Recruitment process global information screening and distinguishing method and system based on AI algorithm

Abstract

The invention provides a recruitment process global information screening and distinguishing method and system based on artificial intelligence, and belongs to the field of information system.The method comprises the steps that high-dimensional feature extraction and fusion are conducted on multi-source heterogeneous data through the quantum computing technology, and relevance feature expression is generated through a quantum entanglement encoder and quantum principal component analysis; a causal reasoning technology is combined, a super causal decision tree and an anti-fact causal network are constructed, dynamic modeling and optimization of the causal relationship are realized, and fairness and reliability of decision are improved through a resistance bias elimination module. In addition, the culture gene adapter is used for evaluating the matching degree between the candidates and the post and the organization culture, and the accuracy of the recruitment result is ensured. A meta-architecture evolutionary engine and a holographic verification sand table are introduced into the system design, automatic optimization and real-time verification of the model are achieved, and the efficiency and robustness of recruitment decision making are improved.

Description

Technical Field

[0001] The present invention belongs to the field of information systems, and more specifically relates to a method and system for screening and distinguishing global information in a recruitment process based on an AI algorithm. Background Art

[0002] With the rapid development of artificial intelligence technology, the application of AI algorithms in the recruitment field is gradually becoming a trend. Traditional recruitment processes often rely on manual resume screening, interview assessments, and subjective decision-making. This process is not only inefficient but can also be affected by human bias, making it difficult to ensure the objectivity and fairness of recruitment results. In addition, with the advent of the data age, the sources of candidate information involved in the recruitment process are becoming increasingly diverse, including resume databases, social media, assessment platforms, and biosensors. The heterogeneity and complexity of this data make it difficult for traditional processing methods to fully tap the potential value of the data, resulting in limited decision-making quality. How to use advanced technologies to efficiently integrate and deeply analyze multi-source data, and reduce human bias and improve scientificity in recruitment decisions, has become a technical challenge that needs to be urgently addressed in the industry.

[0003] Existing AI-based recruitment systems typically employ classical machine learning or deep learning models. These models can improve the automation level of the recruitment process to a certain extent, but they still have significant limitations. First, when processing multi-source heterogeneous data, traditional models typically rely on feature engineering or simple fusion strategies, making it difficult to capture the complex correlations between data, especially the potential interactions between cross-modal data. Second, these models typically make predictions based on statistical correlations, while ignoring the ability to model causal relationships and are unable to effectively address causal reasoning problems in the decision-making process. For example, when assessing the match between candidates and job requirements, traditional models may only focus on the correlation of surface features and fail to identify the deep causal factors that lead to the matching results, which may lead to decision-making bias. In addition, existing models are highly sensitive to data bias and are easily affected by the bias implicit in the training data, further reducing the fairness and reliability of recruitment decisions. Summary of the Invention

[0004] This invention proposes a global information screening and discrimination method and system for the recruitment process based on AI algorithms. It aims to solve the problems of low efficiency, decision-making bias and insufficient data processing capabilities in the traditional recruitment process through the deep integration of technologies such as quantum computing, causal reasoning, and reinforcement learning. At the method level, the technology maps multi-source data to a high-dimensional Hilbert space through a quantum entanglement encoder, extracts quantum states with non-local characteristics, and compresses feature dimensions through quantum principal component analysis to generate quantum correlation feature expressions. At the decision-making level, a counterfactual causal network is constructed using a hyper-causal decision tree, and the modeling and optimization of causal relationships are achieved through dynamic do operator inference and adversarial bias elimination modules.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: the method comprises:

[0006] Global information multi-source data collection, using a distributed crawler interface to aggregate information streams from heterogeneous data sources such as resume databases, social media, assessment platforms, and biosensors to form raw feature tensors, which are then pre-processed through Schmidt orthogonalization and a manifold mapping function based on quantum geodesic distance;

[0007] A quantum entanglement encoder is used to map the preprocessed data vector into a 12-dimensional Hilbert space. Quantum amplitude embedding technology is used to generate non-local feature vectors. Quantum principal component analysis (QPCA) is used to compress the feature dimensions to form a feature expression with quantum non-local correlation.

[0008] Construct a hyper-causal decision tree, using counterfactual causal networks and dynamic do operator inference, combined with an adversarial bias elimination module, to establish a causal graph topology for recruitment decisions, forming a closed-loop verification ecosystem.

[0009] Generate cultural gene adapters, encode organizational culture data through quantum convolutional networks, fuse job requirement vectors to generate job-specific evaluation matrices, and calculate the geodesic distance between cultural DNA and job requirements through hyperbolic space fusion;

[0010] The meta-architecture evolution engine is used to automatically optimize the topological structure and hyperparameter configuration of the causal network, and the recruitment decision-making path is dynamically simulated and stress-tested through a holographic verification sandbox driven by reinforcement learning.

[0011] In one embodiment, the data preprocessing uses a quantum-compatible normalization method to perform Schmidt orthogonalization on each feature dimension to eliminate mutual information redundancy.

[0012] In one embodiment, the quantum entanglement encoder is implemented by a 12-qubit variational circuit, whose Hamiltonian construction follows a parameterized rotation gate design, encoding classical data into superposition amplitudes of quantum states.

[0013] In one embodiment, the counterfactual causal network controls the edge set E of the causal graph through dynamic do operator parameterization, and uses a counterfactual generator to automatically trigger retraining of the conditional probability distribution.

[0014] In one embodiment, the cultural gene adapter adopts a two-stream adversarial generative network to fuse cultural DNA with job requirement vectors, and dynamically adjusts job feature weights through a quantum attention mechanism.

[0015] In one embodiment, the meta-architecture evolution engine uses quantum crossover operations and quantum mutation operations to optimize the topological structure of the causal network, and generates a lightweight model through quantum distillation compression.

[0016] In one embodiment, the holographic verification sandbox captures the decision maker's subconscious preferences through a reinforcement learning agent combined with a brain-computer interface module, and encodes them as correction terms of the reinforcement learning reward function.

[0017] In one solution, the holographic verification sandbox simulates extreme scenarios through adversarial generation technology to test the robustness and adaptability of the recruitment decision-making model.

[0018] In another aspect, a global information screening and discrimination system for a recruitment process based on an AI algorithm is provided, wherein the system is applicable to the method described above and comprises:

[0019] The data collection module is used to aggregate data from multiple sources such as resume databases, social media, assessment platforms, and biosensors through a distributed crawler interface;

[0020] The quantum feature fusion module is used to map the preprocessed data vector into the 12-dimensional Hilbert space and generate non-local feature vectors;

[0021] Hypercausal reasoning module, used to construct counterfactual causal networks and infer causal relationships in recruitment decisions through dynamic do operators;

[0022] The cultural gene adaptation module is used to generate the organizational culture DNA and job evaluation matrix and calculate the geodesic distance between the cultural DNA and job requirements;

[0023] Meta-architecture evolution module for optimizing causal network topology and hyperparameter configuration;

[0024] Holographic verification module, used to simulate recruitment decision paths and perform stress testing and robustness testing.

[0025] Beneficial effects of the present invention:

[0026] By combining advanced technologies such as quantum computing, causal reasoning, and reinforcement learning, this paper proposes an AI-based method and system for global information screening and discrimination in the recruitment process. This method efficiently integrates multi-source heterogeneous data, extracts quantum feature expressions with non-local correlations, and uses causal networks to ensure the scientific and interpretable nature of recruitment decisions. Furthermore, this method ensures the fairness and job matching of recruitment results through dynamic bias elimination and cultural gene adapters. It also optimizes decision paths using a meta-architecture evolution engine and a holographic verification sandbox, improving the efficiency, robustness, and reliability of the recruitment process and significantly enhancing the intelligence of recruitment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0028] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate exemplary embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those understood by those skilled in the art to which the present invention pertains. The terms used in the present specification are for the purpose of describing specific embodiments only and are not intended to limit the present invention. To facilitate understanding of the present invention, a more comprehensive description of the present invention will be provided below with reference to the accompanying drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0030] like Figure 1 As shown, a global information screening and discrimination method for the recruitment process based on an AI algorithm includes the following steps:

[0031] Step 1: Collect global information from multiple sources and perform quantum feature fusion;

[0032] Collect multi-source data, use quantum entanglement encoder to map the multi-source data into 12-dimensional Hilbert space, and generate non-local feature vectors.

[0033] In the stage of global information multi-source data collection and quantum feature fusion, the system first aggregates information flows from heterogeneous data sources such as resume databases, social media, evaluation platforms, and biosensors through a distributed crawler interface to form the original feature tensor. (Where N represents the number of samples, and D represents the total dimension of the original features extracted from channels such as resumes, social media, and biosensors.) Data preprocessing uses a quantum-compatible normalization method to perform Schmidt orthogonalization on each feature dimension, eliminating the mutual information redundancy of classical data and generating an intermediate representation. where Φ is the manifold mapping function based on quantum geodesic distance. Schmidt orthogonalization, mentioned in the data preprocessing stage, is a quantum-compatible mathematical operation. Its essence is to eliminate redundant information between different feature dimensions through linear transformations, projecting data from each dimension onto a mutually independent orthogonal basis. This process is implemented by the manifold mapping function Φ based on quantum geodesic distance, which reconstructs the data distribution by simulating the shortest path properties of quantum states in manifold space.

[0034] The core of the quantum entanglement encoder is implemented by a 12-qubit variational circuit, and its Hamiltonian construction follows By parametric revolving door R z (θ k ) encodes classical data into the superposition amplitude of quantum states. ij represents the coupling strength coefficient between the i-th and j-th quantum bits, σ x and σ y is the Pauli operator, corresponding to the spin components of the quantum bit in the X-axis and Y-axis directions, respectively. Represents the tensor product operation between quantum bits, which is used to construct the joint action of multi-body quantum systems. k From the preprocessed data The numerical parameters extracted from the quantum bit are used to control the rotation angle of the quantum bit around the Z axis, thereby encoding the classical information into the amplitude phase of the quantum state.

[0035] The specific mapping process uses quantum amplitude embedding technology to pre-process the data vector Perform a unitary transformation Project each data point into the 12-dimensional Hilbert space H 12 , generating quantum states The extraction of non-local eigenvectors relies on the quantum parallel measurement strategy to perform Pauli operator projection measurement on the entangled state Eigenvectors are obtained by quantum expectation value estimation Where K = 12 is the number of selected measurement bases. Finally, the feature dimension of 144 dimensions is compressed to 12 dimensions through quantum principal component analysis (QPCA), satisfying ’s fidelity constraints, forming a characteristic expression with quantum non-local correlation, providing a data basis with quantum correlation characteristics for subsequent super-causal reasoning.

[0036] U encIt is the evolution operator of the quantum state. By combining the Hamiltonian's time evolution operator with the rotating gate, the initial zero state is transformed into Transformed into an entangled state carrying data information |ψ i >. The Pauli operator projection measurement M mentioned in the process of extracting non-local eigenvectors is composed of multiple σ z The composite observation operator is composed of the tensor product of operators. Its measurement results are converted into 144-dimensional classical feature vectors through quantum expectation value calculation. Finally, the high-dimensional features are compressed into a 12-dimensional latent space through quantum principal component analysis (QPCA). The fidelity constraint condition It means that the compressed quantum state density matrix has more than 95% similarity with its original state, ensuring that the quantum correlation characteristics are retained during the dimensionality reduction process, providing a data basis with quantum non-local correlation for subsequent causal reasoning.

[0037] Step 2. Hyper-causal decision tree; construct a counterfactual causal network, integrating dynamic do operator inference and adversarial bias elimination modules.

[0038] In the construction of the hyper-causal decision tree, the system establishes a dual-channel reasoning network through the structural causal model framework. First, the topological structure of the causal graph G = (V, E) is defined, where the vertex set V = {v1,...,v m} represents the potential factors of the recruitment decision (such as skill level and cultural fit), and the edge set E is parameterized by the dynamic do operator. The conditional probability distribution of each node is implemented using the counterfactual generator: in is the learnable intervention matrix, Δv j Represents the node v j The execution of the dynamic do operator is achieved through parameter adaptive adjustment by the quantum annealing optimizer, and its energy function is defined as where p θ (z) is the data distribution after causal intervention, and q(z) is the unbiased target distribution. The dynamic do operator is an important component in constructing counterfactual causal networks. It plays a role in connecting and controlling the nodes in the causal graph.

[0039] In the causal network, nodes represent potential factors in hiring decisions, such as skill level and cultural fit, while edges are parameterized by dynamic do operators, expressing the causal relationship between these factors.

[0040] The dynamic do operator can simulate different intervention strategies by setting different parameter values, exploring the impact of manipulating a factor on the outcome. The dynamic do operator's "intervention" behavior can simulate various real-world scenarios, such as adjusting educational requirements and work experience constraints. Then, through a counterfactual generator, it calculates the probability distribution under different interventions, using this as a basis for generating the optimal solution.

[0041] In general, dynamic do operators are a key tool for decision-making and reasoning in causal networks. By varying the parameters of the do operator, we can simulate and calculate the effects of different decision-making strategies to arrive at the optimal recruitment strategy.

[0042] The adversarial bias elimination module is implemented through the Wasserstein generative adversarial network to construct the discriminator and generator G ψ :Z→H 12 The game process of the adversarial bias elimination module. φ With the generator G ψ The subscript symbols φ and ψ correspond to the trainable parameters of the two, respectively. 12 represents the 12-dimensional quantum feature space generated in step 1. Its loss function incorporates the causal sufficiency constraint:

[0043] Where (x cf ) is the counterfactual sample, and the maximum mean difference (MMD) kernel function selects the quantum radial basis The quantum radial basis kernel function k(x,y) in the maximum mean difference (MMD) constraint is obtained by the inner product of the quantum state<x|y> Calculate sample similarity, and its parameter σ controls the bandwidth sensitivity of the kernel function.

[0044] Dynamic causal reasoning is realized by tensor contraction operation, and quantum Monte Carlo estimation is performed on the causal effect quantity τ = E[Y|do(T=1)]-E[Y|do(T=0)] to construct a method that satisfies Stable learning dynamics of , where the causal loss term The constraint model output is aligned with the distribution of expert experience. The final decision tree is generated by the hypersurface cutting algorithm, which divides the 128-dimensional causal embedding space into 2 k causal decision units, and the division criteria of each unit is determined by sign(w T Φ(x)+b) is determined, where Φ(x)=ReLU(U·QuantumPooling(f i )) is the quantum-classical hybrid feature mapping function. This architecture achieves parameter optimization through the alternating direction method of multipliers (ADMM), ensuring a causal sufficiency score CSS ≥ 0.92 while maintaining the real-time constraint of a prediction latency of less than 15ms.

[0045] The causal effect size τ in dynamic causal inference uses the quantum Monte Carlo method to estimate the expected difference between the intervention group and the control group, L causal The KL divergence term in the model predicts the distribution p by comparing θ (y i |do(pa(v i ))) and expert experience distribution p exp (y i ) to constrain the rationality of the decision logic. k It means that the 128-dimensional causal embedding space is recursively divided into exponentially growing decision units, and the division criterion function sign(w T In Φ(x)+b), W represents the normal vector of the decision hyperplane, and b represents the bias term. Φ(x) uses the ReLU activation function and quantum pooling to map quantum features into the classical decision space. During the Alternating Direction Multiplier Method (ADMM) optimization process, a causal sufficiency score (CSS) threshold of 0.92 ensures a strong correlation between the causal graph structure and the actual business logic. The 15ms prediction latency constraint is achieved through a parallel pipeline architecture using quantum-classical hybrid computing.

[0046] Step 3. Cultural gene adapter: Encode the organizational cultural DNA through the 128-dimensional latent space and generate a position-specific evaluation matrix in real time.

[0047] In the implementation of the cultural gene adapter, the system constructs the coding system of the organizational cultural DNA through the quantum-classical hybrid autoencoder architecture.

[0048] First, multimodal cultural data such as corporate document library, employee behavior log, and value assessment questionnaire are input into the encoding network composed of 12 layers of quantum convolution. The data is mainly used to encode the organizational culture DNA. Each quantum convolution layer performs the transformation The quantum pooling operation QPool uses an amplitude selection strategy to retain the first 64% of the quantum state amplitude. The encoder output passes through a 128-dimensional quantum principal component projection layer. Dimensionality reduction, generating cultural DNA latent variables where ρ x =|ψ x ><ψ x | is the density matrix representation of the input data.

[0049] The decoder uses a dynamic tensor product structure The position feature vector Generated by the quantum attention mechanism, the attention weight is calculated as d=64 is the scaling dimension. The dynamic tensor product structure D(z c,j) in LSTM k represents the k-th long short-term memory network module, is the position feature vector The kth component of Calculate the latent variable z c The mth dimension and job characteristics j n The quantum state similarity, scaling factor The d=64 in is used to stabilize the gradient update.

[0050] The real-time evaluation matrix generation module is implemented through a two-stream adversarial generation network, and the cultural DNA latent space z c and job demand vector Perform geodesic fusion in hyperbolic space:

[0051] The hyperbolic space operation with curvature κ = -0.5 is implemented through glomeration. and They represent the exponential mapping and logarithmic mapping at the origin of the 128-dimensional hyperbolic space (curvature κ = -0.5), and λ∈[0,1] is the relationship between cultural DNA and job requirements. The fusion coefficient.

[0052] Geodesic fusion refers to the use of geodesic distance to measure and combine various features in hyperbolic space. In hyperbolic space, a geodesic represents the shortest path between two points in space, similar to the straight-line distance we commonly understand on the surface of a sphere or the Earth.

[0053] By calculating the geodesic distance between the cultural DNA latent space and the job requirement vector in hyperbolic space, we can effectively measure the similarities and differences between the two and perform effective feature fusion. This allows us to extract the most critical features that best reflect the job requirements and organizational culture from this high-dimensional and complex feature space, thus informing subsequent hiring decisions. Furthermore, due to the special characteristics of hyperbolic space (such as spatial distortion), it is better able to process and represent data with hierarchical structures, complex relationships, and distributions, such as organizational culture and job requirements.

[0054] The fused features are input into an evaluation matrix generator consisting of 64 quantum gate logic units to perform parameterized quantum operations. Output Tensor satisfy Orthogonality constraints, Σ culture is the quantum estimate of the corporate culture covariance matrix. l The evaluation matrix generated is the quantum state evolution parameter of the lth time step The corporate culture covariance matrix Σ that needs to be satisfied with the quantum estimation culture Frobenius norm constraint in is the preset error tolerance threshold.

[0055] The matrix element dynamic calibration module is adjusted through a real-time feedback loop and uses the quantum back propagation algorithm to calculate the gradient Denotes the parameter θ to the quantum state The partial differential of M target is the target evaluation matrix, and its update process is carried out on FPGA hardware through the real part extraction operation Re[·] to achieve fast parameter adjustment.

[0056] In the implementation of the meme adapter, the transformation operation U of the quantum convolution layer qc The symbol CRZ(θ k ) represents the Kth controlled quantum phase rotation gate, whose rotation angle θ k Through dynamic adjustment of classic optimization algorithms, QPool 3×3 It represents a 3×3 window quantum pooling operation in the quantum state space. It retains the first 64% of the quantum state probability amplitude through the amplitude selection strategy to achieve feature dimensionality reduction. QReLU is a quantum rectified linear unit that enhances the sparsity of the quantum state through nonlinear projection.

[0057] Step 4. Meta-architecture evolution engine: automatically generate model topology based on neural architecture search and optimize hyperparameter configuration using genetic programming.

[0058] In the implementation of the meta-architecture evolution engine, the system constructs an adaptive model generator through a hybrid quantum-classical co-evolution framework. First, the neural architecture search space is defined. The causal decision topology G casual Encoded by a dynamic Bayesian network, the cultural adapter substructure H culture The hypergraph tensor is used for representation, and the hyperparameter space d=128 dimensions covers quantum gate parameters, learning rate decay strategy and other factors. The evolutionary population is initialized using quantum entangled state sampling: each architecture individual A i =(G i ,H i ,θ i )'s genetic sequence is encoded by quantum amplitude in is the phase random parameter. i =(G i ,H i ,θ i )'s gene sequence is encoded by the quantum state amplitude |ψ i > implementation, |g k > and |hk >Represent the quantum state basis of causal topological gene fragments and cultural substructure gene fragments respectively.

[0059] The architecture evaluation phase constructs a multi-objective fitness function F(A)=[f acc ,-f latency ,CSS,CAD], where the causal reasoning accuracy By quantum fidelity calculation, the delay penalty term f latency Measured in real time by the probe. acc Represents the accuracy of causal reasoning based on quantum fidelity, by calculating the prediction density matrix ρ pred and the true label density matrix ρ true The trace product of Measure model performance; f latency is the inference delay penalty term measured by the FPGA hardware probe, which is constrained to meet the 15ms real-time requirement preset in step 2; CSS is the causal sufficiency score defined in step 2, which ensures the consistency between the causal graph structure and the actual decision logic; CAD is the cultural adaptability indicator in step 3, which is evaluated by the matrix M j The degree of match with job requirements ensures the fit between talent and organizational culture

[0060] The evolutionary operator design includes: 1) quantum crossover operation C X (A p ,A m )=QFT -1 (QFT(A p )⊙QFT(A m )), realize the architecture feature fusion in the frequency domain QFT represents quantum Fourier transform, and realize the parent architecture A through frequency domain feature fusion p With the parent architecture A m Gene recombination; 2) The mutation operator uses a parameterized quantum channel The Kraus operator Dynamically generated by the LSTM controller, E mut Kraus operator E k The parameterized unitary operation U generated by the LSTM controller k (θ) constitutes, mutation probability p k Dynamic adjustment through the quantum annealing optimizer in step 2; 3) Cultural gene strengthening operation By increasing the cultural adaptability gradient, the key connection weights are enhanced, and the cultural gene strengthens the operation R boost in represents the Hadamard product, through the cultural fitness gradient Soft threshold filtering (threshold 0.7) is used to enhance the connection weights of key cultural features in the hypergraph H.

[0061] Hyperparameter optimization is implemented through the quantum extension of Differentiable Architecture Search (DARTS), which constructs a two-level optimization problem in a hybrid continuous-discrete search space:

[0062] min α L val (w * (α),α)+λ‖α‖ Hilbert

[0063] st

[0064] w * (α)=argmin w L train (w,α)

[0065] L=E (x,y)～D [‖ <y|ψ w,α (x)>‖ 2 ]+γMMD(p arch ,p prior )

[0066] The architecture parameter α is regularly optimized in Hilbert space, λ‖α‖ Hilbert Geometric complexity of the constrained parameter space; quantum state overlap term‖ <y|ψ w,α (x)>‖ 2 Predicting the quantum state ψ by measuring w,α (x)>‖ 2 The loss function is constructed with the projection amplitude of the label state |y>; the maximum mean difference (MMD) term MMD(p arch ,p prior ) constrains the architecture distribution p through the quantum radial basis kernel function defined in step 2 arch With the prior distribution p prior similarity.

[0067] The architecture parameter α is optimized in Hilbert space, and the quantum state overlap is used as the core term of the loss function. The Pareto frontier uses the quantum annealing-assisted NSGA-II algorithm to construct the energy function Temperature coefficient T k Dynamically adjust to balance multi-objective optimization directions.

[0068] Quantum distillation compression is implemented in the architecture convergence stage to convert the evolved optimal architecture A * Mapped as a lightweight hybrid model:

[0069]

[0070] Quantum distillation compression in the architecture convergence phase maps the optimal architecture to a hybrid model M final , where QConv3×3 represents a 3×3 quantum convolutional layer, inheriting the feature extraction capability of the quantum-classical hybrid encoder in step 3; CausalLSTM 256 It is a 256-dimensional causal long short-term memory network, integrating the intervention logic of the dynamic do operator in step 1; represents an 8-head quantum attention mechanism, whose cultural latent variable z c Culture DNA coder from step 3.

[0071] The quantum superiority of the architecture is verified by quantum entanglement witness technology to ensure that S(ρ arch )=-Tr(ρlog2ρ)>2 N The quantum entropy condition.

[0072] Entropy condition S(ρ arch )>2 N In, ρ arch is the density matrix representation of the architecture, and N is the number of quantum bits, ensuring the system’s quantum supremacy over classical architectures.

[0073] Energy function E(A i ), the temperature coefficient T k Dynamically scale the fitness component F according to the weights of multiple objectives k , the optimization direction of balancing causal accuracy (step 1), cultural adaptability (step 3) and computational efficiency (step 2) through the quantum annealing process.

[0074] Step 5. Holographic verification sandbox; build a reinforcement learning-driven three-dimensional decision mapping system that supports stress testing and path backtracking.

[0075] In the implementation of the holographic verification sandbox, the system constructs a dynamic three-dimensional decision-making mapping environment through a quantum-enhanced reinforcement learning framework. Based on the cultural DNA latent variables generated in step 3 and the meta-architecture optimized in step 4, the sandbox engine first integrates organizational strategic goals, market environment variables, and employee behavior patterns to construct an interactive quantum state holographic space. Each decision node is driven by a 128-dimensional cultural gene vector, projected into a dynamic three-dimensional topological structure via a photonic crystal array. The strength of the association between business units is rendered in real time by the quantum entanglement strength of the cultural adaptability indicator. The reinforcement learning agent uses a dual-channel gating mechanism to simultaneously call the model generated by the meta-architecture evolution engine for strategy verification when exploring decision paths, while simultaneously capturing the multi-dimensional decision ripple effects through an FPGA-accelerated spatiotemporal convolutional network.

[0076] The stress testing module uses adversarial generation technology to create a library of extreme scenarios, simulating crisis states such as market disruptions, cultural conflicts, and organizational entropy increases. The system employs a cultural gene mutation algorithm to dynamically distort the coordinate axes of the latent space, forcing the decision mapping to reconverge within the distorted hyperplane. Quantum tunneling is then used to detect the robustness boundaries of critical paths. The path tracing engine constructs a decision graph based on causal entanglement entropy. It utilizes quantum teleportation principles to encode historical decision chains into reversible operation sequences, enabling butterfly effect analysis from any time slice. Virtual actors in the sandbox utilize neuromorphic chips to achieve millisecond-level decision feedback. Their behavioral patterns are dynamically calibrated by a cultural DNA decoder, ensuring over 93% fidelity between virtual responses during stress testing and the actual organizational culture.

[0077] The system ultimately achieves holographic interaction through a mixed-reality interface. Decision-makers can use gestures to control the rotation angle of quantum bits to adjust the weights of environmental variables, observing in real time the projections of different strategies across the cultural and business dimensions. The sandbox's built-in self-healing network continuously compares simulation results with real-world business data streams. When prediction deviations exceed a threshold, it automatically triggers a retraining protocol for the meta-architecture evolution engine, forming a closed-loop verification ecosystem. The sandbox achieves parallel deduction of tens of millions of decision paths per second on a quantum computing cluster. Using a brain-computer interface module, it captures the decision-maker's subconscious preferences, encoding them as corrections to the reinforcement learning reward function. Ultimately, it outputs a quantum topological fingerprint of the optimal strategy and a multidimensional risk assessment heat map.

[0078] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0079] It should be understood that the detailed description of the technical solutions of the present invention using the preferred embodiments above is illustrative and not restrictive. A person skilled in the art, after reading the present specification, may modify the technical solutions described in the embodiments or replace some of the technical features therein with equivalents; such modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A global information screening and discrimination method for the recruitment process based on an AI algorithm, characterized by: The method includes: Global information multi-source data collection, using a distributed crawler interface to aggregate information streams from heterogeneous data sources such as resume databases, social media, assessment platforms, and biosensors to form raw feature tensors, which are then pre-processed through Schmidt orthogonalization and a manifold mapping function based on quantum geodesic distance; A quantum entanglement encoder is used to map the preprocessed data vector into a 12-dimensional Hilbert space. Quantum amplitude embedding technology is used to generate non-local feature vectors. Quantum principal component analysis is used to compress the feature dimensions to form a feature expression with quantum non-local correlation. Construct a hyper-causal decision tree, using counterfactual causal networks and dynamic do operator inference, combined with an adversarial bias elimination module, to establish a causal graph topology for recruitment decisions, forming a closed-loop verification ecosystem. Generate cultural gene adapters, encode organizational culture data through quantum convolutional networks, fuse job requirement vectors to generate job-specific evaluation matrices, and calculate the geodesic distance between cultural DNA and job requirements through hyperbolic space fusion; The meta-architecture evolution engine is used to automatically optimize the topological structure and hyperparameter configuration of the causal network, and the recruitment decision-making path is dynamically simulated and stress-tested through a holographic verification sandbox driven by reinforcement learning.

2. The method for screening and judging global information in a recruitment process based on an AI algorithm according to claim 1, characterized in that: The data preprocessing adopts a quantum-compatible normalization method and performs Schmidt orthogonalization on each feature dimension to eliminate mutual information redundancy.

3. The method for screening and judging global information in a recruitment process based on an AI algorithm according to claim 1, characterized in that: The quantum entanglement encoder is implemented through a 12-qubit variational circuit, and its Hamiltonian construction follows a parameterized rotation gate design, encoding classical data into the superposition amplitude of quantum states.

4. The method for screening and judging global information in a recruitment process based on an AI algorithm according to claim 1, characterized in that: The counterfactual causal network controls the edge set E of the causal graph through dynamic do operator parameterization, and uses a counterfactual generator to automatically trigger retraining of the conditional probability distribution.

5. The method for screening and judging global information in a recruitment process based on an AI algorithm according to claim 1, characterized in that: The cultural gene adapter adopts a two-stream adversarial generative network to fuse cultural DNA with job requirement vectors, and dynamically adjusts job feature weights through a quantum attention mechanism.

6. The AI algorithm-based global information screening and discrimination method for recruitment process according to claim 1, characterized in that: The meta-architecture evolution engine uses quantum crossover operations and quantum mutation operations to optimize the topological structure of the causal network, and generates a lightweight model through quantum distillation compression.

7. The AI algorithm-based global information screening and discrimination method for recruitment process according to claim 1, characterized in that: The holographic verification sandbox captures the decision maker's subconscious preferences through a reinforcement learning agent combined with a brain-computer interface module, and encodes them as correction items of the reinforcement learning reward function.

8. The AI algorithm-based global information screening and discrimination method for recruitment process according to claim 1, characterized in that: The holographic verification sandbox simulates extreme scenarios through adversarial generation technology to test the robustness and adaptability of the recruitment decision-making model.

9. A global information screening and discrimination system for the recruitment process based on an AI algorithm, wherein the system is applicable to the method according to any one of claims 1 to 8, and is characterized in that: The system comprises: The data collection module is used to aggregate data from multiple sources such as resume databases, social media, assessment platforms, and biosensors through a distributed crawler interface; The quantum feature fusion module is used to map the preprocessed data vector into the 12-dimensional Hilbert space and generate non-local feature vectors; Hypercausal reasoning module, used to construct counterfactual causal networks and infer causal relationships in recruitment decisions through dynamic do operators; The cultural gene adaptation module is used to generate the organizational culture DNA and job evaluation matrix and calculate the geodesic distance between the cultural DNA and job requirements; Meta-architecture evolution module for optimizing causal network topology and hyperparameter configuration; Holographic verification module, used to simulate recruitment decision paths and perform stress testing and robustness testing.

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