prediction results, individual historical performance and real-time data to ensure the integrity of the assessment data.

[0157] Comprehensive evaluation: Based on the fusion analysis model, the system evaluates the individual's psychological state, ability matching and future development potential, taking into account historical data and real-time data.

[0158] Report generation: The system automatically generates a personalized assessment report, which not only contains detailed individual analysis, but also provides a graphical display of sentiment trend charts and ability matching charts to help users understand the results more intuitively.

[0159] Decision support: The system provides forward-looking suggestions in the assessment report to help users identify individual strengths and potential and formulate corresponding capability development or talent selection strategies.

[0160] The assessment report includes the following parts:

[0161] Psychological state analysis: Generate detailed reports on individual psychological states based on emotional and behavioral characteristics, including assessment results of anxiety and stress factors. Display emotional change trends through radar charts or curve charts.

[0162] Competency matching analysis: The report shows the matching degree between the individual's current capabilities and the target position or task, and uses pie charts or bar charts to intuitively display the capability matching situation.

[0163] Prediction of future development potential: Based on historical data and trend analysis, the report predicts an individual’s future work performance and provides personalized career development suggestions.

[0164] Proactive decision support: Based on the assessment results, the system provides specific recommendations to help users make accurate decisions on talent selection and capability development.

[0165] Compared with the prior art, the present invention has the following beneficial effects:

[0166] 1. Improved data processing efficiency and accuracy: Traditional drawing projective test systems usually rely on subjective judgment and a single algorithm, resulting in low data processing efficiency and insufficient accuracy of evaluation results. The present invention achieves significant improvements in data feature extraction and classification prediction by combining the hierarchical quantum evolutionary algorithm with the improved particle swarm algorithm. The hierarchical quantum evolutionary algorithm can accurately extract key features from multimodal data through a multi-level feature optimization mechanism, while the improved particle swarm algorithm improves the system's global search capability and convergence speed by dynamically adjusting weights and speeds, thereby ensuring that the system can quickly and efficiently process large amounts of data, significantly improving classification accuracy.

[0167] 2. Enhanced system adaptability and flexibility: The adaptive prediction module and dynamic evaluation module of the present invention can dynamically adjust algorithm parameters according to real-time changes in data, thereby adapting to different situations and individual needs. This adaptability enables the system to maintain efficient operation in different application scenarios (such as talent selection, career matching, and mental health assessment). Especially when processing complex multi-dimensional data, it can be dynamically adjusted according to individual characteristics and situations to ensure the personalization and accuracy of the evaluation results.

[0168] 3. Support the processing of large-scale multimodal data: Traditional evaluation systems are difficult to effectively process multimodal and large-scale data, resulting in inaccurate or lack of comprehensive evaluation results. The present invention supports real-time processing and quantitative analysis of multimodal data through distributed intelligent quantitative analysis modules and edge computing technology, ensuring the efficient operation of the system in large-scale data scenarios. The collaborative work of distributed nodes further improves the processing power and speed of the system, and can process a large number of candidates' information in a short time, which is suitable for large-scale talent screening tasks.

[0169] 4. Provide personalized and forward-looking evaluation reports: The multi-source data dynamic evaluation module of the present invention generates personalized evaluation reports by integrating historical data and real-time data. The report not only includes the individual's psychological state and ability matching analysis, but also predicts future development potential, helping decision makers make more targeted and forward-looking decisions. Through time series analysis, the system can track the long-term development trend of individuals and provide more accurate auxiliary support for talent management.

[0170] 5. Reduced misjudgment rate and improved objectivity of evaluation: In the prior art, the evaluation system has a high misjudgment rate in classification prediction, which affects the overall accuracy. The present invention uses a hierarchical quantum evolutionary algorithm to extract multimodal data in a refined manner, combined with an improved particle swarm algorithm for dynamic classification prediction, ensuring high accuracy and consistency at each stage. Experimental results show that the system reduces the misjudgment rate by more than 25%, significantly improving the objectivity and stability of the evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0171] Figure 1 :The flowchart of the hierarchical quantum evolutionary algorithm introduced in the present invention;

[0172] Figure 2 : The present invention introduces an improved particle swarm algorithm flow chart;

[0173] Figure 3 : The present invention introduces a multi-source data fusion algorithm flow chart;

[0174] Figure 4 : Due diligence analysis. DETAILED DESCRIPTION

[0175] like Figure 1-3 The present invention provides an application system and method of a drawing projective test in talent selection based on a combination of a hierarchical quantum evolutionary algorithm and an improved particle swarm algorithm. Its core concept is to provide personalized talent screening and psychological assessment support through efficient collaboration of modules such as data acquisition, intelligent preprocessing, feature extraction, quantitative analysis, classification prediction and evaluation generation.

[0176] Dynamic perception and multi-dimensional data acquisition: The first step of the system is to collect multimodal data through the perception data acquisition module. This is not limited to image data, but also covers the behavior and physiological state of the individual when painting. This module captures and transmits multi-dimensional data in real time through multi-layer perception devices, high-resolution cameras (collecting painting images), pressure-sensitive plates and physiological monitors to ensure integrity and real-time performance. The system can dynamically perceive changes in external situations to ensure that data collection adapts to changes in environmental factors, and can encrypt and transmit data wirelessly to ensure its security.

[0177] Specific implementation:

[0178] Painting data acquisition unit: uses a high-resolution camera and pressure-sensitive plate to capture painting details.

[0179] Behavior data collection unit: records painting behavior in real time, including painting speed, pen pressure, and hand movement trajectory.

[0180] Physiological data acquisition unit: monitors physiological states such as heart rate and skin electrical response.

[0181] For example:

[0182] Physiological data monitoring heart rate (HR):

[0183] Unit: bpm (beats per minute)

[0184] Assumed range: 60bpm to 100bpm

[0185] Importance: Heart rate changes reflect the emotional fluctuations of the subjects (increased heart rate indicates increased emotional fluctuations).

[0186] Skin conductance response GSR(t):

[0187] Assumed range: 0.5μS to 5μS

[0188] Importance: Fluctuations in GSR(t) are closely related to emotional state (increased GSR(t) indicates increased emotional state), and is an important indicator of psychological state.

[0189] Physiological data vector representation: [HR(t), GSR(t)]

[0190] Intelligent situation identification and data preprocessing: This module recognizes situations in real time and dynamically, and intelligently adjusts preprocessing parameters by analyzing individual mood swings and behavioral changes. Combined with advanced denoising and enhancement technologies, this module ensures the consistency and accuracy of data processing in changing situations. While denoising image data, the system enhances image quality based on an adaptive algorithm, laying a solid foundation for subsequent feature extraction.

[0191] Specific implementation:

[0192] Data denoising unit: Adopts adaptive denoising algorithm to ensure image clarity in high noise environment.

[0193] Situational Awareness Unit: Analyze emotional fluctuations in real time and adjust processing strategies.

[0194] Behavioral and physiological data synchronization unit: ensuring multimodal data consistency.

[0195] The above module units and algorithms are implemented by writing software codes.

[0196] Multi-level quantum optimization feature extraction: Through the hierarchical quantum evolution algorithm, the system can accurately extract painting features, behavioral features and emotional features from multi-dimensional data (based on the emotional state derived from physiological and behavioral data, to assist in analyzing individual emotions). The system uses quantum algorithms to process complex painting data, such as line smoothness and structural complexity, and performs deep feature optimization to ensure that the details of the data at all levels are effectively preserved. Combined with emotional and behavioral data, the system generates high-dimensional feature vectors for subsequent classification.

[0197] Specific implementation:

[0198] Image feature extraction unit: processes detailed features in paintings, such as lines.

[0199] Behavior feature extraction unit: Analyzes the dynamic performance of painting through behavioral data.

[0200] Emotional feature extraction unit: Analyzes the emotional changes of individuals during the painting process.

[0201] The above module units and algorithms are implemented by writing software codes.

[0202] Distributed intelligent quantitative analysis: In the present invention, the distributed intelligent quantitative analysis module is based on edge computing technology and can efficiently process large-scale data. The system works together through distributed nodes to ensure that quantitative analysis is still efficient even in complex and massive data scenarios. This module generates feature vectors by quantifying features, supports subsequent classification and prediction, and ensures the real-time processing capability and scalability of the system.

[0203] Specific implementation:

[0204] Quantitative feature generation unit: Generates a standardized vector based on the extracted multi-dimensional features.

[0205] Edge computing nodes: Improve computing efficiency through distributed networks.

[0206] Dynamic parameter adjustment unit: adjust the quantitative model in real time according to data characteristics.

[0207] The above module units and algorithms are implemented by writing software codes.

[0208] Adaptive intelligent prediction: The system classifies and predicts quantized feature vectors through an improved particle swarm algorithm. Unlike traditional classification algorithms, the improved particle swarm algorithm dynamically adjusts particle weights and speeds, improving the efficiency of global search and local convergence. The system can optimize model parameters according to real-time changes in data to ensure prediction accuracy in high-dimensional data environments.

[0209] Specific implementation:

[0210] Classification training unit: optimizes the classifier through multiple trainings to ensure the accuracy of predictions.

[0211] Dynamic optimization unit: Optimize the classification model through improved particle swarm algorithm.

[0212] Prediction Validation Unit: Evaluate model performance using cross-validation method.

[0213] Classification is carried out according to the Big Five personality dimensions and the drawing projective test index theory or other drawing projective theories. Since the drawing projective theory is a well-known psychological measurement method, it is not the focus of the present invention. The present invention focuses on the classification method of constructing an algorithm. Taking drawing trees as an example, the psychological state of talents is analyzed and classified into the following types:

[0214] 1) Anxiety and stress: The picture is blacked out in many places, with messy and fragmented lines, and the main body of the picture is too small. The degree of the above painting characteristics is set on a scale of 0-1, and the more degrees, the deeper the value;

[0215] 2) Depressed state: light brushstroke pressure, stiff brushstrokes, poor fluency, and a snowy scene; the degree of the above painting characteristics is set on a scale of 0-1, with the higher the degree, the higher the value;

[0216] 3) Vulnerable state: the trunk is abnormal, and the contact surface between the roots and the ground is unstable; the degree of the above-mentioned painting characteristics is set in the range of 0-1, and the greater the degree, the larger the value.

[0217] In addition, based on the Big Five personality dimensions and the drawing projective test indicators for openness, conscientiousness, extroversion, agreeableness, and negative emotions, the analysis of conscientiousness in terms of talent ability was conducted, such as Figure 4 shown.

[0218] The test is tested for completeness (the range is set to 0-1, the more the degree, the larger the value), and the growth form of the branches in the crown (the range is set to 0-1, the more it matches the real form, the larger the value);

[0219] The efficiency was tested by the fineness of the painting (the range of degrees was set to 0-1, with more degrees increasing the value), and the patency of the crown and trunk (the range of degrees was set to 0-1, with more degrees increasing the value).

[0220] The above module units and algorithms are implemented by writing software codes.

[0221] Personalized dynamic assessment report: This module generates a personalized assessment report through multi-source data fusion, and combines time series analysis to predict the individual's future development. The report generated by the system is not only an analysis of the current psychological state, but also includes long-term ability matching and future potential prediction, providing forward-looking auxiliary decision support.

[0222] Specific implementation:

[0223] Data fusion unit: Combine historical data with real-time data to generate evaluation reports.

[0224] Assessment report generation unit: provides personalized analysis, including psychological status, ability assessment and future potential prediction.

[0225] Long-term development forecasting unit: predict individual future development trends through time series analysis.

[0226] The above module units and algorithms are implemented by writing software codes.

[0227] Specific numerical example analysis

[0228] Assume that the goal is to evaluate a user's drawing behavior data to analyze their psychological state, ability matching, and future development potential. After the drawing behavior data is processed and feature extracted, the system quantifies, classifies, predicts, and comprehensively evaluates it, and finally generates a personalized report.

[0229] The following is a numerical example analysis:

[0230] Feature extraction process:

[0231] Specific characteristics: line structure complexity (e.g. line clutter, tortuosity, roughness (unsmoothness), 0-1); behavioral characteristics (drawing speed: 0-1, pen pressure change: 0-1, modification frequency: 0-1); emotional characteristics:

[0232] (such as anxiety, happiness, etc., 0-1), 0-1 values ​​represent the degree of magnitude;

[0233] Suppose the following features are extracted from the user's drawing behavior data:

[0234] [Feature name: value]

[0235] [Stroke Strength: 0.75]

[0236] [Line structure complexity: 0.65]

[0237] [Drawing speed: 1.2]

[0238] [Pen pressure change: 0.8]

[0239] [Emotional characteristics-anxiety index: 0.55]

[0240] [Emotional characteristics-pleasure index: 0.45]

[0241] These feature data will serve as input for subsequent steps and participate in classification, prediction and comprehensive evaluation.

[0242] Quantitative analysis process:

[0243] Quantization: Standardize and normalize the features and calculate the weights. Assume that the feature weights are as follows:

[0244] [Feature name value after standardization value after normalization value weight]

[0245] [Stroke Strength 0.75 0.68 0.74 0.3]

[0246] [Line structure complexity 0.65 0.62 0.70 0.25]

[0247] [Drawing speed 1.2 0.75 0.85 0.2]

[0248] [Pen pressure change 0.8 0.70 0.72 0.15]

[0249] [Emotional characteristics-anxiety index 0.55 0.60 0.67 0.05]

[0250] [Emotional characteristics-pleasure index 0.45 0.55 0.62 0.05]

[0251] Eigenvector calculation:

[0252] Combine the eigenvalues ​​with the weights to generate the final eigenvector:

[0253] V=[0.3·0.74,0.25·0.70,0.2·0.85,0.15·0.72,0.05·0.67,0.05·0.62] The eigenvector is calculated as:

[0254] V=[0.222,0.175,0.17,0.108,0.0335,0.031]

[0255] Classification and prediction process:

[0256] Classification algorithm: Improved particle swarm algorithm

[0257] Initialize the particle swarm. Assume that the particle swarm is initialized as follows

[0258] Particle position: P1 = [0.22, 0.17, 0.17, 0.11, 0.03, 0.03]

[0259] Particle velocity: V1 = [0.02, -0.01, 0.01, -0.02, 0, 0.01]

[0260] Update particle positions and velocities:

[0261] The particles are updated using inertia weights, individual optimal positions, and global optimal positions. After 50 iterations, the classification model converges and the optimal particle position (classification model parameters) is:

[0262] Pbest=[0.63,0.78,0.67,0.62,0.63,0.63]

[0263] Classification result: Based on the IPSO algorithm, the system classifies the user's characteristic data into the "high anxiety" category (classification range: greater than 0.5 and less than 0.8 means high anxiety, less than 0.5 means moderate anxiety, and greater than 0.8 means great anxiety), indicating that the user is at greater risk of psychological anxiety.

[0264] Comprehensive evaluation process:

[0265] In this step, the system comprehensively evaluates the user's psychological state, ability matching and development potential based on historical data and classification prediction results.

[0266] After integrating historical data and real-time data, the system calculates the user's comprehensive score.

[0267] Assume that the historical score is H = 0.75, the real-time data evaluation is R = 0.68, and the weight γ = 0.6.

[0268] F=0.6·0.75+0.4·0.68=0.72

[0269] According to the risk assessment of the classification results, the psychological state score is: S = 0.72 + 0.15 (classification score) = 0.87

[0270] Evaluation report generation and graphical display process:

[0271] 1. Analysis of psychological state

[0272] Psychological state analysis will be based on an individual's emotional changes and behavioral data, combined with physiological signals (such as heart rate and skin electrical response) to assess psychological states such as anxiety and stress, and generate a detailed analysis report.

[0273] Emotional trend analysis: Use charts (such as radar charts or curve charts) to show the emotional fluctuations of individuals during the painting process. For example, the changing trends of emotions such as anxiety, stress, and relaxation.

[0274] Suppose that in a painting task, an individual's emotions may change as follows: Anxiety index: At the beginning of the painting, the anxiety index may be 7 / 10, and gradually decrease to 4 / 10 as the task progresses;

[0275] Stress Index: The stress is high at the beginning, with a value of 8 / 10. As the task proficiency increases, the stress drops to 5 / 10.

[0276] Relaxation index: It is low at the beginning, with a value of 3 / 10. As the task progresses, the sense of relaxation gradually increases, finally reaching 6 / 10.

[0277] Chart implementation: Use radar chart to represent emotional changes: the changing trends of anxiety, stress, relaxation and other emotions during the painting process.

[0278] Emotional change data:

[0279] Anxiety index: [7,6,5,4] / 10

[0280] Pressure index: [8,7,6,5] / 10

[0281] Relaxation index: [3,4,5,6] / 10

[0282] Time axis: [0,5,10,15] (minutes)

[0283] With these data, a curve chart showing emotional trends can be generated, intuitively presenting the emotional fluctuations of individuals at different time points.

[0284] 2. Capability matching analysis

[0285] Based on the Big Five personality dimensions and the drawing projective test indicators for openness, conscientiousness, extroversion, agreeableness, and negative emotions, the ability match analysis will compare the individual's current ability with the requirements of the target position or task based on the individual's performance in the drawing test. Charts (such as pie charts and bar charts) are used to intuitively display the ability match.

[0286] Ability matching: Evaluate the individual's sense of responsibility, organization, efficiency and other aspects through the quantification of multi-dimensional characteristics.

[0287] Assume that the target position requires a responsibility score of 8 / 10, organization: 7 / 10, and efficiency: 8 / 10, while the individual's actual drawing ability score is 6 / 10, organization: 7 / 10, and efficiency: 8 / 10.

[0288] Chart implementation: Pie chart: shows the proportion of individuals' abilities in different dimensions (such as responsibility, organization, and efficiency), helping to determine which job responsibilities the individual is stronger in and which areas need improvement.

[0289] Bar chart: used to show the match between target position competency requirements and individual actual competencies, facilitating intuitive comparison.

[0290] Target position requirements:

[0291] Responsibility: 8 / 10

[0292] Organization: 7 / 10

[0293] Efficiency: 8 / 10

[0294] Individual ability rating:

[0295] Responsibility: 6 / 10

[0296] Organization: 7 / 10

[0297] Efficiency: 8 / 10

[0298] The comparison between the two can be shown through a bar chart, clearly showing the individual's strengths and areas for improvement.

[0299] 3. Prediction of future development potential

[0300] Future Development Potential Prediction Based on historical data, performance of painting tasks and trend analysis, it predicts individuals' future work performance and provides personalized career development suggestions.

[0301] Trend analysis: By reviewing the individual's performance in multiple tasks, analyze the improvement trend of their ability. For example, suppose the individual's performance in the past 5 painting tasks is:

[0302] Drawing Task 1: Ability Rating 6 / 10

[0303] Drawing Task 2: Ability Rating 6.5 / 10

[0304] Drawing Task 3: Ability Rating 7 / 10

[0305] Drawing Task 4: Ability Rating 7.5 / 10

[0306] Drawing Task 5: Ability Rating 8 / 10

[0307] Based on these data, the individual's ability improvement trend can be predicted.

[0308] The machine learning model of the present invention is used to predict an individual's possible ability score in the next 3 or 6 months with continued practice.

[0309] Drawing ability score: [6, 6.5, 7, 7.5, 8] / 10

[0310] Time: [1,2,3,4,5] (task number)

[0311] Future predictions:

[0312] Based on the data fitting, it is predicted that the drawing ability score for the 6th task will be 8.5 / 10, and after 3 months, the individual's drawing ability is likely to reach 9 / 10.

[0313] Personalized recommendations:

[0314] Combined with the prediction results, the system can provide career development suggestions based on the individual's performance trends, such as continuing to improve creative skills and enhancing the ability to cope with complex tasks.

[0315] Predicting capability improvement in 3 months: Assuming that data analysis shows that an individual has the potential to improve his capability score to 9 / 10 in the next 3 months, the system may recommend that the individual start taking on more complex creative tasks in actual work, such as design project leadership or innovation tasks, to take on challenges with his improved capabilities.

[0316] Proactive decision support process:

[0317] The system provides the following recommendations through the evaluation report:

[0318] Stress management advice: It is recommended that users take stress management courses or reduce high-load tasks in their work schedules to prevent their mental state from deteriorating.

[0319] Career development suggestions: Users are suitable for positions that require complex tasks or detailed analysis, and can further develop leadership capabilities in the future.

[0320] Personalized training plan: The system recommends that users strengthen their emotional management and continue to delve deeper into their current work areas.

[0321] The present invention achieves accurate feature extraction and quantitative analysis of drawing projective tests through the deep integration of systematic data processing flow and intelligent algorithms, thus providing efficient and objective decision support for psychological state assessment and ability matching in talent selection. By innovatively combining hierarchical quantum evolutionary algorithm with improved particle swarm algorithm, the system can provide high-precision prediction and evaluation in complex data environments, significantly improving the efficiency and accuracy of talent screening.

[0322] The algorithm modules involved in the present invention are implemented by writing software codes, or by calling the corresponding algorithm modules (hierarchical quantum evolutionary algorithm and particle swarm algorithm) through MATLAB and calling M function codes. The algorithm can be implemented in a variety of programming environments and run in general computer operating systems, including but not limited to C language, Python, SPSSAU and other tools.

[0323] Although the present invention is described in detail through embodiments, those skilled in the art may make various modifications or equivalent substitutions to the technical solutions in these embodiments without departing from the spirit and principles of the present invention. Any modification, improvement, or equivalent substitution made within the spirit and principles of the present invention shall be deemed to fall within the protection scope of the present invention.

Claims

1. A painting projection talent selection test system based on the combination of hierarchical quantum evolution and particle swarm algorithm, characterized by: The system consists of multiple collaborative modules, including a sequentially connected perception data acquisition module, an intelligent context recognition preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, an adaptive intelligent prediction module, and a multi-source data dynamic evaluation module. Through intelligent painting projection data collection and analysis, it provides evaluation results for talent selection. The perception data acquisition module collects painting images through multimodal perception devices to obtain behavioral data, emotional changes and physiological indicators generated during the painting process; The intelligent context recognition preprocessing module performs dynamic preprocessing adjustments based on the painter's real-time context, including image denoising, enhancement, and standardization, and uses a self-learning model to perform adaptive adjustments based on the changing context to ensure the accuracy and consistency of data processing; The multi-dimensional feature collaborative extraction module uses a hierarchical quantum evolutionary algorithm to achieve hierarchical optimization and collaborative extraction of multimodal data extracted from painting projection data. It performs multi-level optimization on the extraction of detailed features of painting images and efficiently integrates behavioral data and emotional features. Finally, it generates a highly comprehensive, multi-dimensional feature vector to ensure that all key features are fully and accurately extracted. The distributed intelligent quantitative analysis module uses edge computing technology to perform real-time quantitative analysis on multiple distributed computing nodes to extract multi-dimensional features. It dynamically adjusts the parameters of the quantitative algorithm to adapt to different painting features and contextual changes, ensuring that the system can still process large-scale data efficiently. At the same time, the multimodal fusion model ensures that the quantitative processing of all data sources has a high degree of consistency, integrity and reliability. The adaptive intelligent prediction module combines the multi-dimensional optimization mechanism of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm to efficiently classify and predict the quantified feature data, and can adaptively adjust the model parameters according to the complexity and nonlinear characteristics of the data to ensure that the global search capability and local convergence speed of the prediction model are optimal. It is suitable for complex data sets and high-dimensional feature analysis. The multi-source data dynamic assessment module integrates multi-source data fusion technology to interactively compare the real-time collected multimodal data with historical data to generate a personalized dynamic assessment report. The report content covers the psychological assessment of talents, ability matching analysis and future potential prediction. At the same time, it tracks and evaluates the long-term development trends of individuals through time series analysis, provides comprehensive and forward-looking talent selection decision support, and ensures that the assessment results are highly predictive and of reference value.

2. A method for a painting projection talent selection test system based on the combination of hierarchical quantum evolution and particle swarm algorithm, characterized in that: The following steps are involved: S1. Data collection: The perception data collection module uses high-precision sensors and multimodal perception devices to collect multimodal data of individuals in the painting process in real time, including painting images, behavioral data, and physiological status data; the collected data is encrypted and transmitted and stored in the system's storage unit in real time, providing a solid foundation for subsequent data processing; S2. Data preprocessing: The collected painting data is standardized and preprocessed through the intelligent context recognition preprocessing module. The preprocessing process includes image denoising, image enhancement and context recognition. The noise in the data is eliminated through an adaptive denoising algorithm to enhance the clarity and contrast of the image. S3. Feature extraction: Through the multi-dimensional feature collaborative extraction module, a hierarchical quantum evolutionary algorithm is used to extract multiple quantitative features from the pre-processed painting data; the extracted features include the strength of the painting strokes, the complexity of the line structure, behavioral characteristics, and emotional characteristics; The hierarchical quantum evolutionary algorithm achieves refined feature extraction of complex painting data through multi-level feature optimization; S4. Quantitative analysis: After feature extraction is completed, the extracted features are quantified through the distributed intelligent quantitative analysis module; This module uses edge computing technology to run quantitative analysis simultaneously on multiple distributed computing nodes to generate feature vectors for subsequent classification and prediction; S5. Classification and prediction: Based on the quantized feature vector, the system uses the improved particle swarm algorithm for classification and prediction through the adaptive intelligent prediction module; the improved particle swarm algorithm optimizes the classification model by dynamically adjusting the weight and speed of particles, ensuring that the classifier can converge quickly and improve classification accuracy; S6. Evaluation and report generation: The classification and prediction results are comprehensively evaluated through the multi-source data dynamic evaluation module, and a personalized evaluation report is generated; the evaluation report includes the individual's psychological state analysis, ability matching and future development potential prediction, and provides forward-looking auxiliary decision-making suggestions based on the fusion analysis of historical data and real-time data; the evaluation report is detailed, including graphical analysis results and text descriptions, intuitively displaying the individual's evaluation results, and providing accurate support for decision-making in talent selection.

3. The method according to claim 2, characterized in that S1 includes: The system collects multimodal data of individuals in the painting process in real time through the perception data collection module; these data include painting image data, behavior data and physiological status data; Painting image data: The painting image is captured in real time through a high-resolution camera to ensure that the smoothness of the lines, the use of colors, and the details of the graphic structure in the painting are accurately recorded; these image data are stored as a two-dimensional pixel matrix I (x, y), where x and y represent the pixel coordinates of the image, and the pixel value represents the color and grayscale information; this data serves as the main input for subsequent image feature extraction to help identify the geometric features and color composition in the painting; Behavioral data: Through a digital tablet equipped with a pressure sensor, the individual's drawing behavior data, including drawing speed, pen pressure, and hand movement trajectory, is recorded in real time; these data reflect the individual's behavioral characteristics and serve as an important basis for analyzing psychological state and ability performance; the time series of behavioral data is expressed as P(t), where t represents time and P(t) represents the pen pressure value at time point t; changes in pen pressure and drawing speed provide direct information for identifying individual psychological characteristics; Physiological state data: The system collects physiological signals of individuals during the painting process through heart rate sensors and galvanic skin response sensors, including heart rate HR(t) and galvanic skin response GSR(t); these data reflect the emotional state of individuals; During the data collection process, the heart rate HR(t) indicates the change of heart rate over time, and the galvanic skin response GSR(t) reflects the change of skin conductivity, which is usually closely related to emotional fluctuations. Through these physiological data, the system can more comprehensively evaluate the individual's psychological state. Data transmission and storage In order to ensure data security and real-time performance, all collected data are transmitted in encrypted form. During the transmission process, the AES symmetric encryption algorithm is used to protect the integrity of the data. The transmitted data is stored in the system's storage unit to provide support for subsequent processing. The data is stored in the form of matrices and time series, so that the data can be effectively called for further feature extraction and analysis. The encryption formula is as follows: C=E k (M) Among them: C is the encrypted ciphertext; E k is an encryption function, which is encrypted using the key k; M is the original multimodal data, including painting images, behavioral data, and physiological data; The collected multimodal data is modeled as multidimensional time series data. Through these multidimensional time series data, the system extracts features, especially when analyzing individual behavioral characteristics and physiological responses, which can provide high-quality data support for subsequent quantitative analysis and prediction models; The description is as follows: D(t)={I(x,y),P(t),HR(t),GSR(t)} Where: I(x,y) is the pixel matrix of the painting image; P(t) represents the change of pen pressure over time during the painting process; HR(t) is the heart rate data, reflecting the individual's emotional fluctuations; GSR(t) is the skin electrical response data, representing the individual's emotional response.

4. The method according to claim 2, characterized in that S2 include: By combining intelligent context-aware technology, the system can adjust denoising and enhancement parameters in real time according to individual physiological and behavioral data; standardize the collected painting data to provide high-quality input for the subsequent hierarchical quantum evolutionary algorithm and improved particle swarm algorithm, including the following core processing links: image denoising, image enhancement, and context recognition and adaptive processing; Image denoising: In the process of collecting painting image data, noise is inevitable from environmental factors, device resolution, and interference during transmission. The system uses an adaptive denoising algorithm to dynamically adjust according to the noise intensity in the image to ensure that the detailed information in the image is retained while eliminating the noise. Image denoising is modeled as an optimization problem of removing noise n(x, y) and maintaining the original image signal I(x, y): I clean (x,y)=I(x,y)-n(x,y) Where: I clean (x, y) is the denoised image; I(x, y) is the original image collected; n(x, y) represents the distribution of noise; In order to further improve the denoising effect, the system uses median filtering to remove random noise and ensure clear edges; the formula is as follows: Where: N is the neighborhood window, and the noise is smoothed by calculating the median of the pixel neighborhood to avoid blurring of details; median() represents the median operation; Through this adaptive denoising process, the system ensures image clarity and provides a higher quality data foundation for subsequent analysis and feature extraction; Image enhancement: After denoising, the system performs image enhancement processing on the image to improve the contrast, brightness and sharpness of the image so as to better capture details; image enhancement uses histogram equalization and Laplace operator enhancement; Histogram equalization: The purpose of histogram equalization is to stretch the grayscale range of the image and improve the contrast. The formula is as follows: Where: I min and I max Respectively represent the minimum and maximum grayscale values ​​in the image; I enhanced (x, y) is the enhanced image; through this processing, the system significantly improves the contrast between light and dark in the image, making the details more prominent; Laplacian operator enhancement: The Laplacian operator is used to sharpen the edges of the image, making the shape features in the image more obvious; its formula is: Yo sharpened (x,y)=I clean (x,y)+α·ΔI clean (x,y) Where: α is the sharpening strength parameter; Δ is the Laplacian operator, which is used to detect and enhance the edges in the image; through this operator, the details and edges of the image will be more prominent, ensuring that the shape and structural features of the painting can be accurately extracted in the subsequent steps; Context recognition and adaptive preprocessing: In the present invention, the context recognition module dynamically adjusts the preprocessing parameters by analyzing the individual's physiological state and behavioral data to ensure the effectiveness of image processing under different emotional states; the module adaptively adjusts the denoising and enhancement parameters to ensure the stability of the data under different emotional states; the emotional state E(t) is modeled by the following formula: E(t)=f(HR(t),GSR(t),P(t)) Where: HR(t) is the heart rate data; GSR(t) is the galvanic skin response; P(t) is the pen pressure behavior data during the painting process; f is the emotion perception function, which converts these physiological and behavioral data into the individual's emotional state; When the system detects emotional fluctuations in an individual, it adjusts denoising and enhancement parameters based on the fluctuations; when heart rate and skin electrical response increase significantly, indicating that the individual is in an anxious state, the system strengthens the denoising process; conversely, when emotions are stable, the system weakens denoising and enhances image sharpness.

5. The method according to claim 2, characterized in that: S3 includes: The task of the multi-dimensional feature collaborative extraction module is to extract key features that can reflect individual painting behaviors from the preprocessed data, covering quantitative features of images, behaviors, and emotions; By collaboratively extracting these multidimensional features, the system is able to comprehensively analyze the psychological and behavioral characteristics of individuals; The main features extracted include: stroke strength: reflects the degree of force used by the individual when painting, reflecting the control of painting; line structure complexity: reflects the smoothness and detail of the painting lines; behavioral characteristics: painting speed, pen pressure changes, hand trajectory, indicating the individual's painting mode; emotional characteristics: emotional state derived from physiological and behavioral data, assisting in analyzing individual emotions; In the present invention, feature extraction is modeled as an optimization problem, that is, finding the optimal solution among all feature sets; let X be the painting dataset, f(X) be the fitness function, and the goal of feature extraction is to maximize the function maxf(X): maxf(X)=max(f(T 笔画力度 ,T 线条复杂度 ,T 行为特征 ,T 情绪特征 )) Where: T 笔画力度 Indicates the stroke strength characteristics; T 线条复杂度 Indicates the complexity of the line; T 行为特征 Indicates the characteristics of painting behavior; T 情绪特征 represents the emotional features derived by the emotion recognition module; In the feature optimization process, the hierarchical quantum evolutionary algorithm adjusts the weight of each feature through the function of quantum rotating gate, and gradually finds the optimal feature combination that maximizes the fitness function f(X); The hierarchical quantum evolutionary algorithm contains the following main steps: Initialize the quantum population: The individuals in the quantum population represent potential solutions to the painting features; each solution is represented by a superposition state of quantum bits, and the state formula is: |ψ>=α|0>+β|1> Where: |ψ> represents the state of the quantum bit; α and β are complex coefficients, satisfying: |α| 2 +|β| 2 =1 Through this quantum state representation, the system can explore multiple feature vectors in parallel to improve the efficiency of feature extraction; 0> refers to the state of 0; 1> refers to the state of 1; Quantum revolving door update: The feature search process is updated through a quantum revolving door, and the formula is as follows: i (t+1) =θ (t) +Δθ Where: θ (t) is the rotation angle in the current algebra; Δθ is the update step calculated according to the fitness function; through the action of the quantum rotating gate, the probability distribution of the characteristic solution will be gradually optimized and close to the optimal solution; Hierarchical search and local optimization: The hierarchical quantum evolutionary algorithm adopts a hierarchical structure. It first performs a global search to extract the main features in the painting data. Then, at each level, it optimizes the local detail features to capture more detailed features. In the feature extraction of painting data, the hierarchical quantum evolutionary algorithm conducts a global search through the quantum evolutionary algorithm to find the main features in the painting; then local optimization is performed in the features of each level to extract more detailed features, such as the tiny details of stroke changes, behavioral characteristics, and emotional characteristics; multi-level feature optimization can avoid falling into local optimal solutions and ensure that the features finally extracted by the system are globally optimal.

6. The method according to claim 2, characterized in that S4 include: Quantitative analysis is to standardize and normalize different types of multidimensional features, and calculate feature weights to ensure the comparability and importance of features. The features processed by quantitative analysis will be converted into feature vectors to provide unified and accurate data input for subsequent classification and prediction modules. The features of quantitative processing include: stroke strength extracted by hierarchical quantum evolution algorithm: reflects the strength of individual painting; line structure complexity: measures the complexity, fluency and detail of the painting lines; behavioral characteristics: painting speed, pen pressure changes, hand trajectory; emotional characteristics: emotional state derived from behavioral and physiological data; The extracted features are converted into feature vectors, expressed as: V=[v1,v2,v3,…,v n ] Among them, v i Indicates that the i-th quantitative feature n is the total number of features extracted; Introduction of edge computing technology: In order to improve processing efficiency, the system adopts edge computing technology, which distributes quantitative analysis tasks to multiple computing nodes. The advantage of edge computing is that it can improve data processing speed, especially for the processing of large-scale, high-dimensional data; There are M computing nodes in the system, each of which is responsible for the quantization of a part of the features. The quantization task is expressed as: Where: V j is the feature vector processed by the jth computing node; w i is the weight of feature i; n j is the number of features processed by the jth node. Through edge computing, each node completes quantitative analysis independently, and the central system integrates the results of all nodes to finally generate a global feature vector V. Mathematical model of quantitative analysis: The quantitative analysis process mainly includes standardization, normalization and weight calculation. Each step relies on mathematical models to ensure that different features are compared and processed at the same scale. Standardization: The purpose of standardization is to eliminate the impact of unit differences between different features so that all feature values ​​are within the same numerical range; the standardization formula is: Where: x i is the original eigenvalue; μ i is the mean of feature i; σ i is the standard deviation of feature i; through standardization, feature x i The mean of ′ is 0 and the standard deviation is 1, which facilitates the comparison between different features; Normalization: Normalization is to map the standardized feature values ​​to the interval [0,1] to enhance the comparability between different features; the normalization formula is: Where: x min and x max They are the features x i The minimum and maximum values ​​of x i ″ is the normalized feature value; normalization ensures that all features are in the same numerical range, which is convenient for subsequent analysis; Weight calculation: The system uses information gain to calculate the weight of each feature. The weight reflects the importance of the feature to classification and prediction. The weight calculation formula is: Where: w i is the weight of feature i; information gain (T i ) is the contribution of feature i to the classification; After weight calculation, the system calculates each normalized eigenvalue x i ″ is weighted, and the formula is: v i =w i ·x i ″ Among them, v i is the final quantized eigenvalue, w i is the weight of the feature, x i ″ is the normalized eigenvalue; Workflow of distributed quantitative analysis The entire quantitative analysis process is completed through the following steps: Feature distribution: The system distributes features to various computing nodes to ensure parallel processing; Standardization and normalization: Each node standardizes and normalizes the features it is responsible for to ensure that the value range of all features is consistent; ensure that different features are compared at the same scale; Weight calculation and feature integration: The computing nodes calculate the weights of the features they are responsible for respectively and perform weighted processing on these features; giving each feature relative importance in classification and prediction tasks Eigenvector generation: After all nodes complete the calculation, the central system integrates the quantization results of each node to generate a global eigenvector V.

7. The method according to claim 2, characterized in that S5 include: In the classification and prediction steps of the present invention, the system uses an improved particle swarm algorithm to classify and predict the quantized feature vectors through an adaptive intelligent prediction module: Particle initialization: Each particle in the particle swarm algorithm represents a potential classification model solution; in the classification task of the present invention, the position of the particle P i Corresponding to the parameters of the classification model, the particle velocity Vi determines the direction and amplitude of the next parameter update; The particle initialization formula is as follows: Where: P i (0) is the initial position of the ith particle, i.e., the parameter value of the classification model; V i (0) is the initial velocity of the ith particle; P min and P max are the minimum and maximum values ​​of the particle position respectively; V min and V max are the minimum and maximum values ​​of the particle speed respectively; rand() is a random number between [0,1]; Particle update formula: The speed and position of the particle are updated according to its individual optimal solution and the global optimal solution; the speed and position update formula are as follows: V i (t+1)=ω·V i (t)+c1·r1·(P best,i -P i (t))+c2·r2·(P global -P i (t)) P i (t+1)=P i +V i (t+1) Where: V i (t) is the velocity of the ith particle in the tth generation; P i (t) is the position of the ith particle in the tth generation; P best,i is the individual optimal position of the particle; P global is the global optimal position of the group; w is the inertia weight, which is used to control the speed change of the particles; c1 and c2 are acceleration constants, which respectively adjust the influence of the individual optimal position and the global optimal position on the speed; r1 and r2 are random factors to ensure the diversity of the particle swarm algorithm; Dynamically adjust the inertia weight: In order to improve the classification accuracy and speed up the convergence of the algorithm, the present invention adopts a strategy of dynamically adjusting the inertia weight; the inertia weight w gradually decreases with the increase of the number of iterations. This design enhances the exploration ability of particles in the early stage and ensures that particles are concentrated on the optimal solution in the later stage; the adjustment formula of the inertia weight is as follows: Where: w max and w min are the maximum and minimum values ​​of the inertia weight respectively; t is the current iteration number; t max Maximum number of iterations; This dynamic adjustment strategy ensures that the algorithm can conduct a wider search in the early stage, and focus on the convergence of the optimal solution with a smaller weight in the later stage, thereby optimizing the classification effect; Optimization goal of classification and prediction: In the classification model of the present invention, the optimization goal of the improved particle swarm algorithm is to minimize the classification error rate; by continuously adjusting the position and speed of particles, the classification model can gradually reduce classification errors and improve classification accuracy; the optimization objective function is expressed as: Where: E is the classification error; N is the total number of samples; y i is the true label of the i-th sample; is the predicted label of the i-th sample; Is the loss function, the cross entropy loss function.

8. The method according to claim 2, characterized in that S6 include: The core function of dynamic evaluation of multi-source data: The core function of the evaluation and report generation module is to comprehensively evaluate the performance of individuals and generate personalized evaluation reports through the integration analysis of classification and prediction results, historical data and real-time data; The module dynamically analyzes the multi-dimensional characteristics of individuals, including psychological state, ability performance and future development potential; The core assessment content includes: Psychological state analysis: Based on the individual's emotional and behavioral characteristics, dynamically evaluate their psychological state, anxiety, and changes in stress psychological factors; Competency matching assessment: combining individual behavioral characteristics with competency characteristics to analyze the degree of match between their current competency and position or task; Prediction of future development potential: Predicting the development potential of individuals in different tasks or situations through the integration of historical performance and future trends; Mathematical model and logic of data evaluation: The multi-source data dynamic evaluation module combines historical data and real-time data, compares the multi-dimensional characteristics of individuals with classification and prediction results, and uses mathematical models to evaluate the comprehensive performance of individuals; Comprehensive evaluation model: The system uses a multidimensional feature vector V = [v1, v2, v3, …, v n ] represents the individual characteristic data, and generates the prediction result C = [c1, c2, c3, ..., c n ], the goal of the evaluation model is to score the individual's performance S based on these data; the evaluation scoring formula is: Where: w i Represents feature v i The weight of reflects its importance to the evaluation results; i is the characteristic value of the individual; c j represents one of the classification and prediction results; α and β are adjustment parameters used to balance the impact of historical data and real-time data; this formula combines the individual historical characteristics v i And the classification result c j Generate a comprehensive score S to evaluate the overall performance of the individual; Fusion of historical and real-time data: In order to more accurately evaluate the performance of individuals, the system adopts a fusion analysis model that combines historical data with real-time data through a weighted strategy; the fusion analysis formula is as follows: F=γ·H+(1-γ)·R Among them: F represents the final fusion evaluation result; H represents the evaluation result based on historical data; R represents the evaluation result based on real-time data; γ is the fusion weight, which controls the proportion of historical data to real-time data and can be dynamically adjusted according to different application scenarios; through fusion analysis, the system can flexibly integrate the individual's historical performance and current performance, thereby generating more personalized and accurate evaluation results; Core process of report generation Data aggregation: The system aggregates classification and prediction results, individual historical performance and real-time data to ensure the integrity of the assessment data; Comprehensive evaluation: Based on the fusion analysis model, the system evaluates the individual's psychological state, ability matching and future development potential, taking into account historical data and real-time data; Report generation: The system automatically generates a personalized assessment report that not only contains detailed individual analysis, but also provides a graphical display of sentiment trend charts and ability matching charts to help users understand the results more intuitively; Decision support: The system provides forward-looking suggestions in the assessment report to help users identify individual strengths and potential and formulate corresponding capability development or talent selection strategies; The assessment report includes the following parts: Psychological state analysis: Generate detailed reports on individual psychological states based on emotional and behavioral characteristics, including assessment results of anxiety and stress factors; display emotional change trends through radar charts or curve graphs; Competency matching analysis: The report shows the matching degree between the individual's current competence and the target position or task, and uses pie charts or bar charts to intuitively display the competence matching situation; Prediction of future development potential: Based on historical data and trend analysis, the report predicts an individual's future work performance and provides personalized career development suggestions; Proactive decision support: Based on the assessment results, the system provides specific recommendations to help users make accurate decisions on talent selection and capability development.

Images