A knowledge graph entity field conflict detection method based on semantic processing

Abstract

The application discloses a knowledge graph entity field conflict detection method based on semantic processing and relates to the technical field of natural language processing, and comprises the following steps: S1, generating a static semantic feature vector by encoding entity attribute features through semantics; S2, calculating the vector from a semantic particle to a category clustering center as field gravity; S3, iteratively solving the change rate of the generalized coordinates and the generalized momentum of the semantic particle through a Hamilton equation; S4, calculating the tangent space Jacobian matrix of the dynamic state sequence and the local Lyapunov index of the semantic particle; S5, performing reverse time integration on the Hamilton equation; S6, generating a semantic dissipation coefficient by using a Langevin equation parameter; and S7, correcting the entity attribute features. The application overcomes the limitations of traditional methods, such as the ignorance of the dynamic evolution mechanism, the difficulty in detecting implicit conflicts and the non-optimization of the correction strategy, and provides an efficient solution for the dynamic consistency maintenance of a knowledge graph.

Description

Technical Field

[0001] This invention relates to the field of natural language processing technology, and in particular to a knowledge graph entity domain conflict detection method based on semantic processing. Background Technology

[0002] As knowledge graphs continue to expand in scale and their applications extend to complex semantic reasoning, traditional entity representation methods based on statistical probability or simple geometric distance face significant limitations in handling deep semantic relationships and dynamic evolutionary behavior. Existing knowledge graph embedding techniques, such as TransE or GAT models, while capable of capturing static semantic similarity or local topological structures between entities, primarily rely on Euclidean distance in vector space or message passing mechanisms in graph structures. This static geometry or graph structure-based approach ignores the implicit physical dynamics (such as the inertia defined by attribute frequencies) in entity attribute features and the energy state changes of entities in the semantic field. This results in a lack of description of causal dynamic mechanisms when analyzing the evolutionary trends of entities in complex contexts, thus limiting the ability to detect implicit domain conflicts caused by dynamic instabilities (such as chaotic divergences). Furthermore, classical knowledge graph correction methods typically rely solely on the backpropagation of local gradients or constraints imposed by artificial rules. They lack the means to quantify the correction cost and plan the globally optimal escape path from the perspective of thermodynamic dissipation and optimal control. As a result, when dealing with complex constraint conflicts such as entities getting trapped in local potential energy traps, it is often difficult to achieve accurate state correction with minimal energy loss.

[0003] Therefore, how to provide a knowledge graph entity domain conflict detection method based on semantic processing is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This invention proposes a knowledge graph entity domain conflict detection method based on semantic processing. By constructing a controlled Hamiltonian system incorporating corrective external forces, entity attribute features are encoded as semantic particles, and the dynamic evolution of these particles is simulated based on the parameters of the Hamiltonian and Langevin equations. Local extreme points are monitored by calculating the local Lyapunov exponent and potential energy gradient using the tangent space Jacobian matrix. Domain constraint conflicts are identified when the exponent is greater than zero or the dwell time exceeds a threshold. Based on this, the cumulative impulse is calculated by backtracking the trajectory to generate a semantic impact key feature vector, and the semantic dissipation coefficient and potential energy surface height difference are inverted to calculate the minimum work required to escape the current potential energy trap. Using the minimum work as a performance index weight, a two-point boundary value problem is constructed and solved based on the two endpoint constraints at the conflict moment and the target minimum potential energy point to obtain the optimal control force sequence. Then, the semantic impact key feature vector is used to correct entity attribute features, and the dynamic evolution trajectory of semantic particles under the corrective external forces is predicted. The mechanism establishes a closed-loop processing pathway from dynamic stability analysis to thermodynamic optimal control correction, effectively quantifying the dynamic causes and correction energy consumption of semantic conflicts. This ensures that entities can escape local potential traps and converge to the target domain with minimal energy cost, achieving accurate latent conflict detection and optimal state correction. This invention overcomes the limitations of traditional methods, such as ignoring dynamic evolution mechanisms, difficulty in detecting latent conflicts, and non-optimal correction strategies, providing an efficient solution for the dynamic consistency maintenance of knowledge graphs.

[0005] A knowledge graph entity domain conflict detection method based on semantic processing according to an embodiment of the present invention specifically includes:

[0006] S1. Generate static semantic feature vectors by semantically encoding entity attribute features, set attribute frequency values ​​as quality parameters, and combine static semantic feature vectors and quality parameters to output semantic particles.

[0007] S2. Map the knowledge graph category labels to a potential energy surface using the radial basis kernel function, calculate the negative vector of the potential energy gradient to determine the semantic gradient force, and calculate the vector from the semantic particle to the category cluster center to determine the domain gravity.

[0008] S3. Substitute the semantic gradient force and the domain gravity into the Hamiltonian equation to iteratively solve the rate of change of the generalized coordinates and generalized momentum of the semantic particle, update the spatial position of the semantic particle, and output the dynamic state sequence.

[0009] S4. Calculate the tangent space Jacobian matrix of the dynamic state sequence and the local Lyapunov exponent of the semantic particle. If the local Lyapunov exponent is greater than zero or the semantic particle stays at the local extreme point for a longer time than the threshold, then it is determined that there is a conflict of domain constraints.

[0010] S5. Perform reverse time integration on the Hamiltonian equation with the moment when the domain constraint conflict is determined as the endpoint, calculate the dot product of the cumulative impulse vector of the semantic particle and the static semantic feature vector, and generate the semantic impact key feature vector.

[0011] S6. Based on the dynamic state at the moment when there is a conflict in the domain constraint, the friction coefficient and temperature are calculated using the parameters of the Langevin equation to determine the semantic dissipation coefficient. The minimum work required for the semantic particle to escape from the current potential energy trap is calculated by combining the semantic dissipation coefficient and the height difference of the potential energy surface.

[0012] S7. Construct a controlled Hamiltonian system with modified external forces, using the minimum work done to escape the potential energy trap as the performance index weight, and solve the two-point boundary value problem based on the two-endpoint constraints at the moment of conflict and the point of lowest target potential energy to obtain the optimal control force sequence; use semantic impact key feature vectors to construct a transformation matrix to modify entity attribute features.

[0013] Optionally, S1 specifically includes:

[0014] S11. Extract the names and values ​​of entity attribute features, map discrete attribute names to word vector IDs through hash index, extract low-dimensional dense word embeddings through lookup table operation, and perform Z-score standardization on continuous numerical attributes.

[0015] S12. Statistical attribute word frequency distribution in the whole set, substitute into the logarithmic frequency transformation formula to calculate the reciprocal of the frequency, and map the calculation result to continuous semantic weight parameters;

[0016] S13. Perform tensor concatenation between dense word embeddings and standardized numerical vectors in the feature dimension to generate a high-dimensional static semantic feature vector; construct a diagonal matrix using continuous semantic weight parameters as diagonal elements, and define the diagonal matrix as a generalized mass matrix; perform matrix-vector multiplication on the generalized mass matrix and the static semantic feature vector to generate an inertial weighted feature vector, and initialize the inertial weighted feature vector as the generalized coordinates of the semantic particles.

[0017] Optionally, S2 specifically includes:

[0018] S21. Map the knowledge graph category labels to feature vectors, input them into the radial basis function network to calculate the Gaussian kernel response value, and use the Gaussian kernel response value to construct a continuous scalar field in the feature space;

[0019] S22. Perform multivariable differential operations on the continuous scalar field, calculate the first-order partial derivative of the potential energy function with respect to each generalized coordinate component, arrange all the first-order partial derivatives in order of the coordinate axes to form the original gradient vector, perform element-wise inversion on the original gradient vector to generate semantic gradient force.

[0020] S23. Calculate the difference vector between the current generalized coordinates of the semantic particle and the coordinates of the category cluster center, perform normalization on the difference vector and multiply it by the gravity coefficient to generate the domain gravity.

[0021] Optionally, S3 specifically includes:

[0022] S31. Construct the inverse matrix of the generalized mass matrix, calculate the tensor product of the generalized momentum vector and the inverse matrix to obtain the transformed vector, further calculate the inner product of the transformed vector and the current generalized momentum vector and divide by the constant 2 to generate the kinetic energy scalar value; extract the value of the continuous scalar field at the current generalized coordinates as the potential energy scalar value; perform a summation operation on the kinetic energy scalar value and the potential energy scalar value to construct the Hamiltonian;

[0023] S32. Perform tensor differentiation on the Hamiltonian with respect to the generalized momentum to analytically obtain the time derivative expression of the generalized coordinates; perform partial differentiation on the Hamiltonian with respect to the generalized coordinates and invert the result to analytically obtain the time derivative expression of the generalized momentum.

[0024] S33. Based on the time derivative expression, set the iteration time step, calculate the product of the inverse matrix of the generalized mass matrix and the current generalized momentum vector as the rate of change of the generalized coordinates with time, and update the generalized coordinates at the next moment in combination with the time step; calculate the gradient vector of the continuous scalar field based on the updated generalized coordinates as the time derivative of the generalized momentum, and update the generalized momentum at the next moment in combination with the time step.

[0025] S34. Arrange and combine the generalized coordinates and generalized momentum calculated at each iteration time according to the time index to generate a dynamic state sequence.

[0026] Optionally, S4 specifically includes:

[0027] S41. Extract the generalized coordinates and generalized momentum from the dynamic state sequence, construct the orthogonal normalized tangent vector basis of the state transition trajectory, perform a first-order Taylor expansion of the state transition equation at the current moment with respect to the generalized coordinates and generalized momentum, and extract the linear coefficient matrix of the perturbation variable in the expansion as the tangent space Jacobian matrix; perform matrix multiplication operation with the tangent vector at the previous moment using the tangent space Jacobian matrix to calculate the predicted value of the tangent vector at the current moment; perform Gram-Schmidt orthogonalization and normalization processing on the predicted value of the tangent vector at the current moment to obtain the corrected tangent vector; calculate the magnitude of the corrected tangent vector, and take the natural logarithm of the magnitude as the instantaneous logarithmic growth rate at the current moment; calculate the time average of the instantaneous logarithmic growth rates at each moment to obtain the local Lyapunov exponent;

[0028] S42. Extract the potential energy value of the continuous scalar field at the current generalized coordinates, perform gradient operation on the potential energy value with respect to the generalized coordinates to obtain the potential energy gradient vector, calculate the Euclidean norm of the potential energy gradient vector to obtain the gradient magnitude, compare the gradient magnitude with the preset minimum value, if the gradient magnitude is less than the preset minimum value, add the current timer value to the unit time as the local extreme value dwell time.

[0029] S43. Read the local Lyapunov exponent and the local extreme value dwell time, compare the local Lyapunov exponent with the preset chaos threshold, compare the local extreme value dwell time with the preset dwell threshold, and output the comparison result.

[0030] S44. Based on the comparison results, if the local Lyapunov exponent is greater than the preset chaos threshold, or the local extreme value dwell time is greater than the preset dwell time threshold, generate the domain constraint conflict determination result.

[0031] Optionally, S5 specifically includes:

[0032] S51. Receive the domain constraint conflict determination result, lock the moment when the domain constraint conflict is determined as the endpoint of the inverse integration, take the generalized coordinates and generalized momentum at the moment of conflict as the initial values, set the inverse time step, perform numerical inverse integration on the Hamiltonian equation, and backtrack the historical trajectory data of the semantic particle before entering the conflict region.

[0033] S52. Collect the generalized momentum vectors at discrete time points along the historical trajectory data, calculate the difference between the generalized momentum vectors at adjacent time points as the instantaneous impulse vector, perform vectorized summation on all instantaneous impulse vectors, and generate the semantic particle cumulative impulse vector.

[0034] S53. Extract the background semantic feature vector in the static semantic environment, perform L2 norm normalization on the cumulative impulse vector of semantic particles to obtain the unit impulse direction vector, and calculate the inner product of the unit impulse direction vector and the background semantic feature vector as the semantic similarity projection value; perform multiplication operation on the semantic similarity projection value and the original magnitude value of the cumulative impulse vector to generate a weighted impulse scalar; use the weighted impulse scalar as the first dimension feature, the original magnitude value of the cumulative impulse vector as the second dimension feature, and the semantic similarity projection value as the third dimension feature, and concatenate them to generate a multi-dimensional semantic impact key feature vector.

[0035] Optionally, S6 specifically includes:

[0036] S61. Extract the generalized velocity sequence and generalized acceleration sequence at the moment of conflict. Use the generalized mass matrix to perform a left multiplication operation on the generalized acceleration sequence to generate an inertial force vector sequence. Subtract the conservative force vector sequence to obtain a random force sequence. Perform time window averaging on the generalized velocity sequence to calculate the mean vector as the drift term. Use the random force sequence to calculate the autocorrelation matrix with zero delay time as the diffusion term. Establish a linear regression model between the drift term and the generalized velocity, with the drift term vector as the dependent variable and the generalized velocity vector as the independent variable. Use the least squares method to analyze the regression coefficient matrix as the linear damping coefficient matrix. Calculate the sum of the diagonal elements of the autocorrelation matrix as the diffusion coefficient scalar. Divide the diffusion coefficient scalar by the product of the sum of the diagonal elements of the linear damping coefficient matrix and the Boltzmann constant, and perform a division operation to invert and obtain the equivalent temperature. Multiply the sum of the diagonal elements of the linear damping coefficient matrix and the equivalent temperature by the preset first weight coefficient and second weight coefficient, respectively. Add the two product results to calculate and output the semantic dissipation coefficient.

[0037] S62. Starting from the generalized coordinates at the moment of conflict determination, perform gradient ascent iteration on the current potential energy surface until the gradient minimum point is reached. Mark the gradient minimum point as the saddle point position, extract the potential energy value at the saddle point position and the potential energy value at the moment of conflict determination, calculate the difference of potential energy values, and output the potential energy surface height difference.

[0038] S63. Receive the semantic dissipation coefficient and the potential energy surface height difference, use the semantic dissipation coefficient and the potential energy surface height difference as input variables of the Kramers escape rate formula, inversely solve the non-equilibrium work value in the escape process, and output the minimum work required for the semantic particle to escape from the current potential energy trap.

[0039] Optionally, S7 specifically includes:

[0040] S71. Calculate the system kinetic energy based on the generalized mass matrix and generalized velocity vector. Determine the potential energy value based on the current potential energy surface. Construct a Lagrangian function by subtracting the potential energy value from the system kinetic energy. Calculate the partial derivative of the Lagrangian function with respect to the generalized velocity vector to construct a generalized momentum vector. Perform a Legendre transformation to replace the generalized velocity variable with the generalized momentum variable, and calculate the initial Hamiltonian. Introduce the minimum work required for a semantic particle to escape the current potential energy trap as a performance index weight. Construct a quadratic cost function for the modified external force vector. Perform a dot product operation on the modified external force vector and the generalized velocity vector to obtain the work power term. Take the negative of the work power term to construct the control Lagrangian term. Add the quadratic cost function, the initial Hamiltonian, and the control Lagrangian term to obtain the augmented Hamiltonian function containing the modified external force and minimum work weights.

[0041] S72. Read the generalized coordinates and generalized momentum at the moment of conflict as the initial state conditions, extract the generalized coordinates of the lowest potential energy point in the target domain as the terminal state constraint, introduce co-state variables and calculate the partial derivatives of the augmented Hamiltonian function with respect to the generalized coordinates to obtain the co-state equation, calculate the gradient vector of the augmented Hamiltonian function with respect to the modified external force vector and set the gradient vector as the zero vector to obtain the stationary point condition.

[0042] S73. By simultaneously solving the Hamiltonian canonical equation, costate equation, and stationary point condition, a system of nonlinear differential-algebraic equations concerning generalized coordinates, generalized momentum, and costate variables is obtained. A two-point boundary value problem is constructed based on the two-endpoint constraints at the moment of conflict and the point of lowest potential energy in the target area. Random initial values ​​of the costate variables are set at the initial moment. Forward numerical integration is performed on the state variables and costate variables on the discrete time axis. In each step of integration, the modified external force vector is explicitly solved using the algebraic stationary point condition. The difference vector between the generalized coordinates at the end of the integration and the generalized coordinates of the point of lowest potential energy in the target area is calculated as the target residual. The Jacobian matrix of the target residual with respect to the initial costate variables is calculated. The product of the inverse of the Jacobian matrix and the target residual is used to construct the modified step size of the initial value of the costate variables. The initial costate variables are updated, and numerical integration and residual calculation are re-executed. The iterative process is repeated until the Euclidean norm of the target residual converges to the preset threshold. The converged modified external force time series is output as the optimal control force sequence.

[0043] S74. Extract the semantic impact key feature vector, map the values ​​of each dimension of the semantic impact key feature vector to the diagonal elements of the linear transformation coefficient matrix, construct the linear transformation coefficient matrix, perform matrix multiplication operation on the entity attribute feature vector and the linear transformation coefficient matrix, and calculate and generate the corrected entity feature vector.

[0044] S75. The optimal control force sequence is input into the Langevin equation as the driving force. The modified entity feature vector is used as the starting state. Time-step iterative numerical solution is performed to predict the dynamic evolution trajectory of semantic particles under the modified external force.

[0045] The beneficial effects of this invention are:

[0046] (1) This invention effectively solves the problems of static representation of entity states and lack of causal dynamic mechanisms in existing knowledge graphs by constructing a semantic dynamic evolution process based on Hamiltonian equations. This invention innovatively introduces analytical mechanics concepts such as generalized mass, generalized coordinates, and potential energy surfaces, mapping entity attribute frequencies to inertial parameters and accurately describing the trajectory of semantic particles through Hamiltonian canonical equations. This invention can reveal the evolution process of entity states from the perspective of physical dynamics, providing a dynamic physical perspective for understanding deep semantic relationships, and significantly enhancing the accuracy and physical interpretability of knowledge graphs in describing entity dynamic behavior.

[0047] (2) By establishing a conflict determination mechanism based on dynamic stability analysis, this invention uses the Jacobian matrix of the tangent space to calculate the local Lyapunov exponent, which can quantify the sensitivity of semantic trajectories to initial disturbances to identify chaotic states. At the same time, it combines the potential energy gradient to determine the dwell time of local extreme points, thus realizing accurate identification of domain constraint conflicts from the perspective of system stability. This breaks through the limitations of traditional static logic checks and greatly improves the detection rate and reliability of implicit conflicts in complex semantic environments.

[0048] (3) This invention obtains the optimal control force sequence by solving a two-point boundary value problem containing the minimum work weight, realizing an entity state correction process based on the thermodynamic optimality principle. It uses the Langevin equation to invert the semantic dissipation coefficient and equivalent temperature, and combines the Cramers escape rate formula to calculate the minimum work required to escape the current potential energy trap, and sets the minimum work as the performance index weight of the optimal control system. By constructing an augmented Hamiltonian function and using the shooting method to solve the two-point boundary value problem, this invention can plan the optimal trajectory that drives the entity to escape the conflict region and converge to the target domain with the minimum energy consumption, ensuring the energy efficiency and state stability of the correction process, and providing a scientific quantitative control method for the adaptive repair of knowledge graphs. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0050] Figure 1 This is an overall flowchart of a knowledge graph entity domain conflict detection method based on semantic processing proposed in this invention;

[0051] Figure 2 This is a flowchart illustrating the working principle of the conflict determination mechanism based on dynamic stability analysis in the knowledge graph entity domain conflict detection method based on semantic processing proposed in this invention. Detailed Implementation

[0052] The invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0053] refer to Figure 1 and Figure 2 A knowledge graph entity domain conflict detection method based on semantic processing, specifically including:

[0054] S1. Generate static semantic feature vectors by semantically encoding entity attribute features, set attribute frequency values ​​as quality parameters, and combine static semantic feature vectors and quality parameters to output semantic particles.

[0055] S2. Map the knowledge graph category labels to a potential energy surface using the radial basis kernel function, calculate the negative vector of the potential energy gradient to determine the semantic gradient force, and calculate the vector from the semantic particle to the category cluster center to determine the domain gravity.

[0056] S3. Substitute the semantic gradient force and the domain gravity into the Hamiltonian equation to iteratively solve the rate of change of the generalized coordinates and generalized momentum of the semantic particle, update the spatial position of the semantic particle, and output the dynamic state sequence.

[0057] S4. Calculate the tangent space Jacobian matrix of the dynamic state sequence and the local Lyapunov exponent of the semantic particle. If the local Lyapunov exponent is greater than zero or the semantic particle stays at the local extreme point for a longer time than the threshold, then it is determined that there is a conflict of domain constraints.

[0058] S5. Perform reverse time integration on the Hamiltonian equation with the moment when the domain constraint conflict is determined as the endpoint, calculate the dot product of the cumulative impulse vector of the semantic particle and the static semantic feature vector, and generate the semantic impact key feature vector.

[0059] S6. Based on the dynamic state at the moment when there is a conflict in the domain constraint, the friction coefficient and temperature are calculated using the parameters of the Langevin equation to determine the semantic dissipation coefficient. The minimum work required for the semantic particle to escape from the current potential energy trap is calculated by combining the semantic dissipation coefficient and the height difference of the potential energy surface.

[0060] S7. Construct a controlled Hamiltonian system with modified external forces, using the minimum work done to escape the potential energy trap as the performance index weight, and solve the two-point boundary value problem based on the two-endpoint constraints at the moment of conflict and the point of lowest target potential energy to obtain the optimal control force sequence; use semantic impact key feature vectors to construct a transformation matrix to modify entity attribute features.

[0061] In this embodiment, S1 specifically includes:

[0062] S11. Extract the name and numerical information from the entity attribute features. Map the discrete attribute names to unique word vector IDs using a hash index. Extract low-dimensional dense word embeddings through lookup table operations. Simultaneously, perform Z-score standardization on continuous numerical attributes to eliminate dimensional differences. Statistically analyze the word frequency distribution of the attributes in the entire set. Substitute the values ​​into the logarithmic frequency transformation formula to calculate the reciprocal of the frequency. Map the calculation results to continuous semantic weight parameters. Perform tensor concatenation between the dense word embeddings and the standardized numerical vectors in the feature dimension to generate a high-dimensional static semantic feature vector. Construct a diagonal matrix using the continuous semantic weight parameters as diagonal elements. Define the diagonal matrix as a generalized mass matrix. Perform matrix-vector multiplication on the generalized mass matrix and the static semantic feature vector to generate an inertial weighted feature vector. Initialize the inertial weighted feature vector to the generalized coordinates of semantic particles, realizing the transformation of entity attribute features into mass parameters and generalized coordinates.

[0063] S12. For the generated static semantic feature vector, traverse the knowledge graph dataset to count the frequency of each attribute feature in the whole set, smooth the obtained word frequency values, substitute the word frequency values ​​into the preset logarithmic frequency transformation formula to calculate the reciprocal of the frequency, linearly map the calculated reciprocal of the frequency values ​​to the range of 0.1 to 1.0, define the mapped values ​​as continuous semantic weight parameters, the continuous semantic weight parameters are used to characterize the importance of attributes in different contexts, and convert the attribute frequency into a calculable physical quality parameter.

[0064] S13. Perform a head-to-tail concatenation operation on the feature dimension of the dense word embedding and standardized numerical vector obtained in the previous step to generate a high-dimensional static semantic feature vector with uniform dimension. Read the continuous semantic weight parameters and use the continuous semantic weight parameters as the main diagonal elements to construct a square diagonal matrix with the value of 0 and the rest of the elements being 0. Define the square diagonal matrix as the generalized mass matrix. Calculate the product of the generalized mass matrix and the static semantic feature vector to generate an inertial weighted feature vector. Directly assign the inertial weighted feature vector to the generalized coordinates in the initial state to complete the initialization of the physical properties of the semantic particles and establish the mapping relationship from attribute features to dynamic state variables.

[0065] In this embodiment, S2 specifically includes:

[0066] S21. Read the category label text from the knowledge graph, convert the category label text into a 128-dimensional vector through vectorization encoding, input the vector into a radial basis function kernel network containing 64 hidden nodes, set the Gaussian kernel bandwidth parameter of the radial basis function kernel network to 2.0, calculate the Euclidean distance between the vector and the center point of each hidden node, divide the square of the Euclidean distance by the square of the Gaussian kernel bandwidth parameter, perform a negative operation on the calculation result, and then perform a natural exponentiation operation on the negative result to obtain 64 Gaussian kernel response values, and sum the 64 Gaussian kernel response values ​​according to their weights to construct a continuous scalar field;

[0067] S22. Read the constructed continuous scalar field, perform multivariable differentiation operation on the continuous scalar field along the coordinate axis, calculate the first-order partial derivative of the potential energy function with respect to each generalized coordinate component, arrange the calculated first-order partial derivatives in the order of the coordinate axes to form the original gradient vector, perform element-wise inversion operation by multiplying each element in the original gradient vector by negative 1, and define the vector after inversion operation as the semantic gradient force. The semantic gradient force is used to drive the semantic particles to move towards the lower potential energy.

[0068] S23. Obtain the generalized coordinate values ​​of the semantic particle at the current moment and the cluster center coordinate values ​​of the target category. Calculate the difference between the generalized coordinate values ​​and the cluster center coordinate values ​​to obtain the difference vector. Calculate the L2 norm value of the difference vector. Divide each element in the difference vector by the L2 norm value to obtain the normalized vector. Multiply each element of the normalized vector by the gravity coefficient 0.8. The calculation result generates the domain gravity, which is used to pull the semantic particle toward the correct category center.

[0069] In this embodiment, S3 specifically includes:

[0070] S31. Read the generalized mass matrix of the semantic particle, calculate the inverse matrix of the generalized mass matrix, calculate the tensor product of the generalized momentum vector and the inverse matrix to obtain the transformed vector, calculate the inner product value of the transformed vector and the current generalized momentum vector, divide the inner product value by the constant 2 to generate the kinetic energy scalar value, extract the value of the continuous scalar field at the current generalized coordinates as the potential energy scalar value, perform a summation operation on the kinetic energy scalar value and the potential energy scalar value to construct the Hamiltonian;

[0071] S32. Read the constructed Hamiltonian, perform partial differential operations on each component of the generalized momentum vector using the Hamiltonian, calculate the rate of change of the generalized coordinates with time, substitute the rate of change of the generalized coordinates with time into the differential equation, and analytically obtain the time derivative expression of the generalized coordinates. At the same time, perform partial differential operations on each component of the generalized coordinate vector using the Hamiltonian, multiply the partial differential operation result by -1, calculate the rate of change of the generalized momentum with time, substitute the rate of change of the generalized momentum with time into the differential equation, and analytically obtain the time derivative expression of the generalized momentum.

[0072] S33. Set the iteration time step to 0.01 seconds. Calculate the product of the inverse of the generalized mass matrix and the current generalized momentum vector as the rate of change of the generalized coordinates with time. Multiply the rate of change of the generalized coordinates with time by the iteration time step and add it to the generalized coordinates at the current time to update the generalized coordinates at the next time step. Based on the updated generalized coordinates at the next time step, calculate the gradient vector of the continuous scalar field as the rate of change of the generalized momentum with time. Multiply the rate of change of the generalized momentum with time by the iteration time step and add it to the generalized momentum at the current time to update the generalized momentum at the next time step.

[0073] S34. The generalized coordinate values ​​and generalized momentum values ​​calculated at each moment during the iteration process are indexed and combined in chronological order to generate a dynamic state sequence.

[0074] In this embodiment, S4 specifically includes:

[0075] S41. Extract the generalized coordinate and generalized momentum values ​​from the dynamic state sequence, construct the orthogonal normalized tangent vector basis of the state transition trajectory, perform a first-order Taylor expansion of the state transition equation at the current moment with respect to the generalized coordinates and generalized momentum, extract the linear coefficient matrix of the perturbation variable in the expansion as the tangent space Jacobian matrix, perform matrix multiplication operation with the tangent vector at the previous moment using the tangent space Jacobian matrix, calculate the predicted value of the tangent vector at the current moment, perform Gram-Schmidt orthogonalization and normalization processing on the predicted value of the tangent vector at the current moment to obtain the corrected tangent vector, calculate the second norm modulus of the corrected tangent vector, perform natural logarithmic operation on the second norm modulus to obtain the instantaneous logarithmic growth rate at the current moment, perform time averaging of the instantaneous logarithmic growth rate at each moment to obtain the local Lyapunov exponent;

[0076] S42. Extract the potential energy value of the continuous scalar field at the current generalized coordinates, perform gradient operation on the potential energy value with respect to the generalized coordinates to obtain the potential energy gradient vector, calculate the Euclidean norm of the potential energy gradient vector to obtain the gradient magnitude, compare the gradient magnitude with the preset minimum value 0.001, if the gradient magnitude is less than 0.001, add the current timer value to the unit duration of 0.1 seconds as the local extreme value dwell time;

[0077] S43. Read the local Lyapunov exponent and the duration of local extrema, compare the local Lyapunov exponent with the preset chaos threshold of 0, compare the duration of local extrema with the preset dwell threshold of 5.0 seconds, and output the comparison results.

[0078] S44. Based on the comparison results, if the local Lyapunov exponent is greater than 0, or the local extreme value dwell time is greater than 5.0 seconds, generate the domain constraint conflict determination result.

[0079] The conflict determination mechanism based on dynamic stability analysis proposed in this step is similar to the traditional constraint checking method based on static logic verification in that both aim to identify erroneous states in the knowledge graph that violate specific rules or consistency requirements. That is, they detect anomalies by comparing the detection of entity state characteristics with standard conditions, and both use a preset threshold as the benchmark for determining whether a violation has occurred.

[0080] The difference lies in that this invention breaks through the limitations of traditional methods that rely solely on discrete logic matching or static feature distance deviation for error detection. Building upon the traditional model's direct calculation of feature deviation, this invention adds a tangent space Jacobian matrix construction step. This linearizes the state transition trajectory in the tangent space, extracting a linear coefficient matrix for the perturbation variable, rather than relying on simple numerical comparisons. In the feature evolution analysis step, the instantaneous logarithmic growth rate is calculated using the tangent vector magnitude after Gram-Schmidt orthogonalization, and the local extremum dwell time is monitored in conjunction with the potential energy gradient magnitude, rather than through simple rule triggering. Finally, in the conflict determination output step, the neighborhood constraint conflict determination result is jointly generated based on conditions such as a local Lyapunov exponent greater than zero or a dwell time exceeding a threshold, rather than a single state value judgment.

[0081] The beneficial effects of this improvement are that, through tangent space Jacobian matrix analysis and local Lyapunov exponent calculation, the chaotic characteristics of semantic trajectories and their sensitivity to initial perturbations can be incorporated into conflict considerations. This breaks through the limitations of traditional methods in capturing implicit instability in complex dynamic evolution processes, achieving a leap from static logic verification to dynamic system stability analysis. This design significantly enhances the system's ability to perceive deep semantic conflicts, enabling more accurate identification of implicit conflicts that appear logically reasonable but diverge in dynamic trends. The combined judgment mechanism of potential energy gradient monitoring and dwell time statistics effectively improves the reliability and comprehensiveness of conflict detection, enhancing the security and self-diagnostic accuracy of the knowledge graph in dynamic semantic environments.

[0082] In this embodiment, S5 specifically includes:

[0083] S51. Receive the domain constraint conflict determination result, lock the moment when the domain constraint conflict is determined as the endpoint of the reverse integration, take the generalized coordinate value and generalized momentum value at the moment of conflict as the initial value, set the reverse time step value to 0.05 seconds, perform numerical reverse integration on the Hamiltonian equation, reverse the motion state of the semantic particle along the time axis, and trace back the historical trajectory data of the semantic particle before entering the conflict area.

[0084] S52. Collect the generalized momentum vector at discrete time points along the historical trajectory data obtained by backtracking at intervals of 0.1 seconds. Calculate the difference between the generalized momentum vector at the next time point and the generalized momentum vector at the previous time point as the instantaneous impulse vector. Perform vectorized summation operation on the instantaneous impulse vectors at all collection times to generate the semantic particle cumulative impulse vector.

[0085] S53. Extract the background semantic feature vector in the static semantic environment, perform L2 norm normalization on the cumulative impulse vector of semantic particles to obtain the unit impulse direction vector, calculate the inner product of the unit impulse direction vector and the background semantic feature vector as the semantic similarity projection value, perform multiplication operation on the semantic similarity projection value and the original magnitude value of the cumulative impulse vector to generate a weighted impulse scalar, use the weighted impulse scalar as the first dimension feature, use the original magnitude value of the cumulative impulse vector as the second dimension feature, use the semantic similarity projection value as the third dimension feature, and concatenate them to generate a multi-dimensional semantic impact key feature vector.

[0086] In this embodiment, S6 specifically includes:

[0087] S61. Extract the generalized velocity sequence and generalized acceleration sequence values ​​at the moment of conflict determination. Use the generalized mass matrix to perform a left multiplication operation on the generalized acceleration sequence values ​​to generate an inertial force vector sequence value. Subtract the conservative force vector sequence values ​​from the inertial force vector sequence values ​​to obtain the random force sequence values. Perform an average calculation on the generalized velocity sequence values ​​with a time window length of 1 second to obtain the mean vector as the drift term. Use the random force sequence values ​​to calculate the autocorrelation matrix with a zero delay time as the diffusion term. Establish a linear regression model between the drift term and the generalized velocity. Then, use the drift term vector... As the dependent variable, the generalized velocity vector is used as the independent variable. The least squares method is used to analyze the regression coefficient matrix as the linear damping coefficient matrix. The sum of the diagonal elements of the autocorrelation matrix is ​​calculated as the diffusion coefficient scalar. The diffusion coefficient scalar is divided by the product of the sum of the diagonal elements of the linear damping coefficient matrix and the Boltzmann constant 1.38. The equivalent temperature is obtained by performing the division operation. The sum of the diagonal elements of the linear damping coefficient matrix and the equivalent temperature are multiplied by the preset first weighting coefficient 0.5 and the second weighting coefficient 0.5, respectively. The two product results are added together to calculate and output the semantic dissipation coefficient.

[0088] S62. Starting from the generalized coordinate value at the moment of conflict determination, perform gradient ascent iteration on the current potential energy surface. The upper limit of the number of iterations is set to 500 times until the gradient minimum point is reached. Mark the gradient minimum point as the saddle point position, extract the potential energy value at the saddle point position and the potential energy value at the moment of conflict determination, calculate the difference between the two potential energy values, and output the potential energy surface height difference.

[0089] S63. Receive the semantic dissipation coefficient and the potential energy surface height difference value, read the preset Boltzmann constant value, calculate the friction damping ratio by dividing the semantic dissipation coefficient value by the Boltzmann constant value, divide the potential energy surface height difference value by the ambient temperature value of 300 Kelvin to obtain the normalized potential energy ratio value, search the pre-stored Kramers escape rate calculation table, look up the corresponding baseline escape rate value in the table using the friction damping ratio and the normalized potential energy ratio, divide the baseline escape rate value by the natural constant value of 2.718 to obtain the reciprocal of the average escape time, multiply the natural constant value of 2.718 by the normalized potential energy ratio to calculate the exponential term value, divide the exponential term value by the reciprocal of the average escape time to calculate the intermediate variable value, perform a natural logarithmic operation on the intermediate variable value, and the calculation result is the non-equilibrium work value during the escape process. Output the minimum work done by the semantic particle to escape the current potential energy trap.

[0090] The semantic potential well escape analysis process based on non-equilibrium thermodynamics proposed in this step is similar to the conventional stability discrimination method based on static energy barrier in that both are based on the theory of phase transition and energy transition of thermodynamic systems. That is, by analyzing the energy state and dynamic environment parameters of particles on the potential energy surface, the possibility of the system undergoing a state change is assessed by using energy thresholds or dynamic coefficients, and both use numerical calculations to quantify the stability index of the system.

[0091] The difference lies in that this invention breaks through the limitations of traditional methods that calculate escape probability solely based on static potential energy differences or ignore the influence of dissipative environments on dynamic processes. Building upon the traditional model's direct calculation of potential energy differences, this invention adds a stochastic dynamics inversion step. By extracting generalized velocity sequences and stochastic force sequences, it calculates drift and diffusion terms and inverts the linear damping coefficient matrix and equivalent temperature, rather than relying solely on energy values. In the parameter calculation step, the semantic dissipation coefficient is jointly calculated using the diffusion coefficient scalar and the sum of the diagonal elements of the linear damping coefficient matrix, and a weighted summation operation is performed, rather than a simple potential energy gradient calculation. Finally, in the minimum work output step, based on the semantic dissipation coefficient and the potential energy surface height difference, and combined with Kramers escape rate theory, the non-equilibrium work done during the escape process is inverted and solved, rather than relying solely on the escape time.

[0092] The beneficial effects of this improvement are that, through stochastic dynamic parameter inversion and semantic dissipation coefficient calculation, the damping characteristics and thermal fluctuation features of the semantic environment can be integrated into the conflict resolution assessment. This breaks through the limitation of traditional methods that ignore environmental heat bath and frictional dissipation in non-equilibrium processes, leading to inaccurate assessments. It achieves a precise mapping from static energy determination to dynamic non-equilibrium work analysis. This design significantly enhances the system's ability to perceive the semantic conflict evolution mechanism and can more accurately quantify the energy cost and work threshold required for semantic particles to escape from the erroneous state. The minimum work calculation based on Kramers escape rate theory effectively improves the completeness of semantic repair cost prediction and enhances the scientificity and practicality of conflict resolution strategies for knowledge graphs in complex dynamic semantic environments.

[0093] In this embodiment, S7 specifically includes:

[0094] S71. Calculate the system's kinetic energy value based on the generalized mass matrix and generalized velocity vector. Determine the potential energy value based on the current potential energy surface. Construct a Lagrangian function by subtracting the potential energy value from the system's kinetic energy value. Calculate the partial derivative of the Lagrangian function with respect to the generalized velocity vector to construct a generalized momentum vector. Perform a Legendre transformation to replace the generalized velocity variable with the generalized momentum variable. Calculate the initial Hamiltonian. Introduce the minimum work required for the semantic particle to escape the current potential energy trap as a performance index weight. Construct a quadratic cost function for the modified external force vector. Perform a dot product operation between the modified external force vector and the generalized velocity vector to obtain the work power term. Take the negative of the work power term to construct a control Lagrangian term. Add the quadratic cost function, the initial Hamiltonian, and the control Lagrangian term to obtain an augmented Hamiltonian function containing the modified external force and minimum work weight.

[0095] S72. Read the generalized coordinate values ​​and generalized momentum values ​​at the moment of conflict as the initial state conditions, extract the generalized coordinate values ​​of the lowest potential energy point in the target field as the terminal state constraints, introduce co-state variables and calculate the partial derivatives of the augmented Hamiltonian function with respect to the generalized coordinate values ​​to obtain the co-state equation, calculate the gradient vector of the augmented Hamiltonian function with respect to the modified external force vector and set the gradient vector to zero to obtain the stationary point conditions.

[0096] S73. By simultaneously solving the Hamiltonian canonical equation, costate equation, and stationary point condition, a system of nonlinear differential-algebraic equations concerning generalized coordinates, generalized momentum, and costate variables is obtained. A two-point boundary value problem is constructed based on the two-endpoint constraints at the moment of conflict and the target point of minimum potential energy. Random initial values ​​of the costate variables are set at the initial moment. Forward numerical integration is performed on the state variables and costate variables on the discrete time axis. In each step of integration, the modified external force vector is explicitly solved using the algebraic stationary point condition. The difference vector between the generalized coordinate values ​​at the end of the integration and the generalized coordinate values ​​at the target point of minimum potential energy is calculated as the target residual. The Jacobian matrix of the target residual with respect to the initial costate variables is calculated. The product of the inverse of the Jacobian matrix and the target residual is used to construct the modified step size of the initial value of the costate variables. The initial costate variables are updated, and numerical integration and residual calculation are re-executed. The iterative process is repeated until the Euclidean norm of the target residual converges to the preset threshold of 0.001. The converged modified external force time series is output as the optimal control force sequence.

[0097] S74. Extract the semantic impact key feature vector, map the values ​​of each dimension of the semantic impact key feature vector to the diagonal elements of the linear transformation coefficient matrix, construct the linear transformation coefficient matrix, perform matrix multiplication operation on the entity attribute feature vector and the linear transformation coefficient matrix, and calculate and generate the corrected entity feature vector.

[0098] S75. The optimal control force sequence is input into the Langevin equation as the driving force. The modified entity feature vector is used as the starting state. Time-step iterative numerical solution is performed to predict the dynamic evolution trajectory of semantic particles under the modified external force.

[0099] The conflict resolution process based on Hamiltonian optimal control and stochastic dynamics proposed in this step is similar to traditional knowledge graph repair methods based on graph neural network link prediction or entity reordering in that both aim to eliminate erroneous states or conflicting relationships in the knowledge graph by calculating and applying corrections. That is, by constructing an objective function and using optimization algorithms to solve for the optimal correction strategy, and both adopt numerical iteration to gradually approach the ideal conflict-free state.

[0100] The difference lies in that this invention breaks away from the limitations of traditional methods that only update node vectors based on graph topological features or ignore the energy cost of physical evolution. Building upon the traditional model's direct calculation of feature similarity loss, this invention adds an augmented Hamiltonian function construction step, introducing a quadratic cost function with minimum work weight and a control Lagrange term. This unifies kinetic energy, potential energy, and corrected external force work within a physical framework, rather than simply calculating graph embedding distance loss. In the optimization solution step, a system of nonlinear differential-algebraic equations is established regarding generalized coordinates, generalized momentum, and costate variables. The initial values ​​of the costate variables are corrected using the inverse Jacobian matrix, and the two-point boundary value problem is solved using a shooting method, rather than simple gradient descent updates. Finally, in the trajectory prediction step, a corrected entity feature vector is generated based on the linear transformation coefficient matrix and used as the initial state input to the Langevin equation for evolution under optimal control, rather than directly outputting the corrected static vector.

[0101] The beneficial effects of this improvement are that, by constructing an augmented Hamiltonian and solving the two-point boundary value problem, the physical energy constraints of semantic evolution and the cost of non-equilibrium work are integrated into the conflict resolution process. This breaks the limitation of traditional methods that ignore the physical rationality of state transition paths, resulting in repair results that do not conform to dynamic laws. It achieves a precise mapping from static feature approximation to dynamic optimal trajectory planning. This design significantly enhances the system's ability to control the semantic evolution path, and can accurately guide semantic particles out of the conflict potential trap with minimal energy consumption. The dynamic evolution prediction based on the Langevin equation effectively improves the adaptability of the repair process to environmental noise and random disturbances, and enhances the repair accuracy and evolution predictability of the knowledge graph in complex dynamic environments.

[0102] Example 1: To verify the feasibility of this invention in dynamic reasoning and conflict resolution of complex knowledge graphs, the method of this invention was applied to the large-scale general-domain knowledge graph intelligent operation and maintenance platform of an artificial intelligence technology company (hereinafter referred to as "Company Q"). In traditional knowledge graph reasoning and correction systems, logical rule reasoning based on static graph embedding vectors or link prediction algorithms based on deep learning are usually used. These methods not only struggle to accurately capture the dynamic characteristics of entity states in complex dynamic evolution backgrounds, but also fail to accurately quantify the physical mechanisms and repair paths of semantic conflicts, easily leading to misjudgments of implicit deep associations or failure of correction strategies. To solve the above problems, Company Q decided to adopt the knowledge graph entity domain conflict detection method based on semantic processing proposed in this invention.

[0103] During implementation, Company Q first utilized a web crawler system and multi-source heterogeneous databases to acquire entity attribute sequences and relationship change data within the domain. After preprocessing operations such as generalized coordinate mapping, generalized mass matrix construction, and potential field fitting, a semantic dynamic state sequence containing kinetic and potential energy terms was constructed. Simultaneously, Company Q's domain experts precisely labeled the collected historical conflict data with conflict types and calibrated potential energy thresholds, serving as a benchmark for model training and dynamic parameter inversion.

[0104] Company Q extracts the generalized velocity and momentum vectors representing semantic trends using the Hamiltonian canonical equation solving module, and calculates the local Lyapunov exponent using the tangent space Jacobian matrix. Next, it uses the reverse integration backtracking module to fuse the cumulative impulse features, generating a key semantic impact feature vector that effectively eliminates irrelevant semantic noise in the background. Subsequently, the feature vector is input into a stochastic dynamics inversion model. Through analytical analysis of the linear damping coefficient matrix and scalar calculation of the diffusion term, a semantic dissipation coefficient adapted to the non-equilibrium environment is constructed. Combined with Kramers escape rate theory, an adaptive weighted solution is performed, outputting an accurate minimum work value for the semantic particle to escape the potential energy trap.

[0105] In the core repair and prediction phase, this invention simultaneously decouples the optimal control force sequence and the corrected entity feature vector through the augmented Hamiltonian function construction and two-point boundary value problem solving modules, achieving optimal path planning and entity attribute correction for conflict state repair. Subsequently, the Langevin equation evolution discriminator, based on the preset target minimum potential energy constraint and driven by the optimal control force sequence, generates a dynamic evolution trajectory and determines the stability after repair. Finally, the system maps the repair results to the knowledge graph management terminal and downstream recommendation system, triggering update strategies based on the degree of conflict dissipation, thus achieving a closed-loop transition from dynamic detection to physical repair.

[0106] During implementation, the technical team at Company Q discovered that, compared to traditional manual review and conventional static reasoning methods, the method of this invention significantly improves the accuracy of deep conflict detection and the physical interpretability of the repair in knowledge graphs. Traditional methods cannot quantify the dynamic stability of semantic evolution and are poor at recognizing semantic drift caused by implicit conflicts. In contrast, the method of this invention effectively achieves dynamic perception and minimum energy consumption repair of complex semantic conflicts through Lyapunov exponential chaos recognition, impulse feature inversion, and Hamiltonian optimal control repair.

[0107] To further verify the actual performance of the method of the present invention, Company Q conducted a detailed comparative test between the method of the present invention and the traditional method. The specific performance data is shown in Table 1:

[0108] Table 1. Performance Comparison of Constraint Conflict Resolution Methods in Company Q's Knowledge Graph Domain

[0109] index Traditional methods Method of the present invention Increase Accuracy rate of latent conflict identification (%) 75.6 94.2 +18.6% Conflict cause misjudgment rate (%) 15.8 2.1 -86.7% Semantic evolution trajectory prediction bias (%) 22.5 4.3 -80.9% Number of missed detections in complex scenarios (times / thousand records) 45.0 3.5 -92.2% Calculation time (milliseconds) for conflict resolution path 320 110 -65.6% Recurrence rate after repair (%) 18.4 1.5 -91.8% Semantic consistency of entity features (%) 81.0 96.5 +15.5% Automatic map update accuracy (%) 83.2 97.8 +14.6% Cost of manual intervention (person-hours / week) 120 40 -66.7% System maintenance satisfaction (%) 88.0 98.0 +10.0%

[0110] As shown in Table 1, the performance of the constraint conflict handling system in the knowledge graph domain was comprehensively improved after applying the method of this invention. The accuracy of implicit conflict identification increased from 75.6% of the traditional method to 94.2%, and the semantic evolution trajectory prediction bias decreased from 22.5% to 4.3%, significantly improving the accuracy of state evolution perception and providing a reliable basis for subsequent conflict repair. The misjudgment rate of conflict causes decreased from 15.8% to 2.1%, effectively avoiding erroneous graph correction operations. The calculation time for conflict repair paths was significantly shortened from 320 milliseconds to 110 milliseconds, significantly enhancing the system's timeliness. In addition, the recurrence rate of states after repair decreased from 18.4% to 1.5%, and the cost of manual correction intervention decreased from 120 person-hours / week to 40 person-hours / week, significantly reducing operation and maintenance costs. System operation and maintenance satisfaction also significantly improved, from 88.0% to 98.0%.

[0111] Through the method of this invention, Company Q has successfully achieved dynamic-level accurate detection and optimal repair based on physical constraints for deep semantic conflicts in knowledge graphs. This effectively reduces business risks caused by erroneous reasoning, ensures the logical consistency of knowledge graph data, significantly improves the intelligence and physical interpretability of knowledge graph operation and maintenance, significantly reduces the workload of data governance personnel, enhances the stability and robustness of the monitoring system, and provides strong technical support for the construction of next-generation cognitive intelligence.

[0112] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A knowledge graph entity domain conflict detection method based on semantic processing, characterized in that, Includes the following steps: S1. Generate static semantic feature vectors by semantically encoding entity attribute features, set attribute frequency values ​​as quality parameters, and combine static semantic feature vectors and quality parameters to output semantic particles. S2. Map the knowledge graph category labels to a potential energy surface using the radial basis kernel function, calculate the negative vector of the potential energy gradient to determine the semantic gradient force, and calculate the vector from the semantic particle to the category cluster center to determine the domain gravity. S3. Substitute the semantic gradient force and the domain gravity into the Hamiltonian equation to iteratively solve the rate of change of the generalized coordinates and generalized momentum of the semantic particle, update the spatial position of the semantic particle, and output the dynamic state sequence. S4. Calculate the tangent space Jacobian matrix of the dynamic state sequence and the local Lyapunov exponent of the semantic particle. If the local Lyapunov exponent is greater than zero or the semantic particle stays at the local extreme point for a longer time than the threshold, then it is determined that there is a conflict of domain constraints. S5. Perform reverse time integration on the Hamiltonian equation with the moment when the domain constraint conflict is determined as the endpoint, calculate the dot product of the cumulative impulse vector of the semantic particle and the static semantic feature vector, and generate the semantic impact key feature vector. S6. Based on the dynamic state at the moment when there is a conflict in the domain constraint, the friction coefficient and temperature are calculated using the parameters of the Langevin equation to determine the semantic dissipation coefficient. The minimum work required for the semantic particle to escape from the current potential energy trap is calculated by combining the semantic dissipation coefficient and the height difference of the potential energy surface. S7. Construct a controlled Hamiltonian system with modified external forces, using the minimum work done to escape the potential energy trap as the performance index weight, and solve the two-point boundary value problem based on the two-endpoint constraints at the moment of conflict and the point of lowest target potential energy to obtain the optimal control force sequence; use semantic impact key feature vectors to construct a transformation matrix to modify entity attribute features.

2. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S1 specifically includes: S11. Extract the names and values ​​of entity attribute features, map discrete attribute names to word vector IDs through hash index, extract low-dimensional dense word embeddings through lookup table operation, and perform Z-score standardization on continuous numerical attributes. S12. Statistical attribute word frequency distribution in the whole set, substitute into the logarithmic frequency transformation formula to calculate the reciprocal of the frequency, and map the calculation result to continuous semantic weight parameters; S13. Perform tensor concatenation between dense word embeddings and standardized numerical vectors in the feature dimension to generate a high-dimensional static semantic feature vector; construct a diagonal matrix using continuous semantic weight parameters as diagonal elements, and define the diagonal matrix as a generalized mass matrix; perform matrix-vector multiplication on the generalized mass matrix and the static semantic feature vector to generate an inertial weighted feature vector, and initialize the inertial weighted feature vector as the generalized coordinates of the semantic particles.

3. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S2 specifically includes: S21. Map the knowledge graph category labels to feature vectors, input them into the radial basis function network to calculate the Gaussian kernel response value, and use the Gaussian kernel response value to construct a continuous scalar field in the feature space; S22. Perform multivariable differential operations on the continuous scalar field, calculate the first-order partial derivative of the potential energy function with respect to each generalized coordinate component, arrange all the first-order partial derivatives in order of the coordinate axes to form the original gradient vector, perform element-wise inversion on the original gradient vector to generate semantic gradient force. S23. Calculate the difference vector between the current generalized coordinates of the semantic particle and the coordinates of the category cluster center, perform normalization on the difference vector and multiply it by the gravity coefficient to generate the domain gravity.

4. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S3 specifically includes: S31. Construct the inverse matrix of the generalized mass matrix, calculate the tensor product of the generalized momentum vector and the inverse matrix to obtain the transformed vector, further calculate the inner product of the transformed vector and the current generalized momentum vector and divide by the constant 2 to generate the kinetic energy scalar value; extract the value of the continuous scalar field at the current generalized coordinates as the potential energy scalar value; perform a summation operation on the kinetic energy scalar value and the potential energy scalar value to construct the Hamiltonian; S32. Perform tensor differentiation on the Hamiltonian with respect to the generalized momentum to analytically obtain the time derivative expression of the generalized coordinates; perform partial differentiation on the Hamiltonian with respect to the generalized coordinates and invert the result to analytically obtain the time derivative expression of the generalized momentum. S33. Based on the time derivative expression, set the iteration time step, calculate the product of the inverse matrix of the generalized mass matrix and the current generalized momentum vector as the rate of change of the generalized coordinates with time, and update the generalized coordinates at the next moment in combination with the time step; calculate the gradient vector of the continuous scalar field based on the updated generalized coordinates as the time derivative of the generalized momentum, and update the generalized momentum at the next moment in combination with the time step. S34. Arrange and combine the generalized coordinates and generalized momentum calculated at each iteration time according to the time index to generate a dynamic state sequence.

5. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S4 specifically includes: S41. Extract the generalized coordinates and generalized momentum from the dynamic state sequence, construct the orthogonal normalized tangent vector basis of the state transition trajectory, perform a first-order Taylor expansion of the state transition equation at the current moment with respect to the generalized coordinates and generalized momentum, and extract the linear coefficient matrix of the perturbation variable in the expansion as the tangent space Jacobian matrix; perform matrix multiplication operation with the tangent vector at the previous moment using the tangent space Jacobian matrix to calculate the predicted value of the tangent vector at the current moment; perform Gram-Schmidt orthogonalization and normalization processing on the predicted value of the tangent vector at the current moment to obtain the corrected tangent vector; calculate the magnitude of the corrected tangent vector, and take the natural logarithm of the magnitude as the instantaneous logarithmic growth rate at the current moment; calculate the time average of the instantaneous logarithmic growth rates at each moment to obtain the local Lyapunov exponent; S42. Extract the potential energy value of the continuous scalar field at the current generalized coordinates, perform gradient operation on the potential energy value with respect to the generalized coordinates to obtain the potential energy gradient vector, calculate the Euclidean norm of the potential energy gradient vector to obtain the gradient magnitude, compare the gradient magnitude with the preset minimum value, if the gradient magnitude is less than the preset minimum value, add the current timer value to the unit time as the local extreme value dwell time. S43. Read the local Lyapunov exponent and the local extreme value dwell time, compare the local Lyapunov exponent with the preset chaos threshold, compare the local extreme value dwell time with the preset dwell threshold, and output the comparison result. S44. Based on the comparison results, if the local Lyapunov exponent is greater than the preset chaos threshold, or the local extreme value dwell time is greater than the preset dwell time threshold, generate the domain constraint conflict determination result.

6. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S5 specifically includes: S51. Receive the domain constraint conflict determination result, lock the moment when the domain constraint conflict is determined as the endpoint of the inverse integration, take the generalized coordinates and generalized momentum at the moment of conflict as the initial values, set the inverse time step, perform numerical inverse integration on the Hamiltonian equation, and backtrack the historical trajectory data of the semantic particle before entering the conflict region. S52. Collect the generalized momentum vectors at discrete time points along the historical trajectory data, calculate the difference between the generalized momentum vectors at adjacent time points as the instantaneous impulse vector, perform vectorized summation on all instantaneous impulse vectors, and generate the semantic particle cumulative impulse vector. S53. Extract the background semantic feature vector in the static semantic environment, perform L2 norm normalization on the cumulative impulse vector of semantic particles to obtain the unit impulse direction vector, and calculate the inner product of the unit impulse direction vector and the background semantic feature vector as the semantic similarity projection value; perform multiplication operation on the semantic similarity projection value and the original magnitude value of the cumulative impulse vector to generate a weighted impulse scalar; use the weighted impulse scalar as the first dimension feature, the original magnitude value of the cumulative impulse vector as the second dimension feature, and the semantic similarity projection value as the third dimension feature, and concatenate them to generate a multi-dimensional semantic impact key feature vector.

7. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, S6 specifically includes: S61. Extract the generalized velocity sequence and generalized acceleration sequence at the moment of conflict. Use the generalized mass matrix to perform a left multiplication operation on the generalized acceleration sequence to generate an inertial force vector sequence. Subtract the conservative force vector sequence to obtain a random force sequence. Perform time window averaging on the generalized velocity sequence to calculate the mean vector as the drift term. Use the random force sequence to calculate the autocorrelation matrix with zero delay time as the diffusion term. Establish a linear regression model between the drift term and the generalized velocity, with the drift term vector as the dependent variable and the generalized velocity vector as the independent variable. Use the least squares method to analyze the regression coefficient matrix as the linear damping coefficient matrix. Calculate the sum of the diagonal elements of the autocorrelation matrix as the diffusion coefficient scalar. Divide the diffusion coefficient scalar by the product of the sum of the diagonal elements of the linear damping coefficient matrix and the Boltzmann constant, and perform a division operation to invert and obtain the equivalent temperature. Multiply the sum of the diagonal elements of the linear damping coefficient matrix and the equivalent temperature by the preset first weight coefficient and second weight coefficient, respectively. Add the two product results to calculate and output the semantic dissipation coefficient. S62. Starting from the generalized coordinates at the moment of conflict determination, perform gradient ascent iteration on the current potential energy surface until the gradient minimum point is reached. Mark the gradient minimum point as the saddle point position, extract the potential energy value at the saddle point position and the potential energy value at the moment of conflict determination, calculate the difference of potential energy values, and output the potential energy surface height difference. S63. Receive the semantic dissipation coefficient and the potential energy surface height difference, use the semantic dissipation coefficient and the potential energy surface height difference as input variables of the Kramers escape rate formula, inversely solve the non-equilibrium work value in the escape process, and output the minimum work required for the semantic particle to escape from the current potential energy trap.

8. The knowledge graph entity domain conflict detection method based on semantic processing according to claim 1, characterized in that, Specifically, S7 includes: S71. Calculate the system kinetic energy based on the generalized mass matrix and generalized velocity vector. Determine the potential energy value based on the current potential energy surface. Construct a Lagrangian function by subtracting the potential energy value from the system kinetic energy. Calculate the partial derivative of the Lagrangian function with respect to the generalized velocity vector to construct a generalized momentum vector. Perform a Legendre transformation to replace the generalized velocity variable with the generalized momentum variable, and calculate the initial Hamiltonian. Introduce the minimum work required for a semantic particle to escape the current potential energy trap as a performance index weight. Construct a quadratic cost function for the modified external force vector. Perform a dot product operation on the modified external force vector and the generalized velocity vector to obtain the work power term. Take the negative of the work power term to construct the control Lagrangian term. Add the quadratic cost function, the initial Hamiltonian, and the control Lagrangian term to obtain the augmented Hamiltonian function containing the modified external force and minimum work weights. S72. Read the generalized coordinates and generalized momentum at the moment of conflict as the initial state conditions, extract the generalized coordinates of the lowest potential energy point in the target domain as the terminal state constraint, introduce co-state variables and calculate the partial derivatives of the augmented Hamiltonian function with respect to the generalized coordinates to obtain the co-state equation, calculate the gradient vector of the augmented Hamiltonian function with respect to the modified external force vector and set the gradient vector as the zero vector to obtain the stationary point condition. S73. By simultaneously solving the Hamiltonian canonical equation, costate equation, and stationary point condition, a system of nonlinear differential-algebraic equations concerning generalized coordinates, generalized momentum, and costate variables is obtained. A two-point boundary value problem is constructed based on the two-endpoint constraints at the moment of conflict and the point of lowest potential energy in the target area. Random initial values ​​of the costate variables are set at the initial moment. Forward numerical integration is performed on the state variables and costate variables on the discrete time axis. In each step of integration, the modified external force vector is explicitly solved using the algebraic stationary point condition. The difference vector between the generalized coordinates at the end of the integration and the generalized coordinates of the point of lowest potential energy in the target area is calculated as the target residual. The Jacobian matrix of the target residual with respect to the initial costate variables is calculated. The product of the inverse of the Jacobian matrix and the target residual is used to construct the modified step size of the initial value of the costate variables. The initial costate variables are updated, and numerical integration and residual calculation are re-executed. The iterative process is repeated until the Euclidean norm of the target residual converges to the preset threshold. The converged modified external force time series is output as the optimal control force sequence. S74. Extract the semantic impact key feature vector, map the values ​​of each dimension of the semantic impact key feature vector to the diagonal elements of the linear transformation coefficient matrix, construct the linear transformation coefficient matrix, perform matrix multiplication operation on the entity attribute feature vector and the linear transformation coefficient matrix, and calculate and generate the corrected entity feature vector. S75. The optimal control force sequence is input into the Langevin equation as the driving force. The modified entity feature vector is used as the starting state. Time-step iterative numerical solution is performed to predict the dynamic evolution trajectory of semantic particles under the modified external force.

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